Marine cephalopod fishing planning system based on big data

Abstract

The invention relates to the technical field of fishery, in particular to an ocean cephalopod fishing planning system based on big data, which comprises a data acquisition module used for collecting ocean environment data, cephalopod biological distribution data and historical fishing data; the data preprocessing module is connected with the data acquisition module and is used for carrying out cleaning, normalization and feature extraction preprocessing operation on the acquired data so as to improve the data quality and availability; and the risk assessment module assesses the possible risk of the fishing activity according to the analysis result of the data analysis module. By making a scientific and reasonable fishing plan, including accurate planning of a fishing area, fishing time and fishing amount, the fishing efficiency is effectively improved, resource waste is reduced, sustainable development and utilization of marine cephalopod resources are promoted, it is ensured that the fishing operation always adapts to dynamic changes of marine environments and biological resources, and the fishing efficiency is improved. And the safety and sustainability of the fishing operation are further guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of fishery technology, and in particular to a marine cephalopod fishing planning system based on big data. Background Art

[0002] In traditional marine cephalopod fishing operations, fishing decisions often rely on the experience of fishermen and limited marine observation data. On the one hand, the marine environment is complex and changeable, and the distribution and growth of cephalopods are affected by a combination of factors such as ocean temperature, salinity, water flow speed, dissolved oxygen, sea surface temperature, and chlorophyll concentration. It is difficult to accurately grasp the relationship between these complex factors and the distribution and growth of cephalopods based on experience alone, resulting in low fishing efficiency, improper selection of fishing areas, inappropriate fishing times, and other problems, resulting in a waste of resources.

[0003] On the other hand, with the increasing shortage of marine resources, higher requirements have been placed on the sustainable development and utilization of marine cephalopod resources. However, due to the lack of systematic analysis and utilization of historical fishing data in the past, it was impossible to accurately predict the changes in the number and distribution trends of cephalopods, making it difficult to achieve sustainable development of resources while ensuring the economic benefits of fisheries.

[0004] In addition, although big data technology has been widely used in many fields in recent years, the application of big data in the field of marine fishing is still in its infancy. The existing marine fishing planning system lacks comprehensive data collection, efficient data processing and accurate data analysis capabilities, and cannot provide scientific, comprehensive and dynamic planning guidance for fishing operations. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a marine cephalopod fishing planning system based on big data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a marine cephalopod fishing planning system based on big data, comprising:

[0007] A data acquisition module is used to collect marine environmental data, cephalopod biological distribution data, and historical fishing data; the marine environmental data include but are not limited to ocean temperature, salinity, water flow velocity, dissolved oxygen, sea surface temperature, and chlorophyll concentration; the historical fishing data include cephalopod catches, catch types, and fishing methods in different years and regions;

[0008] A data preprocessing module, connected to the data acquisition module, is used to perform cleaning, normalization, and feature extraction preprocessing operations on the collected data to improve data quality and usability;

[0009] A data storage module for storing the data processed by the data preprocessing module;

[0010] A data analysis module, connected to the data storage module, analyzes the stored data based on big data analysis algorithms to predict the distribution and growth trends of cephalopod organisms; the data analysis module uses machine learning algorithms to perform correlation analysis on historical fishing data and marine environmental data, and establishes a mathematical model between the distribution of cephalopod organisms and marine environmental factors;

[0011] A risk assessment module, based on the analysis results of the data analysis module, assesses the risks that fishing activities may face, including but not limited to bad weather, overfishing of resources, and legal restrictions;

[0012] A fishing planning module, based on the analysis results of the data analysis module and the assessment results of the risk assessment module, generates an optimized marine cephalopod fishing plan, including the planning of fishing areas, fishing times, and fishing volumes. At the same time, this plan takes into account the breeding cycle, growth rate, and resource sustainability of cephalopod organisms;

[0013] A dynamic adjustment module, which monitors the changes in the marine environment, the actual distribution of cephalopod organisms, and the progress of fishing operations in real time, and dynamically adjusts the generated fishing plan according to the monitoring results.

[0014] Preferably, the data acquisition module includes a sensor array for real-time acquisition of marine environmental data such as ocean temperature, salinity, water flow velocity, and dissolved oxygen. The data acquisition module also includes satellite remote sensing equipment for obtaining large-area marine environmental data such as ocean surface temperature and chlorophyll concentration.

[0015] Preferably, the marine cephalopod fishing plan includes the following steps:

[0016] S1. Collect marine environmental data, cephalopod organism distribution data, and historical fishing data;

[0017] S2. Preprocess the collected data, including operations such as cleaning, normalization, and feature extraction;

[0018] S3. Store the preprocessed data in the data storage module;

[0019] S4. Use the data analysis module to analyze the stored data to predict the distribution and growth trends of cephalopod organisms;

[0020] S5. According to the data analysis results, assess the risks that fishing activities may face;

[0021] S6. Generate an optimized marine cephalopod fishing plan according to the data analysis results and the risk assessment results;

[0022] S7. Monitor the changes in the marine environment, the actual distribution of cephalopod organisms, and the progress of fishing operations in real time, and dynamically adjust the generated fishing plan.

[0023] Preferably, in step S4, the data analysis includes the following steps:

[0024] S41. Use a multiple linear regression model to predict the number of cephalopod organisms. The calculation formula is: In the formula, N is the predicted number of cephalopod organisms, β i is the regression coefficient, ∈ is the error term, X i is the independent variable matrix;

[0025] S42. Use a logistic regression model to predict the probability of cephalopod organisms appearing in a certain area. The calculation formula is: In the formula, P is the probability value, and z is the variable of relevant environmental and historical fishing factors.

[0026] In step S5, the risk assessment includes the following steps:

[0027] S51. Construct a risk assessment index system. Suppose there are k risk assessment indicators R k , and add a weight w k to each indicator, and satisfy

[0028] S52. The score of each indicator is obtained through expert scoring or historical data statistics, denoted as s k . Then the calculation formula for the comprehensive risk score of fishing activities is:

[0029] Preferably, in step S6, generating an optimized marine cephalopod fishing plan includes the following steps:

[0030] S61. Select the areas where the probability P of cephalopod organisms appearing is greater than the set threshold and the comprehensive risk score S is less than the set threshold as candidate fishing areas;

[0031] S62. According to the breeding cycle and growth rate of cephalopod organisms, combined with the periodic changes of marine environmental factors, analyze and predict the peak period of the growth of cephalopod organisms. The calculation formula is: θ(B)∈ t =φ(B)(1 - B) d Y t , in the formula, φ(B) is the autoregressive operator, (1 - B) d is the difference operator, θ(B) is the moving average operator, Y t is the time series data, ∈ t is the white noise sequence;

[0032] S63. Determine the catch amount according to the predicted number N of cephalopod organisms, the requirements for resource sustainability, and the ecological carrying capacity of the fishing area. The calculation formula is: Q = λN, where Q is the catch amount and λ is the catch coefficient.

[0033] The present invention provides a marine cephalopod fishing planning system based on big data, having the following beneficial effects:

[0034] 1. Through the data acquisition module of the present invention, it is possible to widely collect marine environmental data, cephalopod organism distribution data, and historical fishing data, covering various key factors affecting cephalopod organisms. At the same time, the data preprocessing module performs operations such as data cleaning, normalization, and feature extraction on the collected data, greatly improving the data quality and providing a reliable basis for subsequent data analysis and decision-making.

[0035] 2. Through the data analysis algorithm of the present invention, it is possible to accurately predict the distribution, growth trend, and peak period of the number growth of cephalopod organisms. The fishing planning module can comprehensively consider the breeding cycle, growth rate, resource sustainability, and risk assessment results of cephalopod organisms, and formulate a scientific and reasonable fishing plan, including precise planning of the fishing area, fishing time, and catch amount, effectively improving the fishing efficiency, reducing resource waste, and promoting the sustainable development and utilization of marine cephalopod resources.

[0036] 3. By constructing a scientific risk assessment index system, the present invention quantitatively assesses risks such as bad weather, overfishing of resources, and legal restrictions that may be faced in fishing activities, providing an important reference for fishing decisions. At the same time, the dynamic adjustment module can real-time monitor changes in the marine environment, the actual distribution of cephalopod organisms, and the progress of fishing operations, and timely discover the differences between the actual situation and the prediction and plan by calculating the deviation rate. When the deviation exceeds the set threshold, it quickly readjusts the fishing plan to ensure that the fishing operation always adapts to the dynamic changes of the marine environment and biological resources, further ensuring the safety and sustainability of the fishing operation. Description of the Drawings

[0037] Figure 1 It is a schematic diagram of the system flow of the present invention. Detailed Embodiments

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] AsFigure 1 As shown in the figure, the embodiment of the present invention provides a big data-based ocean cephalopod fishing planning system, including:

[0040] A data collection module, which is used to collect ocean environment data, cephalopod biological distribution data, and historical fishing data; the ocean environment data includes but is not limited to ocean temperature, salinity, water flow velocity, dissolved oxygen, sea surface temperature, and chlorophyll concentration; the historical fishing data includes the cephalopod catch volume, catch species, and fishing methods in different years and different regions;

[0041] A data preprocessing module, which is connected to the data collection module and is used to perform preprocessing operations such as cleaning, normalization, and feature extraction on the collected data to improve data quality and usability;

[0042] A data storage module, which is used to store the data processed by the data preprocessing module;

[0043] A data analysis module, which is connected to the data storage module and analyzes the stored data based on big data analysis algorithms to predict the distribution and growth trend of cephalopod organisms; the data analysis module uses machine learning algorithms to perform correlation analysis on historical fishing data and ocean environment data and establish a mathematical model between the distribution of cephalopod organisms and ocean environmental factors;

[0044] A risk assessment module, which assesses the risks that the fishing activities may face according to the analysis results of the data analysis module, and the risks include but are not limited to bad weather, overfishing of resources, and legal regulations;

[0045] A fishing planning module, which generates an optimized ocean cephalopod fishing plan according to the analysis results of the data analysis module and the assessment results of the risk assessment module, including the planning of fishing areas, fishing times, and catch volumes. At the same time, this plan takes into account the reproduction cycle, growth rate, and resource sustainability of cephalopod organisms;

[0046] A dynamic adjustment module, which monitors the changes in the ocean environment, the actual distribution of cephalopod organisms, and the progress of fishing operations in real time, and dynamically adjusts the generated fishing plan according to the monitoring results.

[0047] Specifically, through the data collection module, it is possible to widely collect ocean environment data, cephalopod biological distribution data, and historical fishing data, covering various key factors affecting cephalopod organisms. At the same time, the data preprocessing module performs operations such as cleaning, normalization, and feature extraction on the collected data, greatly improving the data quality and providing a reliable basis for subsequent data analysis and decision-making.

[0048] In this embodiment, the data acquisition module includes a sensor array for real-time acquisition of marine environmental data such as ocean temperature, salinity, water flow velocity, and dissolved oxygen. The data acquisition module also includes satellite remote sensing equipment for obtaining large-area marine environmental data such as sea surface temperature and chlorophyll concentration.

[0049] In this embodiment, the marine cephalopod fishing plan includes the following steps:

[0050] S1. Collect marine environmental data, cephalopod biological distribution data, and historical fishing data;

[0051] S2. Preprocess the collected data, including operations such as cleaning, normalization, and feature extraction;

[0052] S3. Store the preprocessed data in the data storage module;

[0053] S4. Use the data analysis module to analyze the stored data and predict the distribution and growth trend of cephalopods;

[0054] S5. Evaluate the risks that the fishing activities may face according to the data analysis results;

[0055] S6. Generate an optimized marine cephalopod fishing plan according to the data analysis results and the risk assessment results;

[0056] S7. Real-time monitor the changes in the marine environment, the actual distribution of cephalopods, and the progress of fishing operations, and dynamically adjust the generated fishing plan.

[0057] In this embodiment, in step S4, the data analysis includes the following steps:

[0058] S41. Use a multiple linear regression model to predict the number of cephalopods. The calculation formula is: In the formula, N is the predicted number of cephalopods, β i is the regression coefficient, ∈ is the error term, and X i is the independent variable matrix;

[0059] S42. Use a logistic regression model to predict the probability of cephalopods appearing in a certain area. The calculation formula is: In the formula, P is the probability value, and z is the variable of relevant environmental and historical fishing factors.

[0060] Specifically, through data analysis algorithms, it is possible to accurately predict the distribution, growth trends, and peak periods of cephalopod populations. The fishing planning module can comprehensively consider the cephalopod breeding cycle, growth rate, resource sustainability, and risk assessment results to develop a scientific and reasonable fishing plan, including precise planning of fishing areas, fishing times, and catch volumes. This effectively improves fishing efficiency, reduces resource waste, and promotes the sustainable development and utilization of marine cephalopod resources.

[0061] In step S5, the risk assessment includes the following steps:

[0062] S51. Construct a risk assessment index system. Suppose there are a total of k risk assessment indicators R k , and add a weight w to each indicator k , and satisfy

[0063] S52. The score of each indicator is obtained through expert scoring or historical data statistics, denoted as s k . Then, the calculation formula for the comprehensive risk score of fishing activities is:

[0064] In this embodiment, in step S6, generating an optimized marine cephalopod fishing plan includes the following steps:

[0065] S61. Select areas where the occurrence probability P of cephalopods is greater than the set threshold and the comprehensive risk score S is less than the set threshold as candidate fishing areas;

[0066] S62. According to the cephalopod breeding cycle and growth rate, combined with the periodic changes of marine environmental factors, analyze and predict the peak period of cephalopod population growth. The calculation formula is: θ(B) ∈ t = φ(B)(1 - B) d Y t , where φ(B) is an autoregressive operator, (1 - B) d is a difference operator, θ(B) is a moving average operator, Y t is time series data, ∈ t is a white noise sequence;

[0067] S63. Determine the catch volume according to the predicted cephalopod quantity N, resource sustainability requirements, and the ecological carrying capacity of the fishing area. The calculation formula is: Q = λN, where Q is the catch volume and λ is the catch coefficient.

[0068] Specifically, by constructing a scientific risk assessment index system, the risks that fishing activities may face, such as severe weather, overfishing of resources, and legal restrictions, are quantitatively evaluated, providing important references for fishing decisions. At the same time, the dynamic adjustment module can monitor the changes in the marine environment, the actual distribution of cephalopods, and the progress of fishing operations in real time. By calculating the deviation rate, the differences between the actual situation and the prediction and planning can be detected in a timely manner. When the deviation exceeds the set threshold, the fishing plan is quickly readjusted to ensure that the fishing operations always adapt to the dynamic changes of the marine environment and biological resources, further ensuring the safety and sustainability of fishing operations.

[0069] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The marine cephalopod fishing planning system based on big data is characterized in that, Including: A data acquisition module, which is used to collect marine environmental data, cephalopod distribution data, and historical fishing data; the marine environmental data includes but is not limited to marine temperature, salinity, water flow velocity, dissolved oxygen, sea surface temperature, and chlorophyll concentration; the historical fishing data includes the cephalopod catch volume, catch species, and fishing methods in different years and different regions. A data preprocessing module, connected to the data acquisition module, which is used to perform preprocessing operations such as cleaning, normalization, and feature extraction on the collected data to improve the data quality and usability. A data storage module, which is used to store the data processed by the data preprocessing module. A data analysis module, connected to the data storage module, which analyzes the stored data based on big data analysis algorithms to predict the distribution and growth trend of cephalopods; the data analysis module uses machine learning algorithms to perform correlation analysis on historical fishing data and marine environmental data, and establish a mathematical model between the distribution of cephalopods and marine environmental factors. A risk assessment module, which assesses the risks that fishing activities may face according to the analysis results of the data analysis module, and the risks include but are not limited to bad weather, overfishing of resources, and legal regulations. A fishing planning module, which generates an optimized marine cephalopod fishing plan according to the analysis results of the data analysis module and the assessment results of the risk assessment module, including the planning of fishing areas, fishing times, and catch volumes. At the same time, this plan takes into account the reproduction cycle, growth rate, and resource sustainability of cephalopods. A dynamic adjustment module, which monitors the changes in the marine environment, the actual distribution of cephalopods, and the progress of fishing operations in real time, and dynamically adjusts the generated fishing plan according to the monitoring results.

2. The system for planning ocean cephalopod fishing based on big data according to claim 1, wherein The data acquisition module includes a sensor array, which is used to collect marine environmental data such as marine temperature, salinity, water flow velocity, and dissolved oxygen in real time. The data acquisition module also includes satellite remote sensing equipment, which is used to obtain large-area marine environmental data such as sea surface temperature and chlorophyll concentration.

3. The marine cephalopod fishing planning system based on big data according to claim 1, characterized in that The marine cephalopod fishing plan includes the following steps: S1. Collect marine environmental data, cephalopod distribution data, and historical fishing data. S2. Preprocess the collected data, including operations such as cleaning, normalization, and feature extraction. S3. Store the preprocessed data in the data storage module. S4. Use the data analysis module to analyze the stored data and predict the distribution and growth trend of cephalopods. S5. According to the data analysis results, assess the risks that fishing activities may face. S6. Generate an optimized marine cephalopod fishing plan according to the data analysis results and the risk assessment results. S7. Monitor the changes in the marine environment, the actual distribution of cephalopods, and the progress of fishing operations in real time, and dynamically adjust the generated fishing plan.

4. The ocean cephalopod fishing planning system based on big data according to claim 3, characterized in that, In step S4, the data analysis includes the following steps: S41. Use a multiple linear regression model to predict the cephalopod biomass, and the calculation formula is: In the formula, N is the predicted cephalopod biomass, β i is the regression coefficient, ∈ is the error term, and X i is the independent variable matrix; S42. Use a logistic regression model to predict the probability of cephalopod organisms appearing in a certain area. The calculation formula is as follows: In the formula, P is the probability value, and z is the variable of relevant environmental and historical fishing factors.

5. The marine cephalopod fishing planning system based on big data according to claim 3, characterized in that, In step S5, the risk assessment includes the following steps: S51. Construct a risk assessment index system, assuming there are k risk assessment indicators R k , and assign a weight w to each indicator k , and satisfy S52. The score of each index is obtained through expert scoring or historical data statistics, denoted as s k , then the calculation formula for the comprehensive risk score of fishing activities is:

6. The ocean cephalopod fishing planning system based on big data according to claim 3, wherein, In step S6, generating an optimized marine cephalopod fishing plan includes the following steps: S61. Select the area where the occurrence probability P of cephalopods is greater than the set threshold and the comprehensive risk score S is less than the set threshold as the candidate fishing area; S62. According to the breeding cycle and growth rate of cephalopods, combined with the periodic changes of marine environmental factors, analyze and predict the peak period of the growth of cephalopod populations. The calculation formula is: θ(B) ∈ t = φ(B)(1 - B) d Y t , where φ(B) is the autoregressive operator, (1 - B) d is the difference operator, θ(B) is the moving average operator, Y t is the time series data, ∈ t is the white noise sequence; S63. Determine the fishing volume according to the predicted number N of cephalopods, the requirements for resource sustainability, and the ecological carrying capacity of the fishing area. The calculation formula is: Q = λN, where Q is the fishing volume and λ is the fishing coefficient.

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