Integrated intelligent monitoring sensor for underwater sediment content and flow velocity and calibration method

Abstract

The application discloses an underwater silt content and flow velocity integrated intelligent monitoring sensor and a calibration method, and belongs to the technical field of hydrological monitoring. The sensor comprises an integrated waterproof shell, a composite detection unit, an intelligent processing module, a self-calibration unit and a communication power supply module. The composite detection unit integrates a dual-mode acoustic array, a polarized light scattering assembly, a three-dimensional MEMS micro flow chip and a temperature and pressure sensor, and realizes synchronous acquisition of multiple source signals. The monitoring method is based on an acoustic Doppler frequency shift-light scattering intensity coupling model, cross interference is eliminated through an original coupling calculation algorithm, and accurate parameters are synchronously output. The calibration method adopts a three-level closed loop mechanism to realize in-situ automatic zero point, sensitivity and cross interference calibration. The application solves the pain points of poor synchronism, weak anti-interference and the need for disassembly calibration of existing split devices, has compact structure, low power consumption and anti-pollution, is suitable for multiple water environments, has high measurement accuracy, can realize long-term unattended monitoring, and has extremely high practicality and popularization value.

Description

Technical Field

[0001] This invention relates to the field of hydrological monitoring technology, specifically to an integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity. Background Technology

[0002] In fields such as hydrological and water environment monitoring, river sediment management, safe operation and maintenance of water conservancy projects, and marine environmental observation, water sediment content and water flow velocity are two core synchronous monitoring parameters. These two parameters exhibit strong coupling interference, making synchronous, high-precision, and in-situ continuous monitoring a core technical challenge for the industry. Current underwater monitoring technologies and products generally employ a separate monitoring scheme, where sediment concentration monitoring sensors and flow velocity monitoring sensors are deployed separately, acquire data independently, and perform time-division calculations. This approach has inherent structural defects: First, separate equipment is cumbersome to install, occupies a large space, is difficult to deploy underwater, and easily disturbs the natural flow field, leading to distorted monitoring data. Second, the two data acquisitions are asynchronous, with a time difference of up to hundreds of milliseconds, making it impossible to match the real-time dynamic changes of sediment particles moving with the water flow. Coupling interference cannot be corrected, resulting in large monitoring data errors and poor consistency. Third, existing equipment has extremely poor anti-interference capabilities. In water bodies with high sediment content, conventional acoustic Doppler current meters suffer significant scattering and attenuation of acoustic signals by sediment particles, leading to severe jumps in flow velocity measurement data or even complete failure. Conventional optical turbidity... The current / sediment sensor is susceptible to water turbulence, bubble interference, changes in sediment particle size distribution, and water color, resulting in poor measurement linearity and inability to operate stably under high flow velocity and high sediment content conditions. Fourth, existing sensors lack in-situ integrated self-calibration capabilities. During long-term underwater operation, sediment adhesion, biofilm contamination, component temperature drift, time drift, and sensitivity attenuation on the probe surface will accumulate continuously, requiring disassembly and manual calibration on shore, resulting in extremely high maintenance costs and failing to meet the needs of long-term unattended monitoring in the field. Fifth, existing technologies have not established a coupled calculation model for sediment content and flow velocity, making it impossible to eliminate the mutual interference between the two parameters and achieve high-precision and stable measurement across the entire range and all operating conditions. This makes it difficult to adapt to the continuous monitoring needs of rivers during flood season with high flow velocity, high sediment content, and extreme water environments.

[0003] To address the core defects that are prevalent in the industry and cannot be solved by existing technologies, this invention discloses an integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity. It innovates in all dimensions, including hardware structure, detection principle, calculation algorithm, and in-situ calibration, completely avoiding the technical routes and structural solutions of existing technologies. It achieves synchronous, interference-free, and high-precision measurement with a single probe, and is equipped with a fully closed-loop intelligent calibration method, thus completely solving all the pain points of existing technologies. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the above-mentioned technical defects and provide an integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity.

[0005] To solve the above problems, the technical solution of the present invention is: an integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity, including an integrated waterproof shell, a composite detection unit, an intelligent processing module, a self-calibration unit, and a communication power supply module; The integrated waterproof shell adopts a streamlined titanium alloy structure, with a built-in pressure balance chamber and temperature compensation chamber, a protection level of IP68, a pressure resistance of 0-50m, and is suitable for water environments of 0-45℃. The composite detection unit integrates a dual-mode acoustic detection array, a polarized light scattering detection component, a three-dimensional MEMS microfluidic velocimetry chip, and a high-precision temperature and pressure compensation sensor. The dual-mode acoustic array contains four 1.2MHz / 3.5MHz positive exchange energy transducers to form a three-dimensional detection beam. The polarized light scattering detection component uses a 660nm semiconductor laser and a 360° distributed photodetector to cover 0.1-2000μm sediment particle size. The three-dimensional MEMS microfluidic velocimetry chip integrates a thermistor and a piezoresistive element to realize microfluidic vector measurement. The intelligent processing module adopts a 32-bit floating-point DSP processor, with built-in sediment-flow velocity coupling solution algorithm, adaptive noise suppression model, and dynamic parameter correction engine. It is equipped with 16MB high-speed cache and 4GB Flash storage, and supports 200 data acquisitions and real-time solutions per second. The self-calibration unit includes a standard fluid calibration chamber, a micro-volume standard solution injection system, and a closed-loop feedback calibration circuit, enabling in-situ automatic zero-point calibration, sensitivity calibration, and temperature drift compensation. The communication power supply module supports RS485, LoRa, and NB-IoT multi-protocol communication, adopts an underwater inductive power supply + lithium battery dual power supply mode, has a standby power consumption of ≤15mW, and a continuous working time of ≥180 days.

[0006] Furthermore, the dual-mode acoustic detection array adopts an orthogonal symmetrical layout, with four transducers arranged vertically in pairs. Each transducer includes a transmitting chip and a receiving chip. The transmitting chip is made of piezoelectric ceramic composite material, and the receiving chip is made of high-sensitivity PVDF film. The acoustic wave transmission power is continuously adjustable from 0.5 to 5W, the receiving bandwidth is 1MHz to 4MHz, and the signal-to-noise ratio is ≥60dB. It can simultaneously receive and separate scattered signals of different frequencies, eliminating the effects of acoustic multipath interference and signal attenuation under high sand content.

[0007] Furthermore, the polarization light scattering detection component includes a laser emitting unit, a 360° distributed photoelectric detection array, an optical isolation cavity, and an automatic cleaning mechanism; The laser emitting unit outputs 660nm linearly polarized light with a polarization degree ≥99% and light intensity stability ±0.1% / h; The 360° distributed photodetector array consists of 24 silicon photodiodes evenly distributed, with a receiving angle of 0°-170°, a response wavelength of 400nm-800nm, and a minimum detection light intensity of 1nW. The optical isolation cavity employs an anti-reflective coating and a light-shielding structure to suppress stray light interference from the water. The automatic cleaning mechanism removes mud, sand, and biological dirt from the probe surface through micro-ultrasonic vibration and high-pressure water rinsing. The cleaning frequency is adjustable from 0 to 24 times per day.

[0008] Furthermore, the three-dimensional MEMS microfluidic velocimetry chip is fabricated based on SOI technology, with dimensions of 5mm×5mm×0.5mm. It integrates four thermistor arrays and two piezoresistive elements. The thermistor temperature coefficient is ≥-2% / ℃, the response time is ≤1ms, and the piezoresistive element sensitivity is ≥0.1mV / V / kPa. It can simultaneously measure the x / y / z three-dimensional flow velocity components, with a minimum measured flow velocity of 0.001m / s and a measurement range of 0-10m / s. It achieves flow velocity measurement without mechanical parts through microscale heat transfer and pressure sensing.

[0009] Furthermore, the intelligent processing module incorporates a sediment-velocity coupled solution algorithm, which simultaneously outputs sediment content and velocity data through joint calculation of acoustic Doppler frequency shift and light scattering intensity. The algorithm includes a real-time temperature and pressure compensation module, an adaptive noise filtering module, and a cross-interference correction module. The temperature and pressure compensation range is -10℃ to 60℃ and 0-5MPa, with a compensation accuracy of ±0.05%. Adaptive noise filtering employs a wavelet transform + Kalman filter combination algorithm, achieving a noise suppression ratio ≥40dB; Cross-interference correction eliminates the measurement errors of velocity under high sediment concentration and sediment content under high velocity by establishing a sediment-velocity coupling matrix, and the parameters of the coupling matrix are updated dynamically in real time.

[0010] Furthermore, the following steps are included: (1) Sensor deployment initialization: Fix the sensor at the preset depth of the monitored water body, turn on the power, and the self-calibration unit automatically performs zero-point calibration, collects acoustic baseline signal, light scattering baseline signal, and temperature and pressure baseline data under standard clear water environment, and establishes an initial zero-point drift correction model; (2) Synchronous acquisition of multi-source data: The composite detection unit synchronously acquires dual-mode acoustic scattering signal, polarized light scattering signal, three-dimensional microflow velocity signal, temperature and pressure signal at a frequency of 200Hz, and the synchronous acquisition error of each signal is ≤0.01s; (3) Signal preprocessing: The intelligent processing module amplifies, filters, and converts the acquired signal to A / D, removes high-frequency noise and DC drift, extracts effective feature signals, and improves the signal-to-noise ratio of acoustic signals to ≥65dB and the signal-to-noise ratio of optical signals to ≥60dB; (4) Coupled solution: The three-dimensional velocity components are calculated based on the acoustic Doppler frequency shift formula, the sediment content is calculated based on the light scattering intensity formula, the sound velocity and light attenuation coefficient are corrected by the temperature and pressure compensation coefficient, and the mutual influence between sediment and velocity is eliminated by the cross-interference correction factor. (5) Data output and storage: After the validity of the calculated data is determined, it is uploaded to the monitoring platform in real time through the communication module and stored in the local Flash. The storage period is adjustable from 1 to 365 days. The data format includes timestamp, sediment content, three-dimensional flow velocity, temperature, pressure and calibration status. (6) Abnormal handling: When the monitoring data exceeds the threshold or the signal is abnormal, the abnormal calibration process is automatically started, the abnormal code is recorded and the alarm information is uploaded.

[0011] Furthermore, the acoustic Doppler frequency shift calculation employs a dual-frequency joint calculation model. A 1.2MHz sound wave is used for environments with high sand content (50-300 kg / m³), and a 3.5MHz sound wave is used for environments with low sand content (0-50 kg / m³). The operating frequency is automatically switched based on the amplitude ratio of the frequency shift signal. The calculation formula is as follows:

[0012] in This is the correction factor for sound wave attenuation due to sand content. S represents the sediment content, and the accuracy of the full-range flow velocity measurement is ensured to be ±0.2% FS through real-time iterative calculation.

[0013] Furthermore, the sediment content calculation employs a polarized light scattering-flow velocity joint correction model. The baseline concentration is calculated using the ratio of scattered light intensity, and a flow velocity correction factor is introduced to eliminate the influence of water flow disturbance on light scattering. The calculation formula is as follows:

[0014] Where K is the optical calibration coefficient. This is the flow rate correction factor. This is a temperature and pressure correction factor. T represents temperature, and P represents pressure, ensuring a full-range sediment content measurement accuracy of ±0.5% FS.

[0015] Furthermore, a three-level closed-loop calibration mechanism is adopted, including factory calibration, online periodic calibration, and abnormal trigger calibration. The calibration process is as follows: (1) Zero-point calibration: Place the sensor in a standard clean water environment, start the micro-standard solution injection system to inject zero-concentration calibration solution, stabilize for 10 minutes, continuously collect 100 sets of baseline signals, calculate the average value and standard deviation, and establish a zero-point drift dynamic correction model:

[0016] in Let be the zero-point voltage at time t. The initial zero-point voltage, The zero-point parameters are updated in real time as the temperature and pressure drift coefficient. (2) Sensitivity calibration: Three sets of standard sediment suspensions of 5kg / m³, 50kg / m³ and 200kg / m³ were injected sequentially through the micro-injection system. Each set was stabilized for 15 minutes. 200 sets of response signals were collected. The concentration-response calibration curve was fitted by the least squares method. The dynamic sensitivity coefficient was calculated to eliminate the influence of sensitivity decay of the sensor after long-term use. (3) Cross-interference calibration: Adjust the simulated flow rate to 0.5m / s, 2m / s, and 5m / s, inject standard sediment solutions of different concentrations, collect flow rate-concentration coupling response data, construct a 5×5 coupling interference matrix, solve the interference correction factor by matrix inversion, and realize full compensation of sediment-flow rate cross-interference. (4) Calibration verification: After calibration, inject a standard solution of known concentration and simulate the flow rate. If the measurement error is ≤0.3% FS, the calibration is deemed qualified; otherwise, repeat the calibration process. (5) Parameter storage: Calibration parameters are encrypted and stored in Flash, and historical calibration records can be queried and traced. The calibration records include time, calibration type, parameters, error and environmental conditions.

[0017] Furthermore, the online periodic calibration adopts an adaptive triggering mechanism. The calibration cycle is dynamically adjusted according to the sensor's working status. Under normal working conditions, it is automatically calibrated once every 30 days. When the ambient temperature fluctuates by more than ±5℃ / h, the sediment content exceeds 200kg / m³ for 1 hour, or the flow velocity changes by more than 5m / s, abnormal calibration is triggered immediately. The calibration process does not affect normal monitoring. It adopts background parallel processing, with a calibration time of ≤30 minutes, a micro-volume standard solution injection volume of ≤5mL each time, and a standard solution storage capacity of ≥500mL, meeting the online calibration requirements for 2 years. After calibration is completed, a calibration report is automatically generated, which includes the error before calibration, the error after calibration, the corrected parameters, and the validity judgment. The report is then uploaded to the monitoring platform via the communication module.

[0018] The advantages of this invention compared to existing technologies are: This invention integrates dual-modal acoustic detection, polarized light scattering sediment detection, three-dimensional MEMS microfluidic velocimetry, temperature and pressure compensation, and in-situ self-calibration into a single waterproof housing. It has no external components, resulting in a compact structure and small size. Underwater installation requires no modification to the flow field and does not interfere with the natural water flow. The two core parameter acquisition clocks are completely synchronized with a synchronization error of ≤0.01s, enabling real-time capture of the dynamic changes of sediment particles moving with the water flow. This addresses the common industry problem of asynchronous data and uncorrectable coupling interference in existing separate sensors.

[0019] This invention abandons the existing single acoustic and single optical technical approach, and adopts a dual-modal complementary detection structure of acoustic and optical modes. It automatically matches the optimal detection scheme for different working conditions, completely solving the problem of measurement failure under high sediment content and high flow velocity. Under low sediment content conditions, high-frequency acoustic + optical joint calculation is used to ensure measurement resolution; under high sediment content conditions, it automatically switches to low-frequency acoustic detection to avoid signal attenuation and distortion; the optical module adopts polarized light detection technology to completely filter out stray light, bubbles, and chromaticity interference from the water body, and is not affected by sediment particle size distribution; the full range of sediment content measurement is 0-300 kg / m³, with a measurement accuracy of ±0.5%FS and a resolution of 0.01 kg / m³; the flow velocity measurement range is 0.001-10 m / s, with a measurement accuracy of ±0.2%FS. It can still work stably and continuously in water bodies with extremely high flow velocity and high sediment content during the flood season, without data jumps or measurement blind spots.

[0020] This invention addresses the strong coupling interference between sediment content and flow velocity by establishing an original, proprietary coupling solution model. It incorporates a four-layer algorithm: real-time temperature and pressure compensation, flow velocity disturbance correction for sediment measurement, sediment content attenuation compensation for acoustic signal, and cross-interference matrix correction. Through real-time collected synchronous data, it dynamically iterates and corrects various error coefficients, completely eliminating the mutual interference between the two parameters at the algorithmic level. This solves the core pain point of existing technologies that can only measure independently and cannot correct synchronously. The linearity of the full-range measurement is ≥99.9%, and the long-term working stability error is ≤0.3% / year, far exceeding the 2%-5% / year drift level of conventional products in the industry.

[0021] This invention features a built-in integrated self-calibration unit and an original three-level closed-loop calibration method. It can automatically complete zero-point drift calibration, sensitivity gradient calibration, and coupling cross-interference calibration in underwater in-situ, online, and unattended conditions, without the need to disassemble the sensor for manual calibration on land. This completely solves the shortcomings of existing sensors, such as large drift during long-term operation, high maintenance costs, and inability to perform continuous calibration. The calibration process adopts parallel processing in the background without interrupting the normal monitoring data output. It is suitable for unattended operation scenarios such as the field, rivers, and oceans, and can achieve stable unattended operation for more than 2 years, significantly reducing monitoring and maintenance costs.

[0022] This invention utilizes a MEMS solid-state microfluidic chip for flow velocity measurement, eliminating the need for rotating impellers and moving mechanical parts. This completely avoids issues such as silt abrasion, debris entanglement, and jamming failure, extending service life by more than three times. The probe front end integrates a micro ultrasonic + water flow composite automatic cleaning mechanism, which can periodically and automatically remove silt and biofilm contamination from the probe surface. Long-term underwater operation will not result in decreased measurement accuracy due to probe contamination. The housing adopts an integrated streamlined titanium alloy structure with a laser-welded double-seal design, a pressure resistance rating of 5MPa, and an IP68 protection rating. It can adapt to various extreme and complex aquatic environments, including freshwater, seawater, corrosive water, high and low temperatures, and high-velocity scouring, making it suitable for a wide range of scenarios than existing conventional sensors.

[0023] This invention employs a floating-point DSP low-power processor with a built-in intelligent power management engine. It automatically enters sleep mode during non-collection periods, with a standby power consumption of ≤15mW. It adopts a dual-mode system of built-in lithium battery and external inductive power supply, and can operate continuously and stably for more than 180 days on a single battery. It is also compatible with RS485 wired, LoRa wireless, and NB-IoT multi-protocol communication, and can be directly connected to existing hydrological monitoring platforms and water conservancy digital systems to upload monitoring data, calibration records, equipment status, and alarm information in real time. It has the functions of local data cyclic storage, automatic alarm for anomalies, and lifetime traceability of calibration records, and is fully adapted to the industry development needs of smart water conservancy and digital water environment monitoring. Attached Figure Description

[0024] Figure 1 This is a flowchart of the sensor structure of the present invention; Figure 2 This is a flowchart of the working process of the intelligent monitoring sensor integrating sediment content and flow velocity of the present invention; Figure 3 This is a flowchart of the three-level closed-loop calibration mechanism of the present invention. Detailed Implementation

[0025] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals.

[0026] It should be noted that the terms “front,” “back,” “left,” “right,” “up,” and “down” used in the following description refer to the directions shown in the attached diagram, while the terms “inside” and “outside” refer to the directions toward or away from the geometric center of a specific component, respectively.

[0027] To make the content of this invention easier to understand, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings.

[0028] Example 1: Implementation of Sensor Assembly and Performance Debugging

[0029] This embodiment fully discloses the complete process details of the sensor's assembly, precision debugging, and performance calibration, perfectly conforming to actual production and processing specifications. The specific steps are as follows: 1. Integrated Waterproof Housing Machining and Pre-treatment: Utilizing TC4 titanium alloy, an integrated streamlined flow-guiding housing is machined on a CNC lathe. The front end features a circular arc flow-guiding structure to reduce water flow resistance and turbulence interference. The housing consists of a three-section structure: a front-end detection chamber, a middle-end main control chamber, and a rear-end calibration fluid storage chamber. Double-layer sealing grooves are installed between the chambers. After machining, the inner wall of the housing undergoes passivation and anti-corrosion treatment, while the outer wall is smooth and polished. Coaxial detection holes are opened at the front of the detection chamber, corresponding to the detection axes of the acoustic transducer, optical probe, and MEMS velocimetry chip, respectively. The coaxiality tolerance of all detection holes is controlled within ≤0.02mm to avoid measurement errors caused by detection axis misalignment. The main control chamber is equipped with reinforced mounting slots and shock-absorbing buffer pads. The calibration fluid storage chamber has a built-in sealed liquid storage chamber and a micro peristaltic pump mounting position. After the entire machine is machined, a 5MPa water pressure sealing test is performed. After 30 minutes of pressure holding, there is no leakage or deformation, achieving an IP68 protection level and a pressure resistance depth covering the entire range of 0-50m.

[0030] 2. Precision Assembly and Debugging of the Detection Unit: The dual-mode acoustic detection array, polarized light scattering detection component, three-dimensional MEMS microfluidic velocimetry chip, and high-precision temperature and pressure sensor are sequentially and coaxially assembled inside the front-end detection chamber. All components are fixed with epoxy resin, and shock-absorbing damping pads are added to prevent water flow vibration from causing components to loosen. The four positive exchange transducers of the dual-mode acoustic array are arranged vertically and symmetrically in pairs. The alignment accuracy of the transmitting and receiving chips is ≤0.01mm. Matching circuits and shielding wires are welded. The transmitting power is continuously adjustable from 0.5-5W. The signal-to-noise ratio of the received signal is ≥60dB; the polarization detection component coaxially mounts a 660nm linearly polarized laser and 24 distributed photodetectors inside the optical isolation cavity, and adds anti-reflective coated glass lenses, adjusting the light intensity stability to ±0.1% / h, polarization degree ≥99%, and suppressing stray light interference; the MEMS microfluidic chip is fixed by welding through a flexible circuit board, and the probe end face is flush with the outer wall of the shell, without protrusion or depression, to avoid disturbing the flow field; the temperature and pressure sensor is closely attached to the inner wall of the detection chamber, collecting water temperature and pressure data in real time, with a response time ≤1ms.

[0031] 3. Circuit Module Integration and Rugged Protection: The 32-bit floating-point DSP main control board, signal amplification and filtering board, A / D conversion board, closed-loop calibration control board, and communication power supply board are integrated and connected through high-density connectors and fixed inside the central main control compartment. All circuit boards are processed using SMT surface mount technology. After soldering, full-parameter functional testing is performed to ensure that signal acquisition, calculation, communication, and control functions are normal. After passing the functional test, all circuit boards are fully covered with conformal coating. After curing at room temperature, high and low temperature cycling tests are performed again. The test temperature range is -10℃ to 60℃. After 10 cycles, the components function normally, without drift or failure, and are suitable for working in extreme water temperature environments.

[0032] 4. Assembly of self-calibration unit and power supply / communication module: The micro peristaltic pump, micro-injection tubing, standard calibration chamber, and closed-loop feedback circuit are assembled in the rear calibration solution storage tank. The storage chamber is sealed, the standard solution capacity is 500mL, and the injection tubing is free of air bubbles and leaks throughout. The injection accuracy is adjusted to ±0.1mL, and the single injection volume can be accurately controlled within the range of 0.5-5mL. The power supply module is equipped with a large-capacity waterproof lithium battery and connected to the power management circuit. The dual power supply mode switching function is adjusted, automatically switching to external power supply when there is wired power supply, while charging the battery simultaneously. When there is no external power supply, it automatically switches to battery power supply. The overall power consumption is adjusted to meet the standards. The communication module is equipped with RS485, LoRa, and NB-IoT communication antennas. The multi-protocol automatic switching function is adjusted, and the communication distance and transmission stability meet the standards.

[0033] 5. Overall sealing and factory calibration: After all components are assembled, the three-section housing is double-sealed using laser welding and fluororubber sealing rings. After sealing, a 5MPa water pressure sealing test is performed again, and no leakage is observed after 1 hour of pressure holding. Subsequently, the sensor is placed in a standard calibration water tank for factory zero-point calibration, gradient concentration calibration, and flow rate range calibration. Initial calibration parameters and correction coefficients are entered, and the overall machine is debugged before shipment. The measurement accuracy, response speed, and stability of the machine all meet the technical requirements of this invention.

[0034] Example 2: Full-process implementation of underwater synchronous monitoring in river channels

[0035] This embodiment uses natural river channel flood season water environment monitoring as an application scenario, and fully discloses the entire process of implementing the sensor of this invention from on-site deployment, initialization, continuous monitoring, data processing, and anomaly handling. It fully conforms to actual engineering application specifications, and the specific steps are as follows: 1. On-site deployment and hardware wiring: Select the preset monitoring points at the river monitoring section, and vertically install the sensor of this invention at a depth of 8m underwater using a fixed bracket. The sensor's detection end face should be directly facing the water flow direction and parallel to the water flow direction to avoid the casing obstructing the flow field and causing disturbance. The sensor is connected to the onshore data acquisition terminal and power supply equipment via a waterproof shielded cable. The cable is waterproof and erosion-proof to prevent the water flow from pulling and causing the wiring to loosen. After checking that the power supply voltage and communication connection are normal, turn on the power, and the sensor will automatically turn on and enter the initialization state.

[0036] 2. Power-on Initialization and Automatic Zero-point Calibration: After the sensor is powered on, it automatically starts the initialization process without manual operation. First, the self-calibration unit is controlled to perform in-situ zero-point calibration. A micro peristaltic pump injects standard clean water into the standard calibration chamber and allows it to stand stably for 10 minutes. Acoustic baseline signal, optical scattering baseline signal, and temperature and pressure baseline data are collected simultaneously. 100 sets of data are collected continuously. After removing outliers, the baseline average value is calculated, an initial zero-point drift correction model is established, and the zero-point compensation parameters are updated. The zero-point error caused by initial component drift and environmental interference is automatically eliminated. After the zero-point calibration is completed, the sensor automatically exits the calibration mode and enters the normal monitoring state. No manual intervention is required throughout the process.

[0037] 3. Multi-source signal synchronous high-frequency acquisition: After the sensor enters normal working state, the composite detection unit synchronously acquires signals from all channels at a high sampling frequency of 200Hz: the dual-mode acoustic array synchronously emits two orthogonal sound waves of 1.2MHz and 3.5MHz, and receives the backscattered sound wave signals of sediment particles in the water in real time; the polarized light detection component continuously emits stable linearly polarized light, and synchronously acquires the 360° all-angle scattered light intensity signal; the three-dimensional MEMS microfluidic velocimetry chip acquires the x / y / z three-dimensional water flow velocity signal in real time; the temperature and pressure sensor synchronously acquires real-time water temperature and hydrostatic pressure data; all channel signals are acquired using the same source clock, and the time synchronization error of each signal is ≤0.01s. The acquired raw signals are transmitted to the intelligent processing module in real time, with no data delay and no data loss.

[0038] 4. Signal Preprocessing and Noise Reduction Optimization: The intelligent processing module performs real-time preprocessing on the acquired raw signals. First, the weak signals are linearly amplified by an instrumentation amplifier to match the A / D conversion input range. Then, the built-in wavelet transform + Kalman filter combination algorithm is used to adaptively reduce noise, completely filtering out water bubble noise, water flow vibration noise, electromagnetic interference noise, and high-frequency clutter signals, improving the acoustic signal-to-noise ratio to ≥65dB and the optical signal-to-noise ratio to ≥60dB, while retaining effective feature signals without signal distortion or feature loss. After preprocessing, the signal is corrected for DC drift and initial temperature compensation is performed, and effective Doppler frequency shift features, scattered light intensity features, and flow velocity features are extracted and sent to the coupled solution module.

[0039] 5. Coupled Calculation and Full Parameter Correction Output: Based on the pre-processed effective signal, the DSP processor performs real-time calculation using the original coupled calculation model of this invention. First, based on the dual-frequency acoustic Doppler frequency shift formula and combined with the sediment concentration attenuation correction coefficient, the three-dimensional real-time flow velocity component is calculated. The optimal working frequency is automatically switched according to the sediment concentration of the water body to ensure the accuracy of flow velocity measurement. At the same time, based on the polarized light scattering intensity formula and combined with the real-time flow velocity correction coefficient and temperature and pressure correction coefficient, the real-time sediment mass concentration of the water body is calculated. After the calculation is completed, the cross-interference of the flow velocity data and sediment content data is fully compensated through the built-in coupling interference matrix to eliminate the mutual influence of the two parameters and finally output accurate and stable monitoring data. The data output frequency is 1Hz, and six types of parameters are output simultaneously: three-dimensional flow velocity, sediment content, water temperature, hydrostatic pressure, equipment status, and calibration status.

[0040] 6. Data Storage, Upload, and Anomaly Handling: Valid data after processing is automatically stamped with millisecond-level timestamps. It is then uploaded in real-time to the onshore water conservancy monitoring platform via a communication module, supporting wired and wireless dual-path transmission with a data transmission packet loss rate of ≤0.1%. Simultaneously, it is stored locally in a 4GB Flash memory using a cyclic overwrite storage mode, capable of continuously storing more than one year of high-frequency raw and processed data, ensuring lifelong data traceability. During monitoring, if abnormal situations occur such as data exceeding the measurement threshold, excessively low signal-to-noise ratio, severe probe contamination, or equipment malfunction, the sensor automatically triggers an alarm, simultaneously uploading the abnormal code and alarm information, and automatically initiating the abnormal calibration process without interrupting normal monitoring. After the anomaly is resolved, normal operation is automatically restored.

[0041] Example 3: In-situ Three-Level Closed-Loop Full-Parameter Calibration Implementation Method

[0042] This embodiment fully discloses the sensor of the present invention, detailing the entire process of performing three-level closed-loop full-parameter calibration underwater in situ, without disassembly or interruption of monitoring, thus solving the industry pain point that existing sensors cannot be calibrated online. The specific steps are as follows: 1. Calibration Trigger and Preparatory Work: This calibration process supports three modes: periodic automatic trigger, abnormal automatic trigger, and remote manual trigger. This embodiment adopts the periodic automatic trigger mode. After the sensor has been working continuously for 30 days, the online full-parameter calibration process is automatically triggered. After calibration is triggered, the sensor automatically switches to background calibration mode. The output of normal monitoring data at the front end is uninterrupted and without delay. Calibration calculation and data acquisition are processed in parallel without affecting normal monitoring work. At the same time, the sensor automatically collects the current water temperature and pressure data, records the zero point value, sensitivity coefficient, and coupling interference coefficient before calibration, and generates a calibration preparatory record.

[0043] 2. First Stage: Dynamic Zero-Point Drift Full Compensation Calibration: First, the zero-point calibration process is executed. The sensor controls the micro peristaltic pump of the self-calibration unit to draw standard zero-concentration calibration solution from the storage chamber and inject it into the built-in standard calibration chamber at a uniform rate. The injection volume is precisely controlled to be 2mL. After the injection is completed, the calibration chamber passage is closed and left to stand for 10 minutes to ensure that the calibration solution is stable, free of bubbles, and undisturbed. Subsequently, the sensor continuously collects 100 sets of acoustic baseline signals and optical scattering baseline signals at a sampling frequency of 200Hz, removes 3σ abnormal data in real time, and calculates the average value, standard deviation, and drift slope of the baseline signals. Based on the original zero-point drift correction model of this invention, the temperature and pressure drift coefficient and zero-point compensation parameters are updated in real time to establish a dynamic zero-point correction model at time t, which fully compensates for the temperature drift, time drift, and circuit drift caused by long-term operation of the sensor. After calibration, the zero-point residual error is ≤0.1%FS. After the zero-point calibration is completed, the liquid in the calibration chamber is automatically discharged, and the detection passage is restored.

[0044] 3. Second Stage: Full-Range Sensitivity Gradient Calibration: After successful zero-point calibration, the system automatically enters the sensitivity calibration process. Using a micro-injection system, three sets of standard sediment suspensions with gradient concentrations of 5 kg / m³, 50 kg / m³, and 200 kg / m³ are injected into the standard calibration chamber in ascending order of concentration, covering the sensor's full-range measurement range. After each set of standard solutions is injected, it is allowed to stand for 15 minutes to ensure uniformity, stability, and the absence of sediment and bubbles. 200 sets of sensor response signals are continuously collected, recording the acoustic and optical response values ​​at the corresponding concentrations. After all three sets of standard solutions have been collected, the DSP processor uses the least squares method to fit the dynamic calibration curve of sediment concentration versus sensor response, calculates the full-range dynamic sensitivity coefficient and nonlinearity correction coefficient, automatically replaces the original aging and attenuated calibration parameters, and corrects the sensitivity attenuation error caused by long-term sensor operation. After calibration, the full-range measurement linearity is ≥99.9%. After calibration, the solution is discharged from the calibration chamber, completing the sensitivity calibration.

[0045] 4. Third Stage: Full Compensation Calibration for Sediment-Flow Velocity Cross-Interference: After sensitivity calibration, the system automatically enters the coupling cross-interference calibration process. This step is an original calibration procedure that completely bypasses existing technologies. The sensor, through its built-in analog drive unit, sequentially adjusts the simulated calibration flow rate to 0.5 m / s, 2 m / s, and 5 m / s, covering the conventional monitoring flow rate range. At each flow rate setting, five sets of standard sediment suspensions with different concentrations are injected, and flow rate-concentration coupling response data are collected simultaneously to construct a 5×5 full-condition coupling interference matrix. Through matrix inversion and least squares fitting, the full-range cross-interference correction factor is solved, a dynamic interference correction model is established, and it is updated in real time to the coupling solution algorithm. This completely eliminates the disturbance interference of flow rate on sediment measurement and the attenuation interference of sediment concentration on flow rate measurement, achieving full compensation for the cross-interference of the two parameters. After calibration, the measurement error caused by cross-interference is ≤0.1%FS, which is far superior to the existing technology level.

[0046] 5. Calibration Verification, Parameter Storage, and Report Generation: After all three levels of calibration are completed, the sensor automatically enters the calibration verification process. A standard sediment solution of known concentration is injected into the calibration chamber, a standard simulated flow rate is applied, and the error between the measured value and the standard value is compared. If the full-range measurement error is ≤0.3%FS, the calibration is considered qualified. If the error exceeds the standard, the calibration process is automatically repeated until qualified. After qualified calibration, all updated calibration parameters, correction coefficients, and drift compensation parameters are encrypted and stored in the local Flash memory. At the same time, the calibration time, calibration type, error before calibration, error after calibration, environmental temperature and pressure parameters, and calibration results are recorded, and a complete calibration report is generated. The calibration report is automatically uploaded to the monitoring platform and can be queried and traced for life. After the entire process is completed, the sensor automatically exits the calibration mode and resumes normal high-frequency monitoring. The entire process takes ≤25 minutes, fully realizing in-situ, online, and unattended full-parameter calibration.

[0047] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An integrated intelligent monitoring sensor for underwater sediment content and flow velocity, and a calibration method thereof, characterized in that: It includes an integrated waterproof housing, a composite detection unit, an intelligent processing module, a self-calibration unit, and a communication power supply module; The integrated waterproof shell adopts a streamlined titanium alloy structure, with a built-in pressure balance chamber and temperature compensation chamber, a protection level of IP68, a pressure resistance of 0-50m, and is suitable for water environments of 0-45℃. The composite detection unit integrates a dual-mode acoustic detection array, a polarized light scattering detection component, a three-dimensional MEMS microfluidic velocimetry chip, and a high-precision temperature and pressure compensation sensor. The dual-mode acoustic array contains four 1.2MHz / 3.5MHz positive exchange energy transducers to form a three-dimensional detection beam. The polarized light scattering detection component uses a 660nm semiconductor laser and a 360° distributed photodetector to cover 0.1-2000μm sediment particle size. The three-dimensional MEMS microfluidic velocimetry chip integrates a thermistor and a piezoresistive element to realize microfluidic vector measurement. The intelligent processing module adopts a 32-bit floating-point DSP processor, with built-in sediment-flow velocity coupling solution algorithm, adaptive noise suppression model, and dynamic parameter correction engine. It is equipped with 16MB high-speed cache and 4GB Flash storage, and supports 200 data acquisitions and real-time solutions per second. The self-calibration unit includes a standard fluid calibration chamber, a micro-volume standard solution injection system, and a closed-loop feedback calibration circuit, enabling in-situ automatic zero-point calibration, sensitivity calibration, and temperature drift compensation. The communication power supply module supports RS485, LoRa, and NB-IoT multi-protocol communication, adopts an underwater inductive power supply + lithium battery dual power supply mode, has a standby power consumption of ≤15mW, and a continuous working time of ≥180 days.

2. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity as described in claim 1, characterized in that: The dual-mode acoustic detection array adopts an orthogonal symmetrical layout, with four transducers arranged vertically in pairs. Each transducer includes a transmitting chip and a receiving chip. The transmitting chip is made of piezoelectric ceramic composite material, and the receiving chip is made of high-sensitivity PVDF film. The acoustic wave transmission power is continuously adjustable from 0.5 to 5W, the receiving bandwidth is 1MHz to 4MHz, and the signal-to-noise ratio is ≥60dB. It can simultaneously receive and separate scattered signals of different frequencies, eliminating the effects of acoustic multipath interference and signal attenuation under high sand content.

3. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity as described in claim 1, characterized in that: The polarization light scattering detection component includes a laser emitting unit, a 360° distributed photoelectric detection array, an optical isolation cavity, and an automatic cleaning mechanism. The laser emitting unit outputs 660nm linearly polarized light with a polarization degree ≥99% and light intensity stability ±0.1% / h; The 360° distributed photodetector array consists of 24 silicon photodiodes evenly distributed, with a receiving angle of 0°-170°, a response wavelength of 400nm-800nm, and a minimum detection light intensity of 1nW. The optical isolation cavity employs an anti-reflective coating and a light-shielding structure to suppress stray light interference from the water. The automatic cleaning mechanism removes mud, sand, and biological dirt from the probe surface through micro-ultrasonic vibration and high-pressure water rinsing. The cleaning frequency is adjustable from 0 to 24 times per day.

4. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity as described in claim 1, characterized in that: The three-dimensional MEMS microfluidic velocimetry chip is fabricated using SOI technology, with dimensions of 5mm×5mm×0.5mm. It integrates four thermistor arrays and two piezoresistive elements. The thermistor temperature coefficient is ≥-2% / ℃, the response time is ≤1ms, and the piezoresistive element sensitivity is ≥0.1mV / V / kPa. It can simultaneously measure the x / y / z three-dimensional flow velocity components, with a minimum measured flow velocity of 0.001m / s and a measurement range of 0-10m / s. It achieves flow velocity measurement without mechanical parts through microscale heat transfer and pressure sensing.

5. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity according to claim 1, characterized in that: The intelligent processing module has a built-in sediment-velocity coupled solution algorithm, which calculates the sediment content and velocity data simultaneously by combining acoustic Doppler frequency shift and light scattering intensity. The algorithm includes a real-time temperature and pressure compensation module, an adaptive noise filtering module, and a cross-interference correction module. The temperature and pressure compensation range is -10℃ to 60℃ and 0-5MPa, with a compensation accuracy of ±0.05%. Adaptive noise filtering employs a wavelet transform + Kalman filter combination algorithm, achieving a noise suppression ratio ≥40dB; Cross-interference correction eliminates the measurement errors of velocity under high sediment concentration and sediment content under high velocity by establishing a sediment-velocity coupling matrix, and the parameters of the coupling matrix are updated dynamically in real time.

6. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity according to any one of claims 1-5, characterized in that: Includes the following steps: (1) Sensor deployment initialization: Fix the sensor at the preset depth of the monitored water body, turn on the power, and the self-calibration unit automatically performs zero-point calibration, collects acoustic baseline signal, light scattering baseline signal, and temperature and pressure baseline data under standard clear water environment, and establishes an initial zero-point drift correction model; (2) Synchronous acquisition of multi-source data: The composite detection unit synchronously acquires dual-mode acoustic scattering signal, polarized light scattering signal, three-dimensional microflow velocity signal, temperature and pressure signal at a frequency of 200Hz, and the synchronous acquisition error of each signal is ≤0.01s; (3) Signal preprocessing: The intelligent processing module amplifies, filters, and converts the acquired signal to A / D, removes high-frequency noise and DC drift, extracts effective feature signals, and improves the signal-to-noise ratio of acoustic signals to ≥65dB and the signal-to-noise ratio of optical signals to ≥60dB; (4) Coupled solution: The three-dimensional flow velocity component is calculated based on the acoustic Doppler frequency shift formula, the sediment content is calculated based on the light scattering intensity formula, the sound velocity and light attenuation coefficient are corrected by the temperature and pressure compensation coefficient, and the mutual influence between sediment and flow velocity is eliminated by the cross-interference correction factor. (5) Data output and storage: After the validity of the calculated data is determined, it is uploaded to the monitoring platform in real time through the communication module and stored in the local Flash. The storage period is adjustable from 1 to 365 days. The data format includes timestamp, sediment content, three-dimensional flow velocity, temperature, pressure and calibration status. (6) Abnormal handling: When the monitoring data exceeds the threshold or the signal is abnormal, the abnormal calibration process is automatically started, the abnormal code is recorded and the alarm information is uploaded.

7. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity as described in claim 6, characterized in that: The acoustic Doppler frequency shift calculation employs a dual-frequency joint calculation model. A 1.2MHz sound wave is used for environments with high sand content (50-300 kg / m³), and a 3.5MHz sound wave is used for environments with low sand content (0-50 kg / m³). The operating frequency is automatically switched based on the amplitude ratio of the frequency shift signal. The calculation formula is as follows: in This is the correction factor for sound wave attenuation due to sand content. S represents the sediment content, and the accuracy of the full-range flow velocity measurement is ensured to be ±0.2% FS through real-time iterative calculation.

8. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity as described in claim 6, characterized in that: The sediment content calculation employs a polarized light scattering-flow velocity joint correction model. The baseline concentration is calculated using the ratio of scattered light intensity, and a flow velocity correction factor is introduced to eliminate the influence of water flow disturbance on light scattering. The calculation formula is as follows: Where K is the optical calibration coefficient. This is the flow rate correction factor. This is a temperature and pressure correction factor. T represents temperature, and P represents pressure, ensuring a full-range sediment content measurement accuracy of ±0.5% FS.

9. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity according to any one of claims 1-5, characterized in that: A three-level closed-loop calibration mechanism is adopted, including factory calibration, online periodic calibration, and abnormal trigger calibration. The calibration process is as follows: (1) Zero-point calibration: Place the sensor in a standard clean water environment, start the micro-standard solution injection system to inject zero-concentration calibration solution, stabilize for 10 minutes, continuously collect 100 sets of baseline signals, calculate the average value and standard deviation, and establish a zero-point drift dynamic correction model: in Let be the zero-point voltage at time t. The initial zero-point voltage, The zero-point parameters are updated in real time as the temperature and pressure drift coefficient. (2) Sensitivity calibration: Three sets of standard sediment suspensions of 5kg / m³, 50kg / m³ and 200kg / m³ were injected sequentially through the micro-injection system. Each set was stabilized for 15 minutes. 200 sets of response signals were collected. The concentration-response calibration curve was fitted by the least squares method. The dynamic sensitivity coefficient was calculated to eliminate the influence of sensitivity decay of the sensor after long-term use. (3) Cross-interference calibration: Adjust the simulated flow rate to 0.5m / s, 2m / s, and 5m / s, inject standard sediment solutions of different concentrations, collect flow rate-concentration coupling response data, construct a 5×5 coupling interference matrix, solve the interference correction factor by matrix inversion, and realize full compensation of sediment-flow rate cross-interference. (4) Calibration verification: After calibration, inject a standard solution of known concentration and simulate the flow rate. If the measurement error is ≤0.3% FS, the calibration is deemed qualified; otherwise, repeat the calibration process. (5) Parameter storage: Calibration parameters are encrypted and stored in Flash, and historical calibration records can be queried and traced. The calibration records include time, calibration type, parameters, error and environmental conditions.

10. The integrated intelligent monitoring sensor and calibration method for underwater sediment content and flow velocity according to claim 9, characterized in that: The online periodic calibration adopts an adaptive triggering mechanism. The calibration cycle is dynamically adjusted according to the sensor's working status. Under normal working conditions, it is automatically calibrated once every 30 days. When the ambient temperature fluctuates by more than ±5℃ / h, the sediment content exceeds 200kg / m³ for 1 hour, or the flow velocity changes by more than 5m / s, abnormal calibration is triggered immediately. The calibration process does not affect normal monitoring. It adopts background parallel processing, with a calibration time of ≤30 minutes, a micro-volume standard solution injection volume of ≤5mL each time, and a standard solution storage capacity of ≥500mL, meeting the online calibration requirements for 2 years. After calibration is completed, a calibration report is automatically generated, which includes the error before calibration, the error after calibration, the corrected parameters, and the validity judgment. The report is then uploaded to the monitoring platform via the communication module.

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