CN111667112A - Fishery resource abundance gray prediction model optimization method and application thereof - Google Patents
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Abstract
The invention discloses a fishery resource abundance gray prediction model optimization method and application thereof, and the method comprises the following steps: preferably selecting a CPUE sequence as a standard sequence for establishing a resource abundance grey prediction model; calculating the gray correlation degree of the influence factors of the resource abundance by utilizing a gray correlation analysis method aiming at the standard sequence, and selecting the influence factors with large gray correlation degree as the factors of the resource abundance gray prediction model; establishing a resource abundance gray prediction model by utilizing a discrete GM model and adopting the selected factors, wherein the established resource abundance gray prediction model comprises a GM (0, N) model and a GM (1, N) model; and carrying out effectiveness analysis on each prediction model, and selecting the model with the minimum relative error as the optimal prediction model. The prediction method of the invention preferably selects the CPUE sequence to overcome the defect of poor prediction stability of the current gray system model; GM models with different orders are established, an optimal prediction model is preferably obtained from the GM models, and prediction accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of ocean fishing flood prediction, relates to a fishery resource abundance gray prediction model optimization method and application thereof, and particularly relates to an environmental factor-based fishery resource abundance gray prediction model optimization method and application thereof.
Background
The fish in the middle and upper layers is an important economic species, and the resource abundance change of the fish is closely related to factors such as climate, marine environment and the like. The resource abundance prediction is one of main prediction contents in the fishery situation forecast, and the scientific prediction of the resource amount and the evaluation of the resource abundance are favorable for the sustainable utilization and the scientific management of fishery resources. The problems of too small sample size and too many environmental factors related to forecasting are usually encountered in the building of a fishing situation forecasting model, particularly the forecasting of the annual resource abundance, and the two problems are the difficult points of building the resource abundance forecasting model.
The grey system theory is a subject of uncertain system theory, which has the advantage of allowing a smaller number of samples and obeying an arbitrary distribution, compared to other research methods (multiple linear regression, multiple non-linear regression and habitat index). At present, although the method is applied to resource abundance prediction of some economic types, the problems of low prediction accuracy, poor stability and the like still exist.
Therefore, the resource abundance prediction method based on the grey system theory and high in prediction accuracy and stability is of great practical significance.
Disclosure of Invention
The invention aims to overcome the defects of low prediction precision and poor stability in the prior art, and provides a resource abundance prediction method based on a grey system theory, which is high in prediction precision and stability.
In order to achieve the purpose, the invention provides the following technical scheme:
a fishery resource abundance gray prediction model optimization method is applied to electronic equipment and comprises the following steps:
(1) intercepting the CPUE sequence of any time period in the CPUE sequence of the sea area B, fish C in the time period A, establishing a GM (1,1) model corresponding to each intercepted CPUE sequence, establishing m GM (1,1) models in total, respectively calculating the relative error of each GM (1,1) model, and selecting the CPUE sequence corresponding to the GM (1,1) model with the minimum relative error as a standard sequence for establishing a resource abundance gray prediction model;
(2) aiming at the standard sequence selected in the step (1), calculating the gray correlation degree of the influence factors of the resource abundance by using a gray correlation analysis method;
(3) selecting an influence factor with large grey correlation degree as a factor of a resource abundance grey prediction model;
(4) utilizing a discrete GM model, establishing a resource abundance gray prediction model by adopting the factors selected in the step (3), wherein the established resource abundance gray prediction model comprises a GM (0, N) model and a GM (1, N) model, the GM (0, N) model and the GM (1, N) model are linear dynamic models, the GM (1, N) model is a first-order derivative calculation model, the calculation is more complex compared with the GM (0, N) model, and meanwhile, establishing GM prediction models of different orders can provide more models for later-stage optimization, and further, an optimal model is selected from the models to improve the prediction accuracy of the models;
(5) and (4) carrying out effectiveness analysis on the prediction model obtained in the step (4), wherein the effectiveness analysis comprises relative error analysis, the relative error is obtained by comparing the CPUE value calculated by using the prediction model with the real CPUE value, and the model with the minimum relative error is selected as the optimal prediction model.
The fishery resource abundance gray prediction model optimization method of the invention comprises the steps of optimizing CPUE sequences before processing (before establishing a resource abundance gray prediction model), intercepting CPUE sequences in any time period and establishing a GM (1,1) model, selecting the CPUE sequence corresponding to the GM (1,1) model with the minimum relative error as a standard sequence for subsequently establishing the resource abundance gray prediction model, overcoming the defect of poor prediction stability of the current gray system model to a certain extent, then when a resource abundance gray prediction model is established, 0-order and 1-order GM prediction models containing different influence factors are established, a model with the minimum relative error is selected from a plurality of prediction models with different orders as an optimal prediction model, the method can greatly improve the precision of the prediction model and provide a basis for more scientific and more effective fishery production. The method can be used for predicting the fishes at the middle and upper layers in the ocean, has good applicability, can play a good guiding role in the production of the marine fishery, can obviously improve the fishing efficiency and reduce the fishing cost, and has great application prospect.
As a preferred technical scheme:
the fishery resource abundance gray prediction model optimization method is characterized in that the time period A is 1998-2016 years; the coordinate ranges of the sea area B are 35-45 degrees N and 140-179 degrees E; the fish C is North Pacific soft fish. The scope of the present invention is not limited to this, and the prediction method of the present invention can be applied to the prediction of the abundance of resources of the fish in the middle and upper layers in any time zone and any sea area, taking the fish in the time zone and the sea area as an example.
According to the fishery resource abundance gray prediction model optimization method, the standard sequence for establishing the resource abundance gray prediction model is 1998-2005, and the coordinate ranges are 35-45 degrees N and 140-179 degrees E CPUE sequence of the northern Pacific soft fish. The standard sequence for establishing the resource abundance gray prediction model is selected based on the aforementioned data, and one skilled in the art can select an appropriate CPUE sequence as the standard sequence according to actual data.
According to the fishery resource abundance gray prediction model optimization method, the relative errors of the multiple GM (1,1) models obtained in the step (1) are the same and are the minimum, and the CPUE sequences corresponding to the models with the minimum variance of the models are selected as standard sequences for establishing the resource abundance gray prediction model.
The fishery resource abundance gray prediction model optimization method comprises the following steps of optimizing a pacific annual shock index (PDO), a spawning site sea surface temperature (SGSST), a fattening site sea surface temperature (FGSST), a spawning site chlorophyll concentration (SGC) and a fattening site chlorophyll concentration (FGC). The protection scope of the invention is not limited to this, and the influence factor of the resource abundance given here only corresponds to the north pacific soft fish, and for other fishes, the influence factor of the resource abundance can be reasonably selected by the person skilled in the art according to the actual situation.
The fishery resource abundance gray prediction model optimization method comprises the following steps of (2): and (3) taking the standard sequence selected in the step (1) as a mother sequence, taking the corresponding influence factors as subsequences, and respectively calculating the gray absolute correlation degree of each subsequence and the mother sequence.
The fishery resource abundance gray prediction model optimization method comprises the steps of respectively measuring the sea surface temperature (FGSST) of a 10-month fattening field, the pacific annual shock index (PDO) of a 10-month fattening field, the sea surface temperature (SGSST) of a 2-month spawning field, the chlorophyll concentration (SGC) of a 3-month spawning field and the chlorophyll concentration (FGC) of a 8-month fattening field. The prediction models mentioned here and below are only partial data to illustrate the operation logic of the prediction method of the present invention, and the protection scope of the present invention is not limited to this, and those skilled in the art can select appropriate data to predict the resource abundance according to the actual needs, and the factors of the resource abundance gray prediction models are not limited to these 5, and of course, the number of the prediction models will vary with the number of the factors of the resource abundance gray prediction models.
The fishery resource abundance gray prediction model optimization method comprises the following steps:
model I, GM (0,6) model comprising five factors of surface temperature of fattening field for 10 months (FGSST), interpolar oscillation index of pacific ocean for 10 months (PDO), surface temperature of spawning field for 2 months (SGSST), chlorophyll concentration of spawning field for 3 months (SGC), and chlorophyll concentration of fattening field for 8 months (FGC);
model II, GM (0,5) model comprising four factors of sea surface temperature (FGSST) of fattening field for 10 months, pacific interpolar oscillation index (PDO) for 10 months, chlorophyll concentration (SGC) of spawning field for 3 months, and chlorophyll concentration (FGC) of fattening field for 8 months;
model III, GM (0,5) model comprising four factors of pacific annual shock index (PDO) in 10 months, sea surface temperature (SGSST) at spawning site in 2 months, chlorophyll concentration (SGC) at spawning site in 3 months, and chlorophyll concentration (FGC) at fattening site in 8 months;
model IV, GM (0,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of fattening field in 8 months (FGC);
model V, GM (0,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of spawning field in 3 months (SGC);
model VI, including a GM (0,5) model of four factors of the surface temperature of the fattening field in month 10 (FGSST), the surface temperature of the spawning field in month 2 (SGSST), the chlorophyll concentration of the spawning field in month 3 (SGC), and the chlorophyll concentration of the fattening field in month 8 (FGC);
model VII, GM (1,6) model comprising five factors of surface temperature of fattening field for 10 months (FGSST), interpolar oscillation index of pacific ocean for 10 months (PDO), surface temperature of spawning field for 2 months (SGSST), chlorophyll concentration of spawning field for 3 months (SGC), and chlorophyll concentration of fattening field for 8 months (FGC);
model VIII, GM (1,5) model comprising four factors of sea surface temperature (FGSST) of fattening field for 10 months, pacific interpolar oscillation index (PDO) for 10 months, chlorophyll concentration (SGC) of spawning field for 3 months, and chlorophyll concentration (FGC) of fattening field for 8 months;
model IX, GM (1,5) model comprising four factors of interpolar oscillation index (PDO) of pacific annual in 10 months, superficial temperature of spawning site in 2 months (SGSST), chlorophyll concentration (SGC) of spawning site in 3 months, and chlorophyll concentration (FGC) of fattening site in 8 months;
model X, GM (1,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of fattening field in 8 months (FGC);
model XI, GM (1,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of spawning field in 3 months (SGC);
model XII, GM (1,5) model comprising four factors of the surface temperature of the fattening field in month 10 (FGSST), the surface temperature of the spawning field in month 2 (SGSST), the chlorophyll concentration of the spawning field in month 3 (SGC) and the chlorophyll concentration of the fattening field in month 8 (FGC).
According to the fishery resource abundance gray prediction model optimization method, the model IV is an optimal prediction model. On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The invention also provides an electronic device comprising one or more processors, one or more memories, one or more programs, and a data gathering apparatus;
the data gathering device is configured to obtain CPUE sequences for marine B fish C during time period a, the one or more programs being stored in the memory, and when executed by the processor, the one or more programs causing the electronic device to perform a fishery resources abundance gray prediction model optimization method as described above.
Has the advantages that:
(1) the fishery resource abundance gray prediction model optimization method comprises the steps of optimizing a CPUE sequence before processing (before establishing a resource abundance gray prediction model), intercepting the CPUE sequence in any time period and establishing a GM (1,1) model, and selecting the CPUE sequence corresponding to the GM (1,1) model with the minimum relative error as a standard sequence for subsequently establishing the resource abundance gray prediction model, so that the defect of poor prediction stability of the current gray system model is overcome to a certain extent;
(2) according to the fishery resource abundance gray prediction model optimization method, when the resource abundance gray prediction model is established, 0-order and 1-order GM prediction models containing different influence factors are established at the same time, and the model with the smallest relative error is selected from a plurality of prediction models with different orders as the optimal prediction model, so that the accuracy of the prediction models can be greatly improved, and a basis is provided for more scientific and more effective fishery production;
(3) the electronic equipment disclosed by the invention is simple in structure and low in cost, and can be used for quickly predicting the abundance of marine fishery resources.
Drawings
FIG. 1 is a schematic flow chart of a fishery resource abundance gray prediction model optimization method of the present invention;
FIG. 2 is a graph of the relative error of multiple GM (1,1) models in step (1);
FIG. 3 is a comparison graph of the average relative error of each prediction model in step (6);
FIG. 4 is a diagram of optimal prediction model prediction value fitting;
fig. 5 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1
A fishery resource abundance gray prediction model optimization method is shown in figure 1 and specifically comprises the following steps:
(1) aiming at the CPUE (annual unit fishing Number force) sequence of the North Pacific Soft Fish in the coordinate range of 35-45 degrees N and 140-179 degrees E in 1998-2016 (wherein the CPUE is calculated according to fishery production statistical data of the North Pacific Soft Fish in the coordinate range of 35-45 degrees N and 140-179 degrees), the CPUE sequence of any time period is cut, a GM (1,1) model is established corresponding to each cut CPUE sequence, m GM (1,1) models are established in total, the relative error of each GM (1,1) model is respectively calculated, the CPUE sequence corresponding to the GM (1,1) model with the smallest relative error is selected as a standard sequence for establishing a resource abundance grey prediction model, such as a plurality of GM (1,1) the relative errors of the models are the same and are the minimum, the corresponding CPUE sequences of the models with the minimum variance are selected as standard sequences for establishing the resource abundance grey prediction model, and the relative error graphs of the selected multiple GM (1,1) models are shown in figure 2, so that the CPUE sequences (with the minimum average relative error of 6.28%) of the North Pacific ocean soft fish with the coordinate ranges of 35-45 degrees N and 140-179 degrees E in 1998-2005 are finally selected as the standard sequences for establishing the resource abundance grey prediction model;
(2) aiming at the standard sequence selected in the step (1), calculating the gray correlation degree of the influence factors of the resource abundance by using a gray correlation analysis method, specifically, taking the standard sequence selected in the step (1) as a mother sequence, and taking the corresponding influence factors as sub-sequences, and respectively calculating the gray absolute correlation degree of each sub-sequence and the mother sequence (the gray correlation coefficient of each obtained influence factor sub-sequence and the CPUE mother sequence is shown in table 1), wherein the influence factors of the resource abundance comprise a pacific annual shock index (PDO), a superficial temperature of a Spawning Ground (SGSST), a superficial temperature of a fattening ground (SST), a chlorophyll concentration of a Spawning Ground (SGC) and a chlorophyll concentration of a Fattening Ground (FGC);
TABLE 1 Grey correlation coefficient of each impact factor subsequence with CPUE mother sequence
(3) As can be seen from table 1, the influence factors with large gray correlation are the sea surface temperature (FGSST) of the fattening field in 10 months, the pacific annual ring index (PDO) in 10 months, the sea surface temperature (SGSST) of the spawning field in 2 months, the chlorophyll concentration (SGC) of the spawning field in 3 months, and the chlorophyll concentration (FGC) of the fattening field in 8 months, and therefore the above influence factors are selected as the factors of the resource abundance gray prediction model;
(4) utilizing a discrete GM model, and adopting the factors selected in the step (3) to establish a resource abundance gray prediction model, wherein the established resource abundance gray prediction model comprises a GM (0, N) model and a GM (1, N) model, and specifically comprises the following models:
model I, GM (0,6) model comprising five factors of surface temperature of fattening field for 10 months (FGSST), interpolar oscillation index of pacific ocean for 10 months (PDO), surface temperature of spawning field for 2 months (SGSST), chlorophyll concentration of spawning field for 3 months (SGC), and chlorophyll concentration of fattening field for 8 months (FGC);
model II, GM (0,5) model comprising four factors of sea surface temperature (FGSST) of fattening field for 10 months, pacific interpolar oscillation index (PDO) for 10 months, chlorophyll concentration (SGC) of spawning field for 3 months, and chlorophyll concentration (FGC) of fattening field for 8 months;
model III, GM (0,5) model comprising four factors of pacific annual shock index (PDO) in 10 months, sea surface temperature (SGSST) at spawning site in 2 months, chlorophyll concentration (SGC) at spawning site in 3 months, and chlorophyll concentration (FGC) at fattening site in 8 months;
model IV, GM (0,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of fattening field in 8 months (FGC);
model V, GM (0,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of spawning field in 3 months (SGC);
model VI, including a GM (0,5) model of four factors of the surface temperature of the fattening field in month 10 (FGSST), the surface temperature of the spawning field in month 2 (SGSST), the chlorophyll concentration of the spawning field in month 3 (SGC), and the chlorophyll concentration of the fattening field in month 8 (FGC);
model VII, GM (1,6) model comprising five factors of surface temperature of fattening field for 10 months (FGSST), interpolar oscillation index of pacific ocean for 10 months (PDO), surface temperature of spawning field for 2 months (SGSST), chlorophyll concentration of spawning field for 3 months (SGC), and chlorophyll concentration of fattening field for 8 months (FGC);
model VIII, GM (1,5) model comprising four factors of sea surface temperature (FGSST) of fattening field for 10 months, pacific interpolar oscillation index (PDO) for 10 months, chlorophyll concentration (SGC) of spawning field for 3 months, and chlorophyll concentration (FGC) of fattening field for 8 months;
model IX, GM (1,5) model comprising four factors of interpolar oscillation index (PDO) of pacific annual in 10 months, superficial temperature of spawning site in 2 months (SGSST), chlorophyll concentration (SGC) of spawning site in 3 months, and chlorophyll concentration (FGC) of fattening site in 8 months;
model X, GM (1,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of fattening field in 8 months (FGC);
model XI, GM (1,5) model comprising four factors of surface temperature of fattening field in 10 months (FGSST), interpolar oscillation index of pacific ocean in 10 months (PDO), surface temperature of spawning field in 2 months (SGSST), and chlorophyll concentration of spawning field in 3 months (SGC);
model XII, GM (1,5) model comprising four factors of the sea surface temperature of the fattening field in month 10 (FGSST), the sea surface temperature of the spawning field in month 2 (SGSST), the chlorophyll concentration of the spawning field in month 3 (SGC), and the chlorophyll concentration of the fattening field in month 8 (FGC);
(5) carrying out effectiveness analysis on each prediction model obtained in the step (4), verifying by using 2006 fishery production data, wherein the verification result is shown in tables 2 and 3, the comparison graph of the average relative error of each prediction model is shown in fig. 3, the left models 1-6 in fig. 3 are respectively in one-to-one correspondence with the models I-VI in the step (4), and the right models 1-6 are respectively in one-to-one correspondence with the models VII-XII in the step (4);
TABLE 2 relative error of Arctic Pacific Flex recourses abundance GM (0, N) prediction model
TABLE 3 relative error of Arctic Pacific Flex recourses abundance GM (1, N) prediction model
The "error" in tables 2 and 3 is the relative error mean, and the "validation" is the error rate of the 2006 year fishery production data and 2006 year fishery production actual data predicted using the model, with the unit in the tables;
as can be seen from fig. 2, the GM (0, N) and GM (1, N) prediction models with the addition of environmental factors are almost all more accurate than the GM (1,1) models (except model XII) and all the 0 th order gray prediction models are more accurate than the 1 st order.
As can be seen from tables 2 and 3, the average model fitting error in each GM (0, N) model is, in order from large to small: model I > model III > model II > model V > model IV > model VI; the average model fitting errors in the GM (1, N) models are as follows from small to large: model X > model VII > model VIII > model IX > model XI > model XII; from the verification results, the model IV and the model X are far higher than other models, the relative error is 1.18% (lowest) and 1.20%, so the model IV is selected as the optimal prediction model of the resource abundance of the North Pacific ocean soft fish.
The model IV is selected for prediction, and a prediction fitting graph of the model IV is shown in fig. 4, which shows that the variation trends of CPUE are basically consistent, and the variation range of the fitting value predicted by the model is small, and from the parameter of the prediction model, the value-a is-1.71 (table 4) and meets the condition (-a < 0.3) of the medium-and-long-term prediction model.
TABLE 4 parameter values of factors of model IV
Through verification, the prediction method of the invention firstly optimizes the CPUE sequence before processing (before establishing the resource abundance grey prediction model), intercepts the CPUE sequence in any time period and establishes the GM (1,1) model, selects the CPUE sequence corresponding to the GM (1,1) model with the minimum relative error as the standard sequence for subsequently establishing the resource abundance grey prediction model, and overcomes the defect of poor prediction stability of the current grey system model; when the resource abundance gray prediction model is established, 0-order and 1-order GM prediction models containing different influence factors are established at the same time, and the model with the smallest relative error is selected from a plurality of prediction models with different orders as the optimal prediction model, so that the accuracy of the prediction models can be greatly improved, a basis is provided for more scientific and more effective fishery production, and the method has a great application prospect.
Example 2
An electronic device, as shown in FIG. 5, comprising one or more processors, one or more memories, one or more programs, and data gathering means;
the data collection apparatus is used to obtain CPUE sequences of marine B fish C in time period a (i.e., CPUE sequences of north pacific soft fish in the coordinate ranges of 35-45 ° N, 140-179 ° E in 1998-2016 in example 1), and one or more programs are stored in the memory, which when executed by the processor, cause the electronic device to perform the fishery resource abundance gray prediction model optimization method as described in example 1.
Although specific embodiments of the present invention have been described above, it will be appreciated by those skilled in the art that these embodiments are merely illustrative and various changes or modifications may be made without departing from the principles and spirit of the invention.
Claims (10)
1. A fishery resource abundance gray prediction model optimization method is applied to electronic equipment and is characterized by comprising the following steps:
(1) intercepting the CPUE sequence of any time period in the CPUE sequence of the sea area B, fish C in the time period A, establishing a GM (1,1) model corresponding to each intercepted CPUE sequence, establishing m GM (1,1) models in total, respectively calculating the relative error of each GM (1,1) model, and selecting the CPUE sequence corresponding to the GM (1,1) model with the minimum relative error as a standard sequence for establishing a resource abundance gray prediction model;
(2) aiming at the standard sequence selected in the step (1), calculating the gray correlation degree of the influence factors of the resource abundance by using a gray correlation analysis method;
(3) selecting an influence factor with large grey correlation degree as a factor of a resource abundance grey prediction model;
(4) establishing a resource abundance gray prediction model by using the discrete GM model and the factors selected in the step (3), wherein the established resource abundance gray prediction model comprises a GM (0, N) model and a GM (1, N) model;
(5) and (4) carrying out effectiveness analysis on the prediction model obtained in the step (4), wherein the effectiveness analysis comprises relative error analysis, the relative error is obtained by comparing the CPUE value calculated by using the prediction model with the real CPUE value, and the model with the minimum relative error is selected as the optimal prediction model.
2. The gray prediction model optimization method for fishery resource abundance according to claim 1, wherein the time period A is 1998-2016 years; the coordinate ranges of the sea area B are 35-45 degrees N and 140-179 degrees E; the fish C is North Pacific soft fish.
3. The fishery resource abundance gray prediction model optimization method according to claim 2, wherein the standard sequence for establishing the resource abundance gray prediction model is 1998-2005, and the coordinate range is 35-45 ° N, 140-179 ° E CPUE sequence of north pacific soft fish.
4. The fishery resource abundance gray prediction model optimization method according to claim 3, wherein the relative errors of the multiple GM (1,1) models obtained in step (1) are the same and are the minimum, and the CPUE sequence corresponding to the model with the minimum variance of the models is selected as the standard sequence for establishing the resource abundance gray prediction model.
5. The fishery resource abundance gray prediction model optimization method according to claim 4, wherein the influence factors of the resource abundance comprise pacific annual ring oscillation index, surface temperature of a spawning site, surface temperature of a fattening site, chlorophyll concentration of the spawning site and chlorophyll concentration of the fattening site.
6. The fishery resource abundance gray prediction model optimization method according to claim 5, wherein the step (2) is specifically as follows: and (3) taking the standard sequence selected in the step (1) as a mother sequence, taking the corresponding influence factors as subsequences, and respectively calculating the gray absolute correlation degree of each subsequence and the mother sequence.
7. The fishery resource abundance gray prediction model optimization method according to claim 6, wherein the factors of the resource abundance gray prediction model are respectively the sea surface temperature of 10-month fattening field, 10-month Pacific annual ring oscillation index, the sea surface temperature of 2-month spawning field, the chlorophyll concentration of 3-month spawning field and the chlorophyll concentration of 8-month fattening field.
8. The fishery resource abundance gray prediction model optimization method according to claim 7, wherein the established resource abundance gray prediction model comprises the following models:
model I, GM (0,6) model comprising five factors of the sea surface temperature of the fattening field in 10 months, the pacific interpersonal oscillation index in 10 months, the sea surface temperature of the spawning field in 2 months, the chlorophyll concentration of the spawning field in 3 months, and the chlorophyll concentration of the fattening field in 8 months;
model II, GM (0,5) model comprising four factors of sea surface temperature of fattening field in 10 months, interpolar oscillation index of Pacific ocean in 10 months, chlorophyll concentration of spawning field in 3 months, and chlorophyll concentration of fattening field in 8 months;
model III, GM (0,5) model comprising four factors of interpolar oscillation index for pacific at month 10, sea surface temperature for spawning site at month 2, chlorophyll concentration for spawning site at month 3 and chlorophyll concentration for fattening site at month 8;
model IV, including the sea surface temperature of the fattening field in 10 months, the pacific interpersonal oscillation index in 10 months, the sea surface temperature of the spawning field in 2 months (and GM (0,5) model of four factors of chlorophyll concentration for the fattening field in 8 months;
model V, including a GM (0,5) model of four factors of the sea surface temperature of the fattening field in 10 months, the interpolar oscillation index of the Pacific ocean in 10 months, the sea surface temperature of the spawning field in 2 months, and the chlorophyll concentration of the spawning field in 3 months;
model VI, including a GM (0,5) model of four factors of the surface temperature of the fattening field in 10 months, the surface temperature of the spawning field in 2 months, the chlorophyll concentration of the spawning field in 3 months and the chlorophyll concentration of the fattening field in 8 months;
model VII, GM (1,6) model comprising five factors of the sea surface temperature of the fattening field in 10 months, the interpolar oscillation index of the pacific ocean in 10 months, the sea surface temperature of the spawning field in 2 months, the chlorophyll concentration of the spawning field in 3 months, and the chlorophyll concentration of the fattening field in 8 months;
model VIII, GM (1,5) model comprising four factors of sea surface temperature for fattening field in 10 months, interpolar oscillation index for pacific ocean in 10 months, chlorophyll concentration for spawning field in 3 months, and chlorophyll concentration for fattening field in 8 months;
model IX, GM (1,5) model comprising four factors of interpolar oscillation index for pacific at month 10, sea surface temperature for spawning site at month 2, chlorophyll concentration for spawning site at month 3, and chlorophyll concentration for fattening site at month 8;
model X, including a GM (1,5) model of four factors of the sea surface temperature of a fattening field in 10 months, the interpolar oscillation index of the Pacific ocean in 10 months, the sea surface temperature of a spawning field in 2 months, and the chlorophyll concentration of the fattening field in 8 months;
model XI, GM (1,5) model comprising four factors of the surface temperature of the fattening field in 10 months, the interpolar oscillation index of the pacific ocean in 10 months, the surface temperature of the spawning field in 2 months, and the chlorophyll concentration of the spawning field in 3 months;
model XII, GM (1,5) model comprising four factors of the surface temperature of the fattening field in month 10, the surface temperature of the spawning field in month 2, the chlorophyll concentration of the spawning field in month 3, and the chlorophyll concentration of the fattening field in month 8.
9. The method of claim 8, wherein the model IV is an optimal prediction model.
10. An electronic device comprising one or more processors, one or more memories, one or more programs, and data gathering means;
the data gathering device is used for acquiring CPUE sequences of marine B fish C in a time period A, the one or more programs are stored in the memory, and when the one or more programs are executed by the processor, the electronic equipment is caused to execute the fishery resources abundance gray prediction model optimization method according to any one of claims 1-9.
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