Tailing pond leachate pool water level intelligent identification device and intelligent treatment method

Abstract

The invention discloses a tailing pond leachate pool water level intelligent identification device and an intelligent processing method, and relates to the technical field of tailing pond water level monitoring, the tailing pond leachate pool water level intelligent identification device comprises a protective shell installed on the pool wall, the upper portion of the protective shell is provided with a telescopic frame, the upper end of the telescopic frame is provided with a base, and the upper portion of the base is provided with a polarization camera group; a sensor and a pulse coding ultrasonic generator are arranged in the protective shell, and probes of the sensor and the pulse coding ultrasonic generator extend out of the protective shell. According to the tailing pond leachate pool water level intelligent identification device and the intelligent treatment method, the monitoring sensor and the pulse coding ultrasonic generator can be cleaned, external dust is prevented from polluting equipment, the sealing performance of the cleaning device can be good through the arrangement of the sealing head, water is prevented from entering the protective shell, and the service life of the cleaning device is prolonged. And therefore, equipment in the protective shell can be protected.

Description

Technical Field

[0001] The present invention relates to the technical field of mine reservoir water level monitoring, and in particular to an intelligent water level identification device and an intelligent processing method for a tailings reservoir leachate pool. Background Art

[0002] In the operation and management of tailings ponds, accurate monitoring of leachate pond water levels is crucial for ensuring the safety of the pond and the stability of the surrounding environment. Currently, the industry has developed a variety of technical approaches for monitoring leachate pond water levels. Common methods include single-point measurement using a single sensor, such as an ultrasonic sensor or pressure sensor, to obtain water level data. Other methods use image recognition to estimate water levels by installing a camera to capture images of the liquid surface. These technologies can, to a certain extent, meet basic water level monitoring needs and provide data support for the daily management of tailings ponds.

[0003] However, existing water level monitoring technologies have many significant drawbacks. On the one hand, single-point measurement methods are extremely susceptible to interference from the local environment. For example, foam, floating objects on the water surface, or abnormal fluctuations in local water flow will cause large deviations in the measurement results and fail to accurately reflect the true water level conditions of the entire leachate pool. On the other hand, whether it is a single sensor or traditional image recognition technology, most of them adopt a fixed-parameter working mode, which is difficult to adapt to complex and changeable working conditions. For example, under different weather conditions, the reflection characteristics of the water surface change significantly, and the fixed-parameter sensors and image recognition algorithms cannot be dynamically adjusted, resulting in a decrease in measurement accuracy. Moreover, the existing technology lacks an effective equipment coordination mechanism and dynamic compensation means. In data processing, a single algorithm is mostly used, which cannot fully integrate multi-source data. At the same time, the self-maintenance function of the equipment is missing, and equipment failures are difficult to detect and resolve in a timely manner, which seriously affects the accuracy and reliability of water level monitoring.

[0004] Therefore, we have made improvements to this problem and proposed an intelligent water level identification device and intelligent processing method for the leachate pool of the tailings pond. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an intelligent water level identification device and an intelligent processing method for a tailings pond leachate pool, which solves the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a tailings pond leachate pool water level intelligent identification device, including a protective shell installed on the pool wall, a telescopic frame is provided on the upper part of the protective shell, a base is provided on the upper end of the telescopic frame, a polarization camera group is provided on the upper part of the base, a sensor and a pulse coded ultrasonic generator are provided inside the protective shell, and the probes of the sensor and the pulse coded ultrasonic generator both extend out of the protective shell.

[0007] The angle of the polarization camera group is adjusted by a pan-tilt platform, and the polarization camera group includes a main camera and an auxiliary camera. The main camera shoots vertically downward, and the auxiliary camera shoots at an inclination angle of 15-30 degrees. There are three intelligent water level identification devices for the tailings pond leachate pool, which are installed around the pool wall at intervals of 120 degrees. The sensors on the three devices are three groups of near-infrared sensors with different wavelengths. Multi-source data is obtained by setting a multi-spectral sensor group to collect liquid surface reflection spectra, a polarization camera group to shoot liquid surface polarization images at multiple angles, and a pulse-coded ultrasonic generator to emit a modulated waveform. A multi-source data fusion model is constructed to generate a three-dimensional water level estimate with a credibility weight, dynamic compensation calibration is performed, and the water level value and abnormal event classification report after environmental interference compensation are output.

[0008] Preferably, the tailings pond leachate pool water level intelligent identification device also includes a self-cleaning component, which includes a motor installed on the top side of the protective shell and an annular seat rotatably arranged on the outer wall of the protective shell, an L-shaped plate is provided on one side of the annular seat, and a cleaning brush for cleaning the sensor and the pulse-coded ultrasonic generator is provided on the inner side of the L-shaped plate. The output shaft of the motor is connected to the main gear, the inner side of the annular seat is provided with an annular groove, and a slave gear is provided inside the annular groove. The inner wall of the protective shell is provided with a through groove corresponding to the main gear, and one side of the main gear passes through the through groove and engages with the slave gear.

[0009] In this solution: when the sensor probe and the pulse coded ultrasonic generator probe are dusty and need to be cleaned, the motor is started to drive the main gear to rotate, the main gear drives the slave gear meshing with it to rotate, and then drives the annular seat to rotate, the annular seat causes the L-shaped plate to rotate, and the cleaning brush on the inside of the L-shaped plate will wipe and clean the sensor probe and the pulse coded ultrasonic generator probe.

[0010] Preferably, the self-cleaning component also includes a sealing head arranged at the upper and lower edges of the inner side of the annular seat and two card grooves on the outer wall of the protective shell. The two sealing heads are respectively slidably engaged in the two card grooves. The sealing head includes an annular flange, a cavity is provided inside the annular flange, and an annular strip is slidably embedded in the inner side of the annular flange. A rubber pad is provided on the inner side of the annular strip, and the inner side of the rubber pad is fitted to the inner wall of the card groove, and an annular water-swelling strip is connected between the outer edge of the annular strip and the inner wall of the annular flange. The upper part of the annular flange is densely covered with micropores communicating with its interior.

[0011] In this solution: when moisture enters through the slot on the protective shell, it will first enter the inside of the annular flange through the micropores on the annular flange, and then moisten the annular water-swelling strip to cause it to expand. The annular water-swelling strip will push the annular strip outward, so that the rubber pad fits tightly to the inner wall of the slot, preventing moisture from entering, playing a good sealing role, and preventing moisture inside the protective shell from affecting the operation of the equipment.

[0012] Preferably, the wavelengths of the three groups of near-infrared sensors are 850 nm, 940 nm and 1050 nm respectively.

[0013] A method for intelligently identifying and processing the water level of a tailings pond leachate pool comprises using an intelligent water level identification device for identifying the water level of the tailings pond leachate pool. The identification and processing method comprises the following steps: S1, collecting liquid surface reflection spectrum through a ring-shaped multispectral sensor group; S2. Synchronously start the polarization camera group to capture polarization images of the liquid surface at multiple angles, with the main camera shooting vertically downward and the auxiliary camera shooting at an angle of 15-30 degrees; S3, using a pulse coded ultrasonic generator to transmit a modulated waveform, and the receiving end extracting the effective echo signal through an adaptive threshold algorithm; S4. Construct a multi-source data fusion model: perform spatiotemporal registration of the spectral reflectance sequence, polarization image edge features, and ultrasonic transit time to generate a three-dimensional water level estimate with a credibility weight. S5. Perform dynamic compensation calibration: When foam or floating objects are detected on the water surface, activate the wavelength combination switching mode of the multispectral sensor and adjust the filter rotation angle of the polarization camera; S6. Output the water level value after environmental interference compensation and abnormal event classification report.

[0014] Preferably, the working mode of the multispectral sensor group in step S1 is controlled as follows: In sunny mode, 850nm and 1050nm dual-band differential measurement is used; Automatically switches to the 940nm band in rainy and foggy weather and activates the background scattering compensation algorithm; Liquid surface curvature compensation calculation is achieved through the ring layout of three groups of sensors.

[0015] Preferably, in step S2, the cooperative working mechanism of the polarization camera group is as follows: The main camera uses a linear polarizer to eliminate interference from mirror reflections on the water surface; The auxiliary camera uses a circular polarizer to capture diffuse reflection features at different angles; The dual cameras construct a three-dimensional contour model of the liquid surface through a feature matching algorithm.

[0016] Preferably, the data fusion model of step S4 includes: Spectral data preprocessing: Moving window Fourier transform is used to extract the time-frequency characteristics of reflectance at each wavelength; Image feature extraction: The liquid surface edge is segmented by improving the U-Net network and the pixel-level water level position is calculated; Fusion decision: A dual-channel decision-making mechanism combining convolutional neural network and adaptive weighted fusion is established, where CNN processes image features and Kalman filter processes time series signals.

[0017] Preferably, in the dynamic compensation calibration in step S5, when the multispectral sensor detects a sudden change in reflectivity, floating object interference discrimination is initiated: Compare the consistency of reflectivity changes in the three bands; Use polarization camera to identify surface materials; The compensation algorithm is selected based on the recognition results: the trough tracking method is used for foam interference, and the neighborhood interpolation method is used for solid floating objects.

[0018] Preferably, the method for intelligently identifying and processing the water level of the tailings pond leachate pool further includes establishing an abnormality handling knowledge base, as follows: When a water level anomaly is identified, the solution for similar cases is automatically retrieved; Assess risk levels by combining current meteorological data with dam sensor readings; Generate equipment maintenance suggestions: When specific sensor data is persistently abnormal, it prompts you to check for lens contamination or circuit failure.

[0019] It should be added that the above method also includes a device self-check and optimization mechanism, as follows: The sensor calibration process is triggered daily: the base measurement value is obtained by raising and lowering the telescopic frame; Camera mirror contamination detection: Analyzes the attenuation rate of high-frequency components in the image and automatically activates the built-in cleaning function on the camera to clean the camera and ensure the camera's image quality; Optimize sensor layout based on historical data: Dynamically adjust the weight coefficient of each sensor through genetic algorithm.

[0020] The present invention provides a device for intelligently identifying the water level in a tailings pond leachate pool and a method for intelligently processing the water level, which has the following beneficial effects compared with the prior art: 1. The tailings pond leachate pool water level intelligent identification device and intelligent processing method can clean the monitoring sensor and pulse coded ultrasonic generator to prevent external dust from contaminating the equipment. The setting of the sealing head can make the cleaning device well sealed, preventing moisture from entering the protective shell, thereby protecting the equipment in the protective shell.

[0021] 2. The tailings pond leachate pool water level intelligent identification device and intelligent processing method adopts a circular multi-spectral sensor array and a multi-angle polarization camera for spatial coordination, overcoming the problem that the traditional single-point arrangement scheme is susceptible to local interference. It obtains liquid level information from multiple dimensions and reflects the leachate pool water level more comprehensively and accurately.

[0022] 3. This tailings pond leachate pool intelligent water level identification device and intelligent processing method utilizes wavelength combination switching and polarizer-linked compensation, distinguishing it from traditional fixed-parameter sensor systems. The multispectral sensor's wavelength combination and the polarization camera's filter rotation angle can be flexibly adjusted to accommodate interference from surface foam, floating objects, and other factors, effectively addressing complex operating conditions and improving measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 This is a schematic structural diagram of the self-cleaning component of the present invention; Figure 3 For the present invention Figure 2 A partial enlarged schematic diagram of the structure at point A in the middle; Figure 4 It is a structural schematic diagram of the sealing head of the present invention.

[0024] Indicated in the figure: 1. Protective shell; 2. Telescopic frame; 3. Base; 4. Polarization camera assembly; 5. Self-cleaning component; 51. Motor; 52. Ring seat; 53. L-shaped plate; 54. Cleaning brush; 55. Main gear; 56. Ring groove; 57. Slave gear; 58. Through groove; 59. Sealing head; 591. Ring flange; 592. Ring strip; 593. Rubber pad; 594. Ring water-swelling strip; 595. Micropore; 510. Card slot; 6. Sensor; 7. Pulse-coded ultrasonic generator. DETAILED DESCRIPTION

[0025] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in 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 part of the embodiments of the present invention, not all of them.

[0026] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0027] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0028] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0029] See also Figures 1-4 , the present invention provides three technical solutions: Example 1 See also Figure 1 In an embodiment of the present invention, a tailings pond leachate pool water level intelligent identification device includes a protective shell 1 installed on the pool wall, a telescopic frame 2 is provided on the upper part of the protective shell 1, a base 3 is provided on the upper end of the telescopic frame 2, a polarization camera group 4 is provided on the upper part of the base 3, and a sensor 6 and a pulse coded ultrasonic generator 7 are provided inside the protective shell 1, and the probes of the sensor 6 and the pulse coded ultrasonic generator 7 both extend out of the protective shell 1.

[0030] See also Figure 1 In an embodiment of the present invention, the angle of the polarization camera group 4 is adjusted by a pan / tilt platform, and the polarization camera group 4 includes a main camera and an auxiliary camera. The main camera shoots vertically downward, and the auxiliary camera shoots at an inclination angle of 15-30 degrees. Three tailings pond leachate pool water level intelligent identification devices are provided and installed around the pool wall at intervals of 120 degrees. The sensors 6 on the three devices are three groups of near-infrared sensors with different wavelengths. By setting a multispectral sensor group to collect liquid surface reflection spectra, the polarization camera group 4 to shoot liquid surface polarization images at multiple angles, and the pulse coded ultrasonic generator 7 to emit a modulated waveform to obtain multi-source data, a multi-source data fusion model is constructed to generate a three-dimensional water level estimation value with a credibility weight, dynamic compensation calibration is performed, and the water level value after environmental interference compensation and an abnormal event classification report are output.

[0031] The second embodiment differs from the first embodiment in that: See also Figure 2-Figure 3 In an embodiment of the present invention, the tailings pond leachate pool water level intelligent identification device also includes a self-cleaning component 5, which includes a motor 51 installed on the top side of the protective shell 1 and an annular seat 52 rotatably arranged on the outer wall of the protective shell 1. An L-shaped plate 53 is provided on one side of the annular seat 52, and a cleaning brush 54 for cleaning the sensor 6 and the pulse coded ultrasonic generator 7 is provided on the inner side of the L-shaped plate 53. The output shaft of the motor 51 is connected to the main gear 55, and an annular groove 56 is provided on the inner side of the annular seat 52. A slave gear 57 is provided inside the annular groove 56. The inner wall of the protective shell 1 is provided with a through groove 58 corresponding to the main gear 55. One side of the main gear 55 passes through the through groove 58 and engages with the slave gear 57.

[0032] In the above scheme: when the probe of the sensor 6 and the probe of the pulse coded ultrasonic generator 7 are dusty and need to be cleaned, the starting motor 51 drives the main gear 55 to rotate, and the main gear 55 drives the slave gear 57 meshing with it to rotate, thereby driving the annular seat 52 to rotate, and the annular seat 52 causes the L-shaped plate 53 to rotate, and the cleaning brush 54 on the inside of the L-shaped plate 53 will wipe and clean the probe of the sensor 6 and the probe of the pulse coded ultrasonic generator 7.

[0033] The third embodiment differs from the first embodiment in that: See also Figure 3-Figure 4 In the embodiment of the present invention, the self-cleaning component 5 also includes a sealing head 59 arranged at the upper and lower edges of the inner side of the annular seat 52 and two card grooves 510 on the outer wall of the protective shell 1. The two sealing heads 59 are respectively slidably engaged in the two card grooves 510. The sealing head 59 includes an annular flange 591. A cavity is provided inside the annular flange 591, and an annular strip 592 is slidably embedded in the inner side of the annular flange 591. A rubber pad 593 is provided on the inner side of the annular strip 592. The inner side of the rubber pad 593 is fitted against the inner wall of the card groove 510, and an annular water-swelling strip 594 is connected between the outer edge of the annular strip 592 and the inner wall of the annular flange 591. The upper part of the annular flange 591 is densely covered with micropores 595 communicating with its interior.

[0034] In the above scheme: when moisture enters through the card slot 510 on the protective shell 1, it will first enter the inside of the annular flange 591 through the micropores 595 on the annular flange 591, and then moisten the annular water-swelling strip 594 to cause it to expand. The annular water-swelling strip 594 will push the annular strip 592 outward, so that the rubber pad 593 fits tightly against the inner wall of the card slot 510, preventing the entry of moisture, playing a good sealing role, and preventing the inside of the protective shell 1 from being damp and affecting the operation of the equipment.

[0035] Furthermore, the wavelengths of the three groups of near-infrared sensors are 850 nm, 940 nm, and 1050 nm, respectively.

[0036] A method for intelligently identifying and processing the water level of a tailings pond leachate pool comprises using an intelligent water level identification device for identifying the water level of the tailings pond leachate pool. The identification and processing method comprises the following steps: S1, collecting liquid surface reflection spectrum through a ring-shaped multispectral sensor group; S2. Synchronously start the polarization camera group 4 to capture polarization images of the liquid surface at multiple angles, with the main camera shooting vertically downward and the auxiliary camera shooting at an inclined angle of 15-30 degrees; S3, using the pulse coding ultrasonic generator 7 to transmit the modulated waveform, and the receiving end extracts the effective echo signal through the adaptive threshold algorithm; S4. Construct a multi-source data fusion model: perform spatiotemporal registration of the spectral reflectance sequence, polarization image edge features, and ultrasonic transit time to generate a three-dimensional water level estimate with a credibility weight. S5. Perform dynamic compensation calibration: When foam or floating objects are detected on the water surface, activate the wavelength combination switching mode of the multispectral sensor and adjust the filter rotation angle of the polarization camera; S6. Output the water level value after environmental interference compensation and abnormal event classification report.

[0037] Furthermore, the working mode of the multispectral sensor group in step S1 is controlled as follows: In sunny mode, 850nm and 1050nm dual-band differential measurement is used; Automatically switches to the 940nm band in rainy and foggy weather and activates the background scattering compensation algorithm; Liquid surface curvature compensation calculation is achieved through the ring layout of three groups of sensors 6.

[0038] Furthermore, in step S2, the cooperative working mechanism of the polarization camera group 4 is as follows: The main camera uses a linear polarizer to eliminate interference from mirror reflections on the water surface; The auxiliary camera uses a circular polarizer to capture diffuse reflection features at different angles; The dual cameras construct a three-dimensional contour model of the liquid surface through a feature matching algorithm.

[0039] Furthermore, the data fusion model of step S4 includes: Spectral data preprocessing: Moving window Fourier transform is used to extract the time-frequency characteristics of reflectance at each wavelength; Image feature extraction: The liquid surface edge is segmented by improving the U-Net network and the pixel-level water level position is calculated; Fusion decision: A dual-channel decision-making mechanism combining convolutional neural network and adaptive weighted fusion is established, where CNN processes image features and Kalman filter processes time series signals.

[0040] Furthermore, in the dynamic compensation calibration in step S5, when the multispectral sensor detects a sudden change in reflectivity, floating object interference discrimination is initiated: Compare the consistency of reflectivity changes in the three bands; Use polarization camera to identify surface materials; The compensation algorithm is selected based on the recognition results: the trough tracking method is used for foam interference, and the neighborhood interpolation method is used for solid floating objects.

[0041] Furthermore, the method for intelligently identifying and processing the water level of the tailings pond leachate pool also includes establishing an abnormality handling knowledge base, as follows: When a water level anomaly is identified, the solution for similar cases is automatically retrieved; Assess risk levels by combining current meteorological data with dam sensor readings; Generate equipment maintenance suggestions: When specific sensor data is persistently abnormal, it prompts you to check for lens contamination or circuit failure.

[0042] It should be added that the above method also includes a device self-check and optimization mechanism, as follows: Daily timed triggering of sensor 6 calibration process: obtaining the reference measurement value by raising and lowering the telescopic frame 2; Camera mirror contamination detection: Analyzes the attenuation rate of high-frequency components in the image and automatically activates the built-in cleaning function on the camera to clean the camera and ensure the camera's image quality; Optimizing sensor layout based on historical data: A genetic algorithm dynamically adjusts the weighting coefficients of each sensor 6. Different sensor types have unique measurement principles and performance characteristics. In the complex and changing environment of a tailings pond, a single, fixed weighting coefficient cannot ensure system accuracy and stability. The genetic algorithm fully considers the actual performance of each sensor 6 under different operating conditions and dynamically assigns weights based on factors such as measurement accuracy and environmental interference. This allows each sensor 6 to complement its strengths, minimize measurement errors, and significantly improve water level identification accuracy. Furthermore, the algorithm can detect environmental changes in real time, such as changing weather conditions or the presence of foam or floating objects on the water surface, and rapidly adjust the weighting coefficients, ensuring stable operation in a variety of complex environments and enhancing the system's adaptability and robustness. Furthermore, as sensor 6 performance fluctuates over time, the genetic algorithm can adjust the weights based on real-time data feedback, ensuring the system maintains reliable water level identification over the long term.

[0043] Working principle: When the probe of the sensor 6 and the probe of the pulse coded ultrasonic generator 7 are dusty and need to be cleaned, the motor 51 is started to drive the main gear 55 to rotate, and the main gear 55 drives the slave gear 57 meshing with it to rotate, which in turn drives the annular seat 52 to rotate. The annular seat 52 rotates the L-shaped plate 53, and the cleaning brush 54 on the inside of the L-shaped plate 53 wipes and cleans the probe of the sensor 6 and the probe of the pulse coded ultrasonic generator 7; When moisture enters through the card slot 510 on the protective shell 1, it will first enter the inside of the annular flange 591 through the micropores 595 on the annular flange 591, and then moisten the annular water-swelling strip 594 to cause it to expand. The annular water-swelling strip 594 will push the annular strip 592 outward, so that the rubber pad 593 fits tightly against the inner wall of the card slot 510, preventing the entry of moisture and playing a good sealing role, preventing the inside of the protective shell 1 from being damp and affecting the operation of the equipment.

[0044] All technical features in this embodiment can be freely combined according to actual needs.

[0045] The above embodiments are preferred implementation schemes of the present invention. In addition, the present invention can also be implemented in other ways. Any obvious replacement without departing from the concept of the present technical solution is within the scope of protection of the present invention.

Claims

1. An intelligent water level identification device for a tailings pond leachate pool, comprising a protective shell (1) mounted on the pool wall, characterized in that: A telescopic frame (2) is provided on the upper portion of the protective shell (1), a base (3) is provided on the upper end of the telescopic frame (2), a polarization camera group (4) is provided on the upper portion of the base (3), a sensor (6) and a pulse-coded ultrasonic generator (7) are provided inside the protective shell (1), and probes of the sensor (6) and the pulse-coded ultrasonic generator (7) both extend out of the protective shell (1); The angle of the polarization camera group (4) is adjusted by a pan / tilt platform, and the polarization camera group (4) includes a main camera and an auxiliary camera. The main camera shoots vertically downward, and the auxiliary camera shoots at an inclination angle of 15-30 degrees. The tailings pond leachate pool water level intelligent identification device is provided with three and is installed around the pool wall at intervals of 120 degrees. The sensors (6) on the three devices are respectively three groups of near-infrared sensors with different wavelengths. By setting a multi-spectral sensor group to collect liquid surface reflection spectra, the polarization camera group (4) to shoot liquid surface polarization images at multiple angles, and the pulse coded ultrasonic generator (7) to emit a modulated waveform to obtain multi-source data, a multi-source data fusion model is constructed to generate a three-dimensional water level estimation value with a credibility weight, dynamic compensation calibration is performed, and the water level value after environmental interference compensation and an abnormal event classification report are output.

2. The tailings pond leachate pool water level intelligent identification device according to claim 1, characterized in that: The tailings pond leachate pool water level intelligent identification device further includes a self-cleaning component (5), the self-cleaning component (5) including a motor (51) mounted on the top side of the protective shell (1) and an annular seat (52) rotatably arranged on the outer wall of the protective shell (1), an L-shaped plate (53) being arranged on one side of the annular seat (52), a cleaning brush (54) for cleaning the sensor (6) and the pulse coding ultrasonic generator (7) being arranged on the inner side of the L-shaped plate (53), an output shaft of the motor (51) being connected to a main gear (55), an annular groove (56) being arranged on the inner side of the annular seat (52), a slave gear (57) being arranged inside the annular groove (56), an inner wall of the protective shell (1) being provided with a through groove (58) corresponding to the main gear (55), one side of the main gear (55) passing through the through groove (58) and meshing with the slave gear (57).

3. The device for intelligently identifying water level in a tailings pond leachate pool according to claim 2, characterized in that: The self-cleaning component (5) further comprises a sealing head (59) provided at the upper and lower edges of the inner side of the annular seat (52) and two card grooves (510) on the outer wall of the protective shell (1), the two sealing heads (59) being slidably engaged in the two card grooves (510) respectively, the sealing head (59) comprising an annular flange (591), a cavity being provided inside the annular flange (591), and an annular strip (592) being slidably embedded in the inner side of the annular flange (591), a rubber pad (593) being provided on the inner side of the annular strip (592), the inner side of the rubber pad (593) being fitted to the inner wall of the card groove (510), and an annular water-expandable strip (594) being connected between the outer edge of the annular strip (592) and the inner wall of the annular flange (591), and the upper part of the annular flange (591) is densely covered with micropores (595) communicating with the inner side thereof.

4. The device for intelligently identifying water level in a tailings pond leachate pool according to claim 1, characterized in that: The wavelengths of the three groups of near-infrared sensors are 850 nm, 940 nm and 1050 nm respectively.

5. A method for intelligently identifying and processing the water level of a tailings pond leachate pool, characterized in that: The method comprises using the tailings pond leachate pool water level intelligent identification device according to any one of claims 1 to 4 to identify the water level of the tailings pond leachate pool, wherein the identification and processing method comprises the following steps: S1, collecting liquid surface reflection spectrum through a ring-shaped multispectral sensor group; S2, synchronously starting the polarization camera group (4) to capture polarization images of the liquid surface at multiple angles, wherein the main camera captures vertically downward and the auxiliary camera captures at an inclined angle of 15-30 degrees; S3, using a pulse coded ultrasonic generator (7) to transmit a modulated waveform, and the receiving end extracts a valid echo signal through an adaptive threshold algorithm; S4. Construct a multi-source data fusion model: perform spatiotemporal registration of the spectral reflectance sequence, polarization image edge features, and ultrasonic transit time to generate a three-dimensional water level estimate with a credibility weight. S5. Perform dynamic compensation calibration: When foam or floating objects are detected on the water surface, activate the wavelength combination switching mode of the multispectral sensor and adjust the filter rotation angle of the polarization camera; S6. Output the water level value after environmental interference compensation and abnormal event classification report.

6. A method for intelligently identifying and processing the water level of a tailings pond leachate pool, characterized by: The operating mode of the multispectral sensor group in step S1 is controlled as follows: In sunny mode, 850nm and 1050nm dual-band differential measurement is used; Automatically switches to the 940nm band in rainy and foggy weather and activates the background scattering compensation algorithm; Liquid surface curvature compensation calculation is achieved through the ring arrangement of three sets of sensors (6).

7. A method for intelligently identifying and processing the water level of a tailings pond leachate pool, characterized by: In step S2, the cooperative working mechanism of the polarization camera group (4) is as follows: The main camera uses a linear polarizer to eliminate interference from mirror reflections on the water surface; The auxiliary camera uses a circular polarizer to capture diffuse reflection features at different angles; The dual cameras construct a three-dimensional contour model of the liquid surface through a feature matching algorithm.

8. A method for intelligently identifying and processing the water level of a tailings pond leachate pool, characterized by: The data fusion model of step S4 includes: Spectral data preprocessing: Moving window Fourier transform is used to extract the time-frequency characteristics of reflectance at each wavelength; Image feature extraction: The liquid surface edge is segmented by improving the U-Net network and the pixel-level water level position is calculated; Fusion decision: A dual-channel decision-making mechanism combining convolutional neural network and adaptive weighted fusion is established, where CNN processes image features and Kalman filter processes time series signals.

9. A method for intelligently identifying and processing the water level of a tailings pond leachate pool, characterized by: In the dynamic compensation calibration in step S5, when the multispectral sensor detects a sudden change in reflectivity, floating object interference discrimination is initiated: Compare the consistency of reflectivity changes in the three bands; Use polarization camera to identify surface materials; The compensation algorithm is selected based on the recognition results: the trough tracking method is used for foam interference, and the neighborhood interpolation method is used for solid floating objects.

10. A method for intelligently identifying and processing the water level of a tailings pond leachate pool, characterized by: It also includes the establishment of an exception handling knowledge base, as follows: When a water level anomaly is identified, the solution for similar cases is automatically retrieved; Assess risk levels by combining current meteorological data with dam sensor readings; Generate equipment maintenance suggestions: When specific sensor data is persistently abnormal, it prompts you to check for lens contamination or circuit failure.

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