Construction method of fish community digital management and decision-making system based on digital technology

Abstract

The invention discloses a construction method of a fish community digital management and decision-making system based on a digital technology, and belongs to the technical field of fishery resource management and digital technology application. Comprising the following steps: performing all-around and multi-level data acquisition in a fish inhabiting water area by using underwater photographic equipment, a sonar detector and a plurality of sensors, constructing a fish community database in a database, developing a fish community dynamic model based on a big data analysis technology and an artificial intelligence algorithm, and constructing a management and decision platform. According to the construction method applied to the fish community digital management and decision-making system based on the digital technology, a set of comprehensive and efficient fish community digital management and decision-making system is constructed by integrating the digital technology, so that the scientificity and the accuracy of fish community management are effectively improved; sustainable utilization of fishery resources and protection of the ecological environment of a water area are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery resource management and digital technology application, and specifically to a construction method of a digital management and decision-making system for fish communities based on digital technology. Background Art

[0002] With the development of the fishery, the management of fish communities has become increasingly complex. Traditional management methods have many limitations. For example, data collection is inaccurate and untimely, and it is difficult to comprehensively and meticulously monitor the dynamic changes of fish communities, resulting in a lack of scientific basis for decision-making, and problems such as overfishing and ecological imbalance occurring frequently. The rapid development of science and technology has brought new opportunities for fish community management and can effectively solve the pain points of traditional management methods. Summary of the Invention

[0003] The purpose of the present invention is to provide a construction method of a digital management and decision-making system for fish communities based on digital technology to solve the problems raised in the above background art.

[0004] In view of the above problems, the technical solution proposed by the present invention is as follows: A construction method of a digital management and decision-making system for fish communities based on digital technology, comprising the following steps: Step 1, data collection, which includes the following steps: S1, arrange underwater photography equipment, sonar detectors, and several sensors according to the range, depth, and terrain of the fish inhabiting waters; S2, adjust the shooting resolution, frame rate, and angle of the underwater photography equipment, adjust the detection frequency and pulse width of the sonar detector, and calibrate the sensors according to their measurement ranges and accuracy requirements; S3, the data collected by the underwater photography equipment, sonar detector, and several sensors is transmitted to the data processing center through a wireless transmission module; S4, use data integration software to uniformly organize and convert data from different sources and in different formats; Step 2: Construct a fish community database, which includes the following steps: J1, use a graphic recognition algorithm to process the images collected by the underwater photography equipment, record the types, quantities, and average body lengths of the fish in the images in the form of a table, and store them in the database; J2, for the data collected by the sonar detector, use a signal processing algorithm to remove noise interference, calculate the distribution density and group position information of the fish by analyzing the intensity and time delay of the sonar echo, and convert them into geographic coordinate and density numerical data and store them in the database; J3. The environmental data collected by the sensors is directly stored in the database according to the time series, and a data index is established. J4. Obtain historical fishery data from the fishery management department, digitize and clean the historical fishery data, then store it in the database in a unified format, and associate and integrate it with the real-time collected data. Step Three: Develop a fish community dynamic model. The development of the fish community dynamic model includes the following steps: E1. Extract fish community data and environmental data from the fish community database, and use big data analysis tools to analyze the correlation between the two. E2. Use data mining tools to mine the hidden rules in the historical fishery data. E3. Take the environmental data as the input variable and the fish community data as the output variable, and construct a fish community dynamic model based on the decision tree algorithm. E4. Divide the historical fishery data and real-time monitoring data into a training set and a test set, use the training set to train the fish community dynamic model, use the test set to verify and evaluate the trained fish community dynamic model, and optimize the fish community dynamic model according to the results. Step Four: Construct a management and decision-making platform. The construction of the management and decision-making platform includes the following steps: T1. Design the management and decision-making platform using a hierarchical architecture. T2. According to the fish species quantity prediction structure and growth trend analysis output by the fish community dynamic model, combined with the policies and goals of the fishery management department, use intelligent algorithms to calculate the fishing quota. T3. Based on the fish migration path prediction result output by the fish community dynamic model, use GIS technology to visually display the fish migration routes and key aggregation areas on the map, so as to plan fishing prohibited areas and fishing restricted areas. T4. Generate suggestions for fishery resource conservation and water area ecological restoration according to the changes in environmental data and the characteristics of the fish community structure.

[0005] As a preferred technical solution of the present invention, the sensors include but are not limited to water temperature sensors, water quality sensors, nutrient sensors, and turbidity sensors.

[0006] As a preferred technical solution of the present invention, the management and decision-making platform includes a data layer, a business logic layer, and a user interface layer. The data layer is responsible for data interaction with the fish community database. The business logic layer realizes the invocation of the fish community dynamic model, data analysis and processing, and decision generation. Fishery managers complete input parameters, view analysis results, and decision suggestions through the user interface layer.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: The construction method of the digital management and decision-making system for fish communities based on digital technology constructs a comprehensive and efficient digital management and decision-making system for fish communities by integrating digital technology, effectively improving the scientificity and accuracy of fish community management, and being conducive to the sustainable utilization of fishery resources and the protection of water ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 FIG. is a flowchart of the construction method of the digital management and decision-making system for fish communities based on digital technology disclosed in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The technical solutions in the embodiments of the present invention will be clearly and completely described below 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 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.

[0010] Please refer to Figure 1 , the present invention provides a technical solution: A construction method of a digital management and decision-making system for fish communities based on digital technology, step one, data collection, the data collection includes the following steps: S1. According to the range, depth and terrain of the fish inhabiting waters, underwater photography equipment, sonar detectors, and several sensors are arranged. The sensors include but are not limited to water temperature sensors, water quality sensors, nutrient sensors, and turbidity sensors. For example, in large-area and deep-water areas, multiple high-power sonar detectors can be used to ensure the coverage range; underwater photography equipment is key arranged in fish aggregation areas or key ecological sites to obtain clearer image data; S2. Adjust the shooting resolution, frame rate, and angle of the underwater photography equipment to ensure that the morphological characteristics and behavioral details of fish can be clearly captured. Adjust the detection frequency and pulse width of the sonar detector to adapt to different water environments and fish group characteristics, and accurately detect the fish distribution density and position information. The sensors are calibrated according to their measurement range and accuracy requirements. For example, the accuracy of the temperature sensor should be accurate to 0.1 °C to ensure the accuracy of environmental data; S3. The data collected by the underwater photography equipment, sonar detector, and several sensors is transmitted to the data processing center through a wireless transmission module. The wireless transmission module includes but is not limited to WI-FI, Bluetooth, 4G / 5G network modules. During the transmission process, data encryption technology is used to ensure the security and integrity of the data, preventing the data from being stolen or tampered with; S4. Use data integration software to uniformly organize and convert data from different sources and in different formats. For example, convert image data into a specific format (such as JPEG or PNG), convert the signal data of sonar detectors into an analyzable digital signal format, and convert the environmental data of sensors into a standard data table format for subsequent processing; Step 2: Construct a fish community database. The construction of the fish community database includes the following steps: J1. Use a graphic recognition algorithm (such as a convolutional neural network algorithm based on deep learning) to process the images collected by underwater photography equipment. First, train a large number of images of known fish species so that the algorithm can learn the external characteristics of different fish. When new image data is input, the algorithm automatically identifies the fish species in the image and calculates parameters such as the individual number, body length, and body width of the fish through image analysis technology, and records the fish species, quantity, and average body length in the image in the form of a table and stores them in the database; J2. For the data collected by sonar detectors, use a signal processing algorithm to remove noise interference and enhance the effective signal. By analyzing the intensity and time delay of sonar echoes, calculate the distribution density and group position information of fish, and convert them into geographic coordinates and density numerical data and store them in the database; J3. Directly store the environmental data collected by sensors in the database according to the time series and establish a data index for quickly querying and calling the environmental data of different time periods and different regions; J4. Obtain historical fishery data from the fishery management department, including annual fishing records and fish population survey data, digitally organize and clean the historical fishery data, then store it in the database in a unified format, and associate and integrate it with the real-time collected data to provide a more comprehensive time series data basis for subsequent analysis; Step 3: Develop a fish community dynamic model. The development of the fish community dynamic model includes the following steps: E1. Extract fish community data (species, quantity, distribution, etc.) and environmental data (water temperature, water quality, season, etc.) from the fish community database, and use big data analysis tools to analyze the correlation between the two. For example, use a correlation analysis algorithm to determine the degree of association between water temperature changes and the reproductive behavior of specific fish, and the relationship between food resource richness and fish population growth; E2. Use data mining tools to mine the hidden laws in historical fishery data, such as discovering the migration laws of certain fish within a specific season and specific water temperature range, and the evolution trend of the fish community structure under long-term environmental changes; E3. Use the environmental data as input variables and the fish community data as output variables to construct a fish community dynamic model based on the decision tree algorithm; E4. Divide the historical fishery data and real-time monitoring data into a training set and a test set. Use the training set to train the fish community dynamics model, and adjust the model parameters (such as the weights of the neural network, the branching rules of the decision tree, etc.) so that the model can accurately predict the dynamic changes of the fish community based on the input environmental data. Use the test set to verify and evaluate the trained fish community dynamics model. By comparing the error indicators (such as mean square error, accuracy, etc.) between the predicted results and the actual data, and optimize the fish community dynamics model according to the results; Step 4: Build a management and decision-making platform, and the building of the management and decision-making platform includes the following steps: T1. Design the management and decision-making platform using a layered architecture. The management and decision-making platform includes a data layer, a business logic layer, and a user interface layer. The data layer is responsible for data interaction with the fish community database. The business logic layer realizes the invocation of the fish community dynamics model, data analysis and processing, and decision generation. Fisheries managers complete input parameters, view analysis results, and decision-making suggestions through the user interface layer; T2. According to the prediction structure of the fish species number and the growth trend analysis output by the fish community dynamics model, combined with the policies and goals of the fishery management department, use intelligent algorithms to calculate the fishing quota. For example, when the model predicts that the number of a certain fish population is in the growth period and has not reached the environmental carrying capacity limit, the fishing quota can be appropriately increased, but it should be determined within the scope of sustainable development of the population; T3. Based on the predicted results of the fish migration path output by the fish community dynamics model, use GIS technology to visually display the migration routes and key aggregation areas of fish on the map, so as to plan fishing moratorium areas and restricted fishing areas. For example, during the fish breeding season, set its main breeding sites and migration channels as fishing moratorium areas to protect the breeding and living environment of fish; T4. In response to the changes in environmental data and the characteristics of the fish community structure, use the knowledge base and expert system to generate suggestions for fishery resource conservation and water area ecological restoration. For example, when the water quality monitoring data shows eutrophication of the water body, it is recommended to release algae-eating fish or take water purification measures; when the number of certain key species in the fish community structure decreases, it is recommended to artificially increase the release of corresponding fry varieties and determine the appropriate release quantity and location to optimize the fish community structure and promote the ecological balance of the water area.

Claims

1. A construction method for a digital management and decision-making system of fish communities based on digital technology, characterized in that, The following steps are involved: Step 1: data collection, the data collection includes the following steps: S1, arrange underwater photography equipment, sonar detectors, and several sensors according to the range, depth, and topography of the fish habitat; S2, adjust the shooting resolution, frame rate, and angle of the underwater photography equipment, adjust the detection frequency and pulse width of the sonar detector, and calibrate the sensor according to its measurement range and accuracy requirements; S3, the data collected by underwater photography equipment, sonar detectors, and several sensors are transmitted to the data processing center through the wireless transmission module; S4, using data integration software to unify and convert data from different sources and formats; Step 2: constructing a fish community database, the construction of the fish community database comprises the following steps: J1, uses a pattern recognition algorithm to process the images collected by the underwater photography equipment, and records the types, quantity, and average body length of the fish in the images in the form of a table, and stores them in a database; J2, for the data collected by the sonar detector, use the signal processing algorithm to remove noise interference, calculate the distribution density and group location information of fish by analyzing the intensity and time delay of the sonar echo, and convert it into geographic coordinates and density numerical data and store it in the database; J3, the environmental data collected by the sensor is directly stored in the database according to the time series, and the data index is established; J4, obtain historical fishery data from fishery management departments, digitally organize and clean the historical fishery data, and then store them in a database in a unified format and associate and integrate them with real-time collected data; Step 3: Developing a fish community dynamic model, the developing fish community dynamic model comprises the following steps: E1, extracting fish community data and environmental data from the fish community database and analyzing the correlation between the two using big data analysis tools; E2, using data mining tools to discover hidden patterns in historical fishery data; E3, using environmental data as input variables and fish community data as output variables, constructs a fish community dynamic model based on a decision tree algorithm; E4, divide the historical fishery data and real-time monitoring data into training sets and test sets, use the training set to train the fish community dynamics model, use the test set to verify and evaluate the trained fish community dynamics model, and optimize the fish community dynamics model based on the results; Step 4: Building a management and decision-making platform, which includes the following steps: T1, adopts layered architecture to design management and decision-making platform; T2, based on the predicted structure and growth trend analysis of fish species output by the fish community dynamics model, combined with the policies and goals of the fishery management department, uses intelligent algorithms to calculate fishing quotas; T3, based on the prediction results of fish migration paths output by the fish community dynamic model, the fish migration routes and key gathering areas are visualized on the map through GIS technology, so as to plan fishing bans and restricted fishing areas; T4, generates recommendations for fishery resource conservation and water ecological restoration based on changes in environmental data and characteristics of fish community structure.

2. The construction method of a digital management and decision-making system for fish communities based on digital technology according to claim 1, characterized in that, The sensors include, but are not limited to, water temperature sensors, water quality sensors, nutrient sensors, and turbidity sensors.

3. A method for constructing a digital management and decision-making system for fish communities based on digital technology according to claim 1, characterized in that, The management and decision-making platform includes a data layer, a business logic layer, and a user interface layer. The data layer is responsible for data interaction with the fish community database. The business logic layer realizes the invocation of the fish community dynamic model, data analysis and processing, and decision generation. Fisheries managers complete input parameters, view analysis results, and decision suggestions through the user interface layer.

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