Strip mine crushing station receiving hopper material level monitoring device and method

Abstract

The invention discloses a strip mine crushing station receiving hopper material level monitoring device and method, and belongs to the technical field of strip mines. After the material level detection is completed at the detection starting point position, the sensor is controlled to advance to the next detection point position along the track, a self-adaptive dynamic adjustment model is established according to the real-time monitoring environment, the material level change condition and the system performance index, the speed of the sensor is automatically adjusted, and the detection accuracy is improved. Therefore, the sensor can keep proper sampling density and scanning precision under different monitoring conditions. According to the method, by improving a non-contact material level sensor arrangement mode, a signal processing mode and a dust and water vapor interference prevention mode, the material level monitoring precision, stability and coverage are improved, and therefore high-economy crushing station receiving hopper material level dynamic accurate monitoring is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of open-pit mines, and in particular relates to a device and method for monitoring the material level of a receiving hopper of an open-pit mine crushing station. Background Art

[0002] The receiving hopper is designed to take into account both the unloading of the mining truck and the material capacity, so the top opening is large, the bottom closing is as wide as the feeder, and the hopper height is high. The physical structure of the receiving hopper will lead to uneven distribution of materials in the hopper, especially at the edge or corner of the hopper, which is easy to form material accumulation or gaps. The inclination of the receiving hopper is designed to promote material flow, but it will also cause the material to accumulate on one side in the hopper. At the same time, the dust is large during the unloading process of the open-pit mine crushing station, and when the dust suppression system is started, there is a lot of water vapor on the top of the receiving hopper.

[0003] The current receiving hopper material level monitoring devices can be divided into two types: contact type and non-contact type. Among them, the contact type material level monitoring is represented by the weight level meter, which can only monitor at a single point. When there is local accumulation or uneven material in the receiving hopper, it is impossible to truly feedback the material level of the receiving hopper. At the same time, the contact type material level monitoring device is easily buried by the flowing material during operation, causing functional failure. Non-contact material level monitoring includes single-point ultrasonic, laser, radar level meters, and multi-point monitoring 3D radar level scanners. The high dust and high water vapor environment of the receiving hopper has a great impact on the monitoring accuracy of single-point ultrasonic, laser, and radar level meters, and cannot cope with the uneven material inside the receiving hopper. Although the 3D radar level scanner can provide real-time feedback on the distribution status of the material inside the receiving hopper, it is expensive and costly, and is not economical. Summary of the invention

[0004] In view of the above shortcomings, the object of the present invention is to provide a device and method for monitoring the material level of a receiving hopper of an open-pit mine crushing station.

[0005] The technical solution adopted by the invention is: a material level monitoring device for a receiving hopper of an open-pit mine crushing station, the technical key points of which are that it includes a mounting bracket connected to the top of the receiving hopper, on which is mounted a receiving hopper walking track which is arranged around the top opening of the receiving hopper starting from the equipment room, and a sensor is mounted on the receiving hopper walking track and moves along the receiving hopper walking track.

[0006] A method for monitoring the material level of a receiving hopper of an open-pit mine crushing station comprises the following steps:

[0007] Step 1: Perform material level detection and feed back the material level data of the current detection point to the equipment room in real time through wireless transmission, which is then transmitted to the remote control center through the wired network to display the real-time material level situation;

[0008] Step 2: After completing the material level detection at the detection starting point, control the sensor to move along the track to the next detection point. According to the real-time monitoring environment, material level changes and system performance indicators, an adaptive dynamic adjustment model is established to automatically adjust the speed of the sensor to ensure that the sensor maintains the set sampling density and scanning accuracy under different monitoring conditions;

[0009] Step 3: Repeat steps 1 and 2 until the sensor completes the full coverage detection of the receiving hopper along the walking track;

[0010] Step 4: Receive and calculate single-point material level data in real time, model the complete material pile shape in the receiving hopper, and feed the modeling back to the equipment room through wireless transmission. The wired network between the equipment transmits the material pile modeling to the remote control center to display the material pile model.

[0011] Preferably, the process of establishing the adaptive dynamic adjustment model in step 3 to automatically adjust the speed of the walking mechanism is as follows:

[0012] Step 2.1, collect the current scanning data of the laser radar, calculate the material level change rate and regional complexity, and combine the environmental sensor data to monitor the environmental conditions and sensor status to establish an adaptive model:

[0013]

[0014] Where V(t) is the moving speed at time t; k(t) is the adaptive adjustment coefficient, which is dynamically adjusted based on the current detected material level changes, environmental conditions, and sensor status; d 目标 (t) is the target sampling interval, which is adjusted in real time according to the required resolution and the monitoring requirements of the current area; f(t) is the monitoring rate of the lidar at time t, which is adaptively adjusted according to the sensor status;

[0015] Step 2.2, according to the output of the real-time monitoring module, calculate the adaptive adjustment factor k(t) and the target sampling interval dtarget(t), and output the adjusted moving speed V(t);

[0016] Step 2.3, according to the speed command, the motion parameters of the moving mechanism are adjusted to achieve dynamic adjustment of the speed;

[0017] The adaptive adjustment formula of the target sampling interval is:

[0018]

[0019] Where, d 基准 is the reference sampling interval, which is set according to the initial design requirements; R 基准 is the reference material level change rate, which is set to the reference value of the change rate expected by the system;

[0020] Step 2.4: The system continuously monitors the scanning effect and sends feedback data back to the adaptive controller to further adjust and optimize the scanning parameters.

[0021] Preferably, the formula of the adaptive adjustment factor in step 2.1 is:

[0022]

[0023] In the formula, α, β, γ, and δ are weight coefficients, which are set according to the influence of each factor on the scanning effect.

[0024] Preferably, step 1 also includes processing interference signals of dust, water vapor and environmental noise.

[0025] Preferably, the method further includes the following steps: when the sensor body is low on power or needs cleaning and maintenance, the charging device and cleaning device in the equipment room complete the relevant operations during the period interval.

[0026] The beneficial effects of the present invention are as follows: the device and method for monitoring the level of the receiving hopper of the open-pit mine crushing station, after completing the level detection at the detection starting point, controls the sensor to move along the track to the next detection point, establishes an adaptive dynamic adjustment model based on the real-time monitoring environment, level changes and system performance indicators, and automatically adjusts the speed of the sensor to ensure that the sensor maintains a suitable sampling density and scanning accuracy under different monitoring conditions. The method improves the accuracy, stability and coverage of the level monitoring by improving the layout of the non-contact level sensor, the signal processing method, and the dust and water vapor interference prevention method, thereby achieving highly economical dynamic and accurate monitoring of the level of the receiving hopper of the crushing station. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 This is a schematic diagram of the overall architecture in an embodiment of the present invention;

[0029] Figure 2 A top view of the installation arrangement in an embodiment of the present invention;

[0030] Figure 3 It is a side view of the installation arrangement in an embodiment of the present invention;

[0031] Figure 4 This is a working principle diagram in an embodiment of the present invention;

[0032] Figure 5This is a signal processing flow chart in an embodiment of the present invention;

[0033] The serial numbers in the figure are as follows: 1 equipment room, 2 walking track, 3 sensor, 4 scraper, 5 receiving hopper, 6 mounting bracket. DETAILED DESCRIPTION

[0034] To make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the following is a brief description of the present invention in conjunction with the attached Figure 1-Figure 5 The present invention is further described in detail with reference to the accompanying drawings and specific embodiments.

[0035] The present embodiment adopts a material level monitoring device for a receiving hopper of an open-pit mine crushing station, comprising a mounting bracket 6 connected to the upper edge of the receiving hopper, and a walking track 2 is installed on the mounting bracket 6, and its model is an H-shaped steel of 100×100mm. The walking track 2 starts from the equipment room 1 and is set around the top opening of the receiving hopper and then returns to the equipment room 1. The sensor 3 is installed on the walking track 2 and moves along the walking track 2. The sensor 3 moves along the track to dynamically adjust the monitoring position, covering multiple key areas of the receiving hopper 5, and realizing more comprehensive material level monitoring. A scraper 4 is installed at the bottom of the receiving hopper 5. The scraper 4 conveys materials in real time. As long as the receiving hopper is working, the scraper 4 works. The sensor 3 of the present embodiment is equipped with a main and auxiliary single-point laser radar level meter that can measure the height of the material in the receiving hopper by emitting and receiving laser beams. An edge computing software unit is set in the sensor 3 for processing the received signal. The sensor 3 is provided with a driving wheel driven by a driving motor and a wireless transmission unit, and the driving wheel can move along the walking track 2. Equipment room 1 integrates a charging device, a sensor cleaning device, a heating device for preventing water vapor condensation in a low-temperature environment, and a network interface. The sensor cleaning device and the heating device are used to keep the sensor clean and the working environment stable, effectively preventing the influence of dust and water vapor on the lidar level meter; the network interface is convenient for integration with the PLC and SCADA system of the crushing station to realize data interconnection and automatic control.

[0036] A method for monitoring the material level of a hopper in an open-pit mine crushing station adopted in this embodiment includes the following steps:

[0037] Step 1: The main and auxiliary single-point laser radars carried on the sensor 3 perform material level detection at the starting point. During detection, the main laser radar is used to detect the material level, and the auxiliary laser radar is used to assist in material level judgment (after the main laser radar collects data, the auxiliary laser radar will also collect data from adjacent points. If the difference between the two meets the set threshold, the material level feedback value is considered valid. If the deviation is too large, the material level detection value at this point is invalid and needs to be re-detected). The material level data of the current detection point is fed back to the equipment room 1 in real time via wireless transmission, and is transmitted from the equipment room 1 to the remote control center via a wired network to display the real-time material level situation. In this embodiment, the interference signal must be processed during the detection process to eliminate dust interference, water vapor interference and environmental noise interference. Data fusion algorithms, adaptive noise compensation, signal processing, and environmental perception are used in a variety of ways to further improve signal accuracy and improve anti-interference capabilities. Specifically, the following steps are included:

[0038] Step 1.1: Data fusion algorithm, using a time series model combining weighted average method and Kalman filter to fuse the real-time data of the main and auxiliary sensors to improve detection accuracy.

[0039] The outputs of the main and auxiliary sensors are fused using weighted averaging and Kalman filtering.

[0040] (1) Kalman filter state equation:

[0041] X k =AX k-1 +Bu k +w k

[0042] (2) Kalman filter observation equation:

[0043] Z k =Hx k +v k

[0044] In the formula, x k is the current state (material level height); Z k is the sensor measurement value; A is the state transfer matrix; H is the observation matrix; w k and v k is the process noise and measurement noise, which are set to Gaussian distribution; B is the control input matrix, X k-1 is the state at the last moment, u k is the control input vector;

[0045] (3) Filtering steps:

[0046] Step 1.1.3.1: Prediction

[0047]

[0048] In the formula, is the predicted value of the current state, It is the state value at the previous moment.

[0049] Step 1.1.3.2: Predict the covariance:

[0050]

[0051] In the formula, is the current covariance prediction value, Q process noise covariance matrix.

[0052] Step 1.1.3.3: Kalman gain:

[0053]

[0054] In the formula, K k is the Kalman gain, H is the observation matrix, and R is the observation noise covariance matrix.

[0055] Step 1.1.3.4: Update state estimate:

[0056]

[0057] Step 1.1.3.5: Update covariance:

[0058]

[0059] Where P k is the updated state covariance matrix, and I is the identity matrix.

[0060] Step 1.2: Adaptive noise compensation, using online noise estimation method, adaptively adjust the filter parameters to reduce the scattering interference of dust and water vapor. The multi-model adaptive estimation (MMAE) strategy is used to select the optimal noise compensation model according to different environmental noise levels.

[0061] In complex environments such as dust, water vapor, and temperature changes, MMAE can adaptively select the model that best suits the current environment, improve the detection accuracy of the lidar level meter, and enhance the robustness of the system through parallel model estimation.

[0062] Equation of state:

[0063] x k (i) = A(i)x k-1 (i)+B(i)u k-1 +w k-1 (i)

[0064] Observation equation:

[0065] Z k =H(i)Xk (i)+V k (i)

[0066] In the formula, i represents the i-th model, k represents the time step; x k (i) is the state vector of the i-th model (such as material level height, noise state, etc.); A(i) is the state transfer matrix of the i-th model, which describes how the system state is updated from k-1 to k; B(i) is the control input matrix of the i-th model, which converts the control input u k-1 Transformed into the effect on the state; u k-1 is the control input vector (external control signal); w k-1 (i) is the process noise vector, which is set as Gaussian white noise and describes the uncertainty of the model; Z k is the measurement vector (the measurement value of the lidar).

[0067] Model weight update, by comparing the difference between each model's estimate and the actual observation to calculate the model weight. The weight update formula for the i-th model is:

[0068]

[0069] In the formula, μ k (i) is the weight of the i-th model at time k; p(Z k ∣X k (i)) is a given state X k The measurement probability density at (i) reflects the adaptability of the model to the current observation; N 1 is the total number of candidate models. Step 1.3: Signal processing method, using fast Fourier transform (FFT) and wavelet transform frequency domain analysis method to separate the effective signal and the noise signal, and optimize the signal quality through dynamic threshold adjustment and adaptive gain control.

[0070] Combining the advantages of frequency domain analysis and time-frequency analysis, the signal of the lidar level meter can be effectively separated and optimized by adjusting the dynamic threshold and adaptive gain, thereby improving the detection accuracy. The steps are as follows.

[0071] Step 1.3.1: Perform FFT and wavelet transform on the original signal to obtain the frequency domain signal and wavelet coefficients.

[0072] Step 1.3.2: Apply dynamic threshold adjustment to separate valid signals from noise.

[0073] Step 1.3.3: Use adaptive gain control to optimize signal quality.

[0074] Step 1.3.4: Perform inverse transformation on the optimized signal to obtain the improved time domain signal.

[0075] (1) Fast Fourier Transform (FFT):

[0076]

[0077] Where x(n) is the time domain signal; X(f) is the frequency domain signal; N is the signal length; f is the frequency; and J is the imaginary unit.

[0078] (2) Wavelet transform:

[0079]

[0080] Where x(t) is the time domain signal; W(a,b) is the wavelet coefficient, which represents the signal characteristics at scale a and position b; ψ() is the wavelet function; a is the scale factor, which represents the width of the time window; b is the translation factor, which represents the time position.

[0081] (3) Dynamic threshold adjustment:

[0082] Threshold(t)=a·Median(|X(f)|)

[0083] Where Threshold(t) is the dynamic threshold at time t; α is the adjustment coefficient that controls the sensitivity of the threshold; Median(|X(f)|) is the median of the absolute value of the frequency domain signal.

[0084] (4) Adaptive gain control

[0085]

[0086] Where G(t) is the gain factor at time t; σ 2 (t) is the estimated value of the noise variance; Var({X(f)-Threshold(t)}) is the variance of the effective signal;

[0087] Step 1.4: The environmental perception module monitors the changes in environmental conditions in real time through environmental sensors (temperature and humidity sensors, dust concentration sensors), and predicts the deviation of the lidar signal through the environmental model.

[0088] The environmental conditions are monitored by temperature and humidity sensors and dust sensors, and a multivariate linear regression model is established to predict the impact of the environment on the laser signal. The linear regression model is as follows:

[0089] △h=β0+β1T+β2H+β3D+ε

[0090] Wherein, Δh is the material level detection error, that is, the difference between the actual material level and the material level measured by the lidar; β0 is a constant term, which represents the baseline error when all other variables are zero; β1β2β3 are regression coefficients, which are used to indicate the influence of environmental variables on the detection error; T is temperature, which affects the propagation characteristics of the laser signal (such as changes in air density); H is humidity, which affects the scattering and absorption of the laser in the air; D is the dust concentration, which affects the scattering and attenuation of the laser signal; ∈ is an error term, which represents random noise or other unmodeled factors that cannot be explained by a linear model.

[0091] Step 2: After sensor 3 completes the material level detection at the starting point, the sensor is controlled to move along the track to the next detection point. If the material level at the current detection point is complex or has many interference factors, the coordinated walking mechanism will slow down. If the material level at the current detection point is simple or has fewer interference factors, the coordinated walking mechanism will accelerate (the material level is determined according to the material level changes at the front and rear points. If the difference between the two material levels collected before and after is greater than the set threshold, it means that the shape is complex, and the difference is within the threshold range. Simple shape). The focus is on the uniform dynamic adjustment of the material level detection quality, speed and walking mechanism. This embodiment is implemented through an adaptive dynamic adjustment model. The adaptive dynamic adjustment model aims to automatically adjust the speed of the walking mechanism according to the real-time monitoring environment, material level changes and system performance indicators to ensure that the appropriate sampling density and scanning accuracy are maintained under different monitoring conditions. The core of the model is to dynamically adjust the moving speed according to the monitoring rate of the lidar, the complexity of the current scanning area, and changes in environmental factors to achieve accurate, stable and efficient full coverage scanning. The steps to implement the adaptive dynamic adjustment model are:

[0092] Step 2.1: Real-time monitoring: Collect the current scanning data of the laser radar, calculate the material level change rate and regional complexity, and combine the environmental sensor data to monitor the environmental conditions and sensor status, and establish an adaptive model as follows:

[0093]

[0094] Where V(t) is the moving speed at time t; k(t) is the adaptive adjustment coefficient, which is dynamically adjusted based on the current detected material level changes, environmental conditions, sensor status, etc.; d 目标 (t) is the target sampling interval, which is adjusted in real time according to the required resolution and the monitoring needs of the current area; f(t) is the monitoring rate of the lidar at time t, which is adaptively adjusted according to the sensor status.

[0095] Step 2.2: Calculate the adaptive adjustment factor k(t) and target sampling interval d based on the output of the real-time monitoring module 目标(t), and outputs the adjusted moving speed V(t). The adaptive adjustment factor k(t) of this embodiment is the core part of the model, which is dynamically adjusted according to the following factors:

[0096] Material level change rate (R(t)): Calculate the material level change rate at the current time point through the continuously scanned material level data. Rapidly changing areas require higher sampling density, so the movement speed needs to be reduced.

[0097] Scanning area complexity (C(t)): For areas with high surface complexity (such as slopes or irregular stockpiles), more detailed scanning is required, so the moving speed should be reduced to increase the sampling density.

[0098] Environmental conditions (E(t)): Light interference, dust, temperature changes, etc. may affect the accuracy of the LiDAR. In harsh environments, reduce the speed of movement to increase data reliability.

[0099] Sensor status (S(t)): The current working status of the lidar, including signal quality, data loss rate, etc. When the sensor status is not good, the speed should be reduced to compensate for the sampling error.

[0100] The adaptive adjustment factor can be expressed as:

[0101]

[0102] In the formula, α, β, γ, and δ are weight coefficients, which are set according to the influence of each factor on the scanning effect.

[0103] Step 2.3: According to the speed command output by the controller, adjust the motion parameters of the moving mechanism to achieve dynamic speed adjustment. Target sampling interval d 目标 (t) is the key parameter that determines the distance between each scanning point. Its adjustment needs to be based on the requirements of the scanning area:

[0104] For areas where the material level changes dramatically, shorten the sampling interval to improve the scanning resolution.

[0105] For flat areas or areas with small level changes, the sampling interval can be increased to improve the scanning speed.

[0106] The adaptive adjustment formula of the target sampling interval is:

[0107]

[0108] Where, d 基准 is the reference sampling interval, which is set according to the initial design requirements; R 基准 is the reference material level change rate, which is set to the system's expected change rate reference value.

[0109] Step 2.4: The system continuously monitors the scanning effect and sends feedback data back to the adaptive controller to further adjust and optimize the scanning parameters.

[0110] Step 3: Repeat steps 1 and 2 until the sensor body completes the full coverage detection of the receiving hopper along the walking track.

[0111] Step 4: The edge computing software unit on the sensor receives and calculates the single-point material level data in real time until step 3 is completed. The edge computing unit completes the modeling of the complete material pile shape in the receiving hopper. The material pile shape modeling is to fit the single-point material levels detected one by one in step 3 into a three-dimensional dynamic material pile model, which is a process of accumulating material level data from point to surface and then to three-dimensional. The modeling is fed back to the equipment room 3 through wireless transmission, and the wired network of the equipment room 3 transmits the material pile modeling to the remote control center to display the material pile model.

[0112] Step 5: The sensor wraps around the hopper as a cycle. After a cycle, repeat steps 1-4 to complete the material level detection and material pile modeling of the next cycle. The material pile modeling of the next cycle can cover the modeling of the previous cycle. When the sensor body is low on power or needs cleaning and maintenance, the charging device and cleaning device in the equipment room complete the relevant operations during the cycle interval.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A material level monitoring device for a hopper of an open-pit mine crushing station, characterized in that: It includes a mounting bracket connected above the receiving hopper, on which a receiving hopper walking track is installed, which is set around the top opening of the receiving hopper and starts from the equipment room. The sensor is installed on the receiving hopper walking track and moves along the receiving hopper walking track.

2. A method for monitoring the material level of a hopper in an open-pit mine crushing station, implemented by the device according to claim 1, characterized in that: The following steps are involved: Step 1: Perform material level detection and feed back the material level data of the current detection point to the equipment room in real time through wireless transmission, which is then transmitted to the remote control center through the wired network to display the real-time material level situation; Step 2: After completing the material level detection at the detection starting point, control the sensor to move along the track to the next detection point. According to the real-time monitoring environment, material level changes and system performance indicators, an adaptive dynamic adjustment model is established to automatically adjust the speed of the sensor to ensure that the sensor maintains the set sampling density and scanning accuracy under different monitoring conditions; Step 3: Repeat steps 1 and 2 until the sensor completes the full coverage detection of the receiving hopper along the walking track; Step 4: Receive and calculate single-point material level data in real time, model the complete material pile shape in the receiving hopper, and feed the modeling back to the equipment room through wireless transmission. The wired network between the equipment transmits the material pile modeling to the remote control center to display the material pile model.

3. The method for monitoring the material level of the receiving hopper of an open-pit mine crushing station according to claim 1, characterized in that: The process of establishing the adaptive dynamic adjustment model described in step 3 to automatically adjust the speed of the walking mechanism is as follows: Step 2.1, collect the current scanning data of the laser radar, calculate the material level change rate and regional complexity, and combine the environmental sensor data to monitor the environmental conditions and sensor status to establish an adaptive model: Where V(t) is the moving speed at time t; k(t) is the adaptive adjustment coefficient, which is dynamically adjusted based on the current detected material level changes, environmental conditions, and sensor status; d 目标 (t) is the target sampling interval, which is adjusted in real time according to the required resolution and the monitoring requirements of the current area; f(t) is the monitoring rate of the lidar at time t, which is adaptively adjusted according to the sensor status; Step 2.2, according to the output of the real-time monitoring module, calculate the adaptive adjustment factor k(t) and the target sampling interval dtarget(t), and output the adjusted moving speed V(t); Step 2.3, according to the speed command, the motion parameters of the moving mechanism are adjusted to achieve dynamic adjustment of the speed; The adaptive adjustment formula of the target sampling interval is: Where, d 基准 is the reference sampling interval, which is set according to the initial design requirements; R 基准 is the reference material level change rate, set to the system's expected change rate reference value; Step 2.4: The system continuously monitors the scanning effect and sends feedback data back to the adaptive controller to further adjust and optimize the scanning parameters.

4. The method for monitoring the material level of the receiving hopper of an open-pit mine crushing station according to claim 2 is characterized in that: The formula for the adaptive adjustment factor in step 2.1 is: Where α, β, γ, and δ are weight coefficients, which are set according to the influence of each factor on the scanning effect.

5. The method for monitoring the material level of the receiving hopper of an open-pit mine crushing station according to claim 2, characterized in that: Step 1 also includes processing interference signals of dust, water vapor and environmental noise.

6. The method for monitoring the material level of the receiving hopper of an open-pit mine crushing station according to claim 1, characterized in that: The method also includes the following steps: when the sensor body is low on power or needs cleaning and maintenance, the sensor is charged and cleaned by a charging device in the equipment room during periodic intervals.

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