Marine fishery resource prediction device, marine fishery resource prediction method, and marine fishery resource prediction program

Abstract

A marine fishery resource prediction device, a marine fishery resource prediction method, and a marine fishery resource prediction program. In order to accurately predict the amount of marine fishery resources in a wide sea area at a future time different from the current time, a marine fishery resource amount prediction device (10) includes a fish school observation information acquisition unit (20) that acquires fish school observation information in a predetermined sea area, a sea state prediction information acquisition unit (30) that acquires sea state prediction information in a prediction target sea area including the predetermined sea area, and a prediction information calculation unit (40) that calculates marine fishery resource amount prediction information in the prediction target sea area based on the fish school observation information and the sea state prediction information.

Description

Technical Field

[0001] This invention relates to a technique for predicting marine fishery resources, such as fish stocks. Background Technology

[0002] Traditionally, fishermen have used locations and sea conditions that have yielded excellent fishing results in the past as candidates for prime fishing grounds in the current fishing industry. However, a prime fishing ground in the past may not necessarily be a prime fishing ground this time. Specifically, a prime fishing ground is considered to be a place where fish congregate near the location and where sea conditions are favorable for fish. However, traditional methods can only consider the latter.

[0003] Therefore, we consider presenting information that is actually observed (travel information). However, in this case, there is a problem that the information only exists at the observation point or at the current time.

[0004] To address the aforementioned problems, various systems for predicting marine fishery resources have been designed, as shown in Patent Document 1 and Non-Patent Document 1.

[0005] Existing technical documents Patent documents Patent Document 1: International Publication No. WO2022 / 230333 Non-patent literature Non-Patent Literature 1: Development of saury fishing ground prediction method using AI technology, Takashi Yabuki, JAFIC Technology Review No.1, February 2022 Summary of the Invention

[0006] Technical issues However, traditional methods may not be able to predict the quantity of marine fishery resources in a wide range of sea areas with high accuracy. Traditional methods can predict the quantity of marine fishery resources at the current time, but may not be able to predict the quantity of marine fishery resources at a future time different from the current time.

[0007] Therefore, the purpose of this invention is to accurately predict the quantity of marine fishery resources in a wide range of sea areas, including future times that differ from the current time.

[0008] Solution to the problem According to an embodiment of the present invention, an apparatus for predicting the quantity of marine fishery resources includes: a fish school observation information acquisition unit configured to acquire fish school observation information in a predetermined sea area; a sea state prediction information acquisition unit configured to acquire sea state prediction information in a prediction target sea area including the predetermined sea area; and a prediction information calculation unit configured to calculate prediction information of the quantity of marine fishery resources in the prediction target sea area based on the fish school observation information and the sea state prediction information.

[0009] In this configuration, the quantity of marine fishery resources can be calculated with high precision based on fish school observation information obtained through observation and predicted sea state information.

[0010] In the marine fishery resource prediction device (i.e., marine fishery resource (quantity) prediction device), according to an embodiment of the present invention, the fish school observation information is based on the fish school distribution information in the predetermined sea area.

[0011] In this configuration, fish distribution can be predicted by specifically using fish distribution information as fish observation information.

[0012] In a marine fishery resource prediction device, according to one embodiment of the present invention, the fish school observation information is based on the fish school distribution information in the predetermined sea area and the fish school observation location information set at least in one place in the predetermined sea area.

[0013] In this configuration, fish distribution can be predicted by specifically using fish distribution information as fish observation information, and the accuracy of fish distribution prediction can be improved by using fish observation location information.

[0014] In a marine fishery resource prediction device, according to an embodiment of the present invention, the sea condition prediction information is based on at least one of the water temperature, salinity, and current velocity of the predicted sea area.

[0015] In this configuration, sea state prediction information can be set appropriately according to sea conditions.

[0016] In a marine fishery resource prediction device, according to one embodiment of the present invention, the sea condition prediction information is based on the water temperature, salinity, and current velocity of the predicted sea area.

[0017] In this configuration, sea state prediction information can be set with high precision based on sea state.

[0018] In the marine fishery resource prediction device, according to an embodiment of the present invention, the prediction information calculation unit is further configured to calculate at least one of the marine fishery resource quantity in the target sea area and the time change of the marine fishery resource quantity as the marine fishery resource quantity prediction information.

[0019] In this configuration, the quantity of marine fishery resources and their temporal variations can be calculated with high precision as marine fishery resource quantity prediction information.

[0020] In the method for predicting marine fishery resources, according to an embodiment of the present invention, fish school observation information in a predetermined sea area is obtained by a fish school observation information acquisition unit, sea condition prediction information in a target sea area including the predetermined sea area is obtained by a sea condition prediction information acquisition unit, and marine fishery resource prediction information in the target sea area is calculated by a prediction information calculation unit based on the fish school observation information and the sea condition prediction information.

[0021] According to an embodiment of the present invention, a non-transitory computer-readable medium containing program instructions for causing a computer to execute the method is disclosed in a marine fishery resource prediction device. The method includes: acquiring fish school observation information in a predetermined sea area by a fish school observation information acquisition unit; acquiring sea state prediction information in a prediction target sea area including the predetermined sea area by a sea state prediction information acquisition unit; and calculating marine fishery resource prediction information in the prediction target sea area by a prediction information calculation unit based on the fish school observation information and the sea state prediction information. Attached Figure Description

[0022] Figure 1 This is a functional block diagram illustrating a configuration example of a marine fishery resource prediction device (i.e., a marine fishery resource prediction apparatus) according to a first embodiment of the present invention.

[0023] Figure 2 This is a diagram illustrating an example of setting a target area and individual areas according to a first embodiment of the present invention.

[0024] Figure 3 This is a diagram illustrating an example of the concept of obtaining an observed fish population (i.e., obtaining the number of observed fish populations) according to a first embodiment of the present invention.

[0025] Figure 4 This is a diagram illustrating an example of the concept of a preference index according to a first embodiment of the present invention.

[0026] Figure 5 This is a diagram illustrating an example of a predicted fish population distribution according to a first embodiment of the present invention.

[0027] Figure 6 This is a functional block diagram illustrating an example configuration of a marine fisheries resource prediction device according to a second embodiment of the present invention.

[0028] Figure 7 This is a functional block diagram illustrating an example configuration of a marine fishery resource prediction device according to a third embodiment of the present invention. Detailed Implementation

[0029] First Implementation Scheme - A technique for predicting the quantity of marine fishery resources according to a first implementation scheme of the present invention will be described with reference to the accompanying drawings. Figure 1 This is a functional block diagram illustrating an example configuration of a marine fisheries resource prediction device (i.e., a marine fisheries resource prediction apparatus or a marine fisheries resource (quantity) prediction device) according to a first embodiment of the present invention. The quantity of marine fisheries resources shown in each embodiment including this embodiment is, for example, the quantity of fish. The quantity of fish is, for example, the weight of a group of fish.

[0030] like Figure 1 As shown, the marine fishery resource prediction device (10) includes a fish school observation information acquisition unit (20), a sea state prediction information acquisition unit (30), and a prediction information calculation unit (40). The fish school observation information acquisition unit (20) and the sea state prediction information acquisition unit (30) are interface functional units for obtaining various information acquired from external sources. The prediction information calculation unit (40) includes, for example, an arithmetic processing unit such as a computer and a storage device for storing the marine fishery resource prediction program executed by the arithmetic processing unit.

[0031] Figure 2 This is a diagram illustrating an example of setting a target area and individual areas according to a first embodiment of the present invention.

[0032] like Figure 2 As shown, the predicted target region (Rep) is set by multiple individual regions (Re). These multiple individual regions (Re) correspond to the predetermined region of this invention.

[0033] More specifically, the target region (Rep) is set up as a two-dimensional region extending in both the latitudinal and longitude directions. Multiple individual regions (Re) are set up as two-dimensional arrays arranged in both the latitudinal and longitude directions, respectively.

[0034] For example, in Figure 2 In this case, multiple individual regions (Re) are set up by a 5×5 two-dimensional array arranged in the latitude and longitude directions, with the individual region Re(n, m) as the center.

[0035] Each of the multiple individual regions (Re) is linked to a position in the absolute coordinate system. For example, the position coordinates (Re) of the center of the multiple individual regions are set by the position coordinates in the absolute coordinate system.

[0036] Notice, Figure 2 The example shown is just one example, and other examples can be used if multiple individual regions (Re) are set to a predetermined array pattern for predicting target regions (Rep).

[0037] The fish school observation information acquisition unit (20) acquires the observed fish school quantity in individual areas (Re) contained within the predicted target area (Rep). The observed fish school quantity is obtained, for example, by fish school detectors deployed in buoys in a specific individual area (Re), or by observation data from fishing vessels located in a specific individual area (Re), or by sonar observation data.

[0038] Figure 3 This is a diagram illustrating an example of the concept of obtaining an observed fish population (i.e., obtaining the observed fish population number or the observed fish population quantity) according to a first embodiment of the present invention. Figure 3 In this context, buoys or vessels exist in individual regions Re(n-2, m+2), Re(n-1, m-1), Re(n+1, m+1), and Re(n+2, m-2) within the predicted target area (Rep). The fish school observation information acquisition unit (20) acquires the observed fish school number Wo(n-2, m+2) from the individual region Re(n-2, m+2), the observed fish school number Wo(n-1, m-1) from the individual region Re(n-1, m-1), the observed fish school number Wo(n+1, m+1) from the individual region Re(n+1, m+1), and the observed fish school number Wo(n+2, m-2) from the individual region Re(n+2, m-2). At this time, the fish school observation information acquisition unit (20) acquires the observed fish school number (Wo) in the individual region (Re) associated with the observation time (t).

[0039] The fish school observation information acquisition unit (20) outputs the acquired number of observed fish schools (Wo) to the prediction information calculation unit (40).

[0040] The sea state prediction information acquisition unit (30) acquires the sea state prediction information of the prediction target area (Rep) of the external prediction. The sea state prediction information includes time.

[0041] Sea state prediction information includes, for example, water temperature distribution, salinity distribution, and current velocity. Preferably, the sea state prediction information includes all of water temperature distribution, salinity distribution, and current velocity, but obtaining at least one of them is sufficient. Information that may not have been obtained can be supplemented by input from an input unit not shown, or by information from the past (e.g., immediately preceding or similar times).

[0042] The sea state prediction information acquisition unit (30) outputs the sea state prediction information to the prediction information calculation unit (40).

[0043] The prediction information calculation unit (40) calculates fish distribution prediction information based on the observed fish population (Wo) (i.e., the observed fish population) and sea state prediction information, including the fish population (Wp) (i.e., the predicted fish population) in multiple individual areas (Re) constituting the prediction target area (Rep). More specifically, the prediction information calculation unit (40) calculates the fish distribution prediction information, for example as shown below.

[0044] like Figure 1 As shown, the prediction information calculation unit (40) includes a fish distribution correction unit (41), a preference index model setting unit (42), and a fish distribution prediction unit (43).

[0045] The fish distribution correction unit (41) receives the predicted fish population (Wp) and observed fish population (Wo) for multiple individual regions (Re) from the fish observation information acquisition unit (20). The predicted fish population (Wp) is, for example, the fish population obtained in the prediction immediately preceding the current prediction.

[0046] The fish distribution correction unit (41) corrects the predicted fish population (Wp) for multiple individual regions (Re) by observing the fish population (Wo). For example, the fish distribution correction unit (41) corrects the predicted fish population (Wp) so that the difference between the predicted fish population (Wp) and the observed fish population (Wo) in the same individual region (Re) becomes smaller (e.g., 0).

[0047] The fish distribution correction unit (41) outputs the fish population (Wp) of multiple individual regions (Re) of the predicted target region (Rep) (or the corrected fish population (Wp) of individual regions (Re) of the corrected fish population (Wp)) to the fish distribution prediction unit (43).

[0048] The preference index model setting unit (42) receives sea state prediction information. The preference index model setting unit (42) sets the preference index (P) of the predicted fish population based on the water temperature distribution and salinity distribution in the sea state prediction information.

[0049] The preference index (P) indicates the predicted direction in which the fish school may move. For example, the preference index (P) is set to a value between 0 and 1.

[0050] Figure 4 This is a diagram illustrating an example of the concept of a preference index (P) according to a first embodiment of the present invention. Figure 4 The preference index (P) is shown to indicate the movement from a single region Re(n, m) to multiple single regions Re(n-1, m), Re(n, m-1), Re(n, m+1), and Re(n+1, m) adjacent to the single region Re(n, m).

[0051] The preferred water temperature and salinity vary depending on the fish species. Therefore, if the water temperature and salinity distributions in the target area (Rep) are known, the possible direction of movement of the target species fish population is known. Using this, the preference index model setting unit (42) sets the preference index Pnm(-1,0) from the single region Re(n,m) to the single region Re(n-1,m), sets the preference index Pnm(0,-1) from the single region Re(n,m) to the single region Re(n,m-1), sets the preference index Pnm(0,+1) from the single region Re(n,m) to the single region Re(n,m+1), and sets the preference index Pnm(+1,0) from the single region Re(n,m) to the single region Re(n+1,m). At this time, the preference index model setting unit (42) sets the preference index (P) at the current prediction time.

[0052] The preference index model setting unit (42) sets the preference index (P) for all individual regions (Re) of the prediction target region (Rep). Although the preference index model setting unit (42) can set the preference index (P) using only one of the water temperature distribution and salinity distribution, using both water temperature distribution and salinity distribution can set the preference index (P) with higher accuracy.

[0053] The preference index model setting unit (42) sets the maximum swimming speed (V) for each adjacent individual area (Re) based on the current velocity in the sea state prediction information. The maximum swimming speed (V) is a value obtained by dividing the maximum movement distance of the target fish group by the time interval.

[0054] For example, the preference index model setting unit (42) sets a maximum swimming speed (V) for each adjacent individual region (Re).

[0055] The preference index model setting unit (42) sets the preference index model at the target prediction time by combining the preference index (P) and the maximum swimming speed (V). The preference index model setting unit (42) outputs the set preference index model (preference index (P) and maximum swimming speed (V)) to the fish distribution prediction unit (43).

[0056] The fish distribution prediction unit (43) predicts the number of fish (Wp) in each individual region (Re) of the target area (Rep) based on the number of fish (Wp) (corrected number of fish (Wp) in a single region (Re) of the corrected number of fish (Wp)) from the fish distribution correction unit (41) and the preference index model (i.e., the preference index (P) and maximum swimming speed (V) from the preference index model setting unit (42)).

[0057] More specifically, the fish distribution prediction unit (43) sets up the fish population prediction model based on the preference index model from the preference index model setting unit (42). As the fish population prediction model, for example, a grid-based fish behavior model can be used.

[0058] The fish distribution prediction unit (43) uses a fish population prediction model to predict the fish population (Wp) in each individual region (Re) of the target area (Rep), which uses the fish population (Wp) from the fish distribution correction unit (41) as input. The fish distribution prediction unit (43) uses the fish population (Wp) of multiple individual regions (Re) as a dataset to output the predicted fish population distribution of the target area (Rep).

[0059] Using this configuration and processing, the marine fishery resource prediction device (10) can predict fish populations (Wp) (i.e., fish species populations) with high accuracy not only in the present time but also in the future time. Furthermore, the marine fishery resource prediction device (10) can predict marine fishery resources in a wide sea area with high accuracy, rather than being limited to a narrow range such as the location of fishing vessels.

[0060] The marine fishery resource prediction device (10) can also, for example, display the predicted fish population distribution in the target area (Rep) by providing a display device.

[0061] Figure 5 This is a diagram illustrating an example of a predicted fish population distribution according to a first embodiment of the present invention. Figure 5 As shown, the predicted target area (Rep) is displayed on the screen of the display device. Furthermore, the screen displays map information indicating land, sea, etc., based on the location coordinates of the predicted target area (Rep).

[0062] like Figure 5 As shown, based on the predicted fish population distribution in the predicted target area (Rep), the marine fishery resource prediction device (10) (i.e., the marine fishery resource prediction device) displays graphs of the predicted location, predicted fish population size (i.e., fish population size), direction of movement, and speed of movement of the fish population at multiple time points (e.g., t1, t2, t3, t4). For example, in Figure 5 In this case, the predicted location is represented by the position of the circle, and the predicted fish population size is represented by the size of the circle. The direction of movement is represented by the direction of the arrow, and the speed of movement is represented by the length of the arrow.

[0063] It should be noted that, for example, such as Figure 5As shown, although the center of the fish population may deviate from the center (Re) of multiple individual areas, the coordinates of the center of the fish population at each time can be predicted based on the direction and speed of movement from the maximum swimming speed (V), and the center of the fish population can be displayed based on the prediction results.

[0064] It can also calculate the confidence level [%] and confidence interval (i.e., fish population ± α [ton], etc.) of the predicted fish population (Wp), and display these values, although they are not in the range of... Figure 5 As shown in the image.

[0065] Second Implementation Scheme - The marine fishery resource quantity prediction technique according to the second implementation scheme of the present invention will be described with reference to FIGS. Figure 6 This is a functional block diagram illustrating an example configuration of a marine fishery resource prediction device (10A) according to a second embodiment of the present invention.

[0066] like Figure 6 As shown, the marine fishery resource prediction device (10A) according to the second embodiment (i.e., the marine fishery resource prediction apparatus) differs from the marine fishery resource prediction device (10) according to the first embodiment in that the correction means are different. In the following, only the differences between the marine fishery resource prediction device (10A) and the marine fishery resource prediction apparatus (10) will be described, and similar points will be omitted.

[0067] The marine fishery resource prediction device (10A) does not include a fish distribution correction unit (41) in the prediction information calculation unit (40A), but includes a model correction unit (44).

[0068] The model calibration unit (44) sets model calibration information for the preference index model based on the predicted fish population (Wp) and the observed fish population (Wo). The model calibration information sets calibration values ​​for the preference index model (i.e., preference index (P) and maximum swimming speed (V)) to reduce the difference between the predicted fish population (Wp) and the observed fish population (Wo) (e.g., to zero).

[0069] The model calibration unit (44) can select the best fish behavior model and include the selected fish behavior model in the model calibration information.

[0070] The model calibration unit (44) outputs the model calibration information to the preference index model setting unit (42A).

[0071] The preference index model setting unit (42A) calibrates the preference index model (i.e., preference index (P) and swimming speed (V)) based on model calibration information. The preference index model setting unit (42A) outputs the calibrated preference index model (i.e., preference index (P) and swimming speed (V)) to the fish swarm distribution prediction unit (43A).

[0072] The fish distribution prediction unit (43A) predicts the number of fish (Wp) in each individual region (Re) of the prediction target region (Rep) based on the observed fish population (Wo) from the fish population observation information acquisition unit (20) and the corrected preference index model from the preference index model setting unit (42A).

[0073] Using this configuration and processing, the marine fisheries resource prediction device (10A) can predict fish populations (Wp) with high accuracy in the future (not limited to the present time). Furthermore, the marine fisheries resource prediction device (10A) can predict marine fisheries resources with high accuracy over a wide range of sea areas, not limited to narrow ranges such as the location of fishing vessels.

[0074] Third Embodiment - A marine fishery resource prediction device (10B) according to the third embodiment of the present invention will be described with reference to the accompanying drawings. Figure 7 This is a functional block diagram illustrating an example configuration of a marine fishery resource prediction device (10B) according to a third embodiment of the present invention.

[0075] like Figure 7 As shown, the marine fishery resource prediction device (10B) according to the third embodiment has a configuration that combines the configuration of the marine fishery resource prediction device (10) according to the first embodiment with the configuration of the marine fishery resource prediction device (10) according to the second embodiment.

[0076] Specifically, the prediction information calculation unit (40B) of the marine fishery resource prediction device (10B) includes a fish school distribution correction unit (41), a preference index model setting unit (42B), a fish school distribution prediction unit (43B), and a model correction unit (44). According to the first embodiment, the fish school distribution correction unit (41) is the same as the fish school distribution correction unit (41) of the marine fishery resource prediction unit (10). The preference index model setting unit (42B) is similar to the preference index model setting unit (42) of the marine fishery resource prediction device (10A) according to the second embodiment, and the model correction unit (44) is similar to the model correction unit (44) of the marine fishery resource prediction device (10A) according to the second embodiment.

[0077] The fish distribution prediction unit (43B) predicts the number of fish (Wp) in each individual region (Re) of the prediction target region (Rep) based on the fish population (Wp) corrected by the fish distribution correction unit (41) and the corrected preference index model from the preference index model setting unit (42B).

[0078] Using this configuration and processing, the marine fisheries resource prediction device (10B) can predict fish populations (Wp) with high accuracy in the future, not just at the present time. Furthermore, the marine fisheries resource prediction device (10B) can predict marine fisheries resources in a wide area of ​​the ocean with high accuracy, not just within a narrow range, such as the location of fishing vessels.

[0079] In the aforementioned predetermined implementation scheme, the correction of fish school distribution, model correction, and prediction of fish school distribution are performed by different functional units. However, these corrections and predictions can also be performed by a single computational model.

[0080] The following are examples of embodiments of the present invention.

[0081] (1) A marine fishery resource quantity prediction device (10) includes: a fish school observation information acquisition unit (20) configured to acquire fish school observation information in a predetermined sea area; a sea state prediction information acquisition unit (30) configured to acquire sea state prediction information in a prediction target sea area including the predetermined sea area; and a prediction information calculation unit (40) configured to calculate marine fishery resource quantity prediction information in the prediction target sea area based on the fish school observation information and the sea state prediction information.

[0082] (2) The marine fishery resource prediction device (10) according to claim 1, wherein the fish school observation information is based on the fish school distribution information in the predetermined sea area.

[0083] (3) The marine fishery resource prediction device (10) according to claim 1, wherein the fish school observation information is based on the fish school distribution information in the predetermined sea area and the fish school observation location information set at least at one location in the predetermined sea area.

[0084] (4) The marine fishery resource prediction device (10) according to claim 1, wherein the sea condition prediction information is based on at least one of water temperature, salinity and current velocity in the target sea area.

[0085] (5) The marine fishery resource prediction device (10) according to claim 4, wherein the sea condition prediction information is based on the water temperature, salinity and current velocity in the target sea area.

[0086] (6) The marine fishery resource quantity prediction device (10) according to claim 1, wherein the prediction information calculation unit (40) is further configured to calculate at least one of the marine fishery resource quantity in the prediction target sea area and the time change of the marine fishery resource quantity as the marine fishery resource quantity prediction information.

[0087] (7) A method for predicting marine fishery resources, comprising: acquiring fish school observation information in a predetermined sea area by a fish school observation information acquisition unit; acquiring sea condition prediction information in a target sea area including the predetermined sea area by a sea condition prediction information acquisition unit; and calculating marine fishery resources prediction information in the target sea area based on the fish school observation information and the sea condition prediction information by a prediction information calculation unit.

[0088] (8) A non-transitory computer-readable medium comprising program instructions for causing a computer to perform the following methods: acquiring fish school observation information in a predetermined sea area through a fish school observation information acquisition unit; acquiring sea state prediction information in a predicted target sea area including the predetermined sea area through a sea state prediction information acquisition unit; and calculating marine fishery resource prediction information in the predicted target sea area based on the fish school observation information and the sea state prediction information through a prediction information calculation unit.

[0089] the term It should be understood that not all objectives or benefits can necessarily be achieved according to any particular embodiment described herein. Therefore, for example, those skilled in the art will recognize that certain embodiments may be configured to operate in a manner that achieves or optimizes one or more of the advantages taught herein, without necessarily achieving other objectives or benefits as may be taught or suggested herein.

[0090] All the processes described herein can be embodied in software code modules executed by a computing system comprising one or more computers or processors, and can be fully automated via such software code modules. The code modules can be stored on any type of non-transitory computer-readable medium or other computer storage device. Some or all of the methods can be embodied in dedicated computer hardware.

[0091] Many other variations besides those described herein will be apparent from this disclosure. For example, depending on the implementation, certain actions, events, or functions of any algorithm described herein may be performed in a different order, may be added, combined, or omitted entirely (e.g., not all described actions or events are necessary for the practice of the algorithm). Furthermore, in some implementations, actions or events may be performed simultaneously rather than sequentially, for example, through multithreading, interrupt handling, or on multiple processors or processor cores or other parallel architectures. Moreover, different tasks or processes may be performed by different machines and / or computing systems that can run together.

[0092] The various illustrative logic blocks and modules described in conjunction with the embodiments disclosed herein can be implemented or executed by a machine such as a processor. The processor may be a microprocessor, but alternatively, it may be a controller, microcontroller, or state machine, a combination thereof, etc. The processor may include circuitry configured to process computer-executable instructions. In another embodiment, the processor includes an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable devices that perform logic operations without processing computer-executable instructions. The processor may also be implemented as a combination of computing devices, such as a combination of a digital signal processor (DSP) and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Although this document primarily describes digital technologies, the processor may also primarily include analog components. For example, some or all of the signal processing algorithms described herein may be implemented in analog circuitry or mixed analog and digital circuitry. The computing environment may include any type of computer system, including but not limited to microprocessor-based computer systems, mainframe computers, digital signal processors, portable computing devices, computing engines within device controllers or appliances, to name just a few.

[0093] Unless otherwise specified, conditional language such as “can,” “able,” “may,” or “may” is generally understood in context to convey that certain embodiments include certain features, elements, and / or steps that are not included in other embodiments. Therefore, such conditional language is not generally intended to imply that one or more embodiments require features, elements, and / or steps in any way, or that one or more embodiments must include logic for determining, with or without user input or prompts, whether such features, elements, and / or steps are included in any particular embodiment or will be performed in any particular embodiment.

[0094] Unless otherwise specified, a disjunctive language such as at least one of the phrases “X, Y or Z” is understood in the context to generally indicate that an item, term, etc., can be X, Y or Z or any combination thereof (e.g., X, Y and / or Z). Therefore, such a disjunctive language is generally not intended and should not imply that some implementation requires at least one of X, at least one of Y, or at least one of Z to be present respectively.

[0095] Any process description, element, or block depicted in the flowcharts described herein and / or in the accompanying drawings should be understood as potentially representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or element in the process. As will be understood by those skilled in the art, alternative embodiments are included within the scope of the embodiments described herein, wherein elements or functions may be omitted, performed out of the order shown or discussed, including substantially simultaneously or in reverse order, depending on the functionality involved.

[0096] Unless otherwise expressly stated, items such as “a” or “an” should generally be interpreted as including one or more of the described items. Therefore, phrases such as “configured to” are intended to include one or more of the described devices. Such one or more of the described devices can also be collectively configured to perform the descriptions. For example, a processor configured to perform descriptions A, B, and C may include a first processor configured to perform description A, which works in conjunction with a second processor configured to perform descriptions B and C. The same applies to the use of definite articles used to introduce embodiment descriptions. Furthermore, even when a specific number of introduced embodiment descriptions are explicitly described, those skilled in the art will recognize that such descriptions should generally be interpreted as meaning at least the number described (e.g., a bare description of “two descriptions” without other modifiers generally means at least two descriptions, or two or more descriptions).

[0097] Those skilled in the art will understand that, generally, the terms used herein are intended to be “open-ended” terms (e.g., the term “including” should be interpreted as “including but not limited to”, the term “having” should be interpreted as “having at least”, the term “including” should be interpreted as “including but not limited to”, etc.).

[0098] For illustrative purposes, the term "horizontal" as used herein is defined as a plane parallel to the plane or surface of the floor of the area where the described system is used or the described method is performed, regardless of its orientation. The term "floor" may be used interchangeably with the terms "ground" or "water surface." The term "vertical" refers to a direction perpendicular to the horizontal plane just defined. Terms such as "above," "below," "bottom," "top," "side," "higher," "lower," "above," and "below" are defined relative to the horizontal plane.

[0099] As used herein, unless otherwise stated, the terms “attachment,” “connection,” “fitting,” and other such relational terms should be interpreted as including removable, movable, fixed, adjustable, and / or releasable connections or attachments. Connections / attaches can include direct connections and / or connections with an intermediate structure between the two components in question.

[0100] As used herein, numbers preceded by terms such as “approximately,” “about,” and “substantially” include the numbers and also indicate quantities close to the stated amount that still perform the desired function or achieve the desired result. For example, the terms “approximately,” “about,” and “substantially” may refer to a quantity less than 10% of the stated amount. As used herein, features of embodiments disclosed herein are preceded by terms such as “approximately,” “about,” and “substantially” to indicate features with some variability that still perform the desired function or achieve the desired result of the feature.

[0101] It should be emphasized that many changes and modifications can be made to the above embodiments, and its elements should be understood as existing in other acceptable examples. All such modifications and changes are intended to be included within the scope of this disclosure and protected by the appended claims.

[0102] Symbol Explanation 10, 10A, 10B Marine Fisheries Resource Prediction Equipment 20 Fish School Observation Information Acquisition Unit 30 Sea State Forecasting Information Acquisition Unit 40, 40A, 40B Predictive Information Calculation Units 41 Fish Distribution Correction Unit 42, 42A, 42B Preference Index Model Setting Unit Fish distribution prediction units 43, 43A, and 43B 44 Model calibration unit Rep predicts target area Re: Individual Area Wo observed the number of fish. Wp Fish population P Preference Index V Maximum swimming speed

Claims

1. A marine fishery resource prediction device (10), comprising: Fish school observation information acquisition unit (20) is configured to acquire fish school observation information in a predetermined sea area; The sea state prediction information acquisition unit (30) is configured to acquire sea state prediction information in the predicted target sea area including the predetermined sea area; as well as The prediction information calculation unit (40) is configured to calculate the prediction information of marine fishery resources in the target sea area based on the fish school observation information and the sea state prediction information.

2. The marine fishery resource prediction device (10) according to claim 1, wherein, The fish school observation information is based on the fish school distribution information in the predetermined sea area.

3. The marine fishery resource prediction device (10) according to claim 1, wherein, The fish school observation information is based on the fish school distribution information in the predetermined sea area and the fish school observation location information set at least one location in the predetermined sea area.

4. The marine fishery resource prediction device (10) according to claim 1, wherein, The sea state prediction information is based on at least one of the water temperature, salinity, and current velocity in the target sea area.

5. The marine fishery resource prediction device (10) according to claim 4, wherein, The sea state prediction information is based on the water temperature, salinity, and current velocity in the target sea area.

6. The marine fishery resource prediction device (10) according to claim 1, wherein, The prediction information calculation unit (40) is also configured to calculate at least one of the marine fishery resources in the predicted target sea area and the time change of the marine fishery resources as the marine fishery resources prediction information.

7. A method for predicting marine fishery resources, comprising: Fish school observation information in the predetermined sea area is obtained by the fish school observation information acquisition unit (20); The sea state prediction information acquisition unit (30) acquires sea state prediction information in the target sea area, including the predetermined sea area; and The prediction information calculation unit (40) calculates the prediction information of marine fishery resources in the target sea area based on the fish school observation information and the sea state prediction information.

8. A non-transitory computer-readable medium comprising program instructions for causing a computer to perform the following methods: Fish school observation information in the predetermined sea area is obtained by the fish school observation information acquisition unit (20); The sea state prediction information acquisition unit (30) acquires sea state prediction information in the target sea area, including the predetermined sea area; and The prediction information calculation unit (40) calculates the prediction information of marine fishery resources in the target sea area based on the fish school observation information and the sea state prediction information.

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