Quarry slope radar monitoring system

Abstract

The invention discloses a quarry slope radar monitoring system, relates to the technical field of quarry radar monitoring and processing, and aims to solve the problem of influence of dust and vibration on slope radar detection images by collecting dust information in air, dynamically adjusting radar transmitting power and enabling pre-corrected signals to enter an imaging algorithm. The method is advantaged in that image definition and identifiability are substantially improved, accurate image data are provided for subsequent geological analysis, safety monitoring and other work, vibration acceleration data are acquired, a piezoelectric ceramic micro-actuator is driven to cooperatively offset instant vibration, and the radar equipment can be effectively protected even under the condition that the radar equipment is influenced by vibration. And the generated slope radar image can still maintain relatively high geometric accuracy and phase accuracy, so that the image truthfully reflects the real form of the slope, and a reliable basis is provided for slope stability evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of quarry radar monitoring and processing, in particular to a quarry slope radar monitoring system. Background Art

[0002] If a landslide or other disaster occurs on a quarry's slopes, it poses a significant threat to the safety of on-site workers. Radar monitoring can continuously acquire real-time displacement and deformation data on the slopes, identifying potential landslide hazards in advance. Data analysis can provide early warnings before danger occurs, allowing for timely evacuation and preventing casualties. It also helps rationalize quarrying operations. Monitoring data allows managers to understand changes in slope stability and adjust mining areas and schedules. For example, if a decrease in stability is detected in a particular area of the slope, nearby mining activities can be suspended to prevent further damage to the slope.

[0003] However, existing slope radar monitoring systems are mostly used to monitor slope conditions, and do not take into account the quarry environment. The large amount of dust generated by quarrying operations will scatter and absorb radar waves, causing energy loss and weakening of echo signals, resulting in blurred slope images and affecting the observation of details and clarity. At the same time, ground vibrations caused by blasting operations and heavy equipment operation in the quarry will be transmitted to the radar monitoring equipment, causing internal components and antenna parts to shift or loosen, resulting in beam pointing deviation, affecting image accuracy and stability, and causing image jitter or displacement measurement deviation.

[0004] Therefore, a quarry slope radar monitoring system is needed. Summary of the Invention

[0005] In order to solve all or part of the above problems, the present invention aims to provide a quarry slope radar monitoring system to solve the problem of the influence of dust and vibration on the slope radar detection image.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a quarry slope radar monitoring system, comprising: Dust adaptive compensation unit: collects dust information in the air and feeds it back to the built-in dust-radar wave attenuation model via wireless transmission. Combined with the parameter adjustment algorithm based on particle swarm optimization, it dynamically adjusts the radar transmission power to compensate for signal loss. Vibration cancellation and correction unit: collects vibration acceleration data, calculates the reverse vibration cancellation signal through Kalman filtering and control algorithm, and drives the piezoelectric ceramic microactuator to coordinately cancel the immediate vibration; Radar transmitting unit: After being processed by the vibration cancellation and correction module, the radar generates high-frequency radar waves based on the transmission power adjusted by the dust adaptive compensation module, and directs the radar waves toward the quarry slope; Radar receiving unit: captures the weak radar echo signal reflected by the slope and converts the radar echo signal from a weak electromagnetic signal to an electrical signal; Image generation unit: Receives the signal processed by the radar receiving module and uses an algorithm based on matching radar principles with imaging technology to convert the signal data into a slope radar image, presenting the slope surface morphology and constructing a two-dimensional or three-dimensional image model; Early warning unit: Based on big data analysis and machine learning models, it learns in advance the normal state of images under different slope conditions and establishes a slope abnormal state recognition model. Once the real-time image deviates from the normal state and meets the early warning conditions, the sound and light alarm device is immediately triggered to alert on-site staff.

[0007] Furthermore, the dust adaptive compensation unit includes: Dust sensing submodule: This submodule is composed of a sensing network consisting of multiple dust sensors distributed in the quarry near the slope radar, responsible for collecting dust information in the air in real time; Data transmission submodule: transmits the data collected by the dust sensing submodule to the power calculation submodule via wireless communication; Power calculation submodule: The power calculation submodule contains a built-in dust-radar wave attenuation model. The dust-radar wave attenuation model is constructed by setting the dust concentration to , the particle size distribution parameter is , the component influencing factor is , the attenuation model is obtained as ,in is the attenuation coefficient, is a function related to the dust characteristics. At the same time, let the initial power of the radar be , the adjusted power is , the power adjustment formula is obtained ,in is an adjustment coefficient determined by the particle swarm optimization algorithm; Power control submodule: sends the adjusted transmit power instruction calculated by the power calculation submodule to the radar transmit unit, so that the radar transmit unit transmits according to the new power.

[0008] Furthermore, the particle swarm optimization algorithm logic is as follows: Initialization: Initialize a group of particles, each particle represents a power adjustment coefficient , and initialize the velocity of each particle at the same time; Calculate fitness: For each particle, its corresponding Substitute the value into the power adjustment formula to calculate the adjusted transmission power, and then calculate the fitness of the particle based on the quality of the radar signal at the current power; Update particle position and velocity: Update the position and velocity of each particle based on its own optimal position, the optimal position of the group, and the current velocity; Termination condition judgment: When the preset number of iterations is reached or the convergence condition is met, the algorithm terminates. At this time, the optimal position of the group corresponds to The value is the final power adjustment factor.

[0009] Furthermore, the formula for updating the particle position and velocity is as follows:

[0010]

[0011] in, It is The velocity of a particle at time It is The position of a particle at time is the inertia weight, and is the learning factor, and is a number between 0 and 1, It is The individual optimal position of each particle, is the optimal position of the group.

[0012] Furthermore, the vibration cancellation and correction unit includes: Vibration sensing submodule: This module consists of an accelerometer installed on the radar device base. It is responsible for collecting vibration acceleration data of the radar device base in real time. By measuring the acceleration changes in three axes, it can fully perceive the vibration status of the device. Data transmission submodule: transmits the vibration acceleration data collected by the vibration sensing submodule to the instant vibration cancellation submodule via wireless communication; Instant vibration cancellation submodule: Receives data from the data transmission submodule, uses the Kalman filter algorithm to estimate the vibration acceleration data containing noise, obtains accurate vibration state information, and uses the control algorithm to calculate the reverse vibration cancellation signal based on the vibration state information. The reverse vibration cancellation signal is sent to the piezoelectric ceramic microactuator, which generates a force opposite to the vibration direction, thereby collaboratively canceling the instant vibration.

[0013] Furthermore, the vibration cancellation and correction module also uses a displacement deviation prediction algorithm based on a long short-term memory network to analyze long-term vibration history data, predict the cumulative displacement deviation, automatically start the correction program when the threshold is exceeded, and fine-tune the signal phase and calibrate the receiving angle.

[0014] Furthermore, the control algorithm uses the PID control algorithm, assuming is the vibration state error, is the control signal, and the specific formula is as follows:

[0015] in is the proportional gain, is the integral gain, is the differential gain, The integral term of the time error, Differential of the error.

[0016] Furthermore, the image generation unit includes: Signal adaptation and preprocessing module: This module obtains the processed signal from the radar receiving module and connects to the dust adaptive compensation unit and the vibration cancellation and correction unit to pre-correct the signal before the imaging algorithm is executed. Imaging algorithm execution module: Executes imaging tasks through an algorithm based on deep matching of radar principles and imaging technology, ultimately converting signal data into slope radar images, presenting the slope surface morphology, and constructing a two-dimensional or three-dimensional image model.

[0017] Furthermore, the signal adaptation and preprocessing module is connected to the dust adaptive compensation unit to obtain real-time dust information. Based on the dust-radar wave attenuation model, the known relationship between dust particle size and radar wave wavelength, and the Mie scattering theory, the radar wave phase deviation caused by dust of different particle sizes is calculated, and the received signal is pre-corrected before the imaging algorithm is executed.

[0018] Furthermore, the signal adaptation and preprocessing module is connected to the vibration cancellation and correction unit to obtain vibration acceleration, instant vibration cancellation status and cumulative displacement deviation data. Based on the mechanical vibration and imaging geometric relationship model, the impact mechanism of vibration on the imaging process is analyzed. When the displacement deviation caused by vibration is detected, the coordinate mapping relationship is dynamically adjusted in the imaging algorithm to compensate for the image pixel displacement caused by equipment vibration.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. The radar monitoring system for quarry slopes proposed in this invention collects dust information in the air and transmits it wirelessly to a built-in dust-radar wave attenuation model. Combined with a parameter adjustment algorithm based on particle swarm optimization, it dynamically adjusts radar transmission power, compensates for signal loss, and feeds the pre-corrected signal into the imaging algorithm. This system can effectively reduce image blur, ghosting, and uneven brightness caused by dust interference, significantly improve image clarity and recognizability, and make subtle textures and cracks on the slope surface more clearly presented in the image, providing accurate image data for subsequent geological analysis, safety monitoring, and other tasks.

[0020] 2. The present invention proposes a quarry slope radar monitoring system that collects vibration acceleration data, calculates a reverse vibration cancellation signal through Kalman filtering and control algorithms, and drives piezoelectric ceramic microactuators to collaboratively cancel out immediate vibrations. Even when the radar equipment is affected by vibration, the generated slope radar image can still maintain high geometric precision and phase accuracy, avoiding problems such as image distortion, deformation, and phase confusion, so that the image faithfully reflects the true shape of the slope, providing a reliable basis for slope stability assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a module diagram of the quarry slope radar monitoring system of the present invention; Figure 2 This is a module diagram of the dust adaptive compensation unit of the quarry slope radar monitoring system of the present invention; Figure 3 This is a module diagram of the vibration cancellation and correction unit of the quarry slope radar monitoring system of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] like Figure 1-Figure 3 As shown, a quarry slope radar monitoring system includes: Dust adaptive compensation unit: collects dust information in the air, including dust concentration, particle size distribution, and composition information, and feeds it back to the built-in dust-radar wave attenuation model via wireless transmission. Combined with the parameter adjustment algorithm based on particle swarm optimization, it dynamically adjusts the radar transmission power to compensate for signal loss. The specific dust adaptive compensation unit includes: Dust sensing submodule: This submodule is composed of a sensing network consisting of multiple dust sensors distributed in the quarry near the slope radar, responsible for collecting dust information in the air in real time; Data transmission submodule: transmits the data collected by the dust sensing submodule to the power calculation submodule via wireless communication; Power calculation submodule: The power calculation submodule contains a built-in dust-radar wave attenuation model. The dust-radar wave attenuation model is constructed by setting the dust concentration to , the particle size distribution parameter is , which can be comprehensive parameters such as the average and standard deviation of particle size, and the component influencing factor is , the dust of different components has different attenuation degrees on radar waves, and the attenuation model is obtained as follows: ,in is the attenuation coefficient, is a function related to the dust characteristics; Assume that the initial radar transmission power is , the adjusted power is , the initial power of the radar transmission is , the adjusted power is , the power adjustment formula is obtained ,in is an adjustment coefficient determined by the particle swarm optimization algorithm; The logic of the particle swarm optimization algorithm is as follows: Initialization: Initialize a group of particles, each particle represents a power adjustment coefficient , and initialize the velocity of each particle at the same time; Calculate fitness: For each particle, its corresponding Substitute the value into the power adjustment formula to calculate the adjusted transmission power, and then calculate the fitness of the particle based on the quality of the radar signal at the current power; Update particle position and speed: according to the particle's own best position, that is, the one that makes the fitness the best in history value, the best position of the group, the particle with the best fitness among all particles value, and the current velocity, to update the position of each particle (i.e. value) and speed; The formula for updating particle position and velocity is as follows:

[0024]

[0025] in, It is The velocity of a particle at time It is The position of a particle at time value), is the inertia weight, and is the learning factor, and is a number between 0 and 1, It is The individual optimal position of each particle, is the optimal position of the group; Termination condition judgment: When the preset number of iterations is reached or the convergence condition is met (such as the optimal fitness of the particle swarm no longer changes significantly after multiple iterations), the algorithm terminates. At this time, the optimal position of the group corresponds to The value is the final power adjustment factor; Power control submodule: sends the adjusted transmit power instruction calculated by the power calculation submodule to the radar transmit unit, so that the radar transmit unit transmits according to the new power.

[0026] Vibration cancellation and correction unit: collects vibration acceleration data, calculates the reverse vibration cancellation signal through Kalman filtering and control algorithm, and drives the piezoelectric ceramic microactuator to coordinately cancel the immediate vibration; The vibration cancellation and correction unit includes: Vibration sensing submodule: This module consists of an accelerometer installed on the radar device base. It is responsible for collecting vibration acceleration data of the radar device base in real time. By measuring the acceleration changes in three axes, it can fully perceive the vibration status of the device. Data transmission submodule: transmits the vibration acceleration data collected by the vibration sensing submodule to the instant vibration cancellation submodule via wireless communication; The instant vibration cancellation submodule receives data from the data transmission submodule and uses the Kalman filter algorithm to estimate the noisy vibration acceleration data to obtain accurate vibration state information. The PID control algorithm then calculates a reverse vibration cancellation signal based on this vibration state information. This reverse vibration cancellation signal is sent to the piezoelectric ceramic microactuator, which generates a force opposite to the vibration direction, thereby collaboratively canceling the instant vibration. The PID control algorithm first sets is the vibration state error (the difference between the desired vibration-free state and the actual vibration state), is the control signal (the signal sent to the piezoelectric ceramic microactuator), and the specific formula is as follows:

[0027] in is the proportional gain, is the integral gain, is the differential gain, The integral term of the time error reflects the cumulative effect of the error over a period of time, and its unit is related to the product of the error and time. The differential of the error, that is, the rate of change of the error, reflects the rate of change of the error over time, and the unit is related to the rate of change of the error.

[0028] The vibration cancellation and correction module also uses a displacement deviation prediction algorithm based on a long short-term memory network to analyze long-term vibration history data, predict the cumulative displacement deviation, automatically start the correction program when the threshold is exceeded, and fine-tune the signal phase and calibrate the receiving angle.

[0029] Radar transmitting unit: After being processed by the vibration cancellation and correction module, the radar generates high-frequency radar waves based on the transmission power adjusted by the dust adaptive compensation module, and directs the radar waves toward the quarry slope.

[0030] Radar receiving unit: captures the weak radar echo signal reflected by the slope and converts the radar echo signal from a weak electromagnetic signal into an electrical signal.

[0031] Image generation unit: Receives the signal processed by the radar receiving module and uses an algorithm based on matching radar principles with imaging technology to convert the signal data into a slope radar image, presenting the slope surface morphology and constructing a two-dimensional or three-dimensional image model; The image generation unit includes: Signal Adaptation and Preprocessing Module: This module obtains processed signals from the radar receiving module and interfaces with the dust adaptive compensation unit to obtain real-time dust information. Based on the dust-radar wave attenuation model, the known relationship between dust particle size and radar wave wavelength, and Mie scattering theory, the module calculates the radar wave phase deviation caused by dust particles of different sizes. Furthermore, the module interfaces with the vibration cancellation and correction unit to obtain vibration acceleration, instantaneous vibration cancellation, and accumulated displacement deviation data. Based on the mechanical vibration and imaging geometry relationship model, the module analyzes the impact of vibration on the imaging process. When a displacement deviation caused by vibration is detected, the imaging algorithm dynamically adjusts the coordinate mapping relationship to compensate for the image pixel displacement caused by equipment vibration. The signal is pre-corrected before the imaging algorithm is executed. Imaging algorithm execution module: Executes imaging tasks through an algorithm based on deep matching of radar principles and imaging technology, ultimately converting signal data into slope radar images, presenting the slope surface morphology, and constructing a two-dimensional or three-dimensional image model.

[0032] Early warning unit: Based on big data analysis and machine learning models, it learns in advance the normal state of images under different slope conditions and establishes a slope abnormal state recognition model. Once the real-time image deviates from the normal state and meets the early warning conditions, the sound and light alarm device is immediately triggered to alert on-site staff.

[0033] It should be noted that, in the description of this application, it should be understood that the terms "length", "thickness", "inside", "outside", "axial", "radial", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0034] Furthermore, relational terms such as first and second, etc., are used solely to distinguish one entity or operation from another entity or operation and do not necessarily require or imply any actual relationship or order between such entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed or that are inherent to such process, method, article, or apparatus.

[0035] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A quarry slope radar monitoring system, characterized in that: include: Dust adaptive compensation unit: collects dust information in the air and feeds it back to the built-in dust-radar wave attenuation model via wireless transmission. Combined with the parameter adjustment algorithm based on particle swarm optimization, it dynamically adjusts the radar transmission power to compensate for signal loss. Vibration cancellation and correction unit: collects vibration acceleration data, calculates the reverse vibration cancellation signal through Kalman filtering and control algorithm, and drives the piezoelectric ceramic microactuator to coordinately cancel the immediate vibration; Radar transmitting unit: After being processed by the vibration cancellation and correction module, the radar generates high-frequency radar waves based on the transmission power adjusted by the dust adaptive compensation module, and directs the radar waves toward the quarry slope; Radar receiving unit: captures the weak radar echo signal reflected by the slope and converts the radar echo signal from a weak electromagnetic signal to an electrical signal; Image generation unit: Receives the signal processed by the radar receiving module and uses an algorithm based on matching radar principles with imaging technology to convert the signal data into a slope radar image, presenting the slope surface morphology and constructing a two-dimensional or three-dimensional image model; Early warning unit: Based on big data analysis and machine learning models, it learns in advance the normal state of images under different slope conditions and establishes a slope abnormal state recognition model. Once the real-time image deviates from the normal state and meets the early warning conditions, the sound and light alarm device is immediately triggered to alert on-site staff.

2. A quarry slope radar monitoring system according to claim 1, characterized in that: The dust adaptive compensation unit includes: Dust sensing submodule: This submodule is composed of a sensing network consisting of multiple dust sensors distributed in the quarry near the slope radar, responsible for collecting dust information in the air in real time; Data transmission submodule: transmits the data collected by the dust sensing submodule to the power calculation submodule via wireless communication; Power calculation submodule: The power calculation submodule contains a built-in dust-radar wave attenuation model. The dust-radar wave attenuation model is constructed by setting the dust concentration to , the particle size distribution parameter is , the component influencing factor is , the attenuation model is obtained as ,in is the attenuation coefficient, is a function related to the dust characteristics. At the same time, let the initial power of the radar be , the adjusted power is , the power adjustment formula is obtained ,in is an adjustment coefficient determined by the particle swarm optimization algorithm; Power control submodule: sends the adjusted transmit power instruction calculated by the power calculation submodule to the radar transmit unit, so that the radar transmit unit transmits according to the new power.

3. A quarry slope radar monitoring system according to claim 2, characterized in that: The particle swarm optimization algorithm logic is as follows: Initialization: Initialize a group of particles, each particle represents a power adjustment coefficient , and initialize the velocity of each particle at the same time; Calculate fitness: For each particle, its corresponding Substitute the value into the power adjustment formula to calculate the adjusted transmission power, and then calculate the fitness of the particle based on the quality of the radar signal at the current power; Update particle position and velocity: Update the position and velocity of each particle based on its own optimal position, the optimal position of the group, and the current velocity; Termination condition judgment: When the preset number of iterations is reached or the convergence condition is met, the algorithm terminates. At this time, the optimal position of the group corresponds to The value is the final power adjustment factor.

4. A quarry slope radar monitoring system as claimed in claim 3, characterized in that: The formula for updating particle position and velocity is as follows: in, It is The velocity of a particle at time It is The position of a particle at time is the inertia weight, and is the learning factor, and is a number between 0 and 1, It is The individual optimal position of each particle, is the optimal position of the group.

5. The quarry slope radar monitoring system according to claim 1, characterized in that: The vibration cancellation and correction unit includes: Vibration sensing submodule: This module consists of an accelerometer installed on the radar device base. It is responsible for collecting vibration acceleration data of the radar device base in real time. By measuring the acceleration changes in three axes, it can fully perceive the vibration status of the device. Data transmission submodule: transmits the vibration acceleration data collected by the vibration sensing submodule to the instant vibration cancellation submodule via wireless communication; Instant vibration cancellation submodule: Receives data from the data transmission submodule, uses the Kalman filter algorithm to estimate the vibration acceleration data containing noise, obtains accurate vibration state information, and uses the control algorithm to calculate the reverse vibration cancellation signal based on the vibration state information. The reverse vibration cancellation signal is sent to the piezoelectric ceramic microactuator, which generates a force opposite to the vibration direction, thereby collaboratively canceling the instant vibration.

6. A quarry slope radar monitoring system as claimed in claim 5, characterized in that: The vibration cancellation and correction module also uses a displacement deviation prediction algorithm based on a long short-term memory network to analyze long-term vibration history data, predict the cumulative displacement deviation, automatically start the correction program when the threshold is exceeded, and fine-tune the signal phase and calibrate the receiving angle.

7. A quarry slope radar monitoring system according to claim 5, characterized in that: The control algorithm uses the PID control algorithm. is the vibration state error, is the control signal, and the specific formula is as follows: in is the proportional gain, is the integral gain, is the differential gain, The integral term of the time error, Differential of the error.

8. The quarry slope radar monitoring system according to claim 1, characterized in that: The image generation unit includes: Signal adaptation and preprocessing module: This module obtains the processed signal from the radar receiving module and connects to the dust adaptive compensation unit and the vibration cancellation and correction unit to pre-correct the signal before the imaging algorithm is executed. Imaging algorithm execution module: Executes imaging tasks through an algorithm based on deep matching of radar principles and imaging technology, ultimately converting signal data into slope radar images, presenting the slope surface morphology, and constructing a two-dimensional or three-dimensional image model.

9. A quarry slope radar monitoring system according to claim 8, characterized in that: The signal adaptation and preprocessing module is connected to the dust adaptive compensation unit to obtain real-time dust information. Based on the dust-radar wave attenuation model, the known relationship between dust particle size and radar wave wavelength, and the Mie scattering theory, the radar wave phase deviation caused by dust of different particle sizes is calculated. Before the imaging algorithm is executed, the received signal is pre-corrected.

10. The quarry slope radar monitoring system according to claim 8, characterized in that: The signal adaptation and preprocessing module is connected to the vibration cancellation and correction unit to obtain vibration acceleration, instantaneous vibration cancellation status, and cumulative displacement deviation data. Based on the mechanical vibration and imaging geometric relationship model, the module analyzes the impact mechanism of vibration on the imaging process. When the displacement deviation caused by vibration is detected, the coordinate mapping relationship is dynamically adjusted in the imaging algorithm to compensate for the image pixel displacement caused by equipment vibration.

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