CN115221584A - Method and device for predicting height of burst zone, storage medium and electronic equipment - Google Patents

Method and device for predicting height of burst zone, storage medium and electronic equipment Download PDF

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CN115221584A
CN115221584A CN202210774751.3A CN202210774751A CN115221584A CN 115221584 A CN115221584 A CN 115221584A CN 202210774751 A CN202210774751 A CN 202210774751A CN 115221584 A CN115221584 A CN 115221584A
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刘生优
贺鑫
吕思图
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National Energy Group Guoyuan Power Co Ltd
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Abstract

The disclosure relates to a burst zone height prediction method, a burst zone height prediction device, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring actual measurement data of the fractured zone, wherein the actual measurement data comprises first height data of the fractured zone obtained by actual measurement on a mining working face and vibration data of a plurality of vibration points of the fractured zone; determining the height range of the burst zone through the first height data and the vibration data; determining a plurality of vibration points as a position point where the fracturing zone is generated and a position point where the fracturing zone is not generated through the first height data and the fracturing zone height range; determining a burst zone prediction model through data of the position points where the burst zones are generated and data of the position points where the burst zones are not generated; inputting the measured vibration data of the position to be identified into a burst zone prediction model to obtain a prediction result of the position to be identified, wherein the prediction result comprises: and generating a cracking band at the position to be identified, or generating a cracking band at the position to be identified and second height data of the cracking band at the position point to be identified.

Description

Method and device for predicting height of burst zone, storage medium and electronic equipment
Technical Field
The disclosure relates to the technical field of prevention and control of water damage in mining areas, in particular to a method and a device for predicting the height of a burst zone, a storage medium and electronic equipment.
Background
In the prior art, the method for predicting the height of the coal mine roof cracking zone mainly comprises the following steps: empirical formula method, "two-hole" actual measurement method, and numerical simulation method. The method comprises the following steps of obtaining the height of the fractured zone of an area to be predicted by an empirical formula method through counting the height of the fractured zone, the thickness of a coal bed and the like of different mining areas under different working conditions by using a mathematical fitting method, wherein influence factors considered in the measuring process are limited due to actual measurement, and the complex situation in the actual production process is easily separated; the actual measurement principle of the two holes is to confirm the height of the fractured strip through the construction of the two holes, so that the construction cost is relatively high and the construction period is long; compared with the former two prediction methods, the numerical simulation method considers more influence factors, but has larger deviation from the actual condition due to the over-ideal setting of the construction working condition, so that the prediction result has low accuracy compared with the data in the actual mining process.
Disclosure of Invention
The invention aims to provide a method, a device, a storage medium and electronic equipment for predicting the height of a fractured zone of a coal mine roof, which are used for solving the problem that the height of the fractured zone of the coal mine roof is not accurately predicted in the mining process of a mining area.
In order to achieve the above object, according to a first aspect of embodiments of the present disclosure, the present disclosure provides a method for predicting a burst zone height, including:
acquiring actual measurement data of a fractured zone, wherein the actual measurement data comprises first height data of the fractured zone and vibration data of a plurality of vibration points of the fractured zone, which are obtained by actual measurement on a mining working face;
determining the height range of the fractured zone according to the first height data and the vibration data;
determining the plurality of vibration points as the position points where the fracturing zone is generated and the position points where the fracturing zone is not generated through the first height data and the height range of the fracturing zone;
determining a fractured zone prediction model according to the data of the position points where the fractured zones are generated and the data of the position points where the fractured zones are not generated;
inputting the measured vibration data of the position to be identified into the burst zone prediction model to obtain a prediction result of the position to be identified, wherein the prediction result comprises: and the position to be identified does not generate a cracking zone, or the position to be identified generates a cracking zone and second height data of the cracking zone of the position point to be identified.
Optionally, the obtaining the first height data of the fractured zone includes:
acquiring the actually measured height of a fractured zone obtained by carrying out site survey drilling on the mining working face as the first height data;
the seismic data collected during mining by a plurality of seismic sensors disposed at the mining face.
Optionally, the determining a fracture zone height range through the first height data and the vibration data includes:
establishing a fracturing zone simulation model by utilizing preset engineering simulation software based on the first height data and the vibration data;
and determining the height of each position of the fractured zone in the mining process through the fractured zone simulation model to obtain the height range of the fractured zone.
Optionally, the building a fracturing zone simulation model by using preset engineering simulation software based on the first height data and the vibration data includes:
and determining a fracture zone simulation model according with the first height data through Flac3D or Udec geotechnical engineering numerical simulation software.
Optionally, the determining, by the first height data and the fracture zone height range, a fracture zone generated position point and a fracture zone non-generated position point in the plurality of vibration points includes:
comparing the height data for the plurality of seismic points to the fracture zone height range;
and determining the vibration point of which the height data is in the height range of the fracture zone as a position point where the fracture zone is generated, and determining the vibration point of which the height data is not in the height range of the fracture zone as a position point where the fracture zone is not generated.
Optionally, the determining a fractured zone prediction model by the data of the fractured zone position points generated and the data of the fractured zone position points not generated comprises:
acquiring training data according to the data of the position point of the generated spallation zone and the data of the position point of the non-generated spallation zone, wherein the training data comprises height data of the position point of the generated spallation zone, vibration intensity of the position point of the generated spallation zone and unit area vibration point density;
and training the BP neural network model through the training data to obtain the trained BP neural network model as the burst zone prediction model.
Optionally, the measured vibration data of the position to be identified includes distances from a plurality of vibration points to be identified to the mining working face, vibration intensity and unit area vibration point density, and the measured vibration data of the position to be identified is input into the fracturing zone prediction model to obtain a prediction result of the position to be identified, including:
and inputting the distances from the plurality of seismic points to be identified to the mining working face, the magnitude of the vibration intensity and the density of the seismic points in unit area into the fracturing zone prediction model to obtain the prediction result output by the fracturing zone prediction model.
According to a second aspect of the embodiments of the present disclosure, there is provided a burst zone height prediction apparatus, including:
the acquiring module is configured to acquire first height data of a current fracturing zone;
a first determination module configured to determine a fracture zone height range from the fracture zone first height data;
the second determination module is configured to determine data of a position point where a fracturing zone is generated and data of a position point where a fracturing zone is not generated in the current coal mine according to the first height data of the fracturing zone and the height range of the fracturing zone;
and the output module is configured to output the second height data of the fractured zone through the data of the position points where the fractured zone is generated and the data of the position points where the fractured zone is not generated in the current coal mine.
According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspect of the embodiments of the present disclosure.
According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic apparatus including: a memory having a computer program stored thereon; a processor for executing the computer program in the memory to implement the steps of the method of any one of the first aspect of the embodiments of the present disclosure.
Through the technical scheme, the actual measurement data of the fractured zone are obtained, wherein the actual measurement data comprise the first height data of the fractured zone obtained through actual measurement on the mining working face and the vibration data of a plurality of vibration points of the fractured zone; determining the height range of the fractured zone through the first height data and the vibration data; determining a plurality of vibration points as a position point where the fracturing zone is generated and a position point where the fracturing zone is not generated through the first height data and the height range of the fracturing zone; determining a burst zone prediction model through data of the position points where the burst zones are generated and data of the position points where the burst zones are not generated; inputting the measured vibration data of the position to be identified into a burst zone prediction model to obtain a prediction result of the position to be identified, wherein the prediction result comprises: and generating a common crack zone at the position to be identified or generating a common crack zone at the position to be identified and second height data of the common crack zone at the position point to be identified. In the technical scheme, the actual measurement first height data of the fractured zone and the vibration data of the position of the vibration point are obtained through the microseismic monitoring technology, so that the height range of the fractured zone in the current mining area is determined, the data of the position point where the fractured zone is generated and the data of the position point where the fractured zone is not generated are further classified, model training is performed, a fractured zone prediction model is obtained, the actual measurement data of the current position needing to be identified are processed through the model, a prediction result is obtained, the data obtained through the microseismic monitoring system are more authentic, the height data of the fractured zone can be more accurately predicted, and compared with the traditional field actual measurement punching mode, the prediction efficiency is improved.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a method for fracture zone height prediction according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating another method of burst zone height prediction, according to an exemplary embodiment.
FIG. 3 is a flow chart illustrating another method of fracture zone height prediction in accordance with an exemplary embodiment.
FIG. 4 is a flow chart illustrating another method of fracture zone height prediction in accordance with an exemplary embodiment.
FIG. 5 is a flow chart illustrating yet another method of fracture band height prediction in accordance with an exemplary embodiment.
FIG. 6 is a block diagram illustrating a burst zone height prediction apparatus according to an exemplary embodiment.
Fig. 7 is a block diagram of an electronic device 700 shown in accordance with an example embodiment.
Fig. 8 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment.
Detailed Description
The following detailed description of the embodiments of the disclosure refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
It should be noted that all the actions of acquiring signals, information or data in the present disclosure are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Fig. 1 is a flowchart illustrating a method for predicting a burst zone height according to an exemplary embodiment, as shown in fig. 1, including the steps of:
in step S11, actual measurement data of a fractured zone is obtained, where the actual measurement data includes first height data of the fractured zone and vibration data of a plurality of vibration points of the fractured zone, which are actually measured on a mining working face.
Exemplarily, taking a coal mine as an example, in the process of mining in a coal mine area, a rock stratum with a completely collapsed overlying rock mass of a coal seam caused after mining working face extraction can occur, in order to predict a fractured zone based on measured data, height data of the fractured zone and a water flowing fractured zone can be obtained and recorded as first height data through on-site field drilling survey, and simultaneously, vibration data of a plurality of vibration points are recorded through on-site survey of all vibration points around the current fractured zone.
In step S12, a fracture zone height range is determined from the first height data and the vibration data.
Illustratively, a microseismic monitoring system can be arranged on the site of the fractured zone by actually punching, measuring and recording the site actual measurement first height data of the fractured zone generated in the current coal mine area, wherein the microseismic monitoring system comprises a plurality of vibration sensors and is used for detecting the vibration data of vibration points, and the height data range of the fractured zone in the current coal mine area is determined by counting the site actual measurement first height data of the fractured zone and the vibration data of the vibration points around the fractured zone.
In step S13, the plurality of vibration points are determined as a position point where a fractured zone is generated and a position point where a fractured zone is not generated according to the first height data and the fractured zone height range.
Illustratively, the current mining area is actually surveyed by drilling, statistics are recorded to obtain the first height data of the fractured zone and the height range of the current fractured zone, and among a plurality of vibration points around the fractured zone, vibration points which generate the position points of the fractured zone and vibration points which do not generate the position points of the fractured zone are further marked and divided.
In step S14, a fracture zone prediction model is determined from the data of the position points where the fracture zone has been generated and the data of the position points where the fracture zone has not been generated.
Exemplarily, after acquiring height data of a burst zone and vibration data of vibration points through field drilling actual measurement, data of generated burst zone position points and data of non-generated burst zone position points are obtained, a burst zone simulation model conforming to a burst zone development rule is further established through the actually measured first height data of the burst zone, a burst zone range is simulated by using the burst zone simulation model based on the actually measured first height data, vibration data collected by a microseismic monitoring system are compared with the obtained burst zone range, the vibration points within the range and the vibration points outside the range are counted and classified, the classification can comprise the burst zone generated position points and the non-burst zone generated position points, and the classified data is used as training data to establish the burst zone prediction model. Illustratively, in an implementation manner, the numerical simulation model is obtained by geotechnical engineering simulation software such as Flac3D and Udec, and the burst zone prediction model can be constructed by using a BP (Back Propagation) neural network.
In step S15, inputting the measured vibration data of the to-be-identified position into the burst zone prediction model to obtain a prediction result of the to-be-identified position, where the prediction result includes: and the position to be identified does not generate the cracking zone, or the position to be identified outputs the second height data of the cracking zone generating the cracking zone and the position point to be identified.
Exemplarily, the fractured zone prediction model is a prediction model obtained by performing machine learning on a BP neural network based on the fractured zone position point and the fractured zone non-generated position point, and the prediction result of the to-be-recognized position output by the fractured zone prediction model is obtained by inputting the measured vibration data of the to-be-recognized position of the current mining area into the obtained fractured zone prediction model, wherein the to-be-recognized position refers to a position where whether a fractured zone is generated needs to be recognized. The currently output prediction result may be that the position to be identified does not generate a cracking zone, the position to be identified generates a cracking zone and height data of the position to be identified generates a cracking zone, and the height data may be recorded as second height data.
The application of the method for predicting the height of the fractured zone is not limited to coal mines, and the method can be applied to any mine field with similar geological characteristics to the coal mines.
In the technical scheme, the actual measurement first height data and the vibration point vibration data of the fractured zone are obtained through the microseismic monitoring technology, the height range of the fractured zone is further determined, data division is further carried out on the position points where the fractured zone is generated and the position points where the fractured zone is not generated, a fractured zone prediction model is obtained, the actual measurement data of the current position needing to be identified are processed through the model, a prediction result is obtained, the data obtained through the microseismic monitoring system is more authentic, the fractured zone height data can be more accurately predicted, and compared with the traditional field punching actual measurement mode, the prediction efficiency is improved.
Fig. 2 is a flowchart illustrating another method for predicting the height of a fractured zone according to an exemplary embodiment, where, as shown in fig. 2, the step S11 may include the following steps:
in step S111, the measured height of the fractured zone obtained by site survey drilling of the mining face is obtained as the first height data.
Illustratively, in the coal mining process, a ground site of a mining working face is punched and surveyed, the heights of a overburden caving zone and a water flowing fractured zone are observed by observing the consumption of flushing liquid, the height of the current caving zone of a coal mine is measured, and the actually measured height is recorded and counted to serve as first height data.
The seismic data is collected during mining by a plurality of seismic sensors disposed at the mining face in step S112.
In the process of coal mining, a micro-seismic monitoring system is arranged on a mining working face, and vibration data of vibration points around a burst zone of a current coal mine area and vibration points are collected through the micro-seismic monitoring system.
The microseism monitoring system comprises a plurality of vibration sensors, a data acquisition unit and a server, wherein the plurality of vibration sensors of the microseism monitoring system are arranged on rock walls on two sides of a mining working face and are distributed at different positions; the current mining working face is a working place for mining in the coal mining process, and the data acquired by the current micro-seismic monitoring system is the position and the strength of micro-vibration generated around the mining working face in the mining process.
Fig. 3 is a flowchart illustrating another method for predicting the height of a fractured zone according to an exemplary embodiment, where, as shown in fig. 3, the step S12 may include the following steps:
in step S121, a fracture zone simulation model is established by using preset engineering simulation software based on the first height data and the vibration data.
Exemplarily, in a coal mining site, a cracking zone simulation model conforming to a cracking zone development rule is established by carrying out numerical simulation on first height data obtained by actual measurement of a drill hole and vibration data obtained by a microseismic monitoring system through geotechnical engineering numerical simulation software such as Flac3D and Udec on the basis of the first height data obtained by actual measurement of the drill hole.
In step S122, determining the height of each position of the fractured zone in the mining process through the fractured zone simulation model, and obtaining the fractured zone height range.
Illustratively, through a current fracture zone simulation model, the height of each fracture zone position in the whole mining process is obtained and counted, and a fracture zone height range is obtained.
Fig. 4 is a flowchart illustrating another burst zone height prediction method according to an exemplary embodiment, and as shown in fig. 4, the step S13 may include the following steps:
in step S131, the height data of the plurality of vibration points is compared with the fracture zone height range.
Illustratively, the vibration data of the vibration points of the mining working face acquired by the micro-vibration monitoring system comprise the positions and the intensities of vibration, and the height data of the current multiple vibration point positions are compared with the height range of the fractured zone obtained by the fractured zone simulation model.
In step S132, the vibration point of which the height data is in the height range of the fractured zone is determined as the position point where the fractured zone is generated, and the vibration point of which the height data is not in the height range of the fractured zone is determined as the position point where the fractured zone is not generated.
Illustratively, by comparing the height data of the current multiple vibration point positions with the height range of the burst zone, the vibration point of which the height data of the vibration point position is contained in the height range of the burst zone is recorded as a position point where the burst zone is generated, and the vibration point of which the height data of the vibration point position is not contained in the height range of the burst zone is recorded as a position point where the burst zone is not generated.
By the method, the positions of the plurality of vibration points can be divided into two groups of classification data, namely a group of position points which generate the burst zone and a group of position points which do not generate the burst zone, the two groups of classification data are used as training data and input into a pre-constructed BP neural network model, the ability of the BP neural network to classify the data is trained, and the trained BP neural network model is obtained and used as the burst zone prediction model.
Fig. 5 is a flowchart illustrating a method for predicting the height of a burst zone according to an exemplary embodiment, where, as shown in fig. 5, the step S14 may include the following steps:
in step S141, training data is obtained according to the data of the position point where the fracturing zone has occurred and the data of the position point where the fracturing zone has not occurred, where the training data includes height data of the position point where the fracturing zone has occurred, magnitude of vibration intensity of the position point where the fracturing zone has occurred, and density of vibration points per unit area.
Exemplarily, for the height data of the current multiple vibration point positions, the data of the position points where the fracturing zone has been generated and the data of the position points where the fracturing zone has not been generated, which have been acquired through classification, the height data of the position points where the fracturing zone has been generated are determined as the currently required model training data, and the vibration intensity in the data of the position points where the fracturing zone has been generated is used as the density of the vibration points in a unit area.
In step S142, the BP neural network model is trained according to the training data, and the trained BP neural network model is obtained as the burst zone prediction model.
Exemplarily, the data of the position points where the burst zone is generated and the data of the position points where the burst zone is not generated are input into a BP neural network model as training data for training, the input data includes the magnitude of the vibration intensity and the density of the vibration points in unit area in the data of the position points where the burst zone is generated, and after the training of the BP neural network model is completed, the obtained model is the burst zone prediction model.
FIG. 6 is a block diagram illustrating a burst zone height prediction apparatus according to an exemplary embodiment, as shown in FIG. 6, including the steps of:
an obtaining module 601 configured to obtain first height data of a current fractured zone.
A first determination module 602 configured to determine a fracture zone height range from the fracture zone first height data.
And a second determining module 603 configured to determine data of a position point where a fracture zone is generated and data of a position point where a fracture zone is not generated in the current coal mine according to the first height data of the fracture zone and the height range of the fracture zone.
And the output module 604 is configured to output the second height data of the fractured zone through the data of the position points where the fractured zone is generated and the data of the position points where the fractured zone is not generated in the current coal mine.
Optionally, the obtaining module 601 includes: a first height sub-module and a vibration sub-module;
and the height measurement submodule is used for acquiring the actually measured height of the fractured zone obtained by surveying and drilling the mining working face on site as the first height data.
And the vibration measurement submodule is used for acquiring the vibration data in the mining process through a plurality of vibration sensors arranged on the mining working face.
Optionally, the first determining module 602 further includes: a model prediction sub-module and a height range sub-module;
and the modeling submodule is used for establishing a fracturing zone simulation model by utilizing preset engineering simulation software based on the first height data and the vibration data.
And the simulation submodule is used for determining the height of each position of the fractured zone in the mining process through the fractured zone simulation model to obtain the fractured zone height range.
Optionally, the second determining module 603 includes: a comparison submodule and a classification submodule;
and the comparison submodule is used for comparing the height data of the plurality of vibration points with the height range of the burst zone.
And the classification submodule is used for determining the vibration point of the height data in the height range of the fractured zone as the position point where the fractured zone is generated, and determining the vibration point of the height data not in the height range of the fractured zone as the position point where the fractured zone is not generated.
Optionally, the output module 604 includes: a training submodule and a model determination submodule;
and the training submodule is used for acquiring training data according to the data of the position point of the generated cracking zone and the data of the position point of the non-generated cracking zone, wherein the training data comprises height data of the position point of the generated cracking zone, vibration intensity of the position point of the generated cracking zone and unit area vibration point density.
And the model determining submodule is used for training the BP neural network model through the training data to obtain the trained BP neural network model as the burst zone prediction model.
Optionally, the output module 604 is configured to:
and inputting the distances from the multiple seismic points to be identified to the mining working face, the magnitude of the seismic intensity and the density of the seismic points in unit area into the fracturing zone prediction model to obtain the prediction result output by the fracturing zone prediction model.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 7 is a block diagram illustrating an electronic device 700 in accordance with an example embodiment. As shown in fig. 7, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the above-mentioned burst zone height prediction method. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving an external audio signal. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, near Field Communication (NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 705 may thus include: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described burst band height prediction method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described method of fracture zone height prediction is also provided. For example, the computer readable storage medium may be the memory 702 described above including program instructions that are executable by the processor 701 of the electronic device 700 to perform the burst zone height prediction method described above.
Fig. 8 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be provided as a server. Referring to fig. 8, an electronic device 800 includes a processor 822, which may be one or more in number, and a memory 832 for storing computer programs executable by the processor 822. The computer programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processor 822 may be configured to execute the computer program to perform the above-described burst zone height prediction method.
Additionally, the electronic device 800 may also include a power component 826 and a communication component 850, the power component 826 may be configured to perform power management of the electronic device 800, and the communication component 850 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 800. The electronic device 800 may also include input/output (I/O) interfaces 858. The electronic device 800 may operate based on an operating system stored in the memory 832, such as Windows Server, mac OS XTM, unixTM, linux, and the like.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the burst zone height prediction method described above is also provided. For example, the non-transitory computer readable storage medium may be the memory 832 described above that includes program instructions executable by the processor 822 of the electronic device 800 to perform the burst zone height prediction method described above.
In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned method of fracture zone height prediction when executed by the programmable apparatus.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.

Claims (10)

1. A method for predicting the height of a fractured zone is characterized by comprising the following steps:
acquiring actual measurement data of a fractured zone, wherein the actual measurement data comprises first height data of the fractured zone and vibration data of a plurality of vibration points of the fractured zone, which are obtained by actual measurement on a mining working face;
determining the height range of the fractured zone according to the first height data and the vibration data;
determining the plurality of vibration points as the position points where the fracturing zone is generated and the position points where the fracturing zone is not generated through the first height data and the height range of the fracturing zone;
determining a fractured zone prediction model according to the data of the position points where the fractured zones are generated and the data of the position points where the fractured zones are not generated;
inputting the measured vibration data of the position to be identified into the burst zone prediction model to obtain a prediction result of the position to be identified, wherein the prediction result comprises: and the position to be identified does not generate a cracking zone, or the position to be identified generates a cracking zone and second height data of the cracking zone of the position point to be identified.
2. The method of claim 1, wherein the obtaining a burst zone first height data comprises:
acquiring the actually measured height of a fractured zone obtained by carrying out site survey drilling on the mining working face as the first height data;
the seismic data collected during mining by a plurality of seismic sensors disposed at the mining face.
3. The method of claim 1, wherein determining a fracture zone height range from the first height data and the shock data comprises:
establishing a fracturing zone simulation model by utilizing preset engineering simulation software based on the first height data and the vibration data;
and determining the height of each position of the fractured zone in the mining process through the fractured zone simulation model to obtain the fractured zone height range.
4. The method of claim 3, wherein building a fracture zone simulation model using pre-set engineering simulation software based on the first height data and the seismic data comprises:
and determining a burst zone simulation model according with the first height data through Flac3D or Udec geotechnical engineering numerical simulation software.
5. The method of claim 1, wherein the seismic data of the seismic points comprises height data of the seismic points, and wherein determining the locations of the plurality of seismic points where a fractured zone has been generated and the locations where a fractured zone has not been generated from the first height data and the fractured zone height range comprises:
comparing the height data for the plurality of seismic points to the fracture zone height range;
and determining the vibration point of the height data in the height range of the fractured zone as the position point where the fractured zone is generated, and determining the vibration point of the height data not in the height range of the fractured zone as the position point where the fractured zone is not generated.
6. The method of claim 1, wherein determining a fractured zone prediction model from the data of the fractured zone location points generated and the data of the fractured zone location points not generated comprises:
acquiring training data according to the data of the position points where the fracturing zones are generated and the data of the position points where the fracturing zones are not generated, wherein the training data comprises height data of the position points where the fracturing zones are generated, vibration intensity of the position points where the fracturing zones are generated and unit area vibration point density;
and training the BP neural network model through the training data to obtain the trained BP neural network model as the fracturing zone prediction model.
7. The method of claim 1, wherein the measured vibration data of the position to be identified comprises distances from a plurality of vibration points to be identified to the mining working face, vibration intensity and vibration point density per unit area, and the step of inputting the measured vibration data of the position to be identified into the spalling zone prediction model to obtain the prediction result of the position to be identified comprises the following steps:
and inputting the distances from the plurality of seismic points to be identified to the mining working face, the magnitude of the vibration intensity and the density of the seismic points in unit area into the fracturing zone prediction model to obtain the prediction result output by the fracturing zone prediction model.
8. A burst zone height prediction device, comprising:
the acquiring module is configured to acquire first height data of a current burst zone;
a first determination module configured to determine a fracture zone height range from the fracture zone first height data;
the second determination module is configured to determine data of a position point where a fracturing zone is generated and data of a position point where a fracturing zone is not generated in the current coal mine according to the first height data of the fracturing zone and the height range of the fracturing zone;
and the output module is configured to output the second height data of the fractured zone through the data of the position points where the fractured zone is generated and the data of the position points where the fractured zone is not generated in the current coal mine.
9. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
CN202210774751.3A 2022-07-01 2022-07-01 Method and device for predicting height of burst zone, storage medium and electronic equipment Pending CN115221584A (en)

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