CN114519392A - Rock burst early warning method and device, electronic equipment and storage medium - Google Patents
Rock burst early warning method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The disclosure discloses a rock burst early warning method and device, electronic equipment and a storage medium, relates to the technical field of data processing, and particularly relates to a rock burst early warning method and device, electronic equipment and a storage medium. Acquiring microseismic time sequence data to be detected, segmenting the microseismic time sequence data to obtain microseismic time sequence data subsections, and sequentially inputting all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify characteristic information in all the microseismic time sequence data subsections and output corresponding safety attribute information according to the characteristic information; and carrying out early warning on rock burst according to the safety attribute information. The characteristic information in the microseismic time sequence data subsections can be accurately identified based on a preset early warning model so as to output different safety attribute information corresponding to each characteristic information, and the danger early warning of rock burst is realized based on the safety attribute information.
Description
Technical Field
The disclosure relates to the technical field of data processing, in particular to a rock burst early warning method and device, electronic equipment and a storage medium.
Background
Coal mine rock burst is also called coal explosion, and refers to a power phenomenon of sudden and violent damage caused by instantaneous release of elastic deformation energy of coal bodies around a coal mine roadway or a working face, and is often accompanied by phenomena of coal body instantaneous displacement, throwing, loud sound, air wave and the like, and the coal mine rock burst has great destructiveness and is one of major disasters of a coal mine. With the increasing of the mining intensity and the mining depth of the mine, the frequency and the intensity of coal rock dynamic disasters such as rock burst, coal and gas outburst, roof caving, roadway deformation and the like are also increased, and the safety production of the mine is seriously threatened. The sudden and sharp occurrence of rock burst and the rapid destruction to surrounding rocks of the roadway bring serious threats to the safe production of mines, and seriously cause serious economic loss and serious casualties.
At present, two main solutions are provided for predicting the coal mine rock burst danger: firstly, researching a rock burst generation mechanism by an experimental means based on mechanical knowledge, and predicting danger by combining a rock state and a mechanical behavior; secondly, mining the dangerous information of the rock burst from the monitoring data based on the modern advanced computer technology and combining with an artificial intelligence algorithm.
The inventor of the invention finds that although the impact pressure can be warned for a certain degree in the implementation of the two solutions, the warning accuracy is not high.
Disclosure of Invention
The disclosure provides a method and a device for a rock burst hazard prediction algorithm based on microseismic time sequence data, electronic equipment and a storage medium.
According to a first aspect of the present disclosure, there is provided a method for warning of rock burst, including:
acquiring microseismic time sequence data to be detected, and segmenting the microseismic time sequence data to obtain microseismic time sequence data subsections;
sequentially inputting all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify characteristic information in all the microseismic time sequence data subsections and obtain safety attribute information corresponding to different microseismic time sequence data subsections which are output according to the characteristic information;
and carrying out early warning on rock burst according to the safety attribute information.
Optionally, the microseismic time series data includes index information of rock burst;
the segmenting the microseismic timing data comprises:
carrying out format normalization processing on the index information in the microseismic time sequence data;
And segmenting the microseismic time sequence data after the normalization processing according to a preset time interval to obtain the microseismic time sequence data subsections.
Optionally, before all the microseismic time series data subsections are sequentially input into the preset early warning model, the method further includes:
performing first labeling on the safety attribute information of the sample microseismic time sequence data subsections based on a preset quantity classifier; the safety attribute information is obtained by labeling the same sample microseismic time sequence data subsegment by each classifier, and the safety attribute information comprises the following steps: a hazard property and a safety property;
selecting a total number of first sample microseismic time sequence data subsections and a preset number of second sample microseismic time sequence data subsections, wherein the first sample microseismic time sequence data subsections are sample microseismic time sequence data subsections with the safety attribute information as the danger attribute, and the second sample microseismic time sequence data subsections are sample microseismic time sequence data subsections with the safety attribute information as the safety attribute;
training according to the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment to generate the preset early warning model, wherein the preset early warning model comprises characteristic information corresponding to the danger attribute and characteristic information corresponding to the safety attribute.
Optionally, the method further includes:
carrying out second marking of safety attribute information on the sub-segments of the micro-seismic time series data based on the preset log file; the test microseismic time sequence data subsections are obtained by segmenting the test microseismic time sequence data according to time intervals;
and inputting the test microseismic time sequence data subsections into the preset early warning model so as to test the preset early warning model according to the output result of the preset early warning model and the second label.
Optionally, the training according to the first sample microseismic timing data subsegment and the second sample microseismic timing data subsegment includes:
and training the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment based on a K-medoids clustering algorithm.
According to a second aspect of the present disclosure, there is provided a rock burst warning device comprising:
the acquiring unit is used for acquiring microseismic time sequence data to be detected;
the segmentation unit is used for segmenting the microseismic time sequence data to obtain microseismic time sequence data subsections;
the input unit is used for sequentially inputting all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify the characteristic information in all the microseismic time sequence data subsections and output corresponding safety attribute information according to the characteristic information;
And the early warning unit is used for early warning the rock burst according to the safety attribute information.
Optionally, the microseismic time series data includes index information of rock burst;
the segmentation unit includes:
the processing module is used for carrying out format normalization processing on the index information in the microseismic time sequence data;
and the segmentation module is used for segmenting the microseismic time sequence data after the normalization processing according to a preset time interval to obtain the microseismic time sequence data subsegment.
Optionally, the apparatus further comprises:
the first labeling unit is used for performing first labeling on the safety attribute information of the sample microseismic time sequence data subsections based on a preset quantity classifier before all the microseismic time sequence data subsections are sequentially input into a preset early warning model by the input unit; the safety attribute information is obtained by labeling the same sample microseismic time sequence data subsegment by each classifier, and the safety attribute information comprises the following steps: a hazard property and a safety property;
the selecting unit is used for selecting a total number of first sample microseismic time sequence data subsections and a preset number of second sample microseismic time sequence data subsections, wherein the first sample microseismic time sequence data subsections are sample microseismic time sequence data subsections of which the safety attribute information is a danger attribute, and the second sample microseismic time sequence data subsections are sample microseismic time sequence data subsections of which the safety attribute information is a safety attribute;
And the training unit is used for training according to the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment to generate the preset early warning model, and the preset early warning model comprises characteristic information corresponding to the danger attribute and characteristic information corresponding to the safety attribute.
Optionally, the apparatus further comprises:
the second labeling unit is used for carrying out second labeling on the safety attribute information of the microseismic time sequence data subsections tested based on the preset log file; the sub-section of the test microseismic time sequence data is obtained by segmenting the test microseismic time sequence data according to time intervals;
and the test unit is used for inputting the test microseismic time sequence data subsections into the preset early warning model so as to test the preset early warning model according to the output result of the preset early warning model and the second label.
Optionally, the training unit is further configured to train the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment based on a K-medoids clustering algorithm.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method according to the first aspect.
According to the method, the device, the electronic equipment and the storage medium for predicting the rock burst, microseismic time sequence data to be detected are obtained, the microseismic time sequence data are segmented to obtain microseismic time sequence data subsections, all the microseismic time sequence data subsections are sequentially input into a preset early warning model, so that the preset early warning model can identify characteristic information in all the microseismic time sequence data subsections, and corresponding safety attribute information is output according to the characteristic information; and carrying out early warning on rock burst according to the safety attribute information. Compared with the prior art, the method and the device have the advantages that the characteristic information in the microseismic time sequence data subsections can be accurately identified based on the preset early warning model, so that different safety attribute information corresponding to each characteristic information is output, and the danger early warning of rock burst is realized based on the safety attribute information.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flowchart of a method for predicting rock burst according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating a method for segmenting microseismic time series data according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a method for constructing a preset early warning model according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an early warning device for rock burst according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of another rock burst warning device provided in the embodiment of the present disclosure;
fig. 6 is a schematic block diagram of an example electronic device 600 provided by embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
A method, an apparatus, an electronic device, and a storage medium for predicting rock burst according to the embodiments of the present disclosure are described below with reference to the drawings.
Fig. 1 is a schematic flowchart of a method for predicting rock burst according to an embodiment of the present disclosure.
As shown in fig. 1, the method comprises the following steps:
In the practical application process, the microseismic events can be collected and analyzed through the microseismic monitoring equipment, and finally, microseismic time sequence data can be obtained, wherein the microseismic time sequence data comprises 3 indexes of microseismic occurrence time, coordinates and energy, and the microseismic time sequence data in practical application can also comprise indexes of coal-rock body fracture time, space, intensity and the like.
Due to the uncertain occurrence time of the microseisms, the time intervals of the acquired microseismic time sequence data are not uniform, so that the microseismic time sequence data based on different time intervals are required to be segmented for realizing the accuracy of microseismic monitoring. Based on the characteristic of non-uniformity of time intervals, the following modes can be adopted in the segmentation, but are not limited to the following modes, for example: the microseismic time series data is segmented by a fixed window length, the microseismic time series data is segmented by a fixed interval duration, or other segmentation modes in any forms. In the following embodiments, the microseismic time sequence data is segmented by using a fixed window length, but it should be noted that the implementation manner is not a specific limitation to the segmentation means.
It should be noted that although the segmentation time interval is preset, it is not a constant one, and it may be flexibly changed according to actual requirements to segment microseismic time series data into subsections of different numbers.
And 102, sequentially inputting all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify the characteristic information in all the microseismic time sequence data subsections and output corresponding safety attribute information according to the characteristic information.
Based on the study data, the following two practical results were obtained, the first: the larger the time interval between the current microseism and the last microseism is, the larger the outburst of the microseism is, and the smaller the influence of the last microseism is; and the second method comprises the following steps: each sudden micro-shock drives nearby micro-shocks, and the closer the micro-shock generating point is, the greater the influence is. In the embodiment of the present application, a solution for the particularity of microseismic time series data is provided according to the two practical results: namely, a preset early warning model is constructed, the preset early warning model can identify characteristic information in all microseismic time sequence data subsections and output corresponding safety attribute information according to the characteristic information, and the safety attribute information comprises: hazardous properties and safety properties.
In practical application, safety attribute information output by the preset early warning model and corresponding to the microseismic time series data subsections can be obtained only by inputting all the microseismic time series data subsections into the preset early warning model, and the implementation mode is simple and easy to operate.
And 103, early warning of rock burst is carried out according to the safety attribute information.
And early warning the microseism time sequence data subsections with dangerous attributes according to the safety attribute information corresponding to the microseism time sequence data subsections. In the practical application process, the early warning can be realized by adopting the following modes: triggering an alarm to perform early warning, flashing a warning light to perform early warning, and the like. Specifically, the embodiment of the present application does not limit the early warning manner.
The method for predicting rock burst provided by the embodiment of the disclosure obtains microseismic time sequence data to be detected, segments the microseismic time sequence data to obtain microseismic time sequence data subsections, and sequentially inputs all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify characteristic information in all the microseismic time sequence data subsections and obtain safety attribute information corresponding to different microseismic time sequence data subsections according to the characteristic information; and carrying out early warning on rock burst according to the safety attribute information. Compared with the prior art, the method and the device have the advantages that the characteristic information in the microseismic time sequence data subsections can be accurately identified based on the preset early warning model, so that different safety attribute information corresponding to each characteristic information is output, and the danger early warning of rock burst is realized based on the safety attribute information.
As a refinement to the above embodiment, when the step 101 is executed to segment the microseismic time series data, the following manner may be adopted, as shown in fig. 2, and fig. 2 is a flowchart of a method for segmenting microseismic time series data according to an embodiment of the present application, as shown in fig. 2, the method includes:
The format normalization processing is to normalize the coordinates and the energy in the index information in the microseismic time sequence data into a numerical value between 0 and 1; the purpose of performing the format normalization is to facilitate subsequent data processing.
For convenience of understanding, a segmentation mode is taken as a fixed window length for explanation, in a specific implementation, the coordinate index and the energy index are normalized to be a numerical value between 0 and 1, in a segmentation mode, a preset time interval may be set based on an actual requirement, where the preset time interval is the length of the fixed window, for example, the preset time interval is one hour, or half an hour, or one day, and the like.
The embodiment of the present application further provides a method for constructing a preset early warning model, as shown in fig. 3, the method includes:
And 302, segmenting the sample microseismic time sequence data according to a preset time interval to obtain a sample microseismic time sequence data subsection.
It should be noted that, the execution process of step 302 may refer to the execution process of step 202 in the above embodiment, and the principle is the same, and is not described herein again.
The preset number of classifiers are used for classifying the safety attributes of the sample microseismic time sequence data subsections, and each classifier is used for classifying the same sample microseismic time sequence data subsections. For convenience of understanding, the following description is given in an exemplary form, and it is assumed that the number of the preset classifiers is 10, all the classifiers may classify the same sample microseismic time series data subsegment simultaneously or sequentially, if one classifier labels a danger attribute to the sample microseismic time series data subsegment, the danger probability is labeled on the sample microseismic time series data subsegment to be 0.1, if two classifiers label a danger attribute to the sample microseismic time series data subsegment, the danger probability is labeled on the sample microseismic time series data subsegment by the two classifiers to be 0.1, that is, the danger probability of the sample microseismic time series data subsegment is 0.2 at this time. And so on.
And 304, selecting a total number of first sample microseismic time sequence data subsections and a preset number of second sample microseismic time sequence data subsections, wherein the first sample microseismic time sequence data subsections are sample microseismic time sequence data subsections with the safety attribute information being the danger attribute, and the second sample microseismic time sequence data subsections are sample microseismic time sequence data subsections with the safety attribute information being the safety attribute.
The purpose of selecting the total number of the first sub-segments of the sample microseismic timing data is: enabling further training to identify all characteristic information of the hazard property. In practical application, the number of the first sample microseismic time sequence data subsections is possibly far smaller than that of the second sample microseismic time sequence data subsections, so that a preset number of second sample microseismic time sequence data subsections are required to be randomly selected from the second sample microseismic time sequence data subsections, and the method aims to reduce the acquisition number of the second sample microseismic time sequence data subsections so as to better perform learning training on the first sample microseismic time sequence data subsections. The preset number is an experience value, but the preset number is smaller than the number of the first sample microseismic time sequence data subsections in the setting process, the purpose of the preset number is to increase the proportion of the first sample microseismic time sequence data subsections in all selected microseismic time sequence data, and the purpose of the preset number is to more accurately identify the characteristic information corresponding to the hazard attribute so as to improve the accuracy of prediction.
And 305, training according to the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment to generate the preset early warning model.
Training the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment, identifying characteristic information corresponding to a danger attribute and characteristic information corresponding to a safety attribute, and generating a preset early warning model based on the characteristic information.
In a specific implementation process, due to the characteristic that the lengths of the data of the sample microseismic time sequence data subsections are different, the first sample microseismic time sequence data subsection and the second sample microseismic time sequence data subsection can be trained based on a K-medoids clustering algorithm during training. The K-medoids clustering algorithm is only an exemplary description, and any implementation manner in the related art may be referred to in a specific implementation process, which is not limited in the embodiment of the present application.
The foregoing steps 301 to 305 are specific implementation processes for generating the preset early warning model, and in order to ensure the accuracy of the identification of the preset early warning model, the generated preset early warning model is verified in this step.
And step 306, carrying out second labeling of the safety attribute information on the sub-segments of the microseismic time sequence data based on the preset log file.
The method comprises the steps that test microseismic time sequence data subsections which are confirmed to be dangerous are recorded in a preset log file, and after second marking of safety attribute information (dangerous attribute) is carried out on the test microseismic time sequence data subsections, inspection is carried out, and the accuracy of a preset early warning model is further improved.
The number of the sub-segments of the microseismic time sequence data to be tested is not limited by the preset log file.
And 307, inputting the sub-segments of the test microseismic time sequence data into the preset early warning model, and testing the preset early warning model according to the output result of the preset early warning model and the second label.
And comparing whether the first label with the safety attribute as the dangerous attribute is consistent with the second label with the safety attribute as the dangerous attribute, and carrying out precision test on the preset early warning model.
To sum up, the method for warning of rock burst provided by the embodiment of the application can achieve the following effects:
(1) the input data microseismic time sequence data of the early warning model is preset, the acquisition is simple and convenient, and the experiment operation cost is low;
(2) the particularity of microseismic time sequence data is fully considered (the occurrence time interval is not uniform), so that the prediction accuracy of the pre-set early warning model is greatly improved;
(3) The false alarm rate of the algorithm is reduced by integrating learning and presetting the early warning model.
Corresponding to the method for warning of rock burst provided in fig. 1, the present disclosure also provides a device for warning of rock burst, and since the device for warning of rock burst provided in the embodiment of the present disclosure corresponds to the method for warning of rock burst provided in the embodiment of fig. 1, the embodiment of the method for warning of rock burst is also applicable to the device for warning of rock burst provided in the embodiment of the present disclosure, and will not be described in detail in the embodiment of the present disclosure.
Fig. 4 is a regulating and controlling device for early warning of rock burst provided in an embodiment of the present application, and as shown in fig. 4, the device includes:
the acquiring unit 41 is configured to acquire microseismic time series data to be detected.
And the segmenting unit 42 is used for segmenting the microseismic time sequence data according to a preset time interval to obtain the microseismic time sequence data subsections.
And the input unit 43 is configured to sequentially input all the microseismic time series data subsections into a preset early warning model, so that the preset early warning model identifies characteristic information in all the microseismic time series data subsections, and outputs corresponding security attribute information according to the characteristic information.
And the early warning unit 44 is used for performing early warning on rock burst according to the safety attribute information.
According to the pre-warning device for rock burst, after the micro-seismic time sequence data to be detected are obtained, the micro-seismic time sequence data subsections are segmented to obtain the micro-seismic time sequence data subsections, all the micro-seismic time sequence data subsections are sequentially input into a preset pre-warning model to obtain safety attribute information corresponding to different micro-seismic time sequence data subsections, and the pre-warning for the rock burst is carried out according to the safety attribute information. Compared with the related art, the method and the device have the advantages that the early warning of the rock burst can be realized and the accuracy of prediction is obviously improved based on the characteristic mining of the microseismic time sequence data of the sample and the repeated learning of the characteristics.
Further, in another implementation manner of the present application, as shown in fig. 5, the microseismic time series data includes rock burst index information;
the segmentation unit 42 includes:
the processing module 421 is configured to perform format normalization processing on the index information in the microseismic time sequence data;
and the segmenting module 422 is configured to segment the microseismic time sequence data after the normalization processing according to a preset time interval to obtain the microseismic time sequence data subsections.
Further, in another implementation manner of the present application, as shown in fig. 5, the apparatus further includes:
The first labeling unit 45 is used for performing first labeling on the safety attribute information of the sample microseismic time sequence data subsections based on a preset quantity classifier before all the microseismic time sequence data subsections are sequentially input into a preset early warning model by the input unit 43; the safety attribute information is obtained by labeling the same sample microseismic time sequence data subsegment by each classifier, and the safety attribute information comprises the following steps: a hazardous property and a safe property;
the selecting unit 46 is configured to select a total number of first sample microseismic time sequence data subsections and a preset number of second sample microseismic time sequence data subsections, where the first sample microseismic time sequence data subsections are sample microseismic time sequence data subsections whose safety attribute information is a danger attribute, and the second sample microseismic time sequence data subsections are sample microseismic time sequence data subsections whose safety attribute information is a safety attribute;
a training unit 47, configured to train according to the first sample microseismic time series data subsegment and the second sample microseismic time series data subsegment to generate the preset early warning model, where the preset early warning model includes feature information corresponding to the risk attribute and feature information corresponding to the safety attribute.
Further, in another implementation manner of the present application, as shown in fig. 5, the apparatus further includes:
the second labeling unit 48 is configured to perform second labeling on the safety attribute information of the sample microseismic time series data subsections based on the preset log file; the sample microseismic time sequence data subsections are obtained by segmenting sample microseismic time sequence data according to time intervals;
and the test unit 49 is configured to input the sub-segment of the sample microseismic time series data into the preset early warning model, so as to test the preset early warning model according to the output result of the preset early warning model and the second label.
Further, in another implementation manner of the present application, the training unit 47 is further configured to train the first sample microseismic time series data subsegment and the second sample microseismic time series data subsegment based on a K-medoids clustering algorithm. It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of the present embodiment, and the principle is the same, and the present embodiment is not limited thereto.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can 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. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the device 600 includes a computing unit 601 which can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 into a RAM (Random Access Memory) 603. In the RAM603, various programs and data necessary for the operation of the device 600 can also be stored. The calculation unit 601, the ROM602, and the RAM603 are connected to each other via a bus 604. An I/O (Input/Output) interface 605 is also connected to the bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, and the like; 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 the computing Unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The calculation unit 601 executes the respective methods and processes described above, such as the warning method of rock burst. For example, in some embodiments, the method of warning of rock burst may be implemented as a computer software program tangibly embodied in 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 ROM602 and/or the communication unit 609. When the computer program is loaded into the RAM603 and executed by the computing unit 601, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (e.g., by means of firmware) to perform the aforementioned method of warning of rock burst.
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, Integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip, System On a Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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: LAN (Local Area Network), WAN (Wide Area Network), internet, and blockchain Network.
The computer system may include clients and servers. A client and server are generally 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 as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge map technology and the like.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein. The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (10)
1. A rock burst early warning method is characterized by comprising the following steps:
acquiring microseismic time sequence data to be detected, and segmenting the microseismic time sequence data to obtain microseismic time sequence data subsections;
sequentially inputting all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify characteristic information in all the microseismic time sequence data subsections and output corresponding safety attribute information according to the characteristic information;
and carrying out early warning on rock burst according to the safety attribute information.
2. The early warning method as claimed in claim 1, wherein the microseismic time series data comprises information of indexes of rock burst;
the segmenting the microseismic timing data comprises:
carrying out format normalization processing on the index information in the microseismic time sequence data;
and segmenting the normalized microseismic time sequence data according to a preset time interval to obtain the microseismic time sequence data subsegment.
3. The warning method according to claim 1, wherein before all microseismic time series data subsections are sequentially input into a preset warning model, the method further comprises:
carrying out first labeling on safety attribute information of the sample microseismic time sequence data subsections based on a preset number classifier; the safety attribute information is obtained by labeling the same sample microseismic time sequence data subsegment by each classifier, and the safety attribute information comprises the following steps: a hazard property and a safety property;
Selecting a total number of first sample microseismic time sequence data subsections and a preset number of second sample microseismic time sequence data subsections, wherein the first sample microseismic time sequence data subsections are sample microseismic time sequence data subsections with the safety attribute information as the danger attribute, and the second sample microseismic time sequence data subsections are sample microseismic time sequence data subsections with the safety attribute information as the safety attribute;
and training according to the first sample microseismic time sequence data subsections and the second sample microseismic time sequence data subsections to generate the preset early warning model.
4. The warning method of claim 3, further comprising:
carrying out second marking of safety attribute information on the sub-segments of the micro-seismic time series data based on the preset log file; the test microseismic time sequence data subsections are obtained by segmenting sample microseismic time sequence data according to time intervals;
and inputting the test microseismic time sequence data subsections into the preset early warning model, and testing the preset early warning model according to the output result of the preset early warning model and the second label.
5. The warning method of claim 3 wherein the training according to the first and second subsections of sample microseismic timing data comprises:
And training the first sample microseismic time sequence data subsegment and the second sample microseismic time sequence data subsegment based on a K-medoids clustering algorithm.
6. A rock burst early warning device is characterized by comprising:
the acquisition unit is used for acquiring microseismic time sequence data to be detected;
the segmentation unit is used for segmenting the microseismic time sequence data to obtain microseismic time sequence data subsections;
the input unit is used for sequentially inputting all the microseismic time sequence data subsections into a preset early warning model so that the preset early warning model can identify the characteristic information in all the microseismic time sequence data subsections and output corresponding safety attribute information according to the characteristic information;
and the early warning unit is used for early warning the rock burst according to the safety attribute information.
7. The early warning device as recited in claim 6, wherein the microseismic time series data comprises information of an index of rock burst;
the segmentation unit includes:
the processing module is used for carrying out format normalization processing on the index information in the microseismic time sequence data;
and the segmentation module is used for segmenting the microseismic time sequence data after the normalization processing according to a preset time interval to obtain the microseismic time sequence data subsegment.
8. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
9. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.
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