Methods, devices, equipment, and media for predicting kelp shedding amount

Abstract

This disclosure relates to a method, apparatus, device, and medium for predicting kelp shedding. The method includes: obtaining the number of cultivation days to be predicted and the area of ​​a target cultivation area corresponding to the number of cultivation days, wherein the area of ​​the target cultivation area is the kelp cultivation surface area extracted based on satellite imagery; determining the kelp shedding rate of the target cultivation area based on the number of cultivation days; and determining the kelp shedding amount of the target cultivation area based on the kelp shedding rate and the area of ​​the target cultivation area. This method utilizes satellite imagery to determine the area of ​​the cultivation area over a large area at the predicted number of cultivation days, and uses the number of cultivation days and the area of ​​the cultivation area to determine the kelp shedding amount corresponding to the target cultivation area, thereby achieving accurate prediction of kelp shedding amount over a large area, improving the estimation speed of kelp shedding amount, and providing a scientific basis for environmental management of kelp cultivation areas, helping to reduce cultivation risks and improve cultivation efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of information processing technology, and in particular to a method, apparatus, equipment and medium for predicting the amount of kelp shedding. Background Technology

[0002] Currently, kelp is typically cultivated vertically on ropes or rafts. During natural growth or in situations with strong winds and waves, kelp is prone to detaching in large quantities from these ropes or rafts. When the detached kelp accumulates downstream in harbor basins, cold water intakes, or waterways, it can increase clearing costs and affect water intake safety, or even cause blockages at cold water intakes or in waterways, production stoppages, or even equipment damage. Therefore, timely monitoring of the amount and spatial distribution of kelp detachment is crucial for disaster damage assessment, production scheduling, and risk warning.

[0003] Existing monitoring methods for kelp shedding are insufficient to meet the real-time quantitative monitoring needs of large-scale kelp cultivation areas. Summary of the Invention

[0004] To address the aforementioned technical issues, this disclosure provides a method, apparatus, equipment, and medium for predicting kelp shedding, in order to meet the current need for real-time quantitative monitoring of large-area kelp cultivation areas.

[0005] The first aspect of this disclosure provides a method for predicting the amount of kelp shedding, comprising:

[0006] Obtain the number of aquaculture days to be predicted and the target aquaculture area corresponding to the number of aquaculture days, wherein the target aquaculture area is the kelp aquaculture surface area extracted based on satellite images;

[0007] Based on the stated number of days of cultivation, determine the kelp shedding rate in the target cultivation area;

[0008] The amount of kelp lost in the target aquaculture area is determined based on the kelp shedding rate and the area of ​​the target aquaculture area.

[0009] In some embodiments of this disclosure, determining the kelp shedding rate of the target aquaculture area based on the number of aquaculture days includes:

[0010] The target aquaculture area is determined; wherein, the target aquaculture area includes a first aquaculture area, a second aquaculture area, and a third aquaculture area; the aquaculture depth of the first aquaculture area is less than a preset water depth, and the distance between the boundary of the first aquaculture area and the boundary of the overall kelp aquaculture area is greater than a preset distance; the aquaculture depth of the second aquaculture area is greater than or equal to the preset water depth; the distance between the boundary of the third aquaculture area and the boundary of the overall kelp aquaculture area is less than or equal to the preset distance.

[0011] The kelp shedding rate of the target aquaculture area is determined based on the target aquaculture area and the number of aquaculture days.

[0012] In some embodiments of this disclosure, determining the kelp shedding rate of the target aquaculture area based on the target aquaculture area and the number of aquaculture days includes:

[0013] If the target aquaculture area is the first aquaculture area, the kelp shedding rate of the target aquaculture area is determined by the following formula:

[0014]

[0015] If the target aquaculture area is the second aquaculture area, the kelp shedding rate of the target aquaculture area is determined by the following formula:

[0016]

[0017] If the target aquaculture area is the third aquaculture area, the kelp shedding rate of the target aquaculture area is determined by the following formula:

[0018]

[0019] In the formula, The seaweed shedding rate in the target aquaculture area; For the first i The rate of kelp shedding over the days; This refers to the number of days of rearing.

[0020] In some embodiments of this disclosure, the first... i The kelp shedding rate per day includes:

[0021] .

[0022] In some embodiments of this disclosure, determining the amount of kelp detachment in the target aquaculture area based on the kelp detachment rate and the area of ​​the target aquaculture area includes:

[0023]

[0024] In the formula, The amount of kelp detached from the target aquaculture area; The kelp farming area is determined based on satellite remote sensing images of the kelp farming area; n The number of kelp units per unit area; The seaweed shedding rate in the target aquaculture area; The wet weight of a single kelp plant.

[0025] In some embodiments of this disclosure, the prediction method further includes:

[0026] Based on the number of days of cultivation, determine the length and width of a single kelp.

[0027] The area of ​​a single kelp is determined by the product of its length and width.

[0028] Based on the area of ​​a single kelp plant, the wet weight of a single kelp plant is determined using the following formula:

[0029]

[0030] In the formula, S This refers to the area of ​​a single kelp plant.

[0031] In some embodiments of this disclosure, the prediction method further includes:

[0032] Based on the number of days of cultivation, the length and width of a single kelp are determined using the following formulas:

[0033]

[0034]

[0035] In the formula, L The length of a single kelp stalk; W The width of a single kelp stalk; D This refers to the number of days of rearing.

[0036] A second aspect of this disclosure provides a device for predicting the amount of kelp shedding, the device comprising:

[0037] The acquisition module is used to acquire the number of aquaculture days to be predicted and the target aquaculture area corresponding to the number of aquaculture days, wherein the target aquaculture area is the kelp aquaculture surface area extracted based on satellite images;

[0038] A processing module, which is used to determine the kelp shedding rate of the target aquaculture area based on the number of aquaculture days;

[0039] The processing module is also used to determine the amount of kelp detachment in the target aquaculture area based on the kelp detachment rate and the area of ​​the target aquaculture area.

[0040] A third aspect of this disclosure provides an electronic device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0041] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0042] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0043] This solution utilizes satellite remote sensing images of kelp farming areas to determine the area of ​​the target farming area at the predicted number of farming days. Then, based on the predicted number of farming days and the target farming area area, it determines the corresponding amount of kelp shedding in the target farming area. This enables accurate prediction of kelp shedding over large areas, improves the estimation speed of kelp shedding, effectively overcomes the limitations of existing technologies, provides a scientific basis for environmental management of kelp farming areas, and offers timely and accurate monitoring and risk warnings for the kelp farming industry, helping to reduce farming risks and improve farming efficiency.

[0044] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this document. Attached Figure Description

[0045] The accompanying drawings, which form part of this document, are used to provide a further understanding of the document. The illustrative embodiments and descriptions herein are used to explain the document and do not constitute an undue limitation thereof. In the drawings:

[0046] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present disclosure of a method for predicting the amount of kelp shedding;

[0047] Figure 2 This is a flowchart illustrating an exemplary embodiment of the present disclosure of a method for predicting the kelp shedding rate in a target aquaculture area;

[0048] Figure 3 This is a graph illustrating the change in kelp shedding rate over time during the seedling stage, as shown in an exemplary embodiment of this disclosure.

[0049] Figure 4 This is a graph illustrating the change in kelp shedding rate over time during the growth period, as shown in an exemplary embodiment of this disclosure.

[0050] Figure 5 This is a graph illustrating the change in kelp shedding rate over time in a first aquaculture area, as shown in an exemplary embodiment of this disclosure.

[0051] Figure 6 This is a graph illustrating the change in kelp shedding rate over time in a second aquaculture area, as shown in an exemplary embodiment of this disclosure.

[0052] Figure 7 This is a graph illustrating the change in kelp shedding rate over time in a third aquaculture area, as shown in an exemplary embodiment of this disclosure.

[0053] Figure 8 This is a graph illustrating the change in kelp shedding rate over time across the entire ocean area, as shown in an exemplary embodiment of this disclosure.

[0054] Figure 9 This is a graph illustrating the variation of the wet weight of a single kelp plant with the area of ​​a single kelp plant, as shown in an exemplary embodiment of this disclosure.

[0055] Figure 10 This is a graph illustrating the variation of the length of a single kelp plant with the number of days of cultivation, as shown in an exemplary embodiment of this disclosure;

[0056] Figure 11 This is a diagram illustrating the variation of the width of a single kelp plant with the number of days of cultivation, as shown in an exemplary embodiment of this disclosure;

[0057] Figure 12 This is a schematic diagram of a kelp shedding prediction device shown in an exemplary embodiment of this disclosure;

[0058] Figure 13 This is a schematic diagram of the structure of a kelp shedding prediction system shown in an exemplary embodiment of this disclosure;

[0059] Figure 14 This is a schematic diagram of an electronic device structure shown in an exemplary embodiment of the present disclosure. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be arbitrarily combined with each other.

[0061] In related technologies, the monitoring of kelp shedding is mainly based on small-scale in-situ observations and laboratory or flume simulations. Small-scale in-situ observations, using underwater counting, fixed-point photography, and pressure or tension sensors, can obtain high-precision shedding rates, but their spatiotemporal coverage is limited and their cost is high. While laboratory or flume simulations, conducted under controlled hydrodynamic conditions, reveal the shedding mechanism, they are difficult to extrapolate to large-scale aquaculture raft areas. These methods generally suffer from drawbacks such as numerous monitoring points but limited coverage, poor timeliness, and high cost, failing to meet the kelp industry's need for continuous, large-scale, and real-time quantitative monitoring.

[0062] Based on this, this disclosure provides a method for predicting kelp shedding amount. It uses satellite remote sensing images of kelp farming areas to determine the farming area of ​​the target farming area at the number of farming days to be predicted, and then determines the kelp shedding amount of the target farming area based on the farming area corresponding to the number of farming days to be predicted, thereby realizing the prediction of kelp shedding amount over a large area, aiming to solve the problems of the prior art.

[0063] Combination Figure 1 As shown, an exemplary embodiment of this disclosure provides a method for predicting the amount of kelp shedding, the method comprising:

[0064] S100. Obtain the number of aquaculture days to be predicted and the target aquaculture area corresponding to the number of aquaculture days. The target aquaculture area is the kelp aquaculture surface area extracted based on satellite images.

[0065] In this step, the target aquaculture area corresponding to the number of aquaculture days is based on the kelp aquaculture surface area corresponding to the target aquaculture area extracted from satellite images.

[0066] By selecting the kelp farming area image corresponding to the predicted number of farming days, the target farming area area corresponding to the number of farming days is obtained.

[0067] S200. Based on the number of days of aquaculture, determine the kelp shedding rate of the target aquaculture area.

[0068] In this step, the kelp shedding rate changes continuously with the duration of kelp cultivation. However, the change in the kelp shedding rate is related to the number of cultivation days. Therefore, the kelp shedding rate of the target cultivation area can be determined based on the number of cultivation days.

[0069] S300. Determine the amount of kelp that has fallen off in the target aquaculture area based on the kelp shedding rate and the area of ​​the target aquaculture area.

[0070] In this step, since the kelp shedding rate is determined based on the kelp farming area, the amount of kelp shedding in the target farming area can be determined based on the kelp shedding rate of the target farming area, the area of ​​the target farming area, and the total amount of kelp per unit farming area.

[0071] In this embodiment, satellite remote sensing images of kelp farming areas are used to determine the area of ​​the target farming area at the predicted number of farming days. Then, based on the predicted number of farming days and the area of ​​the target farming area, the corresponding amount of kelp shedding is determined, thereby achieving accurate prediction of kelp shedding over a large area, improving the estimation speed of kelp shedding, effectively overcoming the limitations of existing technologies, and providing a scientific basis for environmental management of kelp farming areas. It also provides timely and accurate monitoring and risk warning for the kelp farming industry, helping to reduce farming risks and improve farming efficiency.

[0072] In the above embodiments, the extraction of the target aquaculture area can be achieved by identifying regions from static images and then determining the target aquaculture area, or by performing machine learning based on images to determine the target aquaculture area. This technical solution is existing technology and will only be briefly introduced below.

[0073] For example, a Support Vector Machine (SVM) combined with an object-oriented approach is used to perform machine learning on the spectral and texture features of kelp farming areas to extract the area of ​​the target farming area. During the learning process, an appropriate number of training samples are selected from the kelp farming areas, and the radial basis function kernel is used to perform initial classification of the study area.

[0074]

[0075] In the formula, It is the Euclidean distance between two eigenvectors; It is the width parameter of the kernel function, which controls the width of the Gaussian function.

[0076] By combining visual interpretation, area screening (taking patches with an area greater than 1000m² as kelp farming areas) and subsequent processing such as manual correction, high-precision extraction of kelp farming area is achieved.

[0077] Using a spatiotemporal interpolation algorithm for kelp farming areas, the regional shrinkage of single-scene raster data is simulated using an eight-neighbor neighborhood matrix structure. Based on this, a time-progression concept is introduced, using the updated raster data as the initial state for the next time point, and repeating neighborhood condition judgments to reflect the shrinkage process of the target area over a certain period of time.

[0078]

[0079] In the formula, In the first t At the given time point, located in the raster data at the [number]th time point... i line, number j The cell values ​​of the column; ,2,3,…, The range of iterations, from the 1st to the 2nd iteration. Second-rate.

[0080] Repeat the above process iteratively until the kelp farming area reaches the shrinkage target, and finally obtain the daily scale change data of the kelp farming area within the farming area.

[0081] In the above embodiments, reference is made to Figure 2 As shown, the methods for determining the kelp shedding rate in the target aquaculture area include:

[0082] S201. Determine the target breeding area.

[0083] In this step, the target breeding area is the region where the user wants to predict the amount of shedding.

[0084] The target aquaculture area includes at least one of the first aquaculture area, the second aquaculture area, and the third aquaculture area.

[0085] For example, the first aquaculture zone is defined as a zone where the aquaculture depth is less than a preset water depth, and the boundary of the first aquaculture zone is greater than a preset distance from the boundary of the overall kelp aquaculture zone. The first aquaculture zone is a conventional kelp aquaculture zone, where the environment is relatively stable, and the corresponding kelp shedding amount is also relatively stable. Therefore, it can be predicted based on historical kelp shedding amounts.

[0086] Optionally, as the aquaculture depth increases, the aquaculture seedling ropes at deeper depths may become partially entangled, making it impossible to harvest the kelp. This results in additional kelp shedding during the kelp harvesting period. Therefore, the second aquaculture zone is defined as an area with an aquaculture depth greater than or equal to a preset water depth. The amount of kelp shedding in this zone before the harvesting period is the same as that in the first aquaculture zone, but it will be greater than that in the first aquaculture zone during the same period of the kelp harvesting period.

[0087] In some embodiments, the outermost or closest aquaculture rafts in the aquaculture area are often subject to strong external environmental disturbances. The kelp on these aquaculture rafts often falls off due to the action of waves and other external environmental factors. Therefore, the third aquaculture area is defined as the aquaculture area boundary being less than or equal to the boundary of the overall kelp aquaculture area.

[0088] S202. Determine the kelp shedding rate of the target aquaculture area based on the target aquaculture area and the number of aquaculture days.

[0089] In this step, by monitoring historical data of kelp farming in different target farming areas, the correlation between the number of farming days and the kelp shedding rate can be determined. This historical data can be a statistical value of consecutive years or a statistical value of discontinuous years.

[0090] For example, the kelp cultivation period includes the seedling stage, the growth stage, and the harvest stage. The kelp shedding rate is different at different stages, for example:

[0091] During the seedling stage, as the kelp grows after being seedled, the rate of kelp detachment gradually decreases until it reaches zero, at which point the kelp is completely attached to the seedling rope. Based on historical data on the number of days of kelp cultivation and the kelp detachment rate, the correlation between the kelp detachment rate and the number of days of kelp cultivation can be determined.

[0092] During the growth period, kelp shedding mainly occurs through breakage. The breakage can occur at the root or in the middle. Field surveys show that kelp typically breaks at the end (approximately 1 / 3) or at the root. Based on historical data on kelp cultivation days and shedding rates, a fitting formula was derived to correlate the kelp shedding rate with the number of cultivation days.

[0093] During the harvest season, kelp shedding is mainly due to the loss of unharvested portions caused by harvesting. This shedding rate can be determined based on historical data on the amount of kelp shedding during the harvest season.

[0094] In this embodiment, the kelp shedding rate in different aquaculture areas and aquaculture days is determined by historical data, thereby more accurately estimating the kelp shedding rate and improving the accuracy of kelp shedding amount prediction related to the shedding rate.

[0095] In some embodiments, the kelp shedding rate of the target aquaculture area is determined using the following formula.

[0096] If the target aquaculture area is located in the first aquaculture area, the kelp shedding rate of the target aquaculture area is determined using the following formula:

[0097]

[0098] If the target aquaculture area is located in the second aquaculture area, the kelp shedding rate of the target aquaculture area is determined using the following formula:

[0099]

[0100] If the target aquaculture area is located in the third aquaculture zone, the kelp shedding rate of the target aquaculture area is determined using the following formula:

[0101]

[0102] In the formula, The seaweed shedding rate in the target aquaculture area, expressed in % (%). For the first i The daily kelp shedding rate, also known as the daily kelp shedding rate, is expressed in percentages (%). This refers to the number of days of rearing, without a unit.

[0103] For example, in the first aquaculture area, the kelp is less disturbed by the external environment and therefore sheds at the normal growth and shedding rate. After determining the daily kelp shedding rate, the kelp shedding rate for the corresponding number of aquaculture days is the sum of the daily kelp shedding rates for all days prior to that aquaculture day. For example, to calculate the kelp shedding rate for aquaculture day 4. .

[0104] In the second aquaculture area, being located in the deep sea, there is less interference from the external environment. Therefore, the kelp shedding before harvest is consistent with that in the first aquaculture area. However, during the kelp harvesting season, due to the unavoidable entanglement in rope aquaculture, the entangled parts cannot be harvested, resulting in an additional 6% kelp shedding rate on top of the shedding that occurs during growth.

[0105] The third aquaculture area is the outermost kelp cultivation raft area. Because the outermost raft lacks a protective barrier, it is significantly more susceptible to kelp detachment due to environmental factors such as waves. For example, during the survey, the number of kelp on each seedling rope in the aquaculture area was counted. In the low, middle, and high zones, the number was 28-30 kelp per rope, while only the outermost raft had a number of 23±1 kelp per rope. Each kelp seedling rope in the aquaculture area had 35 seedlings attached. Based on an estimated conventional detachment rate of 20%, the detachment rate of the outermost raft in the aquaculture area was 35%, approximately 1.4 times the conventional detachment rate.

[0106] In this embodiment, by defining the relationship between the kelp shedding rate and the daily kelp shedding rate in different time periods and different target aquaculture areas, the kelp shedding rate is determined as a parameter that varies on a daily scale, thereby improving the prediction accuracy of the kelp shedding rate.

[0107] In the above embodiments, the first i The kelp shedding rate per day includes:

[0108] .

[0109] For example, through field investigations of kelp farming areas, the seedling clamping period typically lasts 16 days, with the first 7 days after clamping being the high-risk period for kelp detachment. Based on historical data on kelp detachment during the seedling clamping period in previous years, it can be determined that the overall kelp detachment rate in the farming area during this period is 7%, and the detachment rate gradually decreases throughout the clamping period. Further analysis of the daily kelp detachment rates during the first 7 days of the clamping period reveals the following rates: 3.50%, 1.50%, 1.00%, 0.50%, 0.30%, 0.15%, and 0.05%. As the kelp grows after clamping, the adhesion of its rhizomes to the seedling ropes gradually increases, and kelp detachment decreases daily, reaching almost 0% by the 8th day. The detachment curve can be referenced... Figure 3 As shown.

[0110] Except for seedling shedding, all other kelp shedding occurs during the growth period. Growth period shedding is categorized into kelp shedding and kelp breakage based on the location of the breakage of a single kelp stalk. Kelp shedding occurs in the early stages of kelp growth, resulting in the loss of the entire kelp stalk due to rhizome or seedling rope breakage. Kelp breakage mostly occurs in the mature stage of kelp, manifesting as loss of distal leaf tissue, leaving the remaining leaf stalks with obvious breakage marks. Based on measurement data from the Sangou Bay area, the length of each kelp seedling rope is 2.7m, and the initial number of kelp stalks on each rope is 35.6 ± 0.5. The calculated cumulative shedding rate throughout the entire growth period due to shedding and breakage is 19.7%. Historical data on kelp shedding between days 50 and 270 of kelp growth were statistically analyzed, and curve fitting was performed on the obtained data to obtain the kelp shedding rate for days 60 to 269. (Refer to...) Figure 4As shown, the kelp shedding rate data during the growth period are all distributed around the fitted curve, and the goodness-of-fit index (R²) is high. 2 = 0.9664) is good, so this fitting formula can be used to accurately predict the kelp shedding rate on a specific date during the growth period.

[0111] When the number of days of cultivation reaches 269, the kelp shedding rate reaches its peak, and the kelp shedding rate will remain unchanged at this peak thereafter.

[0112] In this embodiment, the daily kelp shedding rate is defined as a specific value by referring to historical data, thereby accurately obtaining the daily kelp shedding rate corresponding to any number of farming days. This allows for precise prediction of the amount of kelp shedding by using the relationship between the kelp shedding rate and the daily kelp shedding rate, thus improving prediction accuracy.

[0113] Referring to the daily kelp shedding rate mentioned above, the variation of kelp shedding rate with the number of cultivation days in different target aquaculture areas was statistically analyzed. Figure 5 As shown, in the first aquaculture area, the kelp shedding rate was the baseline shedding rate. With increasing aquaculture days, the kelp shedding rate first decreased, then reached 0, then increased again and decreased again. (Reference) Figure 6 As shown, when the aquaculture area includes a second aquaculture area, the kelp shedding rate, based on the baseline shedding rate shown in the first aquaculture area, suddenly increases and then decreases after approximately 200 days. (Referencing...) Figure 7 As shown, in the third aquaculture zone, the kelp shedding rate is 1.4 times the overall kelp shedding rate. Therefore, the kelp shedding rate is the sum of the 1.4 times base shedding rate and the 1.4 times the additional shedding rate in the second aquaculture zone. The curve shape of the kelp shedding rate changing with the number of aquaculture days is similar to... Figure 6 The curves in the data are almost identical, with only the vertical axis value being slightly larger. When the target aquaculture area to be predicted is the entire sea area, it is necessary to statistically analyze the distribution areas of the first, second, and third aquaculture areas to obtain the total kelp shedding rate for the entire sea area. This facilitates obtaining an overall picture of kelp shedding in the aquaculture area. Figure 8 As shown, the kelp shedding rate in the entire sea area is relatively high in the early stage of aquaculture, and then shows a trend of first increasing and then decreasing in the later stage. In addition, due to the influence of the statistical average of the overall kelp amount in the sea area, the kelp shedding rate displayed on the vertical axis will be lower than the kelp shedding rate of a certain target aquaculture area.

[0114] In the above embodiments, the method for determining the amount of kelp detachment in the target aquaculture area based on the kelp detachment rate and the area of ​​the target aquaculture area includes using the following formula:

[0115]

[0116] In the formula, The amount of kelp detached from the target aquaculture area, expressed in kg; For the target aquaculture area, unit m 2 ; n The number of kelp per unit area is a known quantity. In this embodiment of the disclosure... n =20 trees / m 2 ; The seaweed shedding rate in the target aquaculture area, expressed in % (%). The wet weight of a single kelp stalk is expressed in kg / stalk.

[0117] For example, the kelp shedding rate is the proportion of the area of ​​shed kelp to the total kelp cultivation area. Therefore, the product of the kelp shedding rate and the target cultivation area is the surface area occupied by shed kelp. Since kelp is vertically cultivated, the number of kelp cultivated on each seedling rope is fixed, and the number of seedling ropes on each cultivation raft is also fixed. Therefore, the density of kelp cultivation per unit area is known, meaning the number of kelp per unit area is a known quantity. The product of the surface area occupied by shed kelp and the number of kelp per unit area is the amount of shed kelp in the target cultivation area, and the product of the amount of shed kelp in the target cultivation area and the wet weight of a single kelp plant is the total amount of shed kelp in the target cultivation area.

[0118] In this embodiment, the amount of kelp detachment in the target aquaculture area is determined by using the kelp detachment rate and kelp growth distribution corresponding to the number of days of kelp cultivation. This provides timely and accurate monitoring and risk warning for the kelp aquaculture industry, which helps to reduce aquaculture risks and improve aquaculture efficiency.

[0119] In the above embodiments, the wet weight of a single kelp leaf was determined using the following method:

[0120] Based on the number of days of cultivation, determine the length and width of a single kelp plant.

[0121] The area of ​​a single kelp stalk is determined by multiplying its length and width.

[0122] The wet weight of a single kelp plant is determined based on its area.

[0123] The wet weight of a single kelp leaf is determined using the following formula:

[0124]

[0125] In the formula, S The area of ​​a single kelp plant is given in relation to the number of days of cultivation; no unit is provided.

[0126] For example, since kelp is a thin algae with a large surface area, its growth process mainly involves the increase in its size. That is, as the number of days of cultivation increases, the area of ​​a single kelp also increases, and the wet weight of a single kelp also increases with the area of ​​a single kelp. Therefore, historical data on the change of kelp wet weight with kelp area can be obtained, and the above-mentioned relationship between kelp wet weight and kelp area can be obtained after fitting.

[0127] For example, measured data on the area and wet weight of individual kelp trees in Sangou Bay were obtained in 2016, 2017, 2018, and 2023. The data underwent preliminary checks, removing outliers, duplicates, and missing values. The data was then organized into a table format with the area of ​​individual kelp trees as the independent variable and the wet weight of individual kelp trees as the dependent variable. Based on the data distribution and correlation analysis results, an index model was used to establish a fitting formula between the kelp cultivation area and the total fresh weight of kelp in the cultivation area. The goodness-of-fit index (R²) was calculated. 2 =0.9479) to test the reasonableness of the fitted model; see the specific data. Figure 9 As shown, the evaluation model's fit to the data met the expected standards.

[0128] In this embodiment, by limiting the wet weight of a single kelp plant to a function related to its area, the impact of kelp thickness and other attachments on the accuracy of kelp wet weight prediction is reduced, thereby improving the accuracy of predicting kelp shedding related to the wet weight of a single kelp plant. Simultaneously, this scheme also establishes a kelp natural growth curve prediction model based on the biomass relationship between the natural growth area and weight of kelp, providing a scientific basis for environmental management in kelp farming areas.

[0129] Based on the number of days of cultivation, the length and width of a single kelp are determined using the following formulas:

[0130]

[0131]

[0132] In the formula, L The length of a single kelp plant is expressed in centimeters (c). m ; W Width of a single kelp plant, in cm. m ; D This refers to the number of days of rearing, without a unit.

[0133] For example, the change in kelp area can be confirmed by the product of kelp length and width. As the number of days of cultivation increases, the size of kelp also changes continuously, and this change in size is mainly reflected in the length and width of the kelp. Therefore, by obtaining historical data on the changes in kelp length and width over time, the above-mentioned fitting formulas for the changes in the length and width of a single kelp over time can be obtained.

[0134] For example, measured data on the length of individual kelp trees in Sangou Bay were obtained in 2016, 2017, 2018, and 2023. The data underwent preliminary checks, outliers and missing values ​​were removed, and the data was organized into a table with cultivation time as the independent variable and kelp length as the dependent variable. A suitable nonlinear exponential function was selected, and the least squares method was used to estimate the model parameters. The optimal parameter values ​​were determined through iterative calculation. The goodness-of-fit index (R²) was calculated. 2 =0.9080) to test the reasonableness of the fitted model; see the specific data. Figure 10 As shown, the evaluation model's fit to the data met the expected standards.

[0135] Accordingly, measured data on the width of individual kelp trees in Sangou Bay were obtained in 2016, 2017, 2018, and 2023 to ensure data integrity and accuracy. The data underwent preliminary screening to remove outliers and duplicates, ensuring data quality. The data was then sorted chronologically to form a dataset suitable for analysis. A suitable nonlinear exponential curve model was selected, and a nonlinear fitting function was used to fit the data. The fitting parameters of the model were solved using the least squares method. The goodness-of-fit index (R²) was calculated. 2 =0.9385) to test the reasonableness of the fitted model; see the specific data. Figure 11 As shown, the evaluation model's fit to the data met the expected standards.

[0136] In this embodiment, by defining the length and width of kelp as functions related to the cultivation time, the size of the kelp at the predicted number of days can be obtained more accurately, leading to more precise weight information and laying the foundation for accurate calculation of kelp shedding. Simultaneously, this solution also establishes a kelp natural growth curve prediction model, capable of accurately predicting the growth length and width of kelp, providing scientific decision support for kelp aquaculture industry management.

[0137] The above-described contents can be implemented individually or in various combinations, and all such variations are within the scope of this disclosure.

[0138] refer to Figure 12 As shown, the kelp shedding prediction device 50 includes:

[0139] The acquisition module 51 is used to acquire the number of aquaculture days to be predicted and the target aquaculture area corresponding to the number of aquaculture days. The target aquaculture area is the kelp aquaculture surface area extracted based on satellite images.

[0140] Processing module 52 is used to determine the kelp shedding rate of the target aquaculture area based on the number of aquaculture days;

[0141] The processing module 52 is also used to determine the amount of kelp detachment in the target aquaculture area based on the kelp detachment rate and the area of ​​the target aquaculture area.

[0142] Optionally, the processing module 52 is used for:

[0143] The target aquaculture area is determined; wherein, the target aquaculture area includes a first aquaculture area, a second aquaculture area, and a third aquaculture area; the aquaculture depth of the first aquaculture area is less than the preset water depth, and the distance between the boundary of the first aquaculture area and the boundary of the overall kelp aquaculture area is greater than the preset distance; the aquaculture depth of the second aquaculture area is greater than or equal to the preset water depth; and the distance between the boundary of the third aquaculture area and the boundary of the overall kelp aquaculture area is less than or equal to the preset distance.

[0144] The kelp shedding rate of the target aquaculture area is determined based on the target aquaculture area and the number of aquaculture days.

[0145] Optionally, the processing module 52 is used for:

[0146] If the target aquaculture area is the first aquaculture area, the kelp shedding rate of the target aquaculture area is determined by the following formula:

[0147]

[0148] If the target aquaculture area is the second aquaculture area, the kelp shedding rate of the target aquaculture area is determined by the following formula:

[0149]

[0150] If the target aquaculture area is the third aquaculture area, the kelp shedding rate of the target aquaculture area is determined by the following formula:

[0151]

[0152] In the formula, The seaweed shedding rate in the target aquaculture area, expressed in % (%). For the first i Kelp shedding rate per day, in % This refers to the number of days of rearing, without a unit.

[0153] In some embodiments of this disclosure, the processing module 52 is used for:

[0154] .

[0155] In some embodiments of this disclosure, the processing module 52 is used for:

[0156]

[0157] In the formula, The amount of kelp detached from the target aquaculture area, expressed in kg; This refers to the kelp farming area determined based on satellite remote sensing images, in meters (m²). 2 ; n The number of kelp per unit area n =20 trees / m 2 ; The seaweed shedding rate in the target aquaculture area, expressed in % (%). The wet weight of a single kelp stalk is expressed in kg / stalk.

[0158] In some embodiments of this disclosure, the processing module 52 is used for:

[0159] The length and width of a single kelp are determined based on the number of days of cultivation.

[0160] The area of ​​a single kelp is determined by multiplying its length and width.

[0161] Based on the area of ​​a single kelp plant, the wet weight of the single kelp plant is determined using the following formula:

[0162]

[0163] In the formula, S The area of ​​a single kelp plant is expressed in units of... m 2 .

[0164] In some embodiments of this disclosure, the processing module 52 is used for:

[0165] Based on the number of days of cultivation, the length and width of a single kelp are determined using the following formulas:

[0166]

[0167]

[0168] In the formula, L The length of a single kelp plant is expressed in centimeters (c). m ; W Width of a single kelp plant, in cm. m ; D This refers to the number of days of rearing, without a unit.

[0169] The kelp shedding amount prediction device provided in this embodiment can execute the kelp shedding amount prediction method of the above embodiment. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0170] Based on the above embodiments, referring to Figure 13As shown, this disclosure can also construct a kelp shedding amount prediction system, which includes: an aquaculture image acquisition device 60 and a kelp shedding amount prediction device 50, wherein the aquaculture image acquisition device 60 and the kelp shedding amount prediction device 50 are communicatively connected.

[0171] The aquaculture image acquisition device 60 can extract the kelp aquaculture area based on the acquired remote sensing monitoring images of the aquaculture area using a large-scale dynamic estimation method. The kelp shedding prediction device 50 can fit the kelp growth curve and the daily variation of kelp shedding rate based on the input historical kelp aquaculture data, and then combine it with the aquaculture area extracted by the aquaculture image acquisition device 60 to obtain a kelp shedding prediction model within the aquaculture area, thereby more accurately predicting the kelp shedding amount corresponding to any aquaculture date.

[0172] In this embodiment of the invention, electronic devices or main control devices can be divided into functional modules according to the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the module division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0173] In the specific implementation of the aforementioned kelp shedding amount prediction device, each module can be implemented as a processor. The processor can execute computer execution instructions stored in the memory, so that the processor executes the aforementioned kelp shedding amount prediction method.

[0174] Reference Figure 14 As shown, this disclosure also provides an electronic device 70, which includes:

[0175] At least one processor 71 and memory 72.

[0176] The electronic device also includes a communication component 73.

[0177] The processor 71, memory 72, and communication component 73 are connected via bus 74.

[0178] In the specific implementation process, at least one processor 71 executes the computer execution instructions stored in the memory 72, causing at least one processor 71 to execute the seaweed shedding amount prediction method as described above.

[0179] The specific implementation process of processor 71 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0181] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage.

[0182] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0183] The above describes the solutions provided by the embodiments of the present invention for the functions implemented by the electronic device and the main control device.

[0184] It is understandable that electronic devices or main control devices include hardware structures and / or software modules that perform the above functions in order to achieve the above functions.

[0185] By combining the units and algorithm steps of the various examples described in the embodiments of this invention, the embodiments of this invention can be implemented in hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the technical solutions of the embodiments of this invention.

[0186] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements a method for predicting the amount of kelp shedding.

[0187] The computer program product provided in this embodiment can execute the kelp shedding prediction method of the above embodiment. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0188] This disclosure also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement a method for predicting the amount of kelp shedding.

[0189] The computer-readable storage medium provided in this embodiment can execute the kelp shedding prediction method of the above embodiment. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0190] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0191] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0192] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0193] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0194] In this disclosure, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising…” does not exclude the presence of additional identical elements in the article or device that includes said element.

[0195] Although preferred embodiments of the present disclosure have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this disclosure.

[0196] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, the intent of this disclosure also includes these modifications and variations.

Claims

1. A method for predicting the amount of kelp shedding, characterized in that, The prediction method includes: Obtain the number of aquaculture days to be predicted and the target aquaculture area corresponding to the number of aquaculture days, wherein the target aquaculture area is the kelp aquaculture surface area extracted based on satellite images; Based on the stated number of days of cultivation, determine the kelp shedding rate in the target cultivation area; The amount of kelp lost in the target aquaculture area is determined based on the kelp shedding rate and the area of ​​the target aquaculture area. The step of determining the kelp shedding rate of the target aquaculture area based on the number of aquaculture days includes: Determine the target aquaculture area; wherein the target aquaculture area includes at least one of a first aquaculture area, a second aquaculture area, and a third aquaculture area; the aquaculture depth of the first aquaculture area is less than a preset water depth, and the distance between the boundary of the first aquaculture area and the boundary of the overall kelp aquaculture area is greater than a preset distance; the aquaculture depth of the second aquaculture area is greater than or equal to the preset water depth; the distance between the boundary of the third aquaculture area and the boundary of the overall kelp aquaculture area is less than or equal to the preset distance; The kelp shedding rate of the target aquaculture area is determined based on the target aquaculture area and the number of aquaculture days. The step of determining the kelp shedding rate of the target aquaculture area based on the target aquaculture area and the number of aquaculture days includes: If the target aquaculture area is located in the first aquaculture area, the kelp shedding rate of the target aquaculture area is determined using the following formula: If the target aquaculture area is located in the second aquaculture area, the kelp shedding rate of the target aquaculture area is determined using the following formula: If the target aquaculture area is located in the third aquaculture area, the kelp shedding rate of the target aquaculture area is determined using the following formula: In the formula, The seaweed shedding rate in the target aquaculture area; For the first i The rate of kelp shedding over the days; This refers to the number of days of rearing; The first i The kelp shedding rate per day includes: ； The step of determining the amount of kelp detachment in the target aquaculture area based on the kelp detachment rate and the area of ​​the target aquaculture area includes: In the formula, The amount of kelp detached from the target aquaculture area; The target aquaculture area; n The number of kelp units per unit area; The seaweed shedding rate in the target aquaculture area; The wet weight of a single kelp plant.

2. The method for predicting kelp shedding amount according to claim 1, characterized in that, The prediction method further includes: Based on the number of days of cultivation, determine the length and width of a single kelp. The area of ​​a single kelp is determined by the product of its length and width. Based on the area of ​​a single kelp plant, the wet weight of a single kelp plant is determined using the following formula: In the formula, S The area of ​​a single kelp plant is related to the number of days of cultivation.

3. The method for predicting kelp shedding amount according to claim 2, characterized in that, The prediction method further includes: Based on the number of days of cultivation, the length and width of a single kelp are determined using the following formulas: In the formula, L The length of a single kelp stalk; W The width of a single kelp stalk; D This refers to the number of days of rearing.

4. A device for predicting the amount of kelp shedding, characterized in that, include: The acquisition module is used to acquire the number of aquaculture days to be predicted and the target aquaculture area corresponding to the number of aquaculture days, wherein the target aquaculture area is the kelp aquaculture surface area extracted based on satellite images; A processing module, the processing module being used to determine the amount of kelp detachment in a target aquaculture area according to the method of any one of claims 1 to 3.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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