WO2020088615A1 - Todarodes pacificus resource abundance prediction method and application based on pacific decadal oscillation - Google Patents

Abstract

A Todarodes pacificus resource abundance prediction method based on Pacific Decadal Oscillation. The method comprises: acquiring monthly Pacific Decadal Oscillation (PDO) values of the sea areas where Todarodes pacificus was distributed in previous N years; using a time sequence analysis method to perform correlation analysis on a Todarodes pacificus resource abundance CPUE and the monthly PDO values in the previous N years, selecting monthly PDO values which are significantly associated statistically, numbering these selected monthly PDO values in sequence, and marking same as x₁, x₂, x₃...x_z...x_m in sequence; establishing at most 2^m-1 Todarodes pacificus resource abundance prediction models for any one to m climate factors by using a multivariate linear equation, and calculating a P value of each prediction model; and selecting a model with the minimum P value as the optimal model. In the method, Pacific Decadal Oscillation (PDO) values are used to realize Todarodes pacificus resource abundance prediction, which has a guiding function for fishery production, improves fishing efficiency, reduces fishing costs, and has good application prospects.

Description

Forecasting method and application of abundance of Japanese squid resources based on Pacific turbulence index Technical field

The invention belongs to the technical field of squid resource prediction, and relates to a method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index and its application.

Background technique

Todarodes pacificus (also known as Japanese squid) is an important economic cephalopod resource in the world, and it is only distributed in the northwestern Pacific Ocean and the Alaska Bay in the eastern Pacific Ocean. It is mainly distributed in the 21 ° -50 ° N waters of the western Pacific, namely the Japan Sea, the Pacific coast of Japan, and the Yellow Sea and East China Sea in China. It is a warm temperate oceanic shallow sea species, inhabiting the surface layer to 500m water layer, with a wide temperature range. According to the spawning season, growth type and migration path of Pacific pleated fish, it can be divided into three groups: winter group, autumn group and summer group. They have different life cycles, but have the same habits. The winter group is the most widely distributed. Before the 1970s, the number of this group was the largest. Its spawning ground is located on the outer edge of the continental shelf in southwestern Kyushu, mainly concentrated in the central and northern parts of the East China Sea. The spawning period is from January to March. In the spring and summer, the northern part of the Japanese archipelago seeks for bait, and in the autumn and winter, it goes south to spawn.

Pacific pleated squid is one of the earliest species of cephalopods developed and utilized on a large scale in the world. Before the 1970s, its output accounted for 70-80% of the total domestic cephalopod production. According to FAO statistics, the total production of Pacific pleated squid reached its highest level in history in 1968, exceeding 750,000 tons. However, due to the increase in fishing intensity, future production will decline year by year. The lowest output since 1950 was reached in 1986, with only more than 120,000 tons. Since then, there has been a continuous increase, until 1996, annual production reached nearly 700,000 tons. After that, it declined again, and the current production of Pacific pleated squid is stable at 320,000 to 420,000 tons.

The main catch of the autumn shoal of Pacific pleated fish is from the Sea of Japan, and the main fishing season is from May to October. Japan and South Korea are the main fishing countries. Others include North Korea and China, and there are also small amounts of fishing. The coastal waters are mainly small squid fishing boats (less than 30 tons), and the catch is fresh. In the open sea, it is a medium-sized squid fishing boat (30 to 185 tons), and the catch is frozen. In addition to squid fishing, there are fixed net operations and bottom trawl operations. In the late 1970s, Japan ’s output reached 300,000 tons, and then decreased. It was only about 50,000 tons in 1986, and then increased. It stabilized at 70,000 to 180,000 tons in the 1990s. In recent years, the cumulative catches of Japan and South Korea have ranged from 100,000 to 200,000 tons.

The winter community of Pacific pleated soft squid is mainly based on fishing in Japan and South Korea, and the operations mainly include fishing, bottom towing, fixed nets, and purse seine. The main fishing grounds start from Joban in July to the Pacific coast of Sanriku. From September to November, they move to the Pacific coast of Hokkaido. After November, the main fishing grounds move to the side of the Sea of Japan. The fishing season ends (December to February). ) The fishing ground is in the northwestern waters of Kyushu. The catch of the winter life group reached its peak in the 1950s and 1960s. The main fishing ground is in the Pacific Ocean in eastern Hokkaido. In 1968, the catch was 560,000 tons, which accounted for 84% of the catch of Pacific pleated fish in Japan. After that, the catch fell sharply and reached its lowest level in the 1980s. After 1989, the catch has rebounded, reaching 380,000 tons in 1996. Since then, the catch has been changing significantly. At present, the catch is between 200,000 and 300,000 tons.

Pacific pleated fish resources are susceptible to marine environmental factors. Zhang Shuo et al. (2017) based on the unit fishing effort (CPUE) of Pacific pleated herring winter colony from 2000 to 2010, and the spawning site (28 ° ～ 40 ° N, 125 ° ～ 140 ° E) sea surface temperature (SST) data, select statistically significant SST as a factor affecting resource abundance, and establish resource abundance prediction models of multivariate linear and BP neural networks, respectively. Studies have shown that the surface temperatures in the waters near 30 ° ～ 32 ° N, 135 ° ～ 138 ° E and 37 ° ～ 38 ° N, 129 ° ～ 131 ° E represent the warm current of the spawning ground from January to March (Kuroshio and The strength of Ma Hailiu's forces determines the resource abundance of Pacific pleated fish in the year. The BP neural network model can be used as a prediction model for its resource abundance. Feifei Hu et al. (2015) calculated and analyzed the Pacific pleated squid during spawning period based on Japan ’s resource assessment report on the autumn group of Pacific pleated fish, as well as the sea surface temperature (SST) and chlorophyll a concentration (Chl-a) of the spawning ground Correlation between various environmental factors such as the ratio of the optimal surface temperature range to the total area (PS), tSST, Chl-a, etc., which characterize the environment of the spawning ground, and the catch per unit of fishing effort (CPUE) , Establish a variety of resource supplement forecast models based on major environmental factors. The above research shows that scholars at home and abroad have done a very good research on the environmental impact of Japanese squid / winter squid spawning grounds on their resource replenishment, and established a corresponding resource prediction model, but how to use climate factors It is blank to predict its resource amount in advance.

Therefore, it is very practical to develop a method to predict the abundance of Japanese squid resources based on climate factors.

Summary of the invention

The purpose of the present invention is to overcome the problems and deficiencies of the prior art, and to provide a method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index and its application.

To achieve the above objectives, the present invention provides the following technical solutions:

The method for predicting the abundance of Japanese squid resources based on the Pacific Shock Index is applied to electronic equipment to guide the ocean fishing of Japanese squid. The steps are as follows:

1) Obtain the monthly Pacific Oscillation Index PDO value of the sea area where the Japanese squid is distributed in the previous N years. The Japanese squid is the Japanese autumn squid or the Japanese winter squid;

(2) Using the time series analysis method, the correlation analysis of the CPUE of Japanese squid resource abundance and the monthly PDO value of the previous N years is carried out, and the monthly PDO values that are statistically significant correlation are selected, and the monthly PDO values are selected As a climatic factor affecting the abundance of Japanese squid resources, the selected monthly PDO values are numbered in the order of ₁ , ₂ , 3 ... z ... m, and these monthly PDO values are sequentially recorded as x ₁ , x ₂ , and x ₃ … X _z … x _m , where m is the number of monthly PDO values selected;

(3) For any 1 ～ m climatic factors in x ₁ , x ₂ , x ₃ …… x _m , use multivariate linear equations to establish up to 2 ^m -1 prediction models of abundance of Japanese squid resources, and calculate the statistics of each prediction model The value of P on the formula, where the formula for the Japanese squid resource abundance prediction model is as follows:

CPUE ＝ a + b ₁ * x ₁ + b

₂ * x ₂ + b ₃ * x ₃ +… + b _z * x _z +… + b _m * x _m ;

In the formula, CPUE is the daily output of small and medium squid fishing boats in Japan, a is a constant, b ₁ , b ₂ , b ₃ , ... b _z …, b _n are corresponding to x ₁ , x ₂ , x ₃ … x _z … x coefficient of _m ;

The maximum number of Japanese squid resource abundance prediction models is calculated based on the following logic. For a climate factor, there are

Models, for the two climate factors, there are

Models, and so on, for z climate factors, there are

Models, for m climate factors, there are

Models, and then add the number of models to get

(4) Among the above 2 ^m -1 abundance prediction models of Japanese squid resources, the model with the smallest statistical P value is selected as the optimal model.

The method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index of the present invention establishes for the first time the relationship between the prediction of the climate factor (Pacific Oscillation Index) and the abundance of Japanese squid resources. The rapid and accurate prediction of the abundance of squid resources can play a good guiding role in marine fishery production (fishing of Japanese squid in autumn / winter), can significantly improve fishing efficiency, reduce fishing costs, and have great application prospects. At the same time, The optimal model obtained by the present invention is not static, and the optimal model can be obtained again according to the latest data acquired in real time. The method of the present invention has good adaptability and good application prospects.

As the preferred technical solution:

As mentioned above, the prediction method for the abundance of Japanese squid based on the Pacific Shock Index, after obtaining the optimal model, obtain the corresponding x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, and input it Optimal model, the optimal model output Japanese abundance of squid resources is completed to predict the abundance of Japanese squid resources.

As mentioned above, the prediction method for the abundance of Japanese squid resources based on the Pacific Oscillation Index. The Japanese squid prediction model is established for a climatic factor x _z , then b ₁ , b ₂ , b ₃ , ... b _z-1 , b _{z + 1} , ..., b _n are all 0, and so on, and so on, if the Japanese squid resource abundance prediction model is established for the climate factors x ₁ and x _z , then b ₂ , b ₃ , ... b _z-1 , b _{z + 1} , ..., b _n are all 0.

As described above for the prediction method of the abundance of Japanese squid based on the Pacific Oscillation Index, the statistically significant correlation means that the calculated P value is <0.05. Among them, the P value is a parameter statistically used to determine the hypothesis test result. The P value (P value) is the probability that the sample observation result or the extreme result obtained when the original false is set to true. If the P value is small, the probability of the null hypothesis situation is very small, and if it occurs, according to the principle of small probability, there is a reason to reject the null hypothesis. The smaller the P value, the more reason we reject the null hypothesis. In short, the smaller the P value, the more significant the results.

The sea area where squid is distributed in the Japanese autumn group is the sea area of Japan;

The sea area where squid is distributed in the Japanese winter group is the Pacific Ocean area of Hokkaido.

The method for predicting the abundance of Japanese squid resources based on the Pacific volatility index as described above, the Japanese squid is Japanese autumn squid;

In step (2), two statistically significant monthly PDO values were selected, which were the October PDO value of the previous 2 years and the October PDO value of the previous year. The correlation with the PDO value of October in the previous 2 years was significant, showing a negative correlation, and the correlation coefficient was -0.390 (P <0.05). The CPUE of the squid resource abundance in the Japanese autumn group was significantly correlated with the October PDO value in the previous year. It showed a negative correlation with a correlation coefficient of -0.4486 (P <0.05);

According to the correlation analysis between the CPUE of Japanese squid resource abundance and the PDO value of each month of the same year, the correlation between the CPUE of Japanese squid resource abundance and the PDO value of January-December of the same year is not significant.

The number of prediction models mentioned here and below is only a part of the data to demonstrate the operation logic of the prediction method of the present invention. The scope of protection of the present invention is not limited to this. Those skilled in the art can select appropriate data pairs according to actual needs. To predict the abundance of squid resources in Japan, the number of statistically significant monthly PDO values selected is not limited to two, and the number of prediction models will also vary with the number of statistically significant monthly PDO values selected And change.

As mentioned above, the prediction method for the abundance of Japanese squid based on the Pacific Shock Index has established three prediction models in step (3). The prediction models are as follows:

1) Prediction model I

Aiming at the PDO value of October 2 in the first 2 years of the climate factor x ₁₁ and the PDO value of October in the previous year x ₁₂ of the squid resource abundance prediction model of the Japanese Sea Qiusheng Group, specifically:

CPUE = 2.3463-0.1674 * x ₁₁ -0.1977 * x ₁₂ ;

Its F value is 4.9268, P = 0.0161 <0.05; where F value is the statistic value of F test, F test is a test under the null hypothesis (null hypothesis, H0), the statistic value follows the F-distribution, which It is usually used to analyze a statistical model that uses more than one parameter to determine whether all or part of the parameters in the model are suitable for estimating the parent, which is to test the linear relationship;

2) Prediction model II

Aiming at the PDO value x ₁₁ in the first 2 years of the climatic factor, a model for predicting the abundance of squid resources in the Japanese sea squid was established, specifically:

CPUE = 2.3894-0.2127 * x ₁₁ ;

Its F value is 4.4922, P = 0.0442 <0.05;

3) Prediction model III

Based on the October PDO value x _{12 of the} previous year of the climatic factor, a model for predicting the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

CPUE = 2.3958-0.2323 * x ₁₂ ;

The F value is 6.2984, P = 0.0189 <0.05.

As mentioned above, the prediction method of the Japanese squid resource abundance based on the Pacific Shock Index, in step (4), the prediction model I is selected as the optimal model, and the optimal model is:

CPUE = 2.3463-0.1674 * x ₁₁ -0.1977 * x ₁₂ .

The method for predicting the abundance of Japanese squid resources based on the Pacific Shock Index as described above, the Japanese squid is Japanese winter squid;

In step (2), ten statistically significant monthly PDO values were selected, which were the PDO values of October, November and December of the previous 2 years, and the PDO values of 1, 2, 3 and April of the previous year And the PDO values in January, February, March and April of the same year;

Among them, the CPUE of the Japanese squid resource abundance was significantly correlated with the PDO values of October, November and December of the previous two years, showing a negative correlation, and the correlation coefficients were -0.4506 (P <0.05) and -0.4985 (P < 0.05) and -0.5878 (P ＜ 0.01);

The CPUE of the squid resource abundance in the Japanese winter squid was significantly correlated with the PDO values in January, February, March and April of the previous year, showing a negative correlation. The correlation coefficients were -0.4665 (P <0.05) and -0.4365 (P < 0.05), -0.4295 (P <0.05) and -0.5072 (P <0.01);

The CPUE of abundance of squid resources in Japanese winter squid is significantly correlated with the PDO values in January, February, March and April of the same year, showing a negative correlation. , -0.5458 (P <0.01) and -0.5570 (P <0.01).

As mentioned above, the prediction method of the Japanese squid resource abundance based on the Pacific Shock Index has established five prediction models in step (3). The prediction models are as follows:

1) Prediction model one

For the climate factor, the November PDO value x ₂₁ for the first 2 years, the December PDO value x _{22 for the} previous 2 years, the February PDO value x ₂₃ for the same year, the March PDO value x _{24 for the} same year, and the March PDO for the previous year Value x ₂₅ , to establish a prediction model of squid resource abundance in the Qiusheng group in the Japanese Sea, specifically:

CPUE = 1.2048 + 0.0330 * x ₂₂ -0.1811 * x ₂₁ -0.2260 * x ₂₃ + 0.0749 * x ₂₄ -0.0196 * x ₂₅ ;

Its F value is 4.5183, P = 0.0069 <0.01;

2) Prediction model 2

X _21, two years before the December PDO value x _22, the same year in February PDO value x ₂₃ of the same year and in March PDO value x ₂₄ for November PDO value two years ago climatic factors, the establishment of the Sea of Japan Akio squid resource group Abundance prediction model, specifically:

CPUE = 1.1968 + 0.0273 * x ₂₂ -0.1865 * x ₂₁ -0.2290 * x ₂₃ + 0.0740 * x ₂₄ ;

Its F value is 5.9135, P = 0.0026 <0.01;

3) Prediction model three

In the same year the climate factor February PDO value x _23, the same year on March ₂₄ and PDO value before 1 March of PDO value x x _25, the establishment of squid resource abundance Sea of Japan Akio group forecasting models, in particular:

CPUE = 1.3257-0.1120 * x ₂₃ -0.0461 * x ₂₄ -0.1307 * x ₂₅ ;

Its F value is 5.1699, P = 0.0078 <0.01;

4) Prediction model 4

Aiming at the climatic factor's February PDO value x _{23 in the} same year and March PDO value x _{24 in the} same year, a prediction model for the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

CPUE = 1.3093-0.0792 * x ₂₃ -0.1223 * x ₂₄ ;

Its F value is 5.1233, P = 0.0149 <0.05;

5) Prediction model five

Aiming at the November PDO value x _{21 of the} first 2 years of the climatic factor and the December PDO value x _{22 of the} previous 2 years, a model for predicting the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

CPUE = 1.1582 + 0.0383 * x ₂₂ -0.2125 * x ₂₁ ;

Its F value is 5.8894, P = 0.0089 <0.01.

As mentioned above, the prediction method for the abundance of Japanese squid based on the Pacific Shock Index, in step (4), the prediction model 2 is selected as the optimal model, and the optimal model is:

CPUE = 1.1968 + 0.0273 * x ₂₂ -0.1865 * x ₂₁ -0.2290 * x ₂₃ + 0.0740 * x ₂₄ .

On the basis of meeting the common knowledge in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred examples of the present invention.

The invention also provides an electronic device, including one or more processors, one or more memories, one or more programs, and a data collection device;

The data collection device is used to obtain x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, the one or more programs are stored in the memory, when the one or more When the program is executed by the processor, the electronic device is caused to perform the method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index as described above.

Beneficial effect:

(1) The method for predicting the abundance of Japanese squid based on the Pacific Oscillation Index of the present invention uses the Pacific Oscillation Index PDO to realize the prediction of the abundance of Japanese squid resources, which can play a good role in marine fishery production (fishing of Japanese squid) The guiding role can significantly improve fishing efficiency, reduce fishing costs, and has great application prospects;

(2) The optimal method of the Japanese squid resource abundance prediction method based on the Pacific Shock Index of the present invention is not static, and the optimal model can be re-acquired based on the latest data obtained in real time. The method of the present invention has good adaptability and application prospects it is good;

(3) The electronic device of the present invention has a simple structure and low cost, can quickly realize the prediction of the abundance of Japanese squid resources based on the Pacific Oscillation Index PDO, and has great application prospects.

BRIEF DESCRIPTION

1 is a flowchart of a method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index of the present invention;

Figure 2 is a graph of CPUE annual abundance changes of squid resource abundance in Japanese autumn groups from 1990 to 2016;

Figure 3 is the distribution map of the CPUE actual value and predicted value of the squid resource abundance in the Japanese sea autumn group from 1990 to 2016;

Figure 4 is a schematic diagram of the CPUE annual abundance change of the squid resource abundance of Japanese winter squid from 1992 to 2016;

Figure 5 is the distribution map of the actual value and the predicted value of CPUE of the abundance of squid in the Japanese sea winter group from 1992 to 2016;

6 is a schematic structural diagram of an electronic device of the present invention.

detailed description

To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, but not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

As shown in Figure 1, a method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index is used to guide the offshore fishing of Japanese squid, which includes the following steps:

Step 101: Obtain the monthly Pacific Oscillation Index PDO value of the sea area where the Japanese squid is distributed in the previous N years. The Japanese squid is the Japanese autumn squid or the Japanese winter squid, and the Japanese autumn squid is the Japanese sea. The Japanese winter The sea area where the squid is distributed is the Pacific side of Hokkaido;

Step 102: Using a time series analysis method, perform a correlation analysis between the CPUE of Japanese squid resource abundance and the monthly PDO value of the previous N years, and select the monthly PDO values that are statistically significant correlation among them, and select the monthly PDO values of these months As a climatic factor affecting the abundance of Japanese squid resources, the selected monthly PDO values are numbered in the order of ₁ , ₂ , 3 ... z ... m, and these monthly PDO values are sequentially recorded as x ₁ , x ₂ , and x ₃ … X _z … x _m , where m is the number of monthly PDO values selected;

Step 103: Use multivariate linear equations to establish up to 2 ^m -1 prediction models of abundance of Japanese squid resources for any 1 to m climate factors in x ₁ , x ₂ , x ₃ … x _m , and calculate the statistics of each prediction model The value of P on the formula, where the formula for the Japanese squid resource abundance prediction model is as follows:

CPUE = a + b ₁ * x ₁ + b ₂ * x ₂ + b ₃ * x ₃ + ... + b _z * x _z + ... + b _m * x _m ;

In the formula, CPUE is the daily output of small and medium squid fishing boats in Japan, a is a constant, b ₁ , b ₂ , b ₃ , ... b _z …, b _n are corresponding to x ₁ , x ₂ , x ₃ … x _z … x coefficient of _m ;

Step 104: Among the above 2 ^m -1 Japanese squid resource abundance prediction models, select the model with the smallest statistical P value as the optimal model;

Step 105: Obtain x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, and input it into the optimal model, and output the optimal model to the abundance of Japanese squid resources. Degree of prediction.

Example 1

1. Materials and methods

(1) Data source

Japanese autumn squid are widely distributed in the waters around the Sea of Japan, and the main fishing grounds are distributed in the Sea of Japan. The environmental conditions of their spawning and feeding grounds are easily affected by the Pacific Decadal Oscillation Index (PDO). The Pacific Oscillation Index is a phenomenon of Pacific climate change that changes on a 10-year cycle scale. The conversion period is usually 20 to 30 years. The PDO is characterized by an abnormally warm or cold surface seawater temperature in the area north of 20 degrees north latitude of the Pacific Ocean. During the "warm phase" (or "positive phase") of the 10-year Pacific Oscillation, the western Pacific is colder and the eastern Pacific is warmer. During the "cold phase" (or "negative phase"), the western Pacific is warmer and the eastern Pacific is colder. . PDO comes from the website of the University of Washington (http://research.jisao.washington.edu/pdo/PDO.latest.txt), and its time period is from January 1988 to December 2017 (Table 1).

The Japanese abundance squid resource abundance index CPUE (in tons per ship) comes from the production of small and medium-sized squid fishing vessels in Japan from 1990 to 2016 (Table 2).

Table 1 Monthly statistical table of Pacific volatility index from January 1988 to December 2017

years	January	February	March	April	May	June	July	August	September	October	November	December
1988	0.93	1.24	1.42	0.94	1.2	0.74	0.64	0.19	-0.37	-0.1	-0.02	-0.43
1989	-0.95	-1.02	-0.83	-0.32	0.47	0.36	0.83	0.09	0.05	-0.12	-0.5	-0.21
1990	-0.3	-0.65	-0.62	0.27	0.44	0.44	0.27	0.11	0.38	-0.69	-1.69	-2.23
1991	-2.02	-1.19	-0.74	-1.01	-0.51	-1.47	-0.1	0.36	0.65	0.49	0.42	0.09
1992	0.05	0.31	0.67	0.75	1.54	1.26	1.9	1.44	0.83	0.93	0.93	0.53
1993	0.05	0.19	0.76	1.21	2.13	2.34	2.35	2.69	1.56	1.41	1.24	1.07
1994	1.21	0.59	0.8	1.05	1.23	0.46	0.06	-0.79	-1.36	-1.32	-1.96	-1.79
1995	-0.49	0.46	0.75	0.83	1.46	1.27	1.71	0.21	1.16	0.47	-0.28	0.16
1996	0.59	0.75	1.01	1.46	2.18	1.1	0.77	-0.14	0.24	-0.33	0.09	-0.03
1997	0.23	0.28	0.65	1.05	1.83	2.76	2.35	2.79	2.19	1.61	1.12	0.67
1998	0.83	1.56	2.01	1.27	0.7	0.4	-0.04	-0.22	-1.21	-1.39	-0.52	-0.44
1999	-0.32	-0.66	-0.33	-0.41	-0.68	-1.3	-0.66	-0.96	-1.53	-2.23	-2.05	-1.63
2000	-2	-0.83	0.29	0.35	-0.05	-0.44	-0.66	-1.19	-1.24	-1.3	-0.53	0.52
2001	0.6	0.29	0.45	-0.31	-0.3	-0.47	-1.31	-0.77	-1.37	-1.37	-1.26	-0.93
2002	0.27	-0.64	-0.43	-0.32	-0.63	-0.35	-0.31	0.6	0.43	0.42	1.51	2.1
2003	2.09	1.75	1.51	1.18	0.89	0.68	0.96	0.88	0.01	0.83	0.52	0.33
2004	0.43	0.48	0.61	0.57	0.88	0.04	0.44	0.85	0.75	-0.11	-0.63	-0.17
2005	0.44	0.81	1.36	1.03	1.86	1.17	0.66	0.25	-0.46	-1.32	-1.5	0.2
2006	1.03	0.66	0.05	0.4	0.48	1.04	0.35	-0.65	-0.94	-0.05	-0.22	0.14
2007	0.01	0.04	-0.36	0.16	-0.1	0.09	0.78	0.5	-0.36	-1.45	-1.08	-0.58
2008	-1	-0.77	-0.71	-1.52	-1.37	-1.34	-1.67	-1.7	-1.55	-1.76	-1.25	-0.87
2009	-1.4	-1.55	-1.59	-1.65	-0.88	-0.31	-0.53	0.09	0.52	0.27	-0.4	0.08
2010	0.83	0.82	0.44	0.78	0.62	-0.22	-1.05	-1.27	-1.61	-1.06	-0.82	-1.21
2011	-0.92	-0.83	-0.69	-0.42	-0.37	-0.69	-1.86	-1.74	-1.79	-1.34	-2.33	-1.79
2012	-1.38	-0.85	-1.05	-0.27	-1.26	-0.87	-1.52	-1.93	-2.21	-0.79	-0.59	-0.48
2013	-0.13	-0.43	-0.63	-0.16	0.08	-0.78	-1.25	-1.04	-0.48	-0.87	-0.11	-0.41
2014	0.3	0.38	0.97	1.13	1.8	0.82	0.7	0.67	1.08	1.49	1.72	2.51
2015	2.45	2.3	2	1.44	1.2	1.54	1.84	1.56	1.94	1.47	0.86	1.01
2016	1.53	1.75	2.4	2.62	2.35	2.03	1.25	0.52	0.45	0.56	1.88	1.17
2017	0.77	0.7	0.74	1.12	0.88	0.79	0.1	0.09	0.32	0.05	0.15	0.5

Table 2 Daily production of small and medium squid fishing boats in Japan from 1990 to 2016

years	Daily output per ship (ton / ship)
1990	1.525
1991	1.517
1992	1.973
1993	2.149
1994	1.789
1995	2.134
1996	2.999

1997	3.117
1998	2.514
1999	2.754
2000	2.749
2001	3.465
2002	3.692
2003	3.343
2004	2.276
2005	2.293
2006	2.825
2007	2.192
2008	3.146
2009	2.895
2010	2.290
2011	2.374
2012	2.555
2013	2.279
2014	2.076
2015	1.839
2016	1.812

(2) Research methods and steps

Using the daily production CPUE of Japanese small and medium-sized squid fishing vessels as an indicator of the squid resource abundance in the Japanese squid group, using time series analysis methods, the CPUE of the squid resource abundance in the Japanese squid group was correlated with the PDO value from January to December 1988-2016 Analysis, select monthly PDO values that are statistically significant (P value <0.05), and use these monthly PDO values as the climatic factors that affect the abundance of squid resources in the Japanese autumn group, according to 1, 2, 3 ... z … M sequentially number the selected monthly PDO values, and record these monthly PDO values as x ₁ , x ₂ , x ₃ … x _z … x _m , m is the number of selected monthly PDO values;

For any 1 ～ m climatic factors in x ₁ , x ₂ , x ₃ …… x _m , multivariate linear equations are used to establish up to 2 ^m -1 prediction models of squid resource abundance in the Japanese autumn group, and the calculation of each prediction model is statistically The value of P, where the formula for the prediction model of squid resource abundance in the Japanese autumn group is as follows:

CPUE = a + b ₁ * x ₁ + b ₂ * x ₂ + b ₃ * x ₃ + ... + b _z * x _z + ... + b _m * x _m ;

In the formula, CPUE is the daily output of a single ship, a is a constant, b ₁ , b ₂ , b ₃ ,… b _z …, b _n are coefficients corresponding to x ₁ , x ₂ , x ₃ … x _z … x _m , respectively;

Among the above 2 ^m -1 prediction models of squid resource abundance in the Japanese autumn group, the model with the smallest statistical P value is selected as the optimal model;

Obtain the corresponding x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, and input it into the optimal model, and the optimal model outputs the abundance of squid resources of the Japanese squid group. Quantity forecast.

2. Research results

(1) Changes in CPUE during the year

It can be seen from Figure 2 that the CPUE of Japan's autumn squid resource abundance CPUE showed significant inter-annual changes, and it was at a low resource level in 1990-1992, 2001-2002, 2004-2005, and 2015-2016; and 2001-2003 , 2008-2009 was at a high level of resources.

(2) PDO value that affects resource abundance CPUE

The correlation analysis of the resource abundance CPUE and the PDO value of each month of the previous 2 years believes that its resource abundance CPUE has a significant correlation with the PDO value of October of the previous 2 years, and has a negative correlation, and the correlation coefficients are -0.390 ( P ＜ 0.05);

Correlation analysis between the resource abundance CPUE and the PDO value of each month of the previous year believes that its resource abundance CPUE has a significant correlation with the PDO value of October of the previous year and has a negative correlation, and the correlation coefficients are -0.4486 (P <0.05);

The correlation analysis of the resource abundance CPUE and the PDO value of each month of the same year believes that the resource abundance CPUE has no significant correlation with the PDO value of the same year from January to December.

(3) Establish a model for predicting resource abundance

1) Prediction model I

Aiming at the PDO value of October 2 in the first 2 years of the climate factor x ₁₁ and the PDO value of October in the previous year x ₁₂ of the squid resource abundance prediction model of the Japanese Sea Qiusheng Group, specifically:

CPUE = 2.3463-0.1674 * x ₁₁ -0.1977 * x ₁₂ ;

Its F value is 4.9268, P = 0.0161 <0.05;

The statistical table of actual and predicted values is shown in Table 3;

Table 3 Actual and predicted abundances of squid resources in the Japanese Sea Qiusheng Group and their residuals

2) Prediction model II

Aiming at the PDO value x ₁₁ in the first 2 years of the climatic factor, a model for predicting the abundance of squid resources in the Japanese sea squid was established, specifically:

CPUE = 2.3894-0.2127 * x ₁₁ ;

Its F value is 4.4922, P = 0.0442 <0.05;

The statistical table of actual and predicted values is shown in Table 4;

Table 4 Actual and predicted values and residuals of abundance of squid from the Qiusheng Group in the Japanese Sea

3) Prediction model III

Based on the October PDO value x _{12 of the} previous year of the climatic factor, a model for predicting the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

CPUE = 2.3958-0.2323 * x ₁₂ ;

Its F value is 6.2984, P = 0.0189 <0.05;

The statistical table of actual and predicted values is shown in Table 5;

Table 5 Actual and predicted abundances and residuals of squid resource abundance in the Japanese Sea

It can be drawn from the comparison and analysis of the above three models that the prediction model I is selected as the optimal model, and the optimal model is: CPUE = 2.3463-0.1674 * x ₁₁ -0.1977 * x ₁₂ . Enter the x ₁₁ and x ₁₂ corresponding to the results from 1990 to 2016 into the optimal model to obtain the predicted value (take the result of 2000 as an example, x ₁₁ is the PDO value of October 1998, and x ₁₂ is the PDO value of October 1999 ), The change trend of resource abundance between the actual value and the predicted value is shown in FIG. 3, as can be seen from FIG. 3, the change trend of the predicted value and the actual value is basically the same, that is, the method of the present invention can effectively The abundance of squid resources is predicted.

Example 2

1. Materials and methods

(1) Data source

Japanese winter squid are widely distributed in the waters around the Sea of Japan, and the main fishing grounds are also distributed in the Pacific side of Hokkaido. The environmental conditions of their spawning grounds and feeding grounds are susceptible to Pacific Decadal Oscillation (PDO). influences. The Pacific Oscillation Index is a phenomenon of Pacific climate change that changes on a 10-year cycle scale. The conversion period is usually 20 to 30 years. The PDO is characterized by an abnormally warm or cold surface seawater temperature in the area north of 20 degrees north latitude of the Pacific Ocean. During the "warm phase" (or "positive phase") of the 10-year Pacific Oscillation, the western Pacific is colder and the eastern Pacific is warmer. During the "cold phase" (or "negative phase"), the western Pacific is warmer and the eastern Pacific is colder. . PDO comes from the website of the University of Washington (http://research.jisao.washington.edu/pdo/PDO.latest.txt), and its time period is from January 1990 to December 2017 (Table 6).

The Japanese winter squid resource abundance index CPUE (in tons per ship) comes from the production of small and medium-sized squid fishing boats in Japan from 1992 to 2016 (Table 7).

Table 6 Monthly statistical table of Pacific volatility index from January 1990 to December 2017

years	January	February	March	April	May	June	July	August	September	October	November	December
1990	-0.3	-0.65	-0.62	0.27	0.44	0.44	0.27	0.11	0.38	-0.69	-1.69	-2.23
1991	-2.02	-1.19	-0.74	-1.01	-0.51	-1.47	-0.1	0.36	0.65	0.49	0.42	0.09
1992	0.05	0.31	0.67	0.75	1.54	1.26	1.9	1.44	0.83	0.93	0.93	0.53
1993	0.05	0.19	0.76	1.21	2.13	2.34	2.35	2.69	1.56	1.41	1.24	1.07
1994	1.21	0.59	0.8	1.05	1.23	0.46	0.06	-0.79	-1.36	-1.32	-1.96	-1.79
1995	-0.49	0.46	0.75	0.83	1.46	1.27	1.71	0.21	1.16	0.47	-0.28	0.16
1996	0.59	0.75	1.01	1.46	2.18	1.1	0.77	-0.14	0.24	-0.33	0.09	-0.03
1997	0.23	0.28	0.65	1.05	1.83	2.76	2.35	2.79	2.19	1.61	1.12	0.67
1998	0.83	1.56	2.01	1.27	0.7	0.4	-0.04	-0.22	-1.21	-1.39	-0.52	-0.44
1999	-0.32	-0.66	-0.33	-0.41	-0.68	-1.3	-0.66	-0.96	-1.53	-2.23	-2.05	-1.63
2000	-2	-0.83	0.29	0.35	-0.05	-0.44	-0.66	-1.19	-1.24	-1.3	-0.53	0.52
2001	0.6	0.29	0.45	-0.31	-0.3	-0.47	-1.31	-0.77	-1.37	-1.37	-1.26	-0.93
2002	0.27	-0.64	-0.43	-0.32	-0.63	-0.35	-0.31	0.6	0.43	0.42	1.51	2.1
2003	2.09	1.75	1.51	1.18	0.89	0.68	0.96	0.88	0.01	0.83	0.52	0.33
2004	0.43	0.48	0.61	0.57	0.88	0.04	0.44	0.85	0.75	-0.11	-0.63	-0.17
2005	0.44	0.81	1.36	1.03	1.86	1.17	0.66	0.25	-0.46	-1.32	-1.5	0.2
2006	1.03	0.66	0.05	0.4	0.48	1.04	0.35	-0.65	-0.94	-0.05	-0.22	0.14
2007	0.01	0.04	-0.36	0.16	-0.1	0.09	0.78	0.5	-0.36	-1.45	-1.08	-0.58
2008	-1	-0.77	-0.71	-1.52	-1.37	-1.34	-1.67	-1.7	-1.55	-1.76	-1.25	-0.87
2009	-1.4	-1.55	-1.59	-1.65	-0.88	-0.31	-0.53	0.09	0.52	0.27	-0.4	0.08
2010	0.83	0.82	0.44	0.78	0.62	-0.22	-1.05	-1.27	-1.61	-1.06	-0.82	-1.21
2011	-0.92	-0.83	-0.69	-0.42	-0.37	-0.69	-1.86	-1.74	-1.79	-1.34	-2.33	-1.79
2012	-1.38	-0.85	-1.05	-0.27	-1.26	-0.87	-1.52	-1.93	-2.21	-0.79	-0.59	-0.48
2013	-0.13	-0.43	-0.63	-0.16	0.08	-0.78	-1.25	-1.04	-0.48	-0.87	-0.11	-0.41
2014	0.3	0.38	0.97	1.13	1.8	0.82	0.7	0.67	1.08	1.49	1.72	2.51
2015	2.45	2.3	2	1.44	1.2	1.54	1.84	1.56	1.94	1.47	0.86	1.01
2016	1.53	1.75	2.4	2.62	2.35	2.03	1.25	0.52	0.45	0.56	1.88	1.17
2017	0.77	0.7	0.74	1.12	0.88	0.79	0.1	0.09	0.32	0.05	0.15	0.5

Table 7 Daily production of small and medium squid fishing boats in Japan from 1992 to 2016

years	Daily output per ship (ton / ship)
1992	1.62
1993	1.52
1994	1.15
1995	0.99
1996	1.71
1997	1.45
1998	0.62
1999	0.82

2000	1.31
2001	1.46
2002	1.18
2003	1.19
2004	1.20
2005	1.08
2006	0.85
2007	1.71
2008	1.61
2009	1.63
2010	1.25
2011	1.58
2012	1.26
2013	1.25
2014	1.17
2015	0.82
2016	0.42

(2) Research methods and steps

Using the daily production CPUE of small and medium-sized Japanese squid fishing boats as an indicator of the abundance of squid resources in Japanese winter squids, using time series analysis methods, the CPUE and the PDO values of squid resource abundance in Japanese winter squids from January to December in 1990-2016 Correlation analysis was carried out, in which monthly PDO values that were statistically significant (P value <0.05) were selected, and these selected monthly PDO values were used as the climatic factors affecting the abundance of squid resources in Japanese winter squid. 3 ... z ... m sequentially number the selected monthly PDO values, and record these monthly PDO values as x ₁ , x ₂ , x ₃ … x _z … x _m , m is the number of selected monthly PDO values ;

For any 1 ～ m climatic factors in x ₁ , x ₂ , x ₃ …… x _m , multivariate linear equations are used to establish up to 2 ^m -1 prediction models for the abundance of squid resources in winter squid in Japan. The value of P on the formula, where the formula for the prediction model of the abundance of squid in the Japanese winter group is as follows:

CPUE = a + b ₁ * x ₁ + b ₂ * x ₂ + b ₃ * x ₃ + ... + b _z * x _z + ... + b _m * x _m ;

In the formula, CPUE is the daily output of a single ship, a is a constant, b ₁ , b ₂ , b ₃ ,… b _z …, b _n are coefficients corresponding to x ₁ , x ₂ , x ₃ … x _z … x _m , respectively;

Among the above 2 ^m -1 prediction models of squid resource abundance of Japanese winter squid, the model with the smallest statistical P value is selected as the optimal model;

Obtain the x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, and input it into the optimal model. The optimal model outputs the abundance of squid resources of the Japanese winter live group. Forecast of squid resources.

2. Research results

(1) Changes in CPUE during the year

It can be seen from Figure 4 that the CPUE of the Japanese winter squid resource abundance shows significant inter-annual changes, and it is at a high resource level in 1992, 1996, 2007-2009, and 2011; while 1998-1999, 2006, 2015- In 2016, it was at a low resource level.

(2) PDO value that affects resource abundance CPUE

The correlation analysis of the resource abundance CPUE and the PDO value of each month of the previous 2 years believes that its resource abundance CPUE has a significant correlation with the PDO value of October-December of the previous 2 years, and has a negative correlation, and the correlation coefficients are- 0.4506 (P <0.05), -0.4985 (P <0.05), -0.5878 (P <0.01);

The correlation analysis of the resource abundance CPUE and the PDO value of each month of the previous year believes that the resource abundance CPUE has a significant correlation with the PDO value of January-April of the previous year and has a negative correlation, and the correlation coefficients are -0.4665 (P <0.05), -0.4365 (P <0.05), -0.4295 (P <0.05), -0.5072 (P <0.01);

The correlation analysis between the resource abundance CPUE and the PDO value of each month of the same year believes that the resource abundance CPUE has a significant correlation with the PDO value from January to April of the same year, and shows a negative correlation, and the correlation coefficients are -0.4746 (P < 0.05), -0.4837 (P <0.05), -0.5458 (P <0.01), -0.5570 (P <0.01).

(3) Establish a model for predicting resource abundance

1) Prediction model one

For the climate factor, the November PDO value x ₂₁ for the first 2 years, the December PDO value x _{22 for the} previous 2 years, the February PDO value x ₂₃ for the same year, the March PDO value x _{24 for the} same year, and the March PDO for the previous year Value x ₂₅ , to establish a prediction model of squid resource abundance in the Qiusheng group in the Japanese Sea, specifically:

CPUE = 1.2048 + 0.0330 * x ₂₂ -0.1811 * x ₂₁ -0.2260 * x ₂₃ + 0.0749 * x ₂₄ -0.0196 * x ₂₅ ;

Its F value is 4.5183, P = 0.0069 <0.01;

The statistical table of actual and predicted values is shown in Table 8;

Table 8 Actual and predicted abundances of squid resources in the Japanese sea winter group and their residuals

2) Prediction model 2

X _21, two years before the December PDO value x _22, the same year in February PDO value x ₂₃ of the same year and in March PDO value x ₂₄ for November PDO value two years ago climatic factors, the establishment of the Sea of Japan Akio squid resource group Abundance prediction model, specifically:

CPUE = 1.1968 + 0.0273 * x ₂₂ -0.1865 * x ₂₁ -0.2290 * x ₂₃ + 0.0740 * x ₂₄ ;

Its F value is 5.9135, P = 0.0026 <0.01;

The statistical table of actual and predicted values is shown in Table 9;

Table 9 Actual and predicted abundances and residuals of squid resources in Japanese winter

3) Prediction model three

In the same year the climate factor February PDO value x _23, the same year on March ₂₄ and PDO value before 1 March of PDO value x x _25, the establishment of squid resource abundance Sea of Japan Akio group forecasting models, in particular:

CPUE = 1.3257-0.1120 * x ₂₃ -0.0461 * x ₂₄ -0.1307 * x ₂₅ ;

Its F value is 5.1699, P = 0.0078 <0.01;

The statistical table of actual and predicted values is shown in Table 10;

Table 10 Actual and predicted values of abundance of squid resources in the Japanese sea winter group and their residuals

4) Prediction model 4

Aiming at the climatic factor's February PDO value x _{23 in the} same year and March PDO value x _{24 in the} same year, a prediction model for the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

CPUE = 1.3093-0.0792 * x ₂₃ -0.1223 * x ₂₄ ;

Its F value is 5.1233, P = 0.0149 <0.05;

The statistical table of actual and predicted values is shown in Table 11;

Table 11 Actual and predicted abundances of squid resources in the Japanese sea winter group and their residuals

5) Prediction model five

Aiming at the November PDO value x _{21 of the} first two years of the climate factor and the December PDO value x _{22 of the} previous two years, a prediction model for the abundance of squid resources in the Japanese sea autumn group was established, specifically:

CPUE = 1.1582 + 0.0383 * x ₂₂ -0.2125 * x ₂₁ ;

Its F value is 5.8894, P = 0.0089 <0.01;

The statistical table of actual and predicted values is shown in Table 12.

Table 12 Actual and predicted abundances of squid resources in the Japanese sea winter group and their residuals

From the comparative analysis of the above five models, it can be concluded that the prediction model 2 is selected as the optimal model, and the optimal model is:

CPUE = 1.1968 + 0.0273 * x ₂₂ -0.1865 * x ₂₁ -0.2290 * x ₂₃ + 0.0740 * x ₂₄ .

Enter the x ₂₂ , x ₂₁ , x ₂₃ and x ₂₄ corresponding to the results from 1992 to 2016 into the optimal model to obtain the predicted value (take the result of 2000 as an example, x ₂₁ is the PDO value of November 1998 and x ₂₂ is 1998 PDO value in December, x ₂₃ is the PDO value in February 2000, and x ₂₄ is the PDO value in March 2000), and the actual and predicted resource abundance change trends are shown in Figure 5 5 It can be seen that the change trend of the predicted value and the actual value is basically the same, that is, the method of the present invention can effectively predict the resource abundance of the squid resource abundance in the Japanese sea winter group.

Example 3

An electronic device, as shown in FIG. 6, includes one or more processors, one or more memories, one or more programs, and data collection devices;

The data collection device is used to obtain x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, one or more programs are stored in the memory, when one or more programs are executed by the processor, so that The electronic device executes the method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index as described in Example 1 or Example 2.

Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely examples, and the protection scope of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims (11)

1. The method for predicting the abundance of Japanese squid resources based on the Pacific Shock Index is applied to electronic equipment to guide the ocean fishing of Japanese squid. Its characteristics are as follows:

   (1) Obtain the monthly Pacific Oscillation Index PDO value of the sea area where the Japanese squid was distributed in the previous N years. The Japanese squid is the Japanese autumn squid or the Japanese winter squid;

   (2) Using the time series analysis method, the correlation analysis of the CPUE of Japanese squid resource abundance and the monthly PDO value of the previous N years is carried out, and the monthly PDO values that are statistically significant correlation are selected, and the monthly PDO values are selected As a climatic factor affecting the abundance of Japanese squid resources, the selected monthly PDO values are numbered in the order of ₁ , ₂ , 3 ... z ... m, and these monthly PDO values are sequentially recorded as x ₁ , x ₂ , and x ₃ … X _z … x _m , where m is the number of monthly PDO values selected;

   (3) For any 1 ～ m climatic factors in x ₁ , x ₂ , x ₃ …… x _m , use multivariate linear equations to establish up to 2 ^m -1 prediction models of abundance of Japanese squid resources, and calculate the statistics of each prediction model The value of P on the formula, where the formula for the Japanese squid resource abundance prediction model is as follows:

   CPUE = a + b ₁ * x ₁ + b ₂ * x ₂ + b ₃ * x ₃ + ... + b _z * x _z + ... + b _m * x _m ;

   In the formula, CPUE is the daily output of small and medium squid fishing boats in Japan, a is a constant, b ₁ , b ₂ , b ₃ , ... b _z …, b _n are corresponding to x ₁ , x ₂ , x ₃ … x _z … x coefficient of _m ;

   (4) Among the above 2 ^m -1 abundance prediction models of Japanese squid resources, the model with the smallest statistical P value is selected as the optimal model.
2. The method for predicting the abundance of Japanese squid resources based on the Pacific Shock Index according to claim 1, characterized in that after obtaining the optimal model, x ₁ , x ₂ , x ₃ … x _z … corresponding to the optimal model are obtained x _m and input it into the optimal model, and the optimal model outputs the abundance of Japanese squid resources to complete the prediction of the abundance of Japanese squid resources.
3. The method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index according to claim 1, characterized in that the Japanese squid prediction model is established for a climatic factor x _z , then b ₁ , b ₂ , b ₃ , ... b _z-1 , b _{z + 1} , ..., b _n are all 0.
4. The method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index according to claim 1, wherein the statistically significant correlation refers to a calculated P value <0.05;

   The sea area where squid is distributed in the Japanese autumn group is the sea area of Japan;

   The sea area where squid is distributed in the Japanese winter group is the Pacific Ocean area of Hokkaido.
5. The method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index according to claim 1, wherein the Japanese squid is Japanese squid in autumn group;

   In step (2), two statistically significant monthly PDO values were selected, which were the October PDO value of the previous 2 years and the October PDO value of the previous year. The correlation with the PDO value in October of the previous 2 years was significant and showed a negative correlation, with a correlation coefficient of -0.390. The CPUE of the squid resource abundance in the Japanese autumn group was significantly correlated with the PDO value in October of the previous year. The correlation coefficient is -0.4486.
6. The method for predicting the abundance of Japanese squid resources based on the Pacific Shock Index according to claim 5, wherein a total of three prediction models are established in step (3), and the prediction models are as follows:

   1) Prediction model I

   Aiming at the PDO value of October 2 in the first 2 years of the climate factor x ₁₁ and the PDO value of October in the previous year x ₁₂ of the squid resource abundance prediction model of the Japanese Sea Qiusheng Group, specifically:

   CPUE = 2.3463-0.1674 * x ₁₁ -0.1977 * x ₁₂ ;

   Its F value is 4.9268, P = 0.0161 <0.05;

   2) Prediction model II

   Aiming at the PDO value x ₁₁ in the first 2 years of the climatic factor, a model for predicting the abundance of squid resources in the Japanese sea squid was established, specifically:

   CPUE = 2.3894-0.2127 * x ₁₁ ;

   Its F value is 4.4922, P = 0.0442 <0.05;

   3) Prediction model III

   Based on the October PDO value x _{12 of the} previous year of the climatic factor, a model for predicting the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

   CPUE = 2.3958-0.2323 * x ₁₂ ;

   The F value is 6.2984, P = 0.0189 <0.05.
7. The method for predicting Japanese squid resource abundance based on the Pacific Shock Index according to claim 6, characterized in that, in step (4), the prediction model I is selected as the optimal model, and the optimal model is:

   CPUE = 2.3463-0.1674 * x ₁₁ -0.1977 * x ₁₂ .
8. The method for predicting the abundance of Japanese squid resources based on the Pacific Shock Index according to claim 1, wherein the Japanese squid is Japanese winter squid;

   In step (2), ten statistically significant monthly PDO values were selected, which were the PDO values of October, November and December of the previous 2 years, and the PDO values of 1, 2, 3 and April of the previous year And the PDO values in January, February, March and April of the same year;

   Among them, the CPUE of Japanese winter squid resource abundance is significantly correlated with the PDO values of October, November and December of the previous two years, showing a negative correlation, and the correlation coefficients are -0.4506, -0.4985 and -0.5878 respectively;

   The CPUE of the squid resource abundance in Japanese winter squid is significantly correlated with the PDO values in January, February, March and April of the previous year, showing a negative correlation.

   The CPUE of abundance of squid in the Japanese winter squid was significantly correlated with the PDO value in January, February, March and April of the same year, showing a negative correlation, and the correlation coefficients were -0.4746, -0.4837, -0.5458 and -0.5570, respectively.
9. The method for predicting the abundance of Japanese squid based on the Pacific Shock Index according to claim 8, wherein a total of five prediction models are established in step (3), and the prediction models are as follows:

   1) Prediction model one

   For the climate factor, the November PDO value x ₂₁ for the first 2 years, the December PDO value x _{22 for the} previous 2 years, the February PDO value x ₂₃ for the same year, the March PDO value x _{24 for the} same year, and the March PDO for the previous year Value x ₂₅ , to establish a prediction model of squid resource abundance in the Qiusheng group in the Japanese Sea, specifically:

   CPUE = 1.2048 + 0.0330 * x ₂₂ -0.1811 * x ₂₁ -0.2260 * x ₂₃ + 0.0749 * x ₂₄ -0.0196 * x ₂₅ ;

   Its F value is 4.5183, P = 0.0069 <0.01;

   2) Prediction model 2

   X _21, two years before the December PDO value x _22, the same year in February PDO value x ₂₃ of the same year and in March PDO value x ₂₄ for November PDO value two years ago climatic factors, the establishment of the Sea of Japan Akio squid resource group Abundance prediction model, specifically:

   CPUE = 1.1968 + 0.0273 * x ₂₂ -0.1865 * x ₂₁ -0.2290 * x ₂₃ + 0.0740 * x ₂₄ ;

   Its F value is 5.9135, P = 0.0026 <0.01;

   3) Prediction model three

   In the same year the climate factor February PDO value x _23, the same year on March ₂₄ and PDO value before 1 March of PDO value x x _25, the establishment of squid resource abundance Sea of Japan Akio group forecasting models, in particular:

   CPUE = 1.3257-0.1120 * x ₂₃ -0.0461 * x ₂₄ -0.1307 * x ₂₅ ;

   Its F value is 5.1699, P = 0.0078 <0.01;

   4) Prediction model 4

   Aiming at the climatic factor's February PDO value x _{23 in the} same year and March PDO value x _{24 in the} same year, a prediction model for the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

   CPUE = 1.3093-0.0792 * x ₂₃ -0.1223 * x ₂₄ ;

   Its F value is 5.1233, P = 0.0149 <0.05;

   5) Prediction model five

   Aiming at the November PDO value x _{21 of the} first 2 years of the climatic factor and the December PDO value x _{22 of the} previous 2 years, a model for predicting the abundance of squid in the Qiusheng group in the Japanese Sea was established, specifically:

   CPUE = 1.1582 + 0.0383 * x ₂₂ -0.2125 * x ₂₁ ;

   Its F value is 5.8894, P = 0.0089 <0.01.
10. The method for predicting Japanese squid resource abundance based on the Pacific Shock Index according to claim 9, characterized in that, in step (4), the prediction model 2 is selected as the optimal model, and the optimal model is:

    CPUE = 1.1968 + 0.0273 * x ₂₂ -0.1865 * x ₂₁ -0.2290 * x ₂₃ + 0.0740 * x ₂₄ .
11. An electronic device, characterized in that it includes one or more processors, one or more memories, one or more programs and data collection devices;

    The data collection device is used to obtain x ₁ , x ₂ , x ₃ … x _z … x _m corresponding to the optimal model, the one or more programs are stored in the memory, when the one or more When the program is executed by the processor, the electronic device is caused to execute the method for predicting the abundance of Japanese squid resources based on the Pacific Oscillation Index according to any one of claims 1 to 10.

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