A Method and System for Intelligent DNA Acquisition from Aquatic Environments Based on Flow Velocity Sensing

Abstract

This application relates to the field of data processing technology and discloses a method and system for intelligent DNA collection from aquatic environments based on flow velocity sensing. The method includes: collecting flow velocity data, calculating statistical characteristic parameters and identifying water body types; calculating a two-layer dynamic flow velocity threshold based on the flow velocity variation coefficient and water body type; comparing the flow velocity with the threshold in real time to guide the platform to move to a low-flow velocity region; performing multiple validations on the flow velocity sequence to determine stable DNA enrichment regions; and performing parameterized sampling to obtain DNA samples based on the calculated sampling depth and filtration time according to the flow velocity. This application solves the problem of inaccurate DNA monitoring in flowing water bodies caused by traditional fixed-point sampling by establishing an intelligent sampling system based on flow velocity sensing, and also addresses the problem of low efficiency caused by the inability to dynamically adjust sampling parameters according to flow velocity.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and system for intelligent collection of DNA from aquatic environments based on flow velocity sensing. Background Technology

[0002] Environmental DNA (eDNA) technology is an important method for monitoring aquatic biodiversity by collecting cell-free DNA fragments from water bodies. These DNA fragments originate from biological metabolites, feces, mucus, and exfoliated cells. Molecular biological analysis can identify species present in the water without directly capturing individual organisms. Existing eDNA sampling methods primarily employ a fixed-site sampling strategy. This involves setting up sampling points at predetermined geographical locations, using filtration devices to collect a certain volume of water sample at these locations, and then using a filter membrane to trap DNA fragments in the water. After sampling, the filter membrane is sent to a laboratory for DNA extraction and sequencing analysis. This method has been applied in various aquatic environments, including lakes, rivers, and coastal areas, providing technical support for biodiversity monitoring, early warning of invasive alien species, and surveys of endangered species.

[0003] However, existing fixed-point sampling methods have significant shortcomings. The sampling location and parameters are all pre-set manually, making dynamic adjustments impossible based on real-time changes in water flow velocity. In flowing water, flow velocity directly affects the distribution characteristics and enrichment level of DNA. High-velocity areas experience water disturbance, hindering DNA sedimentation and enrichment, resulting in low concentrations. Conversely, low-velocity areas have gentler flow, promoting DNA sedimentation and enrichment, leading to higher concentrations. However, existing methods cannot detect the flow velocity at the sampling point before sampling. If the sampling point happens to be located in a high-velocity area, it can lead to low DNA detection rates or even missed detections of target species. Furthermore, existing methods use fixed sampling depth and filtration time parameters, failing to consider the differences in vertical DNA distribution under different flow velocities. At extremely low flow velocities, DNA mainly settles at the bottom, and fixed-depth sampling misses enrichment areas. At medium flow velocities, DNA distribution is relatively uniform, and excessively prolonged filtration time wastes energy. This rigid parameter design results in low sampling efficiency and difficulty adapting to complex and variable aquatic environments. Summary of the Invention

[0004] This application provides a method and system for intelligent DNA collection in aquatic environments based on flow velocity sensing. By establishing an intelligent sampling system based on flow velocity sensing, it solves the problem of inaccurate DNA monitoring in flowing water by traditional fixed-point sampling and the problem of low efficiency caused by the inability to dynamically adjust sampling parameters according to flow velocity.

[0005] Firstly, this application provides a method for intelligent collection of DNA from aquatic environments based on flow velocity sensing, the method comprising:

[0006] Step S1: Collect flow velocity data within the sliding time window, calculate the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity, identify the current water area type, and obtain a set of flow velocity statistical characteristic parameters;

[0007] Step S2: Assign segmented values ​​to the adjustment coefficient according to the numerical range of the velocity variation coefficient, and superimpose the product of the water area type benchmark threshold, the adjustment coefficient, and the velocity standard deviation to obtain a two-layer dynamic velocity threshold.

[0008] Step S3: Compare the real-time flow rate with the dual-layer dynamic flow rate threshold. When the real-time flow rate exceeds the dual-layer dynamic flow rate threshold, guide the platform to move and record the flow rate change to obtain the flow rate sequence of the target area.

[0009] Step S4: Verify the threshold proportion, fluctuation amplitude, and change trend of the flow velocity sequence in the target region. If all three conditions are met, it is determined to be a stable DNA enrichment region.

[0010] Step S5: Based on the average flow rate of the DNA enrichment stable region, calculate the sampling depth segment and the inverse proportional calculation of the filtration time, control the sampling device to perform parameterized sampling, and obtain environmental DNA filter membrane samples.

[0011] Secondly, this application provides a flow velocity sensing-based intelligent DNA acquisition system for aquatic environments, the flow velocity sensing-based intelligent DNA acquisition system for aquatic environments comprising:

[0012] The acquisition module is used to collect flow velocity data within the sliding time window, calculate the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity, identify the current water area type, and obtain a set of flow velocity statistical characteristic parameters.

[0013] The segmentation module is used to assign segmented values ​​to the adjustment coefficient according to the numerical range of the velocity variation coefficient, and to superimpose the product of the water area type benchmark threshold, the adjustment coefficient and the velocity standard deviation to obtain a two-layer dynamic velocity threshold.

[0014] The discrimination module is used to compare and discriminate the real-time flow velocity with the dual-layer dynamic flow velocity threshold. When the real-time flow velocity exceeds the dual-layer dynamic flow velocity threshold, the platform is guided to move and the flow velocity change is recorded to obtain the flow velocity sequence of the target area.

[0015] The verification module is used to verify the threshold proportion, fluctuation amplitude and change trend of the flow velocity sequence in the target region. If all three conditions are met, it is determined to be a stable region of DNA enrichment.

[0016] The calculation module is used to perform segmented calculation of sampling depth and inverse proportional calculation of filtration time based on the average flow rate of the DNA enrichment stable region, and to control the sampling device to perform parameterized sampling to obtain environmental DNA filter membrane samples.

[0017] Thirdly, a flow velocity sensing-based aquatic environment DNA intelligent acquisition device is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the flow velocity sensing-based aquatic environment DNA intelligent acquisition device to execute the above-described flow velocity sensing-based aquatic environment DNA intelligent acquisition method.

[0018] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the above-described intelligent DNA collection method for aquatic environments based on flow velocity sensing.

[0019] The technical solution provided in this application establishes a multi-dimensional statistical description of the water flow state by collecting flow velocity data within a sliding time window and calculating the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity. Compared with the traditional method that only relies on instantaneous flow velocity for judgment, the sliding time window mechanism can capture the temporal evolution characteristics of flow velocity. The average flow velocity reflects the overall level, the standard deviation of flow velocity reflects the fluctuation amplitude, and the coefficient of variation of flow velocity reflects the standardized fluctuation intensity. The combination of the three comprehensively depicts the dynamic characteristics of water flow. At the same time, by identifying the current water area type and combining it with the flow velocity statistical characteristics to form a flow velocity statistical characteristic parameter set, it provides dual-dimensional environmental characteristic data for subsequent adaptive decision-making, overcoming the shortcomings of traditional methods in terms of insufficient understanding of water area environmental characteristics. Based on the numerical range of the velocity variation coefficient, a segmented adjustment coefficient is assigned, creatively establishing a quantitative relationship between the intensity of velocity fluctuations and the conservatism of the threshold. When the velocity fluctuations are severe, a larger adjustment coefficient is used to make the threshold more conservative and avoid frequent shifts. When the velocity is stable, a smaller adjustment coefficient is used to make the threshold more sensitive and improve the response speed. This segmented assignment strategy has stronger environmental adaptability compared to a fixed adjustment coefficient. The algorithm that superimposes the benchmark threshold of the water type with the product of the adjustment coefficient and the velocity standard deviation to obtain a two-layer dynamic velocity threshold realizes the organic combination of the benchmark layer and the adjustment layer. The benchmark layer ensures the adaptability to different water types, while the adjustment layer realizes the responsiveness to real-time flow conditions. The two-layer structure makes the threshold have both the stability of water characteristics and the dynamism of velocity changes, fundamentally solving the technical problem that traditional fixed thresholds cannot adapt to complex water environments. An active avoidance mechanism that compares real-time flow velocity with a dual-layer dynamic flow velocity threshold and guides the platform to move when the threshold is exceeded changes the traditional passive sampling mode. By recording flow velocity changes, a flow velocity sequence of the target area is obtained, providing complete flow velocity evolution data for subsequent stability verification, realizing a paradigm shift from passive adaptation to active optimization. A multi-verification mechanism for the flow velocity sequence of the target area, including threshold percentage verification, fluctuation amplitude verification, and trend verification, comprehensively evaluates sampling conditions from three dimensions: flow velocity value, fluctuation amplitude, and trend. Threshold percentage verification ensures that the flow velocity remains below the set baseline; fluctuation amplitude verification ensures that the flow velocity fluctuation range is extremely small; and trend verification ensures that the flow velocity remains stable without accelerating or decelerating. The judgment logic that satisfies all three conditions simultaneously has higher reliability than the traditional single threshold judgment, effectively avoiding the problem of insufficient DNA enrichment caused by starting sampling before the flow velocity has stabilized, and ensuring that water samples are collected during the peak DNA concentration period.A parameterized sampling strategy, based on the average flow rate in the DNA enrichment stable region, calculates sampling depth in segments and filtration time inversely proportionally. This establishes a quantitative coupling relationship between flow rate and sampling parameters. The segmented calculation of sampling depth adjusts the sampling position according to the influence of flow rate on the vertical distribution of DNA. At extremely low flow rates, the sampling depth is lowered to collect DNA settled at the bottom layer, while at medium flow rates, the sampling depth is raised to accommodate uniformly distributed DNA. The inversely proportional calculation of filtration time adjusts the filtration time according to the flow rate. At low flow rates, the filtration time is extended to collect more water samples, while at higher flow rates, the filtration time is shortened to avoid energy waste. Compared with traditional fixed-parameter methods, this parameterized sampling significantly improves sampling efficiency and DNA detection rate.

[0020] When applying the two-layer adaptive threshold algorithm and the velocity-parameter coupling algorithm in the field of DNA collection in aquatic environments, the key contributions of algorithm characteristics to the scheme are fully considered. The two-layer adaptive threshold algorithm combines statistical methods with ecological laws through a segmented assignment mechanism of velocity variation coefficient and a superposition mechanism of water type benchmark values. This allows the threshold calculation to reflect both the inherent characteristics of the water body and the instantaneous changes in velocity. The piecewise function design and superposition operation structure of the algorithm directly determine whether the system can accurately identify DNA-rich regions under different water types and different velocity fluctuation conditions. The three-segment assignment rule of the adjustment coefficient in the algorithm reflects the fine control of velocity stability. A smaller coefficient of variation corresponds to a smaller adjustment coefficient, which reflects the trust in the stable flow state. A larger coefficient of variation corresponds to a larger adjustment coefficient, which reflects the caution for the fluctuating flow state. This nonlinear mapping relationship is the core innovation of the algorithm. The velocity-parameter coupling algorithm converts velocity data into specific execution parameters through a piecewise function of sampling depth and an inverse proportional function of filtering time. The selection of the two boundary points of 0.15 m / s and 0.35 m / s in the algorithm is based on the principle of DNA sedimentation dynamics. The setting of three depth offsets of 0.30 m, 0.15 m, and 0 m reflects the differences in the vertical distribution of DNA under different flow velocities. The velocity compensation coefficient of 45 determines the sensitivity of the filtering time to changes in flow velocity. The precise setting of these algorithm parameters and the reasonable selection of function forms enable the sampling depth and filtering time to be adaptively adjusted with the flow velocity, optimizing energy consumption and time cost while ensuring sufficient DNA samples are collected. The operability of the algorithm is guaranteed by clear mathematical expressions and clear piecewise logic, making the technical solution of this application repeatable and reliable in practical applications. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of an embodiment of the intelligent DNA acquisition method for aquatic environments based on flow velocity sensing in this application.

[0023] Figure 2 This is a schematic diagram of an embodiment of the aquatic environment DNA intelligent acquisition system based on flow velocity sensing in this application.

[0024] Figure 3 This is a schematic block diagram of the structure of the intelligent DNA collection device for water environment based on flow velocity sensing in an embodiment of the present invention. Detailed Implementation

[0025] This application provides a method and system for intelligent DNA collection from aquatic environments based on flow velocity sensing. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the aquatic environment DNA intelligent acquisition method based on flow velocity sensing in this application includes:

[0027] Step S1: Collect flow velocity data within the sliding time window, calculate the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity, identify the current water area type, and obtain a set of flow velocity statistical characteristic parameters;

[0028] Step S2: Assign segmented values ​​to the adjustment coefficient based on the numerical range of the velocity variation coefficient, and superimpose the product of the water area type benchmark threshold, the adjustment coefficient, and the velocity standard deviation to obtain the dual-layer dynamic velocity threshold.

[0029] Step S3: Compare the real-time flow rate with the two-layer dynamic flow rate threshold. When the real-time flow rate exceeds the two-layer dynamic flow rate threshold, guide the platform to move and record the flow rate change to obtain the flow rate sequence of the target area.

[0030] Step S4: Verify the threshold proportion, fluctuation amplitude, and change trend of the flow velocity sequence in the target region. If all three conditions are met, it is determined to be a stable region of DNA enrichment.

[0031] Step S5: Based on the average flow rate in the DNA enrichment stable region, calculate the sampling depth segment and the inverse proportional calculation of the filtration time, control the sampling device to perform parameterized sampling, and obtain environmental DNA filter membrane samples.

[0032] It is understood that the executing entity of this application can be a water environment DNA intelligent acquisition system based on flow velocity sensing, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiments use a server as an example for illustration.

[0033] Specifically, in step S1, a propeller-type current velocity sensor deployed at a depth of 0.5 to 1.5 meters underwater collects three-dimensional current velocity component data at a frequency of 1 Hz. The sensor outputs the principal direction component, lateral component, and vertical component of the current velocity. The composite current velocity is obtained by squaring each of the three components, summing them, and then taking the square root. For example, if the principal direction component is measured to be 0.4 m / s, the lateral component 0.2 m / s, and the vertical component 0.1 m / s at a certain moment, the sum of the squares is 0.21, and the square root yields a composite current velocity of 0.458 m / s. This process is repeated for 600 seconds to form a composite current velocity data sequence of 600 data points. All values ​​in this sequence are then accumulated. The average flow velocity is obtained by adding the average and dividing by 600. Then, the difference between the flow velocity at each sampling point and the average is squared, summed, divided by 599, and the square root is taken to obtain the standard deviation of the flow velocity. The ratio of the standard deviation to the average is the coefficient of variation of the flow velocity. At the same time, the platform's GPS positioning module obtains the real-time location coordinates and performs spatial matching with geographical markers such as river boundaries, lake outlines, nearshore areas, and estuary confluences in the electronic water map. The water type label corresponding to the current location is extracted from the pre-stored water feature database. The four data items of average flow velocity, standard deviation of flow velocity, coefficient of variation of flow velocity, and water type label are packaged to form a set of flow velocity statistical feature parameters. Step S2 extracts the velocity variation coefficient from the parameter set and performs a three-range judgment. When the value is less than 0.15, it indicates that the velocity fluctuation is small and the water body is stable, so the adjustment coefficient is assigned a value of 1.2. When the value is between 0.15 and 0.30, it indicates that the velocity fluctuation is moderate, so the adjustment coefficient is assigned a value of 1.5. When the value is greater than or equal to 0.30, it indicates that the velocity fluctuation is drastic, so the adjustment coefficient is assigned a value of 1.8. The larger adjustment coefficient makes the threshold calculation more conservative and avoids frequent shifts. At the same time, the baseline threshold is queried from the preset parameter table according to the water type label. The river type corresponds to 0. The baseline threshold is 55 m / s, lake type corresponds to 0.20 m / s, nearshore type corresponds to 0.35 m / s, and estuary type corresponds to 0.70 m / s. The two-layer dynamic velocity threshold is obtained by adding the product of the queried baseline threshold, the adjustment coefficient, and the standard deviation of the velocity. For example, in a lake area, the standard deviation of the velocity is 0.08 m / s, the coefficient of variation of the velocity is 0.12 (less than 0.15), the adjustment coefficient is assigned a value of 1.2, the baseline threshold of 0.20 m / s plus 1.2 multiplied by 0.08 equals 0.296 m / s, which is the dynamic threshold at that moment.Step S3 continuously compares the real-time collected flow velocity with a dynamic threshold. When the flow velocity exceeds the threshold, a position adjustment command is triggered. Multiple candidate motion trajectories are generated by combining sampling at intervals of 0.1 m / s and 5 degrees / s in the velocity space (0 to 1.2 m / s online velocity, -30° / s to +30° / s angular velocity). When calculating the heading evaluation value for each trajectory, the absolute value of the difference between the trajectory direction angle and the target direction angle is taken, and then subtracted from 180 degrees. The smaller the difference, the larger the heading evaluation value, indicating closer proximity to the target direction. The obstacle distance evaluation value is obtained by calculating the minimum distance from each predicted point on the trajectory to surrounding obstacles. The velocity evaluation value is directly taken from the trajectory. The corresponding linear velocity value and the flow velocity adaptation evaluation value are obtained by querying the historical flow velocity distribution map to obtain the expected flow velocity of the trajectory path. The difference between the expected flow velocity and 0.8 times the dynamic threshold is taken as the absolute value and then the negative value is taken. This makes the system tend to select the path with a flow velocity close to 80% of the threshold, so as to avoid high flow velocities and avoid excessive pursuit of extremely low flow velocities, which would lead to excessive detours. The four evaluation values ​​are multiplied by the weight coefficients 0.20, 0.30, 0.20 and 0.30 respectively, and then summed to obtain the comprehensive evaluation function value. The trajectory with the largest value is selected as the optimal path to control the differential drive platform of the dual propeller propulsion device. During the movement, the position coordinates, corresponding flow velocity and timestamp are recorded every second to form the flow velocity sequence of the target area. Step S4 extracts continuous flow velocity data from the flow velocity sequence and compares them one by one with 0.8 times the dynamic threshold. When the continuous flow velocity is less than the threshold for 180 seconds, the threshold ratio verification is passed. The flow velocity data segment of the most recent 180 seconds is extracted, and the sum of the squares of the differences between each point in the segment and the mean is calculated. The sum of the squares is divided by 179 and the square root is taken to obtain the flow velocity standard deviation. When the standard deviation is less than 0.05 meters per second, it indicates that the flow velocity fluctuation is extremely small and the fluctuation amplitude verification is passed. The flow velocity data segment of the most recent 60 seconds is extracted, and the difference between the flow velocities of adjacent data points is divided by the 1-second time interval to obtain the flow velocity change rate sequence. The change rate sequence is summed and divided by the number of data points to obtain the average change rate. The absolute value is then taken. When the absolute value is less than 0.002 meters per square second, it indicates that the flow velocity is neither accelerating nor decelerating and is in a stable stage. The change trend verification is passed. After all three conditions are met, the current location coordinates, trigger time, and corresponding average flow velocity are recorded to determine the stable DNA enrichment region.Step S5 extracts the average flow rate from the region and performs segmented calculations. When the average flow rate is less than 0.15 m / s, the extremely low flow rate causes DNA to mainly settle in the bottom layer, so the sampling depth is set to the baseline depth of 1.00 m minus 0.30 m, which equals 0.70 m. When the average flow rate is between 0.15 and 0.35 m / s, the low flow rate causes DNA to accumulate in the middle and lower layers, so the sampling depth is set to 1.00 m minus 0.15 m, which equals 0.85 m. When the average flow rate is greater than or equal to 0.35 m / s, the DNA is evenly distributed under moderate flow rates, so the sampling depth is maintained at the baseline depth of 1.00 m. The filtration time is calculated by dividing the flow rate compensation coefficient of 45 by the average flow rate and adding the baseline filtration time of 300 seconds. For example, if the average flow rate is 0.25 m / s... The filtration time is calculated as 300 + 45 divided by 0.25, which equals 480 seconds. Multiplying the filtration pump flow rate of 2.5 liters per minute by the filtration time of 8 minutes yields the expected filtration volume of 20 liters. The depth control motor drives the sampling head to descend to the calculated sampling depth, and the filtration pump is started to operate for the calculated filtration time. When the water sample passes through the 0.45-micron pore size filter membrane, free DNA fragments in the water are trapped on the surface of the filter membrane. During the filtration process, the filtration pressure difference is monitored in real time. When the pressure difference exceeds 80 kPa, it indicates that the filter membrane is blocked, and the filtration automatically stops and the actual filtration volume is recorded. After sampling, the filter membrane number is associated with parameters such as location coordinates, average flow rate, sampling depth, filtration time, and actual filtration volume to form an environmental DNA filter membrane sample.

[0034] In one specific embodiment, step S1 includes:

[0035] The main direction component, transverse component, and vertical component of the underwater velocity sensor are collected. The sum of the squares of the three velocity components is then taken as the square root to obtain the composite velocity data sequence.

[0036] The average flow velocity is obtained by summing the synthesized flow velocity data sequence into a sliding time window of 600 seconds and then dividing the sum by the total number of sampling points.

[0037] The standard deviation of the flow rate is obtained by calculating the difference between the flow rate at each sampling point and the average flow rate in the synthetic flow rate data sequence, summing the squares, dividing by the total number of sampling points minus one, and then taking the square root.

[0038] The coefficient of variation of flow velocity is obtained by comparing the standard deviation of flow velocity with the average flow velocity.

[0039] The platform's real-time location coordinates are obtained and compared with the electronic water area map to identify the current water area type from the water area feature database;

[0040] The average velocity, standard deviation of velocity, coefficient of variation of velocity, and current water type are combined to form a set of statistical characteristic parameters of velocity.

[0041] Specifically, the principal component of the flow velocity refers to the magnitude of the velocity in the main flow direction, the transverse component refers to the magnitude of the horizontal velocity perpendicular to the principal flow direction, and the vertical component refers to the magnitude of the velocity in the vertical direction. These three components together describe the three-dimensional motion state of the water flow. The propeller-type flow velocity sensor outputs the values ​​of these three components by measuring the pushing effect of the water flow on the propeller. The standard method for vector synthesis is to square each of the three components, sum them up, and then take the square root. At a certain moment, the sensor collects the principal component as 0.35 m / s, the transverse component as 0.18 m / s, and the vertical component as 0.09 m / s. Squaring 0.35 gives 0.1225, 0.18 gives 0.0324, and 0.09 gives 0.0081. Summing the three squared values ​​gives 0.163. Taking the square root of 0.163 gives 0.404 m / s, which is the composite flow velocity at that moment. The sensor collects data at a frequency of 1 Hz, i.e., once per second, for 600 seconds, forming a data sequence containing 600 composite flow velocity values. The sliding time window is a fixed-length period of 600 seconds (10 minutes). It contains 600 composite flow velocity data points arranged chronologically. The flow velocity values ​​of all data points within the window are summed. For example, if the first data point has a flow velocity of 0.404 m / s, the second data point has a flow velocity of 0.398 m / s, and so on, up to the 600th data point with a flow velocity of 0.412 m / s, assuming the total sum is 245.6 m / s, dividing the sum of 245.6 by the total number of sampling points (600) yields an average flow velocity of 0.409 m / s. This average value reflects the overall level of water flow velocity within the time window. The standard deviation of flow velocity is used to measure the dispersion, i.e., the fluctuation range, of flow velocity data. The calculation first involves subtracting the flow velocity at each sampling point in the synthetic flow velocity data sequence from the previously calculated average flow velocity of 0.409 m / s. Subtracting 0.409 from the first data point (0.404) yields -0.005, and subtracting 0.409 from the second data point (0.398) yields -0.011. Squaring each difference yields 0.000025 and 0.000121. Squaring all 600 differences and summing them gives an approximate value of 1.44. Dividing this by the total number of sampling points minus one (599) yields 0.0024. Taking the square root of 0.0024 gives the standard deviation of flow velocity, 0.049 m / s. Subtracting one from the total number of sampling points instead of directly dividing by the total number of sampling points is because the standard deviation calculation uses Bessel correction to obtain an unbiased estimate. The velocity variation coefficient is the ratio of the velocity standard deviation to the velocity average. Dividing the velocity standard deviation of 0.049 m / s calculated earlier by the velocity average of 0.409 m / s gives the velocity variation coefficient of 0.12. This coefficient is a dimensionless value used to standardize the relative intensity of velocity fluctuations. Fluctuations under different average velocities become comparable through the coefficient of variation.The platform's real-time location coordinates are obtained through a GPS positioning module, outputting longitude and latitude values. The electronic water area map is a digital map pre-stored in the controller, containing geographic vector data such as river boundary lines, lake outline polygons, nearshore area ranges, and estuary confluence markers. The matching and comparison process involves determining the spatial relationship between the GPS-output location coordinates and the geographic elements in the map, identifying which water area the coordinate point falls within. The water area feature database stores the type labels corresponding to each water area element: river elements are labeled as "river," lake elements as "lake," nearshore elements as "nearshore," and estuary elements as "estuary." Based on the location matching results, the corresponding labels are extracted from the database as the current water area type. The flow velocity statistical characteristic parameter set is a data structure formed by packaging and combining the four data points obtained above: the average flow velocity of 0.409 m / s, the standard deviation of flow velocity of 0.049 m / s, the coefficient of variation of flow velocity of 0.12, and the identified current water body type (lake). The data in this parameter set have a clear correspondence, that is, they all describe the water flow characteristics at the same location within the same time window. The average flow velocity reflects the overall level of flow velocity, the standard deviation of flow velocity reflects the amplitude of flow velocity fluctuation, the coefficient of variation of flow velocity reflects the normalized fluctuation intensity, and the water body type reflects the geographical environment category. These data together constitute a comprehensive description of the water flow state at the current sampling location.

[0042] In one specific embodiment, step S2 includes:

[0043] The velocity variation coefficient is extracted from the set of velocity statistical characteristic parameters. The velocity variation coefficient is then judged according to its numerical range. When the velocity variation coefficient is less than 0.15, the adjustment coefficient is set to 1.2. When the velocity variation coefficient is between 0.15 and 0.30, the adjustment coefficient is set to 1.5. When the velocity variation coefficient is greater than or equal to 0.30, the adjustment coefficient is set to 1.8.

[0044] Based on the current water type in the set of flow velocity statistical feature parameters, the corresponding water type benchmark threshold is queried from the preset parameter table to obtain the benchmark flow velocity threshold of the current water.

[0045] The standard deviation of flow velocity is extracted from the set of flow velocity statistical characteristic parameters. The adjustment coefficient is multiplied with the standard deviation of flow velocity to obtain the real-time adaptive adjustment amount.

[0046] The baseline flow velocity threshold and the real-time adaptive adjustment are added together to obtain the two-layer dynamic flow velocity threshold.

[0047] Specifically, after extracting the coefficient of variation of flow velocity from the set of flow velocity statistical feature parameters, the numerical range is determined. The determination logic is to compare the value with two preset dividing points, 0.15 and 0.30. When the coefficient of variation of flow velocity is less than 0.15, it indicates that the flow velocity fluctuation within the sliding time window is relatively small compared with the average value, that is, the water flow is relatively stable. At this time, the adjustment coefficient is assigned a value of 1.2. When the coefficient of variation of flow velocity is within the closed interval of 0.15 to 0.30, it indicates that there is a moderate degree of flow velocity fluctuation. At this time, the adjustment coefficient is assigned a value of 1.5. When the coefficient of variation of flow velocity is greater than or equal to 0.30, it indicates that the flow velocity fluctuates violently and the water flow is unstable. At this time, the adjustment coefficient is assigned a value of 1.8. The larger adjustment coefficient value makes the subsequent calculated threshold more conservative, thereby avoiding frequent triggering of platform movement when the water flow fluctuates violently. The preset parameter table is a data table pre-stored in the controller's memory. The table stores the mapping relationship between four water body types and their corresponding baseline thresholds. The baseline threshold for the river type is 0.55 m / s because rivers have strong water flow, requiring a relatively high velocity threshold for DNA sedimentation and enrichment. The baseline threshold for the lake type is 0.20 m / s because lakes have slow water flow, and a lower velocity is sufficient for DNA enrichment. The baseline threshold for the nearshore type is 0.35 m / s because nearshore waters are affected by tides, resulting in large velocity variations, requiring a medium threshold. The baseline threshold for the estuary type is 0.70 m / s because estuaries are areas where rivers and oceans meet, with complex flow patterns and higher velocities, requiring a more tolerant threshold. The current water body type label extracted from the velocity statistical feature parameter set is used as an index to find the corresponding baseline threshold value in the preset parameter table. The query operation involves locating the corresponding row in the table based on the water body type label and then reading the baseline threshold column value for that row. The query result is the baseline velocity threshold for the current water body. The standard deviation of flow velocity is extracted from the set of flow velocity statistical characteristic parameters. The adjustment coefficient obtained from the above determination based on the range of flow velocity variation coefficient is multiplied by the standard deviation of flow velocity. The product operation is to directly multiply the two values. The adjustment coefficient is a dimensionless value, while the unit of flow velocity standard deviation is meters per second. The unit of the product result is meters per second. This product result is named the real-time adaptive adjustment amount. The magnitude of the adjustment amount is affected by both the standard deviation of flow velocity and the adjustment coefficient. The larger the standard deviation of flow velocity, the greater the fluctuation of flow velocity and the larger the adjustment amount. The larger the adjustment coefficient, the more conservative the response to fluctuations and the larger the adjustment amount.The baseline flow velocity threshold obtained from the preset parameter table is added to the real-time adaptive adjustment amount just calculated. The addition operation is to directly add the two values ​​together. The units of the baseline flow velocity threshold and the real-time adaptive adjustment amount are both meters per second, and the unit of the added result is still meters per second. This added result is the dual-layer dynamic flow velocity threshold. The meaning of dual-layer is that the threshold consists of two parts. The first layer is the baseline flow velocity threshold, which reflects the inherent characteristics of the water type. The second layer is the real-time adaptive adjustment amount, which reflects the real-time characteristics of the flow velocity fluctuation at the current moment. The meaning of dynamic is that the threshold is not fixed but changes with the flow velocity standard deviation and the flow velocity coefficient of variation. The dual-layer dynamic flow velocity threshold is recalculated every 120 seconds when the sliding time window is updated. A sampling platform operates in a lake area. From the set of flow velocity statistical characteristic parameters, the coefficient of variation for flow velocity is extracted to be 0.12. This value is less than 0.15 and falls into the first interval, so the adjustment coefficient is assigned a value of 1.2. The current water area type is extracted from the parameter set as a lake. The baseline threshold corresponding to the lake type is found to be 0.20 m / s, which is used as the baseline flow velocity threshold for the current water area. The standard deviation of flow velocity is extracted from the parameter set to be 0.049 m / s. Multiplying the adjustment coefficient 1.2 by the standard deviation of flow velocity 0.049 m / s yields 0.0588 m / s, which is used as the real-time adaptive adjustment amount. The baseline flow velocity threshold of 0.20 m / s is added to the real-time adaptive adjustment amount of 0.0588 m / s, resulting in 0.2588. The velocity per second (m / s) represents the dual-layer dynamic velocity threshold at that moment. After 120 seconds, the sliding time window moves forward to cover a new time period, and the velocity variation coefficient is recalculated to 0.18, falling into the second interval. The adjustment coefficient is updated to 1.5, the velocity standard deviation is updated to 0.056 m / s, the water type remains a lake, and the baseline threshold remains 0.20 m / s. The real-time adaptive adjustment is updated to 1.5 multiplied by 0.056, which equals 0.084 m / s. The dual-layer dynamic velocity threshold is updated to 0.20 plus 0.084, which equals 0.284 m / s. The threshold increases from 0.2588 m / s to 0.284 m / s because the increased velocity variation coefficient indicates stronger water flow fluctuations, and the corresponding increase in the adjustment coefficient makes the threshold more conservative.

[0048] In one specific embodiment, step S3 includes:

[0049] The real-time flow rate continuously collected is compared with the two-layer dynamic flow rate threshold. When the real-time flow rate is greater than the two-layer dynamic flow rate threshold, a position adjustment command is triggered.

[0050] Multiple candidate motion trajectories are generated by combining linear velocity and angular velocity sampling in velocity space. For each candidate motion trajectory, the heading evaluation value, obstacle distance evaluation value, velocity evaluation value and flow velocity adaptation evaluation value are calculated respectively.

[0051] The four evaluation values ​​are multiplied by their corresponding weight coefficients and then summed to obtain the comprehensive evaluation function value of each candidate trajectory. The candidate trajectory with the largest comprehensive evaluation function value is selected as the optimal movement path.

[0052] The control propulsion device moves along the optimal path to a region with slower flow, and simultaneously records the position coordinates, corresponding flow velocity, and timestamp during the movement to obtain the flow velocity sequence of the target area.

[0053] Specifically, the flow velocity sensor continuously collects real-time flow velocity values ​​at a frequency of 1Hz. The controller compares the real-time flow velocity collected each time with the two-layer dynamic flow velocity threshold calculated in the previous steps. The comparison operation is to determine whether the real-time flow velocity is greater than the threshold. When the judgment result is true, that is, the real-time flow velocity value is indeed greater than the threshold value, the controller generates a position adjustment command and triggers the subsequent path planning program. The velocity space refers to a two-dimensional space composed of two parameters: linear velocity and angular velocity. Linear velocity refers to the speed at which the platform moves forward, measured in meters per second (m / s), while angular velocity refers to the speed at which the platform turns, measured in degrees per second (° / s). The sampling range for linear velocity is set from 0 to 1.2 m / s with a sampling interval of 0.1 m / s, and the sampling range for angular velocity is set from -30° / s to +30° / s with a sampling interval of 5° / s. Negative values ​​indicate left turns, and positive values ​​indicate right turns. Combined sampling involves combining each sampled value of linear velocity with each sampled value of angular velocity using a Cartesian product. There are 13 sampling points for linear velocity from 0, 0.1, 0.2 up to 1.2 m / s, and 13 sampling points for angular velocity from -30, -25, -20 up to +30° / s. After combination, 169 candidate motion trajectories are generated, and each trajectory is uniquely determined by a pair of linear velocity and angular velocity values. The heading evaluation value is calculated by first obtaining the platform's current orientation angle and the target position's direction angle. The target position is a region with low flow velocity, and its direction angle is obtained by querying historical flow velocity distribution maps. The predicted direction angle of the candidate trajectory is calculated, and the absolute value of the difference between the predicted and target direction angles is taken to obtain the angle deviation. Subtracting the angle deviation from 180 degrees yields the heading evaluation value; the smaller the deviation, the larger the heading evaluation value, indicating that the trajectory is closer to the target direction. The obstacle distance evaluation value is calculated by estimating the spatial coordinates of each predicted point on the platform's trajectory over a future period based on the linear and angular velocities of the candidate trajectory. The distance from each predicted point to the nearest obstacle is calculated from obstacle position data obtained from obstacle detection sensors. All predicted distance values ​​are placed in a set, and the minimum distance value is extracted from the set as the obstacle distance evaluation value for the candidate trajectory. The larger the minimum distance, the safer the trajectory. The speed evaluation value is directly extracted from the linear velocity value corresponding to the candidate trajectory. The higher the speed, the higher the evaluation value, encouraging the platform to maintain a reasonable speed and reach the target area as quickly as possible. The calculation of the velocity adaptation evaluation value first involves querying historical velocity distribution maps, which record velocity measurement data at different locations in the water area. Based on the predicted path coordinates of the candidate trajectory, spatial interpolation is performed on the velocity distribution map to obtain the expected velocity of the path. The target velocity value is obtained by multiplying the dual-layer dynamic velocity threshold by 0.8. The target velocity value is set to 80% of the threshold because it is necessary to avoid high velocity areas exceeding the threshold while preventing excessive pursuit of extremely low velocity, which would lead to excessive detours by the platform and increased energy consumption. The velocity adaptation evaluation value is obtained by taking the absolute value of the difference between the expected velocity and the target velocity value and then taking a negative sign. The smaller the absolute value of the difference, that is, the closer the expected velocity is to the target velocity value, the larger the evaluation value after taking a negative sign, indicating that the velocity adaptation of the trajectory is better.The four evaluation values ​​are heading evaluation, obstacle distance evaluation, speed evaluation, and current speed adaptation evaluation, with corresponding weight coefficients of 0.20, 0.30, 0.20, and 0.30, respectively. These weight coefficients reflect the importance of each evaluation indicator in path selection. Higher weights for obstacle distance and current speed adaptation indicate that safety and current speed adaptation are key considerations in path planning. The heading evaluation value is multiplied by 0.20, the obstacle distance evaluation value by 0.30, the speed evaluation value by 0.20, and the current speed adaptation evaluation value by 0.30. The sum of these four products yields the comprehensive evaluation function value for the candidate trajectory. This calculation is repeated for all 169 candidate trajectories to obtain 169 comprehensive evaluation function values. The trajectory with the largest value is selected as the optimal path. The largest comprehensive evaluation function value indicates that the trajectory achieves the best balance between heading accuracy, safety, speed rationality, and current speed adaptation. The propulsion device includes two propellers located on both sides of the platform. The rotational speed of each propeller is calculated based on the linear velocity and angular velocity corresponding to the optimal movement path. When straight-line movement is required, the rotational speeds of the two propellers are the same. When turning is required, the rotational speeds of the two propellers are different, and turning is achieved through differential speed. The controller sends rotational speed commands to the propeller motors to drive the platform to move along the optimal movement path. During the movement, the GPS positioning module records the platform's position coordinates, including longitude and latitude, once per second. The flow velocity sensor synchronously records the flow velocity value at that location. The clock module records the timestamp of the sampling time. The position coordinates, corresponding flow velocity, and timestamp are arranged in chronological order to form a flow velocity sequence for the target area. This sequence records the changes in position and flow velocity at each moment during the platform's movement from a high-flow velocity area to a slower-flow velocity area.

[0054] In one specific embodiment, the four evaluation values ​​are multiplied by their corresponding weight coefficients and then summed to obtain the comprehensive evaluation function value of each candidate trajectory. The candidate trajectory with the largest comprehensive evaluation function value is selected as the optimal movement path, including:

[0055] The absolute value of the difference between the trajectory direction angle and the target direction angle of each candidate trajectory is calculated and then subtracted from 180 degrees to obtain the heading evaluation value of each candidate trajectory.

[0056] Calculate the set of distances from predicted points to obstacles on each candidate motion trajectory, and extract the minimum distance value from the distance set as the obstacle distance evaluation value for each candidate motion trajectory;

[0057] Extract the linear velocity corresponding to each candidate motion trajectory as the velocity evaluation value of each candidate motion trajectory;

[0058] Query historical velocity distribution maps to obtain the expected velocity of each candidate motion trajectory path. Calculate the absolute value of the difference between the expected velocity and 0.8 times the two-layer dynamic velocity threshold, and take the negative value to obtain the velocity adaptation evaluation value of each candidate motion trajectory.

[0059] The comprehensive evaluation function value of each candidate motion trajectory is obtained by multiplying the heading evaluation value, obstacle distance evaluation value, speed evaluation value and flow velocity adaptation evaluation value with weight coefficients of 0.20, 0.30, 0.20 and 0.30 respectively, and then summing them.

[0060] The trajectory with the largest comprehensive evaluation function value among all candidate trajectories is selected as the optimal movement path.

[0061] Specifically, the trajectory direction angle refers to the angle between the direction of motion of the candidate trajectory and true north. The position coordinates of the platform after a certain prediction time are calculated based on the linear velocity and angular velocity corresponding to the candidate trajectory. The direction of the line connecting the current position coordinates and the current position coordinates is used as the trajectory direction angle. The target direction angle refers to the angle between the direction from the current position to the target area and true north. The target area is the area where the flow velocity is lower than the two-layer dynamic flow velocity threshold in the historical flow velocity distribution map. The trajectory direction angle and the target direction angle are subtracted to obtain the angle difference. The absolute value of the angle difference is taken to eliminate the influence of positive and negative signs. The heading evaluation value is obtained by subtracting the absolute value from 180 degrees. When the trajectory direction is completely consistent with the target direction, the absolute value of the angle difference is 0 degrees and the heading evaluation value is 180 degrees, reaching the maximum value. When the trajectory direction is completely opposite to the target direction, the absolute value of the angle difference is 180 degrees and the heading evaluation value is 0 degrees, reaching the minimum value. Predicted points are the spatial locations the platform will pass through during a future period of time based on the candidate trajectory's linear and angular velocities and the kinematic model. The platform's position coordinates are calculated every 0.5 seconds in the future. Assuming a prediction time of 5 seconds, 10 predicted points are generated. Obstacle position data comes from the detection results of ultrasonic or lidar sensors. The Euclidean distance from each predicted point to all obstacles is calculated, which is the square root of the sum of the squares of the coordinate differences. For the first predicted point, the distances to obstacle A, obstacle B, obstacle C, etc., are calculated, and the minimum value is selected as the nearest obstacle distance for that predicted point. This process is repeated to obtain the nearest obstacle distances for each of the 10 predicted points. These 10 distance values ​​are placed into a distance set, and the smallest distance value is extracted from the distance set as the obstacle distance evaluation value for that candidate trajectory. This minimum distance value represents the distance from the most dangerous location point on the entire trajectory to the obstacle. Linear velocity is the forward motion speed of the platform corresponding to the candidate trajectory. When generating the candidate trajectory, the combination of linear velocity and angular velocity corresponding to the trajectory has been determined. The linear velocity value in this combination is directly extracted as the velocity evaluation value. The unit of the velocity evaluation value is meters per second. The larger the linear velocity, the larger the velocity evaluation value, indicating that the trajectory can enable the platform to reach the target area faster.Historical velocity distribution maps are spatial distribution data of water flow velocities established in advance through multiple measurements. The maps record the velocity measurements corresponding to different latitude and longitude coordinates of the water body. Candidate trajectory paths consist of a sequence of coordinates of predicted points. For each predicted point, the nearest measured velocity value is found in the historical velocity distribution map, or the interpolated velocity at that predicted point is calculated using bilinear interpolation based on the velocity values ​​of the surrounding four measured points. The expected velocity of the candidate trajectory path is obtained by averaging the velocity values ​​of all predicted points on the trajectory. The velocity calculated in the aforementioned steps is then considered... The target velocity value is obtained by multiplying the dual-layer dynamic velocity threshold by 0.8. The target velocity value represents the ideal velocity level that avoids high-velocity areas without excessive detours. The difference between the expected velocity and the target velocity value is obtained by subtracting the expected velocity from the target velocity value. The absolute value of the difference is taken and then multiplied by -1 to obtain the negative value, which is the velocity adaptation evaluation value. When the expected velocity is exactly equal to the target velocity value, the absolute value of the difference is 0. After taking the negative value, the evaluation value is 0, which is the optimal value. The further the expected velocity deviates from the target velocity value, the larger the absolute value of the difference. The more negative the evaluation value, the worse the velocity adaptation of the trajectory. The heading evaluation value reflects the consistency between the trajectory direction and the target direction; the obstacle distance evaluation value reflects the safety of the trajectory; the speed evaluation value reflects the speed of the trajectory; and the flow velocity adaptation evaluation value reflects the degree of matching between the flow velocity along the trajectory path and the target flow velocity. Multiplying the heading evaluation value by a weighting coefficient of 0.20 yields the heading-weighted value; multiplying the obstacle distance evaluation value by a weighting coefficient of 0.30 yields the safety-weighted value; multiplying the speed evaluation value by a weighting coefficient of 0.20 yields the speed-weighted value; and multiplying the flow velocity adaptation evaluation value by a weighting coefficient of 0.30 yields the flow velocity-weighted value. The sum of the four weighting coefficients is 1.0, indicating that this is a return value. The unified weight allocation involves adding and summing the four weighted values ​​to obtain the comprehensive evaluation function value of the candidate trajectory. The comprehensive evaluation function value is a scalar value whose unit depends on the dimension of each evaluation value. The above calculation process is repeated for all candidate motion trajectories to obtain the comprehensive evaluation function value of each trajectory. All comprehensive evaluation function values ​​are traversed and compared. The candidate motion trajectory corresponding to the comprehensive evaluation function value with the largest value is selected as the optimal movement path. The combination of linear velocity and angular velocity corresponding to the optimal movement path will be sent to the propulsion device control module to execute the actual platform motion control.

[0062] In one specific embodiment, step S4 includes:

[0063] Continuous velocity data is extracted from the velocity sequence of the target area. Each velocity data is compared with 0.8 times the two-layer dynamic velocity threshold. When the continuous velocity is less than 0.8 times the two-layer dynamic velocity threshold and the duration is greater than or equal to 180 seconds, the threshold ratio verification is deemed successful.

[0064] Extract the most recent 180-second velocity data segment from the velocity sequence of the target area, calculate the standard deviation of the velocity data segment, and determine that the fluctuation amplitude verification is passed when the standard deviation of the velocity is less than 0.05 meters per second.

[0065] Extract the flow velocity data segment of the most recent 60 seconds from the flow velocity sequence of the target area, calculate the difference between adjacent flow velocity data and divide by the time interval to obtain the flow velocity change rate sequence, calculate the average value of the flow velocity change rate sequence and take the absolute value. When the absolute value is less than 0.002 meters per square second, the change trend is judged to be verified.

[0066] When the three conditions of threshold ratio verification, fluctuation amplitude verification, and change trend verification are met simultaneously, the current location coordinates, trigger time, and corresponding average flow rate are recorded to determine the DNA enrichment stable region.

[0067] Specifically, the target area flow velocity sequence is a set of flow velocity data recorded in chronological order during platform movement. Continuous flow velocity data refers to flow velocity measurements that are adjacent in time within the sequence. The dual-layer dynamic flow velocity threshold is multiplied by 0.8 to obtain the threshold judgment benchmark value. Starting from the beginning of the flow velocity sequence, flow velocity data is extracted one by one and compared with the benchmark value. When a flow velocity data is less than the benchmark value, it is marked as a data point that meets the condition and its timestamp is recorded. The next flow velocity data is extracted and compared. When all the continuously extracted flow velocity data are less than the benchmark value, the time difference between the timestamp of the first data point that meets the condition and the timestamp of the current data point is calculated. The time difference is the duration. When the duration reaches or exceeds 180 seconds, the threshold ratio verification is passed. The 180-second duration requirement ensures that the flow velocity remains stable at a low level rather than fluctuating briefly. If a flow velocity data is greater than or equal to the benchmark value during the continuous judgment process, the current continuous judgment is interrupted and the counting starts again from the next data point. The interception operation extracts a subset of data for a specific time period from the flow velocity sequence of the target area. The most recent 180 seconds refers to the flow velocity data within a time range of 180 seconds prior to the current moment. Assuming the flow velocity data sampling frequency is 1Hz, i.e., one data point per second, the data segment contains 180 flow velocity values. The calculation of the flow velocity standard deviation first sums these 180 flow velocity values ​​and divides by 180 to obtain the average flow velocity of the data segment. Then, the difference between each flow velocity value and the average flow velocity is calculated and squared to obtain the deviation squared value. The sum of the 180 deviation squared values ​​is then divided by 179, i.e., the number of data points is reduced by one. The square root of the division result is the flow velocity standard deviation. The unit of the standard deviation is meters per second. The standard deviation value reflects the fluctuation range of the flow velocity around the average value within the time period. When the standard deviation is less than 0.05 meters per second, it indicates that the flow velocity fluctuation is minimal and the water body is in a relatively static state, and the fluctuation range is verified. The threshold of 0.05 meters per second is set based on the flow velocity stability requirements required for the sedimentation and enrichment of environmental DNA in water. The method for extracting the flow velocity data segment of the most recent 60 seconds is similar to that described above. Flow velocity data is extracted from a time range of 60 seconds prior to the current moment to form a data segment containing 60 values. Adjacent flow velocity data refers to two flow velocity values ​​that are immediately adjacent in time within this data segment. The change in flow velocity is obtained by subtracting the first flow velocity value from the second flow velocity value. The time interval between the two data points is 1 second. The change in flow velocity is divided by the time interval of 1 second to obtain the rate of change of flow velocity. The unit of the rate of change of flow velocity is meters per second squared, representing the speed at which the flow velocity changes over time. The above calculation is performed on the 60 flow velocity values ​​in the data segment to obtain 59 flow velocity rate of change values, forming a sequence of flow velocity rate of change. The sum of the 59 flow velocity rate of change values ​​is accumulated and divided by 59 to obtain the average rate of change of flow velocity. The absolute value of the average rate of change of flow velocity is taken and the sign is eliminated to obtain the amplitude of the rate of change. When the amplitude of the rate of change is less than 0.002 meters per second squared, it indicates that the flow velocity is neither continuously accelerating nor continuously decelerating, but remains in a stable state. The trend determination is verified.Threshold percentage verification ensures that the flow velocity remains below the set baseline value, fluctuation amplitude verification ensures that the flow velocity fluctuation range is extremely small, and change trend verification ensures that the flow velocity change trend is stable. The three verifications evaluate the stability of the water flow from different dimensions. When the judgment results of the three verifications are all passed, the GPS positioning module reads the current position coordinates of the platform, including longitude and latitude values, and the clock module reads the current system time as the trigger time. The average flow velocity calculated from the flow velocity data segment of the most recent 180 seconds is used as the corresponding average flow velocity. The three data points of position coordinates, trigger time, and average flow velocity are associated and stored, and the location is marked as a DNA enrichment stable region. This region meets the flow velocity conditions and stability conditions for the sedimentation and enrichment of environmental DNA in water. After a sampling platform completes its position adjustment and enters a new area, the flow velocity sequence for the target area begins recording flow velocity data. A threshold judgment benchmark is obtained by multiplying the dual-layer dynamic flow velocity threshold by 0.8. Continuous flow velocity data are extracted from the flow velocity sequence and compared one by one with the benchmark value. When all continuous flow velocities are less than the benchmark value and the cumulative duration reaches 180 seconds, the threshold percentage verification is successful. The standard deviation is calculated by extracting the flow velocity data segment from the most recent 180 seconds. The standard deviation is obtained by summing the squared differences between each flow velocity value and the average value of the segment, dividing by the number of data points minus one, and then taking the square root. The fluctuation amplitude verification was passed when the standard deviation was less than 0.05 meters per second. The flow velocity data segment of the most recent 60 seconds was extracted, and the difference between the flow velocities of adjacent data points was divided by the time interval to obtain the flow velocity change rate sequence. The average value of the change rate sequence was calculated and the absolute value was taken. The change trend verification was passed when the absolute value was less than 0.002 meters per square second. After all three verifications were passed, the current location coordinates output by GPS, the trigger time output by clock, and the average flow velocity calculated from the 180-second data segment were recorded. This location was determined as a stable DNA enrichment region and subsequent sampling operations were initiated.

[0068] In one specific embodiment, step S5 includes:

[0069] The average flow rate was extracted from the DNA enrichment stable region. The average flow rate was then used to determine the numerical range. When the average flow rate was less than 0.15 m / s, the sampling depth was calculated as the baseline depth minus 0.30 m. When the average flow rate was between 0.15 and 0.35 m / s, the sampling depth was calculated as the baseline depth minus 0.15 m. When the average flow rate was greater than or equal to 0.35 m / s, the sampling depth was calculated as the baseline depth.

[0070] The flow rate compensation coefficient of 45 is compared with the average flow rate. The result of the ratio is added to the baseline filtration time of 300 seconds to obtain the filtration time.

[0071] The expected filtered water sample volume is obtained by multiplying the filter pump flow rate of 2.5 liters per minute by the filtration time.

[0072] The depth adjustment motor drives the sampling head to descend to the sampling depth, and the filter pump is started to filter the water sample according to the filtration time, trapping environmental DNA on the filter membrane with a pore size of 0.45 micrometers, thus obtaining an environmental DNA filter membrane sample.

[0073] Specifically, the average flow velocity is extracted from the data recorded in the DNA-enriched stable region. This average flow velocity is a statistical value representing the flow velocity level of that region, calculated from the most recent 180-second flow velocity data segment in the previous step. When determining the numerical range of this average flow velocity, it is compared with two boundary points of 0.15 m / s and 0.35 m / s. When the average flow velocity is less than 0.15 m / s, it indicates that the DNA in the extremely slow-moving environment mainly settles to the bottom of the water body and accumulates due to gravity. The baseline depth is set at 1.00 m, representing the standard sampling depth under normal flow velocity conditions. The baseline depth of 1.00 m is then subtracted by 0.3 m / s. A sampling depth of 0.70 meters is obtained from the baseline depth of 1.00 meters. This means that under extremely low flow conditions, the sampling head needs to descend to a deeper position to collect DNA settled at the bottom layer. When the average flow velocity is in the closed interval of 0.15 to 0.35 meters per second, it indicates that the water flow is at a low speed and DNA is enriched in the middle and lower layers of the water body. Subtracting 0.15 meters from the baseline depth of 1.00 meters gives a sampling depth of 0.85 meters, which means that the sampling head descends to a medium depth position. When the average flow velocity is greater than or equal to 0.35 meters per second, it indicates that the water flow is relatively fast and the DNA is more evenly distributed in the water body and has not yet settled sufficiently. The sampling depth is directly taken as the baseline depth of 1.00 meters to maintain the standard sampling position. The flow rate compensation coefficient of 45 is an empirical parameter in meters per second. This coefficient reflects the compensation relationship between flow rate and filtration time. Dividing the flow rate compensation coefficient of 45 by the average flow rate yields the compensation time, with the average flow rate in meters per second. After the division, the compensation time is in seconds. The lower the flow rate, the longer the compensation time, indicating that the filtration time needs to be extended to collect a sufficient amount of water sample. The baseline filtration time of 300 seconds is the basic filtration time set under standard flow rate conditions. Adding the compensation time to the baseline filtration time of 300 seconds yields the filtration time for this sampling, with the filtration time in seconds. This addition operation realizes an adaptive mechanism that dynamically adjusts the filtration time according to the actual flow rate. The filter pump flow rate of 2.5 liters per minute is the rated operating parameter of the filter pump, representing the volume of water sample pumped through the filter membrane per unit time. To convert the filter pump flow rate of 2.5 liters per minute to the flow rate per second, divide by 60 to get a flow rate of approximately 0.0417 liters per second. Multiply the flow rate per second by the previously calculated filtration time in seconds to obtain the expected filtration volume of water sample. The unit of the expected filtration volume of water sample is liters. This volume represents the total amount of water sample that can be processed in the entire filtration process under ideal conditions without considering filter membrane clogging.The depth adjustment motor is the actuator that controls the vertical position of the sampling head. The controller generates a motor control command based on the previously calculated sampling depth value. The command includes the target depth position information. The motor drives the sampling head to move downwards along the guide rod. A depth sensor installed on the sampling head provides real-time feedback on the current depth position. When the depth sensor reading reaches the target sampling depth, the motor stops rotating, and the sampling head stabilizes at that depth position. The filter pump is installed inside the sampling head and includes an inlet, a filter chamber, and an outlet. The inlet faces the water body. The filter chamber is equipped with a 0.45-micron pore size filter membrane. The 0.45-micron pore size filter membrane can trap most bacteria and larger DNA fragments in the water while allowing water molecules to pass through. The outlet discharges the filtered clean water. The controller generates a pump start command based on the previously calculated filtration time. The filter pump motor starts rotating, driving the impeller to rotate and generating negative pressure. The negative pressure draws the surrounding water into the filter chamber through the inlet. When the water sample passes through the filter membrane, the environmental DNA fragments, bacterial cells, biological tissue fragments, and other particulate matter carried in it are larger than the filter membrane pore size. The filtered water is trapped on the surface of the filter membrane, while the clean water flows out from the other side of the filter membrane and is discharged through the outlet. The filtration process continues until the cumulative time reaches the set filtration duration. The differential pressure sensor monitors the pressure difference between the inlet and outlet of the filter chamber in real time. The pressure difference value reflects the degree of filter membrane clogging. When the pressure difference exceeds 80 kPa, it indicates that a large number of particles are trapped on the surface of the filter membrane, causing the filtration resistance to rise sharply. After receiving the differential pressure over-limit signal, the controller immediately stops the filter pump to prevent the filter membrane from rupturing. The flow meter records the actual volume of water sample that passes through from the start to the stop of the filter pump as the actual filtration volume. After filtration is completed, the controller generates a filter membrane removal command. The robotic arm carefully removes the filter membrane from the filter chamber and places it into a sealed container. The environmental DNA fragments trapped on the surface of the filter membrane, along with other biological substances, are preserved on the filter membrane to form an environmental DNA filter membrane sample. This sample is numbered and associated with parameters such as sampling location coordinates, sampling time, average flow rate, sampling depth, filtration duration, and actual filtration volume. The filter membrane sample is then sent to the laboratory for DNA extraction and molecular biological analysis.A sampling platform extracted the average flow rate in a DNA-enriched stable region. This average value was a statistical measure calculated from a 180-second flow rate data segment. When determining the interval for this value, it was compared with two cutoff points: 0.15 and 0.35. Assuming the average flow rate falls within the second interval, the sampling depth of 0.85 meters was calculated by subtracting 0.15 meters from the baseline depth of 1.00 meters. The flow rate compensation coefficient of 45 was divided by this average flow rate to obtain the compensation time. The compensation time was added to the baseline filtration time of 300 seconds to obtain the filtration time. The filtration pump flow rate of 2.5 liters per minute was multiplied by the filtration time to calculate the final filtration time. Once the expected filtered water sample volume is calculated, the controller instructs the depth adjustment motor to drive the sampling head down to a depth of 0.85 meters. After the depth sensor confirms the position, the filtration pump is started. The impeller inside the pump rotates to generate negative pressure and draw in the surrounding water. When the water sample passes through the 0.45-micron pore size filter membrane, environmental DNA is trapped on the surface of the filter membrane. Filtration continues until the cumulative time reaches the set value or the differential pressure sensor detects a pressure difference exceeding 80 kPa. The flow meter records the actual filtered water sample volume. The robotic arm removes the filter membrane and places it into a sealed container to form an environmental DNA filter membrane sample, which is then associated with and stored with all sampling parameters.

[0074] The above describes the intelligent DNA acquisition method for aquatic environments based on flow velocity sensing in the embodiments of this application. The following describes the intelligent DNA acquisition system for aquatic environments based on flow velocity sensing in the embodiments of this application. Please refer to [link to relevant documentation]. Figure 2 One embodiment of the aquatic environment DNA intelligent acquisition system based on flow velocity sensing in this application includes:

[0075] The acquisition module is used to collect flow velocity data within the sliding time window, calculate the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity, identify the current water area type, and obtain a set of flow velocity statistical characteristic parameters.

[0076] The segmentation module is used to assign segmented values ​​to the adjustment coefficient according to the numerical range of the velocity variation coefficient, and to superimpose the product of the water area type benchmark threshold, the adjustment coefficient and the velocity standard deviation to obtain a two-layer dynamic velocity threshold.

[0077] The discrimination module is used to compare and discriminate the real-time flow velocity with the dual-layer dynamic flow velocity threshold. When the real-time flow velocity exceeds the dual-layer dynamic flow velocity threshold, the platform is guided to move and the flow velocity change is recorded to obtain the flow velocity sequence of the target area.

[0078] The verification module is used to verify the threshold proportion, fluctuation amplitude and change trend of the flow velocity sequence in the target region. If all three conditions are met, it is determined to be a stable region of DNA enrichment.

[0079] The calculation module is used to perform segmented calculation of sampling depth and inverse proportional calculation of filtration time based on the average flow rate of the DNA enrichment stable region, and to control the sampling device to perform parameterized sampling to obtain environmental DNA filter membrane samples.

[0080] above Figure 2 The flow velocity sensing-based intelligent DNA acquisition system for water environment in this embodiment of the invention is described in detail from the perspective of modular functional entities. The flow velocity sensing-based intelligent DNA acquisition device for water environment in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0081] Reference Figure 3 This invention also provides a smart DNA acquisition device for aquatic environments based on flow velocity sensing. This device can be a server, and its internal structure can be as follows: Figure 3 As shown, the flow velocity sensing-based aquatic environment DNA intelligent acquisition device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed as a computer, provides computing and control capabilities. The memory of the flow velocity sensing-based aquatic environment DNA intelligent acquisition device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the flow velocity sensing-based aquatic environment DNA intelligent acquisition device stores the data corresponding to this embodiment. The network interface of the flow velocity sensing-based aquatic environment DNA intelligent acquisition device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0082] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the DNA intelligent collection device for water environment based on flow velocity sensing applied thereto.

[0083] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the intelligent DNA collection method for water environment based on flow velocity sensing.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0085] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a flow velocity sensing-based water environment DNA intelligent collection device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent DNA collection from aquatic environments based on flow velocity sensing, characterized in that, The method includes: Step S1: Collect flow velocity data within the sliding time window, calculate the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity, identify the current water type, and obtain a set of statistical characteristic parameters of flow velocity, including: The system collects the main direction component, lateral component, and vertical component of the flow velocity from an underwater flow velocity sensor. The sum of the squares of these three components is then taken as the square root to obtain a synthetic flow velocity data sequence. This synthetic flow velocity data sequence is input into a sliding time window of 600 seconds for cumulative summation, and then divided by the total number of sampling points to obtain the average flow velocity. The difference between the flow velocity at each sampling point and the average flow velocity in the synthetic flow velocity data sequence is calculated, squared, summed, divided by the total number of sampling points minus one, and then the square root is taken to obtain the standard deviation of the flow velocity. The standard deviation of the flow velocity is then compared with the average flow velocity to obtain the coefficient of variation of the flow velocity. The real-time position coordinates of the platform are acquired and compared with an electronic water area map to identify the current water area type from a water area feature database. Finally, the average flow velocity, standard deviation of the flow velocity, coefficient of variation of the flow velocity, and the current water area type are combined to form a set of statistical feature parameters for flow velocity. Step S2: Assign segmented values ​​to the adjustment coefficient according to the numerical range of the velocity variation coefficient, and superimpose the product of the water area type benchmark threshold, the adjustment coefficient, and the velocity standard deviation to obtain a two-layer dynamic velocity threshold. Step S3: Compare the real-time flow velocity with the dual-layer dynamic flow velocity threshold. When the real-time flow velocity exceeds the dual-layer dynamic flow velocity threshold, guide the platform to move and record the flow velocity changes to obtain the flow velocity sequence of the target area. This includes: comparing the continuously collected real-time flow velocity with the dual-layer dynamic flow velocity threshold; triggering a position adjustment command when the real-time flow velocity is greater than the dual-layer dynamic flow velocity threshold; generating multiple candidate motion trajectories by combining linear velocity and angular velocity sampling in the velocity space; calculating the heading evaluation value, obstacle distance evaluation value, velocity evaluation value, and flow velocity adaptation evaluation value for each candidate motion trajectory; multiplying the four evaluation values ​​with their corresponding weight coefficients and summing them to obtain the comprehensive evaluation function value of each candidate motion trajectory; selecting the candidate motion trajectory with the largest comprehensive evaluation function value as the optimal movement path; controlling the propulsion device to move to a region with slower flow velocity according to the optimal movement path, and synchronously recording the position coordinates, corresponding flow velocity, and timestamp during the movement process to obtain the flow velocity sequence of the target area. Step S4: Verify the threshold proportion, fluctuation amplitude, and change trend of the flow velocity sequence in the target region. If all three conditions are met, it is determined to be a stable DNA enrichment region. Step S5: Based on the average flow rate in the DNA enrichment stable region, calculate the sampling depth in segments and the inverse proportional calculation of the filtration time, control the sampling device to perform parameterized sampling, and obtain environmental DNA filter membrane samples. This includes: extracting the average flow rate from the DNA enrichment stable region, determining the numerical range of the average flow rate, calculating the sampling depth as a reference depth minus 0.30 meters when the average flow rate is less than 0.15 meters per second, calculating the sampling depth as a reference depth minus 0.15 meters when the average flow rate is between 0.15 and 0.35 meters per second, and calculating the sampling depth as a reference depth minus 0.15 meters when the average flow rate is less than 0.15 meters per second. When the value is greater than or equal to 0.35 m / s, the sampling depth is calculated as the baseline depth; the flow rate compensation coefficient 45 is compared with the average flow rate, and the ratio result is added to the baseline filtration time of 300 seconds to obtain the filtration time; the filter pump flow rate of 2.5 liters per minute is multiplied by the filtration time to obtain the expected filtered water sample volume; the depth adjustment motor is controlled to drive the sampling head to descend to the sampling depth, and the filter pump is started to filter the water sample according to the filtration time, trapping environmental DNA on the filter membrane with a pore size of 0.45 micrometers to obtain an environmental DNA filter membrane sample.

2. The method for intelligent DNA acquisition from aquatic environments based on flow velocity sensing according to claim 1, characterized in that, Step S2 includes: The flow velocity variation coefficient is extracted from the set of flow velocity statistical characteristic parameters. The numerical range of the flow velocity variation coefficient is determined. When the flow velocity variation coefficient is less than 0.15, the adjustment coefficient is assigned a value of 1.

2. When the flow velocity variation coefficient is in the range of 0.15 to 0.30, the adjustment coefficient is assigned a value of 1.

5. When the flow velocity variation coefficient is greater than or equal to 0.30, the adjustment coefficient is assigned a value of 1.

8. Based on the current water type in the set of flow velocity statistical feature parameters, the corresponding water type benchmark threshold is queried from the preset parameter table to obtain the benchmark flow velocity threshold of the current water area; The standard deviation of flow velocity is extracted from the set of flow velocity statistical feature parameters. The adjustment coefficient is multiplied by the standard deviation of flow velocity to obtain the real-time adaptive adjustment amount. The baseline flow velocity threshold and the real-time adaptive adjustment amount are added together to obtain the dual-layer dynamic flow velocity threshold.

3. The method for intelligent DNA acquisition from aquatic environments based on flow velocity sensing according to claim 1, characterized in that, The process of multiplying the four evaluation values ​​by their corresponding weight coefficients and then summing them to obtain the comprehensive evaluation function value of each candidate trajectory, and selecting the candidate trajectory with the largest comprehensive evaluation function value as the optimal movement path, includes: The absolute value of the difference between the trajectory direction angle and the target direction angle of each candidate trajectory is calculated and then subtracted from 180 degrees to obtain the heading evaluation value of each candidate trajectory. Calculate the set of distances from predicted points to obstacles on each candidate motion trajectory, and extract the minimum distance value from the distance set as the obstacle distance evaluation value for each candidate motion trajectory; Extract the linear velocity corresponding to each candidate motion trajectory as the velocity evaluation value of each candidate motion trajectory; Query historical velocity distribution maps to obtain the expected velocity of each candidate motion trajectory path. Calculate the absolute value of the difference between the expected velocity and 0.8 times the two-layer dynamic velocity threshold, and take the negative value to obtain the velocity adaptation evaluation value of each candidate motion trajectory. The comprehensive evaluation function value of each candidate motion trajectory is obtained by multiplying the heading evaluation value, obstacle distance evaluation value, speed evaluation value and flow velocity adaptation evaluation value with weight coefficients of 0.20, 0.30, 0.20 and 0.30 respectively, and then summing them. The trajectory with the largest comprehensive evaluation function value among all candidate trajectories is selected as the optimal movement path.

4. The intelligent DNA acquisition method for aquatic environments based on flow velocity sensing according to claim 1, characterized in that, Step S4 includes: Continuous velocity data is extracted from the velocity sequence of the target area. Each velocity data is compared with 0.8 times the two-layer dynamic velocity threshold. When the continuous velocity is less than 0.8 times the two-layer dynamic velocity threshold and the duration is greater than or equal to 180 seconds, the threshold ratio verification is deemed successful. Extract the flow velocity data segment of the most recent 180 seconds from the flow velocity sequence of the target area, calculate the standard deviation of the flow velocity data segment, and determine that the fluctuation amplitude verification is passed when the standard deviation of the flow velocity is less than 0.05 meters per second; Extract the flow velocity data segment of the most recent 60 seconds from the flow velocity sequence of the target area, calculate the difference between adjacent flow velocity data and divide by the time interval to obtain the flow velocity change rate sequence, calculate the average value of the flow velocity change rate sequence and take the absolute value. When the absolute value is less than 0.002 meters per square second, the change trend is determined to be verified. When the three conditions of threshold ratio verification, fluctuation amplitude verification, and change trend verification are met simultaneously, the current location coordinates, trigger time, and corresponding average flow rate are recorded to determine the DNA enrichment stable region.

5. A smart DNA acquisition system for aquatic environments based on flow velocity sensing, characterized in that, For implementing the flow velocity sensing-based intelligent DNA acquisition method for aquatic environments as described in any one of claims 1-4, the flow velocity sensing-based intelligent DNA acquisition system for aquatic environments comprises: The data acquisition module is used to collect flow velocity data within a sliding time window, calculate the average flow velocity, standard deviation of flow velocity, and coefficient of variation of flow velocity, identify the current water area type, and obtain a set of flow velocity statistical feature parameters. This includes: acquiring the main direction component, lateral component, and vertical component of flow velocity from an underwater flow velocity sensor; performing a square root operation on the sum of the squares of the three flow velocity components to obtain a synthetic flow velocity data sequence; inputting the synthetic flow velocity data sequence into a sliding time window of 600 seconds for cumulative summation and dividing by the total number of sampling points to obtain the average flow velocity; calculating the difference between the flow velocity at each sampling point in the synthetic flow velocity data sequence and the average flow velocity, summing the squares, dividing by the total number of sampling points minus one, and then taking the square root to obtain the standard deviation of flow velocity; performing a ratio operation between the standard deviation of flow velocity and the average flow velocity to obtain the coefficient of variation of flow velocity; acquiring the real-time location coordinates of the platform and matching them with an electronic water area map to identify the current water area type from the water area feature database; and combining the average flow velocity, standard deviation of flow velocity, coefficient of variation of flow velocity, and current water area type to form a set of flow velocity statistical feature parameters. The segmentation module is used to assign segmented values ​​to the adjustment coefficient according to the numerical range of the velocity variation coefficient, and to superimpose the product of the water area type benchmark threshold, the adjustment coefficient and the velocity standard deviation to obtain a two-layer dynamic velocity threshold. The discrimination module is used to compare and discriminate the real-time flow velocity with the dual-layer dynamic flow velocity threshold. When the real-time flow velocity exceeds the dual-layer dynamic flow velocity threshold, it guides the platform to move and records the flow velocity changes to obtain the flow velocity sequence of the target area. This includes: comparing the continuously collected real-time flow velocity with the dual-layer dynamic flow velocity threshold; triggering a position adjustment command when the real-time flow velocity is greater than the dual-layer dynamic flow velocity threshold; generating multiple candidate motion trajectories by combining linear velocity and angular velocity sampling in the velocity space; calculating the heading evaluation value, obstacle distance evaluation value, velocity evaluation value, and flow velocity adaptation evaluation value for each candidate motion trajectory; multiplying the four evaluation values ​​with their corresponding weight coefficients and summing them to obtain the comprehensive evaluation function value of each candidate motion trajectory; selecting the candidate motion trajectory with the largest comprehensive evaluation function value as the optimal movement path; controlling the propulsion device to move to a region with slower flow velocity according to the optimal movement path; and synchronously recording the position coordinates, corresponding flow velocity, and timestamp during the movement process to obtain the flow velocity sequence of the target area. The verification module is used to verify the threshold proportion, fluctuation amplitude and change trend of the flow velocity sequence in the target region. If all three conditions are met, it is determined to be a stable region of DNA enrichment. The calculation module is used to perform segmented calculation of sampling depth and inverse proportional calculation of filtration time based on the average flow rate in the DNA enrichment stable region, and to control the sampling device to perform parameterized sampling to obtain environmental DNA filter membrane samples. This includes: extracting the average flow rate from the DNA enrichment stable region; determining the numerical range of the average flow rate; calculating the sampling depth as a reference depth minus 0.30 meters when the average flow rate is less than 0.15 meters per second; calculating the sampling depth as a reference depth minus 0.15 meters when the average flow rate is between 0.15 and 0.35 meters per second; and calculating the sampling depth as a reference depth minus 0.15 meters when the average flow rate is within the range of 0.15 to 0.35 meters per second. When the average value is greater than or equal to 0.35 m / s, the sampling depth is calculated as the baseline depth; the flow rate compensation coefficient of 45 is compared with the average flow rate, and the ratio result is added to the baseline filtration time of 300 seconds to obtain the filtration time; the filter pump flow rate of 2.5 liters per minute is multiplied by the filtration time to obtain the expected filtered water sample volume; the depth adjustment motor is controlled to drive the sampling head to descend to the sampling depth, and the filter pump is started to filter the water sample according to the filtration time, trapping environmental DNA on the filter membrane with a pore size of 0.45 micrometers to obtain an environmental DNA filter membrane sample.

6. A smart DNA acquisition device for aquatic environments based on flow velocity sensing, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the aquatic environment DNA intelligent collection method based on flow velocity sensing as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the aquatic environment DNA intelligent collection method based on flow velocity sensing as described in any one of claims 1 to 4.

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