Real-time evaluation system and method for rock weathering rate based on temporal temperature

Abstract

The present invention discloses a real-time evaluation system for rock weathering rate based on sequential temperature, which includes a data receiving and interpreting module, multiple groups of temperature sensors and strain sensors arranged at different depths. The temperature sensors are arranged inside, and the strain sensors are arranged outside. Each group of temperature sensors and strain sensors is connected to a wireless transmission module through a separate data transmission line. The wireless transmission module sends the data collected by the temperature sensors and strain sensors to the data receiving and interpreting module, and the interpretation result is fed back from the cloud to the local for real-time display. The present invention also discloses a real-time evaluation method for rock weathering rate based on sequential temperature. The present invention can in-situ and real-time interpret and visualize the slow rock weathering rate.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic monitoring and interpretation of rock weathering, and particularly to a real-time evaluation system and method for rock weathering rate based on sequential temperature. Background Art

[0002] Weathering is the main factor causing the deterioration of rock mechanical properties. It is of great significance to accurately determine the weathering depth of rocks, accurately evaluate the weathering degree of rocks, and make divisions. At present, for the evaluation and division of rock weathering degree and the determination of rock weathering depth, most engineering geological qualitative methods are adopted, which are determined through comprehensive analysis from aspects such as rock mass structure, rock color, mineral composition, fragmentation degree, and excavation difficulty.

[0003] The division of rock weathering degree and the research on engineering characteristics play a key role in the selection of the construction base surface of large-scale hydropower projects, high-rise buildings, road bridges and other projects, as well as the determination of the foundation design and construction plan, and also have important significance for evaluating the stability of surrounding rocks and slope engineering.

[0004] The thermal diffusion rate is a parameter for evaluating rock weathering. However, existing laboratory technologies cannot measure the thermal diffusion rate of water-bearing rocks, and in-situ monitoring and real-time measurement cannot be carried out. Summary of the Invention

[0005] To solve the problems existing in the prior art, the purpose of the present invention is to provide a real-time evaluation system and method for rock weathering rate based on sequential temperature, which can in-situ and real-time interpret and visualize the slow rock weathering rate.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is a real-time evaluation system for rock weathering rate based on sequential temperature, including a data receiving and interpreting module, multiple groups of temperature sensors and strain sensors arranged at different depths. The temperature sensors are arranged inside, and the strain sensors are arranged outside. Each group of temperature sensors and strain sensors is connected to a wireless transmission module through a separate data transmission line. The wireless transmission module sends the data collected by the temperature sensors and strain sensors to the data receiving and interpreting module, and the interpretation result is fed back from the cloud to the local for real-time display.

[0007] As a further improvement of the present invention, it further includes a power supply module for power supply. The power supply module automatically matches the voltage according to the working state of the sensors to reduce the energy consumption of the system.

[0008] As a further improvement of the present invention, the space between the temperature sensors and the strain sensors is backfilled with a mixture of dense cement and drilled powder, and the drilling outlet is sealed with waterproof cement to prevent rainwater from seeping in.

[0009] The present invention also provides a real-time evaluation method for the weathering rate of rock based on time-series temperature, which is implemented by using the real-time evaluation system for the weathering rate of rock based on time-series temperature as described above. The method includes the following steps:

[0010] Step 1: Set the acquisition frequencies and laying depths of multiple groups of temperature sensors and strain sensors.

[0011] Step 2: Use a multi-channel high-speed data acquisition instrument to regularly collect the analog data of the temperature sensors and strain sensors. Before, during, and after important events, the cloud automatically monitors the data mutation points and automatically adjusts the acquisition frequency to obtain high-frequency data. After the data is integrated and stored locally, it is synchronously transmitted to the cloud platform via a 4G network.

[0012] Step 3: The data reception and interpretation module corrects the received data, saves the original data using a vectorized array, judges the time consistency of the data of multiple groups of sensors, and selects the common time interval. Outliers in the time series are removed, and data points outside 3 times the IQR are deleted. For null values, they are first replaced with the maximum value and then removed. Linear interpolation is used to fill in the missing values. The data interpretation module can resample the data according to requirements to reduce the data scale and subsequent calculation cost.

[0013] Step 4: Perform phase fitting: The interpretation module performs wavelet power spectrum analysis on the data of each sensor to identify the frequency distribution and intensity of the signal, and outputs the signal frequency with high signal-to-noise ratio. Then, filter the corrected data of a specific frequency, that is, use a band-pass filter to extract the signal components of the specific frequency, and set the data window length according to the data length to segment the wave spectrum. Each segment of data can overlap or be independent of each other. Use the diurnal tide theory formula to perform phase fitting on each data window, give initial values to the amplitude, phase, and residual, and use the least squares method to iterate to obtain and extract the amplitude, phase, and residual of each group of temperature sensors and strain sensors, and draw a fitting curve. The interpretation module automatically adjusts the window length, performs a sensitivity test on the phase stability, and outputs the result with the highest fitting accuracy.

[0014] Step 5: Calculate and display the thermal diffusivity of the rock: Calculate the thermal diffusivity from the one-dimensional heat conduction equation. For the phase of the first window length, use the phase difference between adjacent depths and the diurnal tide angular frequency to calculate the thermal diffusivity of the rock mass in the adjacent depth interval. And so on, calculate the thermal diffusivity for each window serial number time period, then draw a scatter plot of time-thermal diffusivity coefficient for the entire time period and fit a trend line to obtain the final real-time rock mass weathering rate characterized by the thermal diffusivity. Similarly, calculate the thermal diffusivity using different diurnal tide components, compare the calculation accuracies of different diurnal tide components, and give the calculation result with the highest accuracy. Finally, feedback the rock mass weathering rate to the local area through the cloud platform and display it on the high-definition electronic board at the monitoring site.

[0015] Step 6: Based on the mutation depth of the thermal diffusivity and the strain mutation depth, obtain the weathering depth of the rock, providing model parameters for the restoration of stone cultural relics, the stability and early warning of rock mass engineering.

[0016] As a further improvement of the present invention, in Step 1, the acquisition frequency of multiple groups of temperature sensors and strain sensors is not less than 4 times per day, and the distribution depth interval is 0.01 m - 0.04 m; within intact rock, the deepest sensor layout depth should reach at least 30 cm, and the monitoring of the stability of engineering rock masses needs to be arranged across important structural planes.

[0017] As a further improvement of the present invention, in Step 1, to ensure the mutual disturbance between sensors and the influence of the temperature distribution change after drilling, each group of sensors is placed in a separate hole and backfilled with cement; analog output type sensors are used.

[0018] The beneficial effects of the present invention are:

[0019] The present invention can in-situ and real-time interpret and visualize the slow rock weathering rate, providing reliable data for climate change and the protection of stone cultural relics. In the verification tests that have been carried out, real-time measurements comparable to laboratory accuracy have been obtained, and the relevant equipment is less affected by the environment, has a simple structure, and stable performance. Description of the Drawings

[0020] Figure 1 It is the system block diagram of the embodiment of the present invention;

[0021] Figure 2 It is the schematic diagram of the data reception and interpretation module in the embodiment of the present invention;

[0022] Figure 3 It is the schematic diagram of the data correction and interpretation result in the embodiment of the present invention;

[0023] Figure 4 It is the schematic diagram of the calculation result of the rock weathering rate (using the thermal diffusion rate as a characterization parameter) in the embodiment of the present invention. Detailed Embodiment

[0024] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0025] Embodiment

[0026] As Figure 1 shown, a real-time evaluation system for rock weathering rate based on sequential temperature includes as Figure 2The data receiving and interpreting module 6 shown, multiple groups of temperature sensors 1 and strain sensors 2 arranged at different depths, with the temperature sensors 1 arranged on the inner side and the strain sensors 2 arranged on the outer side. Each group of temperature sensors 1 and strain sensors 2 is connected to the wireless transmission module 5 through a separate data transmission line 3, and the wireless transmission module 5 sends the data collected by the temperature sensors 1 and strain sensors 2 to the data receiving and interpreting module 6; it also includes a power supply module 4 for power supply.

[0027] The temperature sensors 1 are on the inner side and the strain sensors 2 are on the outer side. After mixing dense cement and drilled powder, the space between the sensors is backfilled. Waterproof cement is used for sealing at the rock outlet. The data transmission of each sensor adopts a separate channel and is connected to the data acquisition and transmission module, which contains a storage medium as a backup, and the Internet of Things card provides online transmission guarantee. The terminal receiver is placed indoors, and data can be synchronized from the cloud in real time for interpretation work.

[0028] This embodiment also provides a real-time evaluation method for the rock weathering rate based on chronological temperature, including:

[0029] The first step: Set the acquisition frequencies of the temperature and strain sensors, and the laying depths of the sensors.

[0030] The interval between the sensors is between 0.01 - 0.04 m, which is determined according to the detection accuracy requirements.

[0031] The second step: Power on for regular acquisition and transmit the data.

[0032] The set acquisition frequency is not less than 4 times a day.

[0033] The third step: The receiving terminal corrects the data.

[0034] For data correction, outliers in the time series need to be removed first, and linear interpolation is used to fill in the missing values.

[0035] The fourth step: Perform phase fitting.

[0036] Filter the corrected data to extract the 12-hour periodic spectrum. Set the data window length (the window length can be adjusted according to needs), divide the wave, use the diurnal tide theory formula to fit each data window, obtain and extract the amplitude, phase, and residual, and draw the fitting curve.

[0037] And so on, obtain the amplitude, phase, and residual of the data of each depth sensor, and draw the fitting curve, as Figure 3 shown.

[0038] The fifth step: As Figure 4 shown, calculate the thermal diffusion rate of the rock and display it on the terminal;

[0039] From the one-dimensional heat conduction equation, calculate the heat diffusion rate: For the phase of the first window length, use the phase interpolation of adjacent depths and the diurnal tidal angular frequency to calculate the heat diffusion coefficient of the rock mass in the adjacent depth interval, and so on. Calculate the heat diffusion coefficient for each time period (window number), then plot the scatter points of time-heat diffusion coefficient for the entire time period and fit the trend line to obtain the final real-time rock mass weathering rate.

[0040] The embodiments described above only represent the specific implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A real-time evaluation method for rock weathering rate based on time series temperature, characterized in that: The real-time rock weathering rate evaluation system based on time series temperature is adopted. The system includes a data receiving and interpretation module and multiple groups of temperature sensors and strain sensors arranged at different depths. The temperature sensors are arranged on the inside and the strain sensors are arranged on the outside. Each group of temperature sensors and strain sensors is connected to a wireless transmission module through a separate data transmission line. The wireless transmission module sends the data collected by the temperature sensors and strain sensors to the data receiving and interpretation module. The interpretation results are fed back from the cloud to the local for real-time display. The method comprises the following steps: Step 1: Set the acquisition frequency and layout depth of multiple groups of temperature sensors and strain sensors; Step 2: Use a multi-channel high-speed data acquisition instrument to regularly collect analog data from temperature sensors and strain sensors. Before, during and after important events, the cloud automatically monitors data mutation points and automatically adjusts the collection frequency to obtain high-frequency data. After the data is integrated and stored locally, it is synchronously transmitted to the cloud platform using the 4G network. Step 3: The data receiving and interpretation module corrects the received data, uses vectorized arrays to save the original data, judges the time consistency of multiple sets of sensor data, and selects the common time interval; removes outliers from the time series, deletes data points outside 3 times the IQR, replaces null values ​​with maximum values ​​before removing them, and uses linear interpolation to fill in missing values; the data interpretation module can resample the data according to needs to reduce the data size and subsequent computing costs; Step 4, perform phase fitting: the interpretation module performs wavelet power spectrum analysis on the data of each sensor, identifies the frequency distribution and intensity of the signal, and outputs the signal frequency with a high signal-to-noise ratio; then, the correction data of a specific frequency is filtered, that is, a bandpass filter is used to extract the signal component of a specific frequency, and the data window length is set according to the data length to segment the spectrum. Each segment of data can overlap or be independent of each other; the phase of each data window is fitted using the daily tide theory formula, and the initial values ​​of the amplitude, phase, and residual are given. The amplitude, phase, and residual of each group of temperature sensors and strain sensors are iteratively obtained and extracted using the least squares method, and the fitting curve is drawn; the interpretation module automatically adjusts the window length, performs a sensitivity test on the phase stability, and outputs the result with the highest fitting accuracy; Step 5, calculate and display the thermal diffusion rate of rock: calculate the thermal diffusivity by the one-dimensional heat conduction equation, and for the phase of the first window length, use the phase difference between adjacent depths and the angular frequency of the daily tide to calculate the thermal diffusivity of the rock mass in adjacent depth intervals; and so on, calculate the thermal diffusivity of each window sequence time period, and then perform a time-thermal diffusion coefficient scatter plot for the entire period, and fit the trend line to obtain the final real-time rock weathering rate represented by the thermal diffusivity; similarly, use different daily tide components to calculate the thermal diffusivity, compare the calculation accuracy of different daily tide components, and give the calculation result with the highest accuracy; finally, feed back the rock weathering rate to the local through the cloud platform and display it on the high-definition electronic board at the monitoring site; Step 6: Based on the mutation depth of thermal diffusivity and strain mutation depth, the weathering depth of rock is obtained to provide model parameters for stone cultural relics restoration, rock engineering stability and early warning.

2. The rock weathering rate evaluation method based on time series temperature according to claim 1 is characterized in that: In step 1, the acquisition frequency of multiple groups of temperature sensors and strain sensors is not less than 4 times a day, and the distribution depth interval is 0.01m-0.04m; in intact rock, the deepest sensor deployment depth should reach at least 30cm, and engineering rock stability monitoring needs to be deployed across important structural surfaces.

3. The rock weathering rate evaluation method based on time series temperature according to claim 1, characterized in that: In step 1, in order to avoid mutual disturbance between sensors and the influence of temperature distribution changes after drilling, each group of sensors is placed in a separate hole and backfilled with cement; analog output type sensors are used.

4. The method for real-time evaluation of rock weathering rate based on time series temperature according to claim 1, characterized in that: It also includes a power supply module for power supply, and the power supply module automatically matches the voltage according to the working state of the sensor to reduce the energy consumption of the system.

5. The method for real-time evaluation of rock weathering rate based on time series temperature according to claim 1, characterized in that: The space between the temperature sensor and the strain sensor is backfilled with a mixture of dense cement and drilled powder, and waterproof cement is used to seal the drill hole outlet to prevent rainwater from seeping in.

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