Underwater plankton sampling method and device

Abstract

The invention provides an underwater plankton sampling method and device, and relates to the technical field of underwater biological sampling, and the technical scheme is characterized in that the method comprises the steps: obtaining the current position information of sampling equipment, the distance information of a next sampling point, and the current water environment parameters; controlling a plurality of adjustable nozzles on the surface of the sampling equipment to carry out washing operation, wherein filtered environmental water is used as a washing medium in the washing operation; according to the distance information, the movement speed of the sampling equipment and the current time, dynamically adjusting the intensity and mode of the scouring operation, so that the intensity of the scouring operation is gradually reduced along with approaching the next sampling point; the scouring effect is monitored in real time, and the scouring strength and mode are adaptively adjusted according to the monitoring result; and the flushing operation is stopped before the sampling equipment reaches the sampling point by a preset distance. The underwater plankton sampling method and device have the advantages that water disturbance is reduced, cross contamination of samples is prevented, and sampling precision and efficiency are improved.

Description

Technical Field

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

[0002] When sampling and monitoring plankton in deep water areas of large reservoirs, sampling equipment needs to frequently travel back and forth to different depths. This process faces two major challenges: first, plankton may be attached to the surface of the sampling equipment, and when the equipment rises or dives, these organisms may fall off and contaminate samples from other water layers. Secondly, when the sampling equipment is running underwater, it will disturb the water environment, causing changes in the distribution of phytoplankton in the water. This disturbance and contamination not only affects the accuracy and representativeness of the sampling, but may also lead to deviations in water quality monitoring and ecological assessment results.

[0003] At present, conventional sampling methods are difficult to effectively solve these problems, especially in deep water areas of large reservoirs where multi-level, high-precision sampling is required. Traditional sampling techniques usually use simple mechanical devices or manual operations, which cannot accurately control water disturbance and biological attachment during the sampling process. In addition, existing sampling equipment often lacks real-time monitoring and adaptive adjustment capabilities, and cannot be dynamically optimized according to the environmental characteristics and biological distribution of different water layers.

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

[0005] In view of the above problems, the existing technology needs to be improved urgently. Summary of the invention

[0006] The purpose of the present 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] The present application provides an underwater plankton sampling method, and the technical solution is as follows: The steps of the method include: obtaining the current position information of the sampling device, the distance information of the next sampling point and the current water body environmental parameters; controlling multiple adjustable nozzles on the surface of the sampling device to perform a flushing operation according to the distance information and the water body environmental 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; real-time monitoring of the flushing effect, and adaptively adjusting the flushing intensity and mode according to the monitoring results; and stopping the flushing operation before the sampling device reaches a predetermined distance from the sampling point.

[0008] 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.

[0009] 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 environmental 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, 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 environmental parameters to achieve a spiral or staggered flushing mode while minimizing disturbance to the water body.

[0010] 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 compensated flushing parameters according to a preset parameter mapping relationship; wherein the preset parameter mapping relationship is dynamically optimized according to historical sampling data and a water body disturbance model to achieve a spiral or staggered flushing pattern while minimizing the disturbance to the water body.

[0011] Furthermore, the present application also proposes that the step of calculating the environmental compensation coefficient based on the water body turbidity value and the water body 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, a turbidity compensation factor and a flow rate compensation factor are calculated using an environmental compensation model optimized based on a water body disturbance model; based on the degree of influence of the turbidity compensation factor and the flow rate compensation factor on the spiral or staggered flushing mode, respective weight coefficients are determined; and 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.

[0012] Furthermore, the present application also proposes that the step of calculating the environmental compensation coefficient based on the water body turbidity value and the water body flow rate value includes: obtaining historical change data of the water body turbidity value and the water body flow rate value within a preset time period; using the water body 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 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 water body turbidity value, and a second ratio of the dynamically adjusted flow rate reference value to the current water body flow rate value; 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 the 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 the flow rate compensation factor to obtain the environmental compensation coefficient.

[0013] 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; based on the optimal wavelength range and intensity range, controlling the LED light source array with adjustable wavelength and intensity; obtaining 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 at the frequency within the optimal lighting time; real-time monitoring of lighting effects and plankton distribution conditions; 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 plankton distribution.

[0014] 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 at 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.

[0015] 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 the 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 flushing intensity F, adjust the water outlet pressure and direction of the multiple adjustable nozzles; monitor the flushing effect in real time, and when the monitoring result shows that the flushing effect is insufficient, adjust the turbidity sensitivity coefficient μ and the energy efficiency weight coefficient λ, and recalculate the flushing intensity.

[0016] Furthermore, the present application also proposes an underwater plankton sampling device, which includes: an information acquisition module, which is used to obtain 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, which is used to control multiple adjustable nozzles on the surface of the sampling equipment to perform flushing operations according to the distance information and the water environment parameters, and the flushing operation uses filtered environmental water as the flushing medium; an intensity adjustment module, which 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 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, which is used to monitor the flushing effect in real time, and adaptively adjust the flushing intensity and mode according to the monitoring results; a stop control module, which is used to stop the flushing operation before the sampling equipment reaches a predetermined distance from the sampling point.

[0017] From the above, it can be seen that the underwater plankton sampling method and device provided by the present 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 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 cross-contamination of samples, and improves sampling accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of an underwater plankton sampling method provided for this application.

[0019] Figure 2 A schematic structural diagram of an underwater plankton sampling device provided in this application.

[0020] 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

[0021] The technical solutions in the present application will be clearly and completely described below in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all 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 present application for protection, but merely represents the 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 belong to the scope of protection of the present application.

[0022] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and 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 cannot be understood as indicating or implying relative importance.

[0023] When sampling and monitoring plankton in deep water areas of large reservoirs, the sampling equipment needs to frequently travel back and forth to different depths. This process faces two major technical challenges: first, plankton may be attached to the surface of the sampling equipment, and these organisms may fall off and contaminate samples from other water layers when the equipment rises or dives. Second, when the sampling equipment is running underwater, it will disturb the water environment, causing changes in the distribution of phytoplankton in the water. This disturbance and contamination not only affects the accuracy and representativeness of the sampling, but may also lead to deviations in water quality monitoring and ecological assessment results. Specifically, the reciprocating movement of the sampling equipment will cause turbulence in the water body, change the direction and speed of local water flow, and thus affect the spatial distribution of plankton. At the same time, the shedding of organisms attached to the surface of the equipment between different water layers will cause cross-contamination of samples, making the collected samples unable to accurately reflect the community structure of the specific water layer.

[0024] For example, when sampling plankton at multiple levels in a large reservoir with a depth of 100 meters, the sampling equipment needs to make multiple round trips within the range of 0-100 meters. Assuming that the sampling equipment moves at a speed of 0.5 m / s, the residence time at each sampling point is 60 seconds, and a total of 10 samples at different depths need to be collected. In this scenario, the total operation time of the sampling equipment is about 2000 seconds, during which a large amount of water disturbance will be generated. Specifically, the equipment will generate downward water flow during the dive process, and upward water flow during the ascent process, which will significantly change the flow field structure of the local water body. At the same time, a large number of plankton may be attached to the surface of the equipment, especially when passing through dense plankton areas. These attached organisms may fall off during the movement of the equipment, resulting in cross-contamination between samples from different water layers. For example, surface phytoplankton attached to the surface of the equipment may fall off during sampling in deep water areas, resulting in the appearance of surface species that should not exist in samples from deep water areas.

[0025] 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 distribution of plankton in the natural state, especially for species that are sensitive to water flow changes, their distribution may change significantly. Second, sample cross-contamination will lead to an erroneous assessment of the structure of plankton communities in different water layers, which may overestimate the distribution range of some species or underestimate the existence 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, the development of 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.

[0026] To solve these problems, the present application first considers the use of physical barriers to prevent the organisms on the surface of the sampling device from falling off. Specifically, a retractable protective cover can be installed on the outside of the sampling device. However, this method may increase the size and weight of the device, and may also affect the water flow and cause new disturbances.

[0027] Next, the application considers the use of 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.

[0028] Considering the limitations of the above methods, the present application turns to explore the possibility of using the water flow itself to clean the sampling device. Specifically, multiple adjustable nozzles can be installed on the surface of the sampling device to use filtered ambient water for flushing. This method can not only effectively remove attached organisms, but also minimize chemical pollution to the water body.

[0029] However, simple flushing may generate new water disturbances. To solve this problem, the present application proposes the concept of dynamically adjusting the flushing intensity. Specifically, the intensity and mode of the flushing operation can be dynamically adjusted according to the distance information from the sampling device to the next sampling point, the movement speed, and the current time. In this way, the flushing intensity can be gradually reduced as the next sampling point is approached, minimizing the disturbance to the water body at the sampling point.

[0030] In order to further optimize the flushing effect, the present 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 according to the monitoring results to ensure the cleaning effect while minimizing water disturbance.

[0031] Finally, in order to reduce the impact of the sampling equipment on the sampling point, the present application proposes a strategy of stopping 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.

[0032] Therefore, refer to Figure 1 , the present application proposes an underwater plankton sampling method, the steps of the method comprising: Obtain the current location information of the sampling device, the distance information of the next sampling point, and the current water environment parameters; According to the distance information and water environment parameters, multiple adjustable nozzles on the surface of the sampling device are controlled to perform flushing operations, and the flushing operations use filtered environmental water as the flushing medium; According to the distance information, the movement speed of the sampling equipment and the current time, the intensity and mode of the flushing operation are dynamically adjusted so that the intensity of the flushing operation gradually decreases as the next sampling point is approached; Monitor the scouring effect in real time and make adaptive adjustments to the scouring intensity and mode based on the monitoring results; Stop flushing operations before the sampling equipment reaches the predetermined distance from the sampling point.

[0033] 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.

[0034] 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.

[0035] 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 and high-flow flushing.

[0036] 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.

[0037] The core innovation of this application is to propose a dynamically adjusted underwater plankton sampling method. This method controls multiple adjustable nozzles on the surface of the sampling device to perform flushing operations, and dynamically adjusts the flushing intensity and mode according to distance information, movement speed and current time, thereby effectively removing attached organisms while minimizing disturbance to the water body during the sampling process. In addition, this application also introduces real-time monitoring and adaptive adjustment mechanisms to further optimize the flushing effect and ensure the accuracy and representativeness of the sampling.

[0038] The working principle of the present application can be described in detail as follows: First, the sampling device is equipped with multiple adjustable nozzles, which are evenly distributed on the surface of the device and can achieve 180-degree flushing coverage. Each nozzle is equipped with an independent pressure regulating valve and direction control mechanism, which can accurately control the water pressure and spray angle.

[0039] During the sampling process, the system first obtains the current location information of the sampling equipment, the distance information of 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.

[0040] Next, the system controls multiple adjustable nozzles to perform flushing operations based on the distance information and water environment parameters. The flushing medium uses microporously filtered ambient water to minimize chemical contamination of the water body. The intensity and mode of the flushing operation are dynamically adjusted and implemented through an algorithm. The algorithm takes into account multiple factors such as distance information, the movement speed of the sampling device, and the current time, so that the intensity of the flushing operation gradually decreases as it approaches the next sampling point.

[0041] Specifically, when the sampling device is far from the next sampling point, the flushing intensity is high, and the nozzle flushes with higher pressure and larger coverage. As the device approaches the sampling point, the flushing 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 the disturbance of the water body to be sampled.

[0042] During the whole process, the system monitors the flushing effect in real time through optical sensors, flow meters and other equipment. If the flushing effect is not good, the system will automatically adjust the flushing intensity and mode according to the monitoring results. For example, if it is detected that there are still a lot of attachments in certain areas, the system may temporarily increase the flushing intensity of the area or change the spray angle.

[0043] Finally, when the sampling device approaches a predetermined distance from the sampling point (e.g. 5 meters from the sampling point), the system will completely stop 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.

[0044] 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 the disturbance to the water body, especially the impact on the water body near the sampling point; finally, the use of filtered environmental water as the flushing medium avoids the introduction of foreign matter that may affect water quality monitoring.

[0045] As a preferred embodiment, the present application can perform multi-level plankton sampling in a large reservoir with 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 the surface. The water outlet pressure of each nozzle can be adjusted within the range of 0-10MPa, and the spray angle can be rotated within the range of 0-180 degrees.

[0046] During the sampling process, the device moves back and forth at a speed of 0.5 m / s in the depth range of 0-100 m. The system obtains the device's location information and water environment parameters every 1 second. When the device is 50 meters away from the next sampling point, the flushing operation begins. The initial flushing intensity is set to 8 MPa and the spray angle is 120 degrees. As the device approaches the sampling point, the flushing intensity gradually decreases. For every meter closer, the pressure decreases by 0.1 MPa and the spray angle decreases by 2 degrees.

[0047] At the same time, the system monitors the flushing effect every 0.1 seconds. If it detects that the cleanliness of an area is less than 90%, the system will temporarily increase the flushing intensity of the area by 10%, which will last for 2 seconds before returning to normal regulation. When the device is 5 meters away from the sampling point, the system completely stops the flushing operation.

[0048] 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 device and environmental factors.

[0049] In this regard, the present application further proposes that the method dynamically adjusts the intensity and mode of the flushing operation according to distance information, the movement speed of the sampling device and the current time.

[0050] The method first obtains the ratio of the movement speed of the sampling device to the maximum movement speed. This ratio reflects the current movement state of the device. Then, the distance attenuation coefficient is calculated based on the distance information. This coefficient is used to adjust the change of the scouring intensity with distance. At the same time, the periodic adjustment coefficient is calculated according to the current time to achieve the periodic change of the scouring intensity. In addition, the environmental impact coefficient is calculated based on the water environment parameters to adapt to different water conditions.

[0051] Substituting the above coefficients into the dynamic scouring intensity control model, the target scouring 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 scouring pattern.

[0052] The core of this dynamic adjustment method is to comprehensively consider multiple influencing factors, including equipment motion state, distance change, time periodicity and environmental conditions. By quantifying these factors into specific coefficients and introducing a dynamic scouring intensity control model, precise regulation of scouring operation intensity and mode is achieved.

[0053] Specifically, the motion speed ratio reflects the current motion state of the device, and the flushing intensity can be adjusted according to the change in speed. The distance attenuation coefficient ensures that the flushing intensity gradually decreases as it approaches the sampling point, which helps to reduce the disturbance to the water body near the sampling point. The periodic adjustment coefficient introduces the time factor, which can achieve periodic changes in flushing intensity and further optimize the flushing effect. The environmental impact coefficient takes into account the specific conditions of the water body, so that the flushing operation can adapt to different water environments.

[0054] Through the dynamic flushing intensity control model, these coefficients are integrated to calculate the optimal flushing parameters. These parameters are directly used to control the water outlet pressure and spray direction of the adjustable nozzle, realizing a spiral or staggered flushing pattern. This pattern can not only effectively clean the surface of the sampling equipment, but also minimize the disturbance of the water body.

[0055] As a preferred implementation, the dynamic scour intensity control model can be in the following form: F = k * (1 - e^(-αd)) * (v / v_max)^β * (1 + γ*sin(ωt)) * E(τ) Among them, F is the scour intensity, d is the distance to the next sampling point, v is the current speed, v_max is the maximum speed, t is the current time, and τ is the water body environmental parameter. k is the basic scour intensity coefficient, α is the distance attenuation coefficient, β is the speed influence index, γ is the periodic adjustment amplitude, ω is the periodic adjustment frequency, and E(τ) is the environmental impact function.

[0056] In practical applications, these parameters can be adjusted according to the specific water environment and sampling requirements. For example, in water bodies with high turbidity, the weight of the environmental impact function E(τ) can be increased; in the case of large changes in the speed of the sampling equipment, the value of the speed influence index β can be adjusted.

[0057] Through this dynamic adjustment method, the present application 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.

[0058] Compared with the prior art, this application takes into account multiple influencing factors instead of simply using fixed flushing intensity and mode. Secondly, by introducing a dynamic flushing intensity control model, accurate calculation and adjustment of flushing parameters are achieved. Finally, the spiral or staggered flushing pattern can more effectively clean the surface of the equipment while minimizing the disturbance to the water body. These advantages work together to significantly improve the accuracy and reliability of underwater plankton sampling.

[0059] In some of the above-mentioned embodiments, during the implementation of the present application, there is still a problem of how to accurately control multiple adjustable nozzles to achieve a spiral or staggered flushing mode.

[0060] In this regard, the present application further proposes that the method includes 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 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, respectively generating water outlet pressure adjustment instructions and spray direction adjustment instructions according to the first difference and the second difference, and controlling 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. Among them, 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.

[0061] The technical solution of the present application realizes a spiral or staggered flushing mode by precisely controlling the water outlet pressure and spray direction of multiple adjustable nozzles. This control method can not only effectively clean the surface of the sampling equipment, but also minimize the disturbance to the water body, thereby improving the accuracy and representativeness of the sampling.

[0062] Specifically, the present application first obtains the current status of multiple adjustable nozzles, including water outlet pressure and spray direction. Then, according to the target flushing parameters and water environment parameters, the target water outlet pressure and target spray direction are generated. This step takes into account the real-time changes in the water environment and ensures that the flushing operation can adapt to different water conditions.

[0063] Next, the present application calculates the difference between the current state and the target state, and generates a water pressure adjustment instruction and a spray direction adjustment instruction respectively. This difference-based adjustment method can achieve precise control and avoid the problem of over-adjustment or under-adjustment.

[0064] When controlling multiple adjustable nozzles, the present application adopts a method of adjusting them sequentially at preset time intervals. This method can achieve a spiral or staggered flushing pattern, effectively covering 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.

[0065] Furthermore, the preset time interval is dynamically adjusted according to the water environment parameters. For example, when the water flow rate is high, the time interval can be shortened to improve the flushing efficiency; when the water body is relatively static, the time interval can be appropriately extended to reduce disturbance. This dynamic adjustment mechanism further optimizes the flushing effect while minimizing the impact on the water body.

[0066] As a preferred implementation, the present application can be implemented by the following specific steps: 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 environment parameters such as water temperature, turbidity, pH value, etc.

[0067] Then, based on the pre-established water disturbance model and historical sampling data, combined with the current target flushing parameters and water environment parameters, the optimal target water outlet pressure and target injection direction are calculated. For example, when the water turbidity is high, a higher water outlet pressure may be required; when the water flow is fast, the injection direction may need to be adjusted to offset the impact of the water flow.

[0068] Next, the 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 of the water outlet pressure can be set to 0.1 MPa and the adjustment step of the spray direction can be set to 1° to achieve fine control.

[0069] When controlling multiple nozzles, a sequential adjustment method with a time interval of 0.5 seconds can be used. For example, for a sampling device with 8 adjustable nozzles, starting from the first nozzle, one nozzle is adjusted every 0.5 seconds, and a round of adjustment is completed in 4 seconds. This method can achieve a spiral flushing mode.

[0070] Finally, the time interval is dynamically adjusted according to the real-time monitored water environment parameters. For example, when the water velocity is detected to be more than 0.5 m / s, the time interval is shortened to 0.3 seconds; when the water turbidity exceeds 50 NTU, the time interval is extended to 0.7 seconds. This dynamic adjustment ensures the best flushing effect and minimum water disturbance under different water conditions.

[0071] Through the above implementation, the present application can achieve precise control of multiple adjustable nozzles and effectively implement spiral or staggered flushing modes. This control method not only improves the cleaning effect of the surface of the sampling equipment, 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 changeable water environments and achieve more efficient and accurate underwater plankton sampling.

[0072] 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 target water outlet pressure and the target injection direction according to the water environment parameters.

[0073] In this regard, the present application further proposes that the method includes obtaining the water turbidity value and water flow rate value in the water environment parameters; calculating the environmental compensation coefficient based on the water turbidity value and water flow rate value; performing weighted calculation on the current flushing parameter and the environmental compensation coefficient to obtain the compensated flushing parameter; and generating the target water outlet pressure and the target injection direction according to the preset parameter mapping relationship based on the compensated flushing parameter. The preset parameter mapping relationship is dynamically optimized according to the historical sampling data and the water disturbance model to realize the spiral or staggered flushing mode while minimizing the disturbance to the water body.

[0074] The technical solution of the present application dynamically adjusts the flushing parameters by introducing water environment parameters, especially water turbidity value and water flow rate value. This method can better adapt to different water environments and improve the accuracy and efficiency of sampling. By calculating the environmental compensation coefficient, the present application realizes the dynamic adjustment of the current flushing parameters, so that the flushing operation can better adapt to the current water environment.

[0075] Specifically, the application first obtains the water turbidity value and water flow rate value. These two parameters have an important impact on the distribution of underwater plankton and the working efficiency of the sampling equipment. The water turbidity value reflects the amount of suspended particles in the water, which directly affects the flushing effect and sampling accuracy. The water flow rate value affects the flow characteristics of the flushing medium and the distribution of flushing intensity.

[0076] Based on the acquired water turbidity value and water flow rate value, the present application calculates the environmental compensation coefficient. This step can adopt a variety of algorithms, such as the weighted average method, which can compare the turbidity value and flow rate value with the preset reference value respectively to obtain normalized indicators, and then assign different weights according to the importance of these indicators, and finally obtain a comprehensive environmental compensation coefficient.

[0077] The calculation of the environmental compensation coefficient can be further refined. For example, turbidity thresholds 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 environment parameters, making the compensation more forward-looking.

[0078] The current flushing parameters and the environmental compensation coefficient are weighted to obtain the compensated flushing parameters. This step realizes the dynamic adjustment of flushing parameters. The weighted calculation can use linear combination or nonlinear function to ensure that the adjusted parameters can adapt to environmental changes without causing excessive fluctuations.

[0079] Finally, according to the compensated flushing parameters, the target water outlet pressure and target spray direction are generated according to the preset parameter mapping relationship. This step converts the abstract flushing parameters into specific execution instructions. The parameter mapping relationship can be implemented by table lookup or function mapping to ensure accurate control of flushing intensity and direction.

[0080] It is worth noting that the preset parameter mapping relationship is not fixed, but dynamically optimized based on historical sampling data and water disturbance models. This optimization mechanism enables the system to continuously learn and improve and adapt to the characteristics and seasonal changes of different waters. For example, a database can be established to record the optimal flushing parameters under different environmental conditions, and the mapping relationship can be continuously updated and optimized through machine learning algorithms.

[0081] The technical solution of this application realizes the 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 with the flushing method with fixed parameters, this method has higher flexibility and adaptability.

[0082] In specific implementation, the following steps can be adopted: First, the turbidity value and flow rate value of the water body are obtained in real time through the water quality sensor. Assume that the currently measured turbidity value is 5 NTU and the flow rate value is 0.3 m / s. Then, these values ​​are compared with the preset reference values ​​(for example, the turbidity reference value is 3 NTU and the flow rate reference value is 0.2 m / s), and the turbidity ratio is calculated to be 1.67 and the flow rate ratio is 1.5.

[0083] Next, the environmental compensation coefficient is calculated using the pre-trained environmental compensation model. Assume that the turbidity compensation factor output by the model is 1.2 and the flow rate compensation factor is 1.1. Considering that the spiral flushing mode is currently used, turbidity has a greater impact on the flushing effect, so a weight of 0.6 is given to the turbidity compensation factor and a weight of 0.4 is given to the flow rate compensation factor. The final calculated environmental compensation coefficient is 1.2 * 0.6 + 1.1 * 0.4 = 1.16.

[0084] The current flushing parameters (assuming the outlet water pressure is 2 bar and the spray angle is 30°) are multiplied by the environmental compensation coefficient to obtain the compensated flushing parameters: outlet water pressure 2.32 bar and spray angle 34.8°. Finally, according to the optimized parameter mapping relationship, these parameters are converted into specific execution instructions, such as adjusting the pump power and the nozzle angle.

[0085] Through this method, the present application can dynamically adjust the flushing parameters according to the real-time water environment, which not only ensures the flushing effect but also minimizes the disturbance to the water body. This precise control not only improves the accuracy of sampling, but also reduces the impact on the water ecological environment, providing reliable technical support for long-term, large-scale underwater plankton monitoring.

[0086] Compared with the prior art, 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.

[0087] In some of the above-mentioned 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.

[0088] In this regard, the present application further proposes that the method first obtains the turbidity reference value and the flow rate reference value determined according to 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. Next, 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 according to these two ratios. Subsequently, based on the degree of influence of these two compensation factors on the spiral or staggered flushing mode, the 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.

[0089] This method of the present application can more accurately reflect the impact of the current water environment on the flushing effect by introducing historical sampling data and water disturbance models. By calculating the relative changes in turbidity and flow rate, it can more flexibly adapt to different water conditions. At the same time, the influence of different environmental factors on the flushing pattern is taken into account, and a more refined environmental compensation adjustment is achieved by introducing weight coefficients.

[0090] Specifically, the method of the present application first obtains the turbidity baseline value and flow rate baseline value determined by the historical sampling data and the water disturbance model. These baseline values ​​reflect the typical conditions under 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.

[0091] Then, the present application introduces an environmental compensation model based on the optimization of the water disturbance model. The model uses the ratio calculated previously to calculate the turbidity compensation factor and the flow rate compensation factor. This step converts the changes in environmental parameters into specific compensation factors, providing a basis for the subsequent adjustment of the flushing intensity.

[0092] Furthermore, the present application takes into account that different environmental factors may have different effects on the flushing effect. Therefore, based on the degree of influence of the turbidity compensation factor and the flow rate compensation factor on the spiral or staggered flushing mode, the respective weight coefficients are determined. This step makes the environmental compensation more accurate and can adjust the importance of different factors according to the actual situation.

[0093] Finally, the present application performs weighted combination of the turbidity compensation factor and the flow rate compensation factor according to the 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.

[0094] As a preferred embodiment, the present application can further optimize the calculation process of the environmental compensation coefficient. For example, the historical change data of the water turbidity value and the water flow rate value within a preset time period can be obtained, such as the turbidity and flow rate records per hour in the past 24 hours. Using the water disturbance model, the turbidity change rate and flow rate change rate in the future sampling period are predicted based on these historical change data. This prediction can be achieved using time series analysis or machine learning algorithms.

[0095] The turbidity baseline value and flow rate baseline value are dynamically adjusted based on the predicted turbidity change rate and flow rate change rate. For example, if the turbidity is predicted to rise by 10% in the next 4 hours, the turbidity baseline value can be increased by 10% accordingly. This dynamic adjustment makes the baseline value closer to the actual environmental conditions that will be faced.

[0096] Next, the ratio of the dynamically adjusted turbidity reference value to the current water turbidity value, and the ratio of the dynamically adjusted flow rate reference 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.

[0097] Substituting these ratios into the environmental compensation model, we can get the updated turbidity compensation factor and flow rate compensation factor. Since the future environmental change trend is taken into account, these updated compensation factors can better adapt to the upcoming water conditions.

[0098] Based on the updated turbidity compensation factor and flow rate compensation factor, combined with the current spiral or staggered flushing mode parameters, the weight of each compensation factor on the flushing effect is calculated. For example, if the spiral flushing mode is currently used and the flow rate is predicted to increase significantly, the weight of the flow rate compensation factor may need to be increased to better cope with the upcoming high flow rate environment.

[0099] Finally, the updated turbidity compensation factor and flow rate compensation factor are weighted and combined using the calculated influence weights to obtain the final environmental compensation coefficient. This method based on prediction and dynamic adjustment enables the environmental compensation coefficient to not only reflect the current environmental conditions, but also to adapt to upcoming environmental changes in advance.

[0100] Through the above optimization method, the present application can more accurately calculate the environmental compensation coefficient, thereby achieving a better adjustment of the flushing intensity and mode. This method not only takes into account the current environmental conditions, but also incorporates historical data analysis and future trend prediction, greatly improving the environmental adaptability and stability of the flushing effect.

[0101] Compared with the prior art, 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.

[0102] In some of the above-mentioned 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.

[0103] In this regard, the present application further proposes a method that obtains historical change data of water turbidity values ​​and water flow velocity values ​​within a preset time period, and uses a water disturbance model to predict the turbidity change rate and flow velocity change rate within a future sampling period, thereby dynamically adjusting the turbidity reference value and flow velocity reference value. This dynamic adjustment mechanism can more accurately reflect the actual conditions of the current water environment and improve the calculation accuracy of the environmental compensation coefficient.

[0104] Specifically, the present application first obtains the historical change data of the water turbidity value and the water flow rate value within a preset time period. These data can be collected in real time by sensors on the sampling equipment, or obtained from the hydrological monitoring system. The preset time period can be set according to actual needs, for example, it can be the data of the past 24 hours or a week.

[0105] Next, the water disturbance model is used to predict the turbidity change rate and flow rate change rate in the future sampling period based on historical change data. The water disturbance model can be a prediction model trained based on a machine learning algorithm or a mathematical model based on the principles of fluid mechanics. The purpose of this step is to predict the changing trend of the water environment so as to better adjust the flushing strategy.

[0106] According to the predicted turbidity change rate and flow rate change rate, the turbidity baseline value and flow rate baseline value are dynamically adjusted. For example, if the turbidity is predicted to increase significantly, the turbidity baseline value can be appropriately increased; if the flow rate is predicted to slow down, the flow rate baseline value can be reduced accordingly. This dynamic adjustment mechanism can make the calculation of the environmental compensation coefficient more in line with the changes in the actual water environment.

[0107] Subsequently, the first ratio of the dynamically adjusted turbidity reference value to the current water turbidity value and the second ratio of the dynamically adjusted flow rate reference 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 reference value and are important bases for calculating the environmental compensation coefficient.

[0108] Substituting the first ratio and the second ratio into the environmental compensation model, an updated turbidity compensation factor and a flow rate compensation factor are obtained. The environmental compensation model can be a nonlinear function for converting the ratio into a corresponding compensation factor. The model can be optimized and adjusted based on historical data and expert experience.

[0109] Based on the updated turbidity compensation factor and flow rate compensation factor, combined with the current spiral or staggered flushing mode parameters, the weight of each compensation factor on the flushing effect is calculated. This step takes into account the differentiated effects of different environmental factors on the flushing effect, so that the final environmental compensation coefficient can more accurately reflect the comprehensive effect of various factors.

[0110] 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, which will be used for subsequent flushing intensity and mode adjustments to achieve more accurate underwater plankton sampling.

[0111] This improved method of the present application realizes 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: First, by analyzing historical data and predicting future trends, this method can better adapt to the dynamic changes of the water environment. This is particularly important for long-term, multi-point underwater sampling tasks, because the water environment may change significantly over time and space.

[0112] Secondly, the mechanism of dynamically adjusting the turbidity baseline value and the flow rate baseline value makes the calculation of the environmental compensation coefficient more flexible and accurate. This mechanism can timely capture sudden changes in the water environment, such as sudden pollution or water flow changes, so as to adjust the sampling strategy in time.

[0113] Furthermore, by calculating the weight of each compensation factor on the flushing effect, this method can more finely balance the influence of different environmental factors. This balancing mechanism helps to achieve the best sampling effect in complex and changeable water environments.

[0114] Finally, this dynamic optimization method 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 that can automatically adjust various parameters according to real-time environmental changes, greatly improving the accuracy and efficiency of sampling.

[0115] As a specific example, assume that the sampling equipment is sampling plankton in the deep water area of ​​a large reservoir. The equipment is equipped with a high-precision turbidity sensor and a flow rate sensor, which can collect water environment data in real time. The system sets the preset time period to the past 12 hours, and records data every 10 minutes, for a total of 72 data points.

[0116] 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 changes in turbidity and flow rate within the next 6 hours.

[0117] Assume that the current water turbidity is 5 NTU and the flow rate is 0.3 m / s. After analyzing the data of the past 12 hours, the system predicts that the turbidity will increase in the next 6 hours, with an average increase rate of 0.2 NTU / hour; the flow rate will remain relatively stable, with a fluctuation range of ±0.05 m / s.

[0118] Based on this prediction, the system dynamically adjusted the turbidity reference value from the original 4.5 NTU to 5.5 NTU, and the flow rate reference value remained unchanged at 0.3 m / s. The first ratio (turbidity) was calculated to be 0.91, and the second ratio (flow rate) was 1.0.

[0119] 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 that the spiral flushing mode is currently used, the system calculates that the influence weight of the turbidity compensation factor is 0.7 and the influence weight of the flow rate compensation factor is 0.3.

[0120] Finally, through weighted combination, the environmental compensation coefficient is: 1.2 * 0.7 + 1.0 * 0.3 = 1.14.

[0121] This environmental compensation factor will be used to adjust the intensity and pattern of the flushing. For example, the system may slightly increase the intensity of the flushing based on this factor, while adjusting the angle and frequency of the spiral flushing to accommodate the expected increase in turbidity in the water.

[0122] Through this dynamic optimization method, the sampling equipment can better respond to changes in the water environment, reduce disturbances to the water body, and improve the accuracy and representativeness of sampling. This is of great significance for long-term monitoring of the distribution and change trends of plankton in deep water areas of reservoirs, and provides more reliable data support for water quality assessment and ecological research.

[0123] In some of the above-mentioned embodiments, during the implementation of the present application, there is still the problem of how to minimize the impact on the distribution of plankton when light illumination is required.

[0124] 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 distribution of plankton 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 distribution of plankton.

[0125] The technical solution of this application intelligently controls the lighting parameters of the LED light source array to minimize the interference with the distribution of plankton in the water while ensuring the normal operation of the sampling equipment. The solution takes into account the sensitivity of different plankton to light, combines the current water environment and the movement state of the sampling equipment, and dynamically adjusts the lighting strategy, thereby achieving a balance between accurate sampling and ecological protection.

[0126] Specifically, the application first obtains the photosensitivity data of different plankton and the current water environment parameters. These data include the response thresholds of various plankton to different wavelengths of light, the transparency of the water body, temperature, pH value, etc. Based on these data, the system can determine the wavelength range and intensity range of the LED light source that is most suitable for the current environment to minimize the stimulation to the plankton.

[0127] Next, the application considers the motion state of the sampling device. Since the movement of the device in the water may affect the effect of illumination and the distribution of plankton, the system will calculate the optimal lighting time and frequency based on information such as the speed and direction of the device. This ensures that lighting is only performed when necessary and in the most appropriate manner, further reducing interference with the water environment.

[0128] During the lighting process, this application adopts a real-time monitoring and dynamic adjustment strategy. The system continuously monitors the lighting effect and the distribution of plankton. If any abnormal or undesirable situation is found, the parameters of the LED light source array will be adjusted immediately. This closed-loop control mechanism ensures that the lighting is always kept in the optimal state and can quickly respond to changes in the environment.

[0129] As a preferred implementation, the present application can adopt the following specific steps to implement the above technical solution: First, a database containing the photosensitivity of common plankton is established. This database can contain information such as the response thresholds, phototropism or photophobia of different types of plankton to various wavelengths of light. For example, some phytoplankton may be more sensitive to blue light (wavelength of about 450-495nm), while some zooplankton may react more strongly to red light (wavelength of about 620-750nm).

[0130] 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.

[0131] Thirdly, an LED light source array with adjustable wavelength and intensity is used. This light source can be precisely adjusted within the visible spectrum range of 400-700nm, and the intensity can be continuously adjusted from 0-100%. The light source array can be installed at different locations of the sampling equipment to achieve all-round lighting.

[0132] 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 is sensitive to blue light, the system may choose 500-600nm green light as the main illumination wavelength to reduce the impact on these organisms.

[0133] The system then obtains the motion status information of the sampling device, including speed, acceleration, direction, etc. Based on this information, the system can predict the motion trajectory of the device in the next few seconds, thereby calculating the optimal lighting time and frequency. For example, if the device is diving quickly, the system may choose a shorter lighting time and a higher frequency to avoid long-term impact of continuous lighting on the same water layer.

[0134] During the lighting process, the system continuously monitors the distribution of plankton in the water. This can be achieved through highly sensitive underwater cameras or acoustic sensors. If a significant change in the density of plankton in certain areas is detected, the system will immediately adjust the parameters of the LED light source in the corresponding area. For example, if the density of plankton in a certain direction is found to suddenly decrease, 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 position.

[0135] Through this fine control and real-time adjustment, the present application can minimize the interference with the distribution of plankton in the water body 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 water ecosystem.

[0136] Compared with the prior art, this application takes into account the photosensitivity of different plankton, rather than simply using lighting of 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 that can quickly respond to environmental changes, which is more flexible and efficient than traditional fixed parameter lighting systems.

[0137] In some of the above-mentioned 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.

[0138] In this regard, the present application further proposes a method for providing lighting at a specific frequency during the optimal lighting time, which specifically includes the following steps: First, obtain the transparency data of the current water body and use it as part of the water body environmental parameters. The transparency data can be measured in real time by a dedicated sensor or obtained from a historical database. This step provides an important environmental information basis for subsequent light source control.

[0139] Next, the optimal output power of the LED light source array is calculated based on the transparency data and the photosensitivity data. The photosensitivity data includes the sensitivity of different plankton to light intensity and wavelength. By taking these two factors into consideration, an output power range that can provide sufficient illumination without overstimulating plankton can be determined.

[0140] Then, the optimal duration of a single pulse is determined based on the optimal output power and water environment parameters. In addition to transparency, water environment parameters may also include factors such as water temperature and salinity that affect light propagation. Determining the optimal duration requires balancing lighting effects and energy consumption, while considering the impact on plankton.

[0141] During the optimal lighting time, the LED light source array is controlled to perform pulsed lighting according to a predetermined frequency and optimal duration. Pulsed lighting can reduce the interference of continuous light on plankton and save energy. The choice of frequency needs to take into account the movement speed of the sampling equipment and the characteristics of the water environment.

[0142] During the illumination process, the pulsed illumination effect and the instantaneous response of plankton are monitored in real time. This can be achieved through integrated optical sensors and image analysis systems. The monitoring data includes the brightness distribution of the illuminated area, the aggregation or dispersion behavior of plankton, etc.

[0143] Finally, based on the monitoring results, the optimal output power and optimal duration are dynamically adjusted to minimize the impact on the distribution of plankton. This real-time feedback mechanism allows the system to make adaptive adjustments based on actual conditions, improving the accuracy and representativeness of sampling.

[0144] This improved method of the present application achieves efficient lighting in different water environments by finely controlling the LED light source array. Specifically, by obtaining transparency data, the system can more accurately evaluate the propagation characteristics of light in water. This provides an important basis for subsequent power and duration calculations.

[0145] This method takes into account transparency and the photosensitive characteristics of plankton when calculating the optimal output power. This approach not only ensures sufficient lighting effects, but also minimizes disturbance to plankton. For example, in water bodies with low transparency, the system may choose a higher output power but shorten the pulse duration to balance lighting needs and biological protection.

[0146] 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 needs. For example, in areas with high plankton density, a shorter pulse duration may be selected to reduce the impact on biological distribution.

[0147] Pulsed illumination is a key feature of this approach. Compared to continuous illumination, pulsed illumination can significantly reduce the long-term impact on plankton. By precisely controlling the frequency and duration of the pulses, the system can provide sufficient illumination while giving plankton time to adapt and recover. This approach is particularly useful for light-sensitive plankton species.

[0148] Real-time monitoring and dynamic adjustment mechanisms further improve the adaptability and accuracy of the method. By continuously analyzing the lighting effects and the response of plankton, the system can quickly respond to environmental changes or unexpected situations. For example, if it detects that certain plankton are unusually sensitive to the current lighting parameters, the system can immediately reduce the output power or adjust the pulse mode.

[0149] As a preferred embodiment, the present application can be applied when performing 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, and a sensor for measuring water transparency. Before starting sampling, the system first measures the transparency of the current water layer, assuming it is 3 meters.

[0150] Based on this transparency data and the pre-stored database of the photosensitivity of plankton, the system calculated that the optimal output power is 50 W. Then, taking into account the current water temperature of 20°C, salinity of 0.5‰ and other water environmental parameters, the system determined that the optimal duration of a single pulse is 0.5 seconds.

[0151] During the 60-second optimal lighting period, the system controls the LED light source array to pulse the lighting at a frequency of 2 Hz. This means that there are two light pulses per second, each lasting 0.5 seconds. During this process, the integrated optical sensor continuously monitors the lighting effect, while the image analysis system processes the distribution changes of plankton in real time.

[0152] Suppose that 30 seconds after the illumination begins, the system detects that certain plankton begin 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 the distribution of plankton during sampling is minimized while still maintaining adequate illumination.

[0153] Through this refined light control method, the present application can achieve high-quality plankton sampling in different water environments. Compared with traditional fixed parameter lighting, this method significantly reduces interference with the aquatic ecosystem and improves the accuracy and representativeness of sampling. At the same time, through real-time adjustment, this method can also adapt to complex and changing underwater environments, providing more reliable data support for long-term, large-scale water quality monitoring and ecological assessment.

[0154] 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.

[0155] 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.

[0156] Specifically, the present 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 state, and provide necessary inputs for subsequent scour intensity calculations.

[0157] Next, the scour intensity F is calculated based on the optimized adaptive scour control model. The model can be expressed as: F(d,v,t,τ) = k * (1 - e^(-αd)) * (v / v_max)^β * (1 + γ*sin(ωt)) * S(τ) * E(p) Among them, F is the scour intensity, d is the distance to the next sampling point, v is the equipment movement speed, t is the 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, and v_max is the maximum equipment speed.

[0158] 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 weight coefficient.

[0159] This model comprehensively considers the impact of multiple factors such as distance, speed, time, turbidity and energy on the flushing intensity. The distance factor (1 - e^(-αd)) ensures that the flushing intensity gradually decreases as it approaches the sampling point. The speed factor (v / v_max)^β reflects the impact of the equipment movement speed on the flushing demand. The time factor (1 + γ*sin(ωt)) introduces periodic changes, which can achieve pulsed flushing. The turbidity influence function S(τ) and the energy efficiency regulation function E(p) respectively consider the regulatory effects of water turbidity and system energy state on the flushing intensity.

[0160] After calculating the flushing intensity F, the system adjusts the water pressure and direction of multiple adjustable nozzles according to this value. This dynamic adjustment ensures that the flushing operation can adapt to different water environments and system conditions, ensuring the flushing effect while avoiding energy waste and water disturbance caused by excessive flushing.

[0161] In order to further optimize the flushing effect, the system will monitor the flushing effect in real time. When the monitoring results show that the flushing effect is insufficient, the flushing intensity will be recalculated by adjusting the turbidity sensitivity coefficient μ and the energy efficiency weight coefficient λ. This adaptive adjustment mechanism enables the system to continuously optimize its performance in actual applications.

[0162] 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 the period is 60 seconds), τ_ref = 5 NTU, μ = 0.8, λ = 0.3. These parameters can be fine-tuned according to the actual application scenario.

[0163] For example, in a sampling task, assuming that the current distance to the next sampling point is 200 meters, the equipment movement speed is 2 meters per second (maximum speed is 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, a specific flushing intensity value can be obtained, thereby accurately controlling the water outlet pressure and direction of the nozzle.

[0164] In this way, the technical solution of the present application can dynamically adjust the flushing intensity according to real-time environmental parameters and system status, and can better adapt to different water environments compared to the flushing method with fixed intensity. In waters with high turbidity, the system will automatically increase the flushing intensity; while in clear waters, it will appropriately reduce the intensity, thereby ensuring the flushing effect while minimizing the disturbance to the water body.

[0165] Secondly, this adaptive control model takes into account the energy state of the system, and can optimize energy utilization while ensuring the flushing effect. When the system has sufficient energy, it can flush more vigorously; when the energy is insufficient, the intensity will be appropriately reduced to extend the working time.

[0166] Furthermore, by introducing distance and speed factors, the system can dynamically adjust the flushing intensity according to the motion state of the sampling device. This not only ensures the flushing effect near the sampling point, but also provides sufficient cleaning force when the device moves at high speed.

[0167] Finally, real-time monitoring and adaptive adjustment mechanisms enable the system to continuously optimize its performance. This closed-loop control method can cope with various complex underwater environments and improve the accuracy and reliability of sampling.

[0168] 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.

[0169] Second, refer to Figure 2 The present application further proposes an underwater plankton sampling device, the device comprising: 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; A flushing control module 220 is used to control 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; 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; A monitoring and adjustment module 240 is used to monitor the flushing effect in real time and to adaptively adjust the flushing intensity and mode according to the monitoring results; The stop control module 250 is used to stop the flushing operation before the sampling device reaches a predetermined distance from the sampling point.

[0170] By obtaining the current position information of the sampling device, the distance information of 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 cross-contamination of samples, and improves sampling accuracy and efficiency.

[0171] In addition, in some preferred embodiments, an underwater plankton sampling device proposed in the present application can perform any step of the above method.

[0172] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope 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 device, the distance information of the next sampling point, and the current water environment parameters; According to the distance information and the water body environmental parameters, a plurality of adjustable nozzles on the surface of the sampling device are controlled to perform a flushing operation, 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 the scouring effect in real time and make adaptive adjustments to the scouring intensity and mode based on the monitoring results; The flushing operation is stopped before the sampling device reaches a predetermined distance from the sampling point.

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; Calculate 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 the 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 the spray direction of the plurality of adjustable nozzles according to the target flushing parameters to achieve a spiral or staggered flushing mode comprises: Acquire the current water outlet pressure and current spray direction of the multiple adjustable nozzles; Generate a target water outlet pressure and a target injection direction according to the target flushing parameter and the water 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; Generate a water outlet pressure adjustment instruction and a spray direction adjustment instruction respectively according to the first difference and the second difference; 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 injection direction according to the target flushing parameter and the water environment parameter comprises: Obtaining water turbidity value and water flow rate value in 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, the target water outlet pressure and the target spray direction are generated according to a preset parameter mapping relationship; The preset parameter mapping relationship is dynamically optimized according to historical sampling data and a water body 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 comprises: 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 turbidity value of the water body to the turbidity reference value, and a second ratio of the flow velocity value of the water body to the flow velocity reference value; According to the first ratio and the second ratio, a turbidity compensation factor and a flow rate compensation factor are calculated using an environmental compensation model optimized based on a water disturbance model; Determining respective weight coefficients based on the 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 comprises: Obtaining historical change data of the turbidity value of the water body and the flow rate value of the water body 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 rate reference value to the current flow rate 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, calculate the influence weight of each compensation factor on the flushing effect; The updated turbidity compensation factor and flow rate compensation factor are weightedly combined using the influence 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 lighting is needed, 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 according to 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 the optimal lighting time and frequency according to 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 at the frequency within the optimal lighting time comprises: Obtain transparency data of the current water body and use 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 instantaneous 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. The underwater plankton sampling method according to claim 1, characterized in that: 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(ωt)) * S(τ) *E(p); Where: F is the scour intensity; d is the distance to the next sampling point; v is the equipment movement speed; t is the 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 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 the energy efficiency weight coefficient λ, and recalculate the flushing intensity.

10. An underwater plankton sampling device, characterized in that: The device includes: An 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, used to control 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; 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 device and the current time, so that the intensity of the flushing operation gradually decreases as the next sampling point is approached; Monitoring and adjustment module, used to monitor the flushing effect in real time and make adaptive adjustments to the flushing intensity and mode based on the monitoring results; The stop control module is used to stop the flushing operation before the sampling device reaches a predetermined distance from the sampling point.

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