Method and system for monitoring water quality of complex water body based on vertical layered sensing

Abstract

The invention discloses a complex water body water quality monitoring method and system based on vertical layered sensing, and particularly relates to the technical field of water body water quality monitoring and intelligent sensing. The method is used for solving the technical problems that data cannot stably correspond to a specific water quality interface due to fixed point measurement in a dynamic water body and the interface evolution process is difficult to accurately analyze in an existing vertical layering monitoring method. The method comprises the following steps: synchronously acquiring water quality parameters and dynamic environment parameters of a vertical section through an intelligent sensor array, judging whether a dynamic water quality interface exists or not based on the dynamic environment parameters, and extracting water quality frontal surface characteristic parameters and background hydrological shear parameters when the dynamic water quality interface exists; the interaction mode of the two is analyzed to identify the real-time spatial position and the dominant evolution mechanism of the interface, then a corresponding tracking control instruction is generated according to the identified dominant evolution mechanism, and the sensing unit is driven to perform active tracking type vertical layered sensing on the dynamic water quality interface.

Description

Technical Field

[0001] This invention relates to the field of water quality monitoring and intelligent sensing technology, and more specifically, to a method and system for monitoring the water quality of complex water bodies based on vertical stratified sensing. Background Technology

[0002] In the field of water environment monitoring, to understand the vertical water quality structure of water bodies, existing technologies typically employ vertical stratified sensing. This involves deploying sensor arrays at different depths within the target water area, or controlling monitoring equipment to perform vertical profile measurements, thereby obtaining information on the distribution of water quality parameters with depth. This method, based on measuring data at different depths from fixed spatial locations, aims to reflect the state of the entire water layer profile through point measurements. It is a commonly used technical approach for assessing the chemical characteristics, biological activity, and vertical migration of pollutants in stratified water bodies.

[0003] However, the aforementioned existing technical solutions have drawbacks: the vertical water quality data they acquire heavily depends on the fixed spatial location of the sensor at the time of measurement. In complex water bodies significantly affected by dynamic processes such as tides, internal waves, or unsteady flows, water particles and their physicochemical properties are not static, but are continuously transported horizontally or fluctuate vertically. This means that the water quality parameter values ​​measured at a fixed geographical coordinate and depth may only represent water masses from a specific source flowing through that point at an instant, and cannot stably correspond to the long-term state of a specific water layer or water quality characteristic. Therefore, the vertical profile and its gradient information reconstructed based on such fixed spatial point measurement data are difficult to accurately use to analyze the evolution process of the real water layer interface or calculate the vertical mass flux, which seriously restricts the effective interpretation and application value of the monitoring data. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for monitoring water quality in complex water bodies based on vertical stratified sensing to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for monitoring water quality in complex water bodies based on vertical stratified sensing includes the following steps:

[0007] S1. The water quality parameters and corresponding dynamic environment parameters of each measuring point on the preset vertical profile are acquired synchronously through an intelligent sensor array.

[0008] S2. Based on dynamic environmental parameters, determine whether there is a continuously changing dynamic water quality interface in the vertical profile dominated by water movement;

[0009] S3. When it is determined that a dynamic water quality interface exists, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters.

[0010] S4. Analyze the interaction patterns between water quality frontal characteristic parameters and background hydrological shear parameters, and identify the real-time spatial location and dominant evolution mechanism of dynamic water quality interfaces based on the interaction patterns.

[0011] S5. Generate tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drive the sensing units in the intelligent sensor array to perform tracking vertical layered sensing of the dynamic water quality interface.

[0012] Furthermore, S1 includes:

[0013] The intelligent sensor array is arranged at preset depth intervals at each measuring point in the vertical profile.

[0014] The system controls each sensor unit in the intelligent sensor array to acquire data synchronously, so as to obtain the water quality parameters and dynamic environment parameters of each measuring point at the same time.

[0015] Furthermore, the dynamic environmental parameters include the flow velocity and direction of the water body at each measuring point, and the water quality parameters include dissolved oxygen concentration, chlorophyll concentration, and nutrient concentration.

[0016] Furthermore, S2 includes:

[0017] Based on the flow velocity and direction of the water at each measuring point, calculate the vertical shear strength of the vertical profile;

[0018] The vertical shear strength is compared with a preset shear strength threshold;

[0019] When the vertical shear strength is greater than the shear strength threshold, the vertical gradient of dissolved oxygen concentration in the vertical profile is calculated based on the dissolved oxygen concentration at each measuring point.

[0020] The vertical gradient of dissolved oxygen concentration is compared with a preset dissolved oxygen concentration gradient threshold.

[0021] When the vertical gradient of dissolved oxygen concentration is greater than the dissolved oxygen concentration gradient threshold, it is determined that there is a dynamic water quality interface in the vertical profile that is dominated by water movement and is constantly changing.

[0022] Furthermore, S3 includes:

[0023] Calculate the vertical distribution of dissolved oxygen concentration gradient in the vertical profile based on dissolved oxygen concentration;

[0024] The depth corresponding to the maximum dissolved oxygen concentration gradient in the vertical distribution of dissolved oxygen concentration gradient is extracted as the location of the water quality front, and the maximum dissolved oxygen concentration gradient value at this depth is extracted as the intensity of the water quality front.

[0025] The vertical velocity distribution of the vertical profile is calculated based on the velocity and direction of the flow.

[0026] The vertical velocity gradient is calculated from the vertical velocity distribution, and the depth corresponding to the maximum vertical velocity gradient is extracted as the location of the background hydrological shear layer. At the same time, the maximum vertical velocity gradient value at this depth is extracted as the background hydrological shear intensity.

[0027] Furthermore, S4 includes:

[0028] Determine whether the location of the water quality front coincides with the location of the background hydrological shear layer;

[0029] When the location of the water quality front coincides with the location of the background hydrological shear layer, the interaction mode is determined to be the dynamic dominant mode, the location of the background hydrological shear layer is identified as the real-time spatial location of the dynamic water quality interface, and the water body shear motion is identified as the dominant evolution mechanism.

[0030] When the location of the water quality front is inconsistent with the location of the background hydrological shear layer, compare the magnitude of the water quality front intensity and the background hydrological shear intensity.

[0031] If the water quality front intensity changes in the opposite direction to the background hydrological shear intensity, the interaction mode is determined to be the frontal stability mode, the water quality front location is identified as the real-time spatial location of the dynamic water quality interface, and the frontal self-organization dominated by biochemical processes is identified as the dominant evolution mechanism.

[0032] Furthermore, S5 includes:

[0033] Select the corresponding tracking strategy based on the dominant evolution mechanism;

[0034] When the dominant evolution mechanism is water shear motion, a high-frequency tracking strategy is adopted to generate high-frequency control commands to adjust the depth of the sensor unit based on the real-time spatial position of the dynamic water quality interface.

[0035] When the dominant evolution mechanism is frontal self-organization dominated by biochemical processes, an adaptive tracking strategy is adopted to generate adaptive control commands to adjust the depth of sensor units based on the real-time spatial position of the dynamic water quality interface.

[0036] Based on the generated high-frequency control commands or adaptive control commands, the corresponding sensor units in the intelligent sensor array are driven to move to the real-time spatial position of the dynamic water quality interface, and vertical stratified sensing is performed at the real-time spatial position.

[0037] Furthermore, the real-time spatial position of the dynamic water quality interface generates high-frequency control commands to adjust the depth of the sensor unit, including:

[0038] The movement trajectory of the dynamic water quality interface in real time is monitored over multiple consecutive monitoring cycles.

[0039] The direction and speed of movement of the dynamic water quality interface are calculated based on the movement trajectory.

[0040] Based on the direction and speed of movement, the predicted location of the dynamic water quality interface in the next monitoring cycle is determined.

[0041] Generate high-frequency control commands to drive the sensor unit to move to the predicted position.

[0042] Furthermore, an adaptive tracking strategy is adopted to generate adaptive control commands for adjusting the depth of the sensor unit based on the real-time spatial position of the dynamic water quality interface, including:

[0043] The depth difference between the real-time spatial location of the dynamic water quality interface and the current location of the sensor unit is monitored.

[0044] Determine whether the depth difference is greater than the preset depth deviation threshold;

[0045] When the depth difference is greater than the depth deviation threshold, an adaptive control command is generated to drive the sensor unit to move to the real-time spatial position at a preset fast adjustment speed.

[0046] When the depth difference is less than or equal to the depth deviation threshold, an adaptive control command is generated to drive the sensor unit to move to the real-time spatial position at a preset fine-tuning speed.

[0047] On the other hand, the present invention provides a water quality monitoring system for complex water bodies based on vertical stratified sensing, comprising the following modules:

[0048] Parameter acquisition module: synchronously acquires water quality parameters and corresponding dynamic environment parameters at each measuring point on a preset vertical profile through an intelligent sensor array;

[0049] Interface judgment module: Based on dynamic environmental parameters, determine whether there is a dynamic water quality interface in the vertical profile that is dominated by water movement and is constantly changing;

[0050] Parameter extraction module: When a dynamic water quality interface is determined to exist, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters.

[0051] Mutual identification module: Analyzes the interaction patterns between water quality front characteristic parameters and background hydrological shear parameters, and identifies the real-time spatial location and dominant evolution mechanism of dynamic water quality interfaces based on the interaction patterns;

[0052] Command-driven module: Generates tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drives the sensing units in the intelligent sensor array to perform tracking-type vertical layered sensing of the dynamic water quality interface.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. By simultaneously acquiring water quality and dynamic parameters and analyzing their coupling relationship, active identification and tracking monitoring of dynamic water quality interfaces were achieved. Compared with traditional fixed-point measurements, by extracting water quality frontal features and background hydrological shear parameters, and analyzing their interaction patterns, the system can accurately distinguish whether interface evolution is dominated by water shear motion or by self-organized front formation dominated by biochemical processes. This allows the monitoring system to understand the intrinsic physical mechanisms of interface changes, thus overcoming the technical deficiency that fixed-point measurement data in dynamic water bodies cannot stably correspond to specific water layers or water quality characteristics. By combining intelligent sensing, real-time analysis, and feedback control, the acquired vertical stratification data is ensured to be spatially anchored to the real, evolving water quality interface, significantly improving the physical representativeness and accuracy of profile data in analyzing interface evolution and calculating vertical fluxes.

[0055] 2. Based on the identification of the dominant evolution mechanism, differentiated intelligent tracking sensing was achieved. For rapidly changing interfaces dominated by water shear motion, a high-frequency predictive tracking strategy was adopted, enabling the sensor unit to respond to interface displacement in advance. For relatively stable interfaces dominated by biochemical processes, an adaptive fine-tuning tracking strategy was adopted, optimizing energy consumption and equipment operation while ensuring tracking accuracy. Mechanism-based tracking control allows limited sensor resources to continuously focus on the most critical water quality frontier, avoiding ineffective measurements in non-critical homogeneous layers. The intelligent sensor is no longer a passive data collector recording fixed points, but becomes an intelligent sensing unit that can actively lock onto and follow the target interface, thus achieving efficient, accurate, and physically meaningful vertical water quality monitoring in complex and dynamic water environments. Attached Figure Description

[0056] Figure 1 This is a flowchart of a water quality monitoring method for complex water bodies based on vertical stratified sensing according to the present invention.

[0057] Figure 2 This is a schematic diagram of the structure of a complex water quality monitoring system based on vertical layered sensing according to the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1: Figure 1 This invention presents a method for monitoring water quality in complex water bodies based on vertical stratified sensing, comprising the following steps:

[0060] S1. The water quality parameters and corresponding dynamic environment parameters of each measuring point on the preset vertical profile are acquired synchronously through an intelligent sensor array.

[0061] S2. Based on dynamic environmental parameters, determine whether there is a continuously changing dynamic water quality interface in the vertical profile dominated by water movement;

[0062] S3. When it is determined that a dynamic water quality interface exists, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters.

[0063] S4. Analyze the interaction patterns between water quality frontal characteristic parameters and background hydrological shear parameters, and identify the real-time spatial location and dominant evolution mechanism of dynamic water quality interfaces based on the interaction patterns.

[0064] S5. Generate tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drive the sensing units in the intelligent sensor array to perform tracking vertical layered sensing of the dynamic water quality interface.

[0065] S1. The water quality parameters and corresponding dynamic environment parameters of each measuring point on the preset vertical profile are acquired synchronously through an intelligent sensor array. The specific implementation is as follows:

[0066] To achieve vertical synchronous monitoring of water quality parameters, the monitoring system must first be deployed and initialized in the target water area. Water quality parameters and corresponding dynamic environmental parameters at each measuring point on a pre-defined vertical profile are acquired synchronously using an intelligent sensor array. Based on historical depth data of the target water body and the resolution requirements of the monitoring task, monitoring personnel pre-plan a virtual vertical line extending from the water surface to the bottom layer as the vertical profile. On this vertical profile, the specific depth locations of several measuring points need to be pre-determined. The number of measuring points and the depth difference between adjacent points, i.e., the pre-defined depth interval, is set based on prior knowledge of the complexity of the water body's vertical structure. For example, historical data indicates that the target water body often forms a strong thermocline between a depth of 10 and 20 meters in summer; therefore, within this depth range, the pre-defined depth interval can be set to a relatively close 1 meter. In the upper mixed layer (0 to 10 meters deep) and in deep homogeneous water bodies below 20 meters deep, the pre-defined depth interval can be set to a relatively wide 5 meters. The specific values ​​for the preset depth intervals were determined after a comprehensive analysis of the maximum water depth, seasonal stratification characteristics, the number of sensors, and deployment feasibility. The aim was to obtain denser observation data in key gradient regions. These pre-determined depth coordinates for each measuring point were input into the main control unit that controls the operation of the entire intelligent sensor array.

[0067] Next, the intelligent sensor array is deployed at preset depth intervals at various measuring points along the vertical profile. The intelligent sensor array consists of multiple independent sensors capable of in-situ measurement and data communication, all fixed to a dedicated cable with load-bearing and communication functions. Each sensor is a sealed underwater instrument chamber, integrating at least water quality and dynamic environment sensing functions. During deployment, the cable is vertically lowered into the water using a winch on a vessel or shore-based platform. During deployment, operators install or activate each sensor at the corresponding position on the cable based on preset depth interval data stored in the main control unit, ensuring that the fixed position of each sensor on the cable matches the depth coordinates of a preset measuring point. For example, when the preset measuring point depths are 0 meters, 5 meters, 10 meters, and 15 meters, the four sensors are respectively configured and locked at positions on the cable 0 meters, 5 meters, 10 meters, and 15 meters above the water surface. To ensure accurate positioning, each sensor contains a pressure sensor that continuously measures the ambient water pressure. The water pressure value is converted into a real-time depth measurement based on the direct proportionality between water pressure and depth. This real-time depth measurement is sent to the main control unit, which compares it with preset depth coordinates. If the deviation exceeds the allowable range (e.g., 0.5 meters), the unit can fine-tune the cable by controlling the winch or prompting manual intervention until the actual depth of each sensor essentially coincides with the preset depth.

[0068] Then, the sensors in the intelligent sensor array are synchronized to acquire data simultaneously, obtaining water quality and dynamic environment parameters at each measuring point at the same time. Synchronization is uniformly coordinated by the main control unit, which has a built-in high-precision clock source. When the planned acquisition time is reached, the main control unit simultaneously sends an acquisition command pulse with a precise time stamp to all sensors via the communication cable in the cable. Upon receiving the acquisition command pulse, each sensor immediately initiates the measurement sequence of all its internal sensing functions. The detection actions of the water quality and dynamic environment sensing functions are initiated sequentially or in parallel within a very short time after receiving the command, for example, within 100 milliseconds. Since the measurement time of each function is much shorter than the typical change cycle of the water body's dynamics or water quality biochemical processes, it can be assumed that the data obtained by all sensors at all measuring points correspond to the same physical moment. Each sensing function converts the detected physical signal into an electrical signal, which is then converted into a digital value by an analog-to-digital converter. These digital data with the same time stamp are then packaged by the sensors and uploaded to the main control unit via the communication cable. The main control unit receives and stores all uploaded data packets, and classifies the data to the corresponding preset measurement point depth based on the sensor identifier carried in the data packets, thereby generating a complete dataset of the vertical profile at that acquisition time with strictly aligned timestamps.

[0069] The dynamic environmental parameters include the flow velocity and direction of the water at each measuring point. Flow velocity is the magnitude of the water flow speed, measured in meters per second. Flow direction is the horizontal angle of the water flow, measured clockwise with true north as the reference point. Each sensor measures flow velocity and direction through its integrated acoustic Doppler velocity measurement function. Its working principle is that the transducer in the sensor emits a beam of sound waves of a fixed frequency into the water. When the sound waves encounter suspended particles moving with the water flow, they are scattered, and the scattered sound waves are received by the transducer. There is a difference between the received sound wave frequency and the emitted sound wave frequency, known as the Doppler shift. The magnitude of the Doppler shift is proportional to the velocity component of the water flow along the direction of the sound beam; based on this physical relationship, the flow velocity along the direction of the sound beam can be calculated. To obtain a two-dimensional horizontal flow velocity vector, two or more sound beams with different directions are usually configured within a single sensor for measurement, or a single beam is configured and supplemented with an electronic compass to measure the sensor's orientation. By vector combining the velocity components in at least two directions, or by combining the azimuth angle with the directional analysis of the single-beam measurement results, the magnitude of the horizontal flow velocity and the angle of flow direction of the water at the measuring point can be obtained. The flow velocity and flow direction data measured by each sensor under the trigger of the synchronous acquisition command represent the water motion state at its measuring point at the moment the acquisition command pulse is issued.

[0070] Water quality parameters include dissolved oxygen concentration, chlorophyll concentration, and nutrient concentration. Dissolved oxygen concentration is the amount of oxygen dissolved in water, measured in milligrams per liter (mg / L). Dissolved oxygen concentration is obtained using a fluorescence quenching method within the sensor. The probe surface of this function is covered with a thin film of oxygen-sensitive fluorescent material. When excitation light of a specific wavelength illuminates the film, it emits fluorescence. Dissolved oxygen molecules in the water quench the fluorescence, resulting in a decrease in fluorescence intensity or lifetime. The degree of decrease in fluorescence intensity or lifetime is quantitatively related to the dissolved oxygen concentration, a relationship pre-determined through calibration experiments using standard solutions with different known dissolved oxygen concentrations. During measurement, the function measures the current fluorescence intensity or lifetime and calculates the real-time dissolved oxygen concentration using a calibration curve. Chlorophyll concentration typically refers to the concentration of chlorophyll a, serving as an indicator of algal biomass, measured in micrograms per liter (µg / L). Chlorophyll concentration is obtained using a fluorescence chlorophyll measurement function. This function emits a beam of excitation light of a specific wavelength into the water sample; chlorophyll a molecules in the sample absorb the light energy and emit red fluorescence of a specific wavelength. The emitted fluorescence intensity is positively correlated with chlorophyll a concentration within a certain range. The functional component measures the emitted fluorescence intensity using an optical detector and calculates the chlorophyll concentration using a fluorescence intensity-concentration conversion factor established in the laboratory. Nutrient concentration here specifically refers to nitrate nitrogen concentration, measured in milligrams per liter (mg / L). Nitrate nitrogen concentration can be obtained using the ultraviolet absorption method. This function utilizes the characteristic absorption of nitrate ions in the ultraviolet spectral region. The functional component passes a beam of ultraviolet light through a flowing water sample and measures the absorbance of the water sample to the ultraviolet light. According to Beer-Lambert's law, absorbance is directly proportional to nitrate concentration. By measuring the attenuation of the ultraviolet light intensity after passing through the water sample and using pre-calibrated absorbance and concentration parameters, the functional component can deduce the nitrate concentration value. Under each synchronous acquisition command, the dissolved oxygen measurement, chlorophyll fluorescence measurement, and nutrient measurement functions within each sensor are triggered, generating the dissolved oxygen concentration, chlorophyll concentration, and nutrient concentration values ​​at the same measurement point at the same time. These values ​​collectively constitute the water quality parameter set for that measurement point. The water quality parameter sets from all measurement points are arranged in order of their preset depths, forming the vertical distribution spectrum of water quality parameters for that vertical profile at the time of acquisition. The logic for setting the preset depth intervals, the depth positioning and calibration of the sensors based on pressure sensors, the global synchronous acquisition mechanism triggered by the high-precision clock of the main control unit, and the implementation of specific measurement methods such as the fluorescence quenching principle, chlorophyll fluorescence principle, and ultraviolet absorption principle upon which each measurement function relies, collectively ensure the acquisition of highly consistent and reliable raw monitoring data sequences from both spatial and temporal dimensions, providing a solid data foundation for dynamic interface identification and tracking in subsequent steps.

[0071] S2. Based on dynamic environmental parameters, determine whether there is a continuously changing dynamic water quality interface in the vertical profile dominated by water movement. Specifically, this is implemented as follows:

[0072] After completing synchronous data acquisition and obtaining the dynamic environment parameters and water quality parameters of each measuring point, the existence determination step of the water quality dynamic interface is executed. First, the vertical shear strength of the vertical profile is calculated based on the flow velocity and direction of the water at each measuring point. Vertical shear strength is a physical quantity used to quantify the degree of drastic change in water velocity in the vertical direction; a larger value indicates a stronger momentum exchange between upper and lower water layers, and it is usually related to dynamic processes such as internal waves or turbulent mixing. The input parameters required for calculating the vertical shear strength are the flow velocity and direction of the water at each measuring point, which were synchronously acquired in step S1. Specifically, the main control unit sorts the measuring points according to their preset depth coordinates from smallest to largest depth, forming an ordered depth sequence and a corresponding flow velocity sequence. For each pair of adjacent upper and lower measuring points, the velocity vector difference is calculated. Since flow velocity is a vector containing both magnitude and direction, the flow velocity at each measuring point needs to be decomposed into east-west and north-south components based on its flow direction angle. Then, for two adjacent measuring points, the differences in their east-west and north-south velocity components are calculated. These two velocity component differences together reflect the change of the flow velocity vector in the horizontal direction. The calculation of vertical shear strength focuses on the rate of change of velocity with depth. Therefore, the above velocity component differences need to be divided by the depth interval between the two measuring points to obtain the vertical velocity shear components in the east-west and north-south directions. The final vertical shear strength value is obtained by calculating the square root of the sum of the squares of these two vertical velocity shear components, which essentially calculates the magnitude of the vertical velocity shear vector. The main control unit traverses all adjacent measuring point pairs on the vertical profile, calculates and records the vertical shear strength in each depth interval, and can choose to take the maximum value or the vertical average value among all intervals to represent the overall vertical shear strength characteristics of the profile.

[0073] Next, the vertical shear intensity is compared with a preset shear intensity threshold. The preset shear intensity threshold is a critical value used to distinguish between weak background shear and strong dynamic shear activity sufficient to significantly affect the vertical structure of water quality. This threshold is not fixed and its setting depends on the type of target water body, seasonal background, and monitoring target. The preset shear intensity threshold can be obtained through a combination of the following methods. The first method is historical data analysis, which involves long-term observation of the same water body, statistically analyzing the range of vertical shear intensity in the background water body under calm weather or weak tidal conditions, and multiplying the upper limit of this range by a safety factor, such as 1.5 or 2, as the preset shear intensity threshold. The second method is empirical estimation using fluid dynamics. For water bodies with known depth and typical stratification strength, the magnitude of shear intensity that may produce significant vertical mixing can be estimated based on theoretical relationships in fluid dynamics regarding internal wave shear or turbulence generation, and this magnitude can be set as the threshold. The third approach is the target-oriented method. If the monitoring focuses specifically on interface changes driven by tides or wind-driven currents, it can be determined through on-site observation or numerical simulation that when the shear strength reaches a certain level, the observed vertical water quality gradient begins to show signs of stretching or distortion. This level is then defined as the threshold. In practical applications, the preset shear strength threshold can be a specific numerical value, such as 0.05 per second per meter. The comparison operation is performed by the main control unit, which compares the calculated vertical shear strength with the preset shear strength threshold stored therein.

[0074] When the vertical shear strength exceeds a preset shear strength threshold, further judgment conditions are triggered, and the operation of calculating the vertical gradient of dissolved oxygen concentration in the vertical profile based on the dissolved oxygen concentration at each measuring point is executed. The vertical gradient of dissolved oxygen concentration characterizes the rate of change of dissolved oxygen concentration with depth and is a key indicator for identifying water quality interfaces such as oxygen-consuming layers or abrupt change layers. The input required for the calculation is the dissolved oxygen concentration data of each measuring point acquired synchronously in step S1. The main control unit processes the dissolved oxygen concentration sequence according to the depth order of the measuring points. The calculation of the vertical gradient of dissolved oxygen concentration is also performed for each pair of adjacent upper and lower measuring points. For a pair of adjacent measuring points, the dissolved oxygen concentration difference is obtained by subtracting the dissolved oxygen concentration value of the upper measuring point from the dissolved oxygen concentration value of the lower measuring point. Then, this concentration difference is divided by the depth interval between the upper and lower measuring points to obtain the average vertical gradient of dissolved oxygen concentration within that depth interval, usually in milligrams per liter per meter. For example, if the dissolved oxygen concentration at the upper measuring point is 8 mg / L and the dissolved oxygen concentration at the lower measuring point is 4 mg / L, with a depth interval of 5 meters, then the vertical gradient of dissolved oxygen concentration is 4 mg / L minus 8 mg / L divided by 5 meters, which equals a negative 0.8 mg / L / meter. The main control unit calculates the vertical gradient of dissolved oxygen concentration between all adjacent measuring points and can find the one with the largest absolute value, or analyze the vertical distribution pattern of the gradient values, such as identifying the depth range where the gradient value is consistently large and negative.

[0075] Then, the step of comparing the vertical gradient of dissolved oxygen concentration with a preset dissolved oxygen concentration gradient threshold is performed. The preset dissolved oxygen concentration gradient threshold is used to determine whether the vertical change in dissolved oxygen concentration is significant enough to constitute a clear water quality interface, rather than a minor natural fluctuation. The setting of this threshold needs to consider the background oxygen consumption rate of the water body, the water temperature stratification, and the measurement accuracy of the sensor. Methods for obtaining the preset dissolved oxygen concentration gradient threshold include the environmental background value method and the process identification method. The environmental background value method involves making multiple measurements in a vertically homogeneous water body or a homogeneous layer far from the interface, statistically analyzing the normal fluctuation range of the vertical gradient of dissolved oxygen concentration, and taking several times its standard deviation as the threshold, for example, twice the standard deviation. The process identification method is based on aquatic ecology knowledge to determine a lower limit of the concentration change rate that can characterize a typical oxygen-consuming process, such as organic matter decomposition or photosynthesis gradient. For example, near the thermocline in eutrophic lakes, a decrease in dissolved oxygen concentration of several milligrams per liter within a depth of several meters is common; therefore, the threshold can be set at 0.5 milligrams per liter per meter. The comparison operation is performed by the main control unit, which compares the calculated vertical gradient of dissolved oxygen concentration, especially its absolute value, with the pre-stored preset threshold value of dissolved oxygen concentration gradient.

[0076] Ultimately, when the vertical gradient of dissolved oxygen concentration is simultaneously greater than the dissolved oxygen concentration gradient threshold, a dynamic water quality interface dominated by water movement and continuously changing is determined to exist in the vertical profile. Here, "simultaneously greater than" refers to a logical AND relationship, meaning both comparison conditions must be met. Specifically, firstly, the vertical shear strength must be greater than a preset shear strength threshold, indicating strong vertical shear movement in the water body. Secondly, in addition to meeting the first condition, the vertical gradient of dissolved oxygen concentration must also be greater than the preset dissolved oxygen concentration gradient threshold, indicating that significant vertical differences in water quality are accompanied by strong shear in the region. Only when both conditions are met simultaneously will the control unit determine the existence of a dynamic water quality interface dominated by water movement and continuously changing. The significance of this determination logic is that it excludes situations with only strong shear without a significant water quality gradient, and also excludes situations where there is a water quality gradient but it is mainly caused by static stratification rather than being dominated by current water movement. For example, in the surface mixed layer, the shear caused by wind and waves may be very strong, but the vertical gradient of dissolved oxygen may be very small, thus it is not determined to be a dynamic water quality interface. Conversely, a stable thermocline in calm water may contain a dissolved oxygen minimum layer with a significant gradient. However, if the vertical shear is weak, it indicates that the interface is relatively static and not driven by current water movement, thus not meeting the definition of a dynamic water quality interface in this step. Only when shear force is observed acting on a significant water quality gradient interface is a dynamic water quality interface whose location and shape may continuously change due to dynamic processes considered to exist. This determination provides a crucial basis for subsequent steps to decide whether to initiate and how to perform interface tracking. The vector calculation method for vertical shear intensity, the diversified setting basis for the preset shear intensity threshold, the specific calculation method for the vertical gradient of dissolved oxygen concentration, the environmental and process-related setting of the preset dissolved oxygen concentration gradient threshold, and the joint determination rule based on the AND of dual threshold logic together constitute a complete, implementable, and well-reasoned method for determining the existence of dynamic interfaces, ensuring the accuracy and reliability of the selection of subsequent analysis objects.

[0077] S3. When a dynamic water quality interface is determined to exist, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters. Specifically, the implementation is as follows:

[0078] When step S2 determines that a dynamic water quality interface dominated by water movement and continuously changing exists in the current vertical profile, the monitoring process enters the stage of extracting characteristic parameters of this interface. First, the vertical distribution of the dissolved oxygen concentration gradient in the vertical profile is calculated based on the dissolved oxygen concentration. The purpose of this operation is to transform the dissolved oxygen concentration data of discrete measuring points into a distribution information that continuously reflects its rate of change with depth. The input data comes from the dissolved oxygen concentration values ​​of each measuring point acquired synchronously in step S1 and confirmed as valid in step S2, as well as the preset depth coordinates corresponding to each measuring point. The main control unit arranges the measuring point data in ascending order of depth, forming a depth sequence and a corresponding dissolved oxygen concentration sequence. The core of calculating the vertical distribution of the dissolved oxygen concentration gradient is to determine the rate of change of dissolved oxygen concentration relative to depth at each depth point. Since the measuring points are discrete, the main control unit needs to use numerical differentiation methods to estimate the gradient. One specific implementation is to calculate the central difference for each measuring point using its own data and that of its adjacent measuring points. For measuring points in the middle of the vertical profile—that is, points that are neither the shallowest nor the deepest—the dissolved oxygen concentration gradient can be obtained by subtracting the dissolved oxygen concentration of the previous adjacent measuring point from the dissolved oxygen concentration of the next adjacent measuring point, and then dividing by the depth difference between the next and previous adjacent measuring points. For the shallowest top-layer measuring points, forward differencing can be used, i.e., subtracting the current measuring point's concentration from the next adjacent measuring point's concentration, and then dividing by the depth difference. For the deepest bottom-layer measuring points, backward differencing can be used, i.e., subtracting the previous adjacent measuring point's concentration from the current measuring point's concentration, and then dividing by the depth difference. In this way, the main control unit calculates a dissolved oxygen concentration gradient value for each measuring point. The physical meaning of this value is the average change in dissolved oxygen concentration per unit depth near the depth of the measuring point, expressed in milligrams per liter per meter. These calculated gradient values ​​are correlated with the depth coordinates of each measuring point, collectively forming the vertical distribution of the dissolved oxygen concentration gradient. This distribution characterizes the continuous vertical variation of the rate of change of dissolved oxygen concentration. Positive values ​​indicate that the dissolved oxygen concentration increases with increasing depth, while negative values ​​indicate that the concentration decreases with increasing depth. The magnitude of the absolute value characterizes the degree of drastic change.

[0079] Next, the process involves extracting the depth corresponding to the maximum dissolved oxygen concentration gradient from the vertical distribution of dissolved oxygen concentration gradients as the location of the water quality front, and extracting the maximum dissolved oxygen concentration gradient value at that depth as the water quality front intensity. The water quality front location refers to the depth surface where the dissolved oxygen concentration changes most drastically vertically, reaching its maximum absolute gradient value. It typically marks the interface where dissolved oxygen characteristics undergo abrupt changes, such as the boundary between the aerobic and anoxic layers. The water quality front intensity is quantified by the maximum absolute gradient value at that location, characterizing the sharpness of the interface change. The extraction process is automatically completed by the main control unit. The main control unit iterates through the calculated dissolved oxygen concentration gradient values ​​at all measuring points, searching for the value with the largest absolute value. Since gradient values ​​can be positive or negative, finding the maximum absolute value means comparing the moduli of all gradient values. The main control unit takes the absolute value of all gradient values ​​and then finds the maximum among these absolute values. The original gradient value corresponding to this maximum value, regardless of its sign, is the maximum dissolved oxygen concentration gradient value. After determining the maximum value, the main control unit retrieves stored data to find the depth coordinates of the measuring point corresponding to this maximum dissolved oxygen concentration gradient value. These depth coordinates represent the extracted water quality front location. For example, if the main control unit calculates and finds that the dissolved oxygen concentration gradient at a measuring point at a depth of 12 meters is negative (1.2 mg / L / m), and this absolute value is the largest among all measuring points, then the water quality front location is 12 meters, and the water quality front intensity is 1.2 mg / L / m. During data processing, multiple adjacent measuring points may have similar and large absolute gradient values. In such cases, the front can be considered to have a certain thickness. The main control unit can set a threshold, such as 80% of the maximum absolute value, and mark all continuous depth intervals where the absolute gradient value exceeds this threshold. The precise depth corresponding to the middle depth or the peak absolute gradient value of this interval is taken as the water quality front location. The water quality front intensity is still taken as the maximum absolute gradient value within this interval. In this way, the core location of abrupt changes in water quality characteristics and the intensity of these changes are accurately located from the continuous vertical gradient distribution.

[0080] Then, the vertical velocity distribution of the vertical profile is calculated based on the flow velocity and direction. The purpose of this operation is to obtain the vertical distribution of the water body's velocity vector, providing a basis for analyzing dynamic shear. The input data are the magnitude of the water body's velocity and the angle of its direction at each measuring point, acquired synchronously in step S1. Since velocity is a vector with both magnitude and direction, the vertical velocity distribution needs to describe the vertical changes in both magnitude and direction. The main control unit first processes the velocity vector at each measuring point. A comprehensive method for characterizing the vertical velocity distribution is to decompose the velocity vector at each measuring point into two fixed horizontal orthogonal components, such as an eastward component and a northward component. The decomposition calculation is based on the magnitude of the velocity and the angle of its direction at each measuring point. Specifically, the eastward velocity component at a given measuring point is equal to the magnitude of the velocity at that point multiplied by the cosine of the angle of its direction; the northward velocity component is equal to the magnitude of the velocity at that point multiplied by the sine of the angle of its direction. The angle is set to 0 degrees in the north direction and increases clockwise. Through this decomposition, the three-dimensional vector information of each measuring point is converted into two scalar components. The main control unit arranges the velocity component sequences in the eastward and northward directions according to the depth order of the measuring points. This yields the distribution of velocity components with depth in two fixed directions, i.e., one expression of the vertical velocity distribution. Another more intuitive but less informative expression focuses only on the vertical distribution of velocity magnitude, ignoring direction and simply arranging the velocity magnitude values ​​of each measuring point according to their depth. However, for subsequent calculations of vertical shear, the component decomposition method is more accurate. The main control unit calculates and stores these component sequences, forming the basic data for the vertical velocity distribution in subsequent analyses.

[0081] Finally, the operation of calculating the vertical velocity gradient from the vertical velocity distribution and extracting the depth corresponding to the maximum vertical velocity gradient as the location of the background hydrological shear layer, and simultaneously extracting the maximum vertical velocity gradient value at that depth as the background hydrological shear intensity, is performed. The location of the background hydrological shear layer refers to the depth layer where the water velocity changes the most vertically, usually corresponding to a strong dynamic shear zone, such as a shear layer caused by internal waves or a boundary shear layer. The background hydrological shear intensity is characterized by the maximum vertical velocity gradient value of this layer. Calculating the vertical velocity gradient requires the aforementioned vertical velocity distribution. Due to the use of component decomposition, the master control unit needs to calculate the vertical gradient of the velocity component in the eastward direction and the vertical gradient of the velocity component in the northward direction, respectively. The method for calculating the gradient is the same as the numerical differentiation method used when calculating the vertical distribution of dissolved oxygen concentration gradient, that is, for each measuring point, the central difference, forward difference, or backward difference is calculated using the velocity component values ​​of its and adjacent measuring points. For example, for the eastward velocity component sequence, the master control unit calculates the vertical gradient of the eastward velocity component at each measuring point, in meters per second; similarly, it calculates the vertical gradient of the northward velocity component. To obtain a comprehensive vertical gradient scalar reflecting the overall velocity change, the master control unit can calculate the combined shear value at each measuring point. Specifically, for each measuring point, the square of the vertical gradient of the eastward velocity component is added to the square of the vertical gradient of the northward velocity component, and the square root is taken. The resulting value is the combined vertical velocity gradient at that measuring point, reflecting the magnitude of the total rate of change of the velocity vector at that depth, also in meters per second. The master control unit calculates the combined vertical velocity gradient values ​​for all measuring points, forming a vertical sequence of combined vertical velocity gradient values. Then, the master control unit finds the largest combined vertical velocity gradient value in this sequence; this maximum value is the maximum vertical velocity gradient value. The depth coordinates of the measuring point corresponding to this maximum value are the extracted location of the background hydrological shear layer. For example, if the main control unit calculates that the sum of the vertical velocity gradient at a measuring point at a depth of 8 meters is 0.08 per second per meter, and this value is the largest among all measuring points, then the location of the background hydrological shear layer is 8 meters, and the background hydrological shear intensity is 0.08 per second per meter. Similarly, if the maximum shear occurs within a depth range, the location of the background hydrological shear layer can be determined by the peak value or the median value of the range, similar to the method used to extract the frontal position. Through the above steps, key parameters characterizing the location and intensity of the strongest shear in water movement are accurately extracted from the dynamic environmental parameters.The specific numerical differentiation method for calculating the vertical distribution of dissolved oxygen concentration gradient based on dissolved oxygen concentration, the logic and algorithm for locating and extracting the location and intensity of water quality fronts from this distribution, the strategy for constructing the vertical distribution of velocity based on velocity and direction through vector decomposition, and the complete process for calculating the combined shear gradient from this distribution and locating and extracting the location and intensity of the background hydrological shear layer, together constitute a systematic, well-defined, and automatically executable feature parameter extraction scheme, providing accurate quantitative input for the next step of analyzing the interaction mode between the two.

[0082] S4. Analyze the interaction patterns between water quality frontal characteristic parameters and background hydrological shear parameters, and identify the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface based on the interaction patterns. Specifically, this is implemented as follows:

[0083] After extracting the characteristic parameters of the water quality front and the background hydrological shear parameters, the monitoring process enters the core analysis and identification stage. First, it determines whether the location of the water quality front coincides with the location of the background hydrological shear layer. This consistency determination does not require the two depth values ​​to be mathematically absolutely equal, but rather that, within the given observation accuracy and spatial resolution, the physical interfaces they represent essentially overlap vertically. The main control unit compares the depth values ​​of the water quality front location with the depth values ​​of the background hydrological shear layer location. The specific standard for determining consistency is based on a preset depth tolerance threshold. This threshold takes into account the sensor array spacing and the physical thickness of the water quality interface itself. For example, if the preset depth interval for the sensors is 5 meters, the preset depth tolerance threshold can be set to approximately half the interval between adjacent sensors, such as 2 to 3 meters. The main control unit then calculates the absolute value of the difference between the depth values ​​of the water quality front location and the background hydrological shear layer location. If the absolute value is less than or equal to the preset depth tolerance threshold, the water quality front position is determined to be consistent with the background hydrological shear layer position; if the absolute value is greater than the preset depth tolerance threshold, the two are determined to be inconsistent. The preset depth tolerance threshold is pre-stored in the main control unit.

[0084] When the location of the water quality front coincides with the location of the background hydrological shear layer, the interaction mode is determined to be the dynamic-dominant mode, and the location of the background hydrological shear layer is identified as the real-time spatial location of the dynamic water quality interface, with water body shear motion identified as the dominant evolution mechanism. The dynamic-dominant mode characterizes the physical situation where the strongest observed water quality gradient interface and the strongest water body shear layer spatially coincide. This coincidence indicates that the current morphological and positional changes of the water quality interface are mainly driven by the dynamic shear process of the water body. In this mode, the master control unit directly identifies the depth value of the background hydrological shear layer extracted in step S3 as the core location of the current dynamic water quality interface in the vertical direction, i.e., the real-time spatial location of the dynamic water quality interface. Simultaneously, the master control unit marks the dominant evolution mechanism as water body shear motion. This indicates that the main physical cause of the continuous change in this dynamic water quality interface is the shear action of the water body.

[0085] When the location of the water quality front is inconsistent with the location of the background hydrological shear layer, an operation is performed to compare the magnitude of the water quality front intensity and the background hydrological shear intensity. Specifically, this comparison involves comparing the direction of their changes over time. To perform this comparison, the main control unit needs to obtain the numerical sequences of the water quality front intensity and the background hydrological shear intensity calculated at the same vertical profile location for the current moment and the previous few consecutive monitoring periods. The main control unit calculates the difference between the current water quality front intensity value and the water quality front intensity value at the most recent historical moment, obtaining the change in water quality front intensity. Similarly, the main control unit calculates the difference between the current background hydrological shear intensity value and the background hydrological shear intensity value at the most recent historical moment, obtaining the change in background hydrological shear intensity. The sign of the product of the change in water quality front intensity and the change in background hydrological shear intensity is used to determine whether the directions of change are opposite. If the product of the change in water quality front intensity and the change in background hydrological shear intensity is less than zero, it is determined that the directions of change of the water quality front intensity and the background hydrological shear intensity are opposite. This is because when two changes have opposite signs, one positive indicates enhancement and the other negative indicates weakening, and their product is negative. For example, if the change in water quality front intensity is positive (0.2 mg / L / m) and the change in background hydrological shear intensity is negative (0.02 mg / s / m), their product is less than zero, indicating opposite directions of change. To improve robustness, the main control unit can check whether the condition of a product being less than zero is met for multiple consecutive monitoring periods.

[0086] If the intensity of the water quality front changes in the opposite direction to the change in the background hydrological shear intensity, the interaction mode is determined as a frontal stability mode, and the location of the water quality front is identified as the real-time spatial location of the dynamic water quality interface. The operation of identifying frontal self-organization dominated by biochemical processes is also performed. The frontal stability mode characterizes the physical condition where the water quality front is spatially separated from the background hydrological shear layer, and the intensity change of the water quality front is inversely correlated with the change in the background hydrological shear intensity. This phenomenon indicates that the formation and maintenance of this water quality interface may be mainly dominated by local biogeochemical processes, which have strong self-organization. External hydrodynamic shearing mainly plays a perturbation or mixing role; when the shear weakens, the biochemical processes can fully develop, leading to an increase in the intensity of the water quality front. In this mode, the master control unit identifies the depth value of the water quality front location extracted in step S3 as the real-time spatial location of the dynamic water quality interface. Simultaneously, the master control unit marks the dominant evolution mechanism as frontal self-organization dominated by biochemical processes. This indicates that the main driving force for the existence and evolution of this interface is the biochemical activity within the water body. Through the complete logical judgment process described above, from the spatial comparison of water quality front characteristic parameters and background hydrological shear parameters to the coupled analysis of their changing trends, this method can clearly identify the two main physical mechanisms for the formation of dynamic water quality interfaces and their corresponding spatial cores.

[0087] S5. Generate tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drive the sensing units in the intelligent sensor array to perform tracking-type vertical layered sensing of the dynamic water quality interface. Specifically, the implementation is as follows:

[0088] After identifying the dominant evolution mechanism and real-time spatial location of the dynamic water quality interface, the monitoring process enters the final closed-loop control and tracking monitoring stage. First, the operation of selecting the corresponding tracking strategy based on the dominant evolution mechanism is executed. The main control unit reads the dominant evolution mechanism identifier output in step S4. If the dominant evolution mechanism identifier is "water shear motion," the main control unit selects and activates the preset high-frequency tracking strategy. If the dominant evolution mechanism identifier is "biological process-driven frontal self-organization," the main control unit selects and activates the preset adaptive tracking strategy. The selection logic of the tracking strategy is directly related to the dynamic characteristics of the interface. Interfaces driven by water shear motion typically change rapidly, requiring more proactive prediction and rapid response; while interfaces dominated by biological processes change relatively slowly but stably, requiring more precise positioning and energy-efficient tracking methods.

[0089] When the dominant evolution mechanism is water shear motion, a high-frequency tracking strategy is adopted. Based on the real-time spatial position of the dynamic water quality interface, high-frequency control commands are generated to adjust the depth of the sensor unit. The core of the high-frequency tracking strategy lies in predicting interface movement and proactively controlling the sensor unit to reach the predicted position. The specific implementation of this strategy includes the following sub-processes: Monitoring the real-time spatial trajectory of the dynamic water quality interface over multiple consecutive monitoring cycles. The main control unit retrieves the real-time spatial position data of the dynamic water quality interface identified in step S4 from the stored historical data, including the current moment and the previous few monitoring cycles (e.g., the first four cycles), forming a sequence of depth values ​​arranged in chronological order, i.e., the movement trajectory. The movement direction and speed of the dynamic water quality interface are calculated based on the movement trajectory. The movement direction is determined by comparing the interface position depth values ​​at the two most recent moments. If the current depth is greater than the previous depth, the movement direction is downward; otherwise, it is upward. The movement speed is obtained by calculating the absolute value of the difference between the interface position depth values ​​at the two most recent moments and dividing it by the time interval between the two monitoring cycles, in meters per second. Based on the direction and speed of movement, the predicted location of the dynamic water interface in the next monitoring cycle is determined. The predicted location is calculated using linear extrapolation, where the predicted depth for the next cycle equals the current real-time spatial depth plus the product of the movement speed and the duration of one monitoring cycle. The sign of the product is determined by the direction of movement: positive for downward and negative for upward. High-frequency control commands are generated to drive the sensor units to the predicted location. The main control unit determines which sensor unit(s) in the intelligent sensor array is closest to the predicted depth and calculates the distance these units need to move. The control commands include the target sensor unit identifier, the target depth value, and the movement instruction. These commands are transmitted to the target sensor unit via control lines in the cable. The sensor unit's built-in drive mechanism, such as a stepper motor or hydraulic adjustment device, receives the command and begins operation, moving the sensor unit along the cable towards the target depth.

[0090] When the dominant evolutionary mechanism is frontal self-organization driven by biochemical processes, an adaptive tracking strategy is adopted. This strategy generates adaptive control commands to adjust the depth of the sensor unit based on the real-time spatial position of the dynamic water quality interface. The core of the adaptive tracking strategy lies in intelligently selecting the adjustment speed based on the real-time positional deviation between the interface and the sensor, achieving precise and energy-efficient tracking. The specific implementation of this strategy includes the following sub-processes: Monitoring the depth difference between the real-time spatial position of the dynamic water quality interface and the current position of the sensor unit. The current position of the sensor unit is determined by the depth value measured and reported in real-time by its internal pressure sensor. The main control unit calculates the difference between the real-time spatial position depth value of the dynamic water quality interface given in step S4 and the current position depth value reported by the target sensor unit, taking the absolute value to obtain the depth difference. Determining whether the depth difference is greater than a preset depth deviation threshold. The preset depth deviation threshold is used to distinguish whether the interface has undergone significant displacement or only minor fluctuations. The threshold is set based on the positioning accuracy of the sensor unit and the physical thickness of the dynamic water quality interface itself. For example, if the sensor unit's depth positioning accuracy is ±0.1 meters and the typical interface thickness is approximately 1 meter, the preset depth deviation threshold can be set to 0.5 to 1 meter to ensure that a large action is only triggered when the core of the interface actually moves beyond its thickness or significantly exceeds the sensor positioning error. The preset depth deviation threshold is stored in the main control unit. When the depth difference is greater than the preset depth deviation threshold, an adaptive control command is generated to drive the sensor unit to move to the real-time spatial position at a preset rapid adjustment speed. The preset rapid adjustment speed is a relatively large value, designed to allow the sensor unit to approach the interface where significant displacement has occurred as quickly as possible. This speed value needs to consider the maximum safe speed of the sensor unit's drive mechanism and water resistance; for example, it may be set to 0.1 meters per second. The control command includes the target depth and rapid adjustment speed parameters. When the depth difference is less than or equal to the preset depth deviation threshold, an adaptive control command is generated to drive the sensor unit to move to the real-time spatial position at a preset fine-tuning speed. The preset fine-tuning speed is a relatively small value, used for fine tracking of the interface or compensation for minor drifts. This speed value is typically set much lower than the rapid adjustment speed, for example, 0.01 meters per second, to achieve smooth, accurate, and low-energy positioning. Control commands include target depth and fine-tuning speed parameters.

[0091] Based on the generated high-frequency control commands or adaptive control commands, the corresponding sensor units in the intelligent sensor array are driven to move to the real-time spatial position of the dynamic water quality interface, and vertical stratification sensing is performed at the real-time spatial position. After the main control unit issues the final control command, the actuator of the target sensor unit begins to work. For the high-frequency tracking strategy, the sensor unit will move to the predicted position; for the adaptive tracking strategy, the sensor unit will move to the real-time spatial position given in step S4. During the movement, the sensor unit continuously reports its actual depth, and the main control unit performs closed-loop comparison until the error between the actual depth and the target depth is less than the stop tolerance, for example, 0.05 meters. When the sensor unit reaches the target depth and stabilizes, the main control unit commands the sensor unit and the sensor units at adjacent depths to perform a new round of sensing and measurement of water quality parameters and dynamic environmental parameters at this moment, according to the synchronous acquisition method defined in step S1. This is equivalent to acquiring a new set of targeted vertical stratification data at the core position of the dynamic water quality interface and its adjacent water layers. Thus, a complete control loop from interface recognition to tracking sensing is completed. The tracking strategy branch logic based on the dominant evolution mechanism, the specific calculation method for predicted position based on linear extrapolation of historical trajectories in the high-frequency tracking strategy, the speed selection logic based on the comparison of depth difference and preset depth deviation threshold in the adaptive tracking strategy, the specific setting basis of preset depth deviation threshold and speed parameters, and the closed-loop control process that ultimately drives the sensor to move and execute synchronous sensing, together constitute a complete, adaptive, and physically implementable dynamic interface tracking and monitoring scheme. This ensures that monitoring resources are always focused on the most critical and active frontier of water quality changes, thereby effectively solving the representativeness mismatch problem of fixed-point measurements in dynamic water bodies.

[0092] Example 2: Figure 2 A schematic diagram of a complex water quality monitoring system based on vertical stratified sensing according to the present invention is provided. The complex water quality monitoring system based on vertical stratified sensing includes the following modules:

[0093] Parameter acquisition module: synchronously acquires water quality parameters and corresponding dynamic environment parameters at each measuring point on a preset vertical profile through an intelligent sensor array;

[0094] Interface judgment module: Based on dynamic environmental parameters, determine whether there is a dynamic water quality interface in the vertical profile that is dominated by water movement and is constantly changing;

[0095] Parameter extraction module: When a dynamic water quality interface is determined to exist, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters.

[0096] Mutual identification module: Analyzes the interaction patterns between water quality front characteristic parameters and background hydrological shear parameters, and identifies the real-time spatial location and dominant evolution mechanism of dynamic water quality interfaces based on the interaction patterns;

[0097] Command-driven module: Generates tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drives the sensing units in the intelligent sensor array to perform tracking-type vertical layered sensing of the dynamic water quality interface.

[0098] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring water quality in complex water bodies based on vertical stratified sensing, characterized in that, Includes the following steps: S1. The water quality parameters and corresponding dynamic environment parameters of each measuring point on the preset vertical profile are acquired synchronously through an intelligent sensor array. S2. Based on dynamic environmental parameters, determine whether there is a continuously changing dynamic water quality interface in the vertical profile dominated by water movement; S3. When it is determined that a dynamic water quality interface exists, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters. S4. Analyze the interaction patterns between water quality frontal characteristic parameters and background hydrological shear parameters, and identify the real-time spatial location and dominant evolution mechanism of dynamic water quality interfaces based on the interaction patterns. S5. Generate tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drive the sensing units in the intelligent sensor array to perform tracking vertical layered sensing of the dynamic water quality interface.

2. The method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 1, characterized in that, S1 includes: The intelligent sensor array is arranged at preset depth intervals at each measuring point in the vertical profile. The system controls each sensor unit in the intelligent sensor array to acquire data synchronously, so as to obtain the water quality parameters and dynamic environment parameters of each measuring point at the same time.

3. The method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 2, characterized in that, The dynamic environmental parameters include the flow velocity and direction of the water body at each measuring point, and the water quality parameters include dissolved oxygen concentration, chlorophyll concentration, and nutrient concentration.

4. The method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 1, characterized in that, S2 include: Based on the flow velocity and direction of the water at each measuring point, calculate the vertical shear strength of the vertical profile; The vertical shear strength is compared with a preset shear strength threshold; When the vertical shear strength is greater than the shear strength threshold, the vertical gradient of dissolved oxygen concentration in the vertical profile is calculated based on the dissolved oxygen concentration at each measuring point. The vertical gradient of dissolved oxygen concentration is compared with a preset dissolved oxygen concentration gradient threshold. When the vertical gradient of dissolved oxygen concentration is greater than the dissolved oxygen concentration gradient threshold, it is determined that there is a dynamic water quality interface in the vertical profile that is dominated by water movement and is constantly changing.

5. A method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 1, characterized in that, S3 include: Calculate the vertical distribution of dissolved oxygen concentration gradient in the vertical profile based on dissolved oxygen concentration; The depth corresponding to the maximum dissolved oxygen concentration gradient in the vertical distribution of dissolved oxygen concentration gradient is extracted as the location of the water quality front, and the maximum dissolved oxygen concentration gradient value at this depth is extracted as the intensity of the water quality front. The vertical velocity distribution of the vertical profile is calculated based on the velocity and direction of the flow. The vertical velocity gradient is calculated from the vertical velocity distribution, and the depth corresponding to the maximum vertical velocity gradient is extracted as the location of the background hydrological shear layer. At the same time, the maximum vertical velocity gradient value at this depth is extracted as the background hydrological shear intensity.

6. The method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 1, characterized in that, S4 include: Determine whether the location of the water quality front coincides with the location of the background hydrological shear layer; When the location of the water quality front coincides with the location of the background hydrological shear layer, the interaction mode is determined to be the dynamic dominant mode, the location of the background hydrological shear layer is identified as the real-time spatial location of the dynamic water quality interface, and the water body shear motion is identified as the dominant evolution mechanism. When the location of the water quality front is inconsistent with the location of the background hydrological shear layer, compare the magnitude of the water quality front intensity and the background hydrological shear intensity. If the water quality front intensity changes in the opposite direction to the background hydrological shear intensity, the interaction mode is determined to be the frontal stability mode, the water quality front location is identified as the real-time spatial location of the dynamic water quality interface, and the frontal self-organization dominated by biochemical processes is identified as the dominant evolution mechanism.

7. A method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 1, characterized in that, S5 include: Select the corresponding tracking strategy based on the dominant evolution mechanism; When the dominant evolution mechanism is water shear motion, a high-frequency tracking strategy is adopted to generate high-frequency control commands to adjust the depth of the sensor unit based on the real-time spatial position of the dynamic water quality interface. When the dominant evolution mechanism is frontal self-organization dominated by biochemical processes, an adaptive tracking strategy is adopted to generate adaptive control commands to adjust the depth of sensor units based on the real-time spatial position of the dynamic water quality interface. Based on the generated high-frequency control commands or adaptive control commands, the corresponding sensor units in the intelligent sensor array are driven to move to the real-time spatial position of the dynamic water quality interface, and vertical stratified sensing is performed at the real-time spatial position.

8. A method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 7, characterized in that, The real-time spatial position of the dynamic water quality interface generates high-frequency control commands to adjust the depth of the sensor unit, including: The movement trajectory of the dynamic water quality interface in real time is monitored over multiple consecutive monitoring cycles. The direction and speed of movement of the dynamic water quality interface are calculated based on the movement trajectory. Based on the direction and speed of movement, the predicted location of the dynamic water quality interface in the next monitoring cycle is determined. Generate high-frequency control commands to drive the sensor unit to move to the predicted position.

9. A method for monitoring water quality in complex water bodies based on vertical stratified sensing according to claim 7, characterized in that, An adaptive tracking strategy is adopted to generate adaptive control commands for adjusting the depth of the sensor unit based on the real-time spatial position of the dynamic water quality interface, including: The depth difference between the real-time spatial location of the dynamic water quality interface and the current location of the sensor unit is monitored. Determine whether the depth difference is greater than the preset depth deviation threshold; When the depth difference is greater than the depth deviation threshold, an adaptive control command is generated to drive the sensor unit to move to the real-time spatial position at a preset fast adjustment speed. When the depth difference is less than or equal to the depth deviation threshold, an adaptive control command is generated to drive the sensor unit to move to the real-time spatial position at a preset fine-tuning speed.

10. A water quality monitoring system for complex water bodies based on vertical stratified sensing, used to implement the water quality monitoring method for complex water bodies based on vertical stratified sensing as described in any one of claims 1-9, characterized in that, Includes the following modules: Parameter acquisition module: synchronously acquires water quality parameters and corresponding dynamic environment parameters at each measuring point on a preset vertical profile through an intelligent sensor array; Interface judgment module: Based on dynamic environmental parameters, determine whether there is a dynamic water quality interface in the vertical profile that is dominated by water movement and is constantly changing; Parameter extraction module: When a dynamic water quality interface is determined to exist, extract the water quality front characteristic parameters reflected by the water quality parameters, and extract the background hydrological shear parameters reflected by the dynamic environment parameters. Mutual identification module: Analyzes the interaction patterns between water quality front characteristic parameters and background hydrological shear parameters, and identifies the real-time spatial location and dominant evolution mechanism of dynamic water quality interfaces based on the interaction patterns; Command-driven module: Generates tracking control commands based on the real-time spatial location and dominant evolution mechanism of the dynamic water quality interface, and drives the sensing units in the intelligent sensor array to perform tracking-type vertical layered sensing of the dynamic water quality interface.

Images