Enteromorpha prolifera green tide salvage ship deployment determination method

Abstract

The application discloses a kind of enteromorpha green tide salvage ship deployment determination method, belong to enteromorpha green tide prevention and control field.This method includes the following steps: a, construct the biomass estimation model based on enteromorpha green tide satellite remote sensing data and field monitoring data;B, construct biomass-salvage volume conversion model;C, construct the calculation model of salvage efficiency under different sea conditions and different salvage modes;D, based on biomass estimation model, biomass-salvage volume conversion model and the calculation model of salvage efficiency under different sea conditions and different salvage modes, construct salvage ship deployment model;E, according to the delineated sea area range, obtain the enteromorpha green tide satellite remote sensing data of the sea area range, then based on the salvage ship deployment model constructed in step d Determine the number of salvage ship demand, carry out salvage ship deployment.The present application constructs salvage ship deployment model etc., based on the dynamic deployment strategy of fine classification, significantly improves the scientificity and operation efficiency of ship resource scheduling.

Description

Technical Field

[0001] This invention relates to the field of green tide control of seaweed, specifically to a method for determining the allocation of vessels for seaweed harvesting. Background Technology

[0002] Green tides of *Ulva prolifera* are characterized by high biomass, long-distance transport, and significant impact. Controlling green tides of *Ulva prolifera* has become a major task in the marine field. Offshore salvage, nearshore interception, and shoreline cleanup constitute the three lines of defense in controlling green tides of *Ulva prolifera*. During offshore salvage operations, accurately determining the number of vessels needed based on complex sea conditions and the distribution of *Ulva prolifera*, and then effectively allocating these vessels, is a pressing issue that needs to be addressed.

[0003] On the one hand, existing methods mostly adopt a simple correspondence between coverage area and biomass, failing to fully consider the dynamic matching relationship between the spatiotemporal density of remote sensing observations and the actual on-site monitoring biomass data, resulting in limited accuracy in biomass estimation, which in turn affects the rational allocation of subsequent salvage vessels.

[0004] Furthermore, in actual salvage operations, the amount of *Ulva prolifera* harvested is usually counted in ton bags. However, because the drainage standard of the *Ulva prolifera* green tide after salvage often differs from the water content during on-site biomass monitoring, and because factors such as the water carried by the algae and the degree of compaction in the marine operating environment are difficult to control uniformly, there is often a significant deviation between the remote sensing biomass estimate for a specific *Ulva prolifera* patch and the actual salvage amount. At the same time, relevant studies both domestically and internationally often use a single, averaged concept of salvage efficiency, without comprehensively considering the combined impact of specific operating conditions such as sea conditions and *Ulva prolifera* distribution density on salvage efficiency. These factors not only restrict the accurate assessment of salvage efficiency but also introduce significant uncertainties into vessel scheduling and operational optimization. Summary of the Invention

[0005] Based on the above-mentioned technical problems, this invention proposes a method for determining the allocation of vessels for salvaging seaweed green tides.

[0006] The technical solution adopted in this invention is:

[0007] A method for determining the allocation of vessels for salvaging seaweed tides includes the following steps:

[0008] a. Construct a biomass estimation model based on satellite remote sensing data and field monitoring data of Ulva prolifera green tide;

[0009] b. Construct a biomass-salvage conversion model;

[0010] c. Construct a calculation model for salvage efficiency under different sea conditions and salvage methods;

[0011] d. Based on the biomass estimation model, the biomass-salvage conversion model, and the calculation model of salvage efficiency under different sea conditions and salvage methods, a salvage vessel allocation model is constructed.

[0012] e. Based on the designated sea area, obtain satellite remote sensing data of the seaweed green tide in that sea area, and then determine the required number of salvage vessels based on the salvage vessel allocation model constructed in step d, and allocate the salvage vessels accordingly.

[0013] The beneficial technical effects of the present invention are as follows:

[0014] (1) This invention constructs a biomass estimation model based on remote sensing density and field monitoring of Ulva prolifera green tide. By systematically analyzing the quantitative relationship between Ulva prolifera density obtained by satellite remote sensing and a large amount of field-monitored biomass, a deep coupling model between the two is established, overcoming the limitation of the disconnect between remote sensing data and field biomass in traditional methods, and realizing for the first time a high-precision, real-time dynamic estimation of Ulva prolifera biomass in a large area of ​​sea.

[0015] (2) The present invention also innovatively introduces a “biomass-salvage volume” conversion model. This model fits parameters through field test data and establishes a quantifiable conversion relationship between the theoretical biomass (W) obtained from monitoring and the actual salvage volume (Q). It effectively solves the problem of the difference between “remote sensing observation” and “actual salvage”, significantly improves the practical application value of remote sensing biomass estimation results, and enables it to directly serve salvage operation decision-making.

[0016] (3) This invention further establishes a method for calculating salvage efficiency and a dynamic allocation model based on refined classification. Distinguishing itself from the single, averaged concept of salvage efficiency for ships, this invention innovatively proposes a salvage efficiency calculation model that correlates salvage efficiency with four specific operating conditions: "sea conditions (adverse / good)" and "Ulva prolifera distribution density (dense / sparse areas)". Based on this, the constructed ship allocation model can dynamically calculate the optimal combination of haul net vessels and automated mechanical vessels according to real-time forecasted sea conditions and satellite-identified spatial distribution of Ulva prolifera. This dynamic allocation strategy based on refined classification significantly improves the scientific nature and operational efficiency of ship resource scheduling. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method for determining the deployment of vessels for salvaging seaweed in the green tide according to the present invention.

[0018] Figure 2 The distribution of the green tide of *Ulva prolifera* in a specific application example of the present invention;

[0019] Figure 3 for Figure 2 A magnified view of a portion of the image. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown, a method for determining the deployment of vessels for salvaging seaweed tides includes the following steps:

[0022] a. Construct a biomass estimation model based on satellite remote sensing data of Ulva prolifera green tide.

[0023] Specifically, the following steps are included:

[0024] a1. Collect on-site biomass (after draining water) of floating seaweed green tides of different areas in different sea areas of the Yellow Sea over a period of 5 years or more (such as the Subei Shoal, coastal Sheyang, sea areas near 35 degrees, near Rizhao and Qingdao, near Yantai and Weihai), at different development stages (early stage, outbreak stage, and decline stage). The biomass data should include no fewer than 100 samples.

[0025] a2. Select satellite images with clear weather that match the period of on-site biomass monitoring, and interpret the satellite images to obtain satellite remote sensing data of the green tide of Ulva prolifera.

[0026] High-resolution (generally preferred to be greater than 50 meters) satellite imagery was selected during clear weather periods that matched the on-site biomass monitoring time. After image preprocessing, the NDVI index was used to extract information on the green tide of *Ulva prolifera*, obtaining the distribution range, latitude and longitude location, and coverage area corresponding to each pixel.

[0027] ;

[0028] In the formula: The reflectivity is in the near-infrared band. This represents the reflectivity in the infrared band.

[0029] a3. Correlation analysis between satellite remote sensing density of Ulva prolifera green tide and on-site monitoring biomass, and calculation of median biomass in dense and sparse areas of Ulva prolifera green tide distribution.

[0030] Based on satellite remote sensing data of *Ulva prolifera* green tides and biomass monitoring data from over 100 different sea areas at different development stages, this study analyzes the distribution of *Ulva prolifera* green tides as perceived by satellite remote sensing and the distribution of biomass in the field. It was found that dense areas of *Ulva prolifera* green tides perceived by satellite remote sensing typically correspond to areas with high biomass in the field; while sparse areas of *Ulva prolifera* green tides perceived by satellite remote sensing correspond to areas with low biomass in the field. Based on this, the in-field biomass is divided into two categories: dense and sparse areas. The numerical boundary between dense and sparse areas can be set at 1.5 kg / m³. 2 .

[0031] Based on biomass data from dense areas, the median biomass of dense areas was calculated using the boxplot method, and the median biomass of sparse areas was obtained using the same method.

[0032] a4. Construct a biomass estimation model based on the density of Ulva prolifera green tides obtained from satellite remote sensing.

[0033] a41. Patch clustering identification;

[0034] A density-based spatial clustering algorithm (DBSCAN) was used to divide the scattered points of Ulva prolifera green tides extracted by satellite remote sensing into patches, identifying pixels with spatial clustering characteristics as independent Ulva prolifera green tide patches. This algorithm can effectively distinguish between densely distributed areas and sparsely distributed points of Ulva prolifera, and is adaptable to the sporadic, strip, or clump-like distribution patterns of Ulva prolifera at different growth stages.

[0035] DBSCAN defines clusters based on density reachability. Each cluster consists of one or more core points and all density-connected samples within their neighborhood. A core point is a point whose neighborhood radius (Eps) contains at least a minimum number of samples (MinPts). If a cluster contains multiple core points, each core point must contain other core points within its Eps neighborhood to ensure density reachability. Ultimately, samples from the neighborhoods of all core points together form a cluster, thus enabling the automatic identification and segmentation of *Ulva prolifera* green tide patches.

[0036] a42. Construct a biomass estimation model;

[0037] Based on the biomass monitoring results from marine field monitoring, the following model for estimating the floating biomass of *Ulva prolifera* green tides based on satellite remote sensing density is constructed:

[0038] (1)

[0039] In equation (1): W represents the biomass of floating seaweed in a designated sea area or a patch within that area; i represents the i-th pixel identified through clustering (this pixel is divided into pixels in the clustered area and pixels in the sparse area); K i K represents the biomass per unit area of ​​the i-th pixel, and its value is determined based on the clustering results: if it is identified as a clustered area, K... i Take the median biomass of the aggregation zone; if identified as a sparse zone, K i Take the median biomass of the sparse zone; S i Let be the coverage area of ​​the i-th pixel. The biomass estimation model can determine the total biomass of *Ulva prolifera* green tides within a specific patch or sea area.

[0040] b. Construct a biomass-salvage conversion model.

[0041] Specifically, the following steps are included:

[0042] b1. Collect data from marine seaweed green tide harvesting experiments, including biomass per unit area, the area of ​​the experimental patch that was harvested, and the amount of experimental patch harvested.

[0043] More specifically: Current methods for harvesting seaweed tides mainly employ scoop nets and automated mechanical harvesting methods. Automated mechanical harvesting is generally used in areas with dense seaweed distribution and under conditions of calm seas and calm waters. Scoop nets are suitable for various sea conditions and different distribution areas.

[0044] Collect data from *Ulva prolifera* harvesting experiments in different sea areas at different development stages (focusing on the outbreak and decline phases). This data should include sea conditions, patch coverage area, harvest volume, on-site biomass monitoring results, and on-site sea conditions, with at least 25 data points. Collect data from both shovel-net vessel harvesting and automated harvesting experiments. Shovel-net harvesting data should include data from densely distributed *Ulva prolifera* areas, sparsely distributed areas, adverse sea conditions (wind speed > 8 m / s), and favorable sea conditions (wind speed ≤ 8 m / s), with at least 5 data points for each condition. Automated harvesting data should include at least 5 data points. Based on the on-site biomass size and the median values ​​of dense and sparse areas, determine whether the experimental patch is sparse or dense.

[0045] Simultaneously, salvage data can be used to obtain satellite remote sensing data for the day, verifying the biomass estimation model.

[0046] b2. Based on the biomass per unit area, the amount of patches harvested, and the coverage area of ​​the harvested patches from the *Ulva prolifera* harvesting experiment data, the biomass of the experimental patches was calculated using formula (1). Based on the harvesting amounts of no fewer than 25 patches and the overall biomass experimental data, a linear equation in one variable was used for fitting to obtain relevant parameters, and a *Ulva prolifera* "biomass-harvesting amount" conversion model was constructed:

[0047] (2)

[0048] In Equation (2), W is the total biomass of the green tide of Ulva prolifera in the test patch, Q is the amount of harvested, and a and b are fitting parameters.

[0049] c. Construct a calculation model for salvage efficiency under different sea conditions and salvage methods.

[0050] Specifically, the following steps are included:

[0051] c1. Calculate the salvage efficiency of similar salvage vessels using grappling nets, using the following formula:

[0052] (3)

[0053] In formula (3): For ship salvage efficiency; i = 1, 2, ..., n, where n is the total number of trials, generally greater than 20; t ij Let m be the operation time of the j-th salvage vessel in the i-th experiment, where j = 1, 2, ..., m; Let be the salvage volume of the i-th experiment.

[0054] The calculated ship salvage efficiency C for each group i The data were divided into four groups based on sea conditions and the distribution of the seaweed green tide: Condition 1: severe sea conditions (wind speed > 8 m / s) + dense distribution area of ​​seaweed green tide; Condition 2: good sea conditions (wind speed ≤ 8 m / s) + dense distribution area of ​​seaweed green tide; Condition 3: severe sea conditions + sparse distribution area of ​​seaweed green tide; Condition 4: good sea conditions + sparse distribution area of ​​seaweed green tide.

[0055] By statistically analyzing these four sets of data and averaging them, the average salvage efficiency under different working conditions can be obtained. The calculation formula is as follows:

[0056] (4)

[0057] In equation (4): k represents different working conditions, k=1,2,3,4; D k C represents the average salvage efficiency under operating condition k (e.g., severe sea state + densely populated area); kl Let l be the ship salvage efficiency of the l-th test under condition k, where l = 1, 2, ..., h; and h be the number of valid test data under this condition.

[0058] c2. When salvaging automated mechanical salvage vessels, the average salvage efficiency is obtained based on n salvage tests (n is not less than 5), and the calculation formula is as follows:

[0059] (5)

[0060] In equation (5): B is the average salvage efficiency of the ship; i = 1, 2, ..., n, where n is the total number of trials, t ij Let m be the operation time of the j-th salvage vessel in the i-th experiment, where j = 1, 2, ..., m; Let be the salvage volume of the i-th experiment.

[0061] d. Based on the biomass estimation model, the biomass-salvage conversion model, and the calculation model of salvage efficiency under different sea conditions and salvage methods, a salvage vessel allocation model is constructed.

[0062] Based on the efficiency of ship salvage, the spatial distribution of seaweed green tides, and considering sea conditions, a salvage ship allocation model based on satellite coverage area is constructed to estimate the number of ships required in a specific area.

[0063] Based on the spatial distribution of the seaweed green tide (dense and sparse areas), and taking into account sea conditions, a mixed strategy is adopted: for sparse areas, vessels suitable for sparse seaweed green tide patches are used; for dense areas, vessels suitable for dense seaweed green tide patches are used (salvage vessels equipped with strafe nets or automated salvage vessels), that is, V mechanized vessels are dispatched (V is less than the minimum required for full mechanization), and the shortfall is supplemented by strafe net vessels.

[0064] The specific calculation method is as follows:

[0065] d1. In adverse sea conditions, salvage operations will be carried out using strafe net vessels. The formula for calculating the number of vessels is as follows:

[0066] (6)

[0067] In formula (6): N1 is the required number of vessels for attacking the net, rounded up; Q s Q represents the salvage volume in sparse areas. d The amount of salvage in the dense area is T, and the salvage time is D1 and D3 are calculated using formula (4).

[0068] d2. Under good sea conditions, only net-scooping vessels will be used for salvage in sparse areas, while a mixed strategy (net-scooping vessels and / or automated mechanical salvage vessels) will be used in dense areas. Assuming that V automated mechanical salvage vessels are dispatched, this number is less than the minimum number of vessels required if all vessels are automated mechanical salvage vessels. The specific number will depend on the number of automated salvage vessels on site.

[0069] The formula for calculating the total number of vessels required for net-catching operations is as follows:

[0070] (7)

[0071] In equation (7): N2 is the required number of vessels for hauling nets, and the number of vessels must be rounded up during actual scheduling; V is the number of automated mechanical salvage vessels; Q s Q represents the salvage volume in sparse areas. d The salvage volume in the dense area is T, the salvage time is D2 and D4 are calculated using formula (4); B is the average salvage efficiency of automated mechanical salvage vessels.

[0072] If there is only a sparse region, then Q d And V is 0.

[0073] e. Based on the designated sea area, obtain satellite remote sensing data on the green tide of seaweed in that sea area, and then determine the required number of salvage vessels based on the salvage vessel allocation model constructed in step d, and allocate the salvage vessels accordingly.

[0074] Specifically, the following steps are included:

[0075] e1. Based on the designated sea area, distinguish between dense and sparse areas using satellite remote sensing monitoring data.

[0076] e2. Estimate the biomass of the dense and sparse areas of Ulva green tide in the above area according to formula (1).

[0077] e3. Calculate the theoretical salvage volume corresponding to the dense area and the sparse area according to formula (2).

[0078] e4. Based on the meteorological and sea condition forecast, determine the sea condition, and then use formula (6) or formula (7) to calculate the required number of scooping net vessels and automated mechanical salvage vessels, and finally give the vessel allocation plan for different salvage methods.

[0079] The invention will be further explained below with reference to specific application examples.

[0080] 1. Collect on-site biomass monitoring results of *Ulva prolifera* green tides in different sea areas at different development stages;

[0081] We collected 206 biomass data points of floating seaweed green tides at different development stages (early stage, outbreak stage, and extinction stage) and coverage areas in the Yellow Sea North Subei Shoal, coastal Sheyang, sea area near 35 degrees, near Rizhao and Qingdao, near Yantai and Weihai from 2018 to 2025.

[0082] 2. Satellite remote sensing interpretation;

[0083] High-resolution satellite imagery of clear weather from 2018 to 2025, matching the on-site biomass monitoring period, was selected (satellite remote sensing images mainly include HY-1C / D / E CZI, HJ, Gaofen-1 and Gaofen-4, Sentinel-2, Landsat-8, etc.). After image preprocessing, the NDVI index was used to extract information on the green tide of *Ulva prolifera*. Through interpretation, the distribution range, latitude and longitude location, and coverage area corresponding to each pixel of the green tide were obtained.

[0084] 3. Correlation analysis between satellite remote sensing density of Ulva prolifera green tide and on-site monitoring biomass to obtain the median biomass in dense and sparse areas of Ulva prolifera green tide distribution;

[0085] Analysis of satellite remote sensing data on the distribution of *Ulva prolifera* green tides and the distribution of biomass in the field revealed that dense areas of *Ulva prolifera* green tides detected by satellite remote sensing typically correspond to high biomass observed in the field, while sparse areas of *Ulva prolifera* green tides detected by satellite remote sensing correspond to low biomass in the field. Based on this, the biomass in the field was divided into two categories according to satellite remote sensing imagery: dense and sparse biomass.

[0086] Based on biomass data from densely populated areas, the median biomass per unit area was calculated using a boxplot, which was found to be 4.1 kg / m². 2 Using the same method, the median biomass per unit area in the sparse zone was found to be 1.0 kg / m². 2 .

[0087] 4. Based on satellite remote sensing monitoring of the density of Ulva prolifera green tides, construct a biomass estimation model for Ulva prolifera green tides;

[0088] Based on the above correlations, a biomass estimation model is constructed, and the specific steps are as follows:

[0089] 1) Patch Clustering Identification: The DBSCAN function in MATLAB is used to cluster satellite remote sensing monitoring points. The parameters are set as follows: the neighborhood radius (ε) is set based on the satellite resolution determined by the ship monitoring mentioned above; the minimum point threshold (MinPts) is used to define the core point density. The DBSCAN algorithm can cluster dense pixels into densely distributed areas of *Ulva prolifera* (seaweed) green tides, while identifying discrete points as sparsely distributed areas. For example, using a HY-1E CZI (20m) resolution, the neighborhood radius is set to 0.005, and the MinPts point count is set to 4.

[0090] 2) Construct a biomass estimation model;

[0091] Based on the median biomass of *Ulva prolifera* green tides at different densities, a floating biomass estimation model for *Ulva prolifera* green tides based on satellite remote sensing density is constructed as follows:

[0092] (1)

[0093] Where W refers to the biomass of all floating seaweed in a certain sea area, i is the i-th pixel identified through clustering (this pixel is divided into dense region pixels and sparse region pixels), and K i K represents the biomass per unit area of ​​the i-th pixel, and its value is determined based on the clustering results: if identified as a dense area, K... i The median biomass of densely populated areas was taken as 4.1 kg / m². 2 ; identified as a sparse region, K i The median biomass in the sparsely populated area was taken as 1.0 kg / m². 2 S i Let be the area covered by the i-th pixel, which is 400m. 2 This biomass estimation model can provide the total biomass of *Ulva prolifera* green tides in a specific patch or sea area.

[0094] 5. Delineate the salvage area based on the distribution area of ​​the green tide, and calculate the biomass and salvage volume of the delineated area;

[0095] like Figure 2 , Figure 3 The distribution of the green tide of *Ulva prolifera* is shown. Green represents dense areas, red represents areas with low concentrations, and blue represents areas to be harvested.

[0096] The biomass in the densely populated area of ​​the salvage site was 190.2 tons, and the biomass in the sparsely populated area was 8.3 tons.

[0097] 6. Collect data from marine biomass salvage experiments;

[0098] Considering that the weighing and measurement method for *Ulva prolifera* harvested on-site mainly uses ton bags, biomass harvesting test data of *Ulva prolifera* at different development stages (focusing on the outbreak and decline stages) in different sea areas during the pre-emergence harvesting period from 2022 to 2025 was collected. This mainly includes the total patch coverage area, harvest volume, and on-site biomass monitoring results for the harvesting area, to determine the patch biomass and harvest volume models. Data from gantry net harvesting and automated mechanical harvesting tests were collected. The gantry net harvesting test data must include test data in densely distributed areas of *Ulva prolifera* in green tides, sparsely distributed areas, adverse sea conditions (wind speed > 8 m / s), and favorable sea conditions (wind speed ≤ 8 m / s), with at least five data points for each condition. Based on the on-site biomass per unit area, if the biomass per unit area is less than 1.5 kg / m², [further data will be collected]. 2 If the biomass is greater than 1.5 kg / m³, it is considered a sparse region. 2 If it is, then it is considered to be a dense area.

[0099] 7. Construct a salvage volume-biomass model;

[0100] Using biomass per unit area from field monitoring in dense and sparse areas, and based on data from 20 harvesting experiments, a linear equation in one variable was used to fit relevant parameters, and a model was constructed to establish the relationship between the harvested amount of *Ulva prolifera* and the total biomass.

[0101] (2)

[0102] In the formula, the total biomass W and Q of the green tide of Ulva prolifera in a specific area or patch can be obtained by multiplying the biomass per unit area of ​​the field monitoring by the monitoring coverage area of ​​ships or drones, and a and b are fitting parameters.

[0103] Based on formula (2) and 20 salvage test data, the fitting parameters a was finally obtained as 1.1 and b as 0.001; the salvage amount in the densely distributed area of ​​the salvage area was calculated to be 209.3 tons, and the salvage amount in the sparsely distributed area was 9.1 tons.

[0104] 8. Calculate the salvage efficiency under different distribution areas, different sea conditions, and different salvage methods;

[0105] 1) Salvage efficiency of similar salvage vessels using scoop nets;

[0106] Based on 30 field tests, the average salvage efficiency of the scooping net vessel was calculated according to formulas (3) and (4). The average salvage efficiency of the high-horsepower salvage vessel in the densely populated areas of severe sea conditions, the densely populated areas of good sea conditions, the sparsely populated areas of severe sea conditions, and the sparsely populated areas of good sea conditions were 0.9 tons / h, 1.4 tons / h, 0.6 tons / h, and 1.2 tons / h, respectively.

[0107] 2) For automated mechanical salvage vessels, based on 10 field tests, the average salvage efficiency B of the vessels is calculated according to formula (5). The salvage efficiency of automated salvage vessels is 2.0 tons / h.

[0108] 9. Construction of a salvage vessel deployment model;

[0109] The salvage volume Q in sparse and dense areas were counted separately. s and Q d According to the forecast of good sea conditions, all seaweed in the designated area will be harvested within 8 hours. One net-hauling vessel will be used in sparse areas. The minimum number of vessels required for dense areas if fully automated harvesting is employed is (…). The number of vessels is 11. The number of automated salvage vessels that can be dispatched in dense areas is determined based on the on-site conditions, and then the number of vessels attacking the net is calculated according to formula (7). Therefore, when the number of automated salvage vessels is 0, a total of 20 vessels attacking the net need to be dispatched (19 in dense areas and 1 in sparse areas); when the number of automated salvage vessels is 1, 18 vessels attacking the net need to be dispatched; the number of salvage vessels required under different conditions is calculated according to the vessel allocation model, as shown in Table 1.

[0110] Table 1

[0111]

[0112] For any parts not mentioned above, existing technologies can be adopted or referenced.

[0113] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.

Claims

1. A method for determining the allocation of vessels for salvaging seaweed tides, characterized in that... Includes the following steps: a. Construct a biomass estimation model based on satellite remote sensing data and field monitoring data of Ulva prolifera green tide; b. Construct a biomass-salvage conversion model; c. Construct a calculation model for salvage efficiency under different sea conditions and salvage methods; d. Based on the biomass estimation model, the biomass-salvage conversion model, and the calculation model of salvage efficiency under different sea conditions and salvage methods, a salvage vessel allocation model is constructed. e. Based on the designated sea area, obtain satellite remote sensing data of the seaweed green tide in that sea area, and then determine the required number of salvage vessels based on the salvage vessel allocation model constructed in step d, and allocate the salvage vessels accordingly. Step b includes the following steps: b1. Collect data from marine seaweed green tide harvesting experiments, including biomass per unit area, the area of ​​the experimental patch that was harvested, and the amount of experimental patch harvested. b2. Based on the biomass per unit area and the coverage area of ​​the test patch that was dredged, the biomass of the test patch was calculated using formula (1); then, based on the biomass of the test patches calculated from multiple sets of data and the corresponding dredging volume of the test patches, a biomass-dredging volume conversion model was constructed, as shown in the following formula: （2） In Equation (2), W is the total biomass of the green tide of Ulva prolifera in the test patch, Q is the amount of harvested, and a and b are fitting parameters; Step d includes the following steps: d1. In adverse sea conditions, salvage operations will be carried out using strafe net vessels. The formula for calculating the number of vessels is as follows: （6） In formula (6): N1 is the required number of vessels for attacking the net, rounded up; Q s Q represents the salvage volume in sparse areas. d The salvage volume in the dense area is T, the salvage time is T, and D1 and D3 are calculated using formula (4). d2. Under good sea conditions, only scooping net vessels are used for salvage in sparse areas, while scooping net vessels and / or automated mechanical salvage vessels are used in dense areas. Assuming that V automated mechanical salvage vessels are deployed, the number of which is less than the minimum number of vessels required if all vessels are automated mechanical salvage vessels, the formula for calculating the required number of scooping net vessels is as follows: （7） In formula (7): N2 is the number of vessels required for grappling net operations, rounded up; V is the number of automated mechanical salvage vessels; Q s Q represents the salvage volume in sparse areas. d The salvage volume in the dense area is T, the salvage time is D2 and D4 are calculated using formula (4); B is the average salvage efficiency of automated mechanical salvage vessels. If there is only a sparse region, then Q d And V is 0.

2. The method for determining the allocation of vessels for salvaging seaweed tides according to claim 1, characterized in that, Step a includes the following steps: a1. Collect on-site biomass monitoring data of floating Ulva prolifera green tides of different areas and at different development stages in different sea areas; a2. Select satellite images of clear weather corresponding to the time period of the on-site biomass monitoring data, and interpret the satellite images to obtain satellite remote sensing data of the green tide of Ulva prolifera. a3. Correlation analysis was performed between the satellite remote sensing data of Ulva prolifera green tide and the field biomass monitoring data. Based on the satellite remote sensing data of Ulva prolifera green tide, the biomass per unit area in the field was divided into two categories: biomass per unit area in dense areas and biomass per unit area in sparse areas. The median biomass per unit area in dense areas and the median biomass per unit area in sparse areas were calculated respectively. a4. Construct a biomass estimation model based on satellite remote sensing data of Ulva prolifera green tides, as detailed below: （1） In formula (1): W is the biomass of floating seaweed in a designated sea area or a patch within that sea area; K i Let K be the biomass per unit area of ​​the i-th pixel, when it is identified as a dense area. i Take the median biomass per unit area of ​​the dense region; when it is identified as a sparse region, K i Take the median biomass per unit area in the sparse region; S i Let i be the coverage area of ​​the i-th pixel. Biomass estimation models can determine the total biomass of green tides of Ulva lactuca in a certain patch or sea area.

3. The method for determining the allocation of vessels for salvaging seaweed tides according to claim 2, characterized in that: In a1, the biomass of the floating seaweed green tide refers to the biomass after draining the water, and the biomass data is no less than 100. In a2, the selected satellite images are those with a resolution greater than 50 meters; the satellite images are used to extract information on the green tide of Ulva prolifera using the NDVI index, to obtain the distribution range, latitude and longitude location, and coverage area corresponding to each pixel of the green tide of Ulva prolifera. In a3, correlation analysis revealed that the dense areas identified by satellite remote sensing data of Ulva prolifera corresponded to the high biomass areas monitored in the field, while the sparse areas corresponded to the low biomass areas monitored in the field. The biomass per unit area of ​​both the dense and sparse areas was calculated using the box plot method to obtain the median biomass per unit area of ​​the dense area and the median biomass per unit area of ​​the sparse area, respectively. In a4, patch clustering identification is also included; a density-based spatial clustering method is used to divide the scattered points of Ulva prolifera green tide extracted by satellite remote sensing into patches, and pixels with clustering characteristics in spatial distribution are identified as independent Ulva prolifera green tide patches.

4. The method for determining the allocation of vessels for salvaging seaweed tides according to claim 2, characterized in that, Step b1 includes the following steps: Methods for harvesting seaweed include scoop net vessel harvesting and automated mechanical harvesting; Collect data on the harvesting of seaweed green tides in different sea areas at different stages of development, including sea conditions, coverage area of ​​test patches, harvesting volume, and biomass monitoring results per unit area, with no fewer than 25 test data points; Collect data from salvage tests using sluice net vessels and automated machinery. The data from sluice net vessels must include data from densely distributed areas of seaweed in green tides, sparsely distributed areas, and different sea conditions, with at least five data points for each condition. The data from automated machinery salvage tests must include at least five data points. Based on the biomass per unit area at the site, and combined with the median per unit area of ​​dense and sparse areas, determine whether the test patch is a sparse or dense patch. Simultaneously, by utilizing the salvage test data, satellite remote sensing data for the same day was obtained to verify the biomass estimation model.

5. The method for determining the allocation of vessels for salvaging seaweed tides according to claim 2, characterized in that, Step c includes the following steps: c1. Calculate the salvage efficiency of each experiment when using a scoop net vessel for salvage, using the following formula: （3） In formula (3): The efficiency of ship salvage; i = 1, 2, ..., n, where n is the total number of trials, t ij Let m be the operation time of the j-th salvage vessel in the i-th experiment, where j = 1, 2, ..., m; Let be the salvage volume of the i-th experiment; The calculated ship salvage efficiency C for each group i Based on sea conditions and the distribution of the seaweed green tide, the working conditions are divided into four groups: Operating Condition 1: Adverse sea conditions, and located in an area with dense distribution of seaweed green tide; Operating Condition 2: Favorable sea conditions, and located in an area with dense distribution of seaweed green tide; Operating Condition 3: Adverse sea conditions, and located in an area with sparse distribution of seaweed green tide; Operating Condition 4: Favorable sea conditions, and located in an area with sparse distribution of seaweed green tide. The four sets of data were statistically analyzed and averaged to obtain the average salvage efficiency under different working conditions. The calculation formula is as follows: （4） In equation (4): k represents different working conditions, k=1,2,3,4; D k C represents the average salvage efficiency under operating condition k; kl Let l be the ship salvage efficiency of the l-th test under working condition k, where l = 1, 2, ..., h; and h be the number of valid test data under this working condition. c2. When salvaging automated mechanical salvage vessels, the average salvage efficiency is obtained based on n salvage tests, and the calculation formula is as follows: （5） In equation (5): B is the average salvage efficiency of the ship; i = 1, 2, ..., n, where n is the total number of trials, t ij Let m be the operation time of the j-th salvage vessel in the i-th experiment, where j = 1, 2, ..., m; Let be the salvage volume of the i-th experiment.

6. The method for determining the allocation of vessels for salvaging seaweed tides according to claim 5, characterized in that, Step e includes the following steps: e1. Based on the designated sea area, distinguish between dense and sparse areas using satellite remote sensing monitoring data; e2. Calculate the total biomass of the green tide of Ulva prolifera in the dense and sparse areas according to formula (1); e3. Calculate the salvage volume corresponding to the dense area and the sparse area according to formula (2); e4. Based on the meteorological and sea condition forecast, determine the sea condition, and then use formula (7) or formula (8) to calculate the required number of scooping net vessels and automated mechanical salvage vessels, and finally give the vessel allocation plan for different salvage methods.

Images