An acoustic doppler-based ecological flow measurement method and system

Abstract

The present application relates to the technical field of ecological flow measurement, and more particularly to an ecological flow measurement method and system based on acoustic Doppler. The method comprises the following steps: obtaining flow measurement water area data, performing ecological flow measurement based on an acoustic Doppler device to obtain water area ecological flow measurement data; compensating flow velocity error for the water area ecological flow measurement data; evaluating water power structure abnormal conditions, detecting ecological suitability gradient attenuation trends, and monitoring ecological system dynamic imbalance conditions according to the compensated data; detecting water quality purification capacity abnormal conditions according to the ecological imbalance conditions, identifying pollution accumulation conditions in combination with the water power structure abnormal conditions, and determining water quality deterioration states; further determining water body chain ecological deterioration trends, evaluating flow velocity abnormal risk, and performing flow velocity regulation optimization; and the present application realizes more stable water area ecological flow velocity through ecological flow measurement processing.

Description

Technical Field

[0001] This invention relates to the field of ecological flow measurement technology, and in particular to an ecological flow measurement method and system based on acoustic Doppler. Background Technology

[0002] In the process of aquatic ecological monitoring and management, accurate measurement of water flow velocity is of great significance for assessing hydrodynamic structure, analyzing water quality evolution trends, and judging the health status of ecosystems. Traditional water flow measurement methods, such as rotor current meters and electromagnetic current meters, are limited in practical applications due to problems such as complex installation, limited measurement points, and poor spatial representativeness, making it difficult to meet the high-precision and wide-coverage monitoring needs of water flow velocity in complex and ever-changing ecological environments. In recent years, Acoustic Doppler Current Profiler (ADCP) technology has been widely used in the measurement of flow velocity in rivers, lakes, and reservoirs due to its advantages of non-contact, real-time, multi-level, and multi-point measurement. However, acoustic Doppler flow measurement still faces many challenges in ecologically complex water bodies. For example, algal aggregation, biofilm coverage, and suspended sediment in the water body can lead to attenuation of acoustic signals or abnormal echoes, forming non-uniform reflection areas, thereby causing multipath propagation effects and increasing measurement errors. Furthermore, traditional acoustic Doppler ecological flow measurement suffers from inaccurate error compensation processing for water flow velocity and inaccurate monitoring of dynamic imbalances in aquatic ecosystems. Summary of the Invention

[0003] Therefore, it is necessary to provide an ecological flow measurement method and system based on acoustic Doppler to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an ecological flow measurement method based on acoustic Doppler includes the following steps:

[0005] Step S1: Acquire flow measurement data of the water area; perform ecological flow measurement on the flow measurement data of the water area based on acoustic Doppler to obtain water area ecological flow measurement data; perform water flow velocity error compensation processing on the water area ecological flow measurement data to obtain water flow velocity error compensated data;

[0006] Step S2: Assess the abnormal hydrodynamic structure of the measured water area based on the water flow velocity error compensation data; detect the gradient decline trend of the water area's ecological suitability based on the abnormal hydrodynamic structure; monitor the dynamic imbalance of the water area's ecosystem based on the gradient decline trend of the water area's ecological suitability and the abnormal hydrodynamic structure.

[0007] Step S3: Detect abnormalities in the ecological water quality purification capacity based on the dynamic imbalance of the aquatic ecosystem; identify the water pollution accumulation status based on the abnormality of the aquatic hydrodynamic structure; determine the water quality deterioration status of the measured water area based on the abnormality in the ecological water quality purification capacity and the water pollution accumulation status.

[0008] Step S4: Determine the trend of water body chain ecological degradation based on the water quality deterioration status of the measured flow area; assess the risk of abnormal flow velocity in the measured flow area based on the trend of water body chain ecological degradation; optimize the flow velocity regulation of the water area based on the risk of abnormal flow velocity in the measured flow area and the trend of water body chain ecological degradation, and obtain the optimized flow velocity regulation data.

[0009] This invention utilizes acoustic Doppler technology for ecological flow measurement in water bodies, enabling precise acquisition of fundamental data encompassing multidimensional hydrodynamic characteristics such as vertical velocity, lateral disturbance, and flow direction changes, thereby improving the accuracy of water flow velocity measurement. Through flow velocity error compensation processing, data deviations caused by local disturbances, boundary effects, or sensor drift are corrected, enhancing the accuracy of hydrodynamic feature identification. Based on this, identified hydrodynamic structural anomalies significantly reflect changes in water body stability, helping to reveal the dynamic degradation process of ecological living space. Analysis of the decay trend of ecological suitability gradients provides a quantitative basis for changes in the ecologically suitable environment, improving the accuracy and response sensitivity of identifying dynamic imbalances in the ecosystem. Anomaly detection of water purification capacity identifies areas where pollutant self-purification mechanisms are weakened, while pollution accumulation identification supplements the spatial assessment of external input loads. The combination of these two methods accurately indicates the spatial evolution trend of water quality deterioration, helping to define ecological intervention areas. The chain-like ecological degradation trend constructs a multi-factor-driven ecological degradation correlation, providing causal chain logic support for flow velocity anomaly risk assessment. Ultimately, the optimization of water flow velocity regulation guides the dynamic adjustment of flow velocity regulation measures by feeding back the coupling results of flow velocity and ecological status, thereby improving the system adaptability and implementation pertinence of the regulation strategy. Therefore, this invention is an optimization of traditional acoustic Doppler-based ecological flow measurement, solving the problems of inaccurate compensation for water flow velocity errors and inaccurate monitoring of dynamic imbalances in aquatic ecosystems. It improves the accuracy of both error compensation and monitoring of dynamic imbalances in aquatic ecosystems.

[0010] The present invention also provides an acoustic Doppler-based ecological flow measurement system for performing the acoustic Doppler-based ecological flow measurement method described above. The acoustic Doppler-based ecological flow measurement system includes:

[0011] The flow velocity error compensation processing module is used to acquire flow measurement water area data; perform ecological flow measurement on the flow measurement water area data based on acoustic Doppler to obtain water area ecological flow measurement data; and perform flow velocity error compensation processing on the water area ecological flow measurement data to obtain water area flow velocity error compensated data.

[0012] The ecosystem dynamic imbalance monitoring module is used to assess the abnormal state of the hydrodynamic structure of the measured water area based on the water flow velocity error compensation data; detect the gradient decline trend of the water area's ecological suitability based on the abnormal state of the water area's hydrodynamic structure; and monitor the dynamic imbalance of the water area's ecosystem based on the gradient decline trend of the water area's ecological suitability and the abnormal state of the water area's hydrodynamic structure.

[0013] The water quality deterioration determination module is used to detect abnormalities in the ecological water quality purification capacity based on the dynamic imbalance of the aquatic ecosystem; identify the water pollution accumulation status based on the abnormality of the water hydrodynamic structure; and determine the water quality deterioration status of the measured water area based on the abnormality in the ecological water quality purification capacity and the water pollution accumulation status.

[0014] The flow velocity regulation and optimization module is used to determine the trend of water body chain ecological deterioration based on the water quality deterioration status of the measured water area; assess the risk status of abnormal flow velocity in the measured water area based on the trend of water body chain ecological deterioration; and optimize the water flow velocity regulation based on the risk status of abnormal flow velocity in the measured water area and the trend of water body chain ecological deterioration to obtain water flow velocity regulation and optimization data.

[0015] The present invention relates to an acoustic Doppler-based ecological flow measurement system. This system can implement any acoustic Doppler-based ecological flow measurement method of the present invention. It serves as a medium for coordinating the operation and signal transmission between various modules to complete the acoustic Doppler-based ecological flow measurement method. The internal modules of the system cooperate with each other and perform multi-parameter coupling analysis based on acoustic Doppler to accurately identify the hydrodynamic and ecosystem change processes of water bodies, thereby improving the comprehensive decision-making efficiency of water flow monitoring and ecological regulation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps involved in an ecological flow measurement method based on acoustic Doppler.

[0017] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0018] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical method 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0022] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] To achieve the above objectives, please refer to Figures 1 to 3 An ecological flow measurement method based on acoustic Doppler includes the following steps:

[0024] Step S1: Acquire flow measurement data of the water area; perform ecological flow measurement on the flow measurement data of the water area based on acoustic Doppler to obtain water area ecological flow measurement data; perform water flow velocity error compensation processing on the water area ecological flow measurement data to obtain water flow velocity error compensated data;

[0025] In this embodiment of the invention, a dedicated acoustic Doppler current meter is used to systematically sample the target flow measurement area, collecting flow velocity data at various vertical levels and spatial locations. During sampling, the device transmits sound waves and receives their reflected signals to accurately capture fluid velocity. During measurement, the acoustic Doppler meter determines preliminary data on the water flow velocity distribution based on the principle of sound wave frequency shift; the obtained data constitutes the preliminary ecological flow measurement data for the water body. The collected raw data includes information such as flow velocity, flow direction, and timestamps at each point, covering the flow velocity changes and spatiotemporal distribution characteristics within the water body. Subsequently, this ecological flow measurement data is input into a flow velocity error compensation module. This module utilizes a multi-factor correction method, combined with on-site collected information on water body complexity, such as algae density, biofilm thickness, and wave interference, to assess the acoustic Doppler flow measurement deviation. The error compensation process identifies non-uniform reflection zones within the water body, calculates the impact of sound wave multipath propagation effects on the signal, further analyzes local abnormal flow velocity measurement offset sections, and constructs a multi-factor correction coefficient for the water body. This correction coefficient integrates the distribution range of the main interference factors and the influence of fluctuation factors. Based on spatial interpolation techniques, it continuously processes the preliminary correction data to form water flow velocity error compensation data. This data, while preserving the original flow velocity characteristics of the measured water area, eliminates and corrects systematic errors and environmental interference from the acoustic measurement process, serving as the basic input for subsequent analysis steps.

[0026] Step S2: Assess the abnormal hydrodynamic structure of the measured water area based on the water flow velocity error compensation data; detect the gradient decline trend of the water area's ecological suitability based on the abnormal hydrodynamic structure; monitor the dynamic imbalance of the water area's ecosystem based on the gradient decline trend of the water area's ecological suitability and the abnormal hydrodynamic structure.

[0027] In this embodiment of the invention, the water velocity error compensation data obtained in step S1 is used to construct a spatial distribution map of the water velocity in the measurement area. By interpolating the velocity data of each sampling point in two-dimensional and three-dimensional coordinate systems, the spatial distribution of the magnitude and direction of the flow velocity in the water area is clarified. Based on the velocity distribution map, a velocity threshold of 0.03 m / s is set, and static flow sections with velocities below this threshold are identified and defined as dead water areas. Further analysis of the velocity direction is performed. When the local velocity direction deviates from the mainstream direction by more than 160 degrees, it is determined to be a local reverse flow area. Combined with the distribution of dead water areas, the degree of local vortex growth is calculated using a vortex identification algorithm, thereby quantifying the high-frequency fluctuation characteristics of the flow velocity. Through a comprehensive evaluation of the high-frequency fluctuations of the flow velocity and the vortex growth status, a hydrodynamic structural anomaly report is generated, clarifying the hydrodynamically unstable areas and anomaly characteristics in the measurement area. Subsequently, based on the hydrodynamic structural anomaly, hydrodynamic theory is used to infer the root scouring of aquatic plants, and drift trend analysis technology is used to monitor the migration status of phytoplankton in the water area, further evaluating the phytoplankton growth capacity attenuation level and the degree of aquatic plant fragmentation. Data collected by dissolved oxygen monitoring instruments was used to calculate the spatial decline trend of dissolved oxygen. Combined with fragmented data on aquatic plants, changes in the quality of biological habitats were assessed, and the decline trend of the aquatic ecological suitability gradient was determined. Finally, based on the superimposed analysis of the decline trend of the ecological suitability gradient and the abnormal state of hydrodynamic structure, the dynamic imbalance of the aquatic ecosystem was monitored, laying the foundation for subsequent ecological water quality assessment.

[0028] Step S3: Detect abnormalities in the ecological water quality purification capacity based on the dynamic imbalance of the aquatic ecosystem; identify the water pollution accumulation status based on the abnormality of the aquatic hydrodynamic structure; determine the water quality deterioration status of the measured water area based on the abnormality in the ecological water quality purification capacity and the water pollution accumulation status.

[0029] In this embodiment of the invention, based on the dynamic imbalance data of the aquatic ecosystem obtained in step S2, an ecological water purification capacity detection device is used, combined with biological and chemical indicator methods, to detect abnormalities in the self-purification function of the water body and monitor changes in nitrogen and phosphorus content and variations in biological community structure. Simultaneously, based on the abnormal hydrodynamic structure, a dynamic sampling device for shoreline soil particles is used to collect scour samples of shoreline soil particles. Using a particle size analyzer and sediment content detection technology, the depositional state and changing trends of shoreline sediment are determined. By comparing the abnormal scour data of shoreline soil particles with the sediment deposition data, the specific areas and degrees of water pollution accumulation are identified. The results of the abnormal ecological water purification capacity detection are comprehensively analyzed with the pollution accumulation data, combined with historical environmental monitoring data and on-site sampling data, to construct a water quality deterioration status assessment model and output a water quality deterioration status report. This report details the eutrophication level of the measured water area, the cumulative distribution of harmful substances, and their impact on the ecological environment, providing precise data support for ecological restoration.

[0030] Step S4: Determine the trend of water body chain ecological degradation based on the water quality deterioration status of the measured flow area; assess the risk of abnormal flow velocity in the measured flow area based on the trend of water body chain ecological degradation; optimize the flow velocity regulation of the water area based on the risk of abnormal flow velocity in the measured flow area and the trend of water body chain ecological degradation, and obtain the optimized flow velocity regulation data.

[0031] In this embodiment of the invention, the water quality deterioration report generated in step S3 is used to analyze the causal relationship between eutrophication, pollution accumulation, and biological community degradation in water bodies based on the ecological chain reaction theory, thus determining the chain-like ecological deterioration trend of the water body. Based on this trend, combined with hydrodynamic models and ecological risk assessment methods, the flow velocity anomaly risk index of the measured flow area is calculated. Through the linkage analysis between the flow velocity anomaly risk index and the water quality deterioration trend, a water flow velocity control scheme is designed, and flow velocity control equipment and hydrodynamic structure optimization measures are adopted to optimize the water flow velocity distribution in the measured flow area. The optimization process specifically includes adjusting the water flow velocity threshold setting, strengthening the flow velocity regulation in key areas, and constructing a flow velocity control optimization database to record the flow velocity control schemes and effect feedback outputs of the water flow velocity control optimization data for each area. This data details the control parameters, implementation schemes, and expected control effects, providing specific technical guidance and implementation basis for water environment management and ecological restoration.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: Obtain flow measurement data;

[0034] In this embodiment of the invention, high-precision aquatic environment monitoring instruments are used to comprehensively collect data from the flow measurement area, covering multi-dimensional parameters of the water body, including environmental indicators such as water depth, water temperature, turbidity, dissolved oxygen content, and suspended solids concentration. Data collection employs a multi-point deployment method, covering the surface, middle, and bottom layers of the water body to ensure uniform coverage of water parameter data within the spatial range of the flow measurement area. The data acquisition equipment samples in real time through a sensor array and stores the data uniformly using a data acquisition system, ensuring data time synchronization and integrity. The collected data undergoes preliminary verification, removing outliers and noise to form a flow measurement area dataset. This dataset serves as the foundation for subsequent complexity detection and ecological flow measurement, directly reflecting the environmental characteristics and basic hydrodynamic state of the flow measurement area, providing accurate environmental background data for subsequent steps.

[0035] Step S12: Detect the water body complexity data of the flow measurement area based on the flow measurement area data;

[0036] In this embodiment of the invention, based on the flow measurement data collected in step S11, a complexity analysis method is used to quantitatively assess the aquatic environment. Specifically, this includes calculating indicators such as the uniformity of suspended solids distribution, algae density distribution, and the thickness of the mixed layer in the water. Using multi-parameter fusion technology, combined with environmental variable data from sampling points, the structural complexity of the flow measurement area is analyzed from multiple dimensions. Spatial statistical analysis is performed on the collected turbidity, suspended solids concentration, and dissolved oxygen content data to calculate the coefficient of variation and spatial correlation indicators between each sampling point, reflecting the heterogeneity of the aquatic environment. Secondly, combined with water temperature gradient data, the distribution of the thermal layer structure is analyzed to assess water stratification. Through comprehensive normalization of the various indicator data, a numerical index of water complexity is constructed, clarifying the water complexity distribution map. This complexity data provides a basis for the analysis of acoustic Doppler data bias in ecological flow measurement, reflecting the influencing factors of the aquatic environment on sound wave propagation.

[0037] Step S13: Set up multiple vertical lines for the flow measurement area data, and set up multiple measurement points on each vertical line;

[0038] In this embodiment of the invention, before conducting flow velocity monitoring in the measurement area, basic survey data of the area is first acquired, including shoreline coordinates, water depth, cross-sectional width, and historical flow distribution. After acquiring the initial measurement area data, step S13 is executed to lay out multiple vertical lines on the measurement cross-section. The laying process uses a total station and an RTK-GNSS positioning system to locate the water surface survey lines, and the vertical line spacing is set equidistantly according to the cross-sectional width, for example, one vertical line every 5 meters. On each vertical line, several measuring points are distributed at equal depths from the water surface to the riverbed. The spacing between measuring points is determined by the water depth, typically set to one measuring point every 0.5 meters, ensuring that the vertical flow velocity distribution can be accurately depicted. The spatial three-dimensional coordinates of each measuring point are determined by the fixed position of the vertical line and the water depth data, and all measuring point numbers and vertical line numbers are synchronously stored in the flow measurement database.

[0039] Step S14: Move the ADCP from one bank of the cross section to the other bank, record the bank, and directly measure the average velocity of the vertical line through the ADCP, and record the start and end times of the velocity measurement;

[0040] In this embodiment of the invention, a four-beam ADCP (Acoustic Doppler Current Profiler) measurement device is used, installed on a dedicated current measurement platform, and slowly moved laterally from one bank to the opposite bank. During the movement across the cross-section, the ADCP continuously collects Doppler frequency shift data at different vertical depths within the water column using the principle of acoustic wave scattering. The system automatically identifies the location of each vertical line and sets a measurement window for that line, taking the average flow velocity value within that window as the representative flow velocity data for that vertical line. To ensure the time accuracy of the data, the ADCP synchronously records the start and end timestamps of the measurement and stores information such as the vertical line number, time, and flow velocity data uniformly. During this process, a shipborne inertial navigation system and an RTK differential positioning system are used to perform real-time positioning correction of the ADCP's movement path, avoiding data errors caused by measurement path deviation.

[0041] Step S15: Apply acoustic Doppler to perform ecological flow measurement processing on the flow measurement water area data and the average flow velocity of the vertical line to obtain the water area ecological flow measurement data;

[0042] In this embodiment of the invention, ecological flow measurement processing is performed based on the acquired vertical average flow velocity data and spatial information of the measurement points. The processing method calculates the flow velocity vector distribution at each measurement point using the acoustic Doppler principle and integrates vertical, lateral, and flow velocity components. During this process, the system performs spatial interpolation on the flow velocity variation trends between different verticals, and combines existing cross-sectional topography and shoreline feature data to construct a hydrodynamic field map of the flow measurement cross-section, obtaining high-precision ecological flow measurement data covering the entire flow measurement cross-section. This data structure contains information such as the flow velocity magnitude, flow direction, and water depth at each measurement point, serving as the core input data for subsequent acoustic deviation analysis and hydrodynamic anomaly structure identification.

[0043] Step S16: Evaluate the degree of acoustic Doppler flow measurement bias based on the water body complexity data of the water body measurement area;

[0044] In this embodiment of the invention, based on the water complexity data obtained in step S12, the acoustic Doppler flow measurement deviation of the aquatic ecological flow measurement data obtained in step S15 is evaluated. This evaluation is achieved by comparing the spatial matching relationship between the flow measurement data and the complexity index. Specifically, the theory of sound wave propagation attenuation and scattering is used to analyze the impact of sound wave signal reflection, refraction, and scattering phenomena on the flow measurement data in areas with high complexity. The complexity data is used to identify multipath propagation and signal attenuation phenomena of acoustic Doppler in areas with high turbidity or dense algae, and the local signal loss rate and noise interference intensity are calculated. Furthermore, based on the acoustic correction factor related to complexity, deviation estimation is performed for each sampling point of the flow measurement data, resulting in a flow measurement error probability distribution map. This map reflects the degree of acoustic Doppler flow measurement deviation caused by environmental complexity in the flow measurement water area, providing a quantitative basis for error compensation.

[0045] Step S17: Use the degree of acoustic Doppler flow measurement deviation to perform water flow velocity error compensation processing on the water ecological flow measurement data to obtain water flow velocity error compensation data.

[0046] In this embodiment of the invention, based on the degree of acoustic Doppler flow measurement deviation obtained in step S16, a multi-factor error compensation algorithm is used to correct the aquatic ecological flow measurement data. The compensation process adjusts the flow velocity data at each sampling point by introducing a correction coefficient based on environmental complexity. This algorithm comprehensively considers the multipath propagation effect of sound waves, environmental noise interference, and signal attenuation, dynamically correcting systematic and random errors in the flow measurement data. Specifically, it involves applying spatial filtering to the original flow velocity data to remove abnormal fluctuations, and using a weighted average adjustment of the flow velocity values ​​based on a complexity index to correct underestimation or overestimation of the measured values. After error compensation, the generated aquatic flow velocity error-compensated data fully reflects the actual water body flow velocity distribution, eliminating the interference of complex environments on acoustic flow measurement. The compensated data possesses higher spatial and temporal continuity, serving as accurate input for subsequent hydrodynamic and ecological analyses.

[0047] Preferably, step S14 includes the following steps:

[0048] Step S161: Identify the large-scale algal aggregation in the water body based on the water body complexity data of the flow measurement area;

[0049] In this embodiment of the invention, based on the acquired water body complexity data of the measured flow area, a high-precision optical imaging device combined with multispectral sensing technology is used to continuously scan the surface layer of the water body to extract the spatial density information of algae distribution on the surface. By analyzing the algae-specific reflection bands in the spectral data, the regions where algae aggregate and their coverage area are identified, thereby quantifying the large-scale algae aggregation status. This process combines the changes in sound wave reflection intensity in the acoustic Doppler flow measurement data with waveform signal processing technology to analyze the influence characteristics of algae aggregation on sound wave propagation, forming a spatial distribution map of large-scale algae aggregation. This graphical data reflects the algae density and spatial continuity in different areas of the water body, providing basic data support for subsequent biofilm detection and identification of non-uniform reflection areas.

[0050] Step S162: Monitor the excessive biofilm in the water body based on the water body complexity data of the flow measurement area;

[0051] In this embodiment of the invention, based on the water complexity data of the measured area, and combined with acoustic sensors and miniature underwater cameras, the thickness of the biofilm covering the water surface and mid-layer is detected in real time. The biofilm thickness is quantified using acoustic reflection time delay analysis and image recognition technology. Specifically, a high-frequency sonar signal transmission and echo reception device is used to measure the time difference of the reflected waveform to calculate the biofilm thickness. A miniature camera assists in verifying the accuracy of the reflection data and targets areas with thick biofilm on the water surface. By integrating acoustic and image data, a biofilm thickness distribution database is established to clarify the distribution and thickness values ​​of areas with excessive biofilm, providing data basis for subsequent judgments of biofilm exceeding 1.2 mm.

[0052] Step S163: Determine the non-uniform reflection area of ​​the water body based on the excessive biofilm exceeding 1.2 mm and the large-scale aggregation of algae in the water body;

[0053] In this embodiment of the invention, based on the large-scale algal aggregation area determined in step S161 and the biofilm thickness data obtained in step S162, and combined with spatial overlay analysis technology, non-uniform reflection areas in the water body are identified. Specifically, monitoring points with a biofilm thickness exceeding 1.2 mm are selected, and combined with the corresponding algal aggregation density data, GIS spatial analysis methods are used to overlay the two types of data to form a joint mapping. This joint mapping displays non-uniform reflection areas in the water body that simultaneously possess the characteristics of dense algae and large biofilm thickness. The data for non-uniform reflection areas include regional boundaries, differences in reflection intensity, and spatial distribution, serving as key input parameters for acoustic multipath propagation effect analysis. After completing this step, the specific range and related numerical indicators of the non-uniform reflection areas in the measured water body are obtained.

[0054] Step S164: Estimate the growth of the multipath propagation effect of sound waves using the non-uniform reflection region of the measured water body;

[0055] In this embodiment of the invention, the data of the non-uniform reflection region of the water body obtained in step S163 is used, combined with the acoustic propagation model and actual acoustic wave reflection echo data, to estimate the growth of the multipath propagation effect of acoustic waves. Specifically, an acoustic wave propagation simulation device is used, and the transmitter and receiver are positioned at key locations in the non-uniform reflection region of the water body to collect time-series signals of reflected waveforms and multipath echoes. A time-frequency analysis method is used to analyze the superposition intensity and time delay distribution of the multipath echoes, and the energy ratio of the multipath propagation signal to the direct signal is calculated. By comparing the difference in acoustic wave energy distribution between the non-uniform reflection region and the normal region, the percentage increase of the multipath propagation effect is obtained. This result quantifies the interference intensity of the non-uniform reflection region on the acoustic flow measurement signal, serving as a core indicator for subsequent judgment of abnormal enhancement conditions.

[0056] Step S165: When the increase of the multipath propagation effect of sound waves exceeds 133%, determine the abnormal enhancement of water echo data;

[0057] In this embodiment of the invention, a threshold comparison method is used to determine whether there is abnormal enhancement in the water echo data based on the estimated increase in the multipath propagation effect of sound waves. The specific process includes comparing the percentage increase in the multipath propagation effect with a preset threshold of 133%. When the increase exceeds this threshold, the sound echo data in that area is marked as having abnormal enhancement. The determination of abnormal enhancement is achieved by automatically recording the echo intensity change trend through the acoustic receiving system and combining it with real-time acoustic signal analysis to identify areas where the peak value of the sound wave reflection signal is abnormally increased. This determination result serves as an important basis for evaluating the quality of acoustic data in the flow measurement area and provides key parameters for subsequent deviation assessment.

[0058] Step S166: Identify the wave interference intensity of the flow measurement area based on the water body complexity data of the flow measurement area;

[0059] In this embodiment of the invention, based on the water complexity data of the flow measurement area, the wave interference intensity of the flow measurement area is identified using high-precision water surface monitoring instruments and wave sensors. Specifically, a laser Doppler velocimeter or a buoy-type wave altimeter is used to measure the water surface wave height, period, and frequency. Combined with changes in the water surface reflection signal from the acoustic flow measurement equipment, the degree of wave interference is quantitatively analyzed. Data processing employs spectrum analysis technology to extract the interference frequency bands in the wave signal and calculate the amplitude of acoustic signal fluctuations caused by waves. A spatial distribution model of wave interference intensity is constructed by combining water complexity indicators. Wave interference intensity, as a key environmental parameter affecting the accuracy of acoustic Doppler flow measurement, provides supporting data for subsequent analysis of flow velocity measurement accuracy attenuation.

[0060] Step S167: Estimate the accuracy attenuation of the flow velocity measurement based on the wave interference intensity of the flow measurement area.

[0061] In this embodiment of the invention, based on the wave interference intensity data obtained in step S166, a statistical regression analysis method is used to estimate the attenuation of the accuracy of the flow velocity measurement in the measured water body. Wave interference intensity is used as the independent variable, and the error statistics of the actual flow measurement data in the measured water body are used as the dependent variable to establish a regression model. The effectiveness of the model is verified through multiple field sampling data, and the quantitative relationship between wave interference intensity and the attenuation of flow measurement accuracy is obtained. Furthermore, based on the wave intensity distribution, the accuracy attenuation coefficient of flow velocity measurement in different areas is calculated. This attenuation coefficient reflects the negative impact of waves on the quality of acoustic Doppler flow measurement data, providing a quantitative basis for subsequent comprehensive evaluation of the degree of deviation.

[0062] Step S168: Evaluate the degree of acoustic Doppler flow measurement deviation of the aquatic ecological flow measurement data based on the abnormal enhancement of water body echo data and the attenuation of the accuracy of water flow velocity measurement.

[0063] In this embodiment of the invention, the degree of acoustic Doppler flow measurement deviation is assessed by combining the abnormal enhancement of water echo data determined in step S165 with the estimated attenuation of flow velocity measurement accuracy in step S167. Specifically, a weighted fusion algorithm is used, employing the abnormal enhancement and accuracy attenuation coefficients as weighting factors, and combining them with the statistical characteristics of the original flow measurement data to calculate the distribution of the deviation degree of the flow measurement data in different regions. During the assessment process, the influence of different environmental factors on the flow measurement data is compared, outliers and noise are removed, and a spatial distribution map of the degree of acoustic Doppler flow measurement deviation is generated. This assessment result reflects in detail the comprehensive impact of environmental complexity on flow measurement accuracy, providing an accurate deviation data basis for subsequent error compensation processing.

[0064] Preferably, step S17 includes the following steps:

[0065] Step S171: Identify abnormal offset sections in water flow velocity measurement based on the degree of acoustic Doppler flow measurement deviation;

[0066] In this embodiment of the invention, based on the acoustic Doppler flow measurement deviation data obtained in step S16, an anomaly detection algorithm is used to systematically analyze the flow velocity data at each monitoring point within the flow measurement area to identify flow velocity measurement sections with significant deviations. Specifically, a statistical control chart method is employed. By calculating the mean and standard deviation of the flow velocity deviation at each point, anomaly sections exceeding control limits are screened out. Spatial continuity analysis is then used to determine the range and boundaries of these anomaly sections. This process relies on real-time flow velocity data collected by a high-precision acoustic Doppler device, combined with quantitative indicators of deviation, to form a spatial distribution map of the anomaly deviation sections. This map accurately reflects the specific location and magnitude of the flow velocity measurement deviation within the water area, serving as the foundational data for subsequent interference factor analysis and achieving seamless integration of data and spatial characteristics.

[0067] Step S172: Determine the distribution range of the main control factors of water disturbance based on the abnormal offset section of water flow velocity measurement;

[0068] In this embodiment of the invention, based on the abnormal offset section of water flow velocity measurement identified in step S171, environmental factor detection equipment is used to monitor the interference factors existing in this section to determine the distribution range of the main controlling factors of water disturbance. These main controlling factors include, but are not limited to, algae density, biofilm thickness, wave intensity, and acoustic multipath effects. Specifically, a multi-parameter water quality analyzer combined with acoustic sensors is used to collect spatial distribution data of each interference factor. Spatial analysis of the data is performed using a Geographic Information System (GIS) to extract the geographical range covered by the main controlling factors. This range is then divided into grid cells using rasterization to ensure the spatial continuity and data accuracy of the main controlling factor distribution, providing a clear regional range and data support for subsequent fluctuation factor extraction.

[0069] Step S173: Extract water disturbance fluctuation factors based on the distribution range of the main control factors of water disturbance;

[0070] In this embodiment of the invention, within the distribution range of the main water disturbance control factors determined in step S172, time series analysis equipment is used to continuously monitor water environmental parameters and extract water disturbance fluctuation factors. Specifically, a high-frequency acoustic wave monitor is used to dynamically sample the intensity of reflection signals from algae and biofilms, combined with a wave monitor to record the changes in water surface wave height and frequency in real time. The fluctuation characteristics of the disturbance signal are separated using spectrum analysis technology. The collected environmental parameter time series data are statistically analyzed to calculate the numerical indicators of the fluctuation factor, such as the standard deviation of reflection intensity and the rate of change of wave height, forming quantitative data describing the disturbance fluctuations. This fluctuation factor reflects the dynamic changes of the main disturbance control factors in space and time, providing a dynamic data basis for constructing multi-factor correction coefficients.

[0071] Step S174: Construct multi-factor correction coefficients for water area based on the distribution range of water area disturbance fluctuation factor and water area disturbance main control factor;

[0072] In this embodiment of the invention, a multi-factor correction coefficient for water area disturbances is constructed based on the distribution range of the main control factors of water disturbances determined in step S172 and the disturbance fluctuation factors extracted in step S173. This coefficient is achieved through a multi-dimensional data fusion method, using a weighted linear combination to integrate each disturbance factor and its fluctuation index. The specific calculation formula is based on empirical weight allocation and normalization processing of measured data. The calculation process of the correction coefficient includes: normalizing each factor to eliminate the influence of dimensions; assigning weights according to the degree of influence of the disturbance factors; and finally obtaining the multi-factor correction coefficient through weighted summation. This coefficient is used to quantify the comprehensive influence of different disturbance factors on flow measurement deviations, forming a continuous spatial distribution map. This correction coefficient provides key parameters for subsequent flow velocity data correction processing, achieving accurate error correction based on multi-factor comprehensive analysis.

[0073] Step S175: Use the water area multi-factor correction coefficient to perform preliminary correction processing on the water flow velocity to obtain preliminary corrected water flow velocity data;

[0074] In this embodiment of the invention, the water flow velocity is initially corrected by using the multi-factor correction coefficients for the water area constructed in step S174 to evaluate the acoustic Doppler flow measurement deviation obtained in step S16. Specifically, the operation involves multiplying the original flow measurement deviation data by the corresponding spatial cell's multi-factor correction coefficient to obtain the adjusted deviation value. This process is performed in batches on a high-performance computing platform to ensure the synchronization and consistency of the spatial data. The correction results form preliminary corrected water flow velocity data, covering the new distribution state of the flow velocity deviation after multi-factor adjustment. This data can be used to identify local weakening areas of flow velocity measurement anomalies and the corrected flow velocity values, serving as the basis for spatial interpolation and error compensation processing.

[0075] Step S176: Identify abrupt changes in water velocity gradient based on preliminary correction data of water velocity;

[0076] In this embodiment of the invention, based on the preliminary corrected water flow velocity data obtained in step S175, a gradient detection algorithm is used to identify abrupt change points in the water flow velocity gradient. Specifically, a differential operator is used to calculate the gradient point by point on the spatially distributed flow velocity data, and combined with a threshold discrimination method, abrupt change points where the rate of change of flow velocity exceeds a preset threshold are extracted. This process utilizes a Geographic Information System (GIS) and numerical analysis tools to display the changes in flow velocity gradient in two-dimensional space through a numerical matrix, accurately locating areas of abnormal flow velocity changes. The identified abrupt change point information includes location coordinates and gradient change intensity, providing key nodes and boundary conditions for subsequent spatial interpolation correction, ensuring the spatial continuity and smoothness of the corrected data.

[0077] Step S177: Perform spatial interpolation correction processing on the water flow velocity gradient abrupt change point information to obtain water data spatial interpolation correction data;

[0078] In this embodiment of the invention, based on the information of abrupt changes in water velocity gradient determined in step S176, spatial interpolation correction processing is performed on the preliminary corrected water velocity data. Specifically, Kriging interpolation is used to spatially fit the preliminary corrected data. The abrupt change points are used as constraints during the interpolation process to prevent interpolation errors caused by gradient abrupt changes. The interpolation processing is based on the spatial autocorrelation characteristics of the velocity data, and the optimal interpolation parameters are determined through variogram analysis. The spatially interpolated corrected water data generated after interpolation retains the characteristics of abrupt changes in velocity while ensuring data continuity and smoothness, achieving reasonable adjustment of outliers and correction of data in surrounding areas. This interpolated data provides an accurate spatial data basis for error compensation.

[0079] Step S178: Perform water flow velocity error compensation processing based on the spatial interpolation correction data of the water area data and the preliminary correction data of the water flow velocity to obtain water flow velocity error compensation data.

[0080] In this embodiment of the invention, water flow velocity error compensation data is obtained by combining the preliminary corrected water flow velocity data obtained in step S175 and the spatial interpolation corrected water flow data obtained in step S177. This processing employs a weighted fusion method, fusing the two sets of data according to spatial weights allocated based on the spatial accuracy and reliability of the data. During the fusion process, the spatial continuity advantage of the interpolation corrected data is preferentially preserved, while the measurement accuracy of the preliminary corrected data is also considered. This results in accurate and stable water flow velocity error compensation output data containing precise spatial distribution information of the flow velocity, which can serve as the basis for subsequent ecological flow measurement analysis and hydrodynamic research, completing the entire closed-loop process from flow measurement deviation assessment to error correction.

[0081] Preferably, the assessment of abnormal hydrodynamic structure in the flow measurement area in step S2 includes:

[0082] A spatial distribution map of the flow velocity in the measured water area is constructed based on the water flow velocity error compensation data.

[0083] In this embodiment of the invention, the water flow velocity data, after error compensation processing, is input into a three-dimensional spatial coordinate system. Geographic Information System (GIS) tools or spatial analysis software based on numerical interpolation are used to spatially map and grid the collected flow velocity data, establishing isovelocity lines and flow velocity vector maps. During this process, interpolation techniques such as Kriging or Inverse Distance Weighted Interpolation (IDW) are used to spatially interpolate the flow velocity data between measuring points, ensuring the continuity and accuracy of the spatial distribution of flow velocity. By performing point-by-point spatial interpolation on the entire measuring water area, a high-resolution spatial distribution map of flow velocity is formed, where each grid cell contains information on the magnitude and direction of flow velocity at the corresponding location, ensuring that the spatial variation characteristics of the flow field are reflected. This spatial distribution map serves as the basis for subsequent analysis, ensuring that the flow velocity characteristics of all measuring points and their intervening regions are completely and accurately expressed.

[0084] Information on the stationary flow segment of the flow measurement area is determined based on the spatial distribution map of the flow velocity in the flow measurement area when the flow velocity is <0.03m / s;

[0085] In this embodiment of the invention, for the constructed spatial distribution map of flow velocity, all grid cells are traversed, and the flow velocity magnitudes are numerically compared. When the flow velocity value in a certain area is lower than 0.03 meters per second, that area is determined to be a stationary flow segment. This determination process employs a threshold screening technique combined with a flow velocity vector directional continuity check to ensure clear spatial boundaries of stationary flow segments. By merging adjacent low-velocity areas, a continuous set of stationary flow segments is formed, and corresponding spatial coordinate range information is generated. During the detection of stationary flow segments, a spatial clustering algorithm is used to assist in region division, eliminating isolated low-velocity points, ensuring the spatial integrity and rationality of stationary flow segment information, and obtaining stationary flow segment information containing the location, area, and flow velocity statistics of all stationary flow segments, providing a data foundation for identifying stagnant water areas.

[0086] Identify stagnant water areas in the flow measurement area based on information from static flow sections with high flow velocity in the measurement area;

[0087] In this embodiment of the invention, based on information about stagnant flow segments, spatial overlay and attribute analysis methods are used to further screen the stagnant flow segments and determine dead water regions. The criteria for determining dead water regions include the area of ​​the stagnant flow segment reaching a certain threshold and its spatial continuity. Through spatial topology analysis, the adjacency and connectivity of stagnant flow segments are identified, removing stagnant flow segments with excessively small areas or isolated distributions, thereby screening out regions with actual hydrodynamic stagnation characteristics. Combining water depth, bottom sediment, and water morphology information, the boundaries of the dead water regions are further confirmed, and the spatial range, area, and corresponding flow velocity characteristic data of the dead water regions are output, providing precise spatial location for subsequent hydrodynamic anomaly analysis.

[0088] When the velocity direction in the spatial distribution map of the flow velocity in the measuring water area deviates by more than 160°, the local reverse flow situation in the measuring water area is identified.

[0089] In this embodiment of the invention, in the velocity spatial distribution map, vector differences are calculated for the velocity directions of adjacent grid cells. By calculating the angle between the velocity vectors, regions with an angle greater than 160 degrees are selected, indicating the presence of local reverse flow in these regions. This calculation uses a vector dot product method to obtain the angle size and combines spatial adjacency relationships to aggregate the reverse flow regions. Through continuity determination, the spatial range and location of the local reverse flow are identified. After this step, the spatial boundary and velocity direction data of the local reverse flow are stored, providing a basis for vortex growth analysis.

[0090] The local eddy growth in the flow measurement area is estimated based on the local reverse flow and stagnant water areas in the flow measurement area.

[0091] In this embodiment of the invention, a spatial overlay analysis is performed on local reverse flow regions and stagnant water regions to identify overlapping or adjacent areas. Using a curl calculation method for velocity vector fields, curl values ​​are extracted from the velocity data in the overlapping regions; a larger curl indicates a higher vortex intensity. Regions with curl values ​​exceeding a set threshold are defined as local vortex growth regions. Simultaneously, combined with velocity time series analysis, the frequency and amplitude of velocity changes in these regions are analyzed to assess the dynamic trend of vortex changes. This process is implemented through numerical calculations, outputting spatial distribution, intensity, and dynamic characteristic data of local vortex growth, providing support for high-frequency fluctuation analysis.

[0092] The high-frequency fluctuations in the flow velocity of the measuring water area are determined based on the local eddy growth pattern in the measuring water area.

[0093] In this embodiment of the invention, based on the flow velocity time series data of the local vortex growth region, spectral analysis technology is used to perform Fourier transform on the flow velocity data to extract high-frequency fluctuation components. By analyzing the energy concentration areas in the spectrum, the frequency range and amplitude of the flow velocity fluctuations are determined. The high-frequency fluctuations are described by frequency, amplitude, and spatial distribution characteristics, and displayed in conjunction with a superimposed flow velocity spatial distribution map. During this process, time-frequency analysis tools are used to filter and denoise the data to ensure accurate extraction of high-frequency fluctuation characteristics, obtaining quantitative data reflecting the high-frequency changes in flow velocity in the measured water area, providing important parameters for the comprehensive assessment of hydrodynamic structural anomalies.

[0094] The abnormal hydrodynamic structure of the measuring water area is assessed based on the high-frequency fluctuations in flow velocity and the local eddy growth in the measuring water area.

[0095] In this embodiment of the invention, the quantitative characteristics of high-frequency velocity fluctuations are comprehensively analyzed with the spatial distribution and intensity data of local eddy growth, and a multi-parameter comprehensive discrimination method is used to form a hydrodynamic structural anomaly index system. Through spatial statistical analysis, the intensity of high-frequency fluctuations and eddy intensities are weighted and superimposed within the flow measurement area to obtain a hydrodynamic anomaly distribution map. Areas with high anomalies are identified as hydrodynamic structural anomaly areas, and the numerical values ​​reflect the severity of the anomaly. Combining the spatial information of velocity stationary sections and local reverse flows, a complete spatial distribution and quantitative description of the hydrodynamic structural anomaly status of the flow measurement area are output, forming key data for ecological flow measurement and subsequent water quality and ecosystem status assessment.

[0096] Preferably, the detection of the gradient decline trend of aquatic ecological suitability in step S2 includes:

[0097] Estimate the erosion status of aquatic plant roots based on abnormal hydrodynamic structure of the water area;

[0098] In this embodiment of the invention, spatial distribution data of abnormal hydrodynamic structures in aquatic waters, combined with spatial distribution maps of flow velocity, are used to analyze the distribution of shear force on the root region of aquatic plants. Specifically, a hydrodynamic shear force calculation method is employed, calculating the shear stress of the water body on the surface of the plant root zone based on flow velocity and direction data, using the formula τ=ρ*u_ 2 Where τ is the shear stress, ρ is the water density, and u_ is the frictional velocity, calculated through velocity profiles. High-resolution hydrodynamic data are used to characterize the velocity gradient of the water boundary layer around the plant roots, accurately obtaining the velocity distribution on the root surface. Spatiotemporal statistics of shear force data are performed to identify regions where shear force exceeds the plant root tolerance threshold, quantifying the scour intensity. Through spatial overlay analysis, a root scour distribution map is generated, including the scour intensity level and its spatial range, providing crucial physical basis data for subsequent drift trend analysis.

[0099] The drift trend of phytoplankton in aquatic areas is detected based on the erosion of the roots of aquatic plants.

[0100] In this embodiment of the invention, a scour intensity distribution map formed by root scour is used and jointly analyzed with spatial distribution and concentration data of phytoplankton in the water. By combining acoustic Doppler current measurement data and phytoplankton sampling results, the transport paths and velocities of phytoplankton are calculated. A particle tracking method is employed to simulate the motion trajectories of floating particles in the water, reflecting the drift trend. The drift trend is described by the spatial distribution of the drift direction vector field and drift rate. Specific methods include continuous monitoring and statistical analysis of phytoplankton concentration changes over time and space, identifying the consistency of drift direction and the gradient changes in drift rate. The drift trend results are presented as a series of spatial vector and concentration change curves, supporting growth capacity decay analysis.

[0101] The decline in the growth capacity of phytoplankton in aquatic areas was determined based on the drift trend of phytoplankton and the erosion of their roots.

[0102] In this embodiment of the invention, drift trend and root scour intensity data are integrated to analyze changes in the growth environment of phytoplankton in different water areas. By correlating drift speed and direction in the drift trend with the degree of sediment disturbance caused by root scour, the stability of phytoplankton populations and changes in growth conditions are assessed. Specifically, a growth vigor decay index is used, which reflects the spatial thinning trend of plant distribution and the limitation of local population growth based on the increase in drift speed and the increase in root scour intensity. The growth vigor decay status is presented in numerical form, and the spatial distribution map represents the strength of growth vigor in different areas, laying a data foundation for plant fragmentation analysis.

[0103] Assess the fragmentation status of aquatic plants based on the decline in phytoplankton growth and the root erosion status of aquatic plants.

[0104] In this embodiment of the invention, the connectivity and distribution structure of surviving plant communities are detected by combining the spatial distribution of growth vigor decline and root erosion areas through spatial clustering and morphological analysis. Spatial fracturing and fragmentation indices are used to identify the number, size, and distribution of fracturing points and fragments within the plant community. This step utilizes remote sensing imagery or underwater photography combined with hydrodynamic data to verify the location and morphological characteristics of fragmented areas. By statistically analyzing fragmentation degree indicators, a spatial map of plant fragmentation is generated, clearly identifying areas of weakened community connectivity and providing fundamental data support for ecological suitability assessment.

[0105] The decline in dissolved oxygen content in water bodies is determined based on abnormal hydrodynamic structures in the water area.

[0106] In this embodiment of the invention, based on the spatial distribution of hydrodynamic anomaly areas and combined with data from high-precision dissolved oxygen sensors deployed on-site, the system analyzes the variation characteristics of dissolved oxygen in various hydrodynamic anomaly areas. Specifically, a hybrid data acquisition method combining continuous sensor monitoring and fixed-point manual sampling is employed. Sensors are deployed at different depths and locations within the anomaly areas to achieve real-time dynamic monitoring of dissolved oxygen concentration, acquiring high temporal resolution dissolved oxygen change data. Simultaneously, fixed-point sampling, supplemented by manual water sampling, supplements accurate dissolved oxygen measurements at specific time points and spatial locations, ensuring data integrity and accuracy. By summarizing and integrating the aforementioned spatiotemporal dissolved oxygen data, a detailed spatiotemporal distribution database of dissolved oxygen is established. Based on this database, a dissolved oxygen decay rate calculation model is used to quantitatively analyze the decay trend of dissolved oxygen in each anomaly area. This model comprehensively considers the oxygen consumption rate and oxygen regeneration rate in the water body, combined with hydrodynamic parameters such as flow velocity, local eddy intensity, and fluid turbulence, to form an oxygen dynamic balance equation, which describes the change process of dissolved oxygen content with time and space. Oxygen consumption rate encompasses biochemical processes such as microbial metabolism, organic matter decomposition, and plant respiration, while oxygen regeneration rate includes factors such as photosynthesis and gas exchange. The dissolved oxygen content change curve calculated by this model can accurately reflect the spatiotemporal decay pattern of oxygen concentration in various hydrodynamic anomaly areas. Based on the calculation results, a dissolved oxygen content decay distribution map is plotted, using color gradients or contour lines to represent the distribution and intensity of dissolved oxygen concentration changes. This clearly indicates the oxygen status of the biological habitat environment in different anomaly areas of the water body, providing key environmental parameter data support for subsequent biological habitat quality assessment and ensuring the scientific rigor and relevance of ecological monitoring and management.

[0107] Assess the quality degradation of aquatic habitats based on the decline in dissolved oxygen levels and the fragmentation of aquatic plants.

[0108] In this embodiment of the invention, a spatial overlay analysis is performed on the spatial distribution map of dissolved oxygen content decline in water bodies and the fragmentation status map of aquatic plants. Using a unified geospatial reference system, the data from the two layers are precisely registered to ensure accurate spatial correspondence of each data point. High-resolution raster data format is used to convert the dissolved oxygen content decline data and plant fragmentation status data into spatial raster layers of the same scale, providing foundational data for subsequent comprehensive evaluation. A multi-factor comprehensive evaluation method is employed, using weighted overlay technology to fuse the decline in dissolved oxygen concentration with plant community connectivity indicators according to pre-set weight ratios. The weights are determined based on quantitative analysis of their impact on ecosystem structure and function, ensuring that the contribution of each factor to the habitat quality decline index is scientifically reasonable. In the calculation process, the decrease in dissolved oxygen content is first normalized to quantify the relative reduction in oxygen concentration in different regions. Then, spatial continuity and connectivity analysis is performed on plant fragmentation indicators to assess the degree of fragmentation and evenness of distribution of plant communities. Subsequently, the two indicators are combined to generate a single habitat quality degradation index. This index can comprehensively reflect the health status of biological habitats in the region and the intensity of environmental pressure they are under. The spatial distribution map of biological habitat quality degradation generated by this index clearly shows the key areas in the ecosystem with high degradation risk. In the spatial distribution map, each region is represented by different degrees of degradation in the form of color scales or contour lines.

[0109] The gradient decline trend of aquatic ecological suitability was detected based on the degradation of aquatic habitat quality and the fragmentation of aquatic plants.

[0110] In this embodiment of the invention, the aquatic habitat quality degradation index and the aquatic plant fragmentation index are integrated. The data of both are spatially registered using a Geographic Information System (GIS) platform to ensure accurate correspondence of the index data within the same spatial coordinate system. Using a high-precision raster data format, the degradation and fragmentation indices are converted into continuous spatial distribution data, forming two spatial raster layers. Subsequently, spatial gradient analysis is applied to calculate the spatial rate of change of the data in both layers point by point. Specifically, the differences in index values ​​and gradient directions between adjacent raster cells are calculated to analyze the spatial variation trends of habitat quality and plant fragmentation indices within the aquatic area. Through gradient calculation, spatial regions where ecological suitability gradually declines from high to low are identified, and gradient vector fields and contour maps are plotted to visually present the spatial trend characteristics of suitability decline. Simultaneously, combined with the spatial distribution data of hydrodynamic structural anomalies, these anomalies are used as weights or influencing factors and incorporated into the spatial gradient model to construct a spatial gradient degradation model for ecological suitability. This model can quantify the correlation between ecological suitability decline and hydrodynamic anomalies, and enhance the explanatory power of the gradient trend map. The output ecological suitability gradient decline trend curve reflects the variation law of ecological suitability index with the spatial location of water area, while the spatial distribution map shows in detail the distribution characteristics of suitability gradient in different regions, providing scientific basis and spatial guidance for water ecological management and restoration measures.

[0111] Preferably, the monitoring of the dynamic imbalance of the aquatic ecosystem in step S2 includes:

[0112] Assess the increasing complexity of aquatic ecosystem survival based on the gradient decline trend of aquatic ecological suitability.

[0113] In this embodiment of the invention, based on a constructed ecological suitability gradient decay trend map, spatial data analysis technology is used to quantitatively extract and compare the ecological suitability index of different regions in the gradient map. GIS spatial analysis tools are used to partition the gradient map, dividing ecological suitability into multiple levels from high to low, and extracting the ecological suitability value and its spatial distribution characteristics for each level. Then, by statistically analyzing the changing trends of ecological factors (such as plant diversity index, plankton abundance, and habitat complexity) within different level levels, a quantitative index system for ecological survival complexity is established. This system assesses whether the survival complexity of the ecosystem is increasing by comparing the changes in ecological factors within each gradient interval, obtaining spatial distribution data and quantitative values ​​of ecological survival complexity, providing a basis for subsequent analysis.

[0114] Determine the extent of exceedance of hydrodynamic thresholds in a water area based on abnormal hydrodynamic structure conditions.

[0115] In this embodiment of the invention, based on detailed data of abnormal hydrodynamic structures, threshold standards for several key hydrodynamic parameters are set, including velocity fluctuation range, velocity direction deviation, and vortex intensity. High-precision hydrodynamic parameter data measured by an acoustic Doppler current meter are used, combined with the aforementioned threshold standards, to screen and determine abnormal data points. Specifically, this involves real-time monitoring of velocity and vortex data over continuous time periods, and using statistical methods to identify abnormal fluctuation periods and spatial locations exceeding preset thresholds. Spatial clustering analysis is performed on data areas exceeding the thresholds to clarify the specific range and degree of hydrodynamic anomalies. The resulting data on hydrodynamic threshold exceedances is used to guide subsequent analysis of their impact on the ecosystem.

[0116] Monitor the degree of damage to aquatic biological migration channels based on the extent to which hydrodynamic thresholds in the water area exceed limits;

[0117] In this embodiment of the invention, based on the obtained hydrodynamic threshold exceedance area and combined with the channel distribution information of fish and other migratory organisms in the water area, acoustic Doppler current velocity detectors and underwater cameras are deployed to monitor the velocity distribution and flow pattern changes along the target migration path in detail. By comparing the velocity patterns under normal hydrodynamic conditions with those within the threshold exceedance area, the obstructive impact of velocity anomalies on the migratory channels is analyzed. Specifically, this includes assessing the impact range and severity of velocity abrupt change points, flow reversal points, and eddy intensity changes within the channel on the migration paths of migratory organisms. Combined with biological behavioral data, the effective passage rate and degree of damage of the migratory channels are quantitatively calculated to obtain spatially distributed data on the degree of damage to the migratory channels, serving as an important parameter for the dynamic changes of the ecosystem.

[0118] The trend of hindered reproduction of aquatic organisms is estimated based on the degree of damage to aquatic organism migration channels and the exceedance of aquatic hydrodynamic thresholds.

[0119] In this embodiment of the invention, a comprehensive analysis of the spatial distribution of disrupted migratory channels and areas exceeding hydrodynamic thresholds is conducted. A system of factors influencing reproductive obstruction is constructed to quantitatively assess reproductive obstruction trends. Data on the quantity and distribution of fish eggs, larvae, and adults obtained through multi-point sampling is used, combined with hydrodynamic anomaly parameters, to analyze the overlap between spatial distribution changes in the biological population and hydrodynamic anomaly areas during the reproductive period. Through time series analysis and spatial cross-comparison, an obstruction impact coefficient is calculated, reflecting the degree of obstruction to the biological reproductive process caused by hydrodynamic anomalies and channel disruption. Ecological indicators such as reproductive success rate, hatching rate, and larval survival rate are also used to verify the accuracy of the reproductive obstruction trend. The obtained data on reproductive obstruction trends provide a basis for population conservation and ecological regulation.

[0120] The decline in the regeneration capacity of aquatic organisms is determined based on the trend of hindered reproduction and the degree of damage to aquatic organism migration channels;

[0121] In this embodiment of the invention, data on the trend of hindered biological reproduction are comprehensively analyzed with data on the degree of damage to biological migration channels. Using the principles of population dynamics, the renewal rate and replacement rate of biological populations are calculated. The operational process includes: collecting long-term monitored data on the structure and age distribution of biological populations; quantifying the decline in population renewal capacity by comparing and analyzing the population renewal rate under normal ecological conditions with the population dynamics under abnormal hydrodynamic conditions. This calculation process incorporates indicators such as flow velocity changes, habitat connectivity, and reproductive success rate to clarify the spatial range and temporal trend of hindered population renewal. The quantitative results of the decline in population renewal capacity are used to generate spatial distribution maps and numerical reports to guide ecological restoration plans.

[0122] The degree of decline in aquatic species richness is determined based on the decline in the regeneration capacity of aquatic biological populations and the increase in the complexity of aquatic ecological survival.

[0123] In this embodiment of the invention, data on biological population renewal capacity and ecological survival complexity are comprehensively utilized. A multi-indicator fusion technique is employed to calculate the species richness decline index. Based on the ecological complexity growth trend map and the spatial distribution of population renewal capacity, key ecological parameters for each region are extracted. Subsequently, a weighted overlay method is used to comprehensively evaluate species diversity, individual density, and ecological complexity as indicators. The spatial variation magnitude and trend of species richness are analyzed to identify hotspots of significant richness decline. By continuously monitoring species richness changes over different time periods, the dynamic evolution of the degree of decline is confirmed, forming a quantitative evaluation system and spatial distribution map of species richness decline. This result provides important basic data for ecological protection decision-making.

[0124] Monitoring the dynamic imbalance of aquatic ecosystems based on the degree of decline in aquatic species richness.

[0125] In this embodiment of the invention, the degree of species richness decline is used as the core indicator, combined with multidimensional dynamic monitoring data of the ecosystem, including biodiversity indicators, habitat connectivity, biological population structure, and hydrodynamic anomaly data. Through a comprehensive ecosystem health assessment method, continuous monitoring of the dynamic imbalance of aquatic ecosystems is achieved. Specifically, this involves using multi-temporal remote sensing data and acoustic Doppler flow measurement data, combined with field ecological survey data, to quantitatively analyze the dynamic changes in ecosystem structure and function. By constructing an ecosystem imbalance index, the decline in species richness is linked to multiple factors such as habitat fragmentation, hydrodynamic anomalies, and abnormal biological behavior, reflecting the dynamic state of the ecosystem in real time. The index outputs a spatial distribution map and temporal variation curve of ecosystem dynamic imbalance, providing a scientific basis for ecosystem management and restoration measures.

[0126] Preferably, step S3 includes the following steps:

[0127] Step S31: Detect abnormalities in the ecological water purification capacity based on the dynamic imbalance of the aquatic ecosystem;

[0128] In this embodiment of the invention, ecosystem health indicators and biological diversity data obtained from the aforementioned monitoring results of dynamic imbalance in aquatic ecosystems are used as a foundation. Multiple water quality monitoring stations are deployed on-site, and high-precision water quality analysis instruments are used to collect key water quality parameters in real time, including but not limited to chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen content, total phosphorus, and total nitrogen concentrations. The collected data are analyzed using time-series methods, combined with ecosystem imbalance indicators, to calculate an ecological water purification capacity index. Specifically, based on hydrodynamic structure data provided by an acoustic Doppler current meter, the spatiotemporal distribution of sampling is adjusted to ensure that the sampling points cover the water characteristics of both dynamically imbalanced areas and adjacent normal areas. Correlation analysis is performed between the monitored water quality parameters and hydrodynamic characteristic data to extract key hydrodynamic factors affecting water purification capacity. By establishing a quantitative relationship between water purification efficiency and the dynamic imbalance of the ecosystem, the degree of decline in purification capacity in abnormal areas is clarified, and an abnormal distribution map of ecological water purification capacity is output, providing a basis for subsequent water quality assessment and treatment.

[0129] Step S32: Estimate the abnormal erosion of soil particles along the shoreline of the water area based on the abnormal hydrodynamic structure of the water area;

[0130] In this embodiment of the invention, hydrodynamic structural anomaly data collected by an acoustic Doppler current meter, including areas of abnormal flow velocity, flow reversal points, and local eddy intensities, are combined with shoreline soil particle size distribution data to assess the risk of abnormal scour. On-site sampling and analysis of shoreline soil particle size composition and cohesion indices are conducted, and qualitative and quantitative analysis of particles is performed using a laser particle size analyzer and scanning electron microscope. Based on the theory of flow velocity threshold and critical shear force of soil particles, the critical scour point for different shoreline sections under hydrodynamic anomaly conditions is calculated. By comparing the correspondence between the current flow velocity and the critical shear force, particle loosening and migration phenomena caused by flow velocity anomalies are identified. Combined with the spatial distribution of hydrodynamic anomalies, areas of abnormal shoreline particle scour are delineated, forming a spatial distribution map of abnormal shoreline soil particle scour conditions. This map reflects the soil scour risk areas affected by hydrodynamic anomalies, supporting the analysis of dynamic changes in sediment and pollutants.

[0131] Step S33: Detect the sediment deposition status of the bottom sediment on the shore of the water body based on the abnormal scouring of soil particles on the shore.

[0132] In this embodiment of the invention, a multi-point sediment sampler is used to set up sampling points in the abnormal scouring area of ​​the shoreline and its adjacent areas, and sediment samples are collected periodically. Particle size distribution analysis, organic matter content determination, and heavy metal content detection are performed on the sampled sediments. Deposition rates are calculated using gravimetric and volumetric methods. Combined with flow field data provided by an acoustic Doppler current meter, the influence mechanism of flow velocity and eddy intensity on sediment deposition is analyzed in detail. A sediment dynamic equilibrium model is used to compare the particle transport caused by abnormal scouring of shoreline soil particles with the sediment deposition amount, quantifying the sediment deposition status. By monitoring the temporal changes in sediment thickness and composition, a dynamic distribution map of shoreline sediment deposition is plotted. This distribution map reveals the spatial evolution of sediment deposition, providing basic data for further analysis of pollutant accumulation and ecological environment changes.

[0133] Step S34: Identify the water pollution accumulation status based on the sediment deposition status of the bottom sediment and the abnormal erosion status of the soil particles along the water's edge;

[0134] In this embodiment of the invention, spatiotemporal distribution analysis of pollutant content is conducted based on the aforementioned dynamic distribution data of sediment deposition and data on soil particle scouring along the shore. A high-sensitivity mass spectrometer is used to quantitatively detect multiple pollutant components (such as heavy metals, polycyclic aromatic hydrocarbons, and organic pollutants) in the sediment samples. Combined with soil particle transport trends, the migration and accumulation pathways of pollutants are inferred. Spatial overlay analysis technology is used to fuse pollutant concentration data with sediment thickness and deposition rate data to form a spatial distribution map of pollution accumulation. By comparing the correlation between changes in pollutant content and the shore scouring and deposition process, the enrichment and migration mechanisms of pollutants under the influence of abnormal hydrodynamics are clarified. This identification of pollution accumulation status supports the scientific formulation of water quality management and pollution prevention and control decisions.

[0135] Step S35: Determine the water quality deterioration status of the measured water area based on the abnormal ecological water purification capacity and the accumulation of water pollution.

[0136] In this embodiment of the invention, abnormal distribution data of ecological water purification capacity are systematically integrated with spatial distribution maps of pollution accumulation. A comprehensive evaluation method is employed to calculate a water quality deterioration index. Areas with declining purification capacity are spatially overlaid with high-risk areas of pollution accumulation. A weighted scoring mechanism is used to assess the spatial extent and severity of water quality deterioration based on pollutant concentration levels, the degree of purification capacity attenuation, and hydrodynamic anomaly parameters. The formation mechanism and diffusion trend of water quality deterioration are analyzed in conjunction with hydrodynamic anomaly characteristics. The output includes a spatial distribution map and numerical report of water quality deterioration status, reflecting the water quality health level throughout the measured flow area. This data provides quantitative evidence for water quality management, ecological restoration, and management measures, and guides subsequent environmental risk assessment and dynamic monitoring.

[0137] Of particular importance, step S32 includes the following steps:

[0138] Step S321: Determine the trend of excessive scouring velocity in the water area based on the abnormal hydrodynamic structure of the water area;

[0139] In this embodiment of the invention, based on three-dimensional velocity data collected by an acoustic Doppler current meter, the spatial distribution of velocity at different locations in the water body is obtained after error compensation and data filtering. Using this velocity spatial distribution map, regions exceeding a specific threshold are identified, with particular attention paid to the velocity change trend along the shoreline. By calculating the velocity change rate and velocity gradient at continuous measuring points, it is determined whether there is a trend of a sharp increase in velocity, especially the difference between the maximum velocity value along the shoreline and the surrounding velocity values. This process, combined with velocity direction data, ensures that the analyzed velocity changes represent an acceleration along the main direction of the water flow. The output includes a spatial distribution map and trend data containing excessive scouring velocity trends, providing accurate velocity baseline data for subsequent shear force calculations. This operation utilizes high-precision acoustic Doppler current measurement equipment combined with numerical calculation tools to ensure accurate identification and spatial positioning of velocity trends.

[0140] Step S322: Calculate the soil shear force growth based on the trend of excessive water flow velocity in the water area;

[0141] In this embodiment of the invention, utilizing the established trend of excessively high water flow velocity and combining water physical parameters such as water density and viscosity, a quantitative analysis of the shear force of the shore-side flow is performed based on the frictional shear force calculation formula in fluid mechanics. Specifically, the nearshore boundary layer theory is employed, and the shear stress is calculated using velocity profile measurement data. The calculation formula is τ=ρ*u_ 2 Where ρ is the water density and u_ is the shear velocity. The shear velocity is obtained by fitting a flow velocity profile. Combined with the measured flow velocity distribution, the shear force values ​​in the bank area are calculated point-by-point, forming a spatial distribution map of shear force. The growth of shear force is calculated by comparing it with historical monitoring data or setting a baseline value, determining the relative percentage increase in shear force to accurately reflect the changing trend of water flow shear stress on the soil surface. This calculation process relies on high-resolution on-site flow velocity data and physical and mechanical formulas to ensure that the shear force growth is quantitatively clear and has temporal and spatial consistency.

[0142] Step S323: Predict the sudden increase trend of water turbulence based on the trend of excessive water flow velocity.

[0143] In this embodiment of the invention, the turbulent behavior of the water flow is analyzed based on the excessive trend of scouring velocity and the characteristics of velocity fluctuations. High-frequency velocity time-series data collected by an acoustic Doppler current meter are used for time-frequency analysis to calculate turbulence intensity indices, such as turbulent kinetic energy (TKE) and vorticity, to quantitatively reflect the severity of velocity fluctuations. Statistical analysis of the amplitude and frequency of velocity fluctuations is used to determine the spatial distribution and temporal evolution of sudden turbulent growth. Statistical methods are used to identify the frequency of high-amplitude fluctuation events in instantaneous velocity pulsations to determine the sudden growth trend of turbulence. This process relies on high-sampling-rate acoustic Doppler data and mathematical statistical analysis to generate a spatial distribution map and trend report of water flow turbulence intensity, providing important dynamic parameter support for subsequent scouring estimation.

[0144] Step S324: Estimate the abnormal scouring of soil particles along the shoreline of the water body based on the sudden increase trend of water flow turbulence and the increase of soil water flow shear force.

[0145] In this embodiment of the invention, a comprehensive assessment of abnormal erosion of riverbank soil particles is conducted by combining the sudden increase trend of water flow turbulence with the increase of soil shear force. Based on the spatial distribution data of shear force and turbulence intensity, areas exhibiting abnormal erosion are identified by setting critical shear force thresholds for soil particles and soil stability parameters. The probability and intensity of soil particle loosening are estimated by superimposing and analyzing turbulence fluctuation frequencies and shear force peak values. An empirical formula combined with field data is used to quantify the erosion intensity, generating a spatial distribution map of abnormal erosion of riverbank soil particles. This map reveals the detailed impact of hydrodynamic anomalies on riverbank soil structure, providing a scientific basis for subsequent riverbank protection and ecological restoration. All calculations and analyses are based on acoustic Doppler velocity data and field physical parameters to ensure the accuracy and practicality of the assessment results.

[0146] Of particular importance, step S33 includes the following steps:

[0147] Step S331: Estimate the degree of looseness of soil particles along the shore based on the abnormal erosion of soil particles along the shore.

[0148] In this embodiment of the invention, during the estimation of soil looseness based on abnormal soil particle impaction patterns along the water's edge, an acoustic Doppler current meter is used to perform high-precision, subdivided mapping of water flow velocity in the boundary area of ​​the water body, obtaining nearshore water body expansion and gradual velocity distribution data. Simultaneously, shoreline topographic information and soil-water ratio quantitative structure data are collected, and particle migration event constants and distribution are observed using accelerated geomagnetic imaging and dedicated particle detection equipment. By regional matching of velocity distribution data with areas of abrupt decrease in surface soil particle density, group structure analysis is used to classify soil looseness into severe, moderate, and slight levels, resulting in a classification distribution map of soil looseness along the water's edge.

[0149] Step S332: Determine the degree of weakening of soil cohesion on the shoreline based on the looseness of soil particles.

[0150] In this embodiment of the invention, based on the looseness of soil particles along the water's edge, effect series analysis and hierarchical framework detection are used to determine the soil burial level and forest morphological structure, accurately assessing the degree of abrupt reduction in the original soil particle holding capacity. By performing dissipative analysis on the distribution of elements within the water body and the combined forces of soil physical structure, the changes in inter-particle forces and the flow field mechanical differences are applied as components to reveal the precision of soil particle binding, yielding the results of the weakened distribution of soil particle binding capacity along the water's edge.

[0151] Step S333: Estimate the soil structure instability trend based on the weakening of soil bonding strength along the water's edge;

[0152] In this embodiment of the invention, based on the weakened distribution results of soil particle binding force, the soil structure stability detection is carried out in zones. The ground-integrated ground exploration equipment is used to conduct planar exploration of the deepened area of ​​the water shore. A linear distortion comparison is performed by adding a chain geotechnical body and a motion model analysis system under auxiliary program control. Combined with the quantitative analysis of water-ink distributed stress field and geological composition, the classification observation results of unstable soil structure areas are obtained according to the movement response of soil layer stress distribution, and the soil structure instability trend analysis is completed.

[0153] Step S334: Predict the growth of soil erosion risk based on the trend of soil structural instability;

[0154] In this embodiment of the invention, based on the analysis results of unstable soil structure areas, a combined geological loss analysis is used to divide each area according to the geological eastward vibration and left-right time phase difference corresponding to the flow field distribution. The decreasing tendency of soil layer utilization and engineering geological control is calculated through a chain-like distributed grid. Based on the analysis of horizontal vortex distribution along the water body and temperature difference, the soil damage quantification of the stress flow field on the water bank is predicted, resulting in a classification distribution of soil erosion risk growth.

[0155] Step S335: Predict the increasing trend of soil subsidence risk based on the growth status of soil erosion risk and the trend of soil structural instability;

[0156] In this embodiment of the invention, based on the analysis results of the degree of soil erosion risk growth and the trend of soil structural instability, stress analysis and full-scale zonal distribution comparison are performed through field verification and topographic data simulation. Water-ink high-level analysis and soil layer loss tendency analysis are carried out. Through thickened planar distribution and source area directional detection, temperature measurement of the soil horizontal layer and comparison of forest source support pressure are achieved. The results of the horizontal ground plane temperature measurement comparison analysis of the soil temperate zone are obtained, thereby predicting the horizontal temperature difference tendency under the horizontal base temperature measurement model of the soil temperature measurement culvert, and obtaining the soil temperate zone temperature difference tendency analysis results.

[0157] Step S336: Detect the sedimentation status of bottom sediments along the shore of the water area based on the increasing trend of soil subsidence risk.

[0158] In this embodiment of the invention, based on the analysis results of soil temperate temperature difference tendency, a combination of ground vibration wave detection and depth imaging is used to conduct high-resolution detection of the distribution of bottom sediment layers on the shore of the water area. According to the detection results and the prior soil melting tendency data, the total amount and distribution statistics of bottom sediment are displayed through layer mapping analysis, and a bottom sediment accumulation distribution map and bottom sediment ground stress analysis table are obtained.

[0159] Preferably, step S4 includes the following steps:

[0160] Step S41: Estimate the eutrophication status of the measured flow body based on the water quality deterioration status of the measured flow area;

[0161] In this embodiment of the invention, based on the spatial distribution data of water quality deterioration obtained in step S3, the main nutrient indicators in the measured flow area are systematically monitored and analyzed. Multi-point water quality sampling equipment is deployed on-site to periodically collect samples of various forms of nitrogen and phosphorus (such as total nitrogen, total phosphorus, ammonia nitrogen, and phosphate) in the water. Combined with flow velocity and flow field structure data provided by an acoustic Doppler flowmeter, the influence of hydrodynamic conditions on nutrient concentration distribution and migration is analyzed. The spatiotemporal variation patterns of nutrient content are determined using laboratory chemical analysis methods. The eutrophication level of the measured flow water body is quantitatively estimated using the correlation between nutrient concentration and water quality deterioration status. The results are expressed as a eutrophication index, forming a spatial distribution map of eutrophication in the water body. This map reflects the spatial differences in eutrophication levels, providing basic data for subsequent ecological chain reaction analysis.

[0162] Step S42: Determine the trend of chain-like ecological degradation of the water body based on the water quality deterioration status and eutrophication of the measured water area;

[0163] In this embodiment of the invention, by integrating water quality deterioration data and eutrophication index, the chain reaction between different ecological processes in the water body is analyzed. Based on the spatial distribution map of water quality deterioration and eutrophication, high-risk areas are identified. Combined with hydrodynamic anomaly data, the spatial overlap between excessive algal proliferation and dissolved oxygen decline in the water body is determined. Phytoplankton samples are collected for biomass and species composition analysis to assess changes in algal community structure. Hydrodynamic data is used to analyze the drift and settling patterns of phytoplankton and study the chain effect of algal blooms on water quality. Based on the spatiotemporal evolution trends of the biological community structure and hydrochemical indicators in the water body, a chain-like ecological deterioration trend description is established. This trend reveals the dynamic process of nutrient accumulation, algal proliferation, dissolved oxygen reduction, and ecological function decline in the water body, forming a chain-like ecological deterioration trend map, providing a basis for risk assessment and regulation.

[0164] Step S43: Assess the risk of abnormal flow velocity in the measured water area based on the trend of chain-like ecological degradation in the water body;

[0165] In this embodiment of the invention, quantitative analysis of the risk of flow velocity anomalies within the measured water area is conducted by combining flow velocity anomaly distribution data obtained from acoustic Doppler current measurement and the results of chain-like ecological degradation trend analysis. A spatial flow velocity anomaly risk model is constructed using multi-point flow measurement data, superimposing and comparing the spatial locations of flow velocity anomalies with ecological degradation. Key flow velocity anomaly areas affecting the stability of the ecological chain are identified using flow velocity thresholds and anomaly parameters. The negative impacts of flow velocity anomalies on the ecological functions of the aquatic body are analyzed by combining biological and chemical indicators in the ecological degradation trend. The flow velocity anomaly risk areas are classified, and a spatial distribution map and risk numerical report of flow velocity anomaly risk are output. This map and report clearly define the threat posed by flow velocity anomalies to ecosystem stability, providing decision support for flow velocity regulation.

[0166] Step S44: Optimize the regulation of water flow velocity based on the risk status of abnormal flow velocity in the measured water area and the trend of chain-like ecological deterioration of the water body, and obtain optimized water flow velocity regulation data.

[0167] In this embodiment of the invention, based on the aforementioned velocity anomaly risk distribution map and chain-like ecological degradation trend map, a velocity regulation scheme is designed. Combined with acoustic Doppler current meter monitoring data, the spatial location and severity of key velocity anomaly areas are determined. Based on the ecological degradation trend, regulation objectives are clarified, such as reducing the residence time in eutrophication zones and mitigating velocity reversal phenomena. Various velocity regulation measures are designed, including adjusting the structure of hydraulic facilities, setting up diversion dams, and optimizing the opening and closing sequence of sluice gates. The improvement effect of the design scheme on the flow field is verified through field experiments and numerical simulations. Velocity data after regulation is collected to assess the uniformity of velocity distribution and the degree of reduction in anomaly risk. Combined with ecological indicator monitoring data, the impact of regulation on the ecological chain reaction is quantitatively analyzed to form optimized water flow velocity regulation data, including specific structural parameters, implementation steps of the regulation scheme, optimized velocity distribution, and ecological environment improvement predictions, providing technical support and implementation plans for water ecological regulation.

[0168] The present invention also provides an acoustic Doppler-based ecological flow measurement system for performing the acoustic Doppler-based ecological flow measurement method described above. The acoustic Doppler-based ecological flow measurement system includes:

[0169] The flow velocity error compensation processing module is used to acquire flow measurement water area data; perform ecological flow measurement on the flow measurement water area data based on acoustic Doppler to obtain water area ecological flow measurement data; and perform flow velocity error compensation processing on the water area ecological flow measurement data to obtain water area flow velocity error compensated data.

[0170] The ecosystem dynamic imbalance monitoring module is used to assess the abnormal state of the hydrodynamic structure of the measured water area based on the water flow velocity error compensation data; detect the gradient decline trend of the water area's ecological suitability based on the abnormal state of the water area's hydrodynamic structure; and monitor the dynamic imbalance of the water area's ecosystem based on the gradient decline trend of the water area's ecological suitability and the abnormal state of the water area's hydrodynamic structure.

[0171] The water quality deterioration determination module is used to detect abnormalities in the ecological water quality purification capacity based on the dynamic imbalance of the aquatic ecosystem; identify the water pollution accumulation status based on the abnormality of the water hydrodynamic structure; and determine the water quality deterioration status of the measured water area based on the abnormality in the ecological water quality purification capacity and the water pollution accumulation status.

[0172] The flow velocity regulation and optimization module is used to determine the trend of water body chain ecological deterioration based on the water quality deterioration status of the measured water area; assess the risk status of abnormal flow velocity in the measured water area based on the trend of water body chain ecological deterioration; and optimize the water flow velocity regulation based on the risk status of abnormal flow velocity in the measured water area and the trend of water body chain ecological deterioration to obtain water flow velocity regulation and optimization data.

[0173] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An ecological flow measurement method based on acoustic Doppler, characterized in that, Includes the following steps: Step S1: Acquire flow measurement data of the water area; perform ecological flow measurement on the flow measurement data of the water area based on acoustic Doppler to obtain water area ecological flow measurement data; perform water flow velocity error compensation processing on the water area ecological flow measurement data to obtain water flow velocity error compensated data; Step S2: Assess the abnormal hydrodynamic structure of the measured water area based on the water flow velocity error compensation data; detect the gradient decline trend of the water area's ecological suitability based on the abnormal hydrodynamic structure; monitor the dynamic imbalance of the water area's ecosystem based on the gradient decline trend of the water area's ecological suitability and the abnormal hydrodynamic structure. Step S3: Detect abnormalities in the ecological water quality purification capacity based on the dynamic imbalance of the aquatic ecosystem; identify the water pollution accumulation status based on the abnormality of the aquatic hydrodynamic structure; determine the water quality deterioration status of the measured water area based on the abnormality in the ecological water quality purification capacity and the water pollution accumulation status. Step S4: Determine the trend of water body chain ecological degradation based on the water quality deterioration status of the measured flow area; assess the risk of abnormal flow velocity in the measured flow area based on the trend of water body chain ecological degradation; optimize the flow velocity regulation of the water area based on the risk of abnormal flow velocity in the measured flow area and the trend of water body chain ecological degradation, and obtain the optimized flow velocity regulation data.

2. The ecological flow measurement method based on acoustic Doppler according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain flow measurement data; Step S12: Detect the water body complexity data of the flow measurement area based on the flow measurement area data; Step S13: Set up multiple vertical lines for the flow measurement area data, and set up multiple measurement points on each vertical line; Step S14: Move the ADCP from one bank of the cross section to the other bank, record the bank, and directly measure the average velocity of the vertical line through the ADCP, and record the start and end times of the velocity measurement; Step S15: Apply acoustic Doppler to perform ecological flow measurement processing on the flow measurement water area data and the average flow velocity of the vertical line to obtain the water area ecological flow measurement data; Step S16: Evaluate the degree of acoustic Doppler flow measurement bias based on the water body complexity data of the water body measurement area; Step S17: Use the degree of acoustic Doppler flow measurement deviation to perform water flow velocity error compensation processing on the water ecological flow measurement data to obtain water flow velocity error compensation data.

3. The ecological flow measurement method based on acoustic Doppler according to claim 2, characterized in that, Step S16 includes the following steps: Step S161: Identify the large-scale algal aggregation in the water body based on the water body complexity data of the flow measurement area; Step S162: Monitor the excessive biofilm in the water body based on the water body complexity data of the flow measurement area; Step S163: Determine the non-uniform reflection area of ​​the water body based on the excessive biofilm exceeding 1.2 mm and the large-scale aggregation of algae in the water body; Step S164: Estimate the growth of the multipath propagation effect of sound waves using the non-uniform reflection region of the measured water body; Step S165: When the increase of the multipath propagation effect of sound waves exceeds 133%, determine the abnormal enhancement of water echo data; Step S166: Identify the wave interference intensity of the flow measurement area based on the water body complexity data of the flow measurement area; Step S167: Estimate the accuracy attenuation of the flow velocity measurement based on the wave interference intensity of the flow measurement area. Step S168: Evaluate the degree of acoustic Doppler flow measurement deviation of the aquatic ecological flow measurement data based on the abnormal enhancement of water body echo data and the attenuation of the accuracy of water flow velocity measurement.

4. The ecological flow measurement method based on acoustic Doppler according to claim 2, characterized in that, Step S17 includes the following steps: Step S171: Identify abnormal offset sections in water flow velocity measurement based on the degree of acoustic Doppler flow measurement deviation; Step S172: Determine the distribution range of the main control factors of water disturbance based on the abnormal offset section of water flow velocity measurement; Step S173: Extract water disturbance fluctuation factors based on the distribution range of the main control factors of water disturbance; Step S174: Construct multi-factor correction coefficients for water area based on the distribution range of water area disturbance fluctuation factor and water area disturbance main control factor; Step S175: Use the water area multi-factor correction coefficient to perform preliminary correction processing on the water flow velocity to obtain preliminary corrected water flow velocity data; Step S176: Identify abrupt changes in water velocity gradient based on preliminary correction data of water velocity; Step S177: Perform spatial interpolation correction processing on the water flow velocity gradient abrupt change point information to obtain water data spatial interpolation correction data; Step S178: Perform water flow velocity error compensation processing based on the spatial interpolation correction data of the water area data and the preliminary correction data of the water flow velocity to obtain water flow velocity error compensation data.

5. The ecological flow measurement method based on acoustic Doppler according to claim 1, characterized in that, Step S2, the assessment of abnormal hydrodynamic structures in the flow measurement area, includes: A spatial distribution map of the flow velocity in the measured water area is constructed based on the water flow velocity error compensation data. Information on the stationary flow segment of the flow measurement area is determined based on the spatial distribution map of the flow velocity in the flow measurement area when the flow velocity is <0.03m / s; Identify stagnant water areas in the flow measurement area based on information from static flow sections with high flow velocity in the measurement area; When the velocity direction in the spatial distribution map of the flow velocity in the measuring water area deviates by more than 160°, the local reverse flow situation in the measuring water area is identified. The local eddy growth in the flow measurement area is estimated based on the local reverse flow and stagnant water areas in the flow measurement area. The high-frequency fluctuations in the flow velocity of the measuring water area are determined based on the local eddy growth pattern in the measuring water area. The abnormal hydrodynamic structure of the measuring water area is assessed based on the high-frequency fluctuations in flow velocity and the local eddy growth in the measuring water area.

6. The ecological flow measurement method based on acoustic Doppler according to claim 1, characterized in that, Step S2, the detection of the gradient decline trend of aquatic ecological suitability, includes: Estimate the erosion status of aquatic plant roots based on abnormal hydrodynamic structure of the water area; The drift trend of phytoplankton in aquatic areas is detected based on the erosion of the roots of aquatic plants. The decline in the growth capacity of phytoplankton in aquatic areas was determined based on the drift trend of phytoplankton and the erosion of their roots. Assess the fragmentation status of aquatic plants based on the decline in phytoplankton growth and the root erosion status of aquatic plants. The decline in dissolved oxygen content in water bodies is determined based on abnormal hydrodynamic structures in the water area. Assess the quality degradation of aquatic habitats based on the decline in dissolved oxygen levels and the fragmentation of aquatic plants. The gradient decline trend of aquatic ecological suitability was detected based on the degradation of aquatic habitat quality and the fragmentation of aquatic plants.

7. The ecological flow measurement method based on acoustic Doppler according to claim 1, characterized in that, Step S2, monitoring the dynamic imbalance of the aquatic ecosystem, includes: Assess the increasing complexity of aquatic ecosystem survival based on the gradient decline trend of aquatic ecological suitability. Determine the extent of exceedance of hydrodynamic thresholds in a water area based on abnormal hydrodynamic structure conditions. Monitor the degree of damage to aquatic biological migration channels based on the extent to which hydrodynamic thresholds in the water area exceed limits; The trend of hindered reproduction of aquatic organisms is estimated based on the degree of damage to aquatic organism migration channels and the exceedance of aquatic hydrodynamic thresholds. The decline in the regeneration capacity of aquatic organisms is determined based on the trend of hindered reproduction and the degree of damage to aquatic organism migration channels; The degree of decline in aquatic species richness is determined based on the decline in the regeneration capacity of aquatic biological populations and the increase in the complexity of aquatic ecological survival. Monitoring the dynamic imbalance of aquatic ecosystems based on the degree of decline in aquatic species richness.

8. The ecological flow measurement method based on acoustic Doppler according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Detect abnormalities in the ecological water purification capacity based on the dynamic imbalance of the aquatic ecosystem; Step S32: Estimate the abnormal erosion of soil particles along the shoreline of the water area based on the abnormal hydrodynamic structure of the water area; Step S33: Detect the sediment deposition status of the bottom sediment on the shore of the water body based on the abnormal scouring of soil particles on the shore. Step S34: Identify the water pollution accumulation status based on the sediment deposition status of the bottom sediment and the abnormal erosion status of the soil particles along the water's edge; Step S35: Determine the water quality deterioration status of the measured water area based on the abnormal ecological water purification capacity and the accumulation of water pollution.

9. The ecological flow measurement method based on acoustic Doppler according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Estimate the eutrophication status of the measured flow body based on the water quality deterioration status of the measured flow area; Step S42: Determine the trend of chain-like ecological degradation of the water body based on the water quality deterioration status and eutrophication of the measured water area; Step S43: Assess the risk of abnormal flow velocity in the measured water area based on the trend of chain-like ecological degradation in the water body; Step S44: Optimize the regulation of water flow velocity based on the risk status of abnormal flow velocity in the measured water area and the trend of chain-like ecological deterioration of the water body, and obtain optimized water flow velocity regulation data.

10. An ecological flow measurement system based on acoustic Doppler, characterized in that, For performing the acoustic Doppler-based ecological flow measurement method as described in claim 1, the acoustic Doppler-based ecological flow measurement system comprises: The flow velocity error compensation processing module is used to acquire flow measurement water area data; perform ecological flow measurement on the flow measurement water area data based on acoustic Doppler to obtain water area ecological flow measurement data; and perform flow velocity error compensation processing on the water area ecological flow measurement data to obtain water area flow velocity error compensated data. The ecosystem dynamic imbalance monitoring module is used to assess the abnormal state of the hydrodynamic structure of the measured water area based on the water flow velocity error compensation data; detect the gradient decline trend of the water area's ecological suitability based on the abnormal state of the water area's hydrodynamic structure; and monitor the dynamic imbalance of the water area's ecosystem based on the gradient decline trend of the water area's ecological suitability and the abnormal state of the water area's hydrodynamic structure. The water quality deterioration determination module is used to detect abnormalities in the ecological water quality purification capacity based on the dynamic imbalance of the aquatic ecosystem; identify the water pollution accumulation status based on the abnormality of the water hydrodynamic structure; and determine the water quality deterioration status of the measured water area based on the abnormality in the ecological water quality purification capacity and the water pollution accumulation status. The flow velocity regulation and optimization module is used to determine the trend of water body chain ecological deterioration based on the water quality deterioration status of the measured water area; assess the risk status of abnormal flow velocity in the measured water area based on the trend of water body chain ecological deterioration; and optimize the water flow velocity regulation based on the risk status of abnormal flow velocity in the measured water area and the trend of water body chain ecological deterioration to obtain water flow velocity regulation and optimization data.

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