Hopper loading state monitoring system based on acoustic double-frequency feature extraction and control method

Abstract

The invention discloses a hopper loading state monitoring system based on acoustic double-frequency feature extraction and a control method, and the system comprises acoustic double-frequency sensors which are symmetrically arranged at the two sides of the top of a hopper, and the sensors employ a double-frequency alternating excitation mode; the distance between the acoustic double-frequency sensor and the material in the hopper is calculated according to the preprocessed signal, and the filling state is judged according to the distance threshold interval where the calculated distance is located; and the distances from the acoustic double-frequency sensors on the two sides to the materials in the hopper are compared, the weighting index is calculated, and the weighting early warning level of the hopper is determined according to the weighting index threshold interval where the weighting index obtained through calculation is located. The dual-frequency measurement improves the detection reliability of a complex environment and a complex material surface; multiple sensors cooperate to realize synchronous monitoring of the filling degree and the emphasis, so that the accuracy of data monitoring is guaranteed, and accidents are avoided; a grading early warning mechanism for the filling degree and the emphasis level gives consideration to safety and operability, and a manager and a driver can obtain information more visually and judge the severity rapidly.

Description

Technical Field

[0001] This invention relates to a hopper loading status monitoring system and control method based on acoustic dual-frequency feature extraction, belonging to the field of intelligent monitoring / detection technology for engineering machinery. Background Technology

[0002] Dump trucks (such as mining dump trucks, slag trucks, and bulk material transport vehicles) are key transportation equipment in mining, construction, and port industries, primarily used for the efficient loading, unloading, and transfer of bulk materials (such as ore, sand, and coal). The loading status of their core component—the hopper (cargo compartment)—directly affects transportation efficiency, energy consumption, and operational safety. Currently, hopper management in dump trucks mainly relies on manual experience or simple mechanical checks, which commonly presents the following problems: manual judgment easily leads to uneven hopper loading; overloading increases vehicle wear and fuel consumption, while underloading reduces transportation efficiency; inaccurate hopper filling may result in incomplete unloading or excessively rapid material spillage, causing safety hazards; traditional methods cannot provide real-time loading rate data for intelligent dispatch systems, hindering fleet management optimization. Accurate monitoring of hopper fullness has multiple implications for the operational efficiency of dump trucks, including: avoiding ineffective empty loads or repeated loading and unloading through real-time monitoring, thus optimizing vehicle scheduling; preventing premature damage to tires and suspension systems due to overloading, thereby reducing fuel waste; providing early warning of uneven material distribution in the hopper (such as uneven loading), thus preventing vehicle rollovers or unloading accidents; and providing key data support for unmanned mining trucks and automated logistics systems.

[0003] Currently, the hopper monitoring technologies applied to dump trucks mainly include the following categories: indirectly calculating the load by measuring the force on the axles or suspension system; contact probes detecting material contact signals, which can only determine whether the hopper is full, but cannot quantify the filling degree or reflect the material distribution (such as uneven loading); and analyzing the material height by taking pictures of the inside of the hopper, but this method has poor reliability in dusty, rainy, and foggy environments, requires auxiliary lighting at night, and has high computing power requirements.

[0004] To address the challenges posed by the complex operating conditions (vibration, dust, temperature changes) of dump trucks and the insufficient adaptability of existing monitoring technologies, this invention proposes an acoustic dual-frequency feature extraction and hopper filling monitoring method and system. By integrating multi-band acoustic excitation with high-dimensional features, it overcomes the limitations of traditional single detection methods, achieving high-precision, interference-resistant real-time monitoring and providing key technical support for the intelligentization of engineering machinery.

[0005] Existing technologies for monitoring the loading status of hoppers primarily rely on data such as weight, images, and point clouds for judgment. However, weight-based judgments have low accuracy, failing to pinpoint the exact location of excess or deficiency of material within the hopper, resulting in low monitoring accuracy and reliability. Image and point cloud data require expensive sensors, which are also prone to damage, and data transmission and analysis are demanding. Indirectly calculating load by measuring the forces on the axles or suspension system, and using contact probes to detect material contact signals, only determine if the hopper is full, failing to quantify filling levels or reflect material distribution (e.g., uneven loading). Analyzing material height by capturing images of the hopper's interior is unreliable in dusty or foggy environments, requires auxiliary lighting at night, and demands high computing power. Weighing alone cannot accurately convey the hopper's loading status to the driver, hindering safety assessments. Summary of the Invention

[0006] This invention provides a hopper loading status monitoring system and control method based on acoustic dual-frequency feature extraction, which solves the problems disclosed in the background art.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A hopper loading status monitoring system based on acoustic dual-frequency feature extraction includes:

[0009] The hopper size information module is used to obtain the length, width, and depth of the hopper;

[0010] The acoustic dual-frequency sensor module includes several sets of acoustic dual-frequency sensors symmetrically arranged on both sides of the top of the hopper. The sensors adopt a dual-frequency alternating excitation mode to dynamically adjust the transmission voltage and automatically adjust the driving voltage according to the ambient noise.

[0011] The signal preprocessing module is used to preprocess the signals acquired by the acoustic dual-frequency sensor;

[0012] The filling status module is used to calculate the distance from the acoustic dual-frequency sensor to the material in the hopper based on the pre-processed signal, and to determine the filling status based on the distance threshold range in which the calculated distance is located.

[0013] The bias status module is used to compare the distances from the acoustic dual-frequency sensors on both sides to the material in the hopper, calculate the bias index, and determine the bias warning level of the hopper based on the bias index threshold range in which the calculated bias index is located.

[0014] Furthermore, it also includes a display module for real-time display of the hopper's material filling status and the level of unbalanced warning.

[0015] Furthermore, the acoustic dual-frequency sensor is a transceiver integrated sensor with a diffusion angle θ=8° and is equipped with a parabolic reflector, which limits the effective detection area to within the cross-section of the hopper.

[0016] Furthermore, the acoustic dual-frequency sensor's heating cover is a 304 stainless steel sealed housing, with a 0.5mm thick titanium alloy filter screen at the front end, and the mounting bracket is equipped with a shock-absorbing spring assembly to suppress signal interference caused by vehicle bumps.

[0017] Furthermore, the dual-frequency alternating excitation mode is as follows: a 40kHz pulse lasts for 2ms, and after a 1ms interval, a 60kHz pulse is switched, with a cycle period of 10ms.

[0018] Furthermore, methods for preprocessing the signals acquired by the acoustic dual-frequency sensor include:

[0019] The signal is bandpass filtered, with a range of 35kHz-65kHz.

[0020] The short-time Fourier transform analysis method is used to segment the signal and perform Fourier transforms to obtain local time-frequency information. The STFT is defined as:

[0021] ;

[0022] in, It is the raw signal being analyzed. It is a time variable. A window function is used to "trap" the signal, focusing only on the signal segment around time t. This represents the offset relative to time t, and is "truncated" by a window function, only affecting signals around time t. Performing a Fourier transform yields the frequency response of the signal around time t. It is a complex exponential function, the kernel of the Fourier transform, used to convert signals from the time domain to the frequency domain. It is a frequency variable, and j is the imaginary unit. The propagation distance of sound signals.

[0023] Furthermore, the method for calculating the distance from the acoustic dual-frequency sensor to the material in the hopper is as follows:

[0024] Extract the propagation time t corresponding to the reflected sound signals from the material at frequencies of 40kHz and 60kHz, respectively. The speed of sound in air is known to be... Then calculate the propagation distance of the sound signal. .

[0025] Furthermore, the method for determining the filling state based on the distance threshold range of the calculated distance is as follows: when d=0cm, the filling state is overloaded; when 20cm≥d>0cm, the filling state is fully loaded; when 40cm≥d>20cm, the filling state is slightly underloaded; when d>40cm, the filling state is underloaded.

[0026] Furthermore, by comparing the distances from the acoustic dual-frequency sensors on both sides to the material in the hopper, the method for calculating the eccentricity index is as follows:

[0027] ;

[0028] in, The bias index is N, where N is half the number of sensors and i is the sensor number. The difference in distance between the acoustic dual-frequency sensors on both sides and the material in the hopper.

[0029] Furthermore, the method for determining the hopper's eccentricity warning level based on the calculated eccentricity index threshold range is as follows: when 20cm ≥ When the warning level is normal, the emphasis is on the warning level; when 40cm ≥ A reading >20cm indicates a Level 1 overweight warning; a reading ≥60cm indicates an underweight warning. When the height is >40cm, it is a Level II heavy warning; when When the value is greater than 60, it is a Level III (slightly severe) warning.

[0030] The beneficial effects achieved by this invention are as follows:

[0031] This invention improves the reliability of detection in complex environments and on complex material surfaces through dual-frequency measurement; multi-sensor collaboration enables simultaneous monitoring of fullness and weight imbalance, ensuring the accuracy of data monitoring and preventing accidents; and a graded early warning mechanism for fullness and weight imbalance levels balances safety and operability, allowing managers and drivers to obtain information more intuitively and quickly determine the severity. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0033] Figure 2 This is a schematic diagram showing the deployment of the hopper and sensors of the present invention;

[0034] Figure 3 This is a schematic diagram of the warning display interface of the present invention. Detailed Implementation

[0035] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0036] Example 1

[0037] like Figure 1 As shown, this embodiment provides a hopper loading status monitoring system based on acoustic dual-frequency feature extraction, including: a hopper size information module, which inputs the hopper length L, width W, and depth H through a human-machine interface to establish a three-dimensional coordinate system, defining the bottom of the hopper as the origin (0,0,0) and the top opening as (L,W,H).

[0038] like Figure 2 As shown, the acoustic dual-frequency sensor module symmetrically arranges eight sets of sensors (A1-A4, B1-B4) on both sides of the top of the hopper according to the hopper's length L, width W, and acoustic sensor diffusion angle, with a horizontal spacing of L / 4. A transceiver integrated sensor with a diffusion angle θ=8° is selected, and the effective detection area is limited to the hopper's cross-section by a parabolic reflector. It employs a dual-frequency alternating excitation mode of 40kHz and 60kHz, with 40kHz used to penetrate dusty environments and 60kHz improving resolution to ±2mm. A time-division multiplexing mode is used for acoustic signal excitation, i.e., a 40kHz pulse lasting 2ms, followed by a 1ms interval before switching to a 60kHz pulse, with a cycle period of 10ms. The transmission voltage is dynamically adjusted, automatically adjusting the drive voltage (20Vpp to 80Vpp) according to ambient noise. The sensor housing is made of 304 stainless steel with a 0.5mm thick titanium alloy filter at the front end. The mounting bracket is designed with shock-absorbing springs to suppress signal interference caused by vehicle bumps.

[0039] The signal preprocessing module is used to preprocess the signals acquired by the acoustic dual-frequency sensor;

[0040] The filling status module is used to calculate the distance from the acoustic dual-frequency sensor to the material in the hopper based on the pre-processed signal, and to determine the filling status based on the distance threshold range in which the calculated distance is located.

[0041] The bias status module is used to compare the distances from the acoustic dual-frequency sensors on both sides to the material in the hopper, calculate the bias index, and determine the bias warning level of the hopper based on the bias index threshold range in which the calculated bias index is located.

[0042] The display module is used to display the fullness status of the material loaded in the hopper and the level of unbalanced warning in real time.

[0043] Example 2

[0044] This embodiment provides a control method for a hopper loading status monitoring system based on acoustic dual-frequency feature extraction;

[0045] First, the signal preprocessing module performs bandpass filtering on the received signal, with a bandpass filtering range of 35kHz-65kHz.

[0046] The Short-Time Fourier Transform (STFT) analysis method is used to segment the signal and perform Fourier transforms to obtain local time-frequency information. The STFT is defined as: ;

[0047] The filling state module extracts the propagation time of the reflected sound signals from the material at frequencies of 40kHz and 60kHz, respectively, given the speed of sound in air. Then calculate the propagation distance of the sound signal. for: ;

[0048] Based on the distance from the acoustic sensor to the material in the hopper, the load is divided into four categories: overload, full load, slightly underload, and underload. The specific strategies are as follows:

[0049]

[0050] like Figure 3 As shown, the display module establishes a virtual simulation 3D model of the hopper and its filling level under normal conditions.

[0051] The distance data obtained from actual monitoring is linked to the 3D model to dynamically display the positions of overload, normal, slightly underload, and underload conditions in the hopper, indicated by red, green, yellow, and orange respectively.

[0052] The eccentricity status module uses a symmetrical sensor comparison algorithm to calculate the eccentricity of the hopper and defines an eccentricity index: ;

[0053] According to the bias index Based on the calculation results, the eccentricity warning level of the hopper is determined, and the specific strategy is as follows:

[0054]

[0055] like Figure 3 As shown, the imbalance warning in the display module is displayed in real time on the monitoring system interface. If the imbalance warning still exists after the vehicle is loaded, the driver and loading personnel need to be notified to make appropriate adjustments. The warning output sends a warning signal to the driver through a three-color status light and a tiered audible alarm.

[0056] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A hopper loading status monitoring system based on acoustic dual-frequency feature extraction, characterized in that, include: The hopper size information module is used to obtain the length, width, and depth of the hopper; The acoustic dual-frequency sensor module includes several sets of acoustic dual-frequency sensors symmetrically arranged on both sides of the top of the hopper. The sensors adopt a dual-frequency alternating excitation mode to dynamically adjust the transmission voltage and automatically adjust the driving voltage according to the ambient noise. The signal preprocessing module is used to preprocess the signals acquired by the acoustic dual-frequency sensor; The filling status module is used to calculate the distance from the acoustic dual-frequency sensor to the material in the hopper based on the pre-processed signal, and to determine the filling status based on the distance threshold range in which the calculated distance is located. The bias status module is used to compare the distances from the acoustic dual-frequency sensors on both sides to the material in the hopper, calculate the bias index, and determine the bias warning level of the hopper based on the bias index threshold range in which the calculated bias index is located.

2. The hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, It also includes a display module for real-time display of the hopper's material filling status and the level of unbalanced warning.

3. The hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, The acoustic dual-frequency sensor is a transceiver integrated sensor with a diffusion angle θ=8° and is equipped with a parabolic reflector, which limits the effective detection area to within the cross-section of the hopper.

4. The hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, The acoustic dual-frequency sensor's heating cover is a 304 stainless steel sealed housing with a 0.5mm thick titanium alloy filter screen at the front end. The mounting bracket is equipped with a shock-absorbing spring assembly to suppress signal interference caused by vehicle bumps.

5. The control method for the hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, The dual-frequency alternating excitation mode is as follows: a 40kHz pulse lasts for 2ms, and after a 1ms interval, a 60kHz pulse is switched, with a cycle period of 10ms.

6. The control method for the hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, Methods for preprocessing signals acquired by acoustic dual-frequency sensors include: The signal is bandpass filtered, with a range of 35kHz-65kHz. The short-time Fourier transform analysis method is used to segment the signal and perform Fourier transforms to obtain local time-frequency information. The STFT is defined as: ； in, It is the raw signal being analyzed. It is a time variable. A window function is used to "trap" the signal, focusing only on the signal segment around time t. This represents the offset relative to time t, and is "truncated" by a window function, only affecting signals around time t. Performing a Fourier transform yields the frequency response of the signal around time t. It is a complex exponential function, the kernel of the Fourier transform, used to convert signals from the time domain to the frequency domain. It is a frequency variable, and j is the imaginary unit. The propagation distance of sound signals.

7. The control method for the hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, The method for calculating the distance from the acoustic dual-frequency sensor to the material in the hopper is as follows: Extract the propagation time t corresponding to the reflected sound signals from the material at frequencies of 40kHz and 60kHz, respectively. The speed of sound in air is known to be... Then calculate the propagation distance of the sound signal. .

8. The control method for the hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 7, characterized in that, The method for determining the filling state based on the distance threshold range of the calculated distance is as follows: when d=0cm, the filling state is overloaded; when 20cm≥d>0cm, the filling state is fully loaded; when 40cm≥d>20cm, the filling state is slightly underloaded; when d>40cm, the filling state is underloaded.

9. The control method for the hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 1, characterized in that, The method for calculating the eccentricity index is as follows: (This is based on comparing the distances from the acoustic dual-frequency sensors on both sides to the material in the hopper.) ； in, The bias index is N, where N is half the number of sensors and i is the sensor number. The difference in distance between the acoustic dual-frequency sensors on both sides and the material in the hopper.

10. The control method for the hopper loading status monitoring system based on acoustic dual-frequency feature extraction according to claim 9, characterized in that, The method for determining the hopper's eccentricity warning level based on the calculated eccentricity index threshold range is as follows: when 20cm ≥ At that time, the warning level was considered normal. When 40cm≥ A reading >20cm indicates a Level 1 overweight warning; a reading ≥60cm indicates an underweight warning. When the height is >40cm, it is a Level II heavy warning; when When the value is greater than 60, it is a Level III (slightly severe) warning.

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