A quarry slope radar monitoring system

Abstract

This invention discloses a quarry slope radar monitoring system, belonging to the field of quarry radar monitoring and processing technology. In order to solve the problem of the influence of dust and vibration on slope radar detection images, the system collects dust information in the air, dynamically adjusts the radar transmission power, and inputs the pre-corrected signal into the imaging algorithm, significantly improving image clarity and recognizability. This provides accurate image data for subsequent geological analysis, safety monitoring, and other work. By collecting vibration acceleration data, the system drives piezoelectric ceramic micro-actuators to cooperate in canceling instantaneous vibrations. Even when the radar equipment is affected by vibration, the generated slope radar image can still maintain high geometric accuracy and phase accuracy, so that the image truly reflects the real shape of the slope and provides a reliable basis for slope stability assessment.

Description

Technical Field

[0001] This invention relates to the field of radar monitoring and processing technology for quarries, and particularly to a radar monitoring system for quarry slopes. Background Technology

[0002] Landslides and other disasters on quarry slopes pose a significant threat to the lives of on-site workers. Radar monitoring can acquire real-time, continuous data on slope displacement and deformation, enabling the early detection of potential landslide hazards. Data analysis allows for early warnings before danger occurs, facilitating timely evacuation and preventing casualties. Furthermore, it helps in the rational planning of quarry operations; monitoring data allows managers to understand changes in slope stability, thereby adjusting mining areas and schedules. For example, if a decrease in stability is detected in a certain area of ​​the slope, nearby mining activities can be suspended to prevent further damage to the slope.

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

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

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

[0006] To achieve the above objectives, the present invention provides the following technical solution: a quarry slope radar monitoring system, comprising:

[0007] Dust adaptive compensation unit: Collects dust information in the air, transmits it wirelessly to the built-in dust-radar wave attenuation model, and combines it with a parameter adjustment algorithm based on particle swarm optimization to dynamically adjust the radar transmission power and compensate for signal loss.

[0008] Vibration cancellation and correction unit: Collects vibration acceleration data, calculates the reverse cancellation vibration signal through Kalman filtering and control algorithm, and drives the piezoelectric ceramic micro actuator to cooperate in canceling instantaneous vibration;

[0009] Radar transmitting unit: After being processed by the vibration cancellation and correction unit, the radar generates high-frequency radar waves based on the transmission power adjusted by the dust adaptive compensation unit, and projects the radar waves directionally toward the quarry slope.

[0010] Radar receiving unit: captures weak radar echo signals reflected back from the slope and converts the radar echo signals from weak electromagnetic signals into electrical signals;

[0011] Image generation unit: Receives the signal processed by the radar receiving unit, uses an algorithm based on radar principle and imaging technology to convert the signal data into a slope radar image, presents the slope surface morphology, and constructs a two-dimensional or three-dimensional image model.

[0012] Early warning unit: Based on big data analysis and machine learning models, it learns the normal state of images under different slope conditions in advance and establishes a slope abnormal state identification model. Once the real-time image deviates from the normal state and reaches the early warning conditions, it immediately triggers the audible and visual alarm device to issue an alarm to the on-site staff.

[0013] Furthermore, the dust adaptive compensation unit includes:

[0014] Dust sensing subunit: It consists of a sensing network composed of multiple dust sensors distributed near the radar on the slope of the quarry, which is responsible for collecting dust information in the air in real time.

[0015] Data transmission submodule: Transmits the data collected by the dust sensing subunit to the power calculation submodule via wireless communication;

[0016] Power Calculation Submodule: This submodule includes a built-in dust-radar wave attenuation model. The dust-radar wave attenuation model is based on a dust concentration of... The particle size distribution parameters are The component influence factor is The attenuation model is derived as follows: ,in It is the attenuation coefficient. It is a function related to the characteristics of dust. Meanwhile, let the initial radar transmission power be... The adjusted power is The power adjustment formula is derived. ,in It is an adjustment coefficient determined by the particle swarm optimization algorithm;

[0017] Power control submodule: Sends the adjusted transmit power command calculated by the power calculation submodule to the radar transmitting unit, so that the radar transmitting unit transmits according to the new power.

[0018] Furthermore, the particle swarm optimization algorithm logic is as follows:

[0019] Initialization: Initialize a group of particles, each particle representing a power adjustment coefficient. At the same time, the velocity of each particle is initialized;

[0020] Calculate fitness: For each particle, assign its corresponding... Substitute the values ​​into the power adjustment formula to calculate the adjusted transmission power, and then calculate the particle fitness based on the quality of the radar signal at the current power.

[0021] Update particle position and velocity: Update the position and velocity of each particle based on its own optimal position, the group's optimal position, and its current velocity;

[0022] Termination condition determination: The algorithm terminates when the preset number of iterations is reached or the convergence condition is met. At this point, the optimal position of the population corresponds to... The value is the final determined power adjustment coefficient.

[0023] Furthermore, the formulas for updating the particle position and velocity are as follows:

[0024]

[0025]

[0026] in, It is the first The velocity of each particle at time t. It is the first The position of each particle at time t. It is inertial weight. and It is a learning factor. and It is a number between 0 and 1. It is the first The optimal position of each individual particle. It is the optimal position for the group.

[0027] Furthermore, the vibration cancellation and correction unit includes:

[0028] Vibration sensing submodule: Composed of acceleration sensors installed on the radar equipment base, it is responsible for collecting vibration acceleration data of the radar equipment base in real time. By measuring the acceleration changes in three axes, it can comprehensively sense the vibration state of the equipment.

[0029] Data transmission submodule: transmits the vibration acceleration data collected by the vibration sensing submodule to the instant vibration cancellation submodule via wireless communication;

[0030] Instantaneous vibration cancellation submodule: Receives data from the data transmission submodule, uses a Kalman filter algorithm to estimate the noisy vibration acceleration data to obtain accurate vibration state information, and calculates a reverse cancellation vibration signal based on the vibration state information using a control algorithm. The reverse cancellation vibration signal is sent to the piezoelectric ceramic micro-actuator, which generates a force opposite to the vibration direction, thereby coordinating to cancel the instantaneous vibration.

[0031] Furthermore, the vibration cancellation and correction unit also uses a displacement deviation prediction algorithm based on a long short-term memory network to analyze long-term vibration history data, predict cumulative displacement deviation, automatically start the correction program when the threshold is exceeded, and fine-tune the signal phase and calibrate the receiving angle.

[0032] Furthermore, the control algorithm uses a PID control algorithm, assuming... It is a vibration state error. It is a control signal, and the specific formula is as follows:

[0033]

[0034] in It is proportional gain. It is integral gain. It is the differential gain. The integral term of the time error, The differential term of the error.

[0035] Furthermore, the image generation unit includes:

[0036] Signal adaptation and preprocessing module: It acquires the processed signal from the radar receiving unit and connects to the dust adaptive compensation unit and the vibration cancellation and correction unit to perform pre-correction processing on the signal before the imaging algorithm is executed.

[0037] Imaging algorithm execution module: It performs imaging tasks through an algorithm based on radar principles and imaging technology depth matching, and finally converts the signal data into slope radar images, presents the slope surface morphology, and constructs two-dimensional or three-dimensional image models.

[0038] Furthermore, the signal adaptation and preprocessing module interfaces with the dust adaptive compensation unit to obtain real-time dust information. Based on the dust-radar wave attenuation model and the known relationship between dust particle size and radar wave wavelength, combined with 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.

[0039] Furthermore, the signal adaptation and preprocessing module interfaces with the vibration cancellation and correction unit to acquire vibration acceleration, instantaneous vibration cancellation status, and cumulative displacement deviation data. Based on the mechanical vibration and imaging geometric relationship model, it analyzes the influence mechanism of vibration on the imaging process. When a 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.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. The quarry slope radar monitoring system proposed in this invention collects dust information in the air, transmits it wirelessly to a built-in dust-radar wave attenuation model, and combines a parameter adjustment algorithm based on particle swarm optimization to dynamically adjust the radar transmission power, compensate for signal loss, and input the pre-corrected signal into the imaging algorithm. This effectively reduces problems such as image blurring, ghosting, and uneven brightness caused by dust interference, significantly improves image clarity and recognizability, and makes the fine textures, cracks, and other features of the slope surface more clearly presented in the image, providing accurate image data for subsequent geological analysis, safety monitoring, and other work.

[0042] 2. The quarry slope radar monitoring system proposed in this invention collects vibration acceleration data, calculates the reverse cancellation vibration signal through Kalman filtering and control algorithm, drives piezoelectric ceramic micro actuators to cooperate in canceling instantaneous vibration, and even when the radar equipment is affected by vibration, the generated slope radar image can still maintain high geometric accuracy and phase accuracy, avoiding problems such as image distortion, deformation and phase disorder, so that the image truly reflects the real shape of the slope and provides a reliable basis for slope stability assessment. Attached Figure Description

[0043] Figure 1 This is a block diagram of the quarry slope radar monitoring system of the present invention;

[0044] Figure 2 This is a block diagram of the dust adaptive compensation unit of the quarry slope radar monitoring system of the present invention;

[0045] Figure 3 This is a block diagram of the vibration cancellation and correction unit of the quarry slope radar monitoring system of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] like Figures 1-3 As shown, a quarry slope radar monitoring system includes:

[0048] Dust adaptive compensation unit: Collects dust information in the air, including dust concentration, particle size distribution and composition information, and wirelessly transmits it back to the built-in dust-radar wave attenuation model. Combined with the parameter adjustment algorithm based on particle swarm optimization, it dynamically adjusts the radar transmission power to compensate for signal loss.

[0049] The specific dust adaptive compensation unit includes:

[0050] Dust sensing subunit: It consists of a sensing network composed of multiple dust sensors distributed near the radar on the slope of the quarry, which is responsible for collecting dust information in the air in real time.

[0051] Data transmission submodule: Transmits the data collected by the dust sensing subunit to the power calculation submodule via wireless communication;

[0052] Power Calculation Submodule: This submodule includes a built-in dust-radar wave attenuation model. The dust-radar wave attenuation model is based on a dust concentration of... The particle size distribution parameters are It can be a comprehensive parameter such as the average value and standard deviation of particle size, and the component influence factor is... Different dust components attenuate radar waves to varying degrees, leading to the following attenuation model: ,in It is the attenuation coefficient. It is a function related to dust properties;

[0053] Assume 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 derived. ,in It is an adjustment coefficient determined by the particle swarm optimization algorithm;

[0054] The logic of the particle swarm optimization algorithm is as follows:

[0055] Initialization: Initialize a group of particles, each particle representing a power adjustment coefficient. At the same time, the velocity of each particle is initialized;

[0056] Calculate fitness: For each particle, assign its corresponding... Substitute the values ​​into the power adjustment formula to calculate the adjusted transmission power, and then calculate the particle fitness based on the quality of the radar signal at the current power.

[0057] Update particle position and velocity: based on the particle's own optimal position, i.e., the position that historically optimizes fitness. Value, the optimal position of the population, the particle that optimizes fitness among all particles. The value, and the current velocity, update the position of each particle (i.e. (value) and speed;

[0058] The formulas for updating particle position and velocity are as follows:

[0059]

[0060]

[0061] in, It is the first The velocity of each particle at time t. It is the first The position of each particle at time (i.e. value), It is inertial weight. and It is a learning factor. and It is a number between 0 and 1. It is the first The optimal position of each individual particle. It is the optimal position for the group;

[0062] Termination condition determination: The algorithm terminates when the preset number of iterations is reached or the convergence condition is met (e.g., the optimal fitness of the particle swarm no longer changes significantly after multiple iterations). At this point, the optimal position of the swarm corresponds to... The value is the final determined power adjustment coefficient;

[0063] Power control submodule: Sends the adjusted transmit power command calculated by the power calculation submodule to the radar transmitting unit, so that the radar transmitting unit transmits according to the new power.

[0064] Vibration cancellation and correction unit: Collects vibration acceleration data, calculates the reverse cancellation vibration signal through Kalman filtering and control algorithm, and drives the piezoelectric ceramic micro actuator to cooperate in canceling instantaneous vibration;

[0065] The vibration cancellation and correction unit includes:

[0066] Vibration sensing submodule: Composed of acceleration sensors installed on the radar equipment base, it is responsible for collecting vibration acceleration data of the radar equipment base in real time. By measuring the acceleration changes in three axes, it can comprehensively sense the vibration state of the equipment.

[0067] Data transmission submodule: transmits the vibration acceleration data collected by the vibration sensing submodule to the instant vibration cancellation submodule via wireless communication;

[0068] Instant vibration cancellation submodule: Receives data from the data transmission submodule and uses a Kalman filter algorithm to estimate the noisy vibration acceleration data to obtain accurate vibration state information. The PID controller calculates the reverse cancellation vibration signal based on the vibration state information using the control algorithm. The reverse cancellation vibration signal is sent to the piezoelectric ceramic micro-actuator, which generates a force opposite to the vibration direction, thereby coordinating to cancel the instant vibration.

[0069] The PID control algorithm first sets It is the vibration state error (the difference between the expected vibration-free state and the actual vibration state). It is the control signal (the signal sent to the piezoelectric ceramic micro actuator), and the specific formula is as follows:

[0070]

[0071] in It is proportional gain. It is integral gain. It is the differential gain. It is the integral term of the error, reflecting the cumulative effect of the error over a period of time. Its unit is related to the product of error and time. It is the differential term of the error, that is, the rate of change of the error, which reflects how quickly the error changes over time, and its unit is related to the rate of change of the error.

[0072] The vibration cancellation and correction unit also uses a displacement deviation prediction algorithm based on a long short-term memory network to analyze long-term vibration history data, predict cumulative displacement deviation, automatically start the correction program when the threshold is exceeded, and fine-tune the signal phase and calibrate the receiving angle.

[0073] Radar transmitting unit: After being processed by the vibration cancellation and correction unit, the radar generates high-frequency radar waves based on the transmission power adjusted by the dust adaptive compensation unit, and then projects the radar waves directionally toward the quarry slope.

[0074] Radar receiving unit: captures weak radar echo signals reflected back from the slope and converts the weak electromagnetic signals into electrical signals.

[0075] Image generation unit: Receives the signal processed by the radar receiving unit, uses an algorithm based on radar principle and imaging technology to convert the signal data into a slope radar image, presents the slope surface morphology, and constructs a two-dimensional or three-dimensional image model.

[0076] The image generation unit includes:

[0077] Signal Adaptation and Preprocessing Module: This module acquires the processed signal from the radar receiving unit. It interfaces with the dust adaptive compensation unit to obtain real-time dust information. Based on the dust-radar wave attenuation model and the known relationship between dust particle size and radar wave wavelength, combined with Mie scattering theory, it calculates the radar wave phase deviation caused by dust of different particle sizes. Simultaneously, the module interfaces with the vibration cancellation and correction unit to acquire vibration acceleration, instantaneous vibration cancellation status, and cumulative displacement deviation data. Based on the mechanical vibration and imaging geometry relationship model, it analyzes the impact mechanism of vibration on the imaging process. When a 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. Before the imaging algorithm is executed, the signal is pre-corrected.

[0078] Imaging algorithm execution module: It performs imaging tasks through an algorithm based on radar principles and imaging technology depth matching, and finally converts the signal data into slope radar images, presents the slope surface morphology, and constructs two-dimensional or three-dimensional image models.

[0079] Early warning unit: Based on big data analysis and machine learning models, it learns the normal state of images under different slope conditions in advance and establishes a slope abnormal state identification model. Once the real-time image deviates from the normal state and reaches the early warning conditions, it immediately triggers the audible and visual alarm device to issue an alarm to the on-site staff.

[0080] It should be noted that, in the description of this application, the terms "length," "thickness," "inner," "outer," "axial," "radial," etc., indicate the orientation or positional relationship based on the orientation or positional relationship 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, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0081] Furthermore, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0082] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A quarry slope radar monitoring system, characterized by, include: Dust adaptive compensation unit: Collects dust information in the air, transmits it wirelessly to the built-in dust-radar wave attenuation model, and combines it with a parameter adjustment algorithm based on particle swarm optimization to dynamically adjust the radar transmission power and compensate for signal loss. Vibration cancellation and correction unit: Collects vibration acceleration data, calculates the reverse cancellation vibration signal through Kalman filtering and control algorithm, and drives the piezoelectric ceramic micro actuator to cooperate in canceling instantaneous vibration; Radar transmitting unit: After being processed by the vibration cancellation and correction unit, the radar generates high-frequency radar waves based on the transmission power adjusted by the dust adaptive compensation unit, and projects the radar waves directionally toward the quarry slope. Radar receiving unit: captures the weak radar echo signal reflected back from the slope and converts the radar echo signal from a weak electromagnetic signal into an electrical signal; Image generation unit: Receives the signal processed by the radar receiving unit, uses an algorithm based on radar principle and imaging technology to convert the signal data into a slope radar image, presents the slope surface morphology, and constructs a two-dimensional or three-dimensional image model. Early warning unit: Based on big data analysis and machine learning models, it learns the normal state of images under different slope conditions in advance, establishes a slope abnormal state identification model, and once the real-time image deviates from the normal state and reaches the early warning conditions, it immediately triggers the sound and light alarm device to issue an alarm to the on-site staff. The dust adaptive compensation unit includes: Dust sensing submodule: It consists of a sensing network composed of multiple dust sensors distributed near the radar on the slope of the quarry, which is 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; The power calculation sub-module comprises a built-in dust-radar wave attenuation model, the dust-radar wave attenuation model is obtained by setting the dust concentration as , the particle size distribution parameter as , the component influence factor as , and the attenuation model as , wherein is an attenuation coefficient, is a function related to the dust characteristics, at the same time, the radar initial transmission power is set as , the adjusted power is , and the power adjustment formula is obtained as , wherein is an adjustment coefficient determined by a particle swarm optimization algorithm; Power control submodule: Sends the adjusted transmit power command calculated by the power calculation submodule to the radar transmitting unit, so that the radar transmitting unit transmits according to the new power.

2. The quarry slope radar monitoring system as described in claim 1, characterized in that, The particle swarm optimization algorithm logic is as follows: Initialization: Initialize a group of particles, each particle representing a power adjustment coefficient. At the same time, the velocity of each particle is initialized; Calculate fitness: For each particle, assign its corresponding... Substitute the values ​​into the power adjustment formula to calculate the adjusted transmission power, and then calculate the particle fitness 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 group's optimal position, and its current velocity; Termination condition determination: The algorithm terminates when the preset number of iterations is reached or the convergence condition is met. At this point, the optimal position of the population corresponds to... The value is the final determined power adjustment coefficient.

3. The quarry slope radar monitoring system as described in claim 2, characterized in that, The formulas for updating the particle position and velocity are as follows: in, It is the first Individual particles The speed of time, It is the first Individual particles Location at any given moment It is inertial weight. and It is a learning factor. and It is a number between 0 and 1. It is the first The optimal position of each individual particle. It is the optimal position for the group.

4. The quarry slope radar monitoring system as described in claim 1, characterized in that, The vibration cancellation and correction unit includes: Vibration sensing submodule: Composed of acceleration sensors installed on the radar equipment base, it is responsible for collecting vibration acceleration data of the radar equipment base in real time. By measuring the acceleration changes in three axes, it can comprehensively sense the vibration state of the equipment. Data transmission submodule: transmits the vibration acceleration data collected by the vibration sensing submodule to the instant vibration cancellation submodule via wireless communication; Instantaneous vibration cancellation submodule: Receives data from the data transmission submodule, uses a Kalman filter algorithm to estimate the noisy vibration acceleration data to obtain accurate vibration state information, and calculates a reverse cancellation vibration signal based on the vibration state information using a control algorithm. The reverse cancellation vibration signal is sent to the piezoelectric ceramic micro-actuator, which generates a force opposite to the vibration direction, thereby coordinating to cancel the instantaneous vibration.

5. A quarry slope radar monitoring system as described in claim 4, characterized in that, The vibration cancellation and correction unit also uses a displacement deviation prediction algorithm based on a long short-term memory network to analyze long-term vibration history data, predict cumulative displacement deviation, automatically start the correction program when the threshold is exceeded, and fine-tune the signal phase and calibrate the receiving angle.

6. A quarry slope radar monitoring system as described in claim 4, characterized in that, The control algorithm uses the PID control algorithm, assuming... It is a vibration state error. It is a control signal, and the specific formula is as follows: in It is proportional gain. It is integral gain. It is the differential gain. It is the integral term of the error. It is the differential term of the error.

7. A quarry slope radar monitoring system as described in claim 1, characterized in that, The image generation unit includes: Signal adaptation and preprocessing module: It acquires the processed signal from the radar receiving unit and connects to the dust adaptive compensation unit and the vibration cancellation and correction unit to perform pre-correction processing on the signal before the imaging algorithm is executed. Imaging algorithm execution module: It performs imaging tasks through an algorithm based on radar principles and imaging technology depth matching, and finally converts the signal data into slope radar images, presents the slope surface morphology, and constructs two-dimensional or three-dimensional image models.

8. A quarry slope radar monitoring system as described in claim 7, characterized in that, The signal adaptation and preprocessing module interfaces with the dust adaptive compensation unit to obtain real-time dust information. Based on the dust-radar wave attenuation model and the known relationship between dust particle size and radar wave wavelength, combined with 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.

9. A quarry slope radar monitoring system as described in claim 7, characterized in that, The signal adaptation and preprocessing module interfaces with the vibration cancellation and correction unit to acquire vibration acceleration, instantaneous vibration cancellation status, and cumulative displacement deviation data. Based on the mechanical vibration and imaging geometric relationship model, it analyzes the influence mechanism of vibration on the imaging process. When a 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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