Water pollution big data monitoring and early warning method and system

Abstract

The invention discloses a water pollution big data monitoring and early warning method and system, and relates to the technical field of water pollution monitoring, and the method comprises the steps: transversely arranging a plurality of flow velocity measurement points on a main river and branch flow convergence section, obtaining the flow velocity data of each flow velocity measurement point, and constructing a transverse flow velocity profile according to the flow velocity data; determining a shearing area according to the flow velocity difference value and the flow velocity change rate between the adjacent flow velocity measuring points, and determining the central position of a mixing zone; controlling a water quality sampling probe mounted on a transverse guide structure to transversely move along the section according to the central position of the mixing zone, so that the water quality sampling probe is positioned at the central position of the mixing zone; and acquiring real-time water level data, and calculating the target sampling depth according to the real-time water level data. A transverse flow velocity profile is constructed on a main river and branch flow convergence section, and the central positions of a shearing area and a mixing zone are determined based on a flow velocity difference value and a flow velocity change rate, so that a water quality sampling probe can be dynamically positioned in a key area of a hydrodynamic structure.

Description

A method and system for big data monitoring and early warning of water pollution Technical Field

[0001] This invention relates to the field of water pollution monitoring technology, specifically to a method and system for big data monitoring and early warning of water pollution. Background Technology

[0002] With the increasing demand for refined management of watershed water environment, online water quality monitoring systems for river sections have been widely deployed in main channels and important inflow sections. Existing water pollution monitoring technologies typically involve setting up fixed monitoring points at the section to obtain water quality concentration data and combining it with flow data for pollution load estimation and early warning analysis. Under most stable flow conditions, this type of fixed monitoring can meet routine water quality supervision needs and plays an important role in daily management. However, at the confluence sections of main rivers and tributaries, the hydrodynamic structure is usually more complex. When the tributary flow changes, a shear zone with a significant transverse velocity gradient is easily formed in the inflow area, and the mixing process of pollutants in the transverse and longitudinal directions has certain temporal and spatial differences. When mixing is not yet fully completed, transverse zonation may occur within the section. The present invention addresses the differences in concentration between different water layers. Existing monitoring methods based on single-point, fixed-depth sampling may not adequately reflect the overall water quality distribution characteristics of a cross-section under such hydrodynamic conditions. Furthermore, the hydrodynamic structure and mixing zone location of the cross-section may dynamically change under conditions such as abrupt changes in tributary flow proportion, water level fluctuations, or enhanced turbulence. If the sampling location is not adjusted synchronously with the hydrodynamic structure, the representativeness and stability of the acquired water quality data may be affected. Therefore, it is necessary to provide a water pollution monitoring and early warning method that can combine the characteristics of the cross-section hydrodynamic structure, achieve dynamic positioning in both the horizontal and vertical directions, and has a multi-layer verification mechanism to improve the spatial representativeness and temporal stability of cross-section monitoring data. Consequently, this invention discloses a water pollution big data monitoring and early warning method and system. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for big data monitoring and early warning of water pollution, so as to solve the problems mentioned in the background art.

[0004] This invention can be achieved through the following technical solution: A method for big data monitoring and early warning of water pollution, comprising: Step 1, setting up multiple velocity measuring points laterally at the confluence section of the main river and its tributaries, acquiring velocity data from each measuring point, and constructing a transverse velocity profile based on the velocity data; determining the shear zone based on the velocity difference and velocity change rate between adjacent measuring points, and determining the center position of the mixing zone; Step 2, controlling the water quality sampling probe installed on the transverse guide structure to move laterally along the section according to the center position of the mixing zone, so that the water quality sampling probe is positioned at the center position of the mixing zone; Step 3, acquiring real-time water level data, calculating the target sampling depth based on the real-time water level data, and controlling the water quality sampling probe to move longitudinally to... The target sampling depth is set to maintain the relative water depth ratio of the water quality sampling probe. Step 4: During water quality sampling, the center position of the mixing zone, the lateral coordinate of the water quality sampling probe, and the relative water depth ratio are recorded simultaneously. It is determined whether the distance between the lateral coordinate of the water quality sampling probe and the center position of the mixing zone is less than the set lateral deviation threshold, and whether the relative water depth ratio is within the set longitudinal deviation threshold range. When both the lateral deviation threshold and the longitudinal deviation threshold are met, the corresponding water quality data is output. Step 5: When the distance between the center position of the mixing zone and the lateral coordinate of the water quality sampling probe is greater than the set lateral deviation threshold, or when the real-time water level change rate is greater than the set water level change rate threshold, steps 1 to 4 are repeated.

[0005] A further technical improvement of the present invention is as follows: In step one, after constructing a transverse velocity profile based on the velocity data, the transverse shear strength value is calculated based on the velocity difference between adjacent velocity measuring points and the distance between the measuring points, and a transverse shear strength distribution is formed along the transverse section; the position of the peak shear strength is determined based on the transverse shear strength distribution, and the transverse position corresponding to the decrease of the transverse shear strength values ​​on both sides of the peak shear strength to a set proportional threshold is taken as the boundary of the effective mixing interval; the water quality sampling probe is controlled to be positioned within the effective mixing interval, and during the sampling process, when the water quality sampling probe is located within the effective mixing interval and the rate of change of the transverse shear strength peak is less than the set rate of change threshold in multiple consecutive sampling cycles, the corresponding water quality data is output.

[0006] A further technical improvement of the present invention is as follows: taking the center position of the mixing zone as the center, a set lateral buffer zone is determined within the effective mixing interval. The distance between the left and right boundaries of the set lateral buffer zone and the center position of the mixing zone is less than or equal to a set lateral deviation threshold, and less than the distance between the boundary of the effective mixing interval and the center position of the mixing zone. A continuous update sequence of the center position of the mixing zone is obtained, and the lateral fluctuation amplitude of the center position of the mixing zone is calculated based on the continuous update sequence. When the lateral coordinate of the water quality sampling probe is within the set lateral buffer zone, the lateral coordinate of the water quality sampling probe remains unchanged. When the lateral coordinate of the water quality sampling probe exceeds the set lateral buffer zone but is still within the effective mixing interval, the target lateral displacement is calculated based on the difference between the center position of the mixing zone and the lateral coordinate of the water quality sampling probe, and the water quality sampling probe is controlled to move laterally along the cross-section so that the water quality sampling probe re-enters the set lateral buffer zone. When the lateral coordinate of the water quality sampling probe exceeds the effective mixing interval, step five is triggered.

[0007] A further technical improvement of the present invention is as follows: Step three involves acquiring real-time water level data and calculating the target sampling depth based on the real-time water level data; acquiring water quality concentration data at multiple depths along the longitudinal direction of the cross-section and calculating the water quality concentration gradient value along the longitudinal direction based on the water quality concentration data; determining the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer based on the water quality concentration gradient value; and determining the stable sampling depth range based on the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer; when the target sampling depth is within the stable sampling depth range, controlling the water quality sampling probe to move to the target sampling depth in the longitudinal direction and maintaining the set relative water depth ratio. For example, when the target sampling depth exceeds the stable sampling depth range, the target sampling depth is corrected to the corrected sampling depth within the stable sampling depth range, and the water quality sampling probe is controlled to move longitudinally to the corrected sampling depth while maintaining the set relative water depth ratio. During the water quality sampling process, the rate of change of water quality concentration gradient near the corrected sampling depth is calculated. When the rate of change of water quality concentration gradient is less than the set gradient threshold, the corresponding water quality data is output. When the rate of change of water quality concentration gradient is greater than or equal to the set gradient threshold, the corrected sampling depth is redefined, and the longitudinal movement and water quality concentration gradient change rate judgment are repeated.

[0008] A further technical improvement of the present invention is as follows: In step four, during the water quality sampling process, the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio are continuously recorded according to the sampling cycle. The lateral offset between the lateral coordinates of the water quality sampling probe and the center position of the mixing zone, and the longitudinal offset of the relative water depth ratio relative to the set relative water depth ratio are calculated respectively. The lateral and longitudinal coupling offsets are calculated based on the lateral and longitudinal offsets, and the lateral and longitudinal coupling offsets of multiple consecutive sampling cycles are accumulated to obtain the cumulative coupling offset. When both the set lateral deviation threshold and the set longitudinal deviation threshold are met, it is further determined whether the cumulative coupling offset is greater than the set cumulative offset threshold. When the cumulative coupling offset is less than or equal to the set cumulative offset threshold, the corresponding water quality data is output. When the cumulative coupling offset is greater than the set cumulative offset threshold, step five is triggered, and after triggering step five, the cumulative coupling offset is cleared and the accumulation starts again.

[0009] A further technical improvement of the present invention is as follows: In step one, after acquiring the velocity data of each velocity measuring point, the historical velocity data of each velocity measuring point within a preset time window is acquired and the historical velocity fluctuation amplitude is calculated. The instantaneous fluctuation amplitude of the velocity data within the current sampling period is calculated, and the velocity difference between each velocity measuring point and adjacent velocity measuring points is calculated. The reliability coefficient of each velocity measuring point is determined based on the historical velocity fluctuation amplitude, the instantaneous fluctuation amplitude, and the velocity difference. The velocity data is corrected based on the reliability coefficient of each velocity measuring point, and the corrected velocity data is used as the data basis for constructing the transverse velocity profile in step one and subsequently calculating the transverse shear strength value, determining the effective mixing interval boundary, and determining the center position of the mixing zone. A corrected transverse velocity profile is constructed based on the corrected velocity data, and the corrected mixing zone center position is determined according to the method in step one of determining the shear region and determining the center position of the mixing zone based on the velocity difference and velocity change rate between adjacent velocity measuring points. The transverse movement control in step two is then executed based on the corrected mixing zone center position.

[0010] A further technical improvement of the present invention is as follows: In step one, the main river flow data and tributary flow data are acquired upstream of the confluence section of the main river and tributary, respectively, and the main river flow data and tributary flow data are continuously acquired according to the sampling period within a preset time window; the tributary flow ratio is calculated based on the main river flow data and tributary flow data, and the tributary flow ratio change rate is calculated based on the tributary flow ratio of multiple consecutive sampling periods; it is determined whether the tributary flow ratio change rate is greater than a set ratio change threshold. When the tributary flow ratio change rate is greater than the set ratio change threshold, the continuous acquisition of transverse velocity data in step one is triggered and the update period of the mixing zone center position is shortened; when the tributary flow ratio change rate is less than or equal to the set ratio change threshold, the acquisition period of transverse velocity data and the update period of the mixing zone center position in step one are kept unchanged.

[0011] The present invention also discloses a water pollution big data monitoring and early warning system, which adopts any of the above-mentioned big data monitoring and early warning methods.

[0012] Compared with existing technologies, this invention has the following advantages: By constructing a transverse velocity profile at the confluence of the main river and tributaries, and determining the center positions of the shear zone and mixing zone based on the velocity difference and velocity change rate, this invention enables the water quality sampling probe to be dynamically positioned in key areas of the hydrodynamic structure. Simultaneously, by combining transverse shear intensity distribution, effective mixing intervals, and the establishment of transverse buffer zones, stable control of the sampling position is achieved, thereby improving the representativeness of the sampling data for the transverse pollution distribution of the cross-section. Furthermore, in the longitudinal direction, this invention calculates the target sampling depth using real-time water level data and determines a stable sampling depth interval by combining water quality concentration gradient analysis, effectively avoiding the influence of surface disturbance layers and bottom sediment disturbance layers on the sampling results. Furthermore, by continuously determining the rate of change of water quality concentration gradient near the sampling depth, the longitudinal sampling position is dynamically corrected, thereby improving the longitudinal stability and anti-disturbance capability of the sampling data. On the other hand, this invention further introduces a multi-layer progressive verification mechanism that combines instantaneous deviation determination and cumulative coupling offset determination. On the basis of meeting the horizontal and vertical deviation thresholds, it adds a stability determination in the time dimension and triggers the re-execution of the adaptive adjustment process of steps one to four in abnormal situations. At the same time, combined with the tributary flow ratio change rate determination mechanism, it realizes the pre-identification and response to changes in the hydrodynamic structure of the inflow, thereby improving the continuous operation capability and early warning reliability of the monitoring system under complex hydrodynamic conditions. Attached Figure Description

[0013] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0014] Figure 1 is a schematic diagram of the method logic of the present invention. Detailed Implementation

[0015] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0016] Example 1 (See Figure 1) This invention provides a water pollution big data monitoring and early warning method, including: Step 1: Multiple velocity measuring points are laterally deployed at the confluence of the main river and tributaries to obtain velocity data from each measuring point, and a lateral velocity profile is constructed based on the velocity data; the shear zone is determined based on the velocity difference and velocity change rate between adjacent velocity measuring points, and the center location of the mixing zone is determined; multiple laterally deployed velocity measuring points form a cross-sectional velocity observation basis, enabling the lateral velocity profile to reflect the lateral velocity distribution characteristics of the main river and tributary water bodies after confluence; further, the shear zone corresponding to the abrupt change in lateral velocity gradient is identified by the velocity difference and velocity change rate between adjacent velocity measuring points, using the shear zone as the most significant hydrodynamic structural marker of the exchange and mixing between the main river and tributary water bodies, thereby determining the center location of the mixing zone and providing a clear spatial target for subsequent lateral control of water quality sampling locations; specifically, Step 1 involves constructing a lateral velocity profile based on the velocity data... After constructing the transverse velocity profile, the transverse shear strength value is calculated based on the velocity difference between adjacent velocity measuring points and the distance between the measuring points, and a transverse shear strength distribution is formed along the cross-section. Multiple velocity measuring points are arranged transversely at the confluence of the main river and its tributaries. For example, nine velocity measuring points are set sequentially from the left bank to the right bank along the cross-section, with a distance of 5 meters between the measuring points. Velocity data for each measuring point is obtained, and a transverse velocity profile is constructed. Within the same sampling period, the velocity difference between adjacent velocity measuring points is calculated in pairs, and the transverse shear strength value is obtained by combining the distance between the measuring points. For example, if the velocities of two adjacent measuring points are 0.42 m / s and 0.58 m / s respectively, and the distance between the measuring points is 5 meters, then the transverse shear strength value of this adjacent interval is 0.032 m / s. The transverse shear strength values ​​of each adjacent interval are arranged in order of transverse position on the cross-section to form a transverse shear strength distribution covering the entire cross-section, thus transforming the transverse velocity profile into basic data on the transverse shear strength distribution of the cross-section that can characterize the shear structure at the confluence.

[0017] The peak position of shear intensity is determined based on the transverse shear intensity distribution, and the effective mixing interval boundary is defined as the transverse position where the transverse shear intensity values ​​on both sides of the peak value decrease to a set proportional threshold. Specifically, after forming the transverse shear intensity distribution, the maximum value within the distribution is searched to determine the peak position. For example, if the transverse shear intensity distribution reaches a maximum value of 0.085 per second at a distance of 20 meters from the left bank, this location is determined as the peak position. Furthermore, a proportional threshold of 0.60 is set, and values ​​are calculated along the shear intensity peak... The search for the lateral shear strength value first drops to 0.085 × 0.60 = 0.051 per second when moving left and right. For example, if the value drops to 0.050 per second at 15 meters from the left bank when moving left and to 0.049 per second at 28 meters from the left bank when moving right, then 15 meters and 28 meters from the left bank are respectively determined as the effective mixing interval boundaries. Thus, a quantifiable and reproducible method for determining the effective mixing interval boundaries is established with the peak shear strength position as the core and a set proportional threshold as a constraint, so that subsequent sampling and positioning have a clear spatial constraint range.

[0018] The water quality sampling probe is positioned within the effective mixing zone. During the sampling process, when the water quality sampling probe is located within the effective mixing zone and the rate of change of the peak transverse shear strength is less than the set rate of change threshold in multiple consecutive sampling cycles, the corresponding water quality data is output. After obtaining the boundary of the effective mixing zone, the water quality sampling probe is laterally controlled to enter the effective mixing zone along the cross-section. For example, the lateral coordinate of the water quality sampling probe is controlled at 18 meters from the left bank and kept within the effective mixing zone between 15 meters and 28 meters from the left bank. During subsequent sampling, the peak value of the lateral shear strength corresponding to the peak position of the lateral shear strength is continuously obtained according to the sampling cycle, and the rate of change of the peak value of the lateral shear strength in multiple consecutive sampling cycles is calculated. For example, if the peak values ​​of the lateral shear strength in three consecutive sampling cycles are 0.085 seconds, 0.083 seconds, and 0.084 seconds, the maximum rate of change is |0.085-0.083| / 0.085=0.0235. The rate of change threshold is set to 0.05. When the rate of change is less than the set rate of change threshold and the water quality sampling probe is always within the effective mixing zone, the corresponding water quality data is output. This ensures that the water quality data output not only meets the constraint of being within the effective mixing zone in space, but also meets the constraint that the hydrodynamic structure is in a stable state in time, reducing the sampling representativeness fluctuation caused by the rapid drift of the shear structure.

[0019] Through the above three progressive steps, the cross-sectional observation results of the "lateral velocity profile" are further refined into the structural criterion of "lateral shear strength distribution". Then, the "shear strength peak position" and "effective mixing interval boundary" are used to form an executable spatial constraint. On this basis, the stability criterion of "the rate of change of the lateral shear strength peak is less than the set rate of change threshold in multiple consecutive sampling periods" is used to complete the construction of water quality data output conditions. This makes the sampling location and data output have a quantifiable and reproducible implementation path in both spatial and temporal dimensions.

[0020] Step Two: Based on the center position of the mixing zone, control the water quality sampling probe installed on the transverse guide structure to move laterally along the cross-section, positioning the water quality sampling probe at the center position of the mixing zone. By changing the water quality sampling point from a fixed position to a position adjusted according to the changes in the cross-sectional hydrodynamic structure, the water quality sampling probe is laterally positioned around the center position of the mixing zone. This ensures that the sampling position is consistently in the area where the water exchange between the main river and tributaries is most sufficient, reducing the sampling representativeness deviation caused by transverse zonal flow phenomena and establishing a stable transverse position basis for subsequent longitudinal control and effectiveness judgment. Specifically, with the center position of the mixing zone as the center, determine and set transverse buffer zones within the effective mixing interval. The distance between the left and right boundaries of the set transverse buffer zones and the center position of the mixing zone is less than or equal to the set transverse deviation threshold, and less than a certain value. The distance between the effective mixing interval boundary and the center of the mixing zone; wherein, after determining the effective mixing interval boundary, the center of the mixing zone is obtained as the reference for constructing the buffer zone. For example, the effective mixing interval boundary is located at 15 meters and 28 meters from the left bank, respectively, and the center of the mixing zone is located at 20 meters from the left bank; the lateral deviation threshold is set to 2 meters, then the left and right boundaries of the set lateral buffer zone are set to 18 meters and 22 meters from the left bank, respectively, with the center of the mixing zone as the center, so that the distance between the left and right boundaries of the set lateral buffer zone and the center of the mixing zone is 2 meters and less than the distance between the effective mixing interval boundary and the center of the mixing zone, thereby constructing a set lateral buffer zone that is linked in the same direction as the center of the mixing zone within the effective mixing interval, providing a clear spatial constraint range for subsequent maintenance and repositioning.

[0021] A continuous update sequence of the center position of the mixed zone is obtained, and the lateral fluctuation amplitude of the center position of the mixed zone is calculated based on the continuous update sequence. Specifically, after the lateral buffer is determined, the center position of the mixed zone is continuously updated according to the sampling period to form a continuous update sequence of the center position of the mixed zone. For example, the center positions of the mixed zone for six consecutive sampling periods are 20.0 meters, 20.4 meters, 19.8 meters, 20.2 meters, 20.1 meters, and 19.9 meters from the left bank, respectively. The lateral fluctuation amplitude of the center position of the mixed zone is calculated based on this continuous update sequence. For example, if the difference between the maximum value of 20.4 meters and the minimum value of 19.8 meters in the continuous update sequence is used as the lateral fluctuation amplitude of the center position of the mixed zone, the lateral fluctuation amplitude is 0.6 meters. This quantifies the degree of lateral change of the center position of the mixed zone into a lateral fluctuation amplitude that can be used for control decisions and provides a consistent update basis for subsequent maintenance and relocation strategies.

[0022] When the lateral coordinate of the water quality sampling probe is within the set lateral buffer zone, the lateral coordinate of the water quality sampling probe remains unchanged; when the lateral coordinate of the water quality sampling probe exceeds the set lateral buffer zone but is still within the effective mixing zone, the target lateral displacement is calculated based on the difference between the center position of the mixing zone and the lateral coordinate of the water quality sampling probe, and the water quality sampling probe is controlled to move laterally along the cross-section, so that the water quality sampling probe re-enters the set lateral buffer zone; wherein, after obtaining the continuous update sequence of the center position of the mixing zone and calculating the lateral fluctuation amplitude of the center position of the mixing zone, the lateral coordinate of the water quality sampling probe is synchronously acquired and The system determines the positional relationship between the water quality sampling probe and the set horizontal buffer zone. For example, if the set horizontal buffer zone is 18 meters to 22 meters from the left bank, and the horizontal coordinate of the water quality sampling probe is 21.3 meters from the left bank, then the horizontal coordinate of the water quality sampling probe is within the set horizontal buffer zone, and the horizontal coordinate of the water quality sampling probe remains unchanged. When the center position of the mixed zone is updated, the boundary of the set horizontal buffer zone is updated synchronously with the updated center position of the mixed zone as the center. For example, if the center position of the mixed zone is updated to 21.6 meters from the left bank, the set horizontal buffer zone is updated to 19.6 meters to 23.6 meters from the left bank. When the center position of the mixing zone is updated to 21.6 meters from the left bank and the lateral coordinate of the water quality sampling probe is 24.0 meters from the left bank, the lateral coordinate of the water quality sampling probe exceeds the right boundary of the set lateral buffer zone (23.6 meters from the left bank) but is still within the effective mixing zone (within 28 meters). Then, the target lateral displacement is calculated based on the difference between the center position of the mixing zone and the lateral coordinate of the water quality sampling probe. For example, if the difference is 2.4 meters, the water quality sampling probe is controlled to move 2.4 meters to the left, so that the probe returns to the center position of the mixing zone and re-enters the set lateral buffer zone. This achieves graded control of "staying still" and "quantitative return" within the effective mixing zone, reducing the impact of frequent small adjustments on sampling stability and ensuring that the water quality sampling probe is continuously constrained by the set lateral buffer zone.

[0023] When the lateral coordinate of the water quality sampling probe exceeds the effective mixing range, step five is triggered. This involves continuously assessing the relationship between the lateral coordinate of the water quality sampling probe and the boundary of the effective mixing range, based on the aforementioned hold and callback control. For example, if the boundary of the effective mixing range is 15 meters to 28 meters from the left bank, and the lateral coordinate of the water quality sampling probe is 28.6 meters from the left bank, then the lateral coordinate exceeds the effective mixing range, triggering step five. This allows for the re-acquisition and updating of the mixing zone center position and the effective mixing range boundary when the water quality sampling probe deviates outside the range, ensuring that lateral control returns to a control benchmark centered on the mixing zone center position and constrained by the effective mixing range.

[0024] Step 3: Acquire real-time water level data, calculate the target sampling depth based on the real-time water level data, and control the water quality sampling probe to move longitudinally to the target sampling depth, maintaining the set relative water depth ratio. Use the real-time water level data to establish a correspondence between the sampling depth and cross-sectional water level changes, allowing the target sampling depth to be dynamically updated with water level rises and falls. By maintaining the set relative water depth ratio, the sampling layer maintains a consistent relative position in the longitudinal direction, thus avoiding water level fluctuations causing the water quality sampling probe to drift. This ensures the comparability and continuity of water quality data between different sampling periods and provides a benchmark for the relative water depth ratio recording and longitudinal deviation judgment in Step 4. Specifically... Step 3 involves acquiring real-time water level data and calculating the target sampling depth based on this data. Specifically, water level measurement locations are set up along the cross-section to continuously acquire real-time water level data. The target sampling depth is calculated using a set relative water depth ratio as the longitudinal positioning reference. For example, if the real-time water level is 3.20 meters and the set relative water depth ratio is 0.60, the target sampling depth is 1.92 meters. When the real-time water level is updated to 3.50 meters, the target sampling depth is updated to 2.10 meters, allowing the target sampling depth to be dynamically adjusted according to changes in the real-time water level data. This provides longitudinal positioning input consistent with water level conditions for subsequently determining a stable sampling depth range based on water quality concentration gradient values.

[0025] Water quality concentration data at multiple depths along the longitudinal direction of the cross-section are acquired, and the water quality concentration gradient value is calculated based on the water quality concentration data along the longitudinal direction. The lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer are determined based on the water quality concentration gradient value, and the stable sampling depth interval is determined based on the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer. Specifically, after obtaining the target sampling depth, multiple sampling depths are set along the longitudinal direction of the same cross-section, and the corresponding water quality concentration data are obtained. For example, water quality concentration data of 0.18 mg / L, 0.22 mg / L, 0.24 mg / L, 0.25 mg / L, 0.26 mg / L, and 0.33 mg / L are obtained at 0.20 m, 0.60 m, 1.00 m, 1.40 m, 1.80 m, and 2.20 m from the water surface, respectively. The water quality concentration gradient value is calculated based on the difference in water quality concentration between adjacent depths and the depth difference. For example, the water quality concentration gradient value between 1.80 m and 2.20 m is... If the concentration difference is 0.07 mg / L and the depth difference is 0.40 m, the corresponding water quality concentration gradient is 0.175 mg / L / m. Further, based on the abrupt changes in the longitudinal direction of the water quality concentration gradient, the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer are determined. For example, when the water quality concentration gradient value between 0.20 m and 0.60 m above the water surface is consistently greater than 0.10 mg / L / m, 0.60 m above the water surface is defined as the lower boundary of the surface disturbance layer. When the water quality concentration gradient value between 1.80 m and 2.20 m above the water surface is consistently greater than 0.10 mg / L / m, 1.80 m above the water surface is defined as the upper boundary of the bottom sediment disturbance layer. Thus, a stable sampling depth range is obtained by sandwiching the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer. For example, the stable sampling depth range is 0.60 m to 1.80 m above the water surface, ensuring that subsequent sampling depth selection avoids the influence zones of surface and bottom sediment disturbance.

[0026] When the target sampling depth is within the stable sampling depth range, the water quality sampling probe is controlled to move longitudinally to the target sampling depth while maintaining the set relative water depth ratio. When the target sampling depth exceeds the stable sampling depth range, the target sampling depth is corrected to a corrected sampling depth within the stable sampling depth range, and the water quality sampling probe is controlled to move longitudinally to the corrected sampling depth while maintaining the set relative water depth ratio. Specifically, after obtaining the stable sampling depth range, the inclusion relationship between the target sampling depth and the stable sampling depth range is determined. For example, if the target sampling depth is 1.20 meters from the water surface and the stable sampling depth range is 0.60 meters to 1.80 meters from the water surface, then the target sampling depth is within the stable sampling depth range, and the water quality sampling probe is controlled accordingly. The probe moves longitudinally to a distance of 1.20 meters from the water surface and maintains the set relative water depth ratio. When the target sampling depth is 2.10 meters from the water surface and the stable sampling depth range is still 0.60 meters to 1.80 meters from the water surface, the target sampling depth exceeds the stable sampling depth range. In this case, the target sampling depth is corrected to a corrected sampling depth within the stable sampling depth range. For example, the corrected sampling depth is set to 1.70 meters from the water surface, and the water quality sampling probe is controlled to move longitudinally to a distance of 1.70 meters from the water surface and maintain the set relative water depth ratio. Thus, when the target sampling depth drifts due to changes in real-time water level data, the sampling depth is still corrected to limit the sampling layer to the stable sampling depth range, ensuring that the sampling layer is representative and repeatable.

[0027] During water quality sampling, the rate of change of water quality concentration gradient near the corrected sampling depth is calculated. When the rate of change of water quality concentration gradient is less than the set gradient threshold, the corresponding water quality data is output. When the rate of change of water quality concentration gradient is greater than or equal to the set gradient threshold, the corrected sampling depth is redefined and the longitudinal movement and water quality concentration gradient change rate judgment are repeated. The process involves moving the water quality sampling probe to the corrected sampling depth and maintaining a set relative water depth ratio. Two adjacent depths above and below the corrected sampling depth are selected to acquire water quality concentration data, and the water quality concentration gradient value is continuously calculated. For example, the water quality concentration gradient value is calculated using water quality concentration data at 1.60 meters and 1.80 meters above the water surface. The changes in the water quality concentration gradient value are compared over multiple consecutive sampling periods to obtain the water quality concentration gradient change rate. For instance, if the water quality concentration gradient values ​​for three consecutive sampling periods are 0.040 mg / L / m, 0.042 mg / L / m, and 0.041 mg / L / m, then the water quality concentration gradient change rate is 0.002 / 0.040 = 0.05. A gradient threshold of 0.10 is set. When the water quality concentration gradient... When the rate of change of water quality is less than the set gradient threshold, the corresponding water quality data is output. When the water quality concentration gradient values ​​for three consecutive sampling periods are 0.060 mg / L / m, 0.085 mg / L / m, and 0.110 mg / L / m, respectively, the rate of change of water quality concentration gradient is 0.050 / 0.060 = 0.833, which is greater than or equal to the set gradient threshold. Then, the corrected sampling depth is redefined. For example, the corrected sampling depth is adjusted to 1.40 meters from the water surface, and the longitudinal movement and water quality concentration gradient change rate judgment are repeated. Thus, the water quality concentration gradient change rate is used as the longitudinal stability criterion to perform closed-loop verification of the corrected sampling depth, so that the output water quality data simultaneously meets the stable sampling depth interval constraint and the gradient change rate stability constraint in the longitudinal direction.

[0028] Step 4: During water quality sampling, simultaneously record the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio; determine whether the distance between the lateral coordinates of the water quality sampling probe and the center position of the mixing zone is less than the set lateral deviation threshold, and whether the relative water depth ratio is within the set longitudinal deviation threshold range; when both the lateral and longitudinal deviation thresholds are met, output the corresponding water quality data; by simultaneously recording the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio, establish a one-to-one correspondence between the sampling data and the sampling spatial state, providing traceable state evidence for the sampling effectiveness; further, by setting the lateral... The deviation threshold and the set longitudinal deviation threshold form a two-dimensional constraint on the sampling location, ensuring that the corresponding water quality data is only confirmed for output when both the lateral position and the longitudinal stratigraphic level simultaneously meet the spatial deviation requirements. This transforms the "matching of sampling points with hydrodynamic structures" into a quantifiable judgment condition, reducing the impact of abnormal location or instantaneous disturbances on the water quality data output. Specifically, in step four, during the water quality sampling process, the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio are continuously recorded according to the sampling cycle. The lateral offset between the lateral coordinates of the water quality sampling probe and the center position of the mixing zone, as well as the relative water depth ratio relative to the set threshold, are calculated respectively. The longitudinal offset of the relative water depth ratio; wherein, during water quality sampling, the center position of the mixing zone, the lateral coordinate of the water quality sampling probe, and the relative water depth ratio are continuously recorded at a fixed sampling period. For example, if the sampling period is 10 seconds, the center position of the mixing zone recorded for 5 consecutive sampling periods are 20.0 meters, 20.3 meters, 20.1 meters, 20.4 meters, and 20.2 meters from the left bank, respectively; the lateral coordinates of the water quality sampling probe are 20.6 meters, 20.7 meters, 20.6 meters, 20.8 meters, and 20.7 meters from the left bank, respectively; and the relative water depth ratios are 0.60, 0.61, 0.59, 0.60, and 0.62, respectively; the phase is set. For a water depth ratio of 0.60, the lateral offset between the lateral coordinate of the water quality sampling probe and the center position of the mixing zone is calculated cycle by cycle. For example, the lateral offset is 0.6 meters in the first sampling cycle and 0.4 meters in the second sampling cycle. The longitudinal offset relative to the set relative water depth ratio is also calculated cycle by cycle. For example, the longitudinal offset is 0.01 in the second sampling cycle and 0.02 in the fifth sampling cycle. This transforms the degree of lateral and longitudinal deviation during the sampling process into quantifiable lateral and longitudinal offsets, establishing a unified offset input sequence for subsequent lateral and longitudinal coupling determination.

[0029] The horizontal and vertical coupling offsets are calculated based on the horizontal and vertical offsets, and the cumulative coupling offsets are obtained by summing the horizontal and vertical coupling offsets of multiple consecutive sampling periods. Specifically, after obtaining the horizontal and vertical offsets corresponding to each sampling period, the horizontal and vertical offsets are combined into a horizontal and vertical coupling offset using a consistent coupling calculation rule. For example, the horizontal and vertical coupling offsets are set as a weighted sum of the horizontal and vertical offsets while keeping the weights unchanged. If the horizontal offset is 0.6 meters and the vertical offset is 0.00 in the first sampling period, then the horizontal and vertical coupling offsets are 0. 6; The lateral offset of the second sampling period is 0.4 meters and the longitudinal offset is 0.01 meters, so the lateral and longitudinal coupling offset is 0.41 meters. Based on this, the lateral and longitudinal coupling offsets of multiple consecutive sampling periods are accumulated to obtain the cumulative coupling offset. For example, if the lateral and longitudinal coupling offsets of five consecutive sampling periods are 0.60, 0.41, 0.51, 0.40 and 0.52 respectively, the cumulative coupling offset is 2.44 meters. This expands the instantaneous offset state into a cumulative coupling offset that reflects the duration of time, so that subsequent judgments can identify sampling states that are qualified for a short time but drift for a long time.

[0030] When both the set lateral deviation threshold and the set longitudinal deviation threshold are met, it is further determined whether the cumulative coupling offset is greater than the set cumulative offset threshold. When the cumulative coupling offset is less than or equal to the set cumulative offset threshold, the corresponding water quality data is output. Specifically, within each sampling period, it is first determined whether the distance between the lateral coordinate of the water quality sampling probe and the center position of the mixing zone is less than the set lateral deviation threshold, and whether the relative water depth ratio is within the set longitudinal deviation threshold range. For example, if the set lateral deviation threshold is 1.0 meter and the set longitudinal deviation threshold range is 0.58 to 0.62, then the lateral offset in the second sampling period is 0.4 meters and the relative water depth ratio is 0.61, which meets the set lateral deviation threshold and the set longitudinal deviation threshold range. Under the premise that both are met, it is further determined whether the cumulative coupling offset is greater than the set cumulative offset threshold. For example, if the set cumulative offset threshold is 3.00, when the cumulative coupling offset is 2.48, it is less than or equal to the set cumulative offset threshold, and the corresponding water quality data is output. Thus, based on satisfying the instantaneous spatial constraints, a time cumulative constraint is introduced, so that the water quality data output has the dual conditions of spatial compliance and temporal stability.

[0031] When the cumulative coupling offset exceeds the set cumulative offset threshold, step five is triggered. After triggering step five, the cumulative coupling offset is cleared and accumulation restarts. Specifically, if both the set lateral deviation threshold and the set longitudinal deviation threshold are met, but the cumulative coupling offset continues to accumulate and exceeds the set cumulative offset threshold (e.g., in a subsequent sampling period, the cumulative coupling offset rises to 3.25 and the set cumulative offset threshold is 3.00), then the cumulative coupling offset is determined to be greater than the set cumulative offset threshold, and step five is triggered. This re-executes steps one through four to rematch the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio. After triggering step five, the cumulative coupling offset is cleared and accumulation restarts, ensuring that the accumulation process of the cumulative coupling offset is consistent with the update process of the new mixing zone center position, thereby avoiding continuous interference from the historical accumulation state on the sampling determination after repositioning.

[0032] Step 5: When the distance between the center of the mixing zone and the lateral coordinate of the water quality sampling probe is greater than the set lateral deviation threshold, or when the real-time water level change rate is greater than the set water level change rate threshold, repeat steps 1 to 4. When the lateral deviation exceeds the limit, it indicates that the matching relationship between the water quality sampling probe and the center of the mixing zone has deviated from the predetermined spatial constraints. It is necessary to re-execute steps 1 to 4 to reacquire velocity data, reconstruct the lateral velocity profile, and update the center of the mixing zone. Then, the lateral and longitudinal positioning should be re-completed, and the validity judgment should be re-performed. When the real-time water level change rate exceeds the limit, it indicates that the water level is in a state of rapid change, and the maintenance of the target sampling depth and the relative water depth ratio may be affected. It is necessary to re-execute steps 1 to 4 to synchronously update the lateral and longitudinal control benchmarks to achieve re-matching of the sampling position and the hydrodynamic state of the cross-section, thereby ensuring the continuity and reliability of water quality data output during rapid changes in the hydrodynamic structure.

[0033] Example 2, based on Example 1, in step one, after acquiring the flow velocity data of each flow velocity measuring point, acquires the historical flow velocity data of each flow velocity measuring point within a preset time window and calculates the historical flow velocity fluctuation amplitude, calculates the instantaneous fluctuation amplitude of the flow velocity data within the current sampling period, and calculates the flow velocity difference between each flow velocity measuring point and its adjacent flow velocity measuring points. The preset time window can be set to 300 seconds. Within this time window, the flow velocity data of each flow velocity measuring point is continuously recorded according to the sampling period. For example, the maximum flow velocity of a certain flow velocity measuring point within the preset time window is 0.82 m / s and the minimum flow velocity is 0.68 m / s. The historical velocity fluctuation amplitude is 0.14 m / s. In the current sampling period, if three consecutive instantaneous sampling values ​​are 0.75 m / s, 0.79 m / s, and 0.73 m / s, the instantaneous fluctuation amplitude is 0.06 m / s. At the same time, the velocity difference between this velocity measuring point and the adjacent velocity measuring points is calculated. For example, if the velocity at this measuring point is 0.75 m / s in the current sampling period and the velocity at the adjacent measuring point is 0.61 m / s, the velocity difference is 0.14 m / s. Thus, the temporal stability and spatial consistency of each velocity measuring point are transformed into three quantitative indicators: historical velocity fluctuation amplitude, instantaneous fluctuation amplitude, and velocity difference.

[0034] The reliability coefficient of each velocity measuring point is determined based on the historical velocity fluctuation amplitude, instantaneous velocity fluctuation amplitude, and velocity difference. The allowable upper limits for historical velocity fluctuation amplitude, instantaneous velocity fluctuation amplitude, and velocity difference can be preset, for example, set to 0.20 m / s, 0.10 m / s, and 0.25 m / s respectively. The reliability coefficient of the measuring point is calculated based on the proportional relationship between each indicator and its corresponding allowable upper limit. For example, when the historical velocity fluctuation amplitude is 0.14 m / s, the instantaneous velocity fluctuation amplitude is 0.06 m / s, and the velocity difference is 0.14 m / s, the reliability coefficient of the measuring point can be proportionally converted to 0.82. When the historical velocity fluctuation amplitude of a certain velocity measuring point is 0.25 m / s and exceeds the set upper limit, the reliability coefficient of that velocity measuring point is reduced to below 0.60, thus allowing the reliability coefficient to reflect the stability of the velocity measuring point in both time and space dimensions.

[0035] The velocity data is corrected based on the reliability coefficient of each velocity measuring point. This corrected velocity data serves as the data basis for constructing the transverse velocity profile in step one, as well as for subsequent calculations of transverse shear strength, determination of the effective mixing zone boundary, and determination of the mixing zone center position. A corrected transverse velocity profile is constructed based on the corrected velocity data. Following the method used in step one to determine the shear zone and mixing zone center position based on the velocity difference and velocity change rate between adjacent velocity measuring points, the corrected mixing zone center position is determined. The transverse movement control in step two is then executed based on the corrected mixing zone center position. The correction of the velocity data can be achieved by combining the velocity data with the measured velocity data... The method involves multiplying the reliability coefficients of the points. For example, if the original velocity data of a certain velocity measuring point is 0.75 m / s and the reliability coefficient of the measuring point is 0.82, then the corrected velocity data is 0.615 m / s. The corrected transverse velocity profile is reconstructed using all the corrected velocity data, and the transverse shear strength value and transverse shear strength distribution are recalculated to determine the corrected mixing zone center position. For example, if the original mixing zone center position is located 20 meters from the left bank, and the corrected mixing zone center position is updated to be 19.4 meters from the left bank, then the transverse movement control in step two is executed according to the corrected mixing zone center position to keep the transverse coordinates of the water quality sampling probe matched with the corrected mixing zone center position.

[0036] In step one, main river flow data and tributary flow data are acquired upstream of the confluence sections of the main river and tributaries, respectively, and continuously acquired within a preset time window according to the sampling period. The tributary flow percentage is calculated based on the main river and tributary flow data, and the rate of change of the tributary flow percentage is calculated based on the tributary flow percentage over multiple consecutive sampling periods. It is determined whether the rate of change of the tributary flow percentage is greater than a set percentage change threshold. If the rate of change of the tributary flow percentage is greater than the set percentage change threshold, the continuous acquisition of transverse velocity data in step one is triggered, and the update period for the center position of the mixing zone is shortened. If the rate of change of the tributary flow percentage is less than or equal to the set percentage change threshold, the acquisition period of the transverse velocity data and the update period for the center position of the mixing zone in step one remain unchanged. For example, if the main river flow data is 120 cubic meters per second and the tributary flow data is 30 cubic meters per second at a certain moment, then… The tributary flow percentage is 0.20; the tributary flow percentages for three consecutive sampling periods are 0.20, 0.27, and 0.35, respectively. Therefore, the tributary flow percentage change rate can be calculated as (0.35-0.20) / 0.20=0.75. If the percentage change threshold is set to 0.50, the tributary flow percentage change rate will be greater than the set threshold, triggering the continuous acquisition of transverse velocity data in step one, and shortening the update cycle of the mixing zone center position from 60 seconds to 10 seconds to improve the response speed to changes in the mixing zone center position. When the tributary flow percentage change rate recovers to less than or equal to the set threshold, the acquisition cycle of transverse velocity data and the update cycle of the mixing zone center position are restored to the original set values. This allows the data correction mechanism and the flow mutation response mechanism to work together to improve the stability and timeliness of mixing zone center position identification and water quality sampling control under tributary flow percentage mutation conditions.

[0037] On the other hand, Embodiments 1 and 2 also disclose a water pollution big data monitoring and early warning system, which adopts the big data monitoring and early warning method in the corresponding embodiments.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for big data monitoring and early warning of water pollution, characterized in that, include: Step 1: Establish multiple velocity measuring points laterally at the confluence of the main river and its tributaries to acquire velocity data at each point and construct a transverse velocity profile based on the data. Determine the shear zone and the center location of the mixing zone based on the velocity difference and velocity change rate between adjacent measuring points. Step 2: Based on the center location of the mixing zone, control the water quality sampling probe installed on the transverse guide structure to move laterally along the cross-section, positioning the probe at the center of the mixing zone. Step 3: Acquire real-time water level data, calculate the target sampling depth based on the data, and control the water quality sampling probe to move longitudinally to the target sampling depth, maintaining the probe at the set depth. Step 4: During water quality sampling, simultaneously record the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio; determine whether the distance between the lateral coordinates of the water quality sampling probe and the center position of the mixing zone is less than the set lateral deviation threshold, and whether the relative water depth ratio is within the set longitudinal deviation threshold range; when both the lateral deviation threshold and the longitudinal deviation threshold are met, output the corresponding water quality data; Step 5: When the distance between the center position of the mixing zone and the lateral coordinates of the water quality sampling probe is greater than the set lateral deviation threshold, or when the real-time water level change rate is greater than the set water level change rate threshold, repeat steps 1 to 4.

2. The water pollution big data monitoring and early warning method according to claim 1, characterized in that, Step 1 involves constructing a transverse velocity profile based on the velocity data. Then, the transverse shear strength value is calculated based on the velocity difference between adjacent velocity measuring points and the distance between the measuring points, forming a transverse shear strength distribution along the cross-section. The peak shear strength position is determined based on this distribution, and the effective mixing zone boundary is defined by the transverse position where the transverse shear strength values ​​on both sides of the peak decrease to a set percentage threshold. The water quality sampling probe is positioned within the effective mixing zone. During sampling, when the water quality sampling probe is within the effective mixing zone and the rate of change of the transverse shear strength peak is less than a set rate of change threshold over multiple consecutive sampling periods, the corresponding water quality data is output.

3. The water pollution big data monitoring and early warning method according to claim 2, characterized in that, Centered on the center of the mixing zone, a set lateral buffer zone is determined within the effective mixing interval. The distance between the left and right boundaries of the set lateral buffer zone and the center of the mixing zone is less than or equal to a set lateral deviation threshold, and less than the distance between the boundary of the effective mixing interval and the center of the mixing zone. A continuous update sequence of the center position of the mixing zone is obtained, and the lateral fluctuation amplitude of the center position of the mixing zone is calculated based on the continuous update sequence. When the lateral coordinate of the water quality sampling probe is within the set lateral buffer zone, the lateral coordinate of the water quality sampling probe remains unchanged. When the lateral coordinate of the water quality sampling probe exceeds the set lateral buffer zone but is still within the effective mixing interval, the target lateral displacement is calculated based on the difference between the center position of the mixing zone and the lateral coordinate of the water quality sampling probe, and the water quality sampling probe is controlled to move laterally along the cross-section, so that the water quality sampling probe re-enters the set lateral buffer zone. When the lateral coordinate of the water quality sampling probe exceeds the effective mixing interval, step five is triggered.

4. The water pollution big data monitoring and early warning method according to claim 1, characterized in that, Step 3: Acquire real-time water level data and calculate the target sampling depth based on the real-time water level data; acquire water quality concentration data at multiple depths along the longitudinal direction and calculate the water quality concentration gradient value along the longitudinal direction based on the water quality concentration data; determine the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer based on the water quality concentration gradient value; determine the stable sampling depth range based on the lower boundary of the surface disturbance layer and the upper boundary of the bottom sediment disturbance layer; when the target sampling depth is within the stable sampling depth range, control the water quality sampling probe to move to the target sampling depth in the longitudinal direction and maintain the set relative water depth ratio; when the target sampling depth exceeds the stable sampling depth range, correct the target sampling depth to the corrected sampling depth within the stable sampling depth range, and control the water quality sampling probe to move to the corrected sampling depth in the longitudinal direction and maintain the set relative water depth ratio; during the water quality sampling process, calculate the rate of change of water quality concentration gradient near the corrected sampling depth; when the rate of change of water quality concentration gradient is less than the set gradient threshold, output the corresponding water quality data; when the rate of change of water quality concentration gradient is greater than or equal to the set gradient threshold, redetermine the corrected sampling depth and repeat the longitudinal movement and water quality concentration gradient change rate judgment.

5. The water pollution big data monitoring and early warning method according to claim 1, characterized in that, In step four, during water quality sampling, the center position of the mixing zone, the lateral coordinates of the water quality sampling probe, and the relative water depth ratio are continuously recorded according to the sampling cycle. The lateral offset between the lateral coordinates of the water quality sampling probe and the center position of the mixing zone, and the longitudinal offset of the relative water depth ratio relative to the set relative water depth ratio are calculated respectively. The lateral and longitudinal coupling offsets are calculated based on the lateral and longitudinal offsets, and the lateral and longitudinal coupling offsets of multiple consecutive sampling cycles are accumulated to obtain the cumulative coupling offset. When both the set lateral deviation threshold and the set longitudinal deviation threshold are met, it is further determined whether the cumulative coupling offset is greater than the set cumulative offset threshold. When the cumulative coupling offset is less than or equal to the set cumulative offset threshold, the corresponding water quality data is output; when the cumulative coupling offset is greater than the set cumulative offset threshold, step five is triggered, and after triggering step five, the cumulative coupling offset is cleared and the accumulation starts again.

6. The water pollution big data monitoring and early warning method according to claim 1, characterized in that, In step one, after acquiring the velocity data of each velocity measuring point, the historical velocity data of each velocity measuring point within a preset time window is acquired and the historical velocity fluctuation amplitude is calculated. The instantaneous fluctuation amplitude of the velocity data within the current sampling period is calculated, and the velocity difference between each velocity measuring point and adjacent velocity measuring points is calculated. The reliability coefficient of each velocity measuring point is determined based on the historical velocity fluctuation amplitude, instantaneous fluctuation amplitude, and velocity difference. The velocity data is corrected based on the reliability coefficient of each velocity measuring point, and the corrected velocity data is used as the data basis for constructing the transverse velocity profile in step one and for subsequent calculation of transverse shear strength, determination of effective mixing zone boundaries, and determination of the mixing zone center position. A corrected transverse velocity profile is constructed based on the corrected velocity data, and the corrected mixing zone center position is determined according to the method in step one of determining the shear zone and determining the mixing zone center position based on the velocity difference and velocity change rate between adjacent velocity measuring points. The transverse movement control in step two is then executed based on the corrected mixing zone center position.

7. The water pollution big data monitoring and early warning method according to claim 1, characterized in that, In step one, the main river flow data and tributary flow data are obtained upstream of the confluence section of the main river and the tributary, respectively, and the main river flow data and tributary flow data are continuously obtained according to the sampling period within the preset time window; The proportion of tributary flow is calculated based on the main river flow data and tributary flow data, and the rate of change of tributary flow proportion is calculated based on the tributary flow proportion of multiple consecutive sampling periods. It is determined whether the rate of change of tributary flow proportion is greater than the set proportion change threshold. When the rate of change of tributary flow proportion is greater than the set proportion change threshold, the continuous collection of transverse velocity data in step one is triggered and the update cycle of the center position of the mixing zone is shortened. When the rate of change of tributary flow proportion is less than or equal to the set proportion change threshold, the collection cycle of transverse velocity data and the update cycle of the center position of the mixing zone in step one remain unchanged.

8. A water pollution big data monitoring and early warning system, characterized in that, The monitoring and early warning system adopts the big data monitoring and early warning method according to any one of claims 1-7.

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