A distributed mine-used wireless microseismic monitoring system

Abstract

The application discloses a kind of distributed mine wireless microseismic monitoring systems, comprising: acquisition module, for real-time acquisition in mine monitoring signal, the monitoring signal includes vibration signal, temperature signal and infrasound signal;Synchronization module is used to time synchronization to the monitoring signal;Transmission module is used to transmit time-synchronized monitoring signal to data processing module by ethernet technology;Processing module is used to process analysis by convolution neural network model to monitoring signal, obtain monitoring information, for dynamic demonstration by visual three-dimensional interface to the monitoring information.The application changes the limitation of relying on single kind sensor for monitoring by comprehensive multi-parameter microseismic monitoring early warning technology, provides basis for the safety evaluation of coal mining.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mine application, and particularly relates to a distributed mine wireless microseismic monitoring system. BACKGROUND

[0002] At present, very primitive methods are mostly used in the monitoring of mine areas, such as stress variation monitoring, displacement and form monitoring. The limitation of these monitoring methods is that only local points of rock mass can be monitored, and it is difficult to comprehensively evaluate the stability of large-scale rock mass.

[0003] In recent years, due to the rapid development of microelectronics and computer technology, microseismic monitoring technology has been widely used in mine safety monitoring, and has made great contributions in solving the spatial distribution characteristics of rock mass rupture, predicting gas outburst and water inrush, and the stability near rock and coal surface. At present, this technology is being developed as a standard technology for monitoring and management of major disasters in mines.

[0004] Microseismic monitoring technology mainly collects the vibration signals released in the process of coal and rock mass rupture through vibration sensors, and determines the position and energy level of the vibration source after positioning and energy calculation of the collected, transmitted and calculated data. However, in recent years, the instruments used for monitoring generally have low positioning accuracy, poor synchronous signal capture ability, and single monitoring sensor, which cannot realize accurate monitoring of mine areas. SUMMARY

[0005] The purpose of the present application is to provide a distributed mine wireless microseismic monitoring system to solve the problems existing in the prior art.

[0006] To achieve the above purpose, the present application provides a distributed mine wireless microseismic monitoring system, comprising:

[0007] A collection module is used for real-time collection of monitoring signals in a mine, and the monitoring signals include vibration signals, temperature signals and infrasound signals;

[0008] A synchronization module is connected with the collection module and is used for time synchronization of the monitoring signals;

[0009] A transmission module is connected with the synchronization module and is used for transmission of the time-synchronized monitoring signals to a data processing module through Ethernet technology;

[0010] A processing module is connected with the transmission module and is used for processing and analysis of the monitoring signals through a convolutional neural network model to obtain monitoring information, wherein the monitoring information includes seismic source parameter data, microseismic event positioning and microseismic event level;

[0011] A monitoring module is connected with the processing module and is used for dynamic demonstration of the monitoring information through a visual three-dimensional interface.

[0012] Optionally, the data acquisition module comprises a plurality of data acquisition units, each data acquisition unit comprising at least one vibration sensor, a temperature sensor and an infrasound sensor;

[0013] The vibration sensor is configured to acquire a vibration signal in the mine and locate a vibration source.

[0014] The temperature sensor is configured to acquire a temperature signal of a temperature field of the vibration source region.

[0015] The infrasound sensor is configured to acquire an infrasound signal of the vibration source region.

[0016] Optionally, the synchronization module comprises:

[0017] The time unit is configured to time the acquired monitoring signal.

[0018] The synchronization unit is configured to time synchronize the time unit based on a standard time.

[0019] Optionally, the transmission module comprises:

[0020] The physical layer transmission unit is configured to combine the Ethernet and the PON technology, adopt a point-to-multipoint structure and perform wireless transmission.

[0021] The link layer transmission unit is configured to access the Ethernet based on a PON topology structure and perform wireless transmission through an Ethernet protocol.

[0022] Optionally, the processing module comprises:

[0023] The preprocessing unit is configured to convert the monitoring signal into monitoring data.

[0024] The processing unit is configured to process the monitoring data through a convolutional neural network model to obtain monitoring information.

[0025] Optionally, the preprocessing unit comprises:

[0026] The signal amplification unit is configured to amplify the monitoring signal to generate an amplified signal.

[0027] The filtering processing unit is configured to filter the amplified signal and transmit the filtered signal to the digital conversion unit.

[0028] The digital conversion unit is configured to convert the amplified signal filtered through the filtering processing unit into monitoring data.

[0029] Optionally, the monitoring module comprises:

[0030] The monitoring unit is configured to generate a monitoring report, a monitoring graph or a monitoring column chart according to the monitoring information, and display the monitoring report, the monitoring graph or the monitoring column chart in real time on the visualized three-dimensional interface.

[0031] The early warning unit is configured to generate an early warning level according to the monitoring information, and send an early warning signal to a mobile terminal.

[0032] The present application has the following technical effects:

[0033] The monitoring system of the present application comprehensively monitors the vibration signals, temperature fields and surrounding rock secondary wave signals of a mining area, changes the limitation of relying on a single type of sensor for monitoring, opens up a new direction for mine earthquake monitoring, helps to predict the occurrence of mine earthquakes, and provides a basis for safety evaluation of coal mining.

[0034] The present application uses the synchronization unit to synchronize the time of the time-providing unit, thereby improving the accuracy of the time marked on the vibration signals by the time-providing unit, and transmitting the time-provided vibration signals to the processing module for microseismic event analysis, thereby avoiding the situation that the microseismic events in the mine cannot be accurately found due to inaccurate time-provided vibration signals.

[0035] The present application can intelligently monitor and identify microseismic signals, and automatically and intelligently monitor microseismic events. Compared with traditional manual identification, the present application increases the clarity of microseismic signals, makes the microseismic signals processed by the control system more accurate, and is less likely to produce missed or incorrect judgments due to fuzzy microseismic signals. The present application uses a convolutional neural network model to process microseismic signals, efficiently learns microseismic signals, automatically judges and processes microseismic signals after the convolutional neural network model is learned, does not require additional human intervention, makes the classification result more reliable, has higher real-time performance, and saves a lot of manpower and resources. BRIEF DESCRIPTION OF DRAWINGS

[0036] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the present application illustrated in the drawings and their descriptions are used to explain the present application and are not intended to limit the present application. In the drawings:

[0037] Figure 1 FIG. 1 is a structural schematic diagram of a mine wireless microseismic monitoring system according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0040] Example 1

[0041] like Figure 1 As shown, this embodiment provides a distributed wireless microseismic monitoring system for mining, including:

[0042] The system comprises four modules: an acquisition module for real-time acquisition of monitoring signals from the mine, including vibration, temperature, and infrasound signals; a synchronization module connected to the acquisition module for time synchronization of the monitoring signals; a transmission module connected to the synchronization module for transmitting the time-synchronized monitoring signals to the data processing module via Ethernet technology; a processing module connected to the transmission module for processing and analyzing the monitoring signals using a convolutional neural network model to obtain monitoring information, including source parameters, microseismic event locations, and microseismic event levels; and a monitoring module connected to the processing module for dynamic demonstration of the monitoring information through a visual 3D interface. The basic architecture of this monitoring system adopts a distributed network topology, using Ethernet to connect the acquisition, processing, and monitoring modules, and the transmission module can wirelessly transmit monitoring data to a remote management equipment system.

[0043] In practice, the data acquisition module includes several data acquisition units, each of which includes at least one vibration sensor, one temperature sensor, and one infrasound sensor; the vibration sensor is used to acquire vibration signals in the mine and locate the source of the vibration; the temperature sensor is used to acquire temperature signals of the temperature field in the source area; and the infrasound sensor is used to acquire infrasound signals in the source area.

[0044] Specifically, vibration sensors collect vibration signals released during mine vibration, perform location and energy calculations to determine the location and energy level of the vibration source. Temperature sensors measure the temperature field in hazardous areas. Under internal stress, the temperature field of coal mine rock strata changes, and as stress increases, the temperature field changes significantly. By measuring the temperature field, the stress state of the surrounding rock can be predicted in advance, playing a predictive role in rockbursts. At the same time, the stress changes in the rock strata will generate infrasound responses. By monitoring infrasound at specific frequencies, the stress state of the surrounding rock can be predicted in advance, also playing a predictive role in rockbursts.

[0045] The feasible synchronization module includes: a time synchronization unit for synchronizing the collected monitoring signals with time; and a synchronization unit for synchronizing the time synchronization unit with time based on a standard time.

[0046] This embodiment utilizes a synchronization unit to synchronize the time of the timing unit, thereby improving the accuracy of the time annotation on the collected vibration signals. The synchronized vibration signals are then transmitted to the processing module for microseismic event analysis, preventing the failure to accurately detect microseismic events within the mine due to inaccurate vibration signal timing. In existing microseismic monitoring systems, data is first collected and then uploaded to the ground before timing is synchronized, or time data is simply transmitted over the network. This results in unavoidable time delay errors. This embodiment directly synchronizes the time of the collected vibration data, avoiding time delays in the data transmission path and time errors caused by relay network switches, thus improving the accuracy of vibration data acquisition.

[0047] The monitoring system in this embodiment also includes a standard time server, which is connected to a synchronization module. The standard time server is used to generate a standard time based on the satellite signals received in real time, and the synchronization module is also used to acquire the standard time generated by the standard time server.

[0048] In this embodiment, the standard time server receives satellite signals in real time and generates accurate real-time time, i.e., standard time. Thus, the synchronization module connected to the standard time server can obtain the standard time from the standard time server in order to synchronize time with the authorized unit.

[0049] The feasible transmission module includes: a physical layer transmission unit for combining Ethernet and PON technologies to perform wireless transmission in a point-to-multipoint structure; and a link layer transmission unit for accessing Ethernet based on the PON topology and performing wireless transmission via the Ethernet protocol.

[0050] The transmission module, connected to the acquisition module, processing module, control module, and cloud service platform, is used for data transmission via Ethernet Passive Optical Network (PON). Ethernet PON combines Ethernet and PON technologies, employing a point-to-multipoint structure and passive fiber transmission. It provides various services over Ethernet, utilizes PON technology at the physical layer and the Ethernet protocol at the link layer, leveraging the PON topology to achieve Ethernet access, significantly improving data transmission efficiency.

[0051] Ethernet Passive Optical Network (PON) systems utilize single-mode, single-core optical fiber and employ wavelength division multiplexing (WDM) technology to achieve bidirectional transmission (uplink wavelength: 1310nm, downlink wavelength: 1490nm). At the physical coding sublayer, it inherits the existing Gigabit Ethernet standard, achieving a symmetrical uplink and downlink data transmission rate of 1Gbit / s. At the data link layer, the Multipoint MAC Control Protocol (MPCP) simulates point-to-point communication within a point-to-multipoint Ethernet PON system, primarily handling ONU discovery and registration, ONU resource requests, OLT dynamic bandwidth allocation, ONU status statistics, and reporting.

[0052] The transmission principle of Ethernet Passive Optical Network (PON) is briefly described as follows: When the OLT starts up, it periodically broadcasts discovery time slot information allowing access. After the ONU powers on, it actively initiates a registration request based on the access permission information broadcast by the OLT. The OLT authenticates the ONU, allows the ONU to access, and assigns a unique logical link identifier to the ONU requesting registration. At the same time, the OLT performs ranging on the ONU, measures the loop delay RTT between the OLT and the ONU, and provides time slot compensation parameters for subsequent bandwidth allocation. The OLT also sends registration response time slot information to the ONU. The ONU uploads the registration response within the registration response time slot. After the OLT confirms that it has received the registration response information within the registration response time slot, the ONU system access is completed, and normal data communication can be carried out. Downlink data is broadcast from the OLT to all ONUs. Each ONU receives its own data according to the downlink data identifier information and discards data from other users. Uplink data uses TDMA technology, and the ONU sends uplink services according to the bandwidth authorization sent by the OLT.

[0053] The feasible processing module includes: a preprocessing unit for converting monitoring signals into monitoring data; and a processing unit for processing the monitoring data through a convolutional neural network model to obtain monitoring information.

[0054] The preprocessing unit can be implemented by including: a signal amplification unit for amplifying the monitoring signal to generate an amplified signal; a filtering unit for filtering the amplified signal and transmitting it to a digital conversion unit; and a digital conversion unit for converting the filtered amplified signal into monitoring data.

[0055] The processing module in this embodiment intelligently monitors and identifies the monitoring signals, automatically and intelligently monitoring micro-vibrations. Compared with traditional manual identification, the classification results are more reliable, more real-time, and save manpower and resources. Multi-level filtering of the signals increases the clarity of the micro-vibration signals, making the micro-vibration signals processed by the control system more accurate and less prone to missed or misjudged cases due to blurred micro-vibration signals. A convolutional neural network model is used to process the micro-vibration signals. The convolutional neural network model learns the micro-vibration signals efficiently and can automatically judge and process the micro-vibration signals after the model has completed learning, without the need for additional manual intervention.

[0056] The monitoring system in this embodiment is also equipped with software for microseismic data acquisition, microseismic data location and energy calculation, microseismic signal analysis (capable of filtering, spectrum calculation, noise reduction, and wavelet analysis), three-dimensional display of microseismic results, and remote monitoring. Each software is self-contained, with a user-friendly interface and simple operation. The software includes software for extracting microseismic signals (vibrations), visualizing and analyzing them, separating and filtering wave groups, and software for precise three-dimensional location and energy calculation of rock mass vibration signals. Supporting software includes software for analyzing the frequency, energy, and source concentration patterns of seismic events, as well as daily reports; SURFER daily graphical analysis report software; and software for analyzing the spatiotemporal distribution patterns of microseismic events in conjunction with a three-dimensional CAD geological model (microseismic three-dimensional visualization software).

[0057] The feasible monitoring module includes: a monitoring unit, used to generate monitoring reports, monitoring curves or monitoring bar charts based on monitoring information, and display them in real time on a visual 3D interface; and an early warning unit, used to generate early warning levels based on monitoring information and send early warning signals to mobile terminals.

[0058] The interactive 3D display allows for event relocation, selection of various event types within a user-defined time range, and output of microseismic event location maps, cumulative event counts, and MSWORD or MSEXCEL reports of various source parameters. Users can view event information as needed.

[0059] In practice, the monitoring system of this embodiment can acquire various triggering or continuous monitoring signals generated on-site in real time and can record and save the acquired data for more than 15 days, allowing users to view and reprocess the data acquired from remote sites at any time. It automatically records, displays, and permanently saves microseismic event data.

[0060] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A distributed wireless microseismic monitoring system for mining, characterized in that, include: The acquisition module is used to acquire monitoring signals in the mine in real time, including vibration signals, temperature signals and infrasound signals; A synchronization module, connected to the acquisition module, is used to synchronize the monitoring signal in time. A transmission module, connected to the synchronization module, is used to transmit the time-synchronized monitoring signal to the data processing module via Ethernet technology. The processing module, connected to the transmission module, is used to process and analyze the monitoring signal through a convolutional neural network model to obtain monitoring information, including source parameter data, microseismic event location, and microseismic event level. A monitoring module, connected to the processing module, is used to dynamically demonstrate the monitoring information through a visual 3D interface; The acquisition module includes several data acquisition units, and each data acquisition unit includes at least one vibration sensor, one temperature sensor and one infrasound sensor. The vibration sensor is used to collect vibration signals in the mine and locate the vibration source; The temperature sensor is used to collect temperature signals from the temperature field in the earthquake source region; The infrasound sensor is used to collect infrasound signals from the earthquake source area; The synchronization module includes: The timing unit is used to synchronize the time of the collected monitoring signals; The synchronization unit is used to synchronize the time of the time synchronization unit based on the standard time. The transmission module includes: The physical layer transmission unit is used to combine Ethernet and PON technologies to perform wireless transmission in a point-to-multipoint structure. The link layer transmission unit is used to connect Ethernet based on the PON topology and to perform wireless transmission via the Ethernet protocol. The processing module includes: A preprocessing unit is used to convert the monitoring signal into monitoring data; The processing unit is used to process the monitoring data through a convolutional neural network model to obtain monitoring information; The preprocessing unit includes: The signal amplification unit is used to amplify the monitoring signal and generate an amplified signal. A filtering unit is used to filter the amplified signal and transmit it to a digital conversion unit; The digital conversion unit is used to convert the amplified signal after filtering into monitoring data; The monitoring module includes: The monitoring unit is used to generate monitoring reports, monitoring curves or monitoring bar charts based on monitoring information, and to display them in real time on a visual 3D interface. The early warning unit is used to generate an early warning level based on the monitoring information and send an early warning signal to the mobile terminal.

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