Mobile water quality treatment system based on precise feeding and aeration system

Abstract

The invention discloses a mobile water quality treatment system based on a precise feeding and aeration system. The mobile water quality treatment system comprises an autonomous underwater robot platform, a multi-parameter water quality sensor, a precise feeding unit, a precise aeration unit and a cooperative control unit. According to the system, the AUV carries integrated navigation to realize precise hovering positioning, an integrated sensor is used for collecting water quality data in real time, and an edge computing node is used for carrying out data fusion and abnormality diagnosis. When ammonia nitrogen is detected to exceed the standard or dissolved oxygen is not enough, the system automatically controls the AUV to sail to an abnormal area, the precise feeding or aeration unit is started respectively, and the feeding flow and the aeration intensity are adjusted based on a dynamic algorithm. The system supports automatic switching between a wide-area cruise mode and a fixed-point operation mode, has the functions of effect evaluation and intermittent monitoring, forms closed-loop control of'monitoring-governance-feedback ', and realizes accurate, efficient and adaptive governance of a wide-range water area.

Description

Technical Field

[0001] This invention belongs to the field of environmental protection and water ecological governance technology, specifically relating to a mobile water quality treatment system based on a precise dosing and aeration system. Background Technology

[0002] With increasing emphasis on environmental protection and ecological restoration, the demand for the management and maintenance of rivers, lakes, reservoirs, and nearshore waters has grown dramatically. Against this backdrop, water aeration and the application of chemicals / bacterial agents have become key means to improve water quality, eliminate black and odorous water, and regulate the ecosystem. In recent years, with the development of robotics technology, there have been some attempts to use small unmanned surface vessels (USVs) for surface monitoring or spraying operations. However, most of these solutions suffer from limited functionality and poor coordination. Therefore, there is an urgent need for a new type of technological equipment that integrates an intelligent mobile platform, precise sensing and detection, and efficient underwater execution units, capable of autonomously or remotely reaching any designated underwater location and performing precise, efficient, and collaborative operations based on commands or real-time water quality data. Summary of the Invention

[0003] [Technical Issues] The technical problem to be solved by this invention is how to overcome the problems of single function, poor coordination, inability to achieve precise underwater positioning and on-demand operation in existing water treatment technologies, and how to resolve the contradiction between large-scale water area monitoring and precise fixed-point treatment.

[0004] [Technical Solution] To address the above problems, this invention provides a mobile water treatment system based on a precise dosing and aeration system.

[0005] In a first aspect, the present invention provides a mobile water treatment system based on a precise dosing and aeration system, comprising: An autonomous underwater robot platform equipped with a combined navigation system; A multi-parameter water quality sensor is integrated into the autonomous underwater robot platform for real-time collection of water quality data. The precision dosing unit, integrated into the autonomous underwater robot platform, includes a bacterial agent storage tank, a metering pump, and a dosing pipeline; The precision aeration unit is integrated on the autonomous underwater robot platform and includes a micro-nano bubble generator and an aeration disc. The collaborative control unit is set on the autonomous underwater robot platform and is communicatively connected to the multi-parameter water quality sensor, the precision dosing unit and the precision aeration unit, respectively. The collaborative control unit is configured to execute the following collaborative control logic: The system receives the water quality data and performs spatiotemporal alignment and fusion processing on the multi-source water quality data to generate a high-confidence water quality state estimate. It supports automatic switching between wide-area patrol monitoring mode and fixed-point precision operation mode, wherein the wide-area patrol monitoring mode is used for patrol monitoring of large water areas, and the fixed-point precision operation mode is used for precise treatment of areas with abnormal water quality. When the estimated water quality indicates that the ammonia nitrogen concentration exceeds the first preset threshold, the autonomous underwater robot platform is controlled to switch from the wide-area cruise monitoring mode to the fixed-point precision operation mode, sail to the water area with abnormal ammonia nitrogen concentration, and then the precision dosing unit is activated to perform the dosing operation. When the estimated water quality indicates that the dissolved oxygen concentration is lower than the second preset threshold, the autonomous underwater robot platform is controlled to switch from the wide-area cruise monitoring mode to the fixed-point precision operation mode, sail to the water area with abnormal dissolved oxygen concentration, and then start the precision aeration unit to perform aeration operation. The system dynamically adjusts operating parameters based on real-time water quality data and enters intermittent monitoring mode once the water quality meets the standards.

[0006] Optionally, the spatiotemporal alignment and fusion processing includes: unifying the timestamps of multi-source water quality data to ensure data temporal consistency; associating the data with spatial coordinates and mapping them to the current position of the underwater robot; and eliminating noise and generating high-confidence water quality state estimates through an improved Kalman filter algorithm.

[0007] Optionally, the improved Kalman filter algorithm includes a noise covariance adaptive mechanism and an anomaly detection and fault tolerance mechanism; The noise covariance adaptive mechanism includes process noise covariance and observation noise covariance; the process noise covariance is dynamically adjusted based on the residual sequence. The calculation method is as follows:

[0008] Observation noise covariance dynamically adjusted based on sensor confidence The calculation method is as follows:

[0009] in, It is a residual sequence; and Forgetting factor; The observation matrix; for Kalman gain at time step; for The prediction error covariance matrix at time 1; The anomaly detection and fault tolerance mechanism is as follows: when the residual norm... An abnormal alarm is triggered when the threshold is exceeded, and the weighting coefficient of the abnormal sensor is adaptively reduced.

[0010] Optionally, the collaborative control unit is further configured to: When controlling the precise dosing unit, the dosing flow rate of the metering pump is adjusted proportionally based on the difference between the measured value of ammonia nitrogen concentration and the first preset threshold. The specific calculation formula is: flow rate = K × (measured concentration - threshold), where K is a proportionality coefficient, and its value is negatively correlated with the water volume and positively correlated with the degradation efficiency of the bacterial agent used. When controlling the precision aeration unit, the aeration intensity of the micro-nano bubble generator is adjusted proportionally based on the difference between the measured value of dissolved oxygen concentration and the second preset threshold. The specific calculation formula is: aeration flow rate = V × (threshold - measured value) × α, where V is the water volume and α is the oxygen transfer coefficient. The oxygen transfer coefficient α is calculated using a pre-set empirical model based on the principle of oxygen mass transfer kinetics. The pre-set empirical model specifically includes temperature compensation using the Arrhenius temperature correction formula, salinity correction using the Weiss salinity compensation formula, and an interface update frequency algorithm based on the theory of turbulent energy dissipation. The implementation method of the interface update frequency algorithm is as follows:

[0011] in, For interface update frequency, It is an empirical constant. The turbulent energy dissipation rate, The viscosity coefficient of water for movement; The formula for calculating the Arrhenius temperature correction is as follows:

[0012] In the formula, Standard oxygen transfer coefficient, unit: ; This is the temperature correction factor; This is the real-time water temperature, in °C. The formula for calculating Weiss salinity compensation is as follows:

[0013] In the formula, S is the real-time salinity; The formula for calculating the oxygen transfer coefficient α is as follows:

[0014] in, These are system calibration coefficients used to correct for device-specific differences; The quantization function for the interface update frequency has the following form: , where the index This is the turbulence influence factor, with a value range of 0.5 to 1.0.

[0015] Optionally, the wide-area cruise monitoring mode adopts an adaptively generated bow-shaped coverage path, and the data collection frequency is once every 5 minutes; the fixed-point precision operation mode is triggered based on the result of the data fusion processing; wherein, the adaptively generated bow-shaped coverage path prioritizes patrols of areas with frequent recent pollution based on historical pollution data, and dynamically optimizes the cruise path according to the real-time sensor reading gradient; The adaptively generated bow-shaped coverage path is achieved by optimizing a cost function, which balances full coverage efficiency with monitoring intensity in key areas; the cost function is:

[0016] in, , , This refers to a weighting coefficient; the weighting coefficient can be dynamically adjusted according to the governance objectives, and in emergency situations, it increases the weight of repeated monitoring rates in key areas. .

[0017] Optionally, the collaborative control unit is further configured to: after performing the treatment operation, evaluate the effect based on the water quality data collected again, and dynamically adjust the subsequent treatment strategy; the effect evaluation takes the continuous and stable compliance of water quality parameters as the main standard; if the water quality parameters meet the standards, it enters the intermittent monitoring mode, and the monitoring frequency is once every 2 hours; The effect evaluation includes determining whether the water quality parameters meet the standards for three consecutive samplings; the dynamic adjustment of subsequent treatment strategies includes adjusting the dosage or aeration intensity, adjusting the dwell time of the autonomous underwater robot, and switching the type of bacterial agent according to changes in pollutant composition, at least one of the following.

[0018] Optionally, the collaborative control unit is further configured to: when the estimated water quality status indicates that both ammonia nitrogen concentration and dissolved oxygen concentration are excessive, adopt a coordinated control strategy of parallel preparation and sequential execution, based on the principle of aerobic degradation by microorganisms, prioritize the initiation of aeration to create a suitable aerobic environment, and then initiate the addition of microbial agents after the dissolved oxygen concentration recovers to the condition for microbial effectiveness, so as to improve the microbial degradation efficiency; wherein, the condition for microbial effectiveness is that the dissolved oxygen concentration recovers to above 3.0 mg / L.

[0019] Secondly, the present invention provides a mobile water quality precision treatment method, using the mobile water quality treatment system described above, the method comprising: Step 1: Control the autonomous underwater robot to cruise along the preset path of the wide-area cruise monitoring mode and collect water quality data in real time; Step 2: Perform spatiotemporal alignment and fusion processing on the collected water quality data to generate a high-confidence water quality state estimate, and compare it with a preset threshold; when it is determined that the ammonia nitrogen concentration exceeds the first preset threshold or the dissolved oxygen concentration is lower than the second preset threshold, control the autonomous underwater robot to switch from the wide-area cruise monitoring mode to the fixed-point precision operation mode, and navigate to the water area with abnormal ammonia nitrogen concentration or dissolved oxygen concentration. Step 3: Depending on the type of water quality anomaly, control the precision dosing unit to perform a dosing operation or control the precision aeration unit to perform an aeration operation; Step 4: Collect water quality parameters of the treated area again to evaluate the effect. If the standard is not met, repeat steps 2 to 3. If the water quality parameters meet the standard, control the system to enter the intermittent monitoring mode and conduct periodic monitoring. Restart the treatment operation when the water quality index rebounds to form a closed-loop control. In step 3, the operation is controlled according to the type of water quality abnormality, including: when triggered by excessive ammonia nitrogen concentration, the precise dosing unit is controlled to perform dosing operation; when triggered by insufficient dissolved oxygen concentration, the precise aeration unit is controlled to perform aeration operation.

[0020] [Beneficial Effects] The system of this invention achieves precise hovering positioning technology through a combination of inertial measurement unit, Doppler log, and ultra-short baseline underwater acoustic positioning system. The positioning accuracy error is no more than 0.3 meters, enabling the accurate deployment of the execution unit above or inside the core pollution area. Simultaneously, the integrated multiple sensors, such as pH, dissolved oxygen, and ammonia nitrogen, provide the system with comprehensive real-time water quality sensing capabilities, providing a complete data foundation for accurate decision-making.

[0021] The system of this invention, by setting up edge computing nodes locally on an autonomous underwater robot platform and performing spatiotemporal alignment and fusion processing, can generate high-confidence water quality state estimates in complex underwater environments. It overcomes the limitations of single-sensor data and uses an airborne data fusion algorithm to perform spatiotemporal alignment and fusion processing on multi-source data, providing a reliable and accurate decision-making basis for subsequent threshold judgment and precise control, thereby improving the robustness and intelligence level of the entire system.

[0022] The system of this invention supports automatic switching between wide-area patrol monitoring mode and fixed-point precision operation mode. This realizes intelligent conversion between general survey mode and detailed survey mode, enabling the system to efficiently complete large-area scanning and immediately focus on abnormal areas and conduct high-frequency detailed surveys every 30 seconds, thus taking into account both monitoring efficiency and precise governance needs.

[0023] The system of this invention evaluates the effectiveness of treatment based on re-collected water quality data after treatment and dynamically adjusts subsequent treatment strategies, forming an intelligent feedback and regulation mechanism. When the water quality meets the standards, the system automatically enters an intermittent monitoring mode and re-triggers operation when the water quality rebounds, forming a complete "monitoring-regulation-dormant" energy-saving cycle. This achieves long-term, autonomous treatment management and significantly reduces manual maintenance costs.

[0024] When faced with combined pollution of excessive ammonia nitrogen and insufficient dissolved oxygen, the system of this invention creates an optimal environment for microbial degradation by coordinating and prioritizing aeration operations. Specifically, through unified scheduling by the collaborative control unit, aeration and dosage are efficiently coordinated, thereby significantly improving treatment efficiency. Prioritized aeration provides the necessary dissolved oxygen for subsequent microbial agents, optimizing the overall efficiency of the degradation process.

[0025] In summary, the system of this invention integrates underwater autonomous and remotely operated vehicle technology, precision sensing and detection technology, and fluid dosing and mixing technology. It enables precise positioning of specific target points or areas in a large area of ​​water, on-demand dosing of substances such as medicines, microbial preparations, and feed, and efficient oxygenation and aeration. It can be widely used in water quality restoration, ecological regulation, and intelligent oxygenation operations in lakes, reservoirs, rivers, marine ranches, aquaculture ponds, and other scenarios. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the system structure provided by the present invention.

[0028] Figure 2 This is a schematic diagram of the governance process provided by the present invention.

[0029] Figure 3 The flowchart for the precision dosing unit monitoring provided by this invention.

[0030] Figure 4 The flowchart of the operation of the precision aeration unit provided by the present invention.

[0031] Figure 5 This is a comparison chart of water quality parameters before and after the application of this system in a certain river, provided by the present invention. Detailed Implementation

[0032] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1 This embodiment provides a mobile water treatment system based on a precise dosing and aeration system, applied to a polluted section of the Y River in XX City (1000m long, 15m average width, 2m average depth; the main pollution problems are ammonia nitrogen exceeding the standard by 5.8mg / L and dissolved oxygen insufficient by only 2.2mg / L). See [link / reference]. Figure 1 The system includes the following components: The autonomous underwater vehicle (AUV) platform utilizes the BlueROV2 Heavy Configuration medium-sized AUV platform, equipped with six T200 thrusters, achieving a maximum speed of 3.0 knots and an operating depth of 20 meters. The platform features a streamlined carbon fiber frame and is equipped with a combined navigation system. This system includes a KVH1750 IMU inertial measurement unit, a Teledyne RDI Explorer DVL Doppler log, and a LinkQuest TrackLink5000USBL ultra-short baseline positioning system, achieving a positioning accuracy error of no more than 0.3 meters and enabling precise hovering positioning above target points. Multi-parameter water quality sensors are integrated into the modular sensor compartment at the front end of the AUV, including: a Hach HQ40d pH sensor with a measurement range of 0-14 and an accuracy of ±0.01; a Thermo Fisher Scientific OrionStar A329 dissolved oxygen sensor with a measurement range of 0-50 mg / L and an accuracy of ±0.1 mg / L; a Greencare G700 ammonia nitrogen sensor with a measurement range of 0-100 mg / L and an accuracy of ±0.5%; a HACH2100Q turbidity sensor with a measurement range of 0-1000 NTU; and a Lubrizol LB-COD-1 COD sensor with a measurement range of 0-200 mg / L. All sensors are equipped with anti-biofouling or automatic cleaning devices.

[0034] The precision dosing unit, integrated into the AUV, includes a 20L polyethylene microbial agent storage tank with a diameter of 0.4m and a height of 0.6m, and a built-in micro-stirrer to prevent sedimentation; a Prominent Beta / 4 precision metering pump with a flow rate range of 0.1-5.0L / min and an accuracy of ±1%; and a vector injection dosing pipeline.

[0035] The precision aeration unit, integrated into the AUV, includes a Venturi self-priming micro-nano bubble generator and a microporous aeration disc. The Venturi self-priming micro-nano bubble generator uses the forward water flow of the AUV to automatically draw in air and generate micro-nano bubbles with a diameter of <50μm. The oxygen transfer efficiency SOTE≥25% and power consumption <150W.

[0036] The collaborative control unit, located on the AUV, is based on the NVIDIA Jetson AGXXavier edge computing node and equipped with the Advantech ADAM-4000 series data acquisition module. It transmits data through dual channels via the USR-G781 series 4G / 5G module and the Evologics S2CR series underwater acoustic communication module, and communicates with the multi-parameter water quality sensor, the precision dosing unit and the precision aeration unit respectively.

[0037] In a preferred embodiment of the present invention, the collaborative control unit adopts a hierarchical hybrid communication architecture: (1) A 4-20mA analog signal is used between the metering pump of the precision dosing unit and the metering pump to achieve stepless flow regulation, so as to ensure the accuracy and real-time performance of the dosing control; (2) The operating parameters are set using the RS-485 / MODBUS digital communication protocol between the micro-nano bubble generator and the precision aeration unit, and the start and stop are controlled by digital I / O signals to achieve a balance between complex parameter settings and fast response; (3) A dual-channel transmission system consisting of a USR-G781 series 4G / 5G module and an Evologics S2CR series underwater acoustic communication module is used to achieve the combination of remote data interaction and reliable underwater communication.

[0038] This hybrid signal architecture combines the advantages of continuous adjustment of analog signals, the ability to configure complex parameters of digital protocols, and the communication requirements of different transmission distances, ensuring the control accuracy and communication reliability of the system in complex underwater environments.

[0039] See Figure 2 , Figure 3 and Figure 4 The collaborative control unit is configured to execute the following collaborative control logic: (1) Data Acquisition and Fusion Processing: After the system starts, the autonomous underwater vehicle cruises at a speed of 1.5 knots along a "bow" shaped path, which is the wide-area cruise monitoring mode. The sensors collect full parameter data every 5 minutes. The airborne edge computing node performs spatiotemporal alignment and fusion processing on the multi-source water quality data, including unifying the timestamps of the multi-source water quality data to ensure the consistency of the data time sequence; associating the data with spatial coordinates and mapping it to the current position of the underwater robot; and eliminating noise and generating high-confidence water quality state estimates through an improved Kalman filter algorithm.

[0040] The state equation of the improved Kalman filter algorithm adopts a first-order linear dynamic model:

[0041] in, The state transition matrix is ​​set according to the sensor sampling interval. For process noise, The state variables include dissolved oxygen (DO), ammonia nitrogen (NH3-N), pH, and turbidity; the observation equations use... ,in The observation matrix is, in this case, a 4×4 identity matrix. To observe noise.

[0042] The improved Kalman filter algorithm includes a prediction phase and an update phase; The prediction phase includes state prediction and error covariance prediction: The formula for calculating state prediction is:

[0043] The calculation method for error covariance prediction is as follows:

[0044] in, It uses the result of the prediction from the previous state. It is the optimal result of the previous state. For a certain moment, the control variable is... Here is the state transition matrix. For the control matrix, for The prediction error covariance matrix at time 1. for The optimal error covariance matrix at time t. The process noise covariance matrix; The update phase includes Kalman gain calculation, state update, and covariance update: The Kalman gain is calculated as follows:

[0045] The formula for calculating state updates is:

[0046] The calculation method for covariance update is as follows:

[0047] in, for The Kalman gain at time t, with a value ranging from 0 to 1. For the observation matrix, To measure the noise covariance matrix, for The optimal state at any given time. for The measured value at time, for The optimal error covariance matrix at time t. It is an identity matrix.

[0048] The improved Kalman filter algorithm also includes a noise covariance adaptive mechanism and anomaly detection and fault tolerance mechanisms; The noise covariance adaptive mechanism includes process noise covariance and observation noise covariance: Process noise covariance based on dynamic adjustment of residual sequence The calculation method is as follows:

[0049] Observation noise covariance dynamically adjusted based on sensor confidence The calculation method is as follows:

[0050] in, It is a residual sequence. and As the forgetting factor, in this embodiment and The preferred value is 0.95; The anomaly detection and fault tolerance mechanism is as follows: when the residual norm An anomaly alarm is triggered when a preset threshold is exceeded, and the weighting coefficient of the anomaly sensor is adaptively reduced. Specifically, the preset threshold is based on the residual sequence. The historical statistical distribution is set. In a preferred embodiment, the system calculates the mean and variance of historical residual data within a sliding time window, and sets a preset threshold as the statistic corresponding to the 99% confidence interval. When the residual norm... If the threshold is exceeded, the corresponding sensor data is determined to be abnormal and an alarm is triggered.

[0051] Adaptively reducing the weight coefficients of anomalous sensors by adjusting the observation noise covariance matrix in the Kalman filter. Implementation. When the collaborative control unit determines that the data from the i-th sensor is abnormal, it will convert the observation noise covariance submatrix corresponding to that sensor. According to the formula Enlarged, among which The amplification factor is greater than 1, preferably ranging from 10 to 100. In the information fusion process of Kalman filtering, the weight of each sensor data point is the inverse of its observation noise covariance. Proportional. Therefore, increasing the anomaly sensor... This is equivalent to reducing the weight of the sensor data in subsequent data fusion steps, thereby improving the overall robustness of the system and the reliability of state estimation when some sensors fail or data is abnormal.

[0052] Through the improved Kalman filter algorithm described above, the system can achieve high-precision fusion processing of multi-source water quality data in complex underwater environments. The specific verification method is as follows: the system conducts auxiliary on-site sampling verification once a week. Using a portable high-precision water quality analyzer, samples are taken at key points along the AUV's cruise path (representative areas upstream, midstream, and downstream). Each sample is measured in triplicate, and the average value is used as a benchmark. By cross-comparing with real-time AUV data, the measurement deviation of key water quality parameters (including dissolved oxygen, ammonia nitrogen, COD, etc.) is controlled within ±5%. This verifies from a practical application perspective that the water quality state estimates generated by the algorithm have high confidence and engineering applicability, providing a reliable basis for subsequent precise governance decisions.

[0053] The bow-shaped coverage path is adaptively generated rather than a fixed path. Its generation is based on the following: determining the geographical boundaries of the river channel based on GPS or Beidou positioning and electronic fence technology, using a ox-plowing search algorithm to achieve full coverage path planning, and combining historical pollution data to optimize the monitoring path, giving priority to covering areas with heavier historical pollution, thereby improving monitoring efficiency and data representativeness.

[0054] The adaptive generation mechanism for the bow-shaped path specifically includes: 1> The system accesses the historical pollution database, identifies areas with frequent recent pollution, and assigns these areas a higher patrol priority when generating patrol routes.

[0055] 2. Real-time Dynamic Adjustment: During the cruise, the system analyzes the spatial gradient of multi-parameter water quality sensor readings in real time. When the rate of change of water quality parameters in a certain area exceeds a preset threshold, the current path will be dynamically adjusted to guide the AUV to focus on inspecting areas with significant abnormal signs.

[0056] 3> The system balances multiple objectives in path planning by optimizing a preset cost function, which is:

[0057] in, , , These are weighting coefficients that can be dynamically configured according to different governance tasks. For example, in a regular survey mode, the weights are set evenly; in an emergency response mode, the system will significantly increase the weight of the duplicate monitoring rate in key areas. To ensure continuous monitoring of the core pollution area, the concentration should be 0.6 or higher.

[0058] (2) Anomaly diagnosis and threshold comparison: The central control module compares the estimated water quality status with the preset thresholds, namely dissolved oxygen <5mg / L and ammonia nitrogen >1.5mg / L, in real time. When an anomaly is detected, a warning status is marked with a "!" symbol.

[0059] (3) Precise positioning and operation mode switching: Once an abnormality is detected, such as ammonia nitrogen concentration >1.5mg / L, the AUV will automatically switch to the fixed-point precision operation mode, and drive to the core pollution area based on the sensor reading gradient, and achieve precise hovering at a water depth of 1.0m above the target point.

[0060] (4) Collaborative Treatment Operation: When the estimated water quality indicates that the ammonia nitrogen concentration exceeds the standard, control the AUV to navigate to the water area with abnormal ammonia nitrogen concentration and activate the precision dosing unit. Calculate the dosing amount according to the formula: Flow rate = K × (Measured concentration - Threshold), where K is a proportionality coefficient based on a water volume of 30,000 cubic meters. The degradation capacity of the microbial agent was calculated and found to be negatively correlated with the water volume and positively correlated with the degradation efficiency of the microbial agent used. The calculation formula is as follows:

[0061] Where: V is the water volume, E is the bacterial agent degradation efficiency, C is the correction constant, and C is the environmental correction factor. Preferably, in this embodiment, K is taken as 0.53. The dosing operation is performed by a metering pump.

[0062] When the estimated water quality indicates insufficient dissolved oxygen, the AUV is guided to the area with abnormal dissolved oxygen concentration, and the precision aeration unit is activated. Aeration is performed using a Venturi device. The aeration intensity is calculated using the formula: Aeration flow rate = V × (threshold - measured value) × α, where V is the water volume and α is the oxygen transfer coefficient, a dynamic variable estimated based on real-time water temperature, salinity, and turbulence intensity using a pre-set empirical model. The pre-set empirical model is based on the principle of oxygen mass transfer kinetics, including temperature compensation using the Arrhenius temperature correction formula. The calculation method is as follows:

[0063] In the formula, Standard oxygen transfer coefficient, unit: ; This is the temperature correction factor; This is the real-time water temperature, in °C. Salinity correction is performed using the Weiss salinity compensation formula, calculated as follows:

[0064] In the formula, S is the real-time salinity; The calculation method, based on the interface update frequency algorithm according to the theory of turbulent energy dissipation, is as follows:

[0065] in, For interface update frequency, It is an empirical constant. The turbulent energy dissipation rate, The viscosity coefficient of water; an empirical constant. The values ​​were calibrated through laboratory water tank tests and verified using particle image velocimetry (PIV) technology. In this embodiment, the preferred value range is 0.1 to 0.2. Turbulent energy dissipation rate Based on real-time flow velocity and motion data measured by the DVL (Doppler log) and IMU (Inertial Measurement Unit) onboard the AUV, and through a pre-set turbulence model, such as... The model may be estimated based on the Reynolds-averaged Navier-Stokes equations. Specifically, the system dynamically calculates based on the velocity gradient and turbulent kinetic energy parameters. value.

[0066] kinematic viscosity coefficient Based on real-time water temperature data measured by a water temperature sensor, such as the Hach HQ40d pH sensor, the solution is calculated using empirical physical formulas, for example, by using an international standard formula: Where T is the water temperature (unit: ).

[0067] This data acquisition method ensures and The real-time performance and accuracy of the oxygen transfer coefficient are improved. The calculation accuracy is high, supporting the adaptive control of the system.

[0068] According to the principles of oxygen mass transfer kinetics, temperature, salinity, and turbulence affect the oxygen transfer coefficient. The effects usually have a product effect, and the calculation method is as follows:

[0069] in, This is the system calibration coefficient, used to correct for equipment-specific differences. It is usually determined through laboratory calibration, and the initial value can be set to 1.0. The quantization function for the interface update frequency has the following form: , where the index The value is the turbulence influence factor, which ranges from 0.5 to 1.0. In this embodiment, the preferred value is 0.75.

[0070] Taking into account the above factors, the real-time oxygen transfer coefficient α value was calculated.

[0071] The collaborative treatment employs an intelligent strategy of parallel preparation and sequential execution, based on the principle of aerobic microbial degradation: nitrifying bacteria, as strict aerobic bacteria, cannot effectively degrade substances in environments with insufficient dissolved oxygen. When both ammonia nitrogen and dissolved oxygen are detected as abnormal, the system immediately activates the aeration unit in parallel and prepares for the addition of microbial agents. By monitoring the dissolved oxygen concentration in real time, the microbial agent addition operation is initiated sequentially only after the dissolved oxygen concentration rises above the critical value of 3.0 mg / L for microbial effectiveness. This control strategy, based on the principle of microbial degradation kinetics, ensures the scientific rigor and efficiency of the treatment operation.

[0072] (5) Dynamic adjustment and effect evaluation: After the treatment operation is performed, the system collects water quality data every 15-30 minutes to evaluate the effect. The main standard for effect evaluation is that the water quality parameters continue to meet the standards. If the dissolved oxygen rises to 4.5 mg / L, the aeration power will be automatically reduced; if it rises to 5.0 mg / L, a shutdown signal will be triggered. If the ammonia nitrogen concentration still exceeds the standard after addition, the addition operation will be repeated.

[0073] (6) Intermittent monitoring mode: After three consecutive water quality parameters meet the standards, the system enters intermittent monitoring mode, monitoring once every 2 hours. If the water quality index rebounds, such as dissolved oxygen <4.8mg / L, the treatment operation is restarted according to the dynamic adjustment of the subsequent treatment strategy, forming a closed-loop control. The dynamic adjustment of the subsequent treatment strategy is a multi-dimensional intelligent adjustment, including but not limited to: adjusting the dosage or aeration intensity based on the pollutant degradation rate, adjusting the residence time of AUV in the polluted area according to the treatment effect, and automatically switching the type of bacterial agent according to the real-time monitoring of pollutant composition changes, so as to achieve precise targeted treatment.

[0074] Changes in pollutant composition are detected through online spectral analysis or laboratory sampling. The data is transmitted in real time to the collaborative control unit for calculation, and multi-dimensional judgment indicators are used, including: 1> Changes in pollutant ratios: including changes in the NH3-N / TN ratio greater than 15%, the BOD5 / COD ratio decreasing from greater than 0.3 to less than 0.2, and the TN / TP ratio exceeding the normal range of 20-30.

[0075] 2> Emergence of new pollutants: Characteristic industrial pollutants such as benzene series compounds and phenols were detected by gas chromatography-mass spectrometry (GC-MS).

[0076] 3> Changes in biotoxicity: The relative luminescence is less than 80% or the Beck biometric index (BDI) is less than 6, as shown by the luminescent bacteria toxicity test.

[0077] 4. Spatial distribution characteristics: The pollution plume distribution varies, with a displacement greater than 10 meters or vertical stratification, and the concentration difference between the surface and bottom layers is greater than 25%. The decision-making logic and priority rules for switching microbial agents are as follows: A three-tiered decision-making mechanism is adopted: 1> Primary decision-making based on pollutant ratios: Selecting appropriate microbial agents, such as compound microbial agents, nitrifying microbial agents, and polyphosphate-accumulating microbial agents, based on thresholds such as BOD5 / COD, NH3-N / TN, and TP concentration. 2> Intermediate decision-making based on new pollutant types: Activating specific degrading bacteria based on detected specific pollutants, such as benzene series compounds, phenols, and petroleum hydrocarbons. 3> Advanced decision-making based on biotoxicity: Prioritizing the use of biological detoxification agents or emergency microbial agents based on the intensity of biotoxicity.

[0078] The priority rule is as follows: the microbial agent switching shall be carried out in the order of "toxic emergency response > specific degradation of new pollutants > optimization of the proportion of major pollutants > nutrient balance".

[0079] To verify the accuracy of real-time monitoring data, the system conducts auxiliary on-site sampling verification once a week: using a portable high-precision water quality analyzer, samples are taken at key points along the AUV's cruise path (representative areas in the upper, middle, and lower reaches). Each sample is measured in triplicate, and the average value is used as a benchmark reference. This average value is then cross-compared with the AUV's real-time data, with the deviation controlled within ±5%. All detection data is transmitted in real-time to the central control platform via underwater acoustic communication and 4G / 5G dual-channel transmission, achieving closed-loop quality control of monitoring, verification, and feedback, significantly improving data authority and the accuracy of governance decisions.

[0080] Table 1. Key Indicator Changes

[0081] See Table 1 and Figure 5Through the practical application of this system in the Y River, significant improvements in water quality were achieved: dissolved oxygen increased from 2.2 mg / L to 5.8 mg / L, an improvement rate of 163.6%; ammonia nitrogen decreased from 5.8 mg / L to 1.2 mg / L, an improvement rate of 79.3%; COD decreased from 35 mg / L to 18 mg / L, an improvement rate of 48.6%; turbidity decreased from 28 NTU to 10 NTU, an improvement rate of 64.3%; and pH value remained stable and met standards. On the 30th day, ammonia nitrogen decreased to 1.2 mg / L (meeting standards), and dissolved oxygen stabilized at 5.8 mg / L, at which point the system entered an "intermittent monitoring - on-demand control" mode. All indicators remained stable and met standards on the 30th day, and the system entered a "monitoring - maintenance" mode, achieving long-term treatment. The treatment coverage increased from the traditional 70% to 98%, the response time was shortened from several hours to within 10 minutes, energy consumption per unit area decreased by 82%, and physical damage to the riverbed ecosystem was avoided.

[0082] The system achieves complete closed-loop control, forming an intelligent governance cycle of "monitoring-judgment-governance-feedback", which greatly reduces the need for manual intervention.

[0083] Example 2 This embodiment provides a mobile water quality precision treatment method using the system described in Embodiment 1, applied to the same river section, specifically including the following steps: Step 1: Control the autonomous underwater vehicle to cruise along a bow-shaped path at a speed of 1.5 knots, collecting water quality data in real time through multi-parameter water quality sensors at a frequency of once every 5 minutes. This stage is a wide-area cruise monitoring mode, achieving full coverage monitoring of the entire river channel.

[0084] Step 2: The collected water quality data is fused to generate a high-confidence water quality status estimate. When the ammonia nitrogen concentration is determined to exceed 1.5 mg / L or the dissolved oxygen concentration is determined to be below 5 mg / L, the AUV is guided to navigate to the water area with abnormal ammonia nitrogen or dissolved oxygen concentrations.

[0085] Specifically, during the treatment of the Y River, when the AUV cruised to the middle reaches, it detected an ammonia nitrogen concentration of 5.8 mg / L > the threshold of 1.5 mg / L. It immediately headed to the core pollution area and achieved precise hovering at a water depth of 1.2m above the target point, with a positioning accuracy error of no more than 0.3m.

[0086] Step 3: Perform appropriate treatment operations based on the type of water quality anomaly: When the ammonia nitrogen concentration exceeds the standard, the precision dosing unit is activated. The dosage is calculated according to the formula: Flow rate = K × (Measured concentration - Threshold), where K is a proportionality coefficient. Based on the water volume of 30,000 m³ and the degradation capacity of the bacterial agent, the value is set to K = 0.53. The metering pump is controlled to add nitrifying bacteria agent at a flow rate of 2.8 L / min.

[0087] When dissolved oxygen is insufficient, activate the precision aeration unit. The aeration flow rate is calculated using the formula: Aeration flow rate = V × (threshold - measured value) × Calculate the aeration intensity and control the micro-nano bubble generator to operate at an aeration rate of 60 m³ / h.

[0088] When both ammonia nitrogen exceedance and dissolved oxygen deficiency exist simultaneously, aeration should be prioritized to create favorable conditions for subsequent microbial degradation.

[0089] Step 4: During and after the treatment operation, water quality parameters of the treated area will be collected again every 30 minutes using water quality sensors to evaluate the treatment effect. If the target is not met, such as ammonia nitrogen > 1.5 mg / L or dissolved oxygen < 5 mg / L, repeat steps 2 to 3. If the target is met, the system will enter maintenance monitoring mode, and the monitoring frequency will be adjusted to once every 2 hours.

[0090] Specifically, in the treatment of the Y River, when the dissolved oxygen level rises to 4.0 mg / L in the 7th hour, the aeration power is automatically reduced to 50%, and when the dissolved oxygen level reaches 5.2 mg / L in the 12th hour, the system enters a dormant state; on the 30th day, when the ammonia nitrogen level drops to 1.2 mg / L, the system enters an intermittent monitoring-on-demand control mode.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A mobile water treatment system based on a precision dosing and aeration system, characterized in that, include: An autonomous underwater robot platform equipped with a combined navigation system; A multi-parameter water quality sensor is integrated into the autonomous underwater robot platform for real-time collection of water quality data. The precision dosing unit, integrated into the autonomous underwater robot platform, includes a bacterial agent storage tank, a metering pump, and a dosing pipeline; The precision aeration unit is integrated on the autonomous underwater robot platform and includes a micro-nano bubble generator and an aeration disc. The collaborative control unit is set on the autonomous underwater robot platform and is communicatively connected to the multi-parameter water quality sensor, the precision dosing unit and the precision aeration unit, respectively. The collaborative control unit is configured to execute the following collaborative control logic: The system receives the water quality data and performs spatiotemporal alignment and fusion processing on the multi-source water quality data to generate a high-confidence water quality state estimate. It supports automatic switching between wide-area patrol monitoring mode and fixed-point precision operation mode, wherein the wide-area patrol monitoring mode is used for patrol monitoring of large water areas, and the fixed-point precision operation mode is used for precise treatment of areas with abnormal water quality. When the estimated water quality indicates that the ammonia nitrogen concentration exceeds the first preset threshold, the autonomous underwater robot platform is controlled to switch from the wide-area cruise monitoring mode to the fixed-point precision operation mode, sail to the water area with abnormal ammonia nitrogen concentration, and then the precision dosing unit is activated to perform the dosing operation. When the estimated water quality indicates that the dissolved oxygen concentration is lower than the second preset threshold, the autonomous underwater robot platform is controlled to switch from the wide-area cruise monitoring mode to the fixed-point precision operation mode, sail to the water area with abnormal dissolved oxygen concentration, and then start the precision aeration unit to perform aeration operation. The system dynamically adjusts operating parameters based on real-time water quality data and enters intermittent monitoring mode once the water quality meets the standards.

2. The mobile water treatment system according to claim 1, characterized in that, The spatiotemporal alignment and fusion processing includes: unifying the timestamps of multi-source water quality data to ensure data temporal consistency; associating the data with spatial coordinates and mapping them to the current position of the underwater robot; and eliminating noise and generating high-confidence water quality state estimates through an improved Kalman filter algorithm.

3. The mobile water treatment system according to claim 2, characterized in that, The improved Kalman filter algorithm includes a noise covariance adaptive mechanism and an anomaly detection and fault tolerance mechanism; The noise covariance adaptive mechanism includes process noise covariance and observation noise covariance; the process noise covariance is dynamically adjusted based on the residual sequence. The calculation method is as follows: Observation noise covariance dynamically adjusted based on sensor confidence The calculation method is as follows: in, It is a residual sequence; and Forgetting factor; The observation matrix; for Kalman gain at time step; for The prediction error covariance matrix at time 1; The anomaly detection and fault tolerance mechanism is as follows: when the residual norm... An abnormal alarm is triggered when the threshold is exceeded, and the weighting coefficient of the abnormal sensor is adaptively reduced.

4. The mobile water treatment system according to claim 1, characterized in that, The collaborative control unit is further configured as follows: When controlling the precise dosing unit, the dosing flow rate of the metering pump is adjusted proportionally based on the difference between the measured value of ammonia nitrogen concentration and the first preset threshold. The specific calculation formula is: flow rate = K × (measured concentration - threshold), where K is a proportionality coefficient, and its value is negatively correlated with the water volume and positively correlated with the degradation efficiency of the bacterial agent used. When controlling the precision aeration unit, the aeration intensity of the micro-nano bubble generator is adjusted proportionally based on the difference between the measured value of dissolved oxygen concentration and the second preset threshold. The specific calculation formula is: aeration flow rate = V × (threshold - measured value) × α, where V is the water volume and α is the oxygen transfer coefficient. The oxygen transfer coefficient α is calculated using a pre-set empirical model based on the principle of oxygen mass transfer kinetics. The pre-set empirical model specifically includes temperature compensation using the Arrhenius temperature correction formula, salinity correction using the Weiss salinity compensation formula, and an interface update frequency algorithm based on the theory of turbulent energy dissipation. The implementation method of the interface update frequency algorithm is as follows: in, For interface update frequency, It is an empirical constant. The turbulent energy dissipation rate, The viscosity coefficient of water for movement; The formula for calculating the Arrhenius temperature correction is as follows: In the formula, Standard oxygen transfer coefficient, unit: ; This is the temperature correction factor; This is the real-time water temperature, in °C. The formula for calculating Weiss salinity compensation is as follows: In the formula, S is the real-time salinity; The formula for calculating the oxygen transfer coefficient α is as follows: in, These are system calibration coefficients used to correct for device-specific differences; The quantization function for the interface update frequency has the following form: , where the index This is the turbulence influence factor, with a value range of 0.5 to 1.

0.

5. The mobile water treatment system according to claim 1, characterized in that, The wide-area patrol monitoring mode adopts an adaptively generated bow-shaped coverage path, with a data collection frequency of once every 5 minutes; the fixed-point precision operation mode is triggered based on the results of the data fusion processing; wherein, the adaptively generated bow-shaped coverage path prioritizes patrols of areas with frequent recent pollution based on historical pollution data, and dynamically optimizes the patrol path according to the real-time sensor reading gradient; The adaptively generated bow-shaped coverage path is achieved by optimizing a cost function, which balances full coverage efficiency with monitoring intensity in key areas; the cost function is: in, , , This refers to a weighting coefficient; the weighting coefficient can be dynamically adjusted according to the governance objectives, and in emergency situations, it increases the weight of repeated monitoring rates in key areas. .

6. The mobile water treatment system according to claim 1, characterized in that, The collaborative control unit is also configured to: after performing the treatment operation, evaluate the effect based on the water quality data collected again, and dynamically adjust the subsequent treatment strategy; the effect evaluation is based on the continuous and stable compliance of water quality parameters as the main standard; if the water quality parameters meet the standards, it enters the intermittent monitoring mode, and the monitoring frequency is once every 2 hours; The effect evaluation includes determining whether the water quality parameters meet the standards for three consecutive samplings; the dynamic adjustment of subsequent treatment strategies includes adjusting the dosage or aeration intensity, adjusting the dwell time of the autonomous underwater robot, and switching the type of bacterial agent according to changes in pollutant composition, at least one of the following.

7. The mobile water treatment system according to claim 1, characterized in that, The collaborative control unit is further configured to: when the water quality status estimate indicates that both ammonia nitrogen concentration and dissolved oxygen concentration are insufficient, adopt a coordinated control strategy of parallel preparation and sequential execution, based on the principle of aerobic degradation by microorganisms, prioritize the initiation of aeration to create a suitable aerobic environment, and then initiate the addition of microbial agents after the dissolved oxygen concentration has recovered to the condition for microbial effectiveness, so as to improve the microbial degradation efficiency; wherein, the condition for microbial effectiveness is that the dissolved oxygen concentration recovers to above 3.0 mg / L.

8. A mobile method for precise water quality treatment, characterized in that, The method uses the mobile water treatment system as described in any one of claims 1-7, and the method includes: Step 1: Control the autonomous underwater robot to cruise along the preset path of the wide-area cruise monitoring mode and collect water quality data in real time; Step 2: Perform spatiotemporal alignment and fusion processing on the collected water quality data to generate a high-confidence water quality state estimate, and compare it with a preset threshold; when it is determined that the ammonia nitrogen concentration exceeds the first preset threshold or the dissolved oxygen concentration is lower than the second preset threshold, control the autonomous underwater robot to switch from the wide-area cruise monitoring mode to the fixed-point precision operation mode, and navigate to the water area with abnormal ammonia nitrogen concentration or dissolved oxygen concentration. Step 3: Depending on the type of water quality anomaly, control the precision dosing unit to perform a dosing operation or control the precision aeration unit to perform an aeration operation; Step 4: Collect water quality parameters of the treated area again to evaluate the effect. If the standard is not met, repeat steps 2 to 3. If the water quality parameters meet the standard, control the system to enter the intermittent monitoring mode and conduct periodic monitoring. Restart the treatment operation when the water quality index rebounds to form a closed-loop control. In step 3, the operation is controlled according to the type of water quality abnormality, including: when triggered by excessive ammonia nitrogen concentration, the precise dosing unit is controlled to perform dosing operation; when triggered by insufficient dissolved oxygen concentration, the precise aeration unit is controlled to perform aeration operation.

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