Underwater plankton sampling method and device

Abstract

The present application provides a method and device for sampling underwater plankton, which relates to the field of underwater biological sampling technology. The key points of its technical solution are: obtaining the current position information of the sampling device, the distance information of the next sampling point, and the current water environment parameters; controlling multiple adjustable nozzles on the surface of the sampling device to perform a flushing operation, using filtered environmental water as the flushing medium; dynamically adjusting the intensity and mode of the flushing operation based on the distance information, the movement speed of the sampling device, and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached; monitoring the flushing effect in real time, and adaptively adjusting the flushing intensity and mode based on the monitoring results; and stopping the flushing operation before the sampling device reaches a predetermined distance from the sampling point. The underwater plankton sampling method and device provided in the present application have the advantages of reducing water disturbance, preventing sample cross-contamination, and improving sampling accuracy and efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of underwater biological sampling, and in particular to a method and device for sampling underwater plankton. Background Art

[0002] When sampling and monitoring plankton in the deep waters of large reservoirs, sampling equipment must frequently travel back and forth to different depths. This process presents two major challenges: First, plankton may adhere to the surface of the sampling equipment. During ascent or descent, these organisms may break off and contaminate samples from other water layers. Second, the sampling equipment's underwater operation disturbs the water environment, altering the distribution of phytoplankton and zooplankton. This disturbance and contamination not only affects the accuracy and representativeness of sampling but can also lead to biased results in water quality monitoring and ecological assessments.

[0003] Currently, conventional sampling methods struggle to effectively address these challenges, particularly in the deepwater areas of large reservoirs, where multi-layer, high-precision sampling is required. Traditional sampling techniques typically employ simple mechanical devices or manual operations, which are unable to precisely control water disturbance and biofouling during the sampling process. Furthermore, existing sampling equipment often lacks real-time monitoring and adaptive adjustment capabilities, making it impossible to dynamically optimize according to the environmental characteristics and biodiversity distribution of different water layers.

[0004] How to effectively minimize water disturbance and prevent cross-contamination during the vertical reciprocating motion of the sampling device to achieve accurate automated sampling of phytoplankton and zooplankton has become a core issue that urgently needs to be addressed in current underwater biological sampling technology. This not only affects the accuracy and reliability of the sampling data, but also directly impacts the scientific nature and effectiveness of aquatic ecosystem research and environmental monitoring.

[0005] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] The purpose of this application is to provide an underwater plankton sampling method and device, which has the advantages of reducing water disturbance, preventing sample cross-contamination, and improving sampling accuracy and efficiency.

[0007] This application provides an underwater plankton sampling method, the technical solution is as follows:

[0008] The method comprises the following steps: obtaining current position information of a sampling device, distance information of a next sampling point, and current water environment parameters; controlling multiple adjustable nozzles on the surface of the sampling device to perform a flushing operation based on the distance information and the water environment parameters, wherein the flushing operation uses filtered environmental water as a flushing medium; dynamically adjusting the intensity and mode of the flushing operation based on the distance information, the movement speed of the sampling device, and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached; monitoring the flushing effect in real time, and adaptively adjusting the flushing intensity and mode based on the monitoring results; and stopping the flushing operation before the sampling device reaches a predetermined distance from the sampling point.

[0009] Furthermore, the present application also proposes that the step of dynamically adjusting the intensity and mode of the flushing operation according to the distance information, the movement speed of the sampling device and the current time includes: obtaining the ratio of the movement speed of the sampling device to the maximum movement speed; calculating the distance attenuation coefficient based on the distance information; calculating the periodic adjustment coefficient based on the current time; calculating the environmental impact coefficient based on the water body environmental parameters; substituting the ratio, the distance attenuation coefficient, the periodic adjustment coefficient and the environmental impact coefficient into the dynamic flushing intensity control model to calculate the target flushing parameters; adjusting the water outlet pressure and the spray direction of the multiple adjustable nozzles according to the target flushing parameters to achieve a spiral or staggered flushing mode.

[0010] Furthermore, the present application also proposes that the step of adjusting the water outlet pressure and the spray direction of the multiple adjustable nozzles according to the target flushing parameters to achieve a spiral or staggered flushing mode includes: obtaining the current water outlet pressure and the current spray direction of the multiple adjustable nozzles; generating the target water outlet pressure and the target spray direction according to the target flushing parameters and the water body environment parameters; calculating the first difference between the current water outlet pressure and the target water outlet pressure, and the second difference between the current spray direction and the target spray direction; generating a water outlet pressure adjustment instruction and a spray direction adjustment instruction according to the first difference and the second difference, respectively; based on the water outlet pressure adjustment instruction and the spray direction adjustment instruction, controlling the multiple adjustable nozzles to adjust in sequence according to a preset time interval until the target water outlet pressure and the target spray direction are reached; wherein the preset time interval is dynamically adjusted according to the water body environment parameters to achieve a spiral or staggered flushing mode while minimizing disturbance to the water body.

[0011] Furthermore, the present application also proposes that the step of generating the target water outlet pressure and the target injection direction according to the target flushing parameters and the water body environmental parameters includes: obtaining the water body turbidity value and the water body flow rate value in the water body environmental parameters; calculating the environmental compensation coefficient based on the water body turbidity value and the water body flow rate value; performing weighted calculation on the current flushing parameters and the environmental compensation coefficient to obtain the compensated flushing parameters; generating the target water outlet pressure and the target injection direction according to the preset parameter mapping relationship based on the compensated flushing parameters; wherein the preset parameter mapping relationship is dynamically optimized according to historical sampling data and the water body disturbance model to achieve a spiral or staggered flushing mode while minimizing the disturbance to the water body.

[0012] Furthermore, the present application also proposes that the step of calculating the environmental compensation coefficient based on the water turbidity value and the water flow rate value includes: obtaining a turbidity baseline value and a flow rate baseline value determined according to historical sampling data and a water body disturbance model; calculating a first ratio of the water body turbidity value to the turbidity baseline value, and a second ratio of the water body flow rate value to the flow rate baseline value; according to the first ratio and the second ratio, using an environmental compensation model optimized based on the water body disturbance model to calculate the turbidity compensation factor and the flow rate compensation factor; based on the degree of influence of the turbidity compensation factor and the flow rate compensation factor on the spiral or staggered flushing mode, determining their respective weight coefficients; and weightedly combining the turbidity compensation factor and the flow rate compensation factor according to the corresponding weight coefficients to obtain the environmental compensation coefficient.

[0013] Furthermore, the present application also proposes that the step of calculating the environmental compensation coefficient based on the water turbidity value and the water flow rate value includes: obtaining historical change data of the water turbidity value and the water flow rate value within a preset time period; using the water disturbance model to predict the turbidity change rate and the flow rate change rate within a future sampling period based on the historical change data; dynamically adjusting the turbidity baseline value and the flow rate baseline value according to the turbidity change rate and the flow rate change rate; calculating a first ratio of the dynamically adjusted turbidity baseline value to the current water turbidity value, and a second ratio of the dynamically adjusted flow rate baseline value to the current water flow rate value; substituting the first ratio and the second ratio into the environmental compensation model to obtain updated turbidity compensation factor and flow rate compensation factor; based on the updated turbidity compensation factor and flow rate compensation factor, combined with the current spiral or staggered flushing mode parameters, calculating the influence weight of each compensation factor on the flushing effect; using the influence weight, weighted combining the updated turbidity compensation factor and flow rate compensation factor to obtain the environmental compensation coefficient.

[0014] Furthermore, the present application also proposes that the method also includes: when a situation requires light illumination, obtaining photosensitivity characteristic data of different plankton and current water environment parameters; determining the optimal wavelength range and intensity range of the LED light source array based on the photosensitivity characteristic data and the water environment parameters; controlling the LED light source array with adjustable wavelength and intensity based on the optimal wavelength range and intensity range; obtaining the current motion state information of the sampling device; calculating the optimal lighting time and frequency based on the motion state information and the water environment parameters; providing lighting according to the frequency within the optimal lighting time; real-time monitoring of the lighting effect and plankton distribution status; and dynamically adjusting the wavelength, intensity, lighting time and frequency parameters of the LED light source array based on the monitoring results to minimize the impact on the plankton distribution.

[0015] Furthermore, the present application also proposes that the step of providing lighting at the frequency within the optimal lighting time includes: obtaining the transparency data of the current water body and taking it as part of the water body environmental parameters; calculating the optimal output power of the LED light source array based on the transparency data and the photosensitivity characteristic data; determining the optimal duration of a single pulse based on the optimal output power and the water body environmental parameters; controlling the LED light source array to perform pulsed lighting according to the frequency and the optimal duration within the optimal lighting time; monitoring the pulse lighting effect and the instantaneous reaction of plankton in real time; and dynamically adjusting the optimal output power and the optimal duration based on the monitoring results to minimize the impact on the distribution of plankton.

[0016] Furthermore, the present application also proposes: obtaining water turbidity τ, current available energy p and system maximum power p_max; calculating the scour intensity F based on the following optimized adaptive scour control model: F(d,v,t,τ) = k * (1- e^(-αd)) * (v / v_max)^β * (1 + γ*sin(ωt)) * S(τ) * E(p); wherein: F is the scour intensity; d is the distance to the next sampling point; v is the equipment movement speed; t is time; τ is the water turbidity; k is the basic scour intensity coefficient; α is the distance attenuation coefficient; β is the speed influence index; γ is the pulse intensity coefficient; ω is the pulse frequency; v_max is the maximum equipment speed; S(τ) is the turbidity influence function, expressed as (τ / τ_ref)^μ; E(p) is the energy efficiency adjustment function, expressed as (1 - λ*(p_max - p) / p_max); p is the current available energy; p_max is the maximum power of the system; μ is the turbidity sensitivity coefficient; λ is the energy efficiency weight coefficient; τ_ref is the reference turbidity value; according to the calculated scouring intensity F, adjust the water outlet pressure and direction of the multiple adjustable nozzles; monitor the scouring effect in real time, and when the monitoring results show that the scouring effect is insufficient, adjust the turbidity sensitivity coefficient μ and the energy efficiency weight coefficient λ, and recalculate the scouring intensity.

[0017] Furthermore, the present application also proposes an underwater plankton sampling device, which includes: an information acquisition module for acquiring the current position information of the sampling equipment, the distance information of the next sampling point, and the current water environment parameters; a flushing control module for controlling multiple adjustable nozzles on the surface of the sampling equipment to perform flushing operations based on the distance information and the water environment parameters, and the flushing operation uses filtered environmental water as the flushing medium; an intensity adjustment module for dynamically adjusting the intensity and mode of the flushing operation based on a preset dynamic flushing intensity control model, according to the distance information, the movement speed of the sampling equipment and the current time, so that the intensity of the flushing operation gradually decreases as it approaches the next sampling point; a monitoring and adjustment module for monitoring the flushing effect in real time, and adaptively adjusting the flushing intensity and mode according to the monitoring results; a stop control module for stopping the flushing operation before the sampling equipment reaches a predetermined distance from the sampling point.

[0018] From the above, it can be seen that the underwater plankton sampling method and device provided in this application obtains the current position information of the sampling equipment, the distance information of the next sampling point and the current water environment parameters, controls the multiple adjustable nozzles on the surface of the sampling equipment to perform flushing operations, and dynamically adjusts the intensity and mode of the flushing operation, monitors the flushing effect in real time and performs adaptive adjustments, effectively reduces water disturbance, prevents sample cross-contamination, and improves sampling accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of an underwater plankton sampling method provided in this application.

[0020] Figure 2 This is a schematic structural diagram of an underwater plankton sampling device provided in this application.

[0021] In the figure: 210, information acquisition module; 220, flushing control module; 230, intensity adjustment module; 240, monitoring and adjustment module; 250, stop control module. DETAILED DESCRIPTION

[0022] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0023] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0024] When sampling and monitoring plankton in the deep waters of large reservoirs, the sampling equipment needs to frequently travel back and forth to different depths. This process presents two major technical challenges: First, plankton may adhere to the surface of the sampling equipment. When the equipment ascends or descends, these organisms may fall off and contaminate samples from other water layers. Second, when the sampling equipment operates underwater, it disturbs the water environment, causing changes in the distribution of phytoplankton and zooplankton in the water. This disturbance and contamination not only affects the accuracy and representativeness of the sampling, but may also lead to deviations in the results of water quality monitoring and ecological assessments. Specifically, the reciprocating motion of the sampling equipment causes turbulence in the water, changing the direction and speed of local water flow, thereby affecting the spatial distribution of plankton. At the same time, the shedding of organisms attached to the surface of the equipment between different water layers can lead to cross-contamination of samples, making the collected samples unable to accurately reflect the biological community structure of a specific water layer.

[0025] For example, when sampling plankton at multiple depths in a large reservoir at a depth of 100 meters, the sampling equipment needs to make multiple round trips within the range of 0-100 meters. Assuming a sampling speed of 0.5 m / s and a dwell time of 60 seconds at each sampling point, a total of 10 samples at different depths need to be collected. In this scenario, the total operating time of the sampling equipment is approximately 2000 seconds, generating significant water disturbance. Specifically, the equipment generates downward currents during descent and upward currents during ascent. These currents can significantly alter the local flow structure. Furthermore, large numbers of plankton may adhere to the equipment surface, especially when passing through dense plankton areas. These attached organisms may dislodge during the equipment's movement, leading to cross-contamination between samples from different water layers. For example, surface phytoplankton attached to the equipment surface may dislodge during sampling in deeper waters, resulting in the presence of surface species that should not be present in the deeper samples.

[0026] Therefore, if these technical problems cannot be effectively solved, it will have a serious impact on the monitoring and assessment of reservoir ecosystems. First, water disturbance will cause the collected samples to fail to truly reflect the natural distribution of plankton. In particular, for species that are sensitive to changes in water flow, their distribution may change significantly. Second, sample cross-contamination will lead to an incorrect assessment of the plankton community structure in different water layers, which may overestimate the distribution range of some species or underestimate the presence of other species. These errors will directly affect the accuracy of water quality assessment. For example, the distribution depth of certain indicator species may be misjudged, resulting in inaccurate water quality conclusions. In addition, these problems will also affect the reliability of long-term ecological monitoring data, making it difficult to accurately assess the changing trends and health status of reservoir ecosystems. Therefore, developing an innovative sampling method that can effectively reduce water disturbance and prevent sample cross-contamination is of great technical significance for improving the accuracy and reliability of plankton sampling in deep water areas of large reservoirs.

[0027] To address these issues, the present applicant first considered using a physical barrier to prevent the shedding of organisms from the surface of the sampling device. Specifically, a retractable protective cover could be installed on the outside of the sampling device. However, this approach would increase the size and weight of the device and could also affect water flow, causing new disturbances.

[0028] Next, the application considered using chemical methods, such as applying an anti-adhesion coating on the surface of the sampling device. Although this method can reduce the attachment of organisms, it may introduce new chemicals and affect the accuracy of water quality monitoring results.

[0029] Considering the limitations of the aforementioned methods, this application explored the possibility of using the water flow itself to clean the sampling device. Specifically, multiple adjustable nozzles could be installed on the surface of the sampling device, using filtered ambient water for flushing. This method not only effectively removes attached organisms but also minimizes chemical contamination of the water.

[0030] However, simple flushing can create new water disturbances. To address this issue, this application proposes the concept of dynamically adjusting flushing intensity. Specifically, the intensity and pattern of the flushing operation can be dynamically adjusted based on the distance from the sampling device to the next sampling point, the speed of movement, and the current time. This allows the flushing intensity to gradually decrease as the next sampling point is approached, minimizing disturbance to the water at the sampling point.

[0031] To further optimize the flushing effect, this application also considers real-time monitoring and adaptive adjustment methods. By real-time monitoring of the flushing effect, the system can adaptively adjust the flushing intensity and mode based on the monitoring results, ensuring the cleaning effect while minimizing water disturbance.

[0032] Finally, to reduce the impact of the sampling equipment on the sampling point, this application proposes a strategy to stop the flushing operation before the sampling equipment reaches a predetermined distance from the sampling point. This ensures that the water body near the sampling point is not directly affected by the flushing operation.

[0033] Therefore, refer to Figure 1 , this application proposes an underwater plankton sampling method, the steps of the method comprising:

[0034] Obtain the current location information of the sampling equipment, the distance information to the next sampling point, and the current water environment parameters;

[0035] According to the distance information and water environment parameters, multiple adjustable nozzles on the surface of the sampling equipment are controlled to perform flushing operations, and the flushing operation uses filtered environmental water as the flushing medium;

[0036] Dynamically adjust the intensity and mode of the flushing operation based on distance information, the speed of the sampling equipment, and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached;

[0037] Monitor scouring effects in real time and make adaptive adjustments to scouring intensity and pattern based on monitoring results;

[0038] Stop flushing operations before the sampling equipment reaches the predetermined distance from the sampling point.

[0039] Among them, the sampling equipment refers to a device used to collect plankton samples underwater, which can be specifically achieved by using an underwater robot with a sampling bottle and a filtration system.

[0040] Among them, the adjustable nozzle refers to a water spraying device that can change the water outlet pressure and spray direction, which can be specifically achieved by a combination of an electric regulating valve and a rotatable nozzle.

[0041] Among them, flushing operation refers to the process of using water flow to clean the surface of the sampling equipment, which can be achieved by high-pressure water flow flushing or low-pressure, high-flow flushing.

[0042] Among them, dynamic adjustment refers to the process of automatically changing parameters according to real-time status, which can be achieved by using an adaptive control algorithm based on sensor feedback.

[0043] The core innovation of this application lies in the proposal of a dynamically adjustable underwater plankton sampling method. This method controls the flushing operation by controlling multiple adjustable nozzles on the surface of the sampling device. The flushing intensity and pattern are dynamically adjusted based on distance information, movement speed, and the current time. This effectively removes attached organisms while minimizing water disturbance during the sampling process. Furthermore, this application introduces real-time monitoring and adaptive adjustment mechanisms to further optimize the flushing effect and ensure the accuracy and representativeness of the sampling.

[0044] The working principle of this application can be described in detail as follows: First, the sampling device is equipped with multiple adjustable nozzles, which are evenly distributed across the device surface, capable of achieving 180-degree flushing coverage. Each nozzle is equipped with an independent pressure regulating valve and directional control mechanism, which can precisely control the water pressure and spray angle.

[0045] During the sampling process, the system first obtains the current location of the sampling device, the distance to the next sampling point, and the current water environment parameters. This information is obtained through GPS, sonar, and various water quality sensors. Based on this information, the control system calculates the optimal flushing strategy.

[0046] Next, the system controls multiple adjustable nozzles to perform flushing operations based on distance information and water environment parameters. The flushing medium uses microporously filtered ambient water to minimize chemical contamination of the water. The intensity and pattern of the flushing operation are dynamically adjusted using an algorithm. This algorithm considers factors such as distance information, the speed of the sampling device, and the current time, gradually reducing the intensity of the flushing operation as the next sampling point is approached.

[0047] Specifically, when the sampling device is farther from the next sampling point, the scouring intensity is higher, and the nozzle flushes with higher pressure and a wider coverage area. As the device approaches the sampling point, the scouring intensity gradually decreases, the nozzle pressure decreases, and the spray angle becomes more focused. This dynamic adjustment not only ensures the effective removal of attached organisms, but also minimizes disturbance to the water being sampled.

[0048] Throughout the entire process, the system monitors flushing effectiveness in real time using optical sensors, flow meters, and other devices. If insufficient flushing is detected, the system automatically adjusts the flushing intensity and pattern based on the monitoring results. For example, if it detects a large amount of debris in a certain area, the system may temporarily increase the flushing intensity or change the spray angle in that area.

[0049] Finally, when the sampling device approaches a predetermined distance from the sampling point (for example, 5 meters from the sampling point), the system completely stops the flushing operation. This distance is determined through experiments to ensure that the water flow generated by the flushing operation is completely dissipated and does not affect the water environment at the sampling point.

[0050] The selection of this dynamically regulated flushing method was based on the following considerations: first, it can effectively remove attached organisms and reduce sample cross-contamination; second, through dynamic regulation, it minimizes disturbances to the water body, especially the impact on the water body near the sampling point; finally, the use of filtered ambient water as the flushing medium avoids the introduction of foreign matter that may affect water quality monitoring.

[0051] As a preferred embodiment, this application can be used to conduct multi-level plankton sampling in a large reservoir at a depth of 100 meters. The sampling equipment uses a cylindrical underwater robot, 2 meters long and 0.5 meters in diameter, with 20 adjustable nozzles evenly distributed on its surface. The water outlet pressure of each nozzle can be adjusted within a range of 0-10 MPa, and the spray angle can be rotated within a range of 0-180 degrees.

[0052] During sampling, the device moves back and forth at a speed of 0.5 m / s within a depth range of 0-100 m. The system acquires the device's location and water environment parameters every second. Flushing begins when the device is 50 meters from the next sampling point. The initial flushing intensity is set at 8 MPa, with a spray angle of 120 degrees. As the device approaches the sampling point, the flushing intensity gradually decreases, with the pressure decreasing by 0.1 MPa and the spray angle decreasing by 2 degrees for every meter of approach.

[0053] The system also monitors flushing effectiveness every 0.1 seconds. If it detects a cleanliness level in an area below 90%, it temporarily increases the flushing intensity by 10% for two seconds before returning to normal operation. When the device is 5 meters from the sampling point, the system completely stops flushing.

[0054] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to dynamically adjust the intensity and mode of the flushing operation according to the motion state of the sampling equipment and environmental factors.

[0055] In this regard, the present application further proposes a method for dynamically adjusting the intensity and mode of the flushing operation according to distance information, the movement speed of the sampling device and the current time.

[0056] The method first obtains the ratio of the sampling device's velocity to its maximum velocity. This ratio reflects the device's current motion state. Next, based on this distance information, a distance attenuation coefficient is calculated. This coefficient adjusts the variation of scour intensity with distance. Furthermore, a periodic adjustment coefficient is calculated based on the current time to achieve periodic variation in scour intensity. Furthermore, an environmental impact coefficient is calculated based on water environmental parameters to adapt to varying water conditions.

[0057] Substituting these coefficients into the dynamic scour intensity control model, the target scour parameters can be calculated. These parameters are then used to adjust the water outlet pressure and spray direction of multiple adjustable nozzles to achieve a spiral or staggered scour pattern.

[0058] The core of this dynamic regulation approach lies in the comprehensive consideration of multiple influencing factors, including equipment motion, distance variation, time periodicity, and environmental conditions. By quantifying these factors into specific coefficients and incorporating a dynamic scouring intensity control model, precise regulation of scouring intensity and pattern is achieved.

[0059] Specifically, the motion speed ratio reflects the device's current motion state, allowing the flushing intensity to be adjusted based on changes in speed. The distance attenuation coefficient ensures that the flushing intensity gradually decreases as the device approaches the sampling point, helping to minimize disturbances to the water near the sampling point. The periodic adjustment coefficient introduces a time factor, enabling periodic changes in flushing intensity and further optimizing flushing effectiveness. The environmental impact coefficient takes into account the specific conditions of the water body, enabling flushing operations to adapt to varying water environments.

[0060] These coefficients are integrated using a dynamic flushing intensity control model to calculate the optimal flushing parameters. These parameters are directly used to control the water pressure and spray direction of the adjustable nozzles, achieving a spiral or staggered flushing pattern. This pattern not only effectively cleans the sampling equipment surface but also minimizes water disturbance.

[0061] As a preferred embodiment, the dynamic scour intensity control model can take the following form:

[0062] F = k * (1 - e^(-αd)) * (v / v_max)^β * (1 + γ*sin(ωt)) * E(τ)

[0063] Where F is the scour intensity, d is the distance to the next sampling point, v is the current velocity, v_max is the maximum velocity, t is the current time, and τ is the water environment parameter. k is the basic scour intensity coefficient, α is the distance attenuation coefficient, β is the velocity influence index, γ is the periodic adjustment amplitude, ω is the periodic adjustment frequency, and E(τ) is the environmental impact function.

[0064] In practical applications, these parameters can be adjusted based on the specific water environment and sampling requirements. For example, in water with high turbidity, the weight of the environmental impact function E(τ) can be increased; in cases where the sampling equipment speed varies greatly, the value of the speed influence exponent β can be adjusted.

[0065] Through this dynamic adjustment method, the present invention can always maintain the best flushing effect under different motion states and environmental conditions. This not only improves the efficiency of cleaning the surface of the sampling equipment, but also minimizes the disturbance of the water body, thereby improving the accuracy and representativeness of the sampling.

[0066] Compared to existing technologies, this application considers multiple influencing factors rather than simply using a fixed flushing intensity and pattern. Secondly, by introducing a dynamic flushing intensity control model, precise calculation and adjustment of flushing parameters are achieved. Finally, a spiral or staggered flushing pattern enables more effective cleaning of the equipment surface while minimizing water disturbance. These advantages combine to significantly improve the accuracy and reliability of underwater plankton sampling.

[0067] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to accurately control multiple adjustable nozzles to achieve a spiral or staggered flushing pattern.

[0068] In this regard, the present application further proposes a method comprising obtaining the current water outlet pressure and current spray direction of multiple adjustable nozzles, generating target water outlet pressure and target spray direction according to target flushing parameters and water environment parameters, calculating a first difference between the current water outlet pressure and the target water outlet pressure, and a second difference between the current spray direction and the target spray direction, generating a water outlet pressure adjustment instruction and a spray direction adjustment instruction according to the first difference and the second difference, and controlling the multiple adjustable nozzles to adjust in sequence according to preset time intervals based on these adjustment instructions until the target water outlet pressure and target spray direction are reached. The preset time interval is dynamically adjusted according to the water environment parameters to achieve a spiral or staggered flushing pattern while minimizing disturbance to the water body.

[0069] The technical solution of this application achieves a spiral or staggered flushing pattern by precisely controlling the water pressure and spray direction of multiple adjustable nozzles. This control method not only effectively cleans the surface of the sampling device, but also minimizes disturbance to the water body, thereby improving the accuracy and representativeness of the sampling.

[0070] Specifically, this application first obtains the current status of multiple adjustable nozzles, including water outlet pressure and spray direction. Then, based on the target flushing parameters and water environment parameters, it generates the target water outlet pressure and target spray direction. This step takes into account real-time changes in the water environment, ensuring that the flushing operation can adapt to different water conditions.

[0071] Next, the application calculates the difference between the current state and the target state, and generates water pressure adjustment instructions and spray direction adjustment instructions respectively. This difference-based adjustment method can achieve precise control and avoid over-adjustment or under-adjustment problems.

[0072] When controlling multiple adjustable nozzles, this application adopts a method of adjusting them sequentially at preset time intervals. This method can achieve a spiral or staggered flushing pattern to effectively cover the surface of the sampling device while avoiding the severe disturbance of the water body that may be caused by adjusting all nozzles at the same time.

[0073] Furthermore, the preset time interval is dynamically adjusted based on water environment parameters. For example, when the water velocity is high, the time interval can be shortened to improve flushing efficiency; when the water is relatively still, the time interval can be appropriately extended to reduce disturbance. This dynamic adjustment mechanism further optimizes flushing effectiveness while minimizing the impact on the water.

[0074] As a preferred implementation, the present application can be implemented using the following specific steps:

[0075] First, multiple sensors are used to monitor the water pressure and spray direction of each adjustable nozzle in real time to obtain current status data. At the same time, water quality sensors are used to obtain water environmental parameters such as water temperature, turbidity, and pH value.

[0076] Then, based on a pre-established water disturbance model and historical sampling data, combined with current target flushing parameters and water environment parameters, the optimal target water outlet pressure and target spray direction are calculated. For example, when water turbidity is high, a higher water outlet pressure may be required; when the water flow is fast, the spray direction may need to be adjusted to offset the influence of the water flow.

[0077] Next, a PID control algorithm is used to calculate the difference between the current state and the target state, generating precise adjustment instructions. Specifically, the adjustment step size of the water outlet pressure can be set to 0.1 MPa and the adjustment step size of the spray direction can be set to 1° to achieve fine control.

[0078] When controlling multiple nozzles, a sequential adjustment method with a 0.5-second interval can be used. For example, for a sampling device with eight adjustable nozzles, starting with the first nozzle, adjust each nozzle every 0.5 seconds, completing a cycle of adjustment in 4 seconds. This method can achieve a spiral flushing pattern.

[0079] Finally, the time interval is dynamically adjusted based on real-time monitoring of water environmental parameters. For example, if the water velocity exceeds 0.5 m / s, the time interval is shortened to 0.3 seconds; if the water turbidity exceeds 50 NTU, the time interval is extended to 0.7 seconds. This dynamic adjustment ensures optimal flushing and minimal water disturbance under varying water conditions.

[0080] Through the above-mentioned implementation, the present application can achieve precise control of multiple adjustable nozzles and effectively implement spiral or staggered flushing patterns. This control method not only improves the cleaning effect of the sampling equipment surface, but also significantly reduces the disturbance to the water body, thereby improving the accuracy and representativeness of the sampling. Compared with traditional fixed nozzles or simple control methods, the technical solution of the present application can better adapt to complex and changing water environments and achieve more efficient and accurate underwater plankton sampling.

[0081] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to dynamically adjust the target water outlet pressure and the target spray direction according to the water environment parameters.

[0082] In response to this, the present application further proposes a method that includes obtaining water turbidity and water flow rate values ​​from water environment parameters; calculating an environmental compensation coefficient based on the water turbidity and water flow rate values; performing a weighted calculation on the current flushing parameters and the environmental compensation coefficient to obtain compensated flushing parameters; and generating a target water outlet pressure and target injection direction based on the compensated flushing parameters according to a preset parameter mapping relationship. The preset parameter mapping relationship is dynamically optimized based on historical sampling data and a water disturbance model to achieve a spiral or staggered flushing pattern while minimizing disturbance to the water body.

[0083] The technical solution of this application dynamically adjusts flushing parameters by incorporating water environment parameters, particularly turbidity and flow rate. This method can better adapt to different water environments and improve sampling accuracy and efficiency. By calculating the environmental compensation coefficient, this application achieves dynamic adjustment of the current flushing parameters, allowing the flushing operation to better adapt to the current water environment.

[0084] Specifically, this application first obtains water turbidity and flow rate values. These two parameters have a significant impact on the distribution of underwater plankton and the efficiency of sampling equipment. Turbidity reflects the amount of suspended particles in the water, directly affecting flushing effectiveness and sampling accuracy. Flow rate affects the flow characteristics of the flushing medium and the distribution of flushing intensity.

[0085] Based on the acquired water turbidity and flow rate values, the present application calculates the environmental compensation coefficient. This step can employ various algorithms, such as a weighted average method, which compares the turbidity and flow rate values ​​with preset baseline values ​​to obtain normalized indices. These indices are then assigned different weights based on their importance to ultimately obtain a comprehensive environmental compensation coefficient.

[0086] The calculation of environmental compensation coefficients can be further refined. For example, turbidity and flow rate thresholds can be set. When the actual value exceeds the threshold, the compensation coefficient changes nonlinearly to better cope with extreme environmental conditions. In addition, time factors can be introduced to consider the changing trends of water environmental parameters, making the compensation more forward-looking.

[0087] The current scour parameters are weighted and added to the environmental compensation coefficient to obtain the compensated scour parameters. This step enables dynamic adjustment of the scour parameters. The weighted calculation can use a linear combination or a nonlinear function to ensure that the adjusted parameters can adapt to environmental changes without causing excessive fluctuations.

[0088] Finally, based on the compensated flushing parameters, the target water outlet pressure and target spray direction are generated according to the preset parameter mapping relationship. This step transforms the abstract flushing parameters into specific execution instructions. The parameter mapping relationship can be implemented through table lookup or function mapping, ensuring precise control of flushing intensity and direction.

[0089] It's worth noting that the preset parameter mappings are not fixed but are dynamically optimized based on historical sampling data and water disturbance models. This optimization mechanism enables the system to continuously learn and improve, adapting to the characteristics and seasonal variations of different water bodies. For example, a database can be established to record optimal flushing parameters under different environmental conditions, and machine learning algorithms can be used to continuously update and optimize the mappings.

[0090] The technical solution of this application achieves dynamic adjustment of flushing parameters by introducing an environmental compensation mechanism. This adjustment not only takes into account the current state of the water body but also predicts and adapts to future changes through historical data and model optimization. Compared to flushing methods with fixed parameters, this approach offers greater flexibility and adaptability.

[0091] To implement this, you can use the following steps: First, use a water quality sensor to obtain real-time turbidity and flow rate values. Assume the current measured turbidity value is 5 NTU and the flow rate is 0.3 m / s. Then, compare these values ​​with preset baseline values ​​(for example, a turbidity baseline value of 3 NTU and a flow rate baseline value of 0.2 m / s) to calculate a turbidity ratio of 1.67 and a flow rate ratio of 1.5.

[0092] Next, use the pre-trained environmental compensation model to calculate the environmental compensation coefficients. Assume the model outputs a turbidity compensation factor of 1.2 and a flow rate compensation factor of 1.1. Considering the spiral flushing mode, turbidity has a greater impact on flushing effectiveness. Therefore, a weight of 0.6 is assigned to the turbidity compensation factor and a weight of 0.4 to the flow rate compensation factor. The final calculated environmental compensation coefficient is 1.2 * 0.6 + 1.1 * 0.4 = 1.16.

[0093] Multiplying the current flushing parameters (assuming a water outlet pressure of 2 bar and a spray angle of 30°) by the environmental compensation coefficient yields the compensated flushing parameters: a water outlet pressure of 2.32 bar and a spray angle of 34.8°. Finally, based on the optimized parameter mapping, these parameters are converted into specific execution instructions, such as adjusting the pump power and nozzle angle.

[0094] Through this method, the present application can dynamically adjust flushing parameters based on the real-time water environment, ensuring flushing effectiveness while minimizing water disturbance. This precise control not only improves sampling accuracy but also reduces the impact on the water ecosystem, providing reliable technical support for long-term, large-scale underwater plankton monitoring.

[0095] Compared with the existing technology, the method of the present application has significant advantages. The traditional fixed parameter flushing method cannot adapt to the complex and changeable water environment, and may under- or over-flushed in some cases. However, the present application can maintain the best flushing effect under various water conditions through real-time environmental perception and dynamic parameter adjustment. In addition, the method of the present application realizes self-learning and self-adaptation through historical data and model optimization. This intelligent feature enables the system to continuously improve its performance and adapt to changes in different waters and seasons, greatly improving the flexibility and reliability of underwater plankton sampling.

[0096] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to accurately calculate the environmental compensation coefficient according to the water environment parameters to optimize the flushing effect.

[0097] In this regard, the present application further proposes that the method first obtains the turbidity reference value and the flow rate reference value determined based on the historical sampling data and the water body disturbance model. Then calculate the first ratio of the current water body turbidity value to the turbidity reference value, and the second ratio of the current water body flow rate value to the flow rate reference value. Then, using the environmental compensation model optimized based on the water body disturbance model, the turbidity compensation factor and the flow rate compensation factor are calculated based on these two ratios. Subsequently, based on the degree of influence of these two compensation factors on the spiral or staggered flushing mode, their respective weight coefficients are determined. Finally, the turbidity compensation factor and the flow rate compensation factor are weightedly combined according to the corresponding weight coefficients to obtain the environmental compensation coefficient.

[0098] This method, by incorporating historical sampling data and a water disturbance model, can more accurately reflect the impact of the current water environment on flushing effectiveness. By calculating the relative changes in turbidity and flow velocity, it can more flexibly adapt to different water conditions. Furthermore, it considers the degree to which different environmental factors affect flushing patterns, and by introducing weighting coefficients, it achieves more refined environmental compensation adjustments.

[0099] Specifically, the method of this application first obtains the turbidity baseline value and flow rate baseline value determined by historical sampling data and the water disturbance model. These baseline values ​​reflect the typical conditions in a specific water environment and provide a reference standard for subsequent calculations. Next, the ratio of the current water turbidity value to the turbidity baseline value, as well as the ratio of the current water flow rate value to the flow rate baseline value, is calculated. This step compares the current environmental conditions with the baseline conditions and quantifies the relative degree of change in the environmental parameters.

[0100] Next, this application introduces an environmental compensation model based on optimization of the water disturbance model. This model uses the ratios calculated previously to calculate turbidity and flow rate compensation factors. This step converts changes in environmental parameters into specific compensation factors, providing a basis for subsequent scour intensity adjustments.

[0101] Furthermore, this application considers that different environmental factors may have varying impacts on flushing effectiveness. Therefore, weighting coefficients for the turbidity compensation factor and the flow rate compensation factor are determined based on their respective impacts on the spiral or staggered flushing pattern. This step makes environmental compensation more precise, allowing the importance of different factors to be adjusted based on actual conditions.

[0102] Finally, the present invention combines the turbidity compensation factor and the flow rate compensation factor according to their corresponding weight coefficients to obtain the final environmental compensation coefficient. This weighted combination method comprehensively considers multiple environmental factors, making environmental compensation more comprehensive and accurate.

[0103] As a preferred embodiment, the present application can further optimize the calculation process of the environmental compensation coefficient. For example, historical change data of water turbidity and water flow rate values ​​within a preset time period can be obtained, such as hourly turbidity and flow rate records for the past 24 hours. Using a water disturbance model, the turbidity change rate and flow rate change rate within a future sampling period can be predicted based on this historical change data. This prediction can be achieved using time series analysis or machine learning algorithms.

[0104] Dynamically adjust the baseline turbidity and flow rate values ​​based on the predicted turbidity and flow rate change rates. For example, if turbidity is predicted to increase by 10% within the next four hours, the baseline turbidity value can be increased by 10%. This dynamic adjustment ensures that the baseline values ​​are more closely aligned with the actual environmental conditions that will be encountered.

[0105] Next, the ratio of the dynamically adjusted turbidity baseline value to the current water turbidity value, as well as the ratio of the dynamically adjusted flow rate baseline value to the current water flow rate value, are calculated. These ratios reflect the degree of deviation of the current environmental parameters from the expected conditions.

[0106] Substituting these ratios into the environmental compensation model yields updated turbidity and flow rate compensation factors. These updated compensation factors, which take into account future environmental trends, are more adaptable to upcoming water conditions.

[0107] Based on the updated turbidity and velocity compensation factors, combined with the current spiral or staggered flushing pattern parameters, the weight of each compensation factor on the flushing effect is calculated. For example, if a spiral flushing pattern is currently in use and a significant increase in flow velocity is predicted, the weight of the velocity compensation factor may need to be increased to better cope with the upcoming high flow rate environment.

[0108] Finally, the calculated impact weights are used to weight the updated turbidity compensation factor and flow rate compensation factor to obtain the final environmental compensation coefficient. This prediction-based and dynamic adjustment method allows the environmental compensation coefficient to not only reflect the current environmental conditions but also adapt to impending environmental changes.

[0109] Through the above optimization method, this application can more accurately calculate the environmental compensation coefficient, thereby achieving better adjustment of the scouring intensity and pattern. This method not only takes into account the current environmental conditions, but also incorporates historical data analysis and future trend prediction, greatly improving environmental adaptability and the stability of the scouring effect.

[0110] Compared with the existing technology, the present application can more comprehensively consider the dynamic changes of the water environment by introducing historical sampling data and water disturbance models, rather than relying solely on instantaneous measurements. Secondly, by calculating the ratio of environmental parameters to baseline values, flexible adaptation to different water conditions is achieved, avoiding the limitations that may be caused by fixed thresholds. Thirdly, by introducing weight coefficients, the importance of different environmental factors can be adjusted according to actual conditions, achieving more refined environmental compensation adjustments. Finally, by predicting future environmental changes and dynamically adjusting baseline values, the method of the present application is forward-looking, can better cope with upcoming environmental changes, and improves the stability and reliability of the sampling process.

[0111] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to optimize the environmental compensation coefficient in real time according to the dynamic changes of the water environment.

[0112] This application further proposes a method that dynamically adjusts the turbidity and flow rate baseline values ​​by acquiring historical data on changes in water turbidity and flow rate over a preset time period and using a water disturbance model to predict the rates of change in turbidity and flow rate over future sampling periods. This dynamic adjustment mechanism more accurately reflects the actual conditions of the current water environment and improves the calculation accuracy of the environmental compensation coefficient.

[0113] Specifically, this application first obtains historical data on water turbidity and flow rate changes over a preset time period. This data can be collected in real time by sensors on sampling equipment or obtained from a hydrological monitoring system. The preset time period can be set based on actual needs, for example, data from the past 24 hours or a week.

[0114] Next, a water disturbance model is used to predict the rate of change in turbidity and flow velocity over future sampling periods based on historical data. This model can be a predictive model trained using a machine learning algorithm or a mathematical model based on fluid mechanics. The purpose of this step is to predict changing trends in the water environment and better adjust flushing strategies.

[0115] Based on the predicted rates of change in turbidity and flow rate, the baseline turbidity and flow rate values ​​are dynamically adjusted. For example, if a significant increase in turbidity is predicted, the baseline turbidity value can be appropriately increased; if a decrease in flow rate is predicted, the baseline flow rate value can be reduced accordingly. This dynamic adjustment mechanism ensures that the calculation of the environmental compensation coefficient is more closely aligned with actual changes in the water environment.

[0116] Subsequently, a first ratio of the dynamically adjusted turbidity baseline value to the current water turbidity value, and a second ratio of the dynamically adjusted flow rate baseline value to the current water flow rate value, are calculated. These two ratios reflect the degree of deviation of the current water environment from the adjusted baseline value and are important bases for calculating the environmental compensation coefficient.

[0117] Substituting the first ratio and the second ratio into the environmental compensation model yields updated turbidity compensation factors and flow rate compensation factors. The environmental compensation model can be a nonlinear function that converts the ratios into corresponding compensation factors. This model can be optimized and adjusted based on historical data and expert experience.

[0118] Based on the updated turbidity and velocity compensation factors, combined with the current spiral or staggered flushing pattern parameters, the weight of each compensation factor on the flushing effect is calculated. This step takes into account the differential impact of different environmental factors on the flushing effect, so that the final environmental compensation coefficient can more accurately reflect the combined effect of various factors.

[0119] Finally, the calculated impact weights are used to weight the updated turbidity compensation factor and flow rate compensation factor to obtain the final environmental compensation coefficient. This environmental compensation coefficient will be used to adjust the subsequent flushing intensity and pattern to achieve more accurate underwater plankton sampling.

[0120] This improved method of the present application achieves dynamic optimization of the environmental compensation coefficient by introducing historical data analysis and future trend prediction. Compared with the static environmental compensation method, this method has the following advantages:

[0121] First, by analyzing historical data and predicting future trends, this method can better adapt to the dynamic changes in the water environment. This is particularly important for long-term, multi-point underwater sampling missions, as the water environment may change significantly over time and space.

[0122] Secondly, the mechanism of dynamically adjusting the turbidity and flow rate baselines makes the calculation of the environmental compensation coefficient more flexible and accurate. This mechanism can promptly capture sudden changes in the water environment, such as sudden pollution or changes in water flow, and thus adjust the sampling strategy in a timely manner.

[0123] Furthermore, by calculating the weight of each compensation factor on the scour effect, this method can more precisely balance the influence of different environmental factors. This balancing mechanism helps achieve optimal sampling results in complex and changing water environments.

[0124] Finally, this dynamic optimization approach can be seamlessly integrated with other sampling control strategies, such as flushing intensity control and LED lighting control, to form a comprehensive adaptive sampling system. This system can automatically adjust various parameters based on real-time environmental changes, greatly improving sampling accuracy and efficiency.

[0125] As a specific example, assume that a sampling device is sampling plankton in the deep waters of a large reservoir. The device is equipped with a high-precision turbidity sensor and a flow rate sensor, enabling real-time collection of water environment data. The system is set to record data every 10 minutes over the past 12 hours, for a total of 72 data points.

[0126] The water disturbance model uses a machine learning algorithm based on the long short-term memory network (LSTM). The model has been trained with a large amount of historical data and can predict turbidity and flow rate changes within the next 6 hours.

[0127] Assume the current water turbidity is 5 NTU and the flow rate is 0.3 m / s. After analyzing the data from the past 12 hours, the system predicts that the turbidity will increase over the next 6 hours, with an average increase rate of 0.2 NTU / hour; the flow rate will remain relatively stable, fluctuating within ±0.05 m / s.

[0128] Based on this prediction, the system dynamically adjusted the baseline turbidity value from 4.5 NTU to 5.5 NTU, while maintaining the baseline flow rate at 0.3 m / s. The calculated first ratio (turbidity) was 0.91, and the second ratio (flow rate) was 1.0.

[0129] Substituting these two ratios into the environmental compensation model, the updated turbidity compensation factor is 1.2 and the flow rate compensation factor is 1.0. Considering the current spiral flushing mode, the system calculates the influence weight of the turbidity compensation factor to be 0.7 and the influence weight of the flow rate compensation factor to be 0.3.

[0130] Finally, through weighted combination, the environmental compensation coefficient is: 1.2 * 0.7 + 1.0 * 0.3 = 1.14.

[0131] This environmental compensation factor is used to adjust the flushing intensity and pattern. For example, the system might slightly increase the flushing intensity based on this factor, while also adjusting the angle and frequency of the spiral flushing to account for an expected increase in water turbidity.

[0132] This dynamic optimization method allows sampling equipment to better respond to changes in the water environment, reduce water disturbance, and improve sampling accuracy and representativeness. This is crucial for long-term monitoring of plankton distribution and trends in deep reservoirs, providing more reliable data for water quality assessment and ecological research.

[0133] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to minimize the impact on plankton distribution when light illumination is required.

[0134] In this regard, the present application further proposes that when a situation requires light illumination, the photosensitivity characteristic data of different plankton and the current water environment parameters are obtained; based on the photosensitivity characteristic data and the water environment parameters, the optimal wavelength range and intensity range of the LED light source array are determined; based on the optimal wavelength range and intensity range, the LED light source array with adjustable wavelength and intensity is controlled; the current motion state information of the sampling device is obtained; based on the motion state information and the water environment parameters, the optimal lighting time and frequency are calculated; lighting is provided according to the frequency within the optimal lighting time; the lighting effect and the plankton distribution status are monitored in real time; and based on the monitoring results, the wavelength, intensity, lighting time and frequency parameters of the LED light source array are dynamically adjusted to minimize the impact on the plankton distribution.

[0135] The technical solution of this application intelligently controls the lighting parameters of an LED light source array, minimizing interference with the distribution of plankton in the water while ensuring the normal operation of the sampling equipment. This solution considers the light sensitivity of different plankton species and dynamically adjusts the lighting strategy based on the current water environment and the motion of the sampling equipment, thus achieving a balance between accurate sampling and ecological protection.

[0136] Specifically, this application first obtains data on the photosensitivity characteristics of different plankton species and the current water environment parameters. This data includes the response thresholds of various plankton species to different wavelengths of light, water transparency, temperature, pH, and more. Based on this data, the system determines the wavelength and intensity range of the LED light source that best suits the current environment, minimizing irritation to the plankton species.

[0137] Next, this application considers the motion of the sampling device. Because the device's movement in the water can affect the illumination and the distribution of plankton, the system calculates the optimal illumination duration and frequency based on information such as the device's speed and direction. This ensures that illumination is applied only when necessary and in the most appropriate manner, further minimizing disturbance to the aquatic environment.

[0138] During the lighting process, this application employs a strategy of real-time monitoring and dynamic adjustment. The system continuously monitors the lighting effect and the distribution of plankton. If any anomalies or unsatisfactory conditions are detected, the parameters of the LED light source array are immediately adjusted. This closed-loop control mechanism ensures that the lighting is always optimized and can quickly respond to environmental changes.

[0139] As a preferred implementation, the present application can implement the above technical solution by adopting the following specific steps:

[0140] First, a database of common plankton photosensitivity characteristics should be established. This database could include information on the response thresholds, phototropism, or photophobia of different plankton species to various wavelengths of light. For example, some phytoplankton may be more sensitive to blue light (wavelengths approximately 450-495 nm), while some zooplankton may respond more strongly to red light (wavelengths approximately 620-750 nm).

[0141] Secondly, it is equipped with a multispectral water quality sensor to monitor water transparency, chlorophyll content, dissolved oxygen and other parameters in real time. These parameters can help the system more accurately assess the impact of the current water environment on light propagation.

[0142] Finally, an LED light source array with adjustable wavelength and intensity is used. This light source can be precisely adjusted across the visible spectrum of 400-700nm, with intensity continuously adjustable from 0-100%. The light source array can be installed at different locations on the sampling device to achieve all-round illumination.

[0143] In actual operation, the system first calculates the optimal wavelength range and intensity range based on the photosensitivity characteristics in the database and the current water environment parameters. For example, if the current water body is mainly composed of phytoplankton that are sensitive to blue light, the system may choose green light of 500-600nm as the main illumination wavelength to reduce the impact on these organisms.

[0144] The system then acquires information about the sampling device's motion status, including speed, acceleration, and direction. Based on this information, the system can predict the device's trajectory over the next few seconds and calculate the optimal lighting duration and frequency. For example, if the device is rapidly descending, the system might choose a shorter lighting duration and a higher frequency to avoid prolonged effects of continuous lighting on the same water layer.

[0145] During the illumination process, the system continuously monitors the distribution of plankton in the water column. This is achieved using highly sensitive underwater cameras or acoustic sensors. If a significant change in plankton density is detected in a certain area, the system immediately adjusts the LED light source parameters in that area. For example, if the plankton density in a certain direction suddenly decreases, the system may reduce the intensity of the LED in that direction or change its wavelength to encourage the plankton to return to its original location.

[0146] Through this precise control and real-time adjustment, the present application can minimize the disturbance to the distribution of plankton in the water while ensuring the normal operation of the sampling equipment. This not only improves the accuracy and representativeness of the sampling, but also helps to protect the stability of the aquatic ecosystem.

[0147] Compared with the existing technology, this application takes into account the photosensitivity of different plankton, rather than simply using lighting with fixed wavelength and intensity. This personalized lighting strategy can better adapt to different water environments and biological communities. Secondly, this application takes the motion state of the sampling equipment into consideration, which has rarely been paid attention to in previous lighting control. By dynamically adjusting the lighting parameters to match the movement of the equipment, this application can more effectively reduce disturbances to the water body. Finally, this application adopts a real-time monitoring and dynamic adjustment method, which can quickly respond to environmental changes, which is more flexible and efficient than traditional fixed-parameter lighting systems.

[0148] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to accurately control the LED light source array in different water environments to minimize the impact on the distribution of plankton.

[0149] In this regard, the present application further proposes a method for providing lighting at a specific frequency within the optimal lighting time, specifically comprising the following steps:

[0150] First, obtain the current water transparency data and use it as part of the water environment parameters. Transparency data can be measured in real time using specialized sensors or obtained from a historical database. This step provides important environmental information foundation for subsequent light source control.

[0151] Next, the optimal output power of the LED light source array is calculated based on the transparency data and photosensitivity data. The photosensitivity data includes the sensitivity of different plankton species to light intensity and wavelength. By considering these two factors, an output power range can be determined that provides sufficient illumination without overstimulating the plankton.

[0152] The optimal duration of a single pulse is then determined based on the optimal output power and water environment parameters. Water environment parameters, in addition to transparency, may also include factors such as temperature and salinity that affect light propagation. Determining the optimal duration requires balancing lighting effectiveness with energy consumption, while also considering the impact on plankton.

[0153] During the optimal illumination time, the LED light array is controlled to pulse illumination at a predetermined frequency and duration. Pulsed illumination reduces the disturbance of continuous illumination to plankton and saves energy. The frequency selection should take into account the speed of the sampling equipment and the characteristics of the water environment.

[0154] During the illumination process, the effects of pulsed lighting and the transient reactions of plankton are monitored in real time. This is achieved through integrated optical sensors and image analysis systems. Monitoring data includes the brightness distribution of the illuminated area and the aggregation or dispersion behavior of plankton.

[0155] Finally, based on the monitoring results, the optimal output power and duration are dynamically adjusted to minimize the impact on plankton distribution. This real-time feedback mechanism allows the system to make adaptive adjustments based on actual conditions, improving the accuracy and representativeness of sampling.

[0156] This improved method achieves efficient illumination in various aquatic environments by precisely controlling the LED light source array. Specifically, by acquiring transparency data, the system can more accurately assess the propagation characteristics of light in water. This provides an important basis for subsequent power and duration calculations.

[0157] This method takes both transparency and the photosensitive properties of plankton into account when calculating the optimal output power. This approach not only ensures adequate illumination but also minimizes disturbance to plankton. For example, in waters with low transparency, the system might select a higher output power but shorten the pulse duration to balance illumination needs with bioprotection.

[0158] The optimal duration of a single pulse is determined dynamically based on multiple factors. This flexibility allows the system to adapt to different water environments and sampling requirements. For example, in areas with high plankton density, a shorter pulse duration might be selected to minimize impacts on biomass distribution.

[0159] A key feature of this method is pulsed illumination. Compared to continuous illumination, it significantly reduces the long-term impact on plankton. By precisely controlling the frequency and duration of the pulses, the system provides sufficient illumination while allowing plankton time to adapt and recover. This approach is particularly useful for light-sensitive plankton species.

[0160] Real-time monitoring and dynamic adjustment mechanisms further enhance the adaptability and accuracy of this method. By continuously analyzing lighting effects and plankton responses, the system can quickly respond to environmental changes or unexpected situations. For example, if certain plankton are detected to be unusually sensitive to current lighting parameters, the system can immediately reduce output power or adjust the pulse pattern.

[0161] As a preferred embodiment, this application can be applied to multi-layer plankton sampling in deep water areas of large reservoirs. Specifically, the sampling equipment is equipped with an LED light source array with adjustable wavelength and intensity, as well as a sensor for measuring water transparency. Before sampling begins, the system first measures the transparency of the current water layer, assuming it is 3 meters.

[0162] Based on this transparency data and a pre-stored database of plankton's photosensitivity, the system calculated an optimal output power of 50 watts. Then, taking into account environmental parameters such as the current water temperature of 20°C and a salinity of 0.5‰, the system determined the optimal duration of a single pulse to be 0.5 seconds.

[0163] During the 60-second optimal illumination period, the system pulses the LED array at a frequency of 2 Hz. This means two 0.5-second light pulses are generated per second. During this process, integrated optical sensors continuously monitor the illumination effects, while an image analysis system processes changes in the distribution of plankton in real time.

[0164] Suppose that 30 seconds after illumination begins, the system detects that certain plankton are beginning to gather around the light source. Based on this feedback, the system immediately reduces the output power to 40 watts and shortens the pulse duration to 0.3 seconds. This dynamic adjustment ensures that the impact on plankton distribution during sampling is minimized while still maintaining adequate illumination.

[0165] Through this refined illumination control method, this application enables high-quality plankton sampling in diverse aquatic environments. Compared to traditional fixed-parameter illumination, this method significantly reduces disruption to aquatic ecosystems and improves sampling accuracy and representativeness. Furthermore, through real-time adjustments, this method can adapt to complex and changing underwater environments, providing more reliable data support for long-term, large-scale water quality monitoring and ecological assessments.

[0166] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to dynamically adjust the flushing intensity according to the water environment parameters and the system energy state.

[0167] In this regard, the present application further proposes an optimized adaptive scour control model, which takes into account factors such as water turbidity, current available energy, and maximum system power to achieve more accurate and efficient scour control.

[0168] Specifically, the application first obtains the water turbidity τ, the current available energy p, and the system maximum power p_max. These parameters reflect the current water environment and system energy status, providing the necessary input for the subsequent scour intensity calculation.

[0169] Next, the scour intensity F is calculated based on the optimized adaptive scour control model. The model can be expressed as:

[0170] F(d,v,t,τ) = k * (1 - e^(-αd)) * (v / v_max)^β * (1 + γ*sin(ωt)) * S(τ) * E(p)

[0171] Where F is the scour intensity, d is the distance to the next sampling point, v is the device speed, t is time, and τ is the water turbidity. k is the basic scour intensity coefficient, α is the distance attenuation coefficient, β is the velocity influence index, γ is the pulse intensity coefficient, ω is the pulse frequency, and v_max is the maximum device speed.

[0172] S(τ) is the turbidity influence function, expressed as (τ / τ_ref)^μ, where τ_ref is the reference turbidity value and μ is the turbidity sensitivity coefficient. E(p) is the energy efficiency adjustment function, expressed as (1 - λ*(p_max - p) / p_max), where λ is the energy efficiency weighting coefficient.

[0173] This model comprehensively considers the impact of multiple factors such as distance, speed, time, turbidity, and energy on scour intensity. The distance factor (1 - e^(-αd)) ensures that the scour intensity gradually decreases as the sampling point is approached. The speed factor (v / v_max)^β reflects the impact of the equipment movement speed on the scour demand. The time factor (1 + γ*sin(ωt)) introduces periodic changes, which can achieve pulsed scour. The turbidity influence function S(τ) and the energy efficiency adjustment function E(p) respectively consider the regulatory effects of water turbidity and system energy state on scour intensity.

[0174] After calculating the scouring intensity F, the system adjusts the water pressure and direction of multiple adjustable nozzles accordingly. This dynamic adjustment ensures that the scouring operation can adapt to different water environments and system conditions, ensuring scouring effectiveness while avoiding energy waste and water disturbance caused by excessive scouring.

[0175] To further optimize flushing effectiveness, the system monitors flushing results in real time. If the monitoring results indicate insufficient flushing, the system recalculates the flushing intensity by adjusting the turbidity sensitivity coefficient μ and the energy efficiency weighting coefficient λ. This adaptive adjustment mechanism enables the system to continuously optimize its performance in practical applications.

[0176] As a preferred implementation, the initial parameters can be set as follows: k = 1.0, α = 0.05, β = 0.5, γ = 0.2, ω = 2π / 60 (assuming a 60-second period), τ_ref = 5 NTU, μ = 0.8, and λ = 0.3. These parameters can be fine-tuned based on the actual application scenario.

[0177] For example, in a sampling mission, assume the current distance to the next sampling point is 200 meters, the device is moving at 2 meters per second (maximum speed 5 meters per second), the water turbidity is 8 NTU, and the current available energy is 800 watts (maximum system power is 1000 watts). Substituting these values ​​into the model yields a specific flushing intensity value, enabling precise control of the nozzle's water pressure and direction.

[0178] In this way, the technical solution of this application can dynamically adjust the flushing intensity based on real-time environmental parameters and system status, better adapting to different water environments compared to fixed-intensity flushing methods. In waters with high turbidity, the system automatically increases the flushing intensity; in clear waters, it appropriately reduces the intensity, thereby ensuring flushing effectiveness while minimizing water disturbance.

[0179] Secondly, this adaptive control model takes into account the system's energy status, optimizing energy utilization while ensuring flushing effectiveness. When the system has sufficient energy, it can flush more vigorously; when energy is insufficient, the intensity is appropriately reduced to extend the working time.

[0180] Furthermore, by introducing distance and speed factors, the system can dynamically adjust the flushing intensity based on the motion of the sampling device. This not only ensures a flushing effect near the sampling point, but also provides sufficient cleaning force when the device is moving at high speeds.

[0181] Finally, real-time monitoring and adaptive adjustment mechanisms enable the system to continuously optimize its performance. This closed-loop control approach can cope with various complex underwater environments and improve sampling accuracy and reliability.

[0182] In summary, the optimized adaptive scour control model proposed in this application can effectively solve the scour control problem in the underwater plankton sampling process, significantly improve the sampling accuracy and efficiency, and minimize the impact on the water environment.

[0183] Secondly, refer to Figure 2 , the present application further proposes an underwater plankton sampling device, the device comprising:

[0184] The information acquisition module 210 is used to obtain the current location information of the sampling device, the distance information of the next sampling point, and the current water environment parameters;

[0185] A flushing control module 220 is configured to control a plurality of adjustable nozzles on the surface of the sampling device to perform a flushing operation based on the distance information and the water environment parameters, wherein the flushing operation uses filtered environmental water as a flushing medium;

[0186] The intensity adjustment module 230 is used to dynamically adjust the intensity and mode of the flushing operation based on the preset dynamic flushing intensity control model according to the distance information, the movement speed of the sampling device and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached;

[0187] The monitoring and adjustment module 240 is used to monitor the scouring effect in real time and adaptively adjust the scouring intensity and mode according to the monitoring results;

[0188] The stop control module 250 is used to stop the flushing operation before the sampling device reaches a predetermined distance from the sampling point.

[0189] By obtaining the current location information of the sampling device, the distance information to the next sampling point, and the current water environment parameters, the multiple adjustable nozzles on the surface of the sampling device are controlled to perform flushing operations, and the intensity and mode of the flushing operations are dynamically adjusted. The flushing effect is monitored in real time and adaptive adjustments are made, which effectively reduces water disturbance, prevents sample cross-contamination, and improves sampling accuracy and efficiency.

[0190] In addition, in some preferred embodiments, the underwater plankton sampling device proposed in this application can perform any one of the steps in the above method.

[0191] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for sampling underwater plankton, characterized in that: The steps of the method include: Obtain the current location information of the sampling equipment, the distance information to the next sampling point, and the current water environment parameters; Controlling a plurality of adjustable nozzles on the surface of the sampling device to perform a flushing operation according to the distance information and the water environment parameters, wherein the flushing operation uses filtered environmental water as a flushing medium; Dynamically adjusting the intensity and mode of the flushing operation according to the distance information, the movement speed of the sampling device and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached; Monitor scouring effects in real time and make adaptive adjustments to scouring intensity and pattern based on monitoring results; stopping the flushing operation before the sampling device reaches a predetermined distance from the sampling point; Also includes: Obtain water turbidity τ, current available energy p, and system maximum power p_max; The scour intensity F is calculated based on the following optimized adaptive scour control model: F(d,v,t,τ)=k*(1-e^(-αd))*(v / v_max)^β*(1+γ*sin(ω t))*S(τ)*E(p); Where: F is the scour intensity; d is the distance to the next sampling point; v is the speed of the equipment; t is the time; τ is the turbidity of the water body; k is the basic scour intensity coefficient; α is the distance attenuation coefficient; β is the velocity influence index; γ is the pulse intensity coefficient; ω is the pulse frequency; v_max is the maximum equipment speed; S(τ) is the turbidity influence function, expressed as (τ / τ_ref)^μ; E(p) is the energy efficiency adjustment function, expressed as (1-λ*(p_max-p) / p_max); p is the current available energy; p_max is the maximum system power; μ is the turbidity sensitivity coefficient; λ is the energy efficiency weight coefficient; τ_ref is the reference turbidity value; According to the calculated flushing intensity F, adjusting the water outlet pressure and direction of the plurality of adjustable nozzles; Monitor the flushing effect in real time. When the monitoring results show that the flushing effect is insufficient, adjust the turbidity sensitivity coefficient μ and energy efficiency weight coefficient λ, and recalculate the flushing intensity.

2. The underwater plankton sampling method according to claim 1, characterized in that: The step of dynamically adjusting the intensity and mode of the flushing operation according to the distance information, the movement speed of the sampling device and the current time includes: Obtaining a ratio of a movement speed of the sampling device to a maximum movement speed; Calculating a distance attenuation coefficient based on the distance information; Calculating a periodic adjustment coefficient according to the current time; Calculating an environmental impact coefficient based on the water body environmental parameters; Substituting the ratio, the distance attenuation coefficient, the periodic adjustment coefficient and the environmental impact coefficient into a dynamic scour intensity control model to calculate a target scour parameter; The water outlet pressure and spray direction of the multiple adjustable nozzles are adjusted according to the target flushing parameters to achieve a spiral or staggered flushing mode.

3. The underwater plankton sampling method according to claim 2, characterized in that: The step of adjusting the water outlet pressure and spray direction of the plurality of adjustable nozzles according to the target flushing parameters to achieve a spiral or staggered flushing pattern includes: Obtaining current water outlet pressure and current spray direction of the multiple adjustable nozzles; generating a target water outlet pressure and a target spray direction according to the target flushing parameter and the water body environment parameter; Calculating a first difference between the current water outlet pressure and the target water outlet pressure, and a second difference between the current spray direction and the target spray direction; generating a water outlet pressure adjustment instruction and a spray direction adjustment instruction according to the first difference and the second difference respectively; Based on the water outlet pressure adjustment instruction and the spray direction adjustment instruction, controlling the plurality of adjustable nozzles to adjust in sequence according to preset time intervals until the target water outlet pressure and the target spray direction are reached; The preset time interval is dynamically adjusted according to the water environment parameters to achieve a spiral or staggered flushing mode while minimizing the disturbance to the water body.

4. The underwater plankton sampling method according to claim 3, characterized in that: The step of generating a target water outlet pressure and a target spray direction according to the target flushing parameter and the water environment parameter comprises: Obtaining water turbidity value and water flow rate value from the water environment parameters; Calculating an environmental compensation coefficient based on the water turbidity value and the water flow rate value; Performing weighted calculation on the current scour parameter and the environmental compensation coefficient to obtain a compensated scour parameter; According to the compensated flushing parameters, generating the target water outlet pressure and the target spray direction according to a preset parameter mapping relationship; The preset parameter mapping relationship is dynamically optimized according to historical sampling data and a water disturbance model to achieve a spiral or staggered flushing pattern while minimizing the disturbance to the water body.

5. The underwater plankton sampling method according to claim 4, characterized in that: The step of calculating the environmental compensation coefficient based on the water turbidity value and the water flow rate value includes: Obtain turbidity baseline values ​​and flow velocity baseline values ​​determined based on historical sampling data and water disturbance models; Calculating a first ratio of the water body turbidity value to the turbidity reference value, and a second ratio of the water body flow rate value to the flow rate reference value; Calculating a turbidity compensation factor and a flow rate compensation factor based on the first ratio and the second ratio using an environmental compensation model optimized based on a water disturbance model; Determining respective weight coefficients based on the degree of influence of the turbidity compensation factor and the flow rate compensation factor on the spiral or staggered flushing pattern; The turbidity compensation factor and the flow rate compensation factor are weighted and combined according to corresponding weight coefficients to obtain the environment compensation coefficient.

6. The underwater plankton sampling method according to claim 5, characterized in that: The step of calculating the environmental compensation coefficient based on the water turbidity value and the water flow rate value includes: Obtaining historical change data of the water turbidity value and the water flow rate value within a preset time period; Using the water disturbance model, predicting the turbidity change rate and flow rate change rate in a future sampling period based on the historical change data; Dynamically adjusting the turbidity reference value and the flow rate reference value according to the turbidity change rate and the flow rate change rate; Calculating a first ratio of the dynamically adjusted turbidity reference value to the current turbidity value of the water body, and a second ratio of the dynamically adjusted flow velocity reference value to the current flow velocity value of the water body; Substituting the first ratio and the second ratio into the environmental compensation model to obtain an updated turbidity compensation factor and a flow rate compensation factor; Based on the updated turbidity compensation factor and flow rate compensation factor, combined with the current spiral or staggered flushing mode parameters, the influence weight of each compensation factor on the flushing effect is calculated; The updated turbidity compensation factor and flow rate compensation factor are weightedly combined using the impact weight to obtain the environmental compensation coefficient.

7. The underwater plankton sampling method according to claim 1, characterized in that: The method further comprises: When light illumination is required, obtain the photosensitivity data of different plankton and the current water environment parameters; Determining the optimal wavelength range and intensity range of the LED light source array based on the photosensitivity characteristic data and the water environment parameters; Based on the optimal wavelength range and intensity range, controlling an LED light source array with adjustable wavelength and intensity; Get the current motion status information of the sampling device; Calculating optimal lighting time and frequency based on the motion state information and the water environment parameters; providing lighting during the optimal lighting time and at the frequency described; Real-time monitoring of lighting effects and plankton distribution; According to the monitoring results, the wavelength, intensity, lighting time and frequency parameters of the LED light source array are dynamically adjusted to minimize the impact on the distribution of plankton.

8. The underwater plankton sampling method according to claim 7, characterized in that: The step of providing lighting according to the frequency within the optimal lighting time comprises: Obtaining transparency data of the current water body and using it as part of the water body environmental parameters; Calculating the optimal output power of the LED light source array according to the transparency data and the photosensitivity characteristic data; Determining an optimal duration of a single pulse based on the optimal output power and the water environment parameters; During the optimal lighting time, controlling the LED light source array to perform pulsed lighting according to the frequency and the optimal duration; Real-time monitoring of pulse lighting effects and transient responses of plankton; According to the monitoring results, the optimal output power and the optimal duration are dynamically adjusted to minimize the impact on plankton distribution.

9. An underwater plankton sampling device, characterized in that: The device includes: The information acquisition module is used to obtain the current location information of the sampling device, the distance information of the next sampling point, and the current water environment parameters; a flushing control module, configured to control a plurality of adjustable nozzles on the surface of the sampling device to perform a flushing operation based on the distance information and the water environment parameters, wherein the flushing operation uses filtered environmental water as a flushing medium; an intensity adjustment module for dynamically adjusting the intensity and mode of the flushing operation based on a preset dynamic flushing intensity control model and according to the distance information, the movement speed of the sampling device, and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached; The monitoring and adjustment module is used to monitor the scouring effect in real time and make adaptive adjustments to the scouring intensity and mode based on the monitoring results; a stop control module, configured to stop the flushing operation before the sampling device reaches a predetermined distance from the sampling point; Also includes: Obtain water turbidity τ, current available energy p, and system maximum power p_max; The scour intensity F is calculated based on the following optimized adaptive scour control model: F(d,v,t,τ)=k*(1-e^(-αd))*(v / v_max)^β*(1+γ*sin(ω t))*S(τ)*E(p); Where: F is the scour intensity; d is the distance to the next sampling point; v is the speed of the equipment; t is the time; τ is the turbidity of the water body; k is the basic scour intensity coefficient; α is the distance attenuation coefficient; β is the velocity influence index; γ is the pulse intensity coefficient; ω is the pulse frequency; v_max is the maximum equipment speed; S(τ) is the turbidity influence function, expressed as (τ / τ_ref)^μ; E(p) is the energy efficiency adjustment function, expressed as (1-λ*(p_max-p) / p_max); p is the current available energy; p_max is the maximum system power; μ is the turbidity sensitivity coefficient; λ is the energy efficiency weight coefficient; τ_ref is the reference turbidity value; According to the calculated flushing intensity F, adjusting the water outlet pressure and direction of the plurality of adjustable nozzles; Monitor the flushing effect in real time. When the monitoring results show that the flushing effect is insufficient, adjust the turbidity sensitivity coefficient μ and energy efficiency weight coefficient λ, and recalculate the flushing intensity.

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