A method, device, equipment and medium for intelligent ultrasonic mud level measurement in the water conservancy industry

Abstract

This application discloses an ultrasonic intelligent measurement method, device, equipment, and medium for mud level in the water conservancy industry, belonging to the field of ultrasonic data processing technology. The method includes: controlling an ultrasonic transducer to emit detection pulses towards the water body to be measured and receiving raw echo signals formed by reflections from different media interfaces in the water body; preprocessing the raw echo signals to construct an echo intensity versus distance distribution curve based on time-domain waveform data; analyzing the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer; calculating the actual distance from the sensor to the mud surface based on the round-trip time difference of the upper interface echo of the sludge layer; converting the actual distance into the current mud level thickness based on a preset sensor installation height; evaluating the confidence level of the mud level thickness; and outputting the mud level measurement result after confirming the data's validity. This technical solution can achieve accurate measurement of mud level thickness under harsh working conditions and can operate stably for extended periods.

Description

Technical Field

[0001] This application belongs to the field of ultrasonic data processing technology, specifically relating to an ultrasonic intelligent measurement method, device, equipment, and medium for water conservancy industry. Background Technology

[0002] In the fields of water conservancy engineering and environmental wastewater treatment, accurate measurement of the sludge interface is crucial for sludge discharge control in sedimentation tanks, sludge concentration adjustment, and process optimization. Currently, the industry commonly uses contact-type measuring instruments for sludge level monitoring, such as float-type, capacitive, or hydrostatic level gauges. These traditional devices acquire data by directly contacting the sludge medium with a physical probe. However, in practical applications, due to the complex composition of wastewater and the high viscosity, strong corrosiveness, and tendency to caking of sludge, contact probes are prone to scaling, clogging, or fiber entanglement, leading to a sharp decrease in sensor sensitivity or even failure. This not only requires frequent on-site cleaning and maintenance by operation and maintenance personnel but also increases labor and time costs. Therefore, how to adapt to the harsh environment of wastewater and accurately measure the sludge level is a technical challenge that urgently needs to be solved in this field. Summary of the Invention

[0003] This application provides an ultrasonic intelligent mud level measurement method, device, equipment, and medium for the water conservancy industry. The purpose is to solve the problems of easy scaling and clogging and high maintenance costs of contact instruments in the prior art. By intelligently determining the mud layer interface and filtering out interference, it can achieve accurate measurement of mud level thickness under harsh working conditions and can operate stably for a long time.

[0004] In a first aspect, embodiments of this application provide an ultrasonic intelligent measurement method for mud level in the water conservancy industry, the method comprising: The ultrasonic transducer is controlled to transmit detection pulses to the water body to be tested and to receive the original echo signals formed by reflections from the interfaces of different media in the water body. The original echo signal is preprocessed, and an echo intensity versus distance distribution curve is constructed based on the preprocessed time-domain waveform data. Analyze the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer; The actual distance from the sensor to the sludge surface is calculated based on the round-trip time difference of the interface echo on the sludge layer. Based on the preset sensor installation height, the actual distance is converted into the current mud thickness; The confidence level of the mud thickness is assessed, and the mud level measurement results are output after the data is confirmed to be valid.

[0005] Furthermore, the analysis of the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer includes: The signal segment in the distribution curve with a step-like increase in amplitude and a stable subsequent decay rate and continuous energy distribution is identified as the upper interface of the sludge layer. Identify signal segments with sharp single peaks in amplitude that rapidly decay to background noise, determine them as hard pool bottoms or solid obstacles, and eliminate them; Signal segments with randomly fluctuating amplitudes and a signal-to-noise ratio below a preset lower limit are identified as bubble or turbulent noise and suppressed by superimposing multiple consecutive echoes.

[0006] Furthermore, including: The ultrasonic transducer is controlled to continuously emit N frames of detection pulses within a preset time window to obtain N sets of echo intensity and distance distribution curves. Perform time-domain overlay analysis on the N sets of distribution curves and calculate the statistical variance of the echo amplitude at the same distance point; If the statistical variance of the echo amplitude at a certain distance point is greater than the preset fluctuation threshold, then the point is determined to be a transient bubble interference, and the amplitude of the point is set to zero in the final composite curve. If the echo amplitude at a certain distance point exhibits a single sharp peak and lacks temporal continuity, it is determined to be a hard obstacle and removed from the candidate interface list. Stable echo signals with temporal continuity are retained as the basis for determining the interface on the sludge layer.

[0007] Furthermore, before calculating the actual distance from the sensor to the mud surface, the method further includes: Obtain real-time environmental parameters of the current water body, including at least water temperature and turbidity; The propagation speed of ultrasound in water is dynamically corrected using a composite compensation formula that includes temperature and turbidity variables, and the corrected sound speed is then substituted into the distance calculation.

[0008] Furthermore, the composite compensation formula is as follows: ; Where V is the corrected current speed of sound, V0 is the speed of sound under standard conditions, Kt is the temperature coefficient, ΔT is the temperature difference between the real-time water temperature and the standard temperature, Ktu is the turbidity coefficient, and TU is the real-time turbidity value.

[0009] Furthermore, the confidence assessment of the mud thickness includes: Record the time series data of mud level thickness within a preset time period, and use the sliding window algorithm to establish a short-term trend prediction model to calculate the rate of rise or fall of mud level. Set multi-level mud level thresholds, wherein the multi-level mud level thresholds include at least a low threshold, a medium threshold, and a high threshold; The predicted mud level change trend is compared with the set multi-level mud level thresholds; When the predicted mud level reaches the aforementioned threshold, a control signal to start the mud discharge equipment is automatically output. When the predicted mud level reaches the high threshold, an emergency alarm signal is output and the measurement output is locked.

[0010] Furthermore, after outputting the mud level measurement results, the method also includes: Record time series data of mud level thickness within a preset time period and establish a short-term trend prediction model; Multiple mud level thresholds can be set. When the predicted mud level reaches the middle threshold, a control signal to start mud discharge will be automatically output. When the high threshold is reached, an emergency alarm signal will be output.

[0011] Secondly, embodiments of this application provide an ultrasonic intelligent mud level measurement device for the water conservancy industry, the device comprising: The echo signal acquisition module is used to control the ultrasonic transducer to send detection pulses to the water body to be tested and to receive the original echo signals formed by reflections from the interfaces of different media in the water body. The distribution curve determination module is used to preprocess the original echo signal and construct the echo intensity and distance distribution curve based on the preprocessed time-domain waveform data. The interface echo identification module is used to analyze the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer. The actual distance calculation module is used to calculate the actual distance from the sensor to the sludge surface based on the round-trip time difference of the interface echo on the sludge layer. The mud thickness calculation module is used to convert the actual distance into the current mud thickness by combining the preset sensor installation height. The confidence assessment module is used to assess the confidence level of the mud thickness and output the mud measurement results after confirming the validity of the data.

[0012] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0013] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0014] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0015] The technical solution provided in this application, by constructing a two-dimensional distribution curve of echo intensity and distance and analyzing its amplitude abrupt change and attenuation characteristics, realizes intelligent determination of the interface on the sludge layer, fundamentally overcoming the failure problem of contact instruments in the prior art caused by probe scaling and clogging. At the same time, by filtering out the interference signals of air bubbles and hard obstacles and combining the confidence evaluation of the output results, this method effectively solves the defects of traditional ultrasonic measurement in high turbidity and variable operating conditions that are easily affected by false echo interference and data drift, thereby significantly reducing maintenance costs and ensuring the stability and accuracy of sludge level measurement in harsh water conservancy environments. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the ultrasonic intelligent mud level measurement method for the water conservancy industry provided in Embodiment 1 of this application. Figure 2 This is a schematic diagram of the ultrasonic mud level gauge provided in Embodiment 1 of this application; Figure 3 This is a schematic diagram of the structure of the ultrasonic mud level intelligent measuring device for the water conservancy industry provided in Embodiment 2 of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 3 of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0018] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0019] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0020] The following detailed description, in conjunction with the accompanying drawings, of the ultrasonic intelligent mud level measurement method, device, equipment, and medium for the water conservancy industry provided in this application, through specific embodiments and application scenarios, will be provided in detail.

[0021] Example 1 Figure 1 This is a flowchart illustrating the ultrasonic intelligent mud level measurement method for the water conservancy industry provided in Embodiment 1 of this application. Figure 1 As shown, the specific steps include the following: S11 controls the ultrasonic transducer to transmit detection pulses to the water body to be tested and to receive the original echo signals formed by reflections from the interfaces of different media in the water body. Ultrasonic transducers are physically sealed underwater probes with overall waterproof sealing. Depending on the degree of water corrosion and turbidity, stainless steel, engineering ceramics, or special anti-corrosion coating materials can be selected. They can work stably for a long time when immersed in sewage and silty water. They also have the dual function of emitting ultrasonic detection signals and receiving reflected echo signals.

[0022] The water body to be tested refers to water environments containing sludge, suspended solids, and impurities, such as sedimentation tanks in the water conservancy industry, equalization tanks in sewage treatment plants, and river dredging monitoring areas. These water bodies generally have the characteristics of high viscosity, easy material adhesion, and easy corrosion of probes.

[0023] A probe pulse is an ultrasonic signal emitted by a transducer that has a very short duration. Its frequency is usually between tens and hundreds of kilohertz. It has concentrated energy and moderate penetration, which can penetrate water to a certain depth and produce effective reflection at the mud-water interface.

[0024] The raw echo signal refers to the reflected acoustic wave electrical signal directly received by the transducer without any algorithm processing. The signal composition is complex, including effective reflection components such as mud, pool bottom, and scum, as well as noise components generated by bubble bursting, water turbulence, and electromagnetic interference. It is the original basic data for subsequent signal processing.

[0025] In this scheme, the main control electronic unit outputs a specific frequency electrical signal through the internal drive circuit to control the ultrasonic transducer to emit detection pulses vertically downwards into the water body; after the pulses propagate in the water and are reflected by different medium interfaces, the transducer converts the reflected sound waves into electrical signals, thus completing the acquisition of the original echo signal.

[0026] S12, preprocess the original echo signal, and construct the echo intensity versus distance distribution curve based on the preprocessed time-domain waveform data; Preprocessing refers to the basic purification and format conversion of the original echo signal, which includes three core steps: low-pass filtering, signal amplification, and analog-to-digital conversion. Low-pass filtering removes high-frequency electromagnetic interference and sudden noise, signal amplification enhances the strength of weak mud layer echo signals, and analog-to-digital conversion converts analog electrical signals into digital signals, providing clean and computable data for subsequent algorithm analysis.

[0027] Time-domain waveform data refers to the digital sequence of echo signal intensity changing over time after preprocessing. Each data point corresponds to a timestamp and signal amplitude, completely recording the signal change pattern of ultrasound from transmission to reception. It is the direct data source for constructing distance dimension curves.

[0028] The echo intensity versus distance distribution curve is a custom core feature curve defined in this method. It is a two-dimensional curve plotted with the ultrasonic propagation distance as the horizontal axis and the echo signal intensity as the vertical axis. It can intuitively present the differences in the strength of the reflected signal at different depths in the water body. It can clearly distinguish the signal characteristics of different reflection sources such as water layer, mud layer, obstacles, and bubbles, and is a key visualization basis for identifying mud layer interfaces.

[0029] Specifically, the signal processing module can filter, amplify, and convert the original echo into digital data according to a preset process; then, combined with the standard propagation speed of ultrasound in water, the waveform data in the time dimension is converted into the distance dimension, and the echo intensity and distance distribution curves are generated.

[0030] S13, Analyze the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer. The amplitude abrupt change characteristic refers to the non-stationary, step-like change pattern of signal amplitude on the echo intensity versus distance distribution curve. Unlike the gentle fluctuation of water layer signals and the sharp abrupt change of obstacle signals, the signal at the mud layer interface will suddenly jump from a low-intensity water layer signal to a high-intensity mud layer reflection signal, forming a clear abrupt inflection point.

[0031] The attenuation rate characteristic refers to the rate at which the echo intensity decreases with increasing propagation distance. It is an important indicator for distinguishing different reflecting media: the sludge layer has a uniform medium and stable reflection, and the echo intensity attenuates slowly and at a constant rate; the signal energy reflected by obstacles or bubbles is concentrated, and the attenuation is extremely fast and irregular; the signal attenuation in the water layer is gradual but the intensity is low.

[0032] The upper interface echo of the sludge layer refers to the echo signal reflected from the outermost layer of the sludge layer (the sludge-water interface) in the water body. This signal has stable intensity, is continuous and uninterrupted, and has a uniform attenuation rate. It is different from the reflection signals from the bottom of the pool, scum, and air bubbles, and is the only effective signal that must be determined to calculate the sludge thickness.

[0033] In this scheme, all amplitude abrupt change points and attenuation rate data on the distribution curve can be extracted and compared one by one with the preset mud layer, obstacle and bubble feature library to eliminate interference signals and accurately determine the echo signal that conforms to the mud layer interface characteristics.

[0034] S14. Calculate the actual distance from the sensor to the sludge surface based on the round-trip time difference of the interface echo on the sludge layer. Round-trip time difference (RTD) refers to the time interval between the time the ultrasonic probe pulse is emitted from the transducer and the time the echo is received from the upper interface of the mud layer. It is measured in microseconds and is the core parameter for distance calculation, directly reflecting the total time it takes for the ultrasonic wave to travel through the water.

[0035] The actual distance refers to the vertical straight-line distance from the installation position of the ultrasonic transducer (sensor) to the upper surface of the sludge layer (sludge surface), denoted as L1, which is the basic distance data for calculating the sludge thickness.

[0036] Specifically, the formula can be substituted into the main control chip. Where v is the speed of ultrasonic wave propagation in water and t is the round-trip time difference, the actual distance from the sensor to the mud surface can be quickly calculated.

[0037] S15, combined with the preset sensor installation height, the actual distance is converted into the current mud thickness; The sensor installation height refers to the fixed vertical distance, denoted as L, from the ultrasonic transducer to the bottom of the sedimentation tank, which is preset during on-site installation. It is entered into the system by the user based on the tank depth and is a fixed parameter.

[0038] Sludge thickness refers to the vertical thickness of the sludge layer in a sedimentation tank from the bottom of the tank to the upper interface of the sludge layer. It is a core indicator for water conservancy sludge discharge control and sludge deposition monitoring, and is denoted as d.

[0039] Specifically, the preset installation height L can be called using the formula. The actual distance from the sensor to the mud surface is converted into the current real-time mud thickness.

[0040] S16, perform a confidence assessment on the mud level thickness, and output the mud level measurement result after confirming the data is valid.

[0041] Confidence assessment is a custom data reliability judgment mechanism that scores data comprehensively from three dimensions: signal-to-noise ratio, waveform stability, and multi-frame data consistency. Signal-to-noise ratio reflects signal purity, waveform stability reflects signal fluctuation, and multi-frame consistency reflects data repeatability reliability. Data is considered valid if the score meets the standard, otherwise it is considered invalid.

[0042] The mud level measurement result refers to the real-time mud level thickness value output by the system after the confidence level assessment confirms its validity. It can be displayed in real time on the screen, stored as historical curve data, or remotely uploaded to the monitoring platform.

[0043] In this solution, the mud thickness data can be verified according to the confidence level assessment rules. If the data is valid, it can be directly displayed, stored and uploaded; if the data is invalid, the data set can be discarded to avoid erroneous output.

[0044] The technical solution provided in this embodiment employs non-contact ultrasonic measurement, fundamentally avoiding the pain points of scaling, clogging, and corrosion failure of contact instrument probes. It is perfectly suited to the harsh working conditions of water conservancy projects involving high viscosity, high turbidity, and easy corrosion. Through raw echo preprocessing, construction of custom intensity and distance curves, and feature comparison to determine the mud layer interface, followed by time difference calculation, installation height conversion, and confidence level verification, interference from air bubbles, suspended solids, and obstacles is filtered layer by layer, significantly improving measurement accuracy, stability, and reliability, meeting the automated, high-precision mud level monitoring needs of the water conservancy industry.

[0045] In one embodiment, optionally, analyzing the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer includes: The signal segment in the distribution curve with a step-like increase in amplitude and a stable subsequent decay rate and continuous energy distribution is identified as the upper interface of the sludge layer. Identify signal segments with sharp single peaks in amplitude that rapidly decay to background noise, determine them as hard pool bottoms or solid obstacles, and eliminate them; Signal segments with randomly fluctuating amplitudes and a signal-to-noise ratio below a preset lower limit are identified as bubble or turbulent noise and suppressed by superimposing multiple consecutive echoes.

[0046] A step-like rise refers to a gradual, step-like change in signal amplitude from low to high on the distribution curve, without abrupt peaks. This closely matches the gradual transition from water to mud at the mud-water interface, distinguishing it from the abrupt peaks of obstacles.

[0047] Among them, continuous energy distribution means that the signal segment is uninterrupted and without obvious gaps, and the amplitude changes are consistent, reflecting that the mud layer interface is uniform and unbroken, which is a typical characteristic of mud layer signals.

[0048] A sharp single peak refers to a momentary, high-amplitude, narrow-width spike signal that appears only once on the distribution curve. It is a reflection characteristic of solid obstacles such as hard pool bottoms, rocks, and metal debris.

[0049] Background noise refers to the basic noise level detected by the system when there is no effective reflected signal. It is low in amplitude and stable, and serves as a benchmark for judging whether the signal has been completely attenuated.

[0050] Signal-to-noise ratio (SNR) is the ratio of the effective signal strength to the noise signal strength. The higher the ratio, the purer the signal. If the ratio is lower than a preset lower limit, the signal is overwhelmed by noise and cannot be used as an effective measurement basis.

[0051] Continuous multi-frame echo superposition refers to a processing method in which the system collects multiple sets of continuous echo signals, accumulates and averages the signal amplitudes at the same distance point, amplifies the stable and effective signal, and cancels out random bubble noise.

[0052] Specifically, the algorithm module can traverse the distribution curve and identify different signal segments one by one according to preset features; determine the signal type based on the features and eliminate obstacle signals; and use multi-frame superposition processing for bubble noise to suppress noise interference.

[0053] This technical solution refines the differential identification rules for three types of signals: mud, obstacles, and bubbles. It accurately distinguishes the signal characteristics of different reflection sources, specifically eliminates interference from solid obstacles and suppresses bubble turbulence noise. From an algorithmic perspective, it further improves the accuracy of mud interface determination, solves the problem of easy misjudgment of interfaces in traditional ultrasonic measurements, provides a purer and more reliable effective signal for subsequent mud level calculation, and adapts to the high-precision measurement needs in complex water environments.

[0054] In one embodiment, optionally, it includes: The ultrasonic transducer is controlled to continuously emit N frames of detection pulses within a preset time window to obtain N sets of echo intensity and distance distribution curves. Perform time-domain overlay analysis on the N sets of distribution curves and calculate the statistical variance of the echo amplitude at the same distance point; If the statistical variance of the echo amplitude at a certain distance point is greater than the preset fluctuation threshold, then the point is determined to be a transient bubble interference, and the amplitude of the point is set to zero in the final composite curve. If the echo amplitude at a certain distance point exhibits a single sharp peak and lacks temporal continuity, it is determined to be a hard obstacle and removed from the candidate interface list. Stable echo signals with temporal continuity are retained as the basis for determining the interface on the sludge layer.

[0055] The preset time window refers to a fixed time period for continuous acquisition of echo signals, which is usually tens to hundreds of milliseconds long, to ensure that enough frames of signals can be acquired for statistical analysis, balancing the real-time performance of the measurement with the ability to resist interference.

[0056] N-frame probe pulses refer to the N probe pulses continuously emitted by the transducer within a preset time window, where N is a positive integer greater than or equal to 3. The more frames, the more reliable the statistical results, but the response speed will be slightly reduced.

[0057] Time-domain overlay analysis is a statistical analysis method that aligns N sets of distribution curves along the time dimension and performs overlay, averaging, and variance calculation on the echo amplitude at the same propagation distance. Its core purpose is to amplify stable signals and highlight random interference.

[0058] Statistical variance refers to the degree to which the amplitudes of N echoes at the same distance point deviate from the average value. The larger the variance, the more drastic the signal fluctuation at that point, and the more likely it is transient interference; the smaller the variance, the more stable the signal, and the more likely it is a valid interface signal.

[0059] The fluctuation threshold is a pre-set variance threshold used by the system to distinguish between stable signals and fluctuation interference. It is determined by a large amount of field test data and is adapted to water environments with different turbidity and different interference intensities.

[0060] Transient bubble interference refers to the reflected signals from bubbles that are temporarily generated in water and rapidly rise and burst. These signals appear randomly, are not fixed in location, and fluctuate wildly in amplitude, making them irregular transient interference.

[0061] Time-domain continuity refers to the continuous existence of an echo signal at a certain distance point in N consecutive frames of data, with small amplitude changes and no sudden disappearance or abrupt changes. It is the core characteristic of a stable interface signal.

[0062] The candidate interface list refers to the set of signal points that the system initially screens out as potential mud layer interfaces. After obstacle removal and bubble suppression, stable signals are ultimately retained as the basis for mud layer interface determination.

[0063] Specifically, the main control unit can control the transducer to periodically transmit multiple frames of pulses, acquire multiple sets of curves, perform time-domain superposition analysis on the curves, calculate the variance of each point, determine the type of interference according to the variance and continuity rules, eliminate obstacles, suppress bubbles, and retain stable signals.

[0064] This technical solution introduces a multi-frame continuous acquisition and time-domain statistical analysis mechanism. By calculating the variance of the echo amplitude, it accurately identifies transient bubble interference and uses the continuity characteristics of the time domain to eliminate occasional obstacle signals. It strengthens the interference filtering capability from multiple dimensions, avoids measurement deviations caused by misjudgment of a single frame signal, and greatly improves the stability and reliability of mud layer interface identification in complex dynamic water environments. It is suitable for harsh water conservancy conditions with high bubble volume and many impurities.

[0065] In one embodiment, optionally, before calculating the actual distance from the sensor to the mud surface, the method further includes: Obtain real-time environmental parameters of the current water body, including at least water temperature and turbidity; The propagation speed of ultrasound in water is dynamically corrected using a composite compensation formula that includes temperature and turbidity variables, and the corrected sound speed is then substituted into the distance calculation.

[0066] Real-time environmental parameters refer to physical parameters that are collected on-site in real time and reflect the state of the water body. They directly affect the propagation speed of ultrasound and are the basic data for sound speed correction. The core parameters include water temperature and turbidity, and can also be expanded to include salinity and pressure.

[0067] Water temperature refers to the real-time temperature of the water being tested, measured in degrees Celsius. Changes in water temperature directly alter the speed of water molecule movement, which in turn affects the speed of ultrasonic wave propagation; the higher the water temperature, the faster the speed of sound.

[0068] Turbidity refers to the level of suspended solids and impurities in water. The unit is turbidity unit (TU). The higher the turbidity, the more suspended particles there are in the water, the greater the scattering loss of ultrasound waves during propagation, and the slightly lower the speed of sound.

[0069] The composite compensation formula refers to the sound velocity correction formula defined in this method that simultaneously includes temperature and turbidity variables. Unlike the traditional single temperature compensation formula, it can simultaneously correct the influence of both water temperature and turbidity on sound velocity, making it suitable for high-turbidity water environments.

[0070] Dynamic correction refers to the system collecting environmental parameters in real time, substituting them into a composite compensation formula to calculate and update the ultrasonic propagation speed in real time, rather than using a fixed sound speed or only correcting according to the season, to ensure that the sound speed always matches the current water condition.

[0071] Specifically, the sensor module can collect water temperature and turbidity data in real time. The system calls the composite compensation formula, substitutes the real-time parameters to dynamically correct the sound velocity, and substitutes the corrected sound velocity into the distance calculation formula to complete the accurate distance calculation.

[0072] This technical solution breaks through the limitations of traditional single temperature compensation by introducing a real-time acquisition mechanism of dual parameters of water temperature and turbidity. Through a custom composite compensation formula, it dynamically corrects the ultrasonic propagation speed, accurately offsetting the dual effects of water temperature changes and water turbidity on sound velocity. This solves the measurement error problem caused by inaccurate sound velocity under hydraulic conditions with high turbidity and large temperature fluctuations, significantly improving the accuracy of distance calculation and providing accurate basic parameters for mud thickness conversion.

[0073] In one embodiment, optionally, the composite compensation formula is: ; Where V is the corrected current speed of sound, V0 is the speed of sound under standard conditions, Kt is the temperature coefficient, ΔT is the temperature difference between the real-time water temperature and the standard temperature, Ktu is the turbidity coefficient, and TU is the real-time turbidity value.

[0074] The corrected current sound velocity (V) refers to the actual propagation speed of ultrasound calculated after compensation for temperature and turbidity, which is adapted to the current water condition. It is a precise sound velocity parameter for distance calculation, and the unit is meters per second (m / s).

[0075] The speed of sound under standard conditions (V0) refers to the reference propagation speed of ultrasound in a standard water environment with a water temperature of 20℃ and turbidity of 0 (clear water). It is a fixed constant, usually taken as 1480m / s, and is the reference value for sound speed compensation.

[0076] The temperature coefficient (Kt) refers to the change in the speed of ultrasonic wave propagation for every 1°C change in water temperature, measured in m / s. The value ℃ is derived from fitting a large amount of water temperature gradient test data and reflects the degree of influence of water temperature on the speed of sound.

[0077] Temperature difference (ΔT) refers to the difference between the real-time water temperature and the standard temperature (20℃). ΔT = real-time water temperature A temperature difference of 20℃ indicates that the water temperature is higher than the standard temperature, while a temperature difference of 20℃ indicates that the water temperature is lower than the standard temperature.

[0078] Turbidity coefficient (Ktu) refers to the change in ultrasonic wave propagation speed for every 1 TU change in turbidity of water, measured in m / s. TU, derived from the fitting of measured data at different turbidity gradients, reflects the degree of influence of turbidity on the speed of sound.

[0079] Real-time turbidity value (TU) refers to the turbidity value of water collected in real time by a turbidity sensor. The unit is turbidity unit (TU). The higher the value, the more turbid the water is and the higher the content of suspended particles.

[0080] This technical solution clarifies the specific expression of the dual-parameter composite compensation formula and the definition of each parameter. By quantifying the influence coefficients of temperature and turbidity on sound velocity, it achieves standardization of sound velocity correction, avoids errors from empirical estimation or single-parameter compensation, and can accurately adapt to water conservancy and water body environments with different water temperatures and turbidities, significantly improving the accuracy of sound velocity correction. It ensures the accuracy of sediment level measurement from the core parameter level and adapts to the high-precision measurement needs under complex water quality conditions.

[0081] In one embodiment, optionally, the confidence assessment of the mud thickness includes: Record the time series data of mud level thickness within a preset time period, and use the sliding window algorithm to establish a short-term trend prediction model to calculate the rate of rise or fall of mud level. Set multi-level mud level thresholds, wherein the multi-level mud level thresholds include at least a low threshold, a medium threshold, and a high threshold; The predicted mud level change trend is compared with the set multi-level mud level thresholds; When the predicted mud level reaches the aforementioned threshold, a control signal to start the mud discharge equipment is automatically output. When the predicted mud level reaches the high threshold, an emergency alarm signal is output and the measurement output is locked.

[0082] Time series data refers to a set of mud level thickness data collected and recorded at fixed time intervals within a preset time period. The data includes timestamps and can clearly reflect the continuous change pattern of mud level over time, serving as the basic data for trend prediction.

[0083] The sliding window algorithm is an algorithm that dynamically extracts data segments and analyzes them one by one. It extracts the latest mud level data according to a fixed window length, and the window slides and updates over time to ensure that the prediction is based only on recent valid data, avoids interference from outdated historical data, and improves the real-time performance and accuracy of trend prediction.

[0084] Short-term trend prediction models are mathematical models constructed based on mud level time series data within a sliding window through linear fitting and regression analysis. They are used to predict the trend and rate of mud level changes in the near future. Unlike long-term trend models, they focus on real-time dynamic prediction.

[0085] The rate of increase or decrease refers to the change in sludge thickness per unit time. A positive value indicates that the sludge level is rising (sludge deposition is accelerating), and a negative value indicates that the sludge level is falling (sludge removal is effective). It is a core indicator for judging the sludge deposition status and predicting changes in sludge level.

[0086] Multi-level sludge level thresholds refer to preset sludge level critical values ​​for different levels based on the sludge discharge control requirements of water conservancy. These values ​​are used to trigger early warning, sludge discharge, and alarm actions in a graded manner. The core of these thresholds includes low threshold, medium threshold, and high threshold. The threshold values ​​can be flexibly set according to the pool capacity and treatment requirements.

[0087] The low threshold refers to the critical sludge level value in the early stage of sludge deposition. When the sludge level is below this threshold, the amount of sludge deposition is small, and there is no need to discharge sludge; only routine monitoring is required.

[0088] The intermediate threshold refers to the critical sludge level at which sludge deposition reaches the point where sludge discharge needs to be initiated. When the sludge level reaches this threshold, the amount of sludge deposition is moderate, and timely sludge discharge can prevent excessive sludge accumulation. It is the trigger threshold for automated sludge discharge control.

[0089] The high threshold refers to the critical sludge level value at which sludge deposition reaches a dangerous state. When the sludge level reaches this threshold, excessive sludge accumulation can easily lead to turbid effluent and equipment blockage. An emergency alarm should be triggered and the measurement should be locked to prevent accidents from occurring.

[0090] Interlocked measurement output refers to the system determining the current measurement output, stopping routine data updates, and prioritizing the push of emergency alarm signals when it detects that the mud level has reached a high threshold dangerous state, in order to prevent erroneous data from interfering with emergency response.

[0091] Specifically, a sliding window algorithm can be used to construct a prediction model and calculate the rate of change of mud level; the system presets multiple thresholds, compares the predicted trend with the thresholds, and outputs control / alarm signals in stages, and locks the measurement output in dangerous situations.

[0092] This technical solution deeply integrates confidence assessment with trend prediction and hierarchical control. It accurately predicts the trend and rate of sludge level change through a sliding window algorithm, and sets three thresholds to achieve hierarchical management of sludge deposition status. The medium threshold automatically triggers sludge discharge, and the high threshold triggers an emergency alarm and lockout, effectively avoiding the problems of excessive sludge accumulation or untimely sludge discharge. It significantly improves the automation level and emergency response capability of water conservancy sludge treatment and is suitable for unattended and automated water conservancy scenarios.

[0093] In one embodiment, optionally, after outputting the mud level measurement results, the method further includes: Record time series data of mud level thickness within a preset time period and establish a short-term trend prediction model; Multiple mud level thresholds can be set. When the predicted mud level reaches the middle threshold, a control signal to start mud discharge will be automatically output. When the high threshold is reached, an emergency alarm signal will be output.

[0094] Short-term trend prediction models are simple prediction models built based on recent historical mud level data. They focus on the short-term change patterns of mud levels, do not require complex algorithms, and can quickly predict the upward trend of mud levels, adapting to the needs of real-time mud discharge control.

[0095] Multi-level sludge level thresholds refer to the critical values ​​used for graded control of sludge deposition. The core includes a medium threshold (sludge discharge trigger) and a high threshold (alarm trigger), which can be flexibly adjusted according to on-site operating conditions to balance sludge discharge efficiency and operational safety.

[0096] This technical solution supplements the trend prediction and graded linkage control links after the sludge level measurement output. Based on historical data, it predicts sludge level changes and accurately triggers sludge discharge and alarm actions through graded thresholds. It eliminates the need for continuous manual monitoring, simplifies the operation process, improves the automation response speed of sludge treatment, and effectively avoids excessive sludge accumulation or sludge discharge delays. It is suitable for the water conservancy industry's needs for low-cost, easy-to-operate, and automated sludge level monitoring and sludge discharge control.

[0097] To enable those skilled in the art to better understand this solution, this application also provides a preferred embodiment.

[0098] In early industrial and environmental applications, contact-type instruments (such as float-type and capacitive types) often failed due to probe scaling and clogging when measuring high-viscosity, corrosive, and easily deposited media like sludge, resulting in extremely high maintenance costs. With the increasing demands for sludge treatment efficiency and automated monitoring in the environmental industry (such as wastewater treatment plants and sedimentation tanks), and the growing need for stable level measurement under harsh conditions in industrial production, the market urgently requires a measurement solution that does not directly contact the medium. This patented invention, an ultrasonic sludge level gauge, employs non-contact measurement and is a highly reliable, cost-effective, and easy-to-install and maintain sludge level measuring instrument. It can display sludge level, distance, echo waveform, and historical curves; built-in temperature compensation, temperature prompts, and sound velocity correction modes enhance measurement accuracy and reliability; the probe is physically sealed, and different probe materials can be selected to adapt to different water quality measurements. The product's measurement utilizes the sonar principle. An ultrasonic transducer submerged in water emits ultrasonic pulses vertically into the water. As suspended particles settle in the water to form a mud layer, the pulses are reflected by the surface of the mud layer and received by the same transducer. By measuring the time interval between the emitted and reflected waves, and given that the speed of ultrasonic wave propagation in water is known, the distance L1 from the sensor to the mud layer can be calculated. The distance L from the sensor to the bottom of the pool is set by the user, thus the thickness of the mud layer, d = L - L1, can be obtained.

[0099] Figure 2 This is a schematic diagram of the ultrasonic mud level gauge provided in Embodiment 1 of this application, as shown below. Figure 2 As shown, the structure of this ultrasonic mud level gauge is as follows: First, the ultrasonic sludge level gauge electronic unit 1 is assembled with the transmitter housing 5. The housing is assembled first, then the electronic unit is secured with screws to form a single unit. One end of the connecting cable 3 is connected to the probe, and the other end is fixed to the electronic unit's terminal block 2 via an electrical interface. The cover is then placed on top and tightened with screws. Finally, the assembled ultrasonic sludge level gauge probe is immersed in water at a depth of 5-10 cm for calibration. The height of the sludge layer is determined by analyzing the ultrasonic wave emission and the echo of suspended solids in the water.

[0100] The technical improvements and beneficial effects of this solution are as follows: 1. Equipped with intelligent echo curve, it can determine whether an obstacle has been detected, whether there is electromagnetic interference, and whether there are multiple echoes from the echo curve; 2. The Chinese and English menus can be switched freely, and the display interface is simple and easy to understand. 3. It has a guided menu, so you can complete the operation even without an instruction manual; 4. Equipped with sound velocity calibration function, it can accurately measure sound velocity even in water tanks where the sound velocity differs from that of clear water; 5. It has a wide measuring range, enabling remote control and alarm functions, and achieving automated control.

[0101] Furthermore, based on this, this solution also provides an optimal configuration: Probe selection: A physically sealed polyvinylidene fluoride (PVDF) transducer is selected, with its inherent frequency set at 1MHz, balancing penetration and resolution, and resisting acid and alkali corrosion.

[0102] Sensor integration: A high-precision NTC temperature sensor is integrated inside the electronic unit, and an optical turbidity sensor is added around the probe to collect the environmental data required for dual-parameter compensation.

[0103] The firmware program executed by the MCU (microcontroller unit) in electronic unit 1 of this embodiment has the following workflow: Step 1: Signal transmission and acquisition; The MCU controls the ultrasonic transducer to vertically emit a beam of ultrasonic pulses with a center frequency of 1MHz into the water. Unlike traditional single-transmission, this embodiment continuously emits 5 frames of pulses within 100ms, and the receiving end transmits the original echo signal back through connecting cable 3.

[0104] Step 2: Construct a two-dimensional distribution curve of echo intensity versus distance; The MCU preprocesses the received 5 frames of echo data (amplification and filtering) and constructs a two-dimensional distribution curve with time as the horizontal axis and echo amplitude as the vertical axis. This curve visually displays the medium distribution at different depths in the water body.

[0105] Step 3: Intelligent interface recognition and interference filtering; MCU analyzes the characteristics of the above curves: If the amplitude at a certain depth point is detected to have a "steep peak" and decays rapidly, it is determined to be a hard object (such as a rock) at the bottom of the pool or submerged, and is removed. If the amplitude at a certain depth point shows a "step-like jump" and then decays steadily, it is determined to be the upper interface of the sludge layer; If random fluctuations in amplitude are detected, it is determined to be bubble interference, and smoothing is performed by weighted averaging of multiple frames of data.

[0106] The system ultimately determined the true upper interface echo of the sludge layer.

[0107] Step 4: Two-parameter dynamic sound velocity correction; Before calculating the distance using the formula L1=V×t / 2, the MCU first reads the current water temperature (T) and turbidity (TU). Instead of using the default 1500m / s, the sound velocity V is dynamically corrected using the composite formula V=V0+Kt×ΔT+Ktu×TU, ensuring accuracy of the sound velocity in wastewater environments.

[0108] Step 5: Calculation and Output; The MCU calculates the distance L1 from the sensor to the mud surface based on the corrected sound velocity and echo time. Combined with the user-preset sensor installation height L (i.e., the vertical distance from the sensor to the bottom of the pool), the distance is calculated using the formula d=L. L1 calculates the final mud thickness d.

[0109] The calculation results are displayed on the screen of the transmitter housing 5 and can be output via 4-20mA or RS485. Based on the mud level change trend, the system automatically outputs a relay signal to control the start and stop of the mud discharge pump when a preset threshold is reached.

[0110] This embodiment employs a phased adaptive calibration, replacing the traditional simple water immersion calibration: Clear water baseline calibration: In the initial stage of empty pool water filling, the system automatically collects sound velocity data at different temperatures and establishes a basic compensation model.

[0111] On-site fitting and calibration: During operation, the sound velocity parameters are finely adjusted based on real-time water temperature and turbidity.

[0112] Mud layer interface calibration: After the stable mud layer is initially identified, the operator only needs to confirm the interface position once, and the system can automatically record the baseline value without subsequent manual intervention.

[0113] This embodiment successfully solves the problems of unstable measurement and susceptibility to interference in traditional mud level gauges. Actual measurements show that even in wastewater environments with turbidity as high as 5000 NTU, this solution can still maintain a measurement accuracy of ±1 cm, and the probe maintenance cycle is extended by more than 3 times.

[0114] Example 2 Figure 3 This is a schematic diagram of the ultrasonic intelligent mud level measurement device for the water conservancy industry provided in Embodiment 2 of this application. Figure 3 As shown, the device includes: The echo signal acquisition module 301 is used to control the ultrasonic transducer to send detection pulses to the water body to be tested and to receive the original echo signals formed by reflections from the interfaces of different media in the water body. The distribution curve determination module 302 is used to preprocess the original echo signal and construct the echo intensity and distance distribution curve based on the preprocessed time-domain waveform data. The interface echo identification module 303 is used to analyze the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer. The actual distance calculation module 304 is used to calculate the actual distance from the sensor to the sludge surface based on the round-trip time difference of the interface echo on the sludge layer. The mud thickness calculation module 305 is used to convert the actual distance into the current mud thickness by combining the preset sensor installation height. The confidence assessment module 306 is used to assess the confidence level of the mud thickness and output the mud measurement result after confirming the validity of the data.

[0115] The ultrasonic mud level intelligent measurement device for the water conservancy industry in this application embodiment can be a system, or a component, integrated circuit, or chip in a terminal. The system can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. The embodiments of this application do not impose specific limitations.

[0116] The ultrasonic mud level intelligent measuring device for the water conservancy industry in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0117] The ultrasonic mud level intelligent measuring device for the water conservancy industry provided in this application embodiment can realize the various processes of the above embodiments, and will not be repeated here to avoid repetition.

[0118] Example 3 like Figure 4As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. When the program or instructions are executed by the processor 401, they implement the various processes of the above-described embodiment of the ultrasonic intelligent mud level measurement method in the water conservancy industry and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0119] It should be noted that the electronic devices in the embodiments of this application include mobile electronic devices and non-mobile electronic devices as described above.

[0120] Example 4 This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiment of the ultrasonic intelligent mud level measurement method in the water conservancy industry and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0121] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0122] Example 5 This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiments of the ultrasonic intelligent mud level measurement method in the water conservancy industry, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0123] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, device chip, chip system, or system-on-a-chip, etc.

[0124] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0126] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms fall within the scope of protection of this application.

[0127] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for intelligent ultrasonic sediment level measurement in the water conservancy industry, characterized in that, The method includes: The ultrasonic transducer is controlled to transmit detection pulses to the water body to be tested and to receive the original echo signals formed by reflections from the interfaces of different media in the water body. The original echo signal is preprocessed, and an echo intensity versus distance distribution curve is constructed based on the preprocessed time-domain waveform data. Analyze the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer; The actual distance from the sensor to the sludge surface is calculated based on the round-trip time difference of the interface echo on the sludge layer. Based on the preset sensor installation height, the actual distance is converted into the current mud thickness; The confidence level of the mud thickness is assessed, and the mud level measurement results are output after the data is confirmed to be valid.

2. The ultrasonic mud level intelligent measurement method according to claim 1, characterized in that, The analysis of amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer includes: The signal segment in the distribution curve with a step-like increase in amplitude and a stable subsequent decay rate and continuous energy distribution is identified as the upper interface of the sludge layer. Identify signal segments with sharp single peaks in amplitude that rapidly decay to background noise, determine them as hard pool bottoms or solid obstacles, and eliminate them; Signal segments with randomly fluctuating amplitudes and a signal-to-noise ratio below a preset lower limit are identified as bubble or turbulent noise and suppressed by superimposing multiple consecutive echoes.

3. The ultrasonic mud level intelligent measurement method according to claim 2, characterized in that, include: The ultrasonic transducer is controlled to continuously emit N frames of detection pulses within a preset time window to obtain N sets of echo intensity and distance distribution curves. Perform time-domain overlay analysis on the N sets of distribution curves and calculate the statistical variance of the echo amplitude at the same distance point; If the statistical variance of the echo amplitude at a certain distance point is greater than the preset fluctuation threshold, then the point is determined to be a transient bubble interference, and the amplitude of the point is set to zero in the final composite curve. If the echo amplitude at a certain distance point exhibits a single sharp peak and lacks temporal continuity, it is determined to be a hard obstacle and removed from the candidate interface list. Stable echo signals with temporal continuity are retained as the basis for determining the interface on the sludge layer.

4. The ultrasonic mud level intelligent measurement method according to claim 1, characterized in that, Before calculating the actual distance from the sensor to the mud surface, the method further includes: Obtain real-time environmental parameters of the current water body, including at least water temperature and turbidity; The propagation speed of ultrasound in water is dynamically corrected using a composite compensation formula that includes temperature and turbidity variables, and the corrected sound speed is then substituted into the distance calculation.

5. The ultrasonic mud level intelligent measurement method according to claim 4, characterized in that, The composite compensation formula is as follows: ； Where V is the corrected current speed of sound, V0 is the speed of sound under standard conditions, Kt is the temperature coefficient, ΔT is the temperature difference between the real-time water temperature and the standard temperature, Ktu is the turbidity coefficient, and TU is the real-time turbidity value.

6. The ultrasonic mud level intelligent measurement method according to claim 1, characterized in that, The confidence assessment of the mud thickness includes: Record the time series data of mud level thickness within a preset time period, and use the sliding window algorithm to establish a short-term trend prediction model to calculate the rate of rise or fall of mud level. Set multi-level mud level thresholds, wherein the multi-level mud level thresholds include at least a low threshold, a medium threshold, and a high threshold; The predicted mud level change trend is compared with the set multi-level mud level thresholds; When the predicted mud level reaches the aforementioned threshold, a control signal to start the mud discharge equipment is automatically output. When the predicted mud level reaches the high threshold, an emergency alarm signal is output and the measurement output is locked.

7. The ultrasonic mud level intelligent measurement method according to claim 1, characterized in that, After outputting the mud level measurement results, the method further includes: Record time series data of mud level thickness within a preset time period and establish a short-term trend prediction model; Multiple mud level thresholds can be set. When the predicted mud level reaches the middle threshold, a control signal to start mud discharge will be automatically output. When the high threshold is reached, an emergency alarm signal will be output.

8. An intelligent ultrasonic mud level measuring device for the water conservancy industry, characterized in that, The device includes: The echo signal acquisition module is used to control the ultrasonic transducer to send detection pulses to the water body to be tested and to receive the original echo signals formed by reflections from the interfaces of different media in the water body. The distribution curve determination module is used to preprocess the original echo signal and construct the echo intensity and distance distribution curve based on the preprocessed time-domain waveform data. The interface echo identification module is used to analyze the amplitude abrupt change characteristics and attenuation rate characteristics on the distribution curve to determine the upper interface echo of the sludge layer. The actual distance calculation module is used to calculate the actual distance from the sensor to the sludge surface based on the round-trip time difference of the interface echo on the sludge layer. The mud thickness calculation module is used to convert the actual distance into the current mud thickness by combining the preset sensor installation height. The confidence assessment module is used to assess the confidence level of the mud thickness and output the mud measurement results after confirming the validity of the data.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the ultrasonic mud level intelligent measurement method for the water conservancy industry as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the ultrasonic mud level intelligent measurement method for the water conservancy industry as described in any one of claims 1-7.

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