ADRC adaptive anti-interference control method of water quality monitoring equipment

Abstract

The application belongs to the technical field of water quality monitoring equipment control, and discloses an ADRC adaptive anti-interference control method of a water quality monitoring equipment. The method comprises the following steps: a processor collects a water body turbulent shear characteristic coefficient, an electrode biofilm impedance drift coefficient and a power supply ripple noise relative coefficient; the processor introduces a turbulent impedance coupling proportion coefficient and an impedance ripple coupling proportion coefficient, and progressively couples and solves the three types of collected coefficients to obtain a three-level progressive coupling disturbance intensity; the processor combines relevant parameters to perform gain solving and signal calibration, and outputs a water quality monitoring output electric signal. The application solves the problem of insufficient anti-interference precision of uncoupled multiple disturbances in the ADRC control in the prior art, realizes accurate calibration of the water quality monitoring signal, and guarantees the stability of the monitoring data.

Description

Technical Field

[0001] This invention belongs to the field of water quality monitoring equipment control technology, specifically relating to an ADRC adaptive disturbance rejection control method for water quality monitoring equipment. Background Technology

[0002] Water quality monitoring equipment is susceptible to various disturbances during actual operation, leading to signal distortion and insufficient anti-disturbance control accuracy. Turbulent shear in water, electrode biofilm impedance drift, and power supply ripple noise are the three main disturbance factors. Existing ADRC adaptive disturbance rejection control methods often employ a single-disturbance-independent processing mode, failing to consider the coupling effect between the three types of disturbances. They rely solely on fixed gain for disturbance rejection control, unable to dynamically adjust control parameters according to disturbance intensity, resulting in poor anti-disturbance performance and difficulty in ensuring the accuracy of monitoring data, thus failing to meet the actual needs of precise water quality monitoring.

[0003] Based on the above problems, there is an urgent need for an ADRC adaptive disturbance rejection control method that can couple multiple types of disturbances and dynamically adjust control parameters. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and proposes an ADRC adaptive disturbance rejection control method for water quality monitoring equipment, comprising: The processor collects the water turbulence shear characteristic coefficient, electrode biofilm impedance drift coefficient, and power supply ripple noise relative coefficient. The processor introduces turbulence impedance coupling proportionality coefficient and impedance ripple coupling proportionality coefficient to perform progressive coupling solution on the three types of coefficients collected, and obtains the three-level progressive coupling disturbance intensity. The processor collects the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period. It then performs gain calculation by combining the three-level progressive coupling disturbance strength to obtain the ADRC extended state observer gain after adaptive correction. The processor acquires the original output electrical signal of the water quality sensor, the gain limit threshold of the ADRC observer, and the electrical conversion calibration coefficient of the water quality signal. It then performs signal calibration by combining the ADRC extended state observer gain after adaptive correction and outputs the water quality monitoring output electrical signal.

[0005] Preferably, the processor collects the water turbulence shear characteristic coefficients using the water flow shear force sensing unit configured in the water quality monitoring equipment; the processor collects the electrode biofilm impedance drift coefficients using the electrode interface impedance detection unit configured in the water quality monitoring equipment; and the processor collects the power supply ripple noise relative coefficients using the DC power supply ripple sampling unit configured in the water quality monitoring equipment.

[0006] In a further preferred embodiment, the turbulence impedance coupling ratio coefficient and the impedance ripple coupling ratio coefficient are fixed coefficients pre-stored in the processor's storage unit. When the processor performs progressive coupling decomposition, it directly calls these two types of fixed coefficients to participate in the calculation.

[0007] In a further preferred embodiment, when the processor performs gain calculation, it introduces the ADRC observer basic fixed gain pre-stored in the processor, and superimposes the ADRC observer basic fixed gain with the calculation results of the three-level progressive coupling disturbance strength, the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period to obtain the ADRC extended state observer gain after adaptive correction.

[0008] In a further preferred embodiment, when the processor performs progressive coupling calculation, it employs specific operational logic, combining the turbulence impedance coupling ratio coefficient and the impedance ripple coupling ratio coefficient, to perform coupling calculations on the water turbulence shear characteristic coefficient, the electrode biofilm impedance drift coefficient, and the power supply ripple noise relative coefficient, thereby obtaining the three-level progressive coupling disturbance intensity.

[0009] In a further preferred embodiment, when the processor performs gain calculation, it employs specific operational logic, combining the three-level progressive coupling disturbance strength, the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period, and performs superposition calculation with the ADRC observer's basic fixed gain to obtain the adaptively corrected ADRC extended state observer gain.

[0010] In a further preferred embodiment, when the processor performs signal calibration, it employs specific operational logic, combining the adaptively corrected ADRC extended state observer gain, the original output electrical signal of the water quality sensor, the ADRC observer gain limit threshold, and the electrical conversion calibration coefficient of the water quality signal, to calibrate the original electrical signal and output the water quality monitoring output electrical signal.

[0011] In a further preferred embodiment, after the processor outputs the water quality monitoring output electrical signal, the deviation data between the water quality monitoring output electrical signal and the preset standard water quality electrical signal is collected by the standard water quality signal comparison unit configured in the water quality monitoring equipment, and the deviation data is transmitted to the processor's progressive coupling solution port.

[0012] In a further preferred embodiment, after receiving the deviation data, the processor re-triggers the progressive coupling calculation, gain calculation, and signal calibration operations according to the pre-set control timing sequence stored in the processor, until the deviation data is less than the pre-set deviation threshold, and then stops the closed-loop operation process.

[0013] In a further preferred embodiment, the water flow shear force sensing unit, the electrode interface impedance detection unit, and the DC power supply ripple sampling unit all transmit the acquired data to the processor through the SPI communication interface. The communication rate of the SPI communication interface is 1Mbps. The processor parses the three types of acquired data according to the sampling timing sequence preset and stored in the processor. After the parsing is completed, the progressive coupling solution operation is started immediately.

[0014] Technical effects: The core inventive technology of this invention is the introduction of turbulence impedance coupling proportionality coefficients and impedance ripple coupling proportionality coefficients to progressively couple and solve three types of disturbances: water turbulence shear, electrode biofilm impedance drift, and power supply ripple noise. This is combined with multi-parameter dynamic adjustment of the ADRC extended state observer gain. This technology precisely solves the problem of insufficient anti-disturbance accuracy caused by ADRC control not considering the coupling of multiple disturbances and having a fixed gain in the prior art, achieving accurate calibration of water quality monitoring signals and ensuring the stability of monitoring data. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the steps of the ADRC adaptive disturbance rejection control method for water quality monitoring equipment; Figure 2 A schematic diagram of the overall hardware architecture of the ADRC adaptive disturbance rejection control system for water quality monitoring equipment; Figure 3 This is a schematic diagram of the hardware structure of a multi-sensor acquisition unit for water quality monitoring equipment. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Traditional ADRC adaptive disturbance rejection control only handles a single type of disturbance independently, without establishing a hierarchical influence correlation model between the three types of disturbances. The control process uses fixed observer gain parameters, which cannot dynamically adjust according to the real-time coupling state of the disturbance. Ultimately, this leads to amplitude distortion and phase shift in the raw electrical signal of water quality monitoring, and the accuracy and stability of the monitoring data cannot meet the requirements of high-precision water quality monitoring scenarios such as surface water, industrial wastewater, seawater, and drinking water.

[0018] Based on this, please refer to Figures 1-3 This embodiment provides an ADRC adaptive disturbance rejection control method for a water quality monitoring device, including: the processor performing multi-dimensional disturbance data acquisition operations to acquire water turbulence shear characteristic coefficients, electrode biofilm impedance drift coefficients, and power supply ripple noise relative coefficients.

[0019] The processor calls pre-stored turbulence impedance coupling ratio coefficients and impedance ripple coupling ratio coefficients to perform progressive coupling calculations on the water turbulence shear characteristic coefficient, the electrode biofilm impedance drift coefficient, and the power supply ripple noise relative coefficient, outputting a three-level progressive coupling disturbance strength. The processor collects the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period, and performs gain calculations based on the three-level progressive coupling disturbance strengths to obtain the adaptively corrected ADRC extended state observer gain.

[0020] The processor acquires the original output electrical signal of the water quality sensor, the gain limit threshold of the ADRC observer, and the electrical conversion calibration coefficient of the water quality signal. It then performs a signal calibration operation based on the adaptively corrected ADRC extended state observer gain and outputs the water quality monitoring output electrical signal.

[0021] The processor is the core execution component of the ADRC adaptive disturbance rejection control in water quality monitoring equipment. It adopts an embedded processor with an ARM Cortex-M4 core and undertakes all tasks such as disturbance data acquisition, coupling calculation, gain adjustment, and signal calibration. The processor's operating clock is synchronized with the sampling clock of the water quality monitoring equipment, with clock accuracy reaching the microsecond level. All calculation operations are executed according to fixed timing logic without adding any redundant calculation steps. The processor has a built-in floating-point unit and can directly perform double-precision floating-point operations without the need for additional coprocessor support.

[0022] The water turbulence shear characteristic coefficient is a core parameter characterizing the mechanical shearing effect of water flow on the surface of the monitoring probe. The change in the value of this parameter directly reflects the magnitude of the water turbulence intensity and is the direct cause of changes in the rate of biofilm shedding and attachment on the electrode surface. The value ranges from zero to one, and the larger the value, the stronger the turbulence shearing effect.

[0023] The electrode biofilm impedance drift coefficient is a parameter characterizing the changes in the thickness and density of the biofilm on the surface of the monitoring electrode. Changes in the biofilm attachment state directly alter the interfacial impedance of the electrode, thereby affecting the acquisition accuracy of the monitoring signal. A coefficient of zero represents no impedance drift, while a larger coefficient indicates a more severe degree of impedance drift.

[0024] The power supply ripple noise relative coefficient is a parameter characterizing the degree of interference of AC ripple in the power supply circuit to the DC power supply signal. Power supply ripple is superimposed on the monitoring signal through circuit coupling, forming noise interference that cannot be removed by conventional filtering. The coefficient is calculated as the ratio of the effective value of the ripple voltage to the rated power supply voltage, and is dimensionless with a value not exceeding one. These three types of parameters together constitute the core disturbance characteristics of water quality monitoring equipment. The absence of any one of these parameters will lead to incomplete quantification of the disturbance state, making accurate anti-disturbance control under multiple disturbance coupling impossible.

[0025] The turbulence impedance coupling proportionality coefficient and the impedance ripple coupling proportionality coefficient are the core parameters for constructing the progressive coupling solution model. These two types of coefficients correspond to the coupling relationship between water turbulence shear and electrode biofilm impedance drift, and the coupling relationship between electrode biofilm impedance drift and power supply ripple noise, respectively. Through the intervention of these two types of coefficients, the originally independent three types of disturbances are transformed into a progressively influencing coupled disturbance system, breaking through the technical limitations of traditional single-disturbance processing. This is also the core feature of the innovative improvement achieved by this technical solution. The three-level progressive coupling disturbance intensity is a comprehensive quantitative result of the coupling effect of the three types of disturbances. This result is directly used as the input benchmark for the gain calculation stage, ensuring that the adjustment range of the observer gain is completely matched with the actual disturbance state.

[0026] The gain calculation stage integrates the three-level progressive coupling disturbance strength, the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period, transforming the traditional fixed gain into an adaptive gain that can dynamically change with the operating conditions, allowing the disturbance observation capability of the ADRC extended state observer to adapt to real-time monitoring scenarios.

[0027] The signal calibration process uses the gain of the adaptively corrected ADRC extended state observer as the core adjustment parameter. Combined with the original output electrical signal of the water quality sensor, the gain limit threshold of the ADRC observer, and the electrical conversion calibration coefficient of the water quality signal, it eliminates signal errors caused by disturbance coupling and the sensor's own system errors layer by layer, ultimately outputting an electrical signal that accurately reflects the water quality parameters. All technical features work together to form a complete closed-loop disturbance rejection control process. This solves the problem of monitoring signal distortion caused by multiple disturbance couplings without the need for additional auxiliary hardware components. The implementation logic of the technical solution is clear and straightforward, and those skilled in the art can directly reproduce it by following the steps.

[0028] Furthermore, the processor acquires the water turbulence shear characteristic coefficient using the water flow shear force sensing unit configured in the water quality monitoring equipment; the processor acquires the electrode biofilm impedance drift coefficient using the electrode interface impedance detection unit configured in the water quality monitoring equipment; and the processor acquires the power supply ripple noise relative coefficient using the DC power supply ripple sampling unit configured in the water quality monitoring equipment.

[0029] The water flow shear force sensing unit adopts an embedded structure and is tightly connected to the 316L stainless steel housing of the water quality monitoring probe. The housing is polished to reduce water adhesion resistance. After the probe is fully immersed in the monitored water, the piezoelectric diaphragm inside the water flow shear force sensing unit senses the tangential mechanical force generated by the water flow in real time. The diaphragm is made of PVDF flexible piezoelectric material. The mechanical force signal is converted into a millivolt-level analog voltage signal. The analog voltage signal is amplified, second-order low-pass filtered, and 16-bit analog-to-digital conversion processed by the signal conditioning circuit inside the unit. The filter cutoff frequency is set to 10Hz, and finally, the digital water turbulence shear characteristic coefficient is output. The detection range of the water flow shear force sensing unit covers the entire scenario from still water to strong turbulence. The signal output is continuous and uninterrupted, with no data loss. The sampling frequency is consistent with the control sampling period of the processor.

[0030] The electrode interface impedance detection unit employs a four-wire measurement structure, directly connecting to the working electrode, reference electrode, and auxiliary electrode for water quality monitoring. The electrodes are made of platinum to prevent water corrosion from affecting measurement accuracy. It collects AC impedance data of the electrode interface in real time, and calculates the electrode biofilm impedance drift coefficient by comparing the difference between the initial calibrated impedance value and the real-time impedance value, combined with the impedance change rate. The electrode interface impedance detection unit uses a 1kHz sinusoidal AC excitation signal with an amplitude of 50mV to avoid the redox effect of DC excitation signals on the electrode biofilm, ensuring the authenticity and accuracy of the drift coefficient acquisition. The DC power supply ripple sampling unit uses a differential sampling structure connected to the output of the water quality monitoring equipment's switching power supply. A 10μF DC blocking capacitor is connected in series at the input. It collects the peak and effective values ​​of the ripple voltage in the power supply circuit in real time, converting the ratio of the effective ripple voltage to the rated power supply voltage into a digital power supply ripple noise relative coefficient. The sampling resolution of the DC power supply ripple sampling unit reaches 1mV, matching the small interference detection requirements of the power supply circuit, and can capture millivolt-level ripple changes and complete precise quantization output.

[0031] The three types of acquisition units have clearly defined functions, each corresponding to the acquisition of a type of disturbance coefficient. The signal transmission paths are independent of each other, and there is no data cross-interference, ensuring that the acquired coefficient data is accurate and reliable, providing real and effective raw data support for subsequent progressive coupling solution operations.

[0032] The turbulence impedance coupling ratio coefficient and the impedance ripple coupling ratio coefficient are fixed coefficients pre-stored in the processor's storage unit. When the processor performs progressive coupling decomposition, it directly calls the two types of fixed coefficients to participate in the calculation.

[0033] The processor storage unit uses a flash memory-type non-volatile storage medium with a storage capacity of 256KB. It can store the turbulent impedance coupling ratio coefficient and the impedance ripple coupling ratio coefficient for a long time when the power is off. After the device is restarted, the processor can directly read the stored data without recalibrating on site.

[0034] The value of the turbulence impedance coupling ratio coefficient was determined through a gradient calibration experiment at the water quality monitoring site. During the calibration process, the intensity of water turbulence was controlled to change in five gradients by an electric stirring device. Simultaneously, the corresponding change data of electrode biofilm impedance drift were collected by the electrode interface impedance detection unit. The acquisition time for each set of gradient data was 5 minutes. The coupling ratio coefficient adapted to the current monitoring scenario was obtained by a linear fitting algorithm. After the coefficient was determined, it was directly stored in the processor storage unit by a serial port write command.

[0035] The impedance ripple coupling ratio coefficients were determined through laboratory power supply interference simulation experiments. A programmable DC power supply was used to simulate power supply ripple noise of varying intensities from 0 to 1V, while simultaneously detecting the response changes in electrode biofilm impedance drift. The corresponding coupling ratio coefficients were obtained through least-squares data fitting. These coefficients can be stored for long-term use, requiring recalibration only when the monitored water type or environment undergoes significant changes. During progressive coupling calculations, the processor directly retrieves the two types of coefficients from the storage unit via internal data read instructions, eliminating the need for real-time coefficient fitting calculations. This effectively reduces the processor's computational load, increases the speed of coupling calculations, and ensures the real-time performance of disturbance rejection control. The retrieval process for these two types of coefficients follows a fixed computational sequence, starting synchronously with the calculation of the three types of disturbance coefficients, ensuring the continuity of the progressive coupling calculation operation and preventing parameter loss or retrieval errors.

[0036] When the processor performs gain calculation, it introduces the ADRC observer basic fixed gain pre-stored in the processor, and superimposes the calculation results of the ADRC observer basic fixed gain with the three-level progressive coupling disturbance strength, the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant and the ADRC control sampling period to obtain the ADRC extended state observer gain after adaptive correction.

[0037] The basic fixed gain of the ADRC observer is the fundamental operating parameter for the stable operation of the ADRC extended state observer. This parameter is pre-calibrated based on the hardware configuration of the water quality monitoring equipment, the sensor response characteristics, and the control requirements. The calibration process is completed by injecting a standard disturbance signal. After calibration, the parameter is stored in the processor's running memory, and the processor can quickly retrieve it at the nanosecond level when performing gain calculation.

[0038] The three-level progressive coupling disturbance strength reflects the comprehensive coupling strength of the three types of disturbances. The larger the disturbance strength value, the greater the adjustment range of the gain calculation needs to be. The relative deviation of the original water quality monitoring signal reflects the degree of deviation between the original monitoring signal and the standard signal. The larger the deviation value, the higher the adjustment amount of the observer gain needs to be. The system anti-disturbance response time constant reflects the response speed of the equipment to the disturbance signal. The larger the time constant value, the smoother the response characteristics that the observer gain needs to match. The ADRC control sampling period reflects the update frequency of the control command. The smaller the sampling period value, the higher the accuracy of the gain adjustment. The processor performs floating-point integrated calculations on the above four types of parameters to obtain the dynamically adjusted gain change value, and then performs arithmetic superposition with the basic fixed gain of the ADRC observer to finally form an adaptive correction gain adapted to the current monitoring conditions. The superposition calculation process adopts double-precision floating-point arithmetic, with a calculation accuracy of four decimal places, ensuring the accuracy of the gain value calculation. The calculation result is directly transmitted to the signal calibration stage without additional data format conversion, ensuring the real-time performance of the gain adjustment.

[0039] The fixed gain of the ADRC observer provides a stable reference for gain adjustment, avoiding gain overflow or abnormal jumps during dynamic calculations, and ensuring the continuous and stable operation of the ADRC extended state observer.

[0040] When performing progressive coupling decomposition, the processor uses a three-level progressive coupling perturbation strength formula to complete all computational operations. The formula is as follows: ; The formula is designed based on the progressive coupling physical characteristics of three types of disturbances: water turbulence shear, electrode biofilm impedance drift, and power supply ripple noise. These three disturbances do not act independently on the water quality monitoring equipment. Water turbulence shear changes the water flow scouring state on the probe surface, accelerating or slowing down the adhesion rate of the biofilm on the electrode surface, directly aggravating or weakening the degree of electrode biofilm impedance drift. Electrode biofilm impedance drift changes the input impedance of the monitoring circuit, thereby amplifying or reducing the interference effect of power supply ripple noise on the sampling electrical signal. This forms a progressive coupling relationship in which water turbulence shear affects electrode biofilm impedance drift, and electrode biofilm impedance drift affects power supply ripple noise. The formula uses three independent operational expressions to characterize the full coupling effect of the three types of disturbances, the pairwise coupling effect between water turbulence and electrode impedance, and the pairwise coupling effect between electrode impedance and power supply ripple, respectively, to achieve accurate quantification of the coupling effect of the three types of disturbances.

[0041] in the formula The turbulent shear characteristic coefficient of water body is dimensionless and has the dimensionless property of . This parameter is acquired in real time by the water flow shear force sensing unit. Its value is directly related to the water flow velocity, probe cross-sectional shape, and water viscosity. It is used to accurately characterize the intensity of the mechanical shear force of water turbulence on the monitoring probe.

[0042] The electrode biomembrane impedance drift coefficient is dimensionless and has a dimensionless value of 1. This parameter is acquired by the electrode interface impedance detection unit. Its value is related to the electrode usage time, water nutrient content, and water temperature environment. It is used to accurately characterize the degree of interface impedance change caused by biofilm adhesion on the electrode surface. The relative coefficient of power supply ripple noise is dimensionless and has the dimension of . This parameter is acquired by the DC power supply ripple sampling unit. Its value is related to the stability of the power supply, the amplitude of grid fluctuations, and the power supply filtering effect. It is used to accurately characterize the electrical interference intensity of the power supply ripple on the monitoring circuit. The proportionality coefficient for turbulent impedance coupling is dimensionless and has the dimension of . This parameter is pre-stored in the processor's storage unit and is used to adjust the coupling weight between the water turbulence shear characteristic coefficient and the electrode biofilm impedance drift coefficient. The value is determined based on the results of the on-site gradient calibration experiment.

[0043] The impedance ripple coupling proportionality coefficient is dimensionless and has dimensions of . This parameter is pre-stored in the processor's memory unit and is used to adjust the coupling weight between the electrode biomembrane impedance drift coefficient and the relative coefficient of power supply ripple noise. The value is determined based on the results of laboratory power supply interference simulation experiments. The three-level progressive coupling perturbation strength is dimensionless, with dimensions of . , is the final calculation result of the formula, which is directly used as the core input parameter for the subsequent gain calculation stage, and fully reflects the comprehensive coupling strength of the three types of disturbances.

[0044] The logical derivation of the formula is as follows: First, the hierarchical influence relationship of the three types of disturbances is verified through experiments, and the electrode biofilm impedance drift is determined as the core coupling node. Three types of fully coupled terms and two types of pairwise coupled terms are constructed. Then, the computational weights of each coupled term are allocated through coupling ratio coefficients. Finally, the three computational results are added together to obtain the comprehensive disturbance intensity value, ensuring that the computational result completely matches the actual disturbance state. Technical personnel in the relevant field can directly complete the computational operation according to the formula and parameter definitions without any creative effort. In the surface water quality monitoring embodiment, the water turbulence shear characteristic coefficient is taken as 0.35, the electrode biofilm impedance drift coefficient is taken as 0.42, the power supply ripple noise relative coefficient is taken as 0.28, the turbulence impedance coupling ratio coefficient is taken as 0.65, and the impedance ripple coupling ratio coefficient is taken as 0.58. The calculated three-level progressive coupling disturbance intensity is 0.213, which accurately reflects the coupling intensity of the three types of disturbances, providing a reliable basis for subsequent gain adjustment. In the industrial wastewater quality monitoring example, the turbulent shear characteristic coefficient of the water body is taken as 0.68, the electrode biofilm impedance drift coefficient is taken as 0.75, the power supply ripple noise relative coefficient is taken as 0.41, the turbulent impedance coupling ratio coefficient is taken as 0.72, and the impedance ripple coupling ratio coefficient is taken as 0.63. The three-level progressive coupling disturbance intensity is calculated to be 0.527, which is significantly higher than that of the surface water scenario, matching the complex working conditions of industrial wastewater.

[0045] When the processor performs gain calculations, it uses the ADRC extended state observer gain adaptive formula to complete all computational operations. The formula is as follows: ; The design of this formula is based on the disturbance observation and control principle of the ADRC extended state observer and the real-time anti-disturbance requirements of water quality monitoring equipment. The observer gain needs to be dynamically adjusted according to the disturbance coupling strength, signal deviation, system response characteristics and control sampling period in order to achieve accurate observation and compensation of disturbance signals. The formula integrates four types of dynamically adjusted parameters through fractional operation, and then performs arithmetic superposition with the basic fixed gain to form an adaptively corrected observer gain, which completely solves the technical problem that traditional fixed gain cannot adapt to real-time operating condition changes.

[0046] in the formula The three-level progressive coupling perturbation strength is dimensionless, with dimensions of . The value is obtained by the three-level progressive coupling disturbance strength formula. It is the core parameter reflecting the comprehensive coupling strength of multiple disturbances. The larger the value, the more significant the disturbance effect, and the greater the required observer gain adjustment range. The relative deviation of the raw water quality monitoring signal is dimensionless, and its dimension is [missing information]. The value is calculated by the processor by comparing the original output electrical signal of the water quality sensor with the preset standard water quality electrical signal. It is used to accurately characterize the degree of deviation of the original monitoring signal. The larger the deviation value, the higher the gain of the observer needs to be increased.

[0047] Let be the system disturbance rejection response time constant, in seconds, with dimensions . The time constant is an inherent hardware parameter of the water quality monitoring equipment, which is pre-stored in the processor and used to accurately characterize the equipment's response speed to disturbance signals. The larger the value of the time constant, the smoother the equipment's response characteristics, and the slower the adjustment rate of the observer gain needs to be matched. The ADRC control sampling period is expressed in seconds, with the dimension being... It is preset according to the real-time requirements of water quality monitoring, and is used to accurately characterize the update frequency of control commands. The smaller the sampling period value, the higher the accuracy of gain adjustment. In fractional operations, it directly affects the overall calculation result as the denominator. The ADRC observer has a base fixed gain, which is dimensionless and has dimensions of . Based on the basic operational requirements of ADRC control, it is pre-calibrated to provide a stable reference value for gain adjustment and avoid numerical anomalies during dynamic calculation.

[0048] The gain of the ADRC extended state observer after adaptive correction is dimensionless and has the dimensionless property of... The final result of the formula is directly used in the subsequent signal calibration process to achieve dynamic adaptive adjustment of the observer gain. The logical derivation of the formula is as follows: First, the core parameters affecting the observer gain are determined through control theory analysis. A positive correlation is established between disturbance intensity, signal deviation, response time constant, and gain, and a negative correlation is established between sampling period and gain. The four types of parameters are integrated into a unified dynamic gain change value through fractional operations. Then, the gain is arithmetically superimposed with the basic fixed gain to obtain the observer gain adapted to the current monitoring conditions. The calculation logic fully conforms to the basic principles of ADRC control, and the parameter definitions are clear and explicit. Technical personnel in the relevant field can directly complete the gain calculation operation according to the formula to ensure the stable operation of the observer.

[0049] In the industrial wastewater quality monitoring embodiment, the three-level progressive coupling disturbance intensity is set to 0.213, the relative deviation of the original water quality monitoring signal is set to 0.18, the system anti-disturbance response time constant is set to 0.02 seconds, the ADRC control sampling period is set to 0.005 seconds, and the basic fixed gain of the ADRC observer is set to 15. After adaptive correction, the ADRC extended state observer gain is calculated to be 16.53. This gain value can accurately match the current disturbance state and improve the disturbance observation accuracy. In the seawater quality monitoring embodiment, the three-level progressive coupling disturbance intensity is set to 0.527, the relative deviation of the original water quality monitoring signal is set to 0.32, the system anti-disturbance response time constant is set to 0.03 seconds, the ADRC control sampling period is set to 0.005 seconds, and the basic fixed gain of the ADRC observer is set to 15. After adaptive correction, the ADRC extended state observer gain is calculated to be 20.12, which is suitable for high-disturbance seawater monitoring scenarios.

[0050] When the processor performs signal calibration, it uses the water quality monitoring closed-loop calibration control formula to complete all calculations. The formula is: .

[0051] This formula is designed based on the influence of disturbance coupling on monitoring signals. The greater the disturbance coupling strength, the higher the observer gain, and the greater the calibration amplitude required for the original signal. The formula adjusts the signal calibration amplitude through a gain proportionality coefficient and compensates for the sensor's own system error by combining an electrical conversion calibration coefficient. Ultimately, it outputs a precise and stable water quality monitoring electrical signal, completely eliminating signal distortion caused by disturbance coupling and sensor errors. In the formula... This is the raw output electrical signal of the water quality sensor, measured in volts, with dimensions of _____. It is the raw analog electrical signal directly collected by the water quality sensor, which includes disturbance coupling interference and the sensor's own system error. It has not undergone any calibration processing, and the signal amplitude ranges from zero to 5 volts. The gain of the ADRC extended state observer after adaptive correction is dimensionless and has the dimensionless property of... It is calculated by the ADRC extended state observer gain adaptive formula and is used to precisely adjust the amplitude of signal calibration. The larger the gain value, the larger the calibration amplitude.

[0052] The ADRC observer gain limit threshold is dimensionless and has the dimensionless value of . The threshold value is preset and stored in the processor to limit the maximum value of the observer gain, so as to avoid excessive gain leading to overcalibration and secondary distortion of the monitoring signal. The threshold value is uniformly set to 25.

[0053] The calibration coefficient for electrical conversion of water quality signals is dimensionless and has a dimension of . The calibration is completed in advance based on the factory calibration data of the water quality sensor to compensate for the linear and nonlinear errors of the sensor and ensure the accuracy of signal conversion. The coefficient value ranges from 0.95 to 1.05. The output electrical signal is for water quality monitoring, with the unit being volts and the dimension being... This is the final result of the formula, the final signal after perturbation calibration and error compensation, which can accurately reflect the water quality parameters of the water body. The logical derivation of the formula is as follows: first, the influence of perturbation on the original electrical signal is analyzed experimentally; then, the correlation between the calibration proportional coefficient and the observer gain is established; and finally, the result is obtained through... The dynamic calibration proportional coefficient is calculated, which changes in real time with the observer gain. The higher the disturbance coupling strength, the larger the gain value, the smaller the calibration proportional coefficient, and the greater the calibration amplitude of the original signal. Then, the calibration proportional coefficient is multiplied with the original electrical signal to eliminate the signal error caused by disturbance coupling. Finally, it is multiplied by the electrical conversion calibration coefficient to compensate for the sensor's own system error, resulting in an accurate and stable monitoring signal. The calculation process fully conforms to the basic principles of electrical signal calibration, with unified and standardized parameter dimensions, and the dimensions on both sides of the equal sign are completely consistent. Technical personnel in the relevant field can directly complete the signal calibration operation according to the formula to ensure the accuracy of the output signal.

[0054] In the drinking water quality monitoring embodiment, the original output electrical signal of the water quality sensor is 2.35 volts, the ADRC extended state observer gain after adaptive correction is 16.53, the ADRC observer gain limit threshold is 25, and the water quality signal electrical conversion calibration coefficient is 1.02. The water quality monitoring output electrical signal is calculated to be 2.21 volts. This signal accurately eliminates the influence of disturbances and errors, and truly reflects the drinking water quality parameters.

[0055] In the seawater quality monitoring embodiment, the original output electrical signal of the water quality sensor is 3.82 volts, the ADRC extended state observer gain after adaptive correction is 20.12, the ADRC observer gain limit threshold is 25, and the water quality signal electrical conversion calibration coefficient is 1.03. The water quality monitoring output electrical signal is calculated to be 3.14 volts, which accurately matches the monitoring requirements of seawater quality.

[0056] After the processor outputs the water quality monitoring output electrical signal, it collects the deviation data between the water quality monitoring output electrical signal and the preset standard water quality electrical signal through the standard water quality signal comparison unit configured in the water quality monitoring equipment, and transmits the deviation data to the processor's progressive coupling solution port.

[0057] The standard water quality signal comparison unit internally stores preset standard water quality electrical signals calibrated by national metrology institutions. These standard signals are stored according to water quality parameter concentration levels, ensuring precise and error-free values. They serve as authoritative reference standards for signal calibration. The unit uses a 16-bit high-precision DAC chip to generate the standard signals, guaranteeing signal accuracy. The standard water quality signal comparison unit receives the water quality monitoring output electrical signal from the processor in real time and performs a high-precision numerical comparison with the preset standard signal. The amplitude and phase deviations are obtained through subtraction. The deviation data is stored in a 16-bit fixed-point format, comprehensively reflecting the degree of deviation of the calibrated signal.

[0058] The deviation data is transmitted to the processor's progressive coupling calculation port via a dedicated parallel data transmission path. The transmission process employs synchronous transmission at a rate of 1 Mbps, ensuring data transmission speed and stability without data loss or transmission delay. This deviation data serves as a real-time correction parameter for the progressive coupling calculation operation, allowing subsequent coupling calculations to dynamically adjust based on the previous calibration deviation. This forms the core of closed-loop control, preventing the continuous accumulation of calibration errors and ensuring the long-term stability of the monitoring signal. The computational speed of the standard water quality signal comparison unit is perfectly matched to the processor's control timing, ensuring zero delay in signal comparison and deviation calculation, guaranteeing timely transmission of deviation data to the processor for subsequent calculations.

[0059] After receiving the deviation data, the processor re-triggers the progressive coupling calculation, gain calculation, and signal calibration operations according to the preset control timing stored in the processor, until the deviation data is less than the preset deviation threshold, and then stops the closed-loop operation process.

[0060] The control timing sequence is pre-stored in the processor's timing control module. The timing logic is strictly set according to the order of progressive coupling decomposition, gain decomposition, and signal calibration. The execution time and interval of each operation are fixed. The progressive coupling decomposition takes 1 millisecond, the gain decomposition takes 0.5 milliseconds, and the signal calibration takes 0.5 milliseconds, ensuring the orderliness and stability of the closed-loop operation. After the processor receives the deviation data, the timing control module immediately triggers the progressive coupling decomposition operation, incorporating the deviation data as a real-time correction parameter into the coupling decomposition process. This dynamically adjusts the calculation results of the three-level progressive coupling disturbance strength, and then sequentially executes the gain decomposition and signal calibration operations to form a complete closed-loop adjustment process. The deviation threshold is preset and stored in the processor. The threshold value is determined according to the accuracy requirements of water quality monitoring. The threshold for drinking water monitoring is set to 0.01 volts, and the threshold for industrial wastewater monitoring is set to 0.05 volts. This is the core standard for judging the calibration effect. When the deviation data is less than the deviation threshold, it means that the output electrical signal of water quality monitoring has reached the preset accuracy requirement and no further adjustment is needed. The processor immediately stops the closed-loop operation and waits for the next round of disturbance parameter acquisition.

[0061] When the deviation data is greater than or equal to the deviation threshold, the processor continues to perform closed-loop adjustment operations until the deviation data meets the threshold requirements. There is no fixed limit to the number of loops in the closed-loop operation; the value of the deviation data is used as the termination criterion to ensure that the calibration effect meets the preset standard.

[0062] Furthermore, the water flow shear force sensing unit, the electrode interface impedance detection unit, and the DC power supply ripple sampling unit all transmit the collected data to the processor through the SPI communication interface. The communication rate of the SPI communication interface is 1Mbps. The processor parses the three types of collected data according to the sampling timing sequence preset and stored in the processor. After the parsing is completed, the progressive coupling solution operation is started immediately.

[0063] The SPI communication interface is a synchronous serial communication interface, employing a master-slave mode. The processor acts as the master, and the three types of acquisition units act as slaves. It features high transmission speed, strong anti-interference capability, and a simple wiring structure, requiring only three signal lines—clock, data, and chip select—to complete communication. This perfectly adapts to the underwater hardware layout requirements of water quality monitoring equipment. All three types of acquisition units establish a stable data connection with the processor through the SPI communication interface. The pin definitions of the communication interface are fixed, eliminating the possibility of wiring errors or signal crosstalk. The communication rate of the SPI communication interface is set to 1 Mbps, which meets the real-time transmission requirements of the three types of disturbance coefficients. The data transmission delay is less than 1 millisecond, ensuring that the acquired data is transmitted to the processor in a timely manner.

[0064] The sampling timing sequence is pre-stored in the processor's data parsing module. The timing logic is set according to the order of the water flow shear force sensing unit, the electrode interface impedance detection unit, and the DC power supply ripple sampling unit. The processor parses the three types of collected data sequentially, using a frame-by-frame parsing method. Each frame of data is 16 bits long, ensuring the accuracy of data parsing and preventing data confusion or parsing errors. After completing the parsing of the three types of data, the processor immediately starts the progressive coupling calculation operation without waiting for additional control commands, effectively reducing computational latency, ensuring the real-time performance of disturbance rejection control, and allowing control commands to quickly respond to real-time changes in disturbance states.

[0065] The processor has a built-in data verification module that performs parity checks on the collected water turbulence shear characteristic coefficient, electrode biofilm impedance drift coefficient, and power supply ripple noise relative coefficient. The verification rule adopts an even parity method to remove abnormal data that occurs during the acquisition process. The criterion for judging abnormal data is that the value exceeds the preset normal range. After removal, the average value of two adjacent valid data is used to replace it to ensure that all disturbance data involved in the calculation are valid data.

[0066] The data verification module performs verification operations synchronously with data acquisition operations, without consuming additional processor processing time. Verification results are stored in the processor's temporary registers in real time, providing a reliable data foundation for progressive coupling decomposition. The introduction of verification operations further improves the accuracy of perturbation data, preventing deviations in coupling decomposition results caused by abnormal data. This ensures that the values ​​of the three-level progressive coupling perturbation strength more closely match the actual perturbation state, guaranteeing the accuracy of subsequent gain adjustments and signal calibration.

[0067] The processor performs a limiting operation on the ADRC extended state observer gain after adaptive correction. The limiting range is from zero to the ADRC observer gain limit threshold. When the gain value exceeds the upper limit, it is directly assigned the ADRC observer gain limit threshold. When it is below the lower limit, it is directly assigned zero. The limiting operation is performed immediately after the gain calculation is completed to avoid abnormal gain values ​​that lead to over- or under-calibration of the signal.

[0068] The limiting logic is executed directly by the processor hardware circuit, which has a fast response speed and no computational delay, ensuring that the gain value entering the signal calibration stage is always within a reasonable range, thus guaranteeing the stability of the output electrical signal of water quality monitoring.

[0069] Throughout the implementation of this technical solution, all hardware components are standard configurations for water quality monitoring equipment. All computational steps adhere to fixed physical logic and mathematical formulas. Parameter definitions, dimensional annotations, and computational rules are complete and clear, without any ambiguity or vague descriptions. Technical personnel in the relevant field can fully reproduce the solution by following the implementation steps and parameter logic without any creative effort. This effectively solves the problem of water quality monitoring signal distortion caused by multi-disturbance coupling, ensuring the stability and accuracy of water quality monitoring data. The computational logic of all formulas in the technical solution has corresponding physical meaning and control theory basis. The relationships between parameters are clear. The progressive coupling solution, gain adaptive solution, and signal calibration are linked step-by-step to form a complete anti-disturbance control closed loop. Every technical feature is necessary to achieve the core control effect; there are no redundant or unnecessary features. The overall solution fully meets the actual operational needs of water quality monitoring equipment in various scenarios, including surface water, drinking water, industrial wastewater, and seawater, and possesses stable implementation effects and wide applicability.

[0070] The technical solution has undergone practical testing in multiple scenarios and can stably achieve multi-disturbance coupling anti-disturbance control under various water quality environments. The distortion rate of the monitoring signal is significantly reduced, and the long-term operational stability meets the usage standards of industrial-grade water quality monitoring equipment, providing reliable control solution support for the accurate measurement of water quality monitoring equipment.

[0071] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An ADRC adaptive disturbance rejection control method for water quality monitoring equipment, characterized in that, include: S1: The processor collects the water turbulence shear characteristic coefficient, electrode biofilm impedance drift coefficient, and power supply ripple noise relative coefficient; S2: The processor introduces turbulent impedance coupling proportional coefficient and impedance ripple coupling proportional coefficient to perform progressive coupling solution on the three types of coefficients collected, and obtains the three-level progressive coupling disturbance intensity. S3: The processor collects the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period. It then combines the three-level progressive coupling disturbance strength to calculate the gain and obtain the ADRC extended state observer gain after adaptive correction. S4: The processor acquires the original output electrical signal of the water quality sensor, the ADRC observer gain limit threshold, and the electrical conversion calibration coefficient of the water quality signal. It then performs signal calibration by combining the ADRC extended state observer gain after adaptive correction and outputs the water quality monitoring output electrical signal.

2. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 1, characterized in that, The processor collects the water turbulence shear characteristic coefficients using the water flow shear force sensing unit configured in the water quality monitoring equipment. The processor collects the electrode biofilm impedance drift coefficients using the electrode interface impedance detection unit configured in the water quality monitoring equipment. The processor collects the power supply ripple noise relative coefficients using the DC power supply ripple sampling unit configured in the water quality monitoring equipment.

3. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 1, characterized in that, The turbulence impedance coupling proportionality coefficient and the impedance ripple coupling proportionality coefficient are fixed coefficients that are pre-stored in the processor's memory unit. When the processor performs progressive coupling decomposition, it directly calls these two types of fixed coefficients to participate in the calculation.

4. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 1, characterized in that, When the processor performs gain calculation, it introduces the ADRC observer basic fixed gain pre-stored in the processor, and superimposes the ADRC observer basic fixed gain with the calculation results of the three-level progressive coupling disturbance strength, the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant and the ADRC control sampling period to obtain the ADRC extended state observer gain after adaptive correction.

5. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 4, characterized in that, When the processor performs progressive coupling calculations, it uses specific operational logic to combine the turbulence impedance coupling ratio coefficient and the impedance ripple coupling ratio coefficient to perform coupling calculations on the water turbulence shear characteristic coefficient, the electrode biofilm impedance drift coefficient, and the power supply ripple noise relative coefficient, thereby obtaining the three-level progressive coupling disturbance intensity.

6. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 5, characterized in that, When the processor performs gain calculation, it uses specific operational logic to combine the three-level progressive coupling disturbance strength, the relative deviation of the original water quality monitoring signal, the system anti-disturbance response time constant, and the ADRC control sampling period, and performs superposition calculation with the ADRC observer's basic fixed gain to obtain the ADRC extended state observer gain after adaptive correction.

7. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 6, characterized in that, When the processor performs signal calibration, it uses specific operational logic to combine the adaptively corrected ADRC extended state observer gain, the original output electrical signal of the water quality sensor, the ADRC observer gain limit threshold, and the electrical conversion calibration coefficient of the water quality signal to calibrate the original electrical signal and output the water quality monitoring output electrical signal.

8. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 1, characterized in that, After the processor outputs the water quality monitoring output electrical signal, the standard water quality signal comparison unit configured in the water quality monitoring equipment collects the deviation data between the water quality monitoring output electrical signal and the preset standard water quality electrical signal, and transmits the deviation data to the processor's progressive coupling calculation port.

9. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 8, characterized in that, After receiving the deviation data, the processor re-triggers the progressive coupling calculation, gain calculation, and signal calibration operations according to the pre-set control timing stored in the processor, until the deviation data is less than the pre-set deviation threshold, and then stops the closed-loop operation process.

10. The ADRC adaptive disturbance rejection control method for water quality monitoring equipment according to claim 2, characterized in that, The water flow shear force sensing unit, the electrode interface impedance detection unit, and the DC power supply ripple sampling unit all transmit the collected data to the processor through the SPI communication interface. The communication rate of the SPI communication interface is 1Mbps. The processor parses the three types of collected data according to the sampling timing sequence preset and stored in the processor. After parsing, the progressive coupling solution operation is started immediately.

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