Rock burst prediction method and device based on wireless passive sensor

Abstract

The invention relates to the technical field of rockburst prediction, discloses a rockburst prediction method and device based on a wireless passive sensor, and aims at solving the problems that an existing rockburst prediction scheme is poor in accuracy and reliability and high in cost. The pressure sensor comprises a plurality of pressure gaskets arranged on the side surface and the bottom surface, and the pressure gaskets are made of piezoelectric materials; the pressure sensor determines stress data of each rock surface corresponding to the pressure gaskets based on the deformation quantity of each pressure gasket and wirelessly sends the stress data; and wirelessly receiving the stress data, and determining whether rock burst occurs in the rock mass to be predicted according to the stress data and the ultimate compressive strength of the rock mass. The method improves the accuracy and reliability of rock burst prediction, reduces the cost, and is suitable for underground construction projects.

Description

Technical Field

[0001] The present invention relates to the technical field of rock burst prediction, and in particular to a rock burst prediction method and device based on wireless passive sensors. Background Art

[0002] Rockburst refers to the phenomenon that the elastic strain energy accumulated in the rock mass is suddenly released under certain conditions in deep underground mining or areas with high tectonic stress, causing the rock to burst and eject. This phenomenon is also called rock burst or mine shock. Rockburst is the most prominent dynamic disaster in deep rock mass. Rockburst not only causes rock collapse, but also produces loud noises and air waves, which may damage the mine structure and even affect ground buildings. Minor rockbursts may only manifest as rock flakes falling, while severe rockbursts can be measured at a higher magnitude, causing significant damage to the surrounding environment and easily causing casualties. Due to the complex causes of rockbursts and numerous influencing factors, the prediction of rockbursts has always been a difficult problem in the engineering field.

[0003] Traditional rockburst prediction schemes mainly include deformation monitoring scheme, microseismic monitoring scheme and stress monitoring scheme.

[0004] The deformation monitoring scheme mainly predicts rockbursts by monitoring the deformation displacement of rock mass. Since rockbursts occur in hard and brittle rock mass, they are sudden and instantaneous. The deformation displacement of rock mass before rockburst is extremely small and difficult to capture. Therefore, it is difficult to accurately predict rockbursts through deformation monitoring schemes.

[0005] Microseismic monitoring mainly uses sensors to collect seismic wave signals emitted by rock destruction or rock fracture. It has the advantages of wide monitoring range and strong timeliness, and is a relatively effective technical means for rock burst disaster monitoring equipment. However, it is difficult to capture rock fracture information. At the same time, the construction environment of deep underground projects is complex and highly interfering, which greatly reduces the ability of microseismic monitoring sensors to collect and locate fracture signals. In addition, the indicators of microseismic monitoring technology applied to rock deformation and fracture processes are very limited. It is difficult to accurately predict the possibility of rock bursts using microseismic monitoring solutions.

[0006] The stress monitoring scheme mainly predicts rock bursts by measuring stress changes. At present, on-site stress monitoring is mainly borehole stress monitoring, which mainly includes borehole stress relief method and borehole stress gauge test method. However, the existing stress monitoring scheme requires the deployment of monitoring sensors and monitoring lines throughout the high-risk rock burst area. The deployment is difficult and expensive, and it is difficult to implement in complex and narrow underground spaces. At the same time, the ground stress in the high-risk rock burst area is extremely high, and the buried lines are at risk of rupture, breakage, and damage, and the reliability is poor. Summary of the invention

[0007] The present invention aims to solve the problems of poor accuracy and reliability and high cost of existing rockburst prediction schemes, and proposes a rockburst prediction method and device based on wireless passive sensors.

[0008] The technical solution adopted by the present invention to solve the above technical problems is:

[0009] In a first aspect, the present invention provides a rockburst prediction method based on wireless passive sensors, the method comprising:

[0010] Arrange a wireless passive pressure sensor in the rock mass to be predicted, wherein the pressure sensor comprises a plurality of pressure pads arranged on the side and bottom surfaces, and the pressure pads are made of piezoelectric material;

[0011] The pressure sensor determines stress data of each rock mass surface corresponding to the pressure pad based on the deformation amount of each pressure pad, and wirelessly transmits the stress data;

[0012] The stress data is wirelessly received, and whether a rock burst will occur in the rock mass to be predicted is determined according to the stress data and the ultimate compressive strength of the rock mass.

[0013] Furthermore, wireless passive pressure sensors are arranged in the rock mass to be predicted, specifically including:

[0014] After drilling a hole in the rock mass to be predicted, place the wireless passive pressure sensor into the hole, place the pressure gaskets on the side and bottom of the pressure sensor on the corresponding rock mass surface, and then fill it fully with concrete. After the concrete solidifies, the rock mass to be predicted, concrete and pressure sensor form an integral structure.

[0015] Furthermore, the pressure sensor is a cube structure, and the pressure gaskets are respectively arranged on the bottom surface and four side surfaces of the cube structure.

[0016] Furthermore, the pressure sensor uses a built-in antenna to wirelessly transmit stress data, and the built-in antenna is arranged on the top surface of the cube structure.

[0017] Furthermore, the pressure sensor determines the stress data of each rock mass surface corresponding to the pressure gasket according to the deformation amount of each pressure gasket, specifically including:

[0018] The pressure sensor includes a piezoelectric vibrator, and the stress data of the corresponding rock surface is determined according to the change in the resonant frequency of the piezoelectric vibrator; the deformation of the pressure gasket causes the change in the internal capacitance of the pressure gasket, and the change in capacitance causes the change in the resonant frequency of the piezoelectric vibrator.

[0019] Further, determining whether the rock mass to be predicted will have rock burst according to the stress data and the ultimate compressive strength of the rock mass specifically includes:

[0020] Determine the stress value corresponding to the stress data of each pressure gasket, and determine the maximum stress value among all stress values. Judge whether the rock mass to be predicted will experience rock burst based on the maximum stress value and the ultimate compressive strength of the rock mass. When it is determined that the rock mass to be predicted will experience rock burst, determine the rock mass surface corresponding to the maximum stress value.

[0021] Further, judging whether the rock mass to be predicted will have rock burst according to the maximum stress value and the ultimate compressive strength of the rock mass specifically includes:

[0022] It is determined whether the ratio of the maximum stress value to the ultimate compressive strength of the rock mass is greater than the rock burst threshold value. If so, it is determined that the rock mass to be predicted will undergo rock burst. Otherwise, it is determined that the rock mass to be predicted will not undergo rock burst.

[0023] Furthermore, when the ratio of the maximum stress value to the ultimate compressive strength of the rock mass is greater than the rock burst threshold, the rock burst level of the rock mass to be predicted is determined according to the ratio, specifically including:

[0024] If the ratio is within the first preset range, the rockburst level of the rock mass to be predicted is a slight rockburst; if the ratio is within the second preset range, the rockburst level of the rock mass to be predicted is a moderate rockburst; if the ratio is within the third preset range, the rockburst level of the rock mass to be predicted is a strong rockburst; if the ratio is within the fourth preset range, the rockburst level of the rock mass to be predicted is an extremely strong rockburst.

[0025] Furthermore, the method further comprises:

[0026] When it is determined that a rock burst may occur in the rock mass to be predicted, a rock burst warning is issued according to the rock burst grade of the rock mass to be predicted.

[0027] In a second aspect, the present invention provides a rockburst prediction device based on a wireless passive sensor, the device comprising:

[0028] A wireless passive pressure sensor, comprising a plurality of pressure pads arranged on the side and bottom surfaces, wherein the pressure pads are made of piezoelectric material; the pressure sensor is arranged in the rock mass to be predicted, and is used to determine stress data of each rock mass surface corresponding to the pressure pad based on the deformation amount of each pressure pad, and wirelessly transmit the stress data;

[0029] A wireless communication module, used for wirelessly receiving stress data and sending the stress data to a controller;

[0030] The control module is used to receive stress data and determine whether a rock burst will occur in the rock mass to be predicted based on the stress data and the ultimate compressive strength of the rock mass.

[0031] The beneficial effects of the present invention are as follows: the rockburst prediction method and device based on wireless passive sensors provided by the present invention, by arranging wireless passive pressure sensors in the rock mass to be predicted in the high-risk area of ​​rockburst, adopts wireless passive sensing technology to collect and transmit stress data, thereby avoiding the layout difficulties and line damage problems existing in wired transmission, and improving the reliability of data transmission and rockburst prediction. At the same time, the present invention arranges pressure gaskets on multiple surfaces of the pressure sensor, and only needs to arrange one pressure sensor to realize stress monitoring of multiple rock mass surfaces of the rock mass to be predicted, thereby reducing the number of sensor arrangements, reducing the difficulty and cost of arranging stress detection sensors, and also improving the accuracy of rockburst prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic flow chart of a rockburst prediction method based on wireless passive sensors provided in an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the structure of a pressure sensor provided by an embodiment of the present invention;

[0034] Figure 3 A schematic structural diagram of a rockburst prediction device based on a wireless passive sensor provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0036] In some of the processes described in the specification of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel, and the sequence numbers of the operations are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0037] The technical solution of the embodiment of the present invention is applicable to application scenarios where rock burst prediction is required for rock masses in high-risk rock burst areas, such as rock burst prediction for hard and brittle rock masses such as granite, sandstone, and diorite in underground construction projects.

[0038] The current rockburst prediction scheme mainly includes monitoring rock deformation displacement, rock fracture signals or rock stress to predict rockburst. The inventors have found through research that it is difficult to accurately predict rockburst by monitoring rock deformation displacement and rock fracture signals because the rock deformation displacement before rockburst occurs is extremely small and difficult to capture, and rock fracture information is difficult to capture. In the existing scheme of monitoring rock stress for rockburst prediction, it is necessary to deploy monitoring sensors and monitoring lines throughout the high-risk rockburst area, which is difficult to deploy and is also prone to line damage due to rock stress.

[0039] Based on this, the technical solution of the present invention is proposed. In an embodiment of the present invention, a wireless passive pressure sensor is arranged in the rock mass to be predicted, and the pressure sensor includes a plurality of pressure pads arranged on the side and the bottom, and the pressure pads are made of piezoelectric material; the pressure sensor determines the stress data of each rock mass surface corresponding to the pressure pad based on the deformation amount of each pressure pad, and wirelessly sends the stress data; the stress data is wirelessly received, and whether a rock burst will occur in the rock mass to be predicted is determined according to the stress data and the ultimate compressive strength of the rock mass.

[0040] Specifically, the embodiment of the present invention adopts wireless passive sensing technology to collect and transmit stress data, avoiding the layout difficulties and line damage problems of wired transmission. At the same time, by arranging pressure gaskets on multiple surfaces of the pressure sensor, the present invention only needs to arrange one pressure sensor to realize stress monitoring of multiple rock surfaces of the rock mass to be predicted, thereby reducing the number of sensor arrangements, reducing the difficulty and cost of arranging stress detection sensors, and improving the accuracy of rock burst prediction.

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0042] Figure 1 A schematic diagram of a rockburst prediction method based on wireless passive sensors is shown in FIG. Figure 1 , the method comprises the following steps:

[0043] S1. Arrange a wireless passive pressure sensor in the rock mass to be predicted. The pressure sensor includes a plurality of pressure pads arranged on the side and bottom surfaces, and the pressure pads are made of piezoelectric material.

[0044] See also Figure 2 In the embodiment of the present invention, the pressure sensor is a cubic structure, and the pressure gaskets are respectively arranged on the bottom surface and four side surfaces of the cubic structure. Figure 2 The black filled part is the pressure gasket.

[0045] It can be understood that a wireless passive pressure sensor is a pressure sensor that does not require an external power supply and transmits data wirelessly. This pressure sensor uses the energy in the measurement environment to power itself, thus achieving a completely passive working mode.

[0046] In practical applications, pressure gaskets made of polymer piezoelectric materials are welded to the bottom and four side surfaces of the cube structure. The sensor circuit is integrated into the cube structure and filled with hard materials. The miniaturized built-in antenna for wireless transmission of stress data is arranged on the top surface of the unwelded pressure gasket. When the pressure sensor is installed, the top surface faces the air surface to facilitate the transmission and reception of stress data.

[0047] In an embodiment of the present invention, a wireless passive pressure sensor is arranged in the rock mass to be predicted, specifically including:

[0048] After drilling a hole in the rock mass to be predicted, place the wireless passive pressure sensor into the hole, place the pressure gaskets on the side and bottom of the pressure sensor on the corresponding rock mass surface, and then fill it fully with concrete. After the concrete solidifies, the rock mass to be predicted, concrete and pressure sensor form an integral structure.

[0049] In actual applications, a hole is first drilled in the rock mass to be predicted, and then the pressure sensor is placed into the hole with the bottom surface facing down. The side orientation of the pressure sensor is adjusted according to actual needs, and then it is fully filled with fast-setting and fast-hardening high-strength concrete. After the concrete solidifies, the surrounding rock, filling concrete and pressure sensor form an integral structure to ensure that the stress of the surrounding rock can act on the pressure sensor through the filling concrete.

[0050] S2. The pressure sensor determines stress data of each rock surface corresponding to the pressure gasket based on the deformation amount of each pressure gasket, and wirelessly transmits the stress data.

[0051] In an embodiment of the present invention, the pressure sensor includes a piezoelectric vibrator, and the stress data of the corresponding rock surface is determined according to the change in the resonant frequency of the piezoelectric vibrator; the deformation of the pressure gasket causes the change in the internal capacitance of the pressure gasket, and the change in capacitance causes the change in the resonant frequency of the piezoelectric vibrator.

[0052] In practical applications, after the pressure sensor is installed, it can be used to collect and transmit stress data. Specifically, when the stress of the rock mass changes, the pressure gasket will also undergo corresponding deformation, which will cause a change in the capacitance inside the pressure gasket, which will cause the natural frequency of the piezoelectric vibrator to shift, and then cause a change in the resonant frequency. The stress data of the corresponding rock surface can be determined by the change in the resonant frequency.

[0053] S3. Wirelessly receive the stress data, and determine whether rock burst will occur in the rock mass to be predicted based on the stress data and the ultimate compressive strength of the rock mass.

[0054] In practical applications, low-frequency data of stress data corresponding to each pressure gasket is received through a low-frequency base station, and then converted into corresponding stress values ​​and saved. Finally, whether rock burst will occur in the rock mass to be predicted is determined based on the stress values ​​corresponding to each pressure gasket and the ultimate compressive strength of the rock mass.

[0055] The embodiment of the present invention first determines the maximum stress value among all stress values, and then determines whether the rock mass to be predicted will have rock burst according to the maximum stress value and the ultimate compressive strength of the rock mass. Specifically, it is determined whether the ratio of the maximum stress value to the ultimate compressive strength of the rock mass is greater than the rock burst threshold. If so, it is determined that the rock mass to be predicted will have rock burst, otherwise, it is determined that the rock mass to be predicted will not have rock burst. That is:

[0056] when When , it is determined that the rock mass to be predicted will have rock burst;

[0057] when When , it is determined that the rock mass to be predicted will not have rock burst;

[0058] Among them, F i Indicates the maximum stress value, F UCS represents the ultimate compressive strength of the rock mass, and m1 represents the rockburst threshold.

[0059] When determining that a rock mass to be predicted will have a rock burst, the orientation of the rock mass surface where the rock burst will occur can be determined based on the installation position of the corresponding stress gasket.

[0060] In an embodiment of the present invention, when the ratio of the maximum stress value to the ultimate compressive strength of the rock mass is greater than the rock burst threshold, the rock burst level of the rock mass to be predicted is determined according to the ratio, specifically including:

[0061] If the ratio is within the first preset range, the rockburst level of the rock mass to be predicted is a slight rockburst; if the ratio is within the second preset range, the rockburst level of the rock mass to be predicted is a moderate rockburst; if the ratio is within the third preset range, the rockburst level of the rock mass to be predicted is a strong rockburst; if the ratio is within the fourth preset range, the rockburst level of the rock mass to be predicted is an extremely strong rockburst. That is:

[0062] when When , it is determined that the rock mass to be predicted will have a slight rockburst;

[0063] when When , it is determined that the rock mass to be predicted will have a moderate rockburst;

[0064] when When , it is determined that the rock mass to be predicted will have a strong rockburst;

[0065] when When , it is determined that the rock mass to be predicted will have an extremely strong rockburst;

[0066] Wherein, (m1, m2] represents the first preset range, (m2, m3] represents the second preset range, (m3, m4] represents the third preset range, and (m4, ∞) represents the fourth preset range.

[0067] After the rockburst level of the rock mass to be predicted is determined, a rockburst warning can be issued according to different rockburst levels. For example, if the rockburst level of the rock mass to be predicted is medium rockburst, a rockburst warning corresponding to medium rockburst will be issued. Rockburst warnings can include sound warnings and light warnings, that is, corresponding sounds and lights are issued according to different rockburst levels, so that staff can take corresponding measures according to the rockburst warning.

[0068] In summary, the rockburst prediction method based on wireless passive sensors provided by the embodiment of the present invention arranges wireless passive pressure sensors in the rock mass to be predicted in the high-risk area of ​​rockburst, and uses wireless passive sensing technology to collect and transmit stress data, thereby avoiding the layout difficulties and line damage problems of wired transmission, and improving the reliability of data transmission and rockburst prediction. At the same time, the present invention arranges pressure gaskets on multiple surfaces of the pressure sensor, and only needs to arrange one pressure sensor to realize stress monitoring of multiple rock mass surfaces of the rock mass to be predicted, thereby reducing the number of sensor arrangements, reducing the difficulty and cost of arranging stress detection sensors, and improving the accuracy of rockburst prediction. By determining the rockburst level of the rock mass to be predicted, the accuracy of rockburst prediction is further improved, and rockburst warning is carried out according to the rockburst level of the rock mass to be predicted, so that the staff can take timely measures to avoid rockburst from damaging the surrounding environment and also avoid casualties.

[0069] See also Figure 3 Based on the above technical solution, this embodiment further proposes a rockburst prediction device based on a wireless passive sensor, the device comprising:

[0070] A wireless passive pressure sensor, comprising a plurality of pressure pads arranged on the side and bottom surfaces, wherein the pressure pads are made of piezoelectric material; the pressure sensor is arranged in the rock mass to be predicted, and is used to determine stress data of each rock mass surface corresponding to the pressure pad based on the deformation amount of each pressure pad, and wirelessly transmit the stress data;

[0071] A wireless communication module, used for wirelessly receiving stress data and sending the stress data to a controller;

[0072] The control module is used to receive stress data and determine whether a rock burst will occur in the rock mass to be predicted based on the stress data and the ultimate compressive strength of the rock mass.

[0073] It can be understood that since the rockburst prediction device based on wireless passive sensors described in the embodiment of the present invention is a device for implementing the rockburst prediction method based on wireless passive sensors described in the embodiment, for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method, which will not be repeated here.

Claims

1. A rockburst prediction method based on wireless passive sensors, characterized in that: The method comprises: Arrange a wireless passive pressure sensor in the rock mass to be predicted, wherein the pressure sensor comprises a plurality of pressure pads arranged on the side and bottom surfaces, and the pressure pads are made of piezoelectric material; The pressure sensor determines stress data of each rock mass surface corresponding to the pressure pad based on the deformation amount of each pressure pad, and wirelessly transmits the stress data; The stress data is wirelessly received, and whether a rock burst will occur in the rock mass to be predicted is determined according to the stress data and the ultimate compressive strength of the rock mass.

2. The rockburst prediction method based on wireless passive sensor according to claim 1 is characterized in that: Wireless passive pressure sensors are arranged in the rock mass to be predicted, specifically including: After drilling a hole in the rock mass to be predicted, place the wireless passive pressure sensor into the hole, place the pressure gaskets on the side and bottom of the pressure sensor on the corresponding rock mass surface, and then fill it fully with concrete. After the concrete solidifies, the rock mass to be predicted, concrete and pressure sensor form an integral structure.

3. The rockburst prediction method based on wireless passive sensor according to claim 1 is characterized in that: The pressure sensor is a cube structure, and the pressure gaskets are respectively arranged on the bottom surface and four side surfaces of the cube structure.

4. The rockburst prediction method based on wireless passive sensor according to claim 3 is characterized in that: The pressure sensor uses a built-in antenna to wirelessly transmit stress data, and the built-in antenna is arranged on the top surface of the cube structure.

5. The rockburst prediction method based on wireless passive sensor according to claim 1 is characterized in that: The pressure sensor determines the stress data of each rock mass surface corresponding to the pressure gasket according to the deformation amount of each pressure gasket, specifically including: The pressure sensor includes a piezoelectric vibrator, and the stress data of the corresponding rock surface is determined according to the change in the resonant frequency of the piezoelectric vibrator; the deformation of the pressure gasket causes the change in the internal capacitance of the pressure gasket, and the change in capacitance causes the change in the resonant frequency of the piezoelectric vibrator.

6. The rockburst prediction method based on wireless passive sensor according to claim 1, characterized in that: Determining whether the rock mass to be predicted will have rock burst based on the stress data and the ultimate compressive strength of the rock mass specifically includes: Determine the stress value corresponding to the stress data of each pressure gasket, and determine the maximum stress value among all stress values. Judge whether the rock mass to be predicted will experience rock burst based on the maximum stress value and the ultimate compressive strength of the rock mass. When it is determined that the rock mass to be predicted will experience rock burst, determine the rock mass surface corresponding to the maximum stress value.

7. The rockburst prediction method based on wireless passive sensor according to claim 6, characterized in that: Judging whether the rock mass to be predicted will have rock burst according to the maximum stress value and the ultimate compressive strength of the rock mass specifically includes: It is determined whether the ratio of the maximum stress value to the ultimate compressive strength of the rock mass is greater than the rock burst threshold value. If so, it is determined that the rock mass to be predicted will undergo rock burst. Otherwise, it is determined that the rock mass to be predicted will not undergo rock burst.

8. The rockburst prediction method based on wireless passive sensor according to claim 7, characterized in that: When the ratio of the maximum stress value to the ultimate compressive strength of the rock mass is greater than the rock burst threshold, the rock burst grade of the rock mass to be predicted is determined according to the ratio, specifically including: If the ratio is within the first preset range, the rockburst level of the rock mass to be predicted is a slight rockburst; if the ratio is within the second preset range, the rockburst level of the rock mass to be predicted is a moderate rockburst; if the ratio is within the third preset range, the rockburst level of the rock mass to be predicted is a strong rockburst; if the ratio is within the fourth preset range, the rockburst level of the rock mass to be predicted is an extremely strong rockburst.

9. The rockburst prediction method based on wireless passive sensor according to claim 8, characterized in that: The method further comprises: When it is determined that a rock burst may occur in the rock mass to be predicted, a rock burst warning is issued according to the rock burst grade of the rock mass to be predicted.

10. A rockburst prediction device based on wireless passive sensors, characterized in that: The device comprises: A wireless passive pressure sensor, comprising a plurality of pressure pads arranged on the side and bottom surfaces, wherein the pressure pads are made of piezoelectric material; the pressure sensor is arranged in the rock mass to be predicted, and is used to determine stress data of each rock mass surface corresponding to the pressure pad based on the deformation amount of each pressure pad, and wirelessly transmit the stress data; A wireless communication module, used for wirelessly receiving stress data and sending the stress data to a controller; The control module is used to receive stress data and determine whether a rock burst will occur in the rock mass to be predicted based on the stress data and the ultimate compressive strength of the rock mass.

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