Ecological flow monitoring system and method based on bottom-mounted doppler profiler

Abstract

The present application relates to the technical field of flow monitoring, in particular to an ecological flow monitoring system and method based on a bottom-mounted Doppler profiler, the monitoring method comprising: step S1, deploying a plurality of monitoring devices; step S2, dividing a monitoring area into a plurality of sub-areas; step S3, electing a certain monitoring device as a regional master node, and the remaining monitoring devices as sub-nodes; and step S4, performing cluster analysis on overlapping area data, generating representative data points, and discarding redundant edge data. The monitoring system is highly intelligent, and the devices can automatically divide the work, correct the positions, and delete the repeated information, so that the task can be finally completed efficiently and accurately. The devices have clear division of labor, and all the devices do not need to work at high intensity all the time, and the division of labor between the monitoring devices can be self-adaptively adjusted. The repeated and conflicting data can be avoided, the redundant data can be reduced, more storage space can be released, the data results are more reliable, the system can be automatically adjusted, and long-term stable operation can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flow monitoring, in particular to an ecological flow monitoring system and method based on a bottom-mounted Doppler profiler. BACKGROUND

[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 monitoring parameters such as flow rate, flow, and water level in real time, ADCP provides scientific basis for ecological restoration, water resource management, and water conservancy engineering planning, and helps to achieve sustainable development of river ecosystems.

[0003] The current monitoring system achieves the purpose of flow monitoring by distributing multiple ADCPs to sample the water flow, but multiple ADCPs are distributed in different spaces, and the monitoring areas of adjacent devices overlap, resulting in redundant data, SUMMARY

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

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: an ecological flow monitoring method based on a bottom-mounted Doppler profiler, the monitoring method comprising:

[0006] Step S1, deploying multiple monitoring devices in the target water area to form a monitoring area and obtaining real-time spatial position information of each monitoring device;

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

[0008] Step S3, obtaining the election index of each monitoring device in the same sub-area, calculating the comprehensive value according to the election index, and electing a certain monitoring device as the regional master node and the remaining monitoring devices as the sub-nodes according to the comprehensive value;

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

[0010] S5, when the flow rate difference of adjacent devices is detected to exceed the threshold value, a device position fine-tuning instruction is triggered, and the device position is adjusted by an anchor chain motor.

[0011] The step S1 comprises:

[0012] S101, multiple monitoring devices are arranged at preset monitoring points in a target water area, and the monitoring devices are distributed along a river cross section and a longitudinal gradient; each monitoring device comprises at least a Doppler flow rate sensor, a water pressure sensor and a wireless communication module;

[0013] S102, a water surface reference coordinate is obtained by using a GPS module;

[0014] A three-dimensional coordinate offset of the device under water is corrected through fusion calculation of the underwater acoustic beacon array and the inertial navigation unit;

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

[0016] The step S2 comprises:

[0017] S201, one monitoring device is marked as one monitoring point, all monitoring points are marked in a three-dimensional model, a certain monitoring point is selected as a starting point, a tolerance range is marked starting from the starting point, and the tolerance range is a range covered by a set river length;

[0018] All monitoring devices in the tolerance range are extracted and used as edge nodes, a closed figure is formed between the starting point and the edge nodes, each closed figure is marked as a sub-region, a plurality of sub-regions are obtained, energy reserves of all monitoring devices in a certain sub-region are obtained, an energy threshold is set, monitoring devices with energy reserves less than the energy threshold are marked as low-energy devices, and monitoring devices with energy reserves greater than the energy threshold are marked as high-energy devices; the number of low-energy devices is divided by the number of all high-energy devices to obtain an extension rate of the sub-region, if the extension rate is less than an extension rate threshold, other sub-regions are extracted and marked as coordination regions, the extension rate of the coordination region is calculated, if the extension rate is greater than the extension rate threshold, low-energy devices in the current sub-region are divided into the coordination region, the extension rate of the coordination region is calculated once for each division, and the division of low-energy devices is stopped until the extension rate of the coordination region is closest to the extension rate threshold; according to the distance between each coordination region and the current sub-region, the distance data is arranged in descending order, and the low-energy devices in the current sub-region are divided into the coordination region in order according to the arrangement order, and finally a plurality of sub-regions are generated after the division is completed, and each sub-region is attached with region information, the region information comprising monitoring devices in the sub-region, energy reserves of the monitoring devices and a coverage range of the sub-region;

[0019] S202, a dynamic geographic fence is generated for each sub-region, and all monitoring devices in the fence are associated as sub-nodes.

[0020] The step S3 comprises:

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

[0022] The election index includes an ability state index E, a communication quality index C, and a data reliability index D score ,

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

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

[0025] The step S4 includes:

[0026] S401, the regional master node receives the original hydrological data packet uploaded by the sub-node, and the original hydrological data packet includes: flow rate measurement value, data acquisition timestamp and monitoring device spatial coordinates (x, y, z);

[0027] S402, set the time window threshold and the spatial tolerance radius;

[0028] The original hydrological data packets of and are clustered; wherein ti, tj are the data acquisition timestamps of the original hydrological data packet i and the original hydrological data packet j;

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

[0030] The OPTICS algorithm is used to identify the density-reachable data cluster, and the median data point in the cluster is reserved as the representative data point;

[0031] S403, consistency verification is performed on the fused representative data points: the flow rate value of the representative data point is compared with the flow rate value in the ADCP walking measurement result, and when the deviation is >5%, data retransmission is triggered.

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

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

[0034] The division module is used to divide the monitoring area into a plurality of sub-regions, and obtain the monitoring devices laid in each sub-region;

[0035] The election module is used for obtaining the election indexes of each monitoring device in the same sub-region, calculating a comprehensive value according to the election indexes, and electing a certain monitoring device as a regional master node according to the comprehensive value, and the remaining monitoring devices as sub-nodes;

[0036] The deduplication module is used for the regional master node to receive the original flow rate data from the sub-nodes, perform fusion on the overlapping region data, generate a cluster center data point and discard redundant edge data.

[0037] The correction module is used for triggering a device position fine-tuning instruction when detecting that the data flow rate difference of adjacent devices exceeds a threshold value, and adjusting the device position through an anchor chain motor.

[0038] The layout module comprises:

[0039] The distance calculation unit calculates the distance between two adjacent monitoring devices in the coverage range of the monitoring device, and arranges a plurality of monitoring devices in the target water area.

[0040] The position marking unit obtains a water surface reference coordinate and monitors the spatial position coordinates of each monitoring device.

[0041] The election module comprises:

[0042] The data retrieval unit obtains environmental data, device data and historical acquisition data of each monitoring device to form a local database.

[0043] The index analysis unit obtains each comprehensive value of the monitoring device according to the data analysis of the local database.

[0044] The rollback unit triggers a dynamic election when detecting a specific event: the nodes around the event occurrence region are preferentially selected, and the election weight is dynamically adjusted according to the distance between the event occurrence region.

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

[0046] 1. The monitoring system of the present application is highly intelligent, and the devices can automatically divide the work, correct the position, and delete the repeated information, so that the task is finally completed efficiently and accurately. The devices have clear division of labor, and all devices do not need to work at high intensity all the time, and the division of labor between the monitoring devices can be self-adaptively adjusted; and the repeated and conflicting data are avoided, the redundant data is reduced, more storage space is released, the data result is more reliable, and the system is automatically adjusted for long-term stable operation. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a flowchart of the ecological flow monitoring method based on the bottom-mounted Doppler profiler. DETAILED DESCRIPTION

[0048] With reference to the drawings and embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0049] Embodiment one: as shown in the figure, the present application provides an ecological flow monitoring method based on a bottom-mounted Doppler profiler, the monitoring method comprising: Figure 1

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

[0051] The step S1 comprises:

[0052] S101, deploying a plurality of monitoring devices at preset monitoring points of the target water area, the monitoring devices being distributed along a river cross section and a longitudinal gradient; each monitoring device comprising at least a Doppler flow velocity sensor, a water pressure sensor and a wireless communication module;

[0053] S102, obtaining a water surface reference coordinate by using a GPS module;

[0054] The three-dimensional coordinate offset of the device under water is corrected through fusion calculation of the underwater acoustic beacon array and the inertial navigation unit;

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

[0056] For example: a plurality of monitoring devices (such as sensors placed on the river bottom) are placed in water, and the positions of each monitoring device are accurately known. Through GPS positioning: the device is positioned by satellite when it floats out of the water (similar to mobile phone navigation), but the underwater signal is poor, so other methods are needed.

[0057] Acoustic beacon: several "underwater loudspeakers" (beacons) are placed on the river bottom, and the device calculates the position of the monitoring device by listening to the time difference of sound waves (similar to judging distance by listening to sound).

[0058] Inertial navigation: the device has a "gyroscope and pedometer" (sensor) inside, which records the moving direction and distance to prevent drift.

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

[0060] Step S2, dividing the monitoring area into a plurality of sub-areas, and obtaining the monitoring devices arranged in each sub-area; ​

[0061] The step S2 comprises:

[0062] S201, mark one monitoring device as a monitoring point, mark all monitoring points in a three-dimensional model, select a certain monitoring point as a starting point, mark a tolerance range starting from the starting point, and the tolerance range is a range covered by a set river length;

[0063] Extract all monitoring devices within the tolerance range as edge nodes, form a closed graph between the starting point and the edge nodes, mark each closed graph as a sub-region, obtain the electric energy reserves of all monitoring devices in a certain sub-region, set an electric energy threshold, mark the monitoring devices with electric energy reserves less than the electric energy threshold as low-energy devices, and mark the monitoring devices with electric energy reserves greater than the electric energy threshold as high-energy devices, divide the number of low-energy devices by the number of all high-energy devices to obtain the extension rate of the sub-region, if the extension rate is less than the extension rate threshold, extract other sub-regions and mark them as coordination regions, calculate the extension rate of the coordination region, if the extension rate is greater than the extension rate threshold, divide the low-energy devices in the current sub-region into the coordination region, calculate the extension rate of the coordination region once for each division, until the extension rate of the coordination region is closest to the extension rate threshold, then stop dividing the low-energy devices; according to the distance between each coordination region and the current sub-region, arrange the distance data in descending order, and according to the arrangement order, divide the low-energy devices in the current sub-region into the coordination region in turn, and finally generate a plurality of sub-regions, and each sub-region is attached with region information, the region information includes monitoring devices in the sub-region, electric energy reserves of the monitoring devices and coverage range of the sub-region;

[0064] S202, generate a dynamic geographic fence for each sub-region, and associate all monitoring devices in the fence as sub-nodes.

[0065] For example: divide the entire water area into small blocks, and each block is responsible for the nearest few devices.

[0066] Observe the change of water flow: divide more blocks in the place where the water flow is fast (such as the turning place of the river), and divide fewer blocks in the place where the water flow is slow.

[0067] Delaunay triangulation: connect the devices into a triangular grid by mathematical method, and ensure that each triangular region does not overlap too much.

[0068] Automatic encryption: if the water flow in a certain block changes dramatically (such as from calm to turbulent), the system will automatically increase the monitoring points.

[0069] Result: the water area is reasonably divided, and multiple devices are avoided to monitor the same place repeatedly.

[0070] Step S3, obtaining the election index of each monitoring device in the same sub-region, calculating a comprehensive value according to the election index, and electing a certain monitoring device as a regional master node and the rest of the monitoring devices as sub-nodes according to the comprehensive value; the step S3 comprises:

[0071] Obtaining the historical sampling records of each monitoring device, and calculating the election index of each monitoring device;

[0072] The election index comprises a capability state index E, a communication quality index C and a data reliability index D score ,

[0073] The calculation formula of the capability state index E is E = (En / Em)*100%, wherein En is the residual power and Em is the total power;

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

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

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

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

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

[0079] Each small region selects a “master device” to manage data.

[0080] Principle:

[0081] Election index:

[0082] More power: prefer to select devices with high power (avoid running out of power in the middle).

[0083] Good signal: select devices with strong signal (data transmission is more stable).

[0084] Data accurate: select devices with small historical data fluctuations (such as the data measured in the past is close to the average value).

[0085] Voting mechanism: 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 aggregating data.

[0087] Step S4, the regional master node receives the hydrological data packets from the sub-nodes, performs cluster analysis on the overlapping region data, generates representative data points, and discards redundant edge data.

[0088] The step S4 comprises:

[0089] S401, the regional master node receives the original hydrological data packets uploaded by the sub-nodes, the original hydrological data packets comprising: flow rate measurement value, data collection timestamp, and monitoring device spatial coordinates (x, y, z);

[0090] S402, setting a time window threshold and a spatial tolerance radius ;

[0091] performing clustering on the original hydrological data packets of and ; wherein t i ,t j is the data collection timestamp 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, x j ,y j are the coordinate values of the original hydrological data packet j;

[0093] adopting the OPTICS algorithm to identify density-reachable data clusters, and retaining the median data points in the clusters as representative data points;

[0094] S403, performing consistency verification on the fused representative data points: comparing the flow rate value of the representative data points with the flow rate value in the ADCP underway measurement result, and triggering data retransmission when the deviation is > 5%.

[0095] Merge data from multiple devices at the same location, leaving only the most accurate one.

[0096] Temporal and spatial constraints: only merge data at the same time (e.g. within 5 seconds) and at the same location (e.g. within 1.5 meters).

[0097] Clustering algorithm: use intelligent algorithms to group close data points, leaving only the middle value in each group (e.g. 1.2 m / s and 1.25 m / s are measured, leaving 1.23 m / s).

[0098] Cross-validation: use a mobile measurement device (e.g. a sensor on a ship) to check the results, and re-measure if the error is large.

[0099] Results: the amount of data is reduced by more than half, and it is more accurate.

[0100] S5、When the adjacent device data flow rate difference exceeds the threshold value, trigger the device position fine-tuning instruction, adjust the device position through the anchor chain motor.

[0101] For example: if the data difference measured by the adjacent device is too large, automatically adjust their positions.

[0102] Calculate the difference: for example, the upstream device measures 1.5 m / s, and the downstream measures 0.8 m / s, the system finds an anomaly.

[0103] Send instructions to adjust the position: retract or loosen the anchor chain of the device through the motor, and move the device a few centimeters to a new position.

[0104] Verify the effect: measure again after adjustment, if the data is consistent, it means the adjustment is successful.

[0105] Result: the device is always in the best position, avoiding "blind area" or repeated monitoring.

[0106] Embodiment two: an ecological flow monitoring system based on a bottom-mounted Doppler profiler, the monitoring system comprising: a deployment module, a division module, an election module, a deduplication module and a correction module;

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

[0108] The division module is used to divide the monitoring area into a plurality of sub-regions and obtain the monitoring devices deployed in each sub-region;

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

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

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

[0112] The deployment module comprises:

[0113] Distance calculation unit: the coverage range of the monitoring device, calculate the distance between two adjacent monitoring devices, and deploy a plurality of 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 comprises:

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

[0117] Index analysis unit: obtain each comprehensive value of the monitoring device according to the data analysis of the local database;

[0118] Rollback unit: if a specific event is detected, trigger dynamic election: the nodes around the event occurrence area are preferentially selected, and the election weight is dynamically adjusted according to the distance between the specific event occurrence area.

[0119] Overall effect:

[0120] Power saving: the devices have clear division of labor, and all devices do not need to work at high intensity all the time.

[0121] Data accuracy: avoid repeated and conflicting data, and the result is more reliable.

[0122] Adaptability: when the water flow changes or the device drifts, the system automatically adjusts and runs stably for a long time.

[0123] A set of "intelligent monitoring system" is installed in the water supply area, and the devices can work, calibrate positions and delete repeated information by themselves, so that the task can be finally completed efficiently and accurately.

[0124] It is apparent to those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be considered as limiting the claims involved.

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 location information of each monitoring device; Step S2: Divide the monitoring area into several sub-areas and obtain the monitoring devices deployed in each sub-area; Step S3: Obtain the election index of each monitoring device in the same sub-region, calculate the comprehensive value based on the election index, and elect a certain monitoring device as the regional master node based on the comprehensive value, and the remaining monitoring devices as child nodes; Step S4: The regional master node receives hydrological data packets from the child nodes, performs cluster analysis on the overlapping area data, generates representative data points, and discards redundant edge data. S5. When the difference in data flow rate between adjacent devices exceeds the threshold, a device position fine-tuning command is triggered to adjust the device position through the anchor chain motor. 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, and mark the tolerance range starting from the starting point. The tolerance range is the range covered by the set river length. Extract all monitoring devices within the tolerance range and treat them as edge nodes. Form closed shapes between the starting point and the edge nodes, and label each closed shape as a sub-region, resulting in several sub-regions. Obtain the power reserve of all monitoring devices in a given sub-region, set a power threshold, and label monitoring devices with power reserves less than the threshold as low-power devices and those with power reserves greater than the threshold as high-power devices. Count the number of low-power devices and divide by the total number of high-power devices to obtain the elongation rate of the sub-region. If the elongation rate of the sub-region is less than the elongation rate threshold, extract other sub-regions and label the current sub-region as a coordination region. Calculate the elongation of the coordination region. If the extension rate of the sub-region is greater than the extension rate threshold, then the low-power devices in the current sub-region are assigned to adjacent coordinated regions. For each assigned region, the extension rate of the coordinated region is calculated once, until the extension rate of the coordinated region is closest to the extension rate threshold, then the assignment of low-power devices is stopped. The sub-region is assigned according to the distance between each coordinated region and the current sub-region. The distance data is sorted in descending order, and the low-power devices in the current sub-region are assigned to the coordinated regions in the order of the sorting. After the assignment is completed, several sub-regions are finally generated, and each sub-region is accompanied by region information, which includes the monitoring devices included in the sub-region, the power reserve of the monitoring devices, and the coverage area of ​​the sub-region. S202. Generate a dynamic geofence for each sub-region and associate all monitoring devices within the fence as child nodes.

2. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, characterized in that: Step S1 includes: S101. Multiple monitoring devices are deployed 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 current velocity sensor, a water pressure sensor, and a wireless communication module. S102. Obtain the water surface reference coordinates through GPS positioning; The device's three-dimensional coordinate offset underwater is corrected by fusion calculation of underwater acoustic beacon array and inertial navigation unit; Based on GPS, acoustic and inertial navigation data, the final positioning result is output, and the real-time spatial location information of each monitoring device is obtained.

3. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, characterized in that: Step S3 includes: Obtain historical sampling records for each monitoring device and calculate the election index for each monitoring device; The election indicators include the capability status index (E), the communication quality index (C), and the data reliability index (D). score , The comprehensive value is calculated using the following formula: W = β1*E + β2*C + β3*D score ; The monitoring device with the highest comprehensive value will be elected as the regional master node.

4. The ecological flow monitoring method based on a bottom-mounted Doppler profiler according to claim 1, characterized in that: Step S4 includes: S401. The regional master node receives the raw hydrological data packet uploaded by the child node. The raw 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 and spatial tolerance radius ; right and Clustering is performed on the original hydrological data packets; where t i ,t j For the data acquisition timestamps of raw hydrological data packet i and raw hydrological data packet j; x i ,y i These are the coordinates of the original hydrological data packet i, x and y, respectively. j ,y j The coordinates of the original hydrological data packet j; The OPTICS algorithm is used to identify data clusters with achievable density, and the median data point within each cluster is retained as the representative data point. S403. Perform consistency verification on the fused representative data points: compare the flow velocity values ​​of the representative data points with the flow velocity values ​​in the ADCP mobile measurement results, and trigger data retransmission when the deviation is >5%.

5. An ecological flow monitoring system based on a bottom-mounted Doppler profiler, applied to the ecological flow monitoring method based on a bottom-mounted Doppler profiler as described in any one of claims 1-4, characterized in that: The monitoring system includes: a deployment module, a division module, an election module, a deduplication module, and a correction module; The deployment module is used to deploy multiple monitoring devices in the target water area to form a monitoring area and obtain the real-time spatial location information of each monitoring device. The division module is used to divide the monitoring area into several sub-areas and obtain the monitoring devices deployed in each sub-area; The election module is used to obtain the election indicators of each monitoring device in the same sub-region, calculate the comprehensive value based on the election indicators, and elect a certain monitoring device as the regional master node based on the comprehensive value, and the remaining monitoring devices as child nodes. The deduplication module is used by the regional master node to receive the original flow rate data from the child nodes, 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 command when it detects that the difference in data flow rate between adjacent devices exceeds a threshold, and adjusts the device position through the anchor chain motor.

6. The ecological flow monitoring system based on a bottom-mounted Doppler profiler according to claim 5, characterized in that: The deployment module includes: Distance calculation unit: The coverage area of ​​the monitoring device is calculated, the distance between two adjacent monitoring devices is calculated, and multiple monitoring devices are deployed in the target water area; Location marker unit: Obtains water surface reference coordinates and monitors the spatial location coordinates of each monitoring device.

7. The ecological flow monitoring system based on a bottom-mounted Doppler profiler according to claim 5, characterized in that: The election module includes: Data retrieval unit: acquires environmental data, equipment data, and historical data from various monitoring devices to form a local database; Indicator Analysis Unit: Based on data analysis from the local database, it obtains various comprehensive values ​​for the monitoring equipment; Rollback Unit: If a specific event is detected, dynamic election is triggered: nodes around the event occurrence area are given priority in election, and the election weight is dynamically adjusted according to the distance between them and the specific event occurrence area.

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