Ecological flow monitoring system and method based on bottom-supported Doppler profiler

Abstract

The invention relates to the technical field of flow monitoring, in particular to an ecological flow monitoring system and method based on a bottom-supported Doppler profiler, and the monitoring method comprises the steps: S1, deploying a plurality of monitoring devices; s2, dividing the monitoring area into a plurality of sub-areas; s3, selecting a certain monitoring device as a regional main node, and selecting other monitoring devices as sub-nodes; and S4, carrying out clustering analysis on the overlapped region data, generating representative data points and discarding redundant edge data. The monitoring system is highly intelligent, automatic labor division, position correction and repeated information deletion can be realized among equipment, and finally, tasks can be efficiently and accurately completed. Equipment division of labor is clear, all equipment does not need to work with high intensity all the time, and division of labor among monitoring devices can be adjusted in a self-adaptive mode; repeated and conflicted data are avoided, redundant data are reduced, and more storage space is released; the data result is more credible; the system is automatically adjusted and stably operates for a long time.

Description

Technical Field

[0001] The present invention relates to the technical field of flow monitoring, and specifically to an ecological flow monitoring system and method based on a bottom-mounted Doppler profiler. Background Art

[0002] The bottom-mounted Doppler profiler (ADCP) has become an important tool in ecological flow monitoring due to its high precision, stability, and real-time performance. By continuously monitoring parameters such as flow velocity, flow rate, and water level, the ADCP provides a scientific basis for ecological restoration, water resource management, and water conservancy project planning, contributing to the sustainable development of river ecosystems.

[0003] Currently, the monitoring system samples the monitored water flow by distributing multiple ADCPs, achieving the purpose of flow monitoring. However, since these multiple ADCPs are distributed in different spaces, the monitoring areas of adjacent devices overlap, resulting in redundant data. Summary of the Invention

[0004] The purpose of the present invention is to provide an ecological flow monitoring system and method based on a bottom-mounted Doppler profiler to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: An ecological flow monitoring method based on a bottom-mounted Doppler profiler, the monitoring method comprising:

[0006] Step S1: Deploy a plurality of monitoring devices in a target water area to form a monitoring area, and obtain the real-time spatial position information of each monitoring device;

[0007] Step S2: Divide the monitoring area into several sub-areas, and obtain the monitoring devices arranged in each sub-area;

[0008] Step S3: Obtain the election indicators of each monitoring device in the same sub-area, calculate a comprehensive value based on the election indicators, and elect a certain monitoring device as the regional master node and the remaining monitoring devices as sub-nodes according to the comprehensive value;

[0009] Step S4: The regional master node receives the hydrological data packets from the sub-nodes, performs cluster analysis on the overlapping area data, generates representative data points, and discards redundant edge data;

[0010] S5: When it is detected that the difference in data flow velocity between adjacent devices exceeds a threshold, trigger a device position fine-tuning instruction, and adjust the device position through the anchor chain motor.

[0011] The step S1 includes:

[0012] S101. Deploy multiple monitoring devices at preset monitoring points in the target water area. The monitoring devices are distributed along the cross-section and longitudinal gradient of the river channel. Each monitoring device includes at least a Doppler flow velocity sensor, a water pressure sensor, and a wireless communication module.

[0013] S102. Use a GPS module to obtain the water surface reference coordinates.

[0014] Through the fusion calculation of the underwater acoustic beacon array and the inertial navigation unit, correct the three-dimensional coordinate offset of the device underwater.

[0015] Based on GPS, acoustic, and inertial navigation data, output the final positioning result and obtain the real-time spatial position information of each monitoring device.

[0016] The step S2 includes:

[0017] S201. Mark one monitoring device as one monitoring point, mark all monitoring points in the three-dimensional model, select a certain monitoring point as the starting point, and starting from the starting point, mark out the tolerance range, where the tolerance range is the range covered by the set river channel length.

[0018] Extract all the monitoring devices within the tolerance range, regard them as edge nodes, form a closed figure between the starting point and the edge nodes, mark each closed figure as a sub-region, obtain several sub-regions, get the power remaining of all the monitoring devices in a certain sub-region, set a power threshold, mark the monitoring devices with power remaining less than the power threshold as low-power devices, and mark the monitoring devices with power remaining greater than the power threshold as high-power devices. Calculate the ratio of the number of low-power devices divided by the number of all high-power devices to get the elongation rate of this sub-region. If the elongation rate is less than the threshold, extract other sub-regions, mark them as coordinated regions, calculate the elongation rate of the coordinated regions. If the elongation rate is greater than the elongation threshold, divide the low-power devices in the current sub-region into the coordinated regions. Each time one is divided, calculate the elongation rate of the coordinated region in turn until the elongation rate of the coordinated region is closest to the elongation threshold, then stop dividing the low-power devices. According to the distances between each coordinated region and the current sub-region, perform a descending order arrangement based on the distance data, and divide the low-power devices in the current sub-region into the coordinated regions in sequence according to the arranged order. After the division is completed, finally generate several sub-regions, and each sub-region is attached with region information, where the region information includes the monitoring devices included in the sub-region, the power remaining of the monitoring devices, and the coverage range of the sub-region.

[0019] S202. Generate a dynamic geographic fence for each sub-region and associate all the monitoring devices within the fence as sub-nodes.

[0020] The step S3 includes:

[0021] Obtain the historical sampling records of each monitoring device, and calculate the election metrics of each monitoring device;

[0022] The election metrics include the capacity status index E, the communication quality index C, and the data reliability index D score ,

[0023] Calculate the comprehensive value according to the following formula: W = β1*E + β2*C + β3*D score ;

[0024] Elect the monitoring device corresponding to the maximum comprehensive value as the regional master node.

[0025] The step S4 includes:

[0026] S401. The regional master node receives the original hydrological data packets uploaded by the slave nodes. The original hydrological data packets include: flow velocity measurement values, data acquisition timestamps, and monitoring device spatial coordinates (x, y, z);

[0027] S402. Set the time window threshold and the spatial tolerance radius;

[0028] Cluster the original hydrological data packets; where ti and tj are the data acquisition timestamps of the original hydrological data packets i and j;

[0029] xi and yi are the coordinate values of the original hydrological data packet i, and xj and yj are the coordinate values of the original hydrological data packet j;

[0030] Use the OPTICS algorithm to identify the density-reachable data clusters, and retain the median data points within the clusters as representative data points;

[0031] S403. Perform consistency verification on the fused representative data points: Compare the flow velocity value of the representative data points with the flow velocity value in the ADCP moving measurement result. When the deviation > 5%, trigger data retransmission.

[0032] Ecological flow monitoring system based on bottom-mounted Doppler profiler: The monitoring system includes: a layout module, a division module, an election module, a deduplication module, and a correction module;

[0033] The layout module is used to deploy multiple monitoring devices in the target water area to form a monitoring area, and obtain the real-time spatial position information of each monitoring device;

[0034] The division module is used to divide the monitoring area into several sub-areas, and obtain the monitoring devices deployed in each sub-area;

[0035] The election module is used to obtain the election metrics of each monitoring device within the same sub-region, calculate a comprehensive value based on the election metrics, and elect a certain monitoring device as the regional master node and the remaining monitoring devices as sub-nodes according to the comprehensive value;

[0036] The deduplication module is used for the regional master node to receive the original flow velocity data from the sub-nodes, perform fusion on the overlapping area data, generate cluster center data points, and discard redundant edge data;

[0037] The correction module is used to trigger a device position fine-tuning instruction when it detects that the data flow velocity difference between adjacent devices exceeds the threshold, and adjust the device position through the anchor chain motor.

[0038] The deployment module includes:

[0039] Distance calculation unit: Calculate the coverage range of the monitoring device and the distance between two adjacent monitoring devices, and deploy multiple monitoring devices in the target water area;

[0040] Position marking unit: Obtain the water surface reference coordinates and monitor the spatial position coordinates of each monitoring device.

[0041] The election module includes:

[0042] Data retrieval unit: Obtain the environmental data, device data, and historical acquisition data of each monitoring device to form a local database;

[0043] Index analysis unit: Obtain the comprehensive values of each monitoring device based on the data analysis of the local database;

[0044] Rollback unit: If a specific event is detected, trigger a dynamic election: The nodes around the event occurrence area have priority to participate in the election, and the election weight is dynamically adjusted according to the distance from the specific event occurrence area.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. The monitoring system of the present invention is highly intelligent. Devices will automatically divide labor, calibrate positions, and delete duplicate information, and finally complete tasks efficiently and accurately. The devices have clear division of labor, and it is not necessary for all devices to work at a high intensity all the time. It can adaptively adjust the division of labor among monitoring devices; and avoid duplicate and conflicting data, reduce redundant data, release more storage space; and the data results are more reliable; the system automatically adjusts and runs stably for a long time. Brief Description of the Drawings

[0047] Figure 1 It is a schematic flow chart of an ecological flow monitoring method based on a bottom-mounted Doppler profiler of the present invention. Detailed Embodiments

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Embodiment 1: As Figure 1 shown, the present invention provides an ecological flow monitoring method based on a bottom-mounted Doppler profiler. The monitoring method includes:

[0050] Step S1: Deploy a plurality of monitoring devices in the target water area to form a monitoring area, and obtain the real-time spatial position information of each monitoring device;

[0051] The step S1 includes:

[0052] S101: Arrange a plurality of monitoring devices at preset monitoring points in the target water area. The monitoring devices are distributed along the river cross-section and longitudinal gradient; each monitoring device at least includes a Doppler velocity sensor, a water pressure sensor, and a wireless communication module;

[0053] S102: Use a GPS module to obtain the water surface reference coordinates;

[0054] Through the fusion calculation of the underwater acoustic beacon array and the inertial navigation unit, correct the three-dimensional coordinate offset of the device underwater;

[0055] Based on the GPS, acoustic, and inertial navigation data, output the final positioning result, and obtain the real-time spatial position information of each monitoring device.

[0056] For example: Place a plurality of monitoring devices in the water (such as placing sensors at the bottom of the river), and accurately know the positions of each monitoring device. Through GPS positioning: When the device emerges from the water surface, satellite positioning is used (similar to mobile phone navigation), but the underwater signal is poor, so other methods are needed.

[0057] Acoustic beacon: Place several "underwater speakers" (beacons) at the bottom of the water. The device calculates the position of the monitoring device through the time difference of listening to sound waves (similar to judging distance by listening to sound).

[0058] Inertial navigation: There are "gyroscopes and pedometers" (sensors) inside the device to record the moving direction and distance and prevent drift.

[0059] Result: Whether on the water surface or underwater, the device can know its accurate position (the error is less than half a meter).

[0060] Step S2: Divide the monitoring area into several sub-areas, and obtain the monitoring devices arranged in each sub-area;

[0061] The said step S2 includes:

[0062] S201. Mark a monitoring device as a monitoring point, mark all monitoring points in the 3D model, select a certain monitoring point as the starting point, and starting from the starting point, mark out the tolerance range, where the tolerance range is the range covered by the set river length;

[0063] Extract all monitoring devices within the tolerance range, take them as edge nodes, form a closed figure between the starting point and the edge nodes, mark each closed figure as a sub-region, obtain several sub-regions, acquire the power energy surplus of all monitoring devices in a certain sub-region, set a power energy threshold, mark the monitoring devices with power energy surplus less than the power energy threshold as low-power devices, mark the monitoring devices with power energy surplus greater than the power energy threshold as high-power devices, calculate the ratio of the number of low-power devices divided by the number of all high-power devices to obtain the elongation rate of this sub-region. If the elongation rate is less than the threshold, extract other sub-regions, mark them as coordination regions, calculate the elongation rate of the coordination regions. If the elongation rate is greater than the elongation threshold, divide the low-power devices within the current sub-region into the coordination regions. For each division, calculate the elongation rate of the coordination region in turn until the elongation rate of the coordination region is closest to the elongation threshold, then stop dividing the low-power devices; Divide according to the distances between each coordination region and the current sub-region, sort in descending order according to the distance data, and successively divide the low-power devices within the current sub-region into the coordination regions according to the sorted order. After the division is completed, finally generate several sub-regions, and each sub-region is attached with region information, where the region information includes the monitoring devices included in the sub-region, the power energy surplus of the monitoring devices, and the coverage range of the sub-region;

[0064] S202. Generate a dynamic geographical fence for each sub-region and associate all monitoring devices within the fence as sub-nodes.

[0065] For example: Divide the entire water area into small pieces, and each piece is responsible for by the nearest several devices.

[0066] Check the water flow changes: Divide more pieces in places with fast water flow (such as river bends), and divide fewer pieces in places with slow water flow.

[0067] Delaunay triangulation: Connect the devices into a triangular grid by mathematical methods to ensure that the areas of each triangle do not overlap too much.

[0068] Automatic encryption: If the water flow in a certain area changes violently (such as from calm to rapid), the system will automatically increase the monitoring points.

[0069] Result: The water area is reasonably divided, avoiding multiple devices from repeatedly monitoring the same place.

[0070] Step S3: Obtain the election metrics of each monitoring device within the same sub-region, calculate the comprehensive value based on the election metrics, and elect a certain monitoring device as the regional master node according to the comprehensive value, and the remaining monitoring devices as sub-nodes; the said Step S3 includes:

[0071] Obtain the historical sampling records of each monitoring device, and calculate the election metrics of each monitoring device;

[0072] The election metrics include the capacity status index E, the communication quality index C, and the data reliability index D score ,

[0073] Among them, the calculation formula of the capacity status index E is: E = (En / Em)*100%, where En is the remaining power and Em is the total power;

[0074] The calculation formula of the communication quality index C is: C = RSSI-(1-P / 100), where RSSI is the signal strength and P is the packet loss rate;

[0075] The data reliability index D score The calculation formula is:

[0076] v i is historical data, is the moving average reference value;

[0077] Calculate the comprehensive value according to the following formula: W = β1*E + β2*C + β3*D score ;

[0078] Elect the monitoring device corresponding to the maximum comprehensive value as the regional master node.

[0079] Select one "master device" for each small region to manage data.

[0080] Principle:

[0081] Election metrics:

[0082] More power: Preferentially select the device with higher power (to avoid running out of power halfway).

[0083] Good signal: Select the device with strong signal (more stable data transmission).

[0084] Accurate data: Select the device with small fluctuations in historical data (for example, the data measured in the past is close to the average value).

[0085] Voting mechanism: The devices "vote" for each other, and the one with the highest comprehensive score becomes the "group leader".

[0086] Result: Each region has a reliable "administrator" responsible for summarizing data.

[0087] Step S4: The regional master node receives the hydrological data packets from the slave nodes, performs clustering analysis on the overlapping area data, generates representative data points, and discards redundant edge data;

[0088] The said step S4 includes:

[0089] S401: The regional master node receives the original hydrological data packets uploaded by the slave nodes. The original hydrological data packets include: flow velocity measurement values, data acquisition timestamps, and spatial coordinates (x, y, z) of the monitoring devices;

[0090] S402: Set the time window threshold T win and the spatial tolerance radius R tol ;

[0091] Cluster the original hydrological data packets for which ‖t i -t j ‖ < T win and ; where t i , t j are the data acquisition timestamps of the original hydrological data packet i and the original hydrological data packet j;

[0092] x i , y i are the coordinate values of the original hydrological data packet i respectively, and x j , y j are the coordinate values of the original hydrological data packet j;

[0093] Adopt the OPTICS algorithm to identify density-reachable data clusters, and retain the median data point within the cluster as the representative data point;

[0094] S403: Conduct consistency verification on the fused representative data points: Compare the flow velocity value of the representative data point with the flow velocity value in the ADCP moving measurement result. When the deviation > 5%, trigger data retransmission.

[0095] Merge the data measured by multiple devices at the same location, and only keep the most accurate one.

[0096] Spatio-temporal constraint: Only merge the data at the same time (e.g., within 5 seconds) and the same location (e.g., within 1.5 meters).

[0097] Clustering algorithm: Use an intelligent algorithm to group the close data points, and only keep the median value in each group (e.g., when measuring 1.2 m / s and 1.25 m / s, keep 1.23 m / s).

[0098] Cross-validation: Use a moving measurement device (such as a sensor installed on a ship) to spot-check the results, and re-measure if the error is large.

[0099] Result: The data volume is reduced by more than half and is more accurate.

[0100] S5. When the data flow rate difference between adjacent devices is detected to exceed the threshold, trigger a device position fine-tuning instruction and adjust the device position through the anchor chain motor.

[0101] For example: If the data measured by adjacent devices differ too much, automatically adjust their positions.

[0102] Calculate the difference degree: For example, if the upstream device measures 1.5 m / s and the downstream device measures 0.8 m / s, the system detects an anomaly.

[0103] Send an instruction to adjust the position: Contract or relax the anchor chain of the device through the motor to move the device a few centimeters to a new position.

[0104] Verify the effect: Measure again after adjustment. If the data is consistent, it indicates that the adjustment is successful.

[0105] Result: The device is always in the optimal position, avoiding "blind spots" or repeated monitoring.

[0106] Embodiment 2: An ecological flow monitoring system based on a bottom-mounted Doppler profiler, the monitoring system includes: a layout module, a division module, an election module, a duplicate removal module, and a correction module;

[0107] The layout module is used to deploy multiple monitoring devices in the target water area to form a monitoring area and obtain the real-time spatial position information of each monitoring device;

[0108] The division module is used to divide the monitoring area into several sub-areas and obtain the monitoring devices arranged in each sub-area;

[0109] The election module is used to obtain the election indicators of each monitoring device in the same sub-area, calculate a comprehensive value according to the election indicators, and elect a certain monitoring device as the regional master node and the remaining monitoring devices as sub-nodes according to the comprehensive value;

[0110] The duplicate removal module is used for the regional master node to receive the original flow velocity data from the sub-nodes, perform fusion on the overlapping area data, generate cluster center data points and discard redundant edge data;

[0111] The correction module is used to trigger a device position fine-tuning instruction when the data flow rate difference between adjacent devices is detected to exceed the threshold, and adjust the device position through the anchor chain motor.

[0112] The layout module includes:

[0113] Distance calculation unit: Calculate the coverage range of the monitoring device, calculate the distance between two adjacent monitoring devices, and deploy multiple monitoring devices in the target water area;

[0114] Position marking unit: Obtain the water surface reference coordinates and monitor the spatial position coordinates of each monitoring device.

[0115] The election module includes:

[0116] Data retrieval unit: Obtain the environmental data, device data, and historical acquisition data of each monitoring device to form a local database;

[0117] Index analysis unit: Obtain various comprehensive values of the monitoring device based on the data analysis of the local database;

[0118] Rollback unit: When a specific event is detected, trigger dynamic election: The nodes around the event occurrence area have priority to participate in the election, and the election weight is dynamically adjusted according to the distance from the specific event occurrence area.

[0119] Overall effect:

[0120] Power saving: The devices have clear division of labor, and it is not necessary for all devices to work at a high intensity all the time.

[0121] Accurate data: Avoid duplicate and conflicting data, and the results are more reliable.

[0122] Adaptive: When the water flow changes or the device drifts, the system automatically adjusts and operates stably in the long term.

[0123] Installed a set of "intelligent monitoring systems" in the water area. The devices will divide labor, calibrate positions, and delete duplicate information by themselves, and finally complete the tasks efficiently and accurately.

[0124] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. An ecological flow monitoring method based on a bottom-mounted Doppler profiler, characterized in that: The monitoring method includes: Step S1: Deploy multiple monitoring devices in the target water area to form a monitoring area, and obtain the real-time spatial position information of each monitoring device; Step S2: Divide the monitoring area into several sub-areas, and obtain the monitoring devices arranged in each sub-area; Step S3: Obtain the election indicators of each monitoring device in the same sub-area, calculate the comprehensive value according to the election indicators, and elect a certain monitoring device as the regional master node according to the comprehensive value, and the remaining monitoring devices as sub-nodes; Step S4: The regional master node receives the hydrological data packets from the sub-nodes, performs clustering analysis on the overlapping area data, generates representative data points and discards redundant edge data; S5: When it is detected that the data flow velocity difference between adjacent devices exceeds the threshold, trigger the device position fine-tuning instruction, and adjust the device position through the anchor chain motor.

2. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, characterized in that: The said step S1 includes: S101: Arrange multiple monitoring devices at preset monitoring points in the target water area, and the monitoring devices are distributed along the river cross-section and longitudinal gradient; each monitoring device at least includes a Doppler velocity sensor, a water pressure sensor and a wireless communication module; S102: Obtain the water surface reference coordinates through GPS positioning; Calculate and correct the three-dimensional coordinate offset of the device underwater through the fusion of the underwater acoustic beacon array and the inertial navigation unit; Based on the GPS, acoustic and inertial navigation data, output the final positioning result and obtain the real-time spatial position information of each monitoring device.

3. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, characterized in that: The said step S2 includes: S201: Mark a monitoring device as a monitoring point, mark all monitoring points in the three-dimensional model, select a certain monitoring point as the starting point, start from the starting point, mark out the tolerance range, and the tolerance range is the range covered by the set river length; Extract all the monitoring devices within the tolerance range, and regard them as edge nodes. Form a closed figure between the starting point and the edge nodes, mark each closed figure as a sub-area, obtain several sub-areas, and obtain the remaining electric energy of all the monitoring devices in a certain sub-area. Set the electric energy threshold, mark the monitoring devices with the remaining electric energy less than the electric energy threshold as low-power devices, and mark the monitoring devices with the remaining electric energy greater than the electric energy threshold as high-power devices. Calculate the number of low-power devices divided by the number of all high-power devices to obtain the elongation rate of this sub-area. If the elongation rate is less than the threshold, extract other sub-areas and mark them as coordinated areas. Calculate the elongation rate of the coordinated areas. If the elongation rate is greater than the elongation threshold, divide the low-power devices in the current sub-area into the coordinated areas. Each time a division is made, calculate the elongation rate of the coordinated areas in turn until the elongation rate of the coordinated areas is closest to the elongation threshold, then stop dividing the low-power devices; Divide according to the distance between each coordinated area and the current sub-area, sort according to the distance data in descending order, and divide the low-power devices in the current sub-area into the coordinated areas in turn according to the sorted order. After the division is completed, several sub-areas are finally generated, and each sub-area is attached with area information, and the area information includes the monitoring devices included in the sub-area, the remaining electric energy of the monitoring devices, and the coverage range of the sub-area; S202. Generate a dynamic geographic fence for each sub-region and associate all monitoring devices within the fence as sub-nodes.

4. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, wherein: The step S3 includes: Obtain the historical sampling records of each monitoring device and calculate the election metrics for each monitoring device; The election metrics include the ability status index E, the communication quality index C, and the data reliability index D score , Calculate the comprehensive value according to the following formula: W = β1 * E + β2 * C + β3 * D score ; Elect the monitoring device corresponding to the largest comprehensive value as the regional master node.

5. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, wherein: The step S4 includes: S401. The regional master node receives the original hydrological data packet uploaded by the sub-node. The original hydrological data packet includes: flow velocity measurement value, data acquisition timestamp, and monitoring device spatial coordinates (x, y, z); S402. Set the time window threshold T win and the spatial tolerance radius R tol ; For ||t i -t j ||<T win And Cluster the original hydrological data packets; where t i ,t j Are the data acquisition timestamps of the original hydrological data packet i and the original hydrological data packet j; x i , y i are the coordinate values of the original hydrological data packet i, x j , y j are the coordinate values of the original hydrological data packet j; Use the OPTICS algorithm to identify density-reachable data clusters and retain the median data point within the cluster as the representative data point; S403. Perform consistency verification on the fused representative data points: Compare the flow velocity value of the representative data point with the flow velocity value in the ADCP traverse measurement result. When the deviation > 5%, trigger data retransmission.

6. An ecological flow monitoring system based on a bottom-mounted Doppler profiler, which is applied to the ecological flow monitoring method based on a bottom-mounted Doppler profiler according to any one of claims 1-5, characterized in that: The monitoring system includes: a layout module, a division module, an election module, a duplicate removal module, and a correction module; The layout module is used to deploy multiple monitoring devices in the target water area to form a monitoring area and obtain the real-time spatial position information of each monitoring device; The division module is used to divide the monitoring area into several sub-regions and obtain the monitoring devices deployed in each sub-region; The election module is used to obtain the election metrics of each monitoring device within the same sub-region, calculate the comprehensive value according to the election metrics, and elect a certain monitoring device as the regional master node according to the comprehensive value, and the remaining monitoring devices as sub-nodes; The duplicate removal module is used for the regional master node to receive the original flow velocity data from the sub-node, perform fusion on the overlapping area data, generate cluster center data points, and discard redundant edge data; The correction module is used to trigger a device position fine-tuning instruction when it detects that the flow velocity difference between adjacent device data exceeds the threshold, and adjust the device position through the anchor chain motor.

7. The ecological flow monitoring system based on a bottom-mounted Doppler profiler according to claim 6, characterized in that: The layout module includes: Distance calculation unit: Calculate the coverage range of the monitoring device, calculate the distance between two adjacent monitoring devices, and deploy multiple monitoring devices in the target water area; Position marking unit: Obtain the water surface reference coordinates and monitor the spatial position coordinates of each monitoring device.

8. The ecological flow monitoring system based on a bottom-mounted Doppler profiler according to claim 6, wherein: The election module includes: Data retrieval unit: Obtain the environmental data, device data, and historical acquisition data of each monitoring device to form a local database; Index analysis unit: Obtain the respective comprehensive values of the monitoring devices based on the analysis of the data in the local database; Rollback unit: If a specific event is detected, trigger a dynamic election: The nodes around the event occurrence area have priority to participate in the election, and the election weight is dynamically adjusted according to the distance from the specific event occurrence area.

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