A river and lake pollutant dynamic tracing system based on multi-device cooperation

Abstract

The present application relates to a kind of river and lake pollutant dynamic tracing system based on multi-device cooperation, it includes a main device and at least one slave device, the main device and slave device all include carrier, sensor unit, control unit, power unit, communication unit, energy unit, the control unit of the main device is equipped with main controller, the main controller is used for path planning and data fusion with slave device, the slave device follows main device and synchronously collects data and returns cloud platform;Wherein, the steps of locating river and lake pollutant discharge source include: step S1, initial preparation;Step S2, cruise search;Step S3, gradient tracking;Step S4, pollution source positioning.The present application breaks through the limitation of prior art by multi-device cooperation, dynamic formation, gradient search and multi-sensor fusion, provides an efficient, accurate solution for river and lake pollution tracing, with significant social and environmental benefits.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a dynamic source tracing system for river and lake pollutants based on multi-device collaboration. Background Technology

[0002] Currently, the main technologies for tracing the source of river and lake pollution include manual source investigation, fixed equipment monitoring and analysis, water quality fingerprinting, and satellite remote sensing and data model analysis. Among these, manual source investigation and fixed equipment monitoring and analysis are the most widely used. While fixed equipment monitoring and analysis and water quality fingerprinting offer high accuracy, they are too costly and lack flexibility. Satellite remote sensing and data model analysis, although relatively low-cost, rely excessively on data and are not timely enough. Manual source investigation, while low-cost and highly flexible, is overly reliant on manpower and struggles to quickly locate pollution sources.

[0003] For example, Chinese patent publication CN119152198A proposes narrowing the source tracing range by dividing sub-regions and evaluation coefficients, but it does not involve the coordination of dynamic mobile devices. Chinese patent publication CN111855945A discloses shipborne monitoring technology, utilizing unmanned vessels equipped with sensors and a navigation command system, but it uses a single device and does not achieve real-time concentration gradient-driven heading adjustment. Furthermore, traditional methods such as the combined use of lidar and UAVs (Chinese patent publication CN114137569A) or the combination of fixed monitoring and mobile monitoring (Chinese patent publication CN114280249A) suffer from slow response speed, low positioning accuracy, or inability to adapt to complex flow fields. While Chinese patent application publication CN115508322A can identify pollution sources through three-dimensional fluorescence spectroscopy, it relies on laboratory analysis and cannot perform real-time dynamic tracking. In addition, although buoy-type monitoring devices (Chinese patent publication CN221976863U) achieve online water quality monitoring, they lack dynamic heading adjustment capabilities, making it difficult to quickly locate mobile pollution sources. Summary of the Invention

[0004] The purpose of this invention is to propose a dynamic source tracing system for river and lake pollutants based on multi-device collaboration. By measuring the concentration difference of specific factors in water through multi-device collaboration, the system automatically corrects the course to locate the source of river and lake pollutant discharge, thereby achieving rapid location of pollution sources and enabling efficient and accurate source tracing of river and lake pollutants.

[0005] To achieve the above objectives, this invention provides a dynamic source tracing system for river and lake pollutants based on multi-device collaboration, comprising a master device and at least one slave device. Both the master and slave devices include a carrier, sensor units, a control unit, a power unit, a communication unit, and an energy unit. The control unit of the master device houses a master controller, which is used for path planning and data fusion with the slave devices. The slave devices synchronously collect data following the master device and transmit it back to a cloud platform. The carrier is an unmanned surface vessel, unmanned underwater vehicle, or waterproof drone, providing physical support and an installation foundation for the various functional units of the device. The sensor units include high-precision water quality sensors and auxiliary sensors, used to sense external environmental and water quality data and convert the data into electrical or digital signals, providing real-time environmental data and operational status information for the device. The control unit receives signals, processes data, and sends commands to coordinate and control the operational logic and actions of each unit of the device. The power unit provides the energy required for the operation of each component of the device according to the commands of the control unit. The communication unit is used to realize data transmission and interaction between the various units within the device and between the device and external systems. The energy unit is used to store, distribute, and manage the energy required for the operation of the device.

[0006] The steps of locating the sources of pollutant discharge in rivers and lakes by the dynamic source tracing system for river and lake pollutants include:

[0007] Step S1, Initial preparation: After receiving the alarm information triggered by the water quality exceeding the standard at section P0, bring the main equipment and slave equipment to the designated location, and turn on, connect, calibrate and debug the main equipment and at least one slave equipment in sequence to enter the standby working state;

[0008] Step S2, Cruise Search: Manually suspend the main device and slave device at a fixed interval perpendicular to the river / lake midstream line at point P0, manually set the source tracing area P0-P, where P is the nearest upstream section of P0 where the water quality does not exceed the standard; detect and record the concentration C0 of a specific factor at point P0, and manually set the gradient tracking trigger concentration C; after setting, send a search command, and the device will sail upstream against the current along the river / lake midstream line at a fixed speed, record the navigation trajectory and sensor unit detection results, and upload them to the cloud platform;

[0009] Step S3, Gradient Tracking: Navigate to a certain section P1, and the main equipment detects the concentration C of a specific factor. n When the concentration is ≥C, the gradient tracking command is triggered, the device enters the gradient tracking mode, adjusts the heading to be perpendicular to the water flow direction to form a lateral baseline, measures the concentration difference and adjusts to move in the direction of increasing concentration according to the concentration gradient direction;

[0010] Step S4, pollution source location: When the concentration difference between the devices is less than the threshold and the concentration reaches the local maximum value, it is determined that the device is close to the pollution source and fixed-point hovering is initiated. At this time, the main device uploads the coordinates (x0, y0) to the cloud platform to generate a pollution hotspot map.

[0011] Preferably, the high-precision water quality sensor includes a fluorescence COD sensor and an electrochemical heavy metal sensor, with a detection accuracy of ±2% and a response time of <10s; the auxiliary sensor includes an RTK-GPS module, an IMU inertial navigation module and an ultrasonic flow meter, wherein the positioning accuracy of the RTK-GPS module is ≤10cm and the heading angle accuracy of the IMU inertial navigation module is ±0.5°.

[0012] Preferably, the gradient tracking trigger concentration C is set by empirical value or taken as C = C0 + 3 times the standard deviation.

[0013] Preferably, the baseline distance between the master device and the slave device is initially maintained at a distance of 5 to 20 meters.

[0014] Preferably, in step S4, when it is necessary to accurately locate the pollution source, another slave device is summoned to form a triangulation, and the extreme point is determined by calculating the second derivative of the concentration field to accurately locate the pollution source.

[0015] Preferably, the data fusion between the master device and the slave device includes multi-sensor fusion positioning enhancement and trajectory estimation;

[0016] The multi-sensor fusion positioning enhancement includes: combining data from the RTK-GPS module, IMU inertial navigation module, and ultrasonic current meter, and eliminating positioning drift through an extended Kalman filter (EKF), as shown in the following formula:

[0017] ;

[0018] Among them, V current V is the device drive speed. device This is the water flow velocity vector;

[0019] The trajectory estimation includes: in GPS blind spots, the position is estimated using the IMU inertial navigation module and the water current velocity vector, as shown in the following formula:

[0020] ;

[0021] Among them, V current V is the device drive speed. device This is the water flow velocity vector.

[0022] Preferably, in step S3, the step of adjusting the movement according to the concentration gradient direction to move in the direction of increasing concentration includes:

[0023] Step S31, Concentration gradient modeling:

[0024] Assuming pollutants diffuse in rivers and lakes in a two-dimensional Gaussian manner, the concentration field is:

[0025] ;

[0026] Where (x0, y0) are the coordinates of the pollution source;

[0027] Concentration gradient calculation: The measured values ​​of the master and slave devices are C respectively. A C B Baseline vector d=P B -P A The concentration gradient is then approximated as:

[0028] ;

[0029] Step S32: Execute the heading control strategy; the master device sends the steering angle Δθ and speed command v to the slave device.

[0030] Data synchronization: The master and slave devices exchange concentration and location data, and calculate the coordinates of the baseline midpoint.

[0031] ;

[0032] Concentration gradient direction calculation:

[0033] ;

[0034] in, The gradient direction angle, Let x be the concentration gradient component. The concentration gradient y component;

[0035] Heading correction:

[0036] The main equipment's heading is adjusted to: In the formula: The heading angle of the main equipment. To avoid obstacles or compensate for water flow angles;

[0037] The slave device moves along the baseline direction, using the master device as a reference.

[0038] Speed ​​control:

[0039] ;

[0040] Where, k p This is the proportionality coefficient. For the forward speed of the equipment, Minimum forward speed, For the concentration gradient.

[0041] Preferably, in step S3, if the main device does not detect the concentration gradient, it enters the spiral search mode to re-detect the concentration gradient; if the spiral search mode still does not detect the concentration gradient, the current search result is recorded and fed back for manual judgment.

[0042] Preferably, the spiral search mode involves the main device searching in a circle with a radius of 5 meters, centered on its current location.

[0043] Based on the above technical solution, the advantages of the present invention are:

[0044] This invention overcomes the limitations of existing technologies by using multi-device collaboration, dynamic formation, gradient search, and multi-sensor fusion, providing an efficient and accurate solution for tracing the source of river and lake pollution, with significant social and environmental benefits.

[0045] This invention proposes a dual-device collaborative dynamic formation strategy, which adjusts the course in real time by adjusting the concentration difference, significantly improving positioning efficiency; it integrates RTK-GPS, IMU and current meter to achieve high-precision positioning in complex environments, with a positioning accuracy of ≤10cm.

[0046] This invention combines concentration gradient ascent with PID control to solve the problem of lag or over-adjustment of heading in traditional methods; solar charging and wireless buoy design improve system endurance and reduce operation and maintenance costs; the distributed consensus algorithm is extended to multi-device collaboration, enhancing robustness in complex flow fields, especially suitable for rapid response to sudden pollution events, locating pollution sources within 2 hours; it can be expanded to multi-device collaboration, such as combining with satellite remote sensing to build an integrated air-space-ground monitoring network; positioning error ≤ 5 meters, convergence time ≤ 30 minutes. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0048] Figure 1 This is a schematic diagram of the hardware unit structure of the river and lake pollutant dynamic source tracing system of the present invention;

[0049] Figure 2 This is a schematic diagram of the multi-device collaborative search path of the present invention;

[0050] Figure 3 This is a flowchart illustrating the steps of locating pollutant emission sources in rivers and lakes according to the present invention.

[0051] Figure 4 Flowchart of the multi-sensor fusion positioning enhancement framework;

[0052] Figure 5 This is a control flow diagram showing the adjustment of the movement towards the direction of increasing concentration based on the concentration gradient. Detailed Implementation

[0053] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0054] This invention provides a dynamic source tracing system for river and lake pollutants based on multi-device collaboration, such as... Figures 1-5 As shown, a preferred embodiment of the present invention is illustrated.

[0055] like Figure 1 , Figure 2 As shown, the dynamic source tracing system for river and lake pollutants includes one master device and at least one slave device. Both the master device and the slave device include a carrier, a sensor unit, a control unit, a power unit, a communication unit, and an energy unit. The control unit of the master device is equipped with a master controller, which is used for path planning and data fusion with the slave device. The slave device follows the master device to collect data synchronously and transmits it back to the cloud platform.

[0056] The carrier provides physical support and installation foundation for each functional unit of the equipment, ensuring stable integration and collaborative operation of each component. It adopts unmanned boats, unmanned underwater vehicles or waterproof drones (suitable for river and lake flow speeds ≤3m / s) and is equipped with a collision-proof shell and buoyancy adjustment device.

[0057] The sensor unit senses external environmental and water quality data and converts the data into electrical or digital signals, providing the equipment with real-time environmental data and operating status information. It adopts high-precision water quality sensors, such as fluorescence COD sensors and electrochemical heavy metal sensors, with a detection accuracy of ±2% and a response time of <10s. Auxiliary sensors include: RTK-GPS module with a positioning accuracy of ≤10cm; IMU inertial navigation module with a heading angle accuracy of ±0.5°; and ultrasonic flow meter.

[0058] The control unit receives signals, processes data, and sends instructions to coordinate the operation logic and actions of various components of the control device, ensuring that the device works accurately and stably according to the predetermined process. It uses an embedded processor, such as a Raspberry Pi 4B or STM32H7, and runs a real-time operating system (RTOS) and gradient search algorithm.

[0059] The power unit provides the energy (such as electrical energy, mechanical energy, hydraulic energy, etc.) required for the operation of each component of the equipment according to the instructions of the control unit. It is the energy core that drives the equipment to perform actions. It adopts dual thrusters and vector servo control to control the direction, supporting forward, backward and turning, with a maximum speed of 1.5m / s.

[0060] The communication unit is responsible for data transmission and interaction between modules within the device and between the device and external systems, such as host computers, other devices, and the cloud. It supports protocol conversion, remote monitoring, and system collaboration. For short-range communication, it uses Bluetooth 5.2 (within 100m) or LoRa modules (1-5km, strong anti-interference). For long-range communication, it uses 4G / 5G modules (to transmit data back to the cloud platform).

[0061] The energy unit is responsible for storing, distributing and managing the energy required for equipment operation (such as electricity and fuel), ensuring a stable energy supply and optimizing its utilization. It uses rechargeable lithium batteries (with a battery life of ≥8 hours) combined with solar panels (for emergency charging).

[0062] The river and lake pollutant dynamic source tracing system of this invention adopts a multi-device collaborative strategy and a dynamic formation mode: 1+N mode, that is, 1 master device + N slave devices.

[0063] Leader device: Equipped with the main controller, responsible for path planning and data fusion.

[0064] Slave device (follower): Follows the master device, synchronously collects data and transmits it back.

[0065] Equipment baseline distance: Initially maintain a distance of 5-20 meters, which can be adjusted according to the flow rate. The faster the flow rate, the smaller the distance should be to reduce concentration diffusion error.

[0066] Multi-device expansion: Distributed consensus algorithms (such as rumor algorithms) can be used to expand to 3-5 devices to improve robustness in complex flow fields.

[0067] Furthermore, such as Figure 3 As shown, the steps of the river and lake pollutant dynamic source tracing system to locate the sources of river and lake pollutant emissions include:

[0068] Step S1, Initial Preparation: After receiving the alarm information triggered by the water quality exceeding the standard at section P0, bring the main equipment and slave equipment to the designated location, and turn on, connect, calibrate and debug the main equipment and at least one slave equipment in sequence to enter the standby working state.

[0069] Data interaction protocol: Real-time data frames (transmitted once per second); Control commands: Master device sends steering angle to slave device. With the speed command v.

[0070] Step S2, Cruise Search: Manually suspend the master and slave devices at a fixed interval perpendicular to the river / lake midstream line at point P0, manually set the source tracing area from P0 to P, where P is the nearest upstream section of P0 where the water quality does not exceed the standard; detect and record the concentration C0 of a specific factor at P0, and manually set the gradient tracking trigger concentration C; after setting, send a search command, and the device will sail upstream against the current along the river / lake midstream line at a fixed speed, record the navigation trajectory and sensor unit detection results, and upload them to the cloud platform.

[0071] Preferably, the gradient tracking trigger concentration C is set by empirical value or taken as C = C0 + 3 times the standard deviation.

[0072] Furthermore, such as Figure 4 As shown, the data fusion between the master and slave devices includes multi-sensor fusion positioning enhancement and trajectory estimation; wherein, multi-sensor fusion positioning enhancement includes: combining data from the RTK-GPS module, IMU inertial navigation module, and ultrasonic current meter, and eliminating positioning drift through an extended Kalman filter (EKF), as shown in the following formula:

[0073] ;

[0074] Among them, V current V is the device drive speed. device This is the water flow velocity vector.

[0075] The trajectory estimation includes: in GPS blind spots (such as areas shaded by bridges), the position is estimated using the IMU inertial navigation module and the water flow velocity vector, as shown in the following formula:

[0076] ;

[0077] Among them, V current V is the device drive speed. device This is the water flow velocity vector.

[0078] Countercurrent / Co-current compensation: Based on the flow meter data, if the water flow velocity V device For speeds >0.5 m / s, the heading needs to be corrected by the current vector. For example, when going against the current, the heading will deviate upstream by an angle: α = arcsin(V current / V device ).

[0079] Step S3, Gradient Tracking: Navigate to a certain section P1, and the main equipment detects the concentration C of a specific factor. n When the concentration is ≥C, a gradient tracking command is triggered, the device enters gradient tracking mode, adjusts its heading to be perpendicular to the water flow direction to form a lateral baseline, measures the concentration difference, and adjusts its movement to move in the direction of increasing concentration according to the concentration gradient direction.

[0080] like Figure 5As shown, further, the step of adjusting the movement according to the concentration gradient direction to move in the direction of increasing concentration includes:

[0081] Step S31, Concentration gradient modeling:

[0082] Assuming pollutants diffuse in rivers and lakes in a two-dimensional Gaussian manner, the concentration field is:

[0083] ;

[0084] Where (x0, y0) are the coordinates of the pollution source;

[0085] Concentration gradient calculation: The measured values ​​of the master and slave devices are C respectively. A C B Baseline vector d=P B -P A The concentration gradient is then approximated as:

[0086] ;

[0087] Step S32: Execute the heading control strategy; the master device sends the steering angle Δθ and speed command v to the slave device.

[0088] Data synchronization: The master and slave devices exchange concentration and location data, and calculate the coordinates of the baseline midpoint.

[0089] ;

[0090] Concentration gradient direction calculation:

[0091] ;

[0092] in, The gradient direction angle, Let x be the concentration gradient component. The concentration gradient y component;

[0093] Heading correction:

[0094] The main equipment's heading is adjusted to: In the formula: The heading angle of the main equipment. To avoid obstacles or compensate for water flow angles;

[0095] The slave device moves along the baseline direction, using the master device as a reference.

[0096] Speed ​​control:

[0097] ;

[0098] Where, k p This is the proportionality coefficient. For the forward speed of the equipment, Minimum forward speed, For the concentration gradient.

[0099] Furthermore, if the main device does not detect a concentration gradient, |C A C B If the concentration gradient is less than the threshold (e.g., 0.01 mg / L), the system enters a spiral search mode to re-detect the concentration gradient. If the spiral search mode still fails to detect the concentration gradient, the current search result is recorded and fed back for manual judgment. Preferably, the spiral search mode involves the main device circling around its current location within a 5-meter radius.

[0100] like Figure 5 As shown, the main device, centered on its current location, sets a 5-meter radius around the target and initiates PID control (including...). proportionality coefficient Integral coefficient, Differential coefficients), real-time acquisition of current heading angle Circling heading angle with the target deviation The PID controller is based on... and the rate of change of deviation (d) / dt), outputs servo steering commands. ( (and the water flow velocity obtained by the current meter) Superimposed water flow compensation angle α (α=arcsin( / Correct the course to ensure a stable circling trajectory; continuously monitor C during the circling process. A C B The system will reassess whether a concentration gradient exists. If the spiral search mode still fails to detect a concentration gradient, the current search trajectory and concentration data will be recorded and fed back to the cloud platform and technical personnel. The system will then manually determine the next steps based on the situation.

[0101] Step S4, pollution source location: When the concentration difference between the devices is less than the threshold and the concentration reaches the local maximum value, it is determined that the device is close to the pollution source and fixed-point hovering is initiated. At this time, the main device uploads the coordinates (x0, y0) to the cloud platform to generate a pollution hotspot map.

[0102] This invention overcomes the limitations of existing technologies by using multi-device collaboration, dynamic formation, gradient search, and multi-sensor fusion, providing an efficient and accurate solution for tracing the source of river and lake pollution, with significant social and environmental benefits.

[0103] This invention proposes a dual-device collaborative dynamic formation strategy, which adjusts the course in real time by adjusting the concentration difference, significantly improving positioning efficiency; it integrates RTK-GPS, IMU and current meter to achieve high-precision positioning in complex environments, with a positioning accuracy of ≤10cm.

[0104] This invention combines concentration gradient ascent with PID control to solve the problem of lag or over-adjustment of heading in traditional methods; solar charging and wireless buoy design improve system endurance and reduce operation and maintenance costs; the distributed consensus algorithm is extended to multi-device collaboration, enhancing robustness in complex flow fields, especially suitable for rapid response to sudden pollution events, locating pollution sources within 2 hours; it can be expanded to multi-device collaboration, such as combining with satellite remote sensing to build an integrated air-space-ground monitoring network; positioning error ≤ 5 meters, convergence time ≤ 30 minutes.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A dynamic source tracing system for river and lake pollutants based on multi-device collaboration, characterized in that: The system comprises one master device and at least one slave device. Both the master and slave devices include a carrier, sensor units, a control unit, a power unit, a communication unit, and an energy unit. The control unit of the master device houses a main controller, which is used for path planning and data fusion with the slave devices. The slave devices synchronously collect data following the master device and transmit it back to a cloud platform. The carrier, such as an unmanned surface vessel, unmanned underwater vehicle, or waterproof drone, provides physical support and a foundation for the various functional units of the system. The sensor units include high-precision water quality sensors and auxiliary sensors, used to sense the external environment and water quality data, converting the data into electrical or digital signals to provide real-time environmental data and operational status information. The control unit receives signals, processes data, and sends commands to coordinate and control the operational logic and actions of each unit within the system. The power unit provides the energy required for the operation of each component of the system according to the commands of the control unit. The communication unit enables data transmission and interaction between internal units and between the system and external systems. The energy unit stores, distributes, and manages the energy required for the system's operation. The steps of locating the sources of pollutant discharge in rivers and lakes by the dynamic source tracing system for river and lake pollutants include: Step S1, Initial preparation: After receiving the alarm information triggered by the water quality exceeding the standard at section P0, bring the main equipment and slave equipment to the designated location, and turn on, connect, calibrate and debug the main equipment and at least one slave equipment in sequence to enter the standby working state; Step S2, Cruise Search: Manually suspend the main device and slave device at a fixed interval perpendicular to the river / lake midstream line at point P0, manually set the source tracing area P0-P, where P is the nearest upstream section of P0 where the water quality does not exceed the standard; detect and record the concentration C0 of a specific factor at point P0, and manually set the gradient tracking trigger concentration C; after setting, send a search command, and the device will sail upstream against the current along the river / lake midstream line at a fixed speed, record the navigation trajectory and sensor unit detection results, and upload them to the cloud platform; Step S3, Gradient Tracking: Navigate to a certain section P1, and the main equipment detects the concentration C of a specific factor. n When the concentration difference is ≥C, a gradient tracking command is triggered, the device enters gradient tracking mode, adjusts its heading to be perpendicular to the water flow direction to form a lateral baseline, measures the concentration difference, and adjusts its movement towards the direction of increasing concentration according to the concentration gradient direction; The steps of measuring the concentration difference and adjusting the movement towards the direction of increasing concentration according to the concentration gradient direction include: Step S31, Concentration gradient modeling: Assume that pollutants diffuse in rivers and lakes in a two-dimensional Gaussian manner; Concentration gradient calculation: The measured values ​​of the master and slave devices are C respectively. A C B Baseline vector d=P B -P A Then the concentration gradient is approximately: ； Step S32: Execute the heading control strategy; the master device sends the steering angle Δθ and speed command v to the slave device. Data synchronization: The master and slave devices exchange concentration and location data, and calculate the coordinates of the baseline midpoint. ； Concentration gradient direction calculation: ； in, The gradient direction angle, For the concentration gradient y component, The concentration gradient x component; Heading correction: The main equipment's heading is adjusted to: In the formula: The heading angle of the main equipment. To avoid obstacles or compensate for water flow angles; The slave device moves along the baseline direction, using the master device as a reference. Speed ​​control: ； Where, k p This is the proportionality coefficient. For the forward speed of the equipment, Minimum forward speed, For concentration gradient; Step S4, pollution source location: When the concentration difference between the devices is less than the threshold and the concentration reaches the local maximum value, it is determined that the device is close to the pollution source and fixed-point hovering is initiated. At this time, the main device uploads the coordinates (x0, y0) to the cloud platform to generate a pollution hotspot map.

2. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration according to claim 1, characterized in that: The high-precision water quality sensor includes a fluorescence COD sensor and an electrochemical heavy metal sensor, with a detection accuracy of ±2% and a response time of <10s; the auxiliary sensors include an RTK-GPS module, an IMU inertial navigation module and an ultrasonic flow meter, with the RTK-GPS module having a positioning accuracy of ≤10cm and the IMU inertial navigation module having a heading angle accuracy of ±0.5°.

3. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration as described in claim 1, characterized in that: The gradient tracking trigger concentration C is set by empirical value or taken as C = C0 + 3 times the standard deviation.

4. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration according to claim 1, characterized in that: The baseline distance between the master device and the slave device should initially be maintained at 5 to 20 meters.

5. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration according to claim 1, characterized in that: In step S4, when it is necessary to accurately locate the pollution source, another slave device is summoned to form a triangulation. The extreme point is determined by calculating the second derivative of the concentration field to accurately locate the pollution source.

6. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration according to claim 2, characterized in that: The data fusion between the master and slave devices includes multi-sensor fusion positioning enhancement and trajectory estimation. The multi-sensor fusion positioning enhancement includes: combining data from the RTK-GPS module, IMU inertial navigation module, and ultrasonic current meter, and eliminating positioning drift through an extended Kalman filter (EKF); the trajectory estimation includes: in GPS blind spots, calculating the position using the IMU inertial navigation module and water flow velocity vector, as shown in the following formula: ； Among them, V device V is the device drive speed. current This is the water flow velocity vector.

7. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration according to claim 1, characterized in that: In step S3, if the main device does not detect the concentration gradient, it enters the spiral search mode to re-detect the concentration gradient; if the spiral search mode still does not detect the concentration gradient, it records and reports the current search result for manual judgment.

8. The dynamic source tracing system for river and lake pollutants based on multi-device collaboration according to claim 7, characterized in that: The spiral search mode is described as the main device searching in a circle with a radius of 5 meters, centered on the current location.

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