Prediction method, device and equipment for kelp falling amount and medium

Abstract

The invention relates to a method, a device and equipment for predicting the falling amount of kelp and a medium. The prediction method of the kelp shedding amount comprises the steps of obtaining a to-be-predicted culture day number and a target culture area corresponding to the culture day number, wherein the target culture area is a kelp culture surface area extracted based on a satellite image; according to the number of culture days, determining the kelp shedding rate of the target culture area; and determining the kelp shedding amount of the target culture area according to the kelp shedding rate of the target culture area and the area of the target culture area. According to the scheme, the culture area of the to-be-predicted culture days can be determined in a large range by using the satellite image, and the kelp shedding amount corresponding to the target culture area is determined by using the culture days and the culture area, so that accurate prediction of the kelp shedding amount in a large area is realized, the estimation speed of the kelp shedding amount is improved, and the prediction accuracy is improved. A scientific basis can be provided for environment management of a kelp culture area, the culture risk can be reduced, and the culture benefit can be improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of information processing, and particularly relates to a kelp shedding amount prediction method and device, equipment and medium. BACKGROUND

[0002] The existing kelp is usually cultivated in a three-dimensional manner on a rope or a floating raft. During natural growth or in a strong wind and wave situation, the kelp is prone to fall off in a sheet from the rope or the floating raft. When the fallen kelp is gathered to a harbor basin, a cold source water intake or a channel, it may at least increase the cost of obstacle removal and affect the safety of water intake, and may even cause the cold source water intake or the channel to be blocked, production to be stopped, and equipment to be damaged. Therefore, timely grasping of the kelp shedding amount and spatial distribution is crucial for disaster loss assessment, production scheduling and risk warning.

[0003] The existing kelp shedding amount monitoring cannot meet the monitoring demand of real-time quantification of a large-area kelp cultivation area. SUMMARY

[0004] In order to solve the above technical problems, the present disclosure provides a kelp shedding amount prediction method, device, equipment and medium to meet the monitoring demand of real-time quantification of a large-area kelp cultivation area.

[0005] In a first aspect, the present disclosure provides a kelp shedding amount prediction method, comprising: obtaining a cultivation day to be predicted and a target cultivation area corresponding to the cultivation day, the target cultivation area being a kelp cultivation surface area extracted based on a satellite image; determining a kelp shedding rate of a target cultivation area according to the cultivation day; determining a kelp shedding amount of the target cultivation area according to the kelp shedding rate of the target cultivation area and the target cultivation area.

[0006] In some embodiments of the present disclosure, the determination of the kelp shedding rate of the target cultivation area according to the cultivation day comprises: determining a target cultivation area; wherein the target cultivation area comprises a first cultivation area, a second cultivation area and a third cultivation area; the cultivation depth of the first cultivation area is less than a preset water depth, and the distance between the boundary of the first cultivation area and the boundary of the overall kelp cultivation area is greater than a preset distance; the cultivation depth of the second cultivation area is greater than or equal to the preset water depth; the distance between the boundary of the third cultivation area and the boundary of the overall kelp cultivation area is less than or equal to the preset distance; determining the kelp shedding rate of the target cultivation area according to the target cultivation area and the cultivation day.

[0007] In some embodiments of the present disclosure, the determination of the kelp shedding rate of the target cultivation area according to the target cultivation area and the cultivation day comprises: If the target cultivation area is the first cultivation area, the kelp shedding rate of the target cultivation area is determined by using the following formula:

[0008] If the target cultivation area is the second cultivation area, the kelp shedding rate of the target cultivation area is determined by using the following formula:

[0009] If the target cultivation area is the third cultivation area, the kelp shedding rate of the target cultivation area is determined by using the following formula:

[0010] In the formula, is the kelp shedding rate of the target cultivation area; is the kelp shedding rate on the nth day; i is the number of cultivation days.

[0011] In some embodiments of the present disclosure, the kelp shedding rate on the nth day includes: i .

[0012] In some embodiments of the present disclosure, the determination of the kelp shedding amount of the target cultivation area according to the kelp shedding rate of the target cultivation area and the area of the target cultivation area includes:

[0013] In the formula, is the kelp shedding amount of the target cultivation area; is the kelp cultivation area determined based on the satellite remote sensing image; n is the number of kelps per unit area; is the kelp shedding rate of the target cultivation area; is the wet weight of a single kelp.

[0014] In some embodiments of the present disclosure, the prediction method further includes: determining the length of a single kelp and the width of a single kelp according to the number of cultivation days; determining the area of a single kelp according to the product of the length of a single kelp and the width of a single kelp; determining the wet weight of a single kelp according to the area of a single kelp by using the following formula:

[0015] In the formula, S is the area of a single kelp.

[0016] In some embodiments of the present disclosure, the prediction method further includes:​​ According to the cultivation days, the length of single kelp and the width of single kelp are determined respectively by using the following formula:

[0017]

[0018] In the formula, L is the length of single kelp; W is the width of single kelp; D is the cultivation days.

[0019] In a second aspect of the present disclosure, a kelp shedding amount prediction device is provided, comprising: an acquisition module configured to acquire cultivation days to be predicted and a target cultivation area corresponding to the cultivation days, the target cultivation area being a kelp cultivation surface area extracted based on a satellite image; a processing module configured to determine a kelp shedding rate of a target cultivation area according to the cultivation days; the processing module is further configured to determine a kelp shedding amount of the target cultivation area according to the kelp shedding rate of the target cultivation area and the target cultivation area.

[0020] In a third aspect of the present disclosure, an electronic device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above method when executing the computer program.

[0021] In a fourth aspect of the present disclosure, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0022] The technical scheme provided by the embodiments of the present disclosure can include the following beneficial effects: The present scheme determines the cultivation area of the target cultivation area at the cultivation days to be predicted by using the kelp cultivation area image shot by satellite remote sensing, and then determines the corresponding kelp shedding amount of the target cultivation area based on the cultivation days to be predicted and the target cultivation area, so as to realize accurate prediction of the kelp shedding amount of a large area, improve the estimation speed of the kelp shedding amount, effectively overcome the limitations of the prior art, and provide a scientific basis for the environmental management of the kelp cultivation area, provide timely and accurate shedding amount monitoring and risk early warning for the kelp cultivation industry, help to reduce the cultivation risk and improve the cultivation benefit.

[0023] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present document and together with the description, serve to explain the present document. In the drawings: Figure 1 is a flowchart of a method for predicting the amount of kelp shedding according to an example embodiment of the present disclosure; Figure 2 is a schematic diagram of a kelp cultivation area based on satellite remote sensing according to an example embodiment of the present disclosure; Figure 3 is a flowchart of a method for predicting the shedding rate of kelp in a target cultivation area according to an example embodiment of the present disclosure; Figure 4 is a graph of the shedding rate of kelp over time during the clipping stage according to an example embodiment of the present disclosure; Figure 5 is a graph of the shedding rate of kelp over time during the growth stage according to an example embodiment of the present disclosure; Figure 6 is a graph of the shedding rate of kelp over time in a first cultivation area according to an example embodiment of the present disclosure; Figure 7 is a graph of the shedding rate of kelp over time in a second cultivation area according to an example embodiment of the present disclosure; Figure 8 is a graph of the shedding rate of kelp over time in a third cultivation area according to an example embodiment of the present disclosure; Figure 9 is a graph of the shedding rate of kelp over time in the entire cultivation area according to an example embodiment of the present disclosure; Figure 10 is a graph of the wet weight of a single kelp plant over the area of a single kelp plant according to an example embodiment of the present disclosure; Figure 11 is a graph of the length of a single kelp plant over the number of days of cultivation according to an example embodiment of the present disclosure; Figure 12 is a graph of the width of a single kelp plant over the number of days of cultivation according to an example embodiment of the present disclosure; Figure 13 is a schematic diagram of a kelp shedding prediction device according to an example embodiment of the present disclosure; Figure 14 is a schematic diagram of the structure of a kelp shedding prediction system according to an example embodiment of the present disclosure; Figure 15 is a schematic diagram of the structure of an electronic device according to an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] To make the purposes, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other in any manner without conflict.

[0026] In the related art, the monitoring of the kelp shedding amount is mainly based on small-scale in-situ observation and laboratory or flume simulation. Among them, the small-scale in-situ observation can obtain high-precision shedding rate by using a diving meter, fixed-point camera, pressure or tension sensor, but the spatio-temporal coverage is limited and the cost is high. The peeling experiment under controllable hydrodynamic conditions in the laboratory or flume simulation can reveal the shedding mechanism, but it is difficult to extrapolate to large-scale cultivation raft areas. The above-mentioned methods generally have the pain points of many points and few surfaces, poor timeliness, and high cost, and it is difficult to meet the continuous, large-scale, and real-time quantitative monitoring needs of the kelp industry.

[0027] Based on this, the present disclosure provides a kelp shedding amount prediction method, which determines the cultivation area of the target cultivation area at the to-be-predicted cultivation day by using the kelp cultivation area image shot by satellite remote sensing, and then determines the corresponding kelp shedding amount of the target cultivation area based on the cultivation area corresponding to the to-be-predicted cultivation day, so as to realize the prediction of the kelp shedding amount in a large area, and solve the problems of the prior art.

[0028] In combination with Figure 1 As shown in the figure, an exemplary embodiment of the present disclosure provides a kelp shedding amount prediction method, which comprises: S100, obtaining a to-be-predicted cultivation day and a target cultivation area corresponding to the cultivation day, the target cultivation area being a kelp cultivation surface area extracted based on a satellite image.

[0029] In this step, the target cultivation area corresponding to the cultivation day is based on the kelp cultivation surface area of the target cultivation area extracted based on the satellite image.

[0030] Referring to Figure 2 As shown in the figure, it is a geographical position schematic diagram shot by satellite remote sensing. After enlarging the part of the marine area on the land boundary, a dark strip pattern can be obtained, which is the kelp cultivation area 11, and then the kelp cultivation surface area of the kelp cultivation area can be extracted. The kelp cultivation area image of the corresponding date of the predicted cultivation day is selected to obtain the target cultivation area corresponding to the cultivation day.

[0031] S200, determining the kelp shedding rate of the target cultivation area according to the cultivation day.

[0032] In this step, as the length of kelp cultivation changes, the shedding rate of kelp also changes, but the change of the shedding rate of kelp is related to the cultivation days, so the shedding rate of kelp in the target cultivation area can be determined according to the cultivation days.

[0033] S300, according to the shedding rate of kelp in the target cultivation area and the area of the target cultivation area, determine the shedding amount of kelp in the target cultivation area.

[0034] In this step, since the shedding rate of kelp is determined based on the kelp cultivation area, based on the shedding rate of kelp in the target cultivation area, the area of the target cultivation area and the total amount of kelp per unit cultivation area, the shedding amount of kelp in the target cultivation area can be determined.

[0035] In this embodiment, the image of the kelp cultivation area taken by satellite remote sensing is used to determine the cultivation area of the target cultivation area at the cultivation days to be predicted, and then based on the cultivation days to be predicted and the area of the target cultivation area, the shedding amount of kelp corresponding to the target cultivation area is determined, so as to realize accurate prediction of the shedding amount of kelp in a large area, improve the estimation speed of the shedding amount of kelp, effectively overcome the limitations of the prior art, and also provide a scientific basis for environmental management of the kelp cultivation area, provide timely and accurate shedding amount monitoring and risk warning for the kelp cultivation industry, help to reduce the cultivation risk and improve the cultivation benefit.

[0036] In the above embodiment, the extraction of the area of the target cultivation area can be to identify the region from the static image and then determine the area of the target cultivation area, or to determine the area of the target cultivation area based on image machine learning. The technical solution is the prior art, which will be only briefly introduced as follows.

[0037] Exemplarily, the spectral and texture features of the kelp cultivation area are machine learned using Support Vector Machine (SVM) combined with object-oriented method to extract the area of the target cultivation area. In the learning process, a proper amount of training samples are selected in the kelp cultivation area, and a radial basis kernel is used to preliminarily classify the study area:

[0038] In the formula, is the Euclidean distance between two feature vectors; is the width parameter of the kernel function, which controls the width of the Gaussian function.

[0039] After combining visual interpretation, area screening (taking patches with an area greater than 1000 m² as kelp cultivation areas) and subsequent processing such as manual correction, high-precision extraction of the kelp cultivation area is realized.

[0040] The regional contraction of single scene raster data is simulated by using the neighborhood matrix structure of eight-neighborhood through the kelp cultivation area spatio-temporal interpolation algorithm. On this basis, the time progression concept is introduced, the updated raster data is taken as the initial state of the next time point, the neighborhood condition judgment is repeated, and the contraction process of the target area within a certain time is reflected:

[0041] In the formula, is the pixel value located in the first row and the first column of the raster data at the first time point; t i ,2,3,…, j is the range of iteration times, from the first time to the

[0042] The above process is repeated for multiple iterations until the kelp cultivation area reaches the contraction target, and the daily scale change data of the kelp cultivation area in the cultivation area is finally obtained.

[0043] In the above embodiment, as shown in reference Figure 3 , the method for determining the kelp shedding rate of the target cultivation area comprises: S201, determining the target cultivation area.

[0044] In this step, the target cultivation area is the area that the user wants to predict the shedding amount.

[0045] Among them, the target cultivation area includes at least one of the first cultivation area, the second cultivation area and the third cultivation area.

[0046] For example, the first cultivation area is defined as the cultivation depth being less than the preset water depth, and the distance between the boundary of the first cultivation area and the boundary of the overall kelp cultivation area being greater than the preset distance. The first cultivation area is a conventional kelp cultivation area, and the environment of the first cultivation area is relatively stable, and the corresponding kelp shedding amount is also relatively stable, so that the kelp shedding amount can be predicted based on the historical record.

[0047] Alternatively, as the cultivation depth increases, the cultivation ropes in the deep part will be partially wound, resulting in the kelp being unable to be harvested, and thus additional kelp shedding will occur in the harvesting period of the kelp. Therefore, the second cultivation area is defined as the cultivation depth being greater than or equal to the preset water depth, and the shedding amount of this area before the harvesting period is the same as that of the first cultivation area, but the shedding amount in the kelp harvesting period will be greater than that of the first cultivation area at the same period.

[0048] ​​​​In some embodiments, the outermost row or the row close to the outermost row of the cultivation area is usually subject to strong external environmental interference, and the kelp on these cultivation rafts often falls off due to the action of external environment such as waves. Therefore, the third cultivation area is defined as the area within a preset distance from the boundary of the cultivation area to the boundary of the overall kelp cultivation area. For example, as shown in FIG. 12, the part between the first dashed line 12 and the second dashed line 13 can be regarded as the third cultivation area, and the part between the second dashed line 13 and the land 14 can be regarded as the first cultivation area. Figure 2

[0049] In S202, the kelp falling rate of the target cultivation area is determined according to the target cultivation area and the cultivation days.

[0050] In this step, the corresponding relationship between the cultivation days and the kelp falling rate can be determined by monitoring the historical data of kelp cultivation in different target cultivation areas. The historical data can be the statistical value of continuous years or the statistical value of discontinuous years.

[0051] For example, the cultivation period of kelp includes the seedling clipping period, the growth period and the harvesting period, and the kelp falling rate is different in different periods. For example: In the seedling clipping period, as the kelp grows after seedling clipping, the kelp falling phenomenon decreases day by day until it is 0, at which time the kelp has been completely attached to the cultivation rope. Based on the statistical data of the kelp cultivation days and the kelp falling rate in the historical data, the corresponding relationship between the kelp falling rate and the kelp cultivation days can be determined.

[0052] In the growth period, the kelp falling is mainly the falling of kelp due to breakage, and the breakage position can be root breakage and middle breakage. According to field research, kelp usually breaks at the end 1 / 3 or at the root. Based on the statistical data of the kelp cultivation days and the kelp falling rate in the historical data, a fitting formula between the kelp falling rate and the kelp cultivation days is obtained after fitting.

[0053] In the harvesting period, the kelp falling is mainly the loss falling of the part that cannot be harvested due to harvesting. The falling rate can be determined based on the falling amount statistics in the harvesting period in the historical data.

[0054] In this embodiment, the kelp falling rate in different cultivation areas and cultivation days is determined through historical data, so as to more accurately estimate the kelp falling rate and further improve the accuracy of the prediction of the kelp falling amount related to the falling rate.

[0055] In some embodiments, the kelp falling rate of the target cultivation area is determined by using the following formula.

[0056] If the target cultivation area is in the first cultivation area, the kelp falling rate of the target cultivation area is determined by using the following formula: If the target cultivation area is in the second cultivation area, the kelp falling rate of the target cultivation area is determined by using the following formula:

[0057] If the target cultivation area is in the second cultivation area, the kelp shedding rate of the target cultivation area is determined by the following formula:

[0058] If the target cultivation area is in the third cultivation area, the kelp shedding rate of the target cultivation area is determined by the following formula:

[0059] In the formula, is the kelp shedding rate of the target cultivation area, with a unit of %; is the kelp shedding rate on the nth day, that is, the daily kelp shedding rate, with a unit of %; i is the cultivation time, with no unit.

[0060] Exemplarily, in the first cultivation area, the kelp is less disturbed by the external environment, and thus will shed according to the normal growth shedding rate. After the daily kelp shedding rate is determined, the kelp shedding rate corresponding to the cultivation time is the sum of all the daily kelp shedding rates before the cultivation time. For example, the kelp shedding rate of the cultivation time of 4 is calculated as .

[0061] In the second cultivation area, since it is in the deep sea range, it is also less disturbed by the external environment, and thus the shedding of the kelp before harvesting is consistent with that in the first cultivation area. However, during the harvesting period of the kelp, due to the inevitable entanglement of the rope cultivation, the entangled part cannot be harvested, which will cause the kelp to have an additional shedding rate of 6% on the basis of the growth shedding.

[0062] The third cultivation area is the outermost row of the kelp cultivation raft area. Since there is no protective barrier in the outermost row, the kelp shedding rate is significantly higher than that of other rafts due to the influence of environmental factors such as sea waves. For example, during the investigation, the number of kelp on each cultivation seedling rope in the kelp cultivation area was counted, among which the low area, the middle area, and the high area were all 28-30 per rope, and only the outermost row of the cultivation area had 23±1 per rope. Each kelp cultivation seedling rope in the cultivation area had 35 seedlings, and the normal shedding rate was calculated to be 20%. The shedding rate of the outermost row of the cultivation area was 35%, which was about 1.4 times the normal shedding rate.

[0063] In this embodiment, by limiting the relationship between the kelp shedding rate in different time periods and different target cultivation areas and the daily kelp shedding rate, the kelp shedding rate is determined as a parameter that changes on a daily scale, thereby improving the prediction accuracy of the kelp shedding rate.

[0064] In the above embodiment, the kelp shedding rate on the nth day includes: i ​​.

[0065] For example, through on-site visits to kelp cultivation areas, the duration of the splicing period is usually 16 days, and the first 7 days after splicing is a high-risk period for kelp shedding. Based on historical data of kelp shedding during the splicing period in previous years, it can be determined that the total kelp shedding rate in the cultivation area during the splicing period is 7%, and the kelp shedding rate gradually decreases during the splicing period. Further statistics of the kelp shedding rate in the first 7 days of the splicing period are as follows: 3.50%, 1.50%, 1.00%, 0.50%, 0.30%, 0.15%, and 0.05% per day. As the kelp grows after splicing, the adhesion of the rhizoids to the rope gradually increases, and the kelp shedding phenomenon decreases day by day, and by the 8th day, the kelp shedding rate is almost 0%. Its shedding curve can be referred to as shown in Figure 4 .

[0066] In addition to shedding during the splicing period, the rest of the kelp shedding is during the growth period. Shedding during the growth period is divided into kelp shedding and kelp breaking according to the breaking position of single kelp. Kelp shedding occurs in the early growth stage of kelp, resulting in whole kelp loss due to rhizoid or rope breaking; kelp breaking often occurs in the adult stage of kelp, resulting in loss of distal leaf tissue, leaving part of the leaf with obvious breaking marks. According to the measurement data in Sanggouwan area, the length of each kelp cultivation rope is 2.7m, and the initial number of kelp on each rope is 35.6±0.5. After calculation, it can be obtained that the cumulative shedding rate of kelp during the growth period due to shedding and breaking is 19.7%. The number of kelp shedding between the 50th day and the 270th day of kelp growth is counted from historical data, and the obtained data is curve fitted to obtain the kelp shedding rate from the 60th day to the 269th day. As shown in Figure 5 , the kelp shedding rate data during the growth period are distributed near the fitting curve, and the goodness of fit index (R 2 = 0.9664) is good, so the fitting formula can be used to accurately predict the kelp shedding rate on a specific date during the growth period.

[0067] When the cultivation days reach 269 days, the kelp shedding rate reaches a peak, and the kelp shedding rate after that will remain unchanged.

[0068] In this embodiment, by referring to historical data, the daily kelp shedding rate is limited to a specific value, so that the kelp shedding rate corresponding to any cultivation day can be accurately obtained, and the relationship between the kelp shedding rate and the daily kelp shedding rate is used to accurately predict the kelp shedding amount, thereby improving the prediction accuracy.

[0069] Referring to the above daily kelp shedding rate, the kelp shedding rate of different target cultivation areas is counted with respect to the cultivation days. Referring to Figure 6As shown in the first culture area, the kelp shedding rate is the basic shedding rate, and with the increase of culture days, the kelp shedding rate presents the trend of first decreasing, then being 0, then increasing and then decreasing. Figure 7 As shown, when the culture area includes the second culture area, the kelp shedding rate is suddenly increased and decreased at about 200 days on the basis of the basic shedding rate shown in the first culture area. While Figure 8 As shown in the third culture area, it is 1.4 times shedding on the basis of the overall kelp shedding rate, so the kelp shedding rate is the superposition of 1.4 times of the basic shedding rate and 1.4 times of the additional shedding rate of the second culture area, and the curve shape of the kelp shedding rate changing with the culture days is almost the same as that in Figure 7 , only the value of the ordinate is larger. When the target culture area to be predicted is the whole sea area, at this time, it is necessary to count according to the distribution area of the first culture area, the second culture area and the third culture area, and then obtain the total shedding rate of the whole sea area, so as to obtain the kelp shedding situation of the whole culture area, and Figure 9 As shown, the kelp shedding rate of the whole sea area is larger in the early culture period, and then presents the trend of first increasing and then decreasing, and the kelp shedding rate displayed by the ordinate will be lower than the kelp shedding rate of a target culture area due to the influence of the average value statistics of the overall kelp amount of the sea area.

[0070] In the above embodiment, the method for determining the kelp shedding amount of the target culture area according to the kelp shedding rate of the target culture area and the area of the target culture area includes using the following formula to determine:

[0071] In the formula, is the kelp shedding amount of the target culture area, unit kg; is the area of the target culture area, unit m 2 ; n is the number of kelp per unit area, which is a known quantity in the embodiment of the present disclosure n =20 m 2 ; is the kelp shedding rate of the target culture area, unit %; is the wet weight of a single kelp, unit kg / plant.

[0072] Exemplarily, the kelp shedding rate is the proportion of the area of the shed kelp to the total kelp cultivation area, and thus the product of the kelp shedding rate and the area of the target cultivation area is the specific surface area occupied by the shed kelp. Since the kelp is cultivated in three dimensions, the number of kelp cultivated on each cultivation rope is determined, and the number of cultivation ropes on the cultivation raft is also a determined number, and thus the planting density of the kelp per unit area is known, i.e., the number of kelp per unit area is a known quantity. The product of the specific surface area occupied by the shed kelp and the number of kelp per unit area is the number of shed kelp in the target cultivation area, and the product of the number of shed kelp in the target cultivation area and the wet weight of a single kelp is the kelp shedding amount in the target cultivation area.

[0073] In this embodiment, the kelp shedding amount in the target cultivation area is determined by using the acquired kelp shedding rate of the target cultivation area corresponding to the cultivation days and the growth distribution of the kelp, so as to provide timely and accurate shedding amount monitoring and risk early warning for the kelp cultivation industry, which helps to reduce the cultivation risk and improve the cultivation benefit.

[0074] In the above embodiment, the wet weight of a single kelp is determined by the following method: According to the cultivation days, the length of a single kelp and the width of a single kelp are determined.

[0075] According to the product of the length of a single kelp and the width of a single kelp, the area of a single kelp is determined.

[0076] According to the area of a single kelp, the wet weight of a single kelp is determined.

[0077] In the above embodiment, the wet weight of a single kelp is determined by the following formula:

[0078] In the formula, A is the area of a single kelp, and has no unit. S is the area of a single kelp related to the cultivation days, and has no unit.

[0079] Exemplarily, since the kelp is a large-area alga with a relatively thin thickness, the growth process thereof mainly lies in the process of increasing the size, i.e., with the increase of the cultivation days, the area of a single kelp also increases, and accordingly the wet weight of a single kelp also increases with the area of a single kelp, and thus the historical data of the change of the kelp wet weight with the kelp area can be acquired, and the relationship between the kelp wet weight and the kelp area is obtained after fitting.

[0080] For example, the measured data of the area and wet weight of single kelp in Sanggou Bay in 2016, 2017, 2018 and 2023 are obtained in the field, the data are preliminarily checked, the abnormal values, repeated data and missing values are eliminated, and the data are arranged in the form of a table with the area of single kelp as the independent variable and the wet weight of single kelp as the dependent variable. According to the data distribution and correlation analysis results, an exponential model is selected to establish a fitting formula of the kelp cultivation area and the total fresh weight of kelp in the cultivation area. The rationality of the fitting model is tested by calculating the goodness-of-fit index (R 2 =0.9479), and the specific data are shown in Figure 10 , and the fitting effect of the model on the data reaches the expected standard.

[0081] In this embodiment, by limiting the wet weight of single kelp as a function related to the area of single kelp, the influence of kelp thickness and other attachments on the prediction accuracy of kelp wet weight is reduced, thereby improving the prediction accuracy of the kelp shedding amount related to the wet weight of single kelp. At the same time, the natural growth curve prediction model of the biomass relationship between the natural growth area and weight of kelp is also established, which provides a scientific basis for the environmental management of kelp cultivation areas.

[0082] According to the cultivation days, the length of single kelp and the width of single kelp are determined by the following formulas, respectively:

[0083]

[0084] In the formula, L is the length of single kelp, unit c m ; W is the width of single kelp, unit c m ; D is the cultivation days, unitless.

[0085] Exemplarily, the change of the area of kelp can be confirmed by the product of the length and the width of kelp. As the cultivation days increase, the size of kelp is also constantly changing, and the change of the size mainly reflects in the length and the width of kelp. Therefore, by obtaining the historical data of the length and the width of kelp changing over time, the fitting formulas of the length of single kelp and the width of single kelp changing over time can be fitted.

[0086] For example, the measured data of the length of single kelp in Sanggou Bay in 2016, 2017, 2018 and 2023 are obtained in the field, the data are preliminarily checked, the abnormal values and missing values are eliminated, and the data are arranged in the form of a table with the cultivation time as the independent variable and the length of kelp as the dependent variable. A suitable nonlinear exponential function form is selected, the least square method is used to estimate the model parameters, and the optimal parameter value is determined by iterative calculation. The goodness-of-fit index (R2 =0.9080) to test the rationality of the fitting model. For specific data, see Figure 11 As shown in Figure 3, the fitting effect of the evaluation model on the data meets the expected standards.

[0087] Accordingly, field data on the width of a single kelp tree in Sanggou Bay in 2016, 2017, 2018, and 2023 were obtained to ensure the integrity and accuracy of the data. The data were preliminarily screened to remove outliers and duplicate data to ensure data quality, and the data were sorted in chronological order to form a data set for analysis. An appropriate nonlinear exponential curve model form was selected, and a nonlinear fitting function was called to fit the data. The fitting parameters of the model were solved by the least squares method. The goodness of fit index (R 2 =0.9385) to test the rationality of the fitting model. For specific data, see Figure 12 As shown in Figure 3, the fitting effect of the evaluation model on the data meets the expected standards.

[0088] In this embodiment, by defining the length and width of kelp as functions related to the cultivation time, the kelp size at the predicted day can be more accurately determined, and thus the kelp weight information can be more accurately obtained, laying the foundation for the subsequent accurate calculation of the kelp shedding amount. Furthermore, this solution also establishes a natural kelp growth curve prediction model that can accurately predict the growth length and width of kelp, providing scientific decision-making support for the management of the kelp cultivation industry.

[0089] The contents described above can be implemented individually or in combination in various ways, and these variations are all within the scope of protection of the present disclosure.

[0090] refer to Figure 13 As shown, the kelp shedding amount prediction device 50 includes: An acquisition module 51 is used to obtain the number of breeding days to be predicted and the target breeding area corresponding to the number of breeding days, where the target breeding area is the kelp breeding surface area extracted based on satellite images; A processing module 52 is used to determine the kelp shedding rate in the target aquaculture area according to the aquaculture days; The processing module 52 is further configured to determine the amount of kelp shedding in the target aquaculture area according to the kelp shedding rate in the target aquaculture area and the area of ​​the target aquaculture area.

[0091] Optionally, the processing module 52 is configured to: Determine a target aquaculture area; 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 entire 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; and the distance between the boundary of the third aquaculture area and the boundary of the entire kelp aquaculture area is less than or equal to a preset distance; The kelp shedding rate in the target aquaculture area is determined based on the target aquaculture area and the number of aquaculture days.

[0092] Optionally, the processing module 52 is configured to: If the target aquaculture area is the first aquaculture area, the following formula is used to determine the kelp shedding rate in the target aquaculture area:

[0093] If the target aquaculture area is the second aquaculture area, the following formula is used to determine the kelp shedding rate in the target aquaculture area:

[0094] If the target aquaculture area is the third aquaculture area, the following formula is used to determine the kelp shedding rate in the target aquaculture area:

[0095] Where, is the kelp shedding rate in the target aquaculture area, unit:%; For the i The daily kelp shedding rate, unit: % It is the number of breeding days, without unit.

[0096] In some embodiments of the present disclosure, the processing module 52 is used to: .

[0097] In some embodiments of the present disclosure, the processing module 52 is used to:

[0098] Where, is the amount of kelp shedding in the target aquaculture area, in kg; The kelp cultivation area is determined based on the kelp cultivation area image taken by satellite remote sensing, unit is m 2 ; n is the number of kelp per unit area, n =20 trees / m 2 ; is the kelp shedding rate in the target aquaculture area, unit:%; It is the wet weight of a single kelp, unit: kg / kelp.

[0099] In some embodiments of the present disclosure, the processing module 52 is configured to: According to the cultivation days, the length of the single kelp and the width of the single kelp are determined; According to the product of the length of the single kelp and the width of the single kelp, the area of the single kelp is determined; According to the area of the single kelp, the wet weight of the single kelp is determined by using the following formula:

[0100] In the formula, S is the area of the single kelp, unit m2 m 2 .

[0101] In some embodiments of the present disclosure, the processing module 52 is configured to: According to the cultivation days, the length of the single kelp and the width of the single kelp are determined by using the following formula:

[0102]

[0103] In the formula, L is the length of the single kelp, unit cm m ; W is the width of the single kelp, unit cm m ; D is the cultivation days, unitless.

[0104] The kelp shedding amount prediction device provided in the present embodiment can perform the kelp shedding amount prediction method of the above-mentioned embodiments, and has similar implementation principles and technical effects. Therefore, the present embodiment will not be described here again.

[0105] Based on the above-mentioned embodiments, referring to FIG. 6, Figure 14 The present disclosure can also construct a kelp shedding amount prediction system. The system includes a cultivation image acquisition device 60 and a kelp shedding amount prediction device 50, wherein the cultivation image acquisition device 60 and the kelp shedding amount prediction device 50 are communicatively connected.

[0106] The cultivation image acquisition device 60 can extract the kelp cultivation area by using a large-scale cultivation area dynamic calculation method based on the obtained cultivation area remote sensing monitoring image. The kelp shedding amount prediction device 50 can obtain the kelp growth curve and the daily variation of the kelp shedding rate by fitting the input kelp cultivation historical data, and then obtain the kelp shedding amount prediction model in the cultivation area by combining the cultivation area extracted from the cultivation image acquisition device 60, so as to more accurately predict the kelp shedding amount corresponding to any cultivation date.

[0107] The embodiments of the present application can divide the function modules of the electronic device or the host device according to the above method examples. For example, each function module can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be realized in the form of hardware or in the form of a software function module. It should be noted that the division of the modules in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, another division manner can be used.

[0108] In the specific implementation of the foregoing kelp shedding amount prediction device, each module can be implemented as a processor, and the processor can execute computer execution instructions stored in a memory, so that the processor executes the foregoing kelp shedding amount prediction method.

[0109] Referring to Figure 15 The present disclosure also provides an electronic device 70, which includes: at least one processor 71 and a memory 72.

[0110] The electronic device also includes a communication component 73.

[0111] The processor 71, the memory 72, and the communication component 73 are connected through a bus 74.

[0112] In the specific implementation process, the at least one processor 71 executes computer execution instructions stored in the memory 72, so that the at least one processor 71 executes the foregoing kelp shedding amount prediction method.

[0113] The specific implementation process of the processor 71 can refer to the method embodiments described above, which has similar implementation principles and technical effects, and will not be described here again.

[0114] In the foregoing embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the disclosed method can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0115] The memory can include a high-speed RAM memory, and can also include a non-volatile storage NVM, for example, at least one disk memory.

[0116] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, the bus in the drawings of the present disclosure does not limit to only one bus or one type of bus.

[0117] The functions implemented by the electronic device and the host device described above are introduced for the scheme provided by the embodiments of the present application.

[0118] It can be understood that, in order to implement the above functions, the electronic device or the host device comprises a hardware structure and / or a software module corresponding to each function.

[0119] In combination with the units and algorithm steps of each example described in the embodiments of the present application, the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in hardware or in the form of 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 beyond the scope of the technical solution of the embodiments of the present application.

[0120] The present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the prediction method of the amount of kelp shedding.

[0121] The computer program product provided by the present embodiment can execute the prediction method of the amount of kelp shedding of the above-mentioned embodiments, and has similar implementation principles and technical effects, which will not be described here again in the present embodiment.

[0122] The present disclosure also provides a computer readable storage medium having stored computer execution instructions, when the processor executes the computer execution instructions, the prediction method of the amount of kelp shedding is implemented.

[0123] The computer readable storage medium provided by the present embodiment can execute the prediction method of the amount of kelp shedding of the above-mentioned embodiments, and has similar implementation principles and technical effects, which will not be described here again in the present embodiment.

[0124] The above computer readable storage medium can be realized by any type of volatile or nonvolatile storage devices 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 that can be accessed by a general or special purpose computer.

[0125] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (devices) and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts 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 device to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing device, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0126] These computer program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0128] In the present disclosure, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that the article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such article or device. Without more limitations, the elements defined by the statement "comprising" do not exclude the presence of additional identical elements in the article or device including the elements.

[0129] While the preferred embodiments of the disclosure have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the foregoing description. Therefore, it is to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true scope of the disclosure. In

[0130] It is clear that the disclosure can be subject to various modifications and alterations, all falling within the scope of the disclosure. Thus, the disclosure is intended to cover all modifications and alterations of this disclosure that fall within the scope of the claims and their equivalents.

Claims

1. A method for predicting the amount of kelp shedding, characterized in that: The prediction method comprises: Obtaining the number of breeding days to be predicted and the target breeding area corresponding to the breeding days, wherein the target breeding area is the kelp breeding surface area extracted based on satellite images; Determining the kelp shedding rate in the target aquaculture area according to the aquaculture days; The amount of kelp shedding in the target aquaculture area is determined according to the kelp shedding rate in the target aquaculture area and the area of ​​the target aquaculture area.

2. The method for predicting the amount of kelp falling off according to claim 1, wherein Determining the kelp shedding rate in the target aquaculture area according to the aquaculture days includes: Determine a 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 entire 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; and the distance between the boundary of the third aquaculture area and the boundary of the entire kelp aquaculture area is less than or equal to a preset distance; The kelp shedding rate in the target aquaculture area is determined according to the target aquaculture area and the aquaculture days.

3. The method for predicting the amount of kelp falling off according to claim 2, wherein: Determining the kelp shedding rate in the target aquaculture area according to the target aquaculture area and the aquaculture days includes: If the target aquaculture area is in the first aquaculture area, the kelp shedding rate in the target aquaculture area is determined using the following formula: If the target aquaculture area is in the second aquaculture area, the kelp shedding rate in the target aquaculture area is determined using the following formula: If the target aquaculture area is in the third aquaculture area, the kelp shedding rate in the target aquaculture area is determined using the following formula: Where, is the kelp shedding rate in the target aquaculture area; For the i day's kelp shedding rate; The number of breeding days.

4. The method for predicting the amount of kelp falling off according to claim 3, wherein: The said i The daily kelp shedding rates include: 。 5. The method for predicting the amount of kelp falling according to any one of claims 1 to 4, characterized in that: Determining the amount of kelp shedding in the target aquaculture area according to the kelp shedding rate in the target aquaculture area and the area of ​​the target aquaculture area includes: Where, is the amount of kelp shedding in the target aquaculture area; is the area of ​​the target breeding area; n is the number of kelp per unit area; is the kelp shedding rate in the target aquaculture area; It is the wet weight of a single kelp.

6. The method for predicting the amount of kelp falling off according to claim 5, wherein: The prediction method further comprises: Determining the length and width of a single kelp plant according to the culture days; Determining the area of ​​a single kelp tree according to the product of the length of the single kelp tree and the width of the single kelp tree; The wet weight of a single kelp tree is determined according to the area of ​​the single kelp tree using the following formula: Where, S It is the area of ​​a single kelp tree related to the number of culture days.

7. The method for predicting the amount of kelp falling off according to claim 6, wherein: The prediction method further comprises: According to the culture days, the length and width of a single kelp are determined using the following formulas: Where, L is the length of a single kelp; W is the width of a single kelp; D The number of breeding days.

8. A device for predicting the amount of kelp shedding, characterized in that: include: An acquisition module, the acquisition module is used to obtain the number of breeding days to be predicted and the target breeding area corresponding to the breeding days, the target breeding area being the kelp breeding surface area extracted based on satellite images; a processing module, the processing module being used to determine a kelp shedding rate in a target aquaculture area according to the aquaculture days; The processing module is further configured to determine the amount of kelp shedding in the target aquaculture area according to the kelp shedding rate in the target aquaculture area and the area of ​​the target aquaculture area.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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