An east pacific squid fishing ground prediction precision improving method based on middle layer hydrological features and ship position state

Abstract

The application discloses a method for improving the prediction accuracy of east Pacific squid fishing ground based on middle layer hydrological characteristics and ship position state, adopts the middle layer water temperature, the isotherm dense area position, the ocean current vortex, the cold and warm vortex and the ship position state characteristics to multiple construct the accurate identification of the squid fishing ground, constructs the method of the center fishing ground through the real-time and predicted hydrological and ship position data indication, realizes the improvement of the fishing ground prediction accuracy, the application adopts the middle layer hydrological element to indicate the east Pacific fishing ground with higher accuracy, it is difficult to reflect the environment characteristics of the actual habitat water layer of the squid, so the middle layer hydrological characteristics more accurately reflect the environment characteristic field of the squid fishing ground; real-time ship position and ship position state analysis are used to grasp the position of the high yield area of the fishing ground in time, further improve the prediction accuracy of the fishing ground, achieve the purpose of improving the prediction accuracy, and are expected to improve 10-20%; effectively solve the difficulty of squid center fishing ground prediction, and provide accurate theoretical guidance for fishery production and fishing ground transfer.

Description

Technical Field

[0001] This invention belongs to the field of marine fishery fishing ground prediction technology, and in particular relates to a method for improving the accuracy of squid fishing ground prediction in the Eastern Pacific Ocean based on mid-level hydrological characteristics and vessel position status. Background Technology

[0002] With the advent of remote sensing, ocean models, vessel position monitoring, and the era of big data, fishing ground forecasting in distant-water fisheries is becoming increasingly refined and precise. This necessitates leveraging three-dimensional spatial exploration of the ocean and characterizing the marine environment at a fine scale. Combined with the patterns of fishing ground changes, refined fishing ground forecasting methods can be established. Furthermore, by utilizing real-time vessel positions and visualized fisheries system software, central fishing ground forecasting based on predicted hydrological environments and fleet dynamics can be achieved. In this scenario, the accuracy of fishing ground forecasting will be significantly improved compared to previous methods based on historical and delayed data.

[0003] The Eastern Pacific is one of the world's most important squid fishing grounds, with the main species being the squid (Saccharito spp.). Saccharito spp. has a wide distribution and high resource levels, supporting significant fishing pressure and yielding substantial catches. It is a crucial fishing ground for coastal states and the high seas. However, due to the significant fluctuations in the fishing grounds and the difficulty in achieving real-time visualization and availability of key reference indicators, accurate prediction of the fishing grounds in this region is challenging, and there is considerable room for improvement in accuracy. Therefore, it is necessary to utilize data mining of mid-level hydrological characteristics and real-time vessel position status to identify refined fishing grounds. Simultaneously, developing methods and processes to improve the accuracy of Eastern Pacific squid fishing ground prediction will help further guide fisheries production and enhance efficiency. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for improving the prediction accuracy of squid fishing grounds in the East Pacific based on mid-level hydrological characteristics and ship position status, which can summarize and generalize the methods for accurate prediction and accuracy improvement of squid fishing grounds in the East Pacific.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A method for improving the prediction accuracy of squid fishing grounds in the Eastern Pacific Ocean based on mid-layer hydrological characteristics and vessel position status is proposed. This method utilizes mid-layer (100-meter or 50-meter water layer) water temperature and the location of isotherm concentration zones, ocean current eddies (counterclockwise or clockwise eddies), warm and cold eddies, and vessel position status characteristics to construct a precise identification of squid fishing grounds. It employs a method that uses real-time and predicted hydrological and vessel position data to indicate the central fishing ground, thereby improving the prediction accuracy. The method includes the following steps:

[0007] (1) The location of squid fishing grounds in the eastern Pacific Ocean was initially determined by using the predicted mid-water temperature (preferably the 100-meter water layer, and secondarily the 50-meter water layer) and the location of the dense isotherm zones. The fishing grounds were further narrowed down by combining the dense isotherm zones around ocean current eddies, and five central fishing grounds were selected (see...). Figure 1 The optimal water temperature at a depth of 100 meters in the fishing grounds is 14.5–15.5℃ in January and 14.4–14.8℃ in February.

[0008] (2) The difference in the optimal water temperature at 100 meters in the fishing grounds for each month is 1 to 2°C; the optimal water temperature at 100 meters in each month is about 15°C.

[0009] (3) The probability of squid center fishing grounds forming within 30 to 60 nautical miles around the cold vortex and upwelling around the 100-meter water layer is relatively high; the probability of squid center fishing grounds forming around the warm vortex is relatively low.

[0010] (4) Determine the vessel position status by combining vessel position trajectory data during the fishing season; the vessel position is mainly divided into fishing and navigation, with vessel speeds of 0.1 to 1 knot during fishing and 4.5 to 8 knots during navigation; based on the vessel position distribution (small black dots, see...) Figure 2 It can roughly determine the concentrated distribution area of ​​squid central fishing grounds, and combined with the identification of real-time vessel position status, it can predict which fishing ground will be better.

[0011] (5) Based on the hydrological spatial characteristics of mid-layer water temperature, eddies, and upwelling currents identified in the preceding steps, the scope and number of squid central fishing grounds can be preliminarily determined. Combined with the real-time distribution of vessel positions and accurate identification of vessel status, the optimal central fishing grounds can be further selected (see...). Figure 3 This effectively improves the accuracy of fishing ground forecasts;

[0012] (6) By adopting a method based on real-time judgment of mid-level hydrological characteristics and vessel position status, the prediction accuracy of squid fishing grounds can be improved by 10-20%.

[0013] As a preferred embodiment, the changing patterns of squid fishing grounds in step (1) can be summarized by identifying real-time hydrological characteristics and vessel position status, as well as production data over a period of more than 3 months.

[0014] As a preferred embodiment, the water temperature in step (1) is preferably at a water layer of 100 meters, and secondarily at a water layer of 50 meters.

[0015] As a preferred embodiment, the isotherm density area and ocean current vortex direction in step (1) are predicted using the data of the fishing period on the same day, and can also predict the hydrological characteristics of the fishing ground for the next 1 to 3 days.

[0016] As a preferred embodiment, the upwelling in step (3) uses the predicted data of the fishing period on the same day, and can also predict the hydrological characteristics of the fishing grounds for the next 1 to 3 days.

[0017] As a preferred embodiment, the vessel position and position status determination in step (4) uses AIS or vessel position monitoring platform data; the vessel position status identification can be determined mainly by the speed threshold method; the vessel position is mainly for fishing and navigation, generally fishing at night local time, and navigating to transfer or explore fishing grounds during the day.

[0018] As a preferred embodiment, the water temperature and ocean current in step (5) should not be based on surface hydrological data, because the surface is affected by wind and waves and cannot accurately express the environmental field of the squid's habitat layer; the initially selected fishing grounds are 4 to 5; the fishing ground locations are further optimized by combining the fishing grounds with the vessel position status to determine the optimal central fishing ground.

[0019] In a preferred embodiment, the habitat water layer is selected from a water layer of 50 to 150 meters.

[0020] As a preferred embodiment, the accuracy of squid fishing ground forecasting in step (6) can be further improved. Short-term forecasts can be achieved by using the hydrological characteristics of the next few days, and provide an effective basis for fishing vessel production decisions and fishing ground relocation.

[0021] Beneficial effects: Compared with the prior art, the present invention has the following advantages and positive effects:

[0022] 1. Using mid-level hydrological elements to indicate the fishing grounds in the eastern Pacific Ocean has higher accuracy. Surface ocean currents and surface water temperature are affected by wind and waves and are difficult to reflect the environmental characteristics of the actual water layer in which squid inhabit. Therefore, mid-level hydrological characteristics more accurately reflect the environmental characteristics of squid fishing grounds.

[0023] 2. By using real-time vessel position and status analysis, the location of high-yield fishing areas can be determined in a timely manner, further improving the accuracy of fishing ground forecasts and achieving the goal of improving forecast precision, which is expected to increase by 10-20%.

[0024] 3. It effectively solves the dilemma of predicting the central fishing grounds of squid, and provides accurate theoretical guidance for fishery production and the relocation of fishing grounds. Attached Figure Description

[0025] Figure 1 A schematic diagram showing the relationship between the central fishing grounds and hydrological characteristics of squid in the eastern Pacific Ocean.

[0026] Figure 2 A schematic diagram showing the relationship between the trajectory characteristics of squid fishing vessels in the eastern Pacific Ocean and the indication of the central fishing grounds.

[0027] Figure 3 A schematic diagram illustrating the process for improving the accuracy of squid fishing ground prediction in the Eastern Pacific. Detailed Implementation

[0028] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0029] Example 1:

[0030] like Figure 1 , 2 As shown in Figure 3, a method for improving the prediction accuracy of squid fishing grounds in the Eastern Pacific Ocean based on mid-layer hydrological characteristics and vessel position status is proposed. This method utilizes mid-layer (100-meter or 50-meter water layer) water temperature and the location of isotherm concentration zones, ocean current eddies (counterclockwise or clockwise eddies), warm and cold eddies, and vessel position status characteristics to construct a multiple framework for accurate squid fishing ground identification. The method constructs a central fishing ground by using real-time and predicted hydrological and vessel position data to indicate the fishing grounds, thereby improving the prediction accuracy. The method includes the following steps:

[0031] (1) The location of squid fishing grounds in the eastern Pacific Ocean was initially determined by using the predicted mid-water temperature (preferably the 100-meter water layer, and secondarily the 50-meter water layer) and the location of the dense isotherm zones. The fishing grounds were further narrowed down by combining the dense isotherm zones around ocean current eddies, and five central fishing grounds were selected (see...). Figure 1 The optimal water temperature at a depth of 100 meters in the fishing grounds is 14.5–15.5℃ in January and 14.4–14.8℃ in February.

[0032] (2) The difference in the optimal water temperature at 100 meters in the fishing grounds for each month is 1 to 2°C; the optimal water temperature at 100 meters in each month is about 15°C.

[0033] (3) The probability of squid center fishing grounds forming within 30 to 60 nautical miles around the cold vortex and upwelling around the 100-meter water layer is relatively high; the probability of squid center fishing grounds forming around the warm vortex is relatively low.

[0034] (4) Determine the vessel position status by combining vessel position trajectory data during the fishing season; the vessel position is mainly divided into fishing and navigation, with vessel speeds of 0.1 to 1 knot during fishing and 4.5 to 8 knots during navigation; based on the vessel position distribution (small black dots, see...) Figure 2 It can roughly determine the concentrated distribution area of ​​squid central fishing grounds, and combined with the identification of real-time vessel position status, it can predict which fishing ground will be better.

[0035] (5) Based on the hydrological spatial characteristics of mid-layer water temperature, eddies, and upwelling currents identified in the preceding steps, the scope and number of squid central fishing grounds can be preliminarily determined. Combined with the real-time distribution of vessel positions and accurate identification of vessel status, the optimal central fishing grounds can be further selected (see...). Figure 3 This effectively improves the accuracy of fishing ground forecasts;

[0036] (6) By adopting a method based on real-time discrimination of mid-level hydrological characteristics and vessel position status, the prediction accuracy of squid fishing grounds can be improved by 10-20%;

[0037] The upwelling in step (3) uses the predicted data of the fishing period on the day, and can also predict the hydrological characteristics of the fishing grounds for the next 1 to 3 days.

[0038] The vessel position and position status determination in step (4) uses AIS or vessel position monitoring platform data; the vessel position status identification mainly uses the speed threshold method; the vessel position is mainly for fishing and navigation, generally fishing at night local time, and navigating to transfer or explore fishing grounds during the day.

[0039] The water temperature and ocean current data in step (5) should not be based on surface hydrological data, as the surface is affected by wind and waves and cannot accurately represent the environmental field of the squid's habitat. The initially selected fishing grounds are 4 to 5. The fishing ground locations are further optimized by combining the fishing grounds with the vessel positions to determine the optimal central fishing ground.

[0040] The selected habitat water layer is a water layer of 50 to 150 meters.

[0041] The accuracy of squid fishing ground forecasting in step (6) can be further improved. Short-term forecasts can be made based on the hydrological characteristics of the next few days, providing an effective basis for fishing vessel production decisions and fishing ground relocation.

[0042] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for improving the prediction accuracy of East Pacific squid fishing grounds based on mesoscale hydrographic features and ship position state, characterized in that, The method comprises the following steps: (1) The position of the central squid fishing ground is determined by using the predicted central water temperature and the position of the isotherm dense area, the ocean current vortex, the cold and warm eddies, and the ship position state characteristics. The prediction accuracy of the fishing ground is improved by indicating the method of constructing the central fishing ground through real-time and predicted hydrological and ship position data, including the following steps: (1) The position of the central squid fishing ground is determined by using the predicted central water temperature and the position of the isotherm dense area, the ocean current vortex, the cold and warm eddies, and the ship position state characteristics. The prediction accuracy of the fishing ground is improved by indicating the method of constructing the central fishing ground through real-time and predicted hydrological and ship position data, including the following steps: (1) The position of the central squid fishing ground is determined by using the predicted central water temperature and the position of the isotherm dense area, the ocean current vortex, the cold and warm eddies, and the ship position state characteristics. The prediction accuracy of the fishing ground is improved by indicating the method of constructing the central fishing ground through real-time and predicted hydrological and ship position data, including the following steps: (2) The difference between the optimal water temperature of the 100-meter water layer in each month is 1-2℃; the optimal water temperature of the 100-meter water layer in each month is 15℃; (3) The cold eddy and the upwelling flow around the 100-meter water layer have a higher probability of forming a central squid fishing ground; the warm eddy around the 100-meter water layer has a lower probability of forming a central squid fishing ground; (4) The ship position state is determined by combining the ship position trajectory data in the fishing season; the ship position is divided into fishing and sailing, the ship speed during fishing is 0.1-1 knots, and the ship speed during sailing is 4.5-8 knots; the central distribution area of the squid central fishing ground can be predicted according to the ship position aggregation distribution characteristics, and the probability of the formation of the fishing ground in the central distribution area of the fishing ground is high by combining the real-time ship position fishing state recognition; (5) The range and number of the central squid fishing ground are preliminarily determined according to the central water temperature, vortex, and upwelling ocean current hydrological space characteristics in the foregoing steps, and the best central fishing ground is further selected by combining the real-time ship position distribution and the accurate recognition of the ship position state, so as to effectively improve the prediction accuracy of the fishing ground; (6) The prediction accuracy of the squid fishing ground can be improved by 10-20% by using the method based on the real-time recognition of the central hydrological characteristics and the ship position state; The variation law of the squid fishing ground in step (1) can be summarized by identifying the real-time hydrological characteristics and the ship position state and production data for more than 3 months; The central water temperature in step (1) is preferably the 100-meter water layer, and the 50-meter water layer is used as a reference; The isotherm dense area and the direction of the ocean current vortex in step (1) use the predicted data during the fishing period of the same day, or the hydrological characteristic field of the fishing ground in the future 1-3 days; The upwelling flow in step (3) uses the predicted data during the fishing period of the same day, or the hydrological characteristic field of the fishing ground in the future 1-3 days; The ship position and ship position state recognition in step (4) use AIS or ship position monitoring platform data; the ship position state recognition can be determined by using the speed threshold method; the ship position is fishing and sailing, fishing at night and sailing during the day to transfer or detect the fishing ground; The water temperature and ocean current in step (5) should not use the surface hydrological data, because the surface is affected by wind and wave mixing, and cannot accurately express the environment field of the squid habitat water layer; the preliminarily selected fishing ground is 4-5; the position of the fishing ground is further optimized by combining the ship position state to determine the optimal central fishing ground; The habitat water layer is selected to be 50-150 meters.

2. The method of claim 1, wherein the method is characterized by, The accuracy of the squid fishing ground prediction of step (6) can be further improved, and short-term prediction can be realized through hydrological characteristics in the next few days, thereby providing effective basis for fishing boat production decision and transfer of fishing ground.

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