Intelligent fishery comprehensive management method and system

Abstract

The invention relates to the technical field of fishery comprehensive management, in particular to an intelligent fishery comprehensive management method and system, and the method comprises the following steps: obtaining fishery information, and classifying the fishery information to obtain a plurality of types of fishery information; obtaining each type of historical fishery information, extracting information features corresponding to each type of historical fishery information, and obtaining a plurality of historical fishery information features based on the plurality of types of historical fishery information; constructing a plurality of regulation and control models based on the plurality of historical fishery information features; obtaining fishery information of the current category, and extracting current fishery information features according to the fishery information of the current category; matching a corresponding regulation and control model based on the category of the current fishery information feature to obtain a target regulation and control scheme; the fishery environment can be monitored and managed in real time, corresponding regulation and control strategies are automatically generated according to different types of fishery information, and therefore intelligent, efficient and optimized management is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated fishery management, and in particular to an intelligent integrated fishery management method and system. Background Art

[0002] As an important part of modern agriculture, the management method of smart fishery is of great significance to improving fishery production efficiency and ensuring the sustainable use of fishery resources. With the continuous development of information technology, the comprehensive management method of smart fishery has emerged, which uses the Internet of Things, big data, artificial intelligence and other technical means to achieve real-time monitoring and intelligent management of fisheries.

[0003] However, the existing integrated management methods of smart fisheries still have some shortcomings in the acquisition, classification, processing of fishery information and the formulation of control plans. For example, the acquisition of fishery information often relies on a single monitoring method, resulting in incomplete information; the classification and processing of fishery information lacks intelligent means, making the formulation of control plans inaccurate; in addition, the formulation of control plans often lacks foresight and is difficult to cope with sudden changes in the fishery environment.

[0004] To this end, we propose a smart fishery integrated management method and system to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a smart fishery integrated management method and system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a smart fishery integrated management method and system, the method comprising the following steps:

[0007] S1: Obtaining fishing ground information and classifying the fishing ground information to obtain multiple categories of fishing ground information, wherein the multiple categories of fishing ground information include water quality information and feeding information; obtaining each category of historical fishing ground information and extracting information features corresponding to each category of historical fishing ground information, and obtaining multiple historical fishing ground information features based on multiple categories of historical fishing ground information; and constructing multiple control models based on multiple historical fishing ground information features;

[0008] S2: Obtain the fishery information of the current category, extract the current fishery information features according to the fishery information of the current category; match the corresponding control model based on the category to which the current fishery information features belong to obtain the target control scheme;

[0009] S3: predicting multiple backup fishing ground information according to the sub-item characteristics of the target control plan, and formulating backup target control plans one by one according to the backup fishing ground information;

[0010] S4: updating the real-time fishing ground information to obtain the changed fishing ground information; if the changed fishing ground information is the backup fishing ground information, starting the backup target control plan; if the changed fishing ground information is not the backup fishing ground information, re-matching the control model to obtain the target control plan.

[0011] Preferably, the step of acquiring the fishing ground information and classifying the fishing ground information to obtain multiple categories of fishing ground information includes:

[0012] Obtain the fishery area information to be managed, divide the fishery area information and deploy monitoring points;

[0013] Obtaining the fishery information of the fishery area based on the monitoring points and uploading it to the database, obtaining the usage category of each fishery information in the database, wherein the usage category includes water quality category and feeding category;

[0014] A plurality of fishing ground information is classified based on usage categories to obtain a plurality of categories of fishing ground information.

[0015] Preferably, the step of constructing multiple control models based on multiple historical fishery information features comprises:

[0016] Obtaining historical fishery information features of multiple categories, and dividing the historical fishery information features of each category into a training set and a validation set;

[0017] Construct a neural network model, and train the neural network model based on the training set to obtain a preliminary control model;

[0018] The preliminary control model is tested based on the validation set, and the preliminary control model that meets the test requirements is used as the target control model;

[0019] Multiple regulation models are obtained based on multiple historical fishery information characteristics.

[0020] Preferably, the step of obtaining a target regulation scheme by matching a corresponding regulation model based on the category to which the current fishery information feature belongs includes:

[0021] Obtaining an output port of a database and multiple control models, and building a matching model based on the output port of the database and the multiple control models;

[0022] Obtain the use category corresponding to the current fishery information feature to obtain the target use category, start the regulation model corresponding to the target use category based on the matching model, and obtain the target regulation model;

[0023] The fishing ground information corresponding to the current fishing ground information characteristics is input into the target control model to obtain the target control plan.

[0024] Preferably, the step of constructing a matching model based on the output port of the database and multiple control models includes:

[0025] Acquire multiple control models, each of which corresponds to a model point, and arrange the multiple model points in a ring to obtain a model ring;

[0026] Obtaining the usage category of the fishery information features corresponding to each control model, each usage category of the fishery information features corresponds to an information point, and arranging multiple information points in a ring to obtain an information ring;

[0027] The information ring is set inside the model ring, and the center points of the information ring and the model ring coincide with each other. The center point of the information ring is set as a common point. An information channel is set between the common point and each information point. The information point and the model point are bound through an association chain.

[0028] The output port of the database is obtained, and the output port is connected to the common point through a communication channel to obtain a matching model.

[0029] Preferably, the step of obtaining the target usage category by acquiring the usage category corresponding to the current fishery information feature, and starting the regulation model corresponding to the target usage category based on the matching model to obtain the target regulation model comprises:

[0030] Obtaining the usage category corresponding to the current fishing ground information feature, extracting the fishing ground information feature of the current fishing ground information corresponding to the usage category to obtain the current fishing ground information feature;

[0031] Obtaining the fishing ground information features at each information point to obtain the benchmark fishing ground information features, and comparing the current fishing ground information features with multiple benchmark fishing ground information features to obtain multiple feature similarities;

[0032] The information point where the benchmark fishing ground information feature corresponding to the highest feature similarity value is located is taken as the target information point, and the regulation model corresponding to the target information point is started based on the association chain to obtain the target regulation model.

[0033] Preferably, the sub-item characteristics corresponding to the target control plan predict multiple backup fishing grounds information, and the step of formulating backup target control plans one by one according to the backup fishing grounds information includes: obtaining sub-item characteristics of the target control plan, predicting trend information of the sub-item characteristics one by one, wherein the trend information is information about possible consequences of the sub-item characteristics; formulating multiple backup fishing grounds information corresponding to the trend information, and formulating a backup target control plan according to the backup fishing ground information.

[0034] Preferably, the step of updating the real-time fishing ground information in real time to obtain the changed fishing ground information, starting the backup target control scheme if the changed fishing ground information is the backup fishing ground information, and re-matching the control model to obtain the target control scheme if the changed fishing ground information is not the backup fishing ground information includes:

[0035] Collecting real-time fishing ground information based on monitoring points, obtaining difference information between the real-time fishing ground information and the current fishing ground information as changed fishing ground information;

[0036] Obtaining sub-item features of the changed fishing ground information, and activating the backup target control plan if the sub-item features of the changed fishing ground information are trend information of the backup fishing ground information;

[0037] If the sub-item characteristics of the changed fishing ground information are not the trend information of the backup fishing ground information, the control model is re-matched to obtain the target control plan.

[0038] A smart fishery integrated management system, applied to any of the above-mentioned fishery integrated management methods, comprising:

[0039] A construction module is used to obtain and classify fishing ground information to obtain multiple categories of fishing ground information, wherein the multiple categories of fishing ground information include water quality information and feeding information; obtain each category of historical fishing ground information and extract information features corresponding to each category of historical fishing ground information, and obtain multiple historical fishing ground information features based on multiple categories of historical fishing ground information; and construct multiple control models based on multiple historical fishing ground information features;

[0040] The information collection module is used to obtain the current category of fishing ground information, extract the current fishing ground information features according to the current category of fishing ground information; match the corresponding control model based on the category to which the current fishing ground information features belong to obtain the target control plan;

[0041] A plan formulation module is used to predict multiple backup fishing ground information corresponding to the sub-item characteristics of the target control plan, and formulate backup target control plans one by one according to the backup fishing ground information;

[0042] The control management module is used to update the real-time fishing ground information in real time to obtain the changed fishing ground information. If the changed fishing ground information is the backup fishing ground information, the backup target control plan is started. If the changed fishing ground information is not the backup fishing ground information, the control model is re-matched to obtain the target control plan.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. By setting up monitoring points, the fishing ground information of the fishing ground area is fully obtained, and the fishing ground information is classified according to the use category to obtain multiple categories of fishing ground information, which can improve the comprehensiveness of the information and provide convenience for subsequent processing and regulation; based on multiple historical fishing ground information features, multiple regulation models are constructed using neural network models. These models can automatically match the target regulation scheme according to the current fishing ground information characteristics, realizing the intelligent management of the fishing ground; according to the sudden changes in the fishing ground environment, by predicting the trend information of the sub-item characteristics, multiple backup fishing ground information and backup target regulation schemes are formulated, which improves the predictability and flexibility of the regulation scheme; it can monitor and manage the fishing ground environment in real time, discover problems in time and take corresponding regulation measures, thereby improving the efficiency of fishery production. At the same time, intelligent management means also reduce the frequency and cost of manual intervention. By collecting and analyzing historical fishing ground information and characteristics, a personalized regulation model suitable for different types of data in the fishing ground is constructed. The model can automatically generate corresponding regulation strategies according to different types of fishing ground information, thereby realizing intelligent, efficient and optimized management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0046] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0047] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example

[0050] See also Figure 1 to Figure 2 The present invention provides a smart fishery integrated management method and system technical solution: a smart fishery integrated management method, comprising the following steps:

[0051] S1: Obtaining fishing ground information and classifying the fishing ground information to obtain multiple categories of fishing ground information, wherein the multiple categories of fishing ground information include water quality information and feeding information; obtaining each category of historical fishing ground information and extracting information features corresponding to each category of historical fishing ground information, and obtaining multiple historical fishing ground information features based on multiple categories of historical fishing ground information; and constructing multiple control models based on multiple historical fishing ground information features;

[0052] It should be noted that: each type of historical fishing ground information is obtained and the information features corresponding to each type of historical fishing ground information are extracted. For example, one type of fishing ground information is dissolved oxygen, which is used to judge water quality, and its features are extracted as water quality features;

[0053] The steps of obtaining the fishery information and classifying the fishery information to obtain multiple categories of fishery information include: obtaining the fishery area information to be managed, dividing the fishery area information and arranging monitoring points; obtaining the fishery information of the fishery area based on the monitoring points and uploading it to a database, obtaining the use category of each fishery information in the database, wherein the use category includes water quality category and feeding category; classifying the multiple fishery information based on the use category to obtain multiple categories of fishery information;

[0054] Specifically, the monitoring points include multiple types of monitors. For example, when monitoring water quality, it is necessary to monitor the dissolved oxygen, pH value, etc. of the water. Different monitors are arranged according to different monitoring objects. For example, when monitoring water temperature, a water temperature measuring instrument is used. By dividing the fishing ground into regions, monitoring points are arranged in each region. The monitoring points obtain all the information of the fishing ground and upload it to the database. In the database, all the information is divided into categories to obtain multiple categories of fishing ground information. The use categories include water quality, feeding, disease and fishing, and the information is classified according to the use category. The use category refers to what the bathing ground information is used for. For example, dissolved oxygen is used to judge water quality and is classified into the water quality category. The feeding amount belongs to the feeding situation of the target fish and is classified into the feeding category.

[0055] The steps of constructing multiple control models based on multiple historical fishery information features include: obtaining multiple categories of historical fishery information features, dividing each category of historical fishery information features into a training set and a validation set; constructing a neural network model, training the neural network model based on the training set to obtain a preliminary control model; testing the preliminary control model based on the validation set, and taking the preliminary control model that meets the test requirements as the target control model; obtaining multiple control models based on multiple historical fishery information features;

[0056] Specifically, various historical information of past fishing grounds is collected and preprocessed, such as the location, time, climate conditions, marine biological communities, etc. of the fishing grounds. Preprocessing can be data cleaning, normalization and other processing to ensure the quality and consistency of the data. The collected historical fishing ground information is divided into input features (X) and output results (Y), where Y represents the target to be predicted. For example, Y can be the result of water quality regulation in fisheries, the result of feeding regulation, the result of disease assessment, and the fishing efficiency of the fishing ground. After the data set is divided, 80% of the training data and 20% of the test data can be used to train and test the performance of the model. Design a multi-layer perceptron (MLP) neural network, which includes an input layer, a hidden layer, and an output layer. A hidden layer is set between the input layer and the output layer to capture complex nonlinear relationships. The structure of the neural network can be adjusted according to the specific problem. For example, if the amount of data is small, a simple two-layer network can be selected; if the amount of data is large, a deeper network can be selected. Use the training data set to train the neural network model. During the training process, the weights and biases can be calculated by the back propagation algorithm, and the network parameters can be adjusted to achieve the best performance. Common loss functions include mean square error (MSE), mean absolute error (MAE), etc., while the optimizer can use stochastic gradient descent (SGD) or other optimization algorithms. After training, the model is applied to the test data set to evaluate the performance of the model. If the model performance is poor, continue to adjust the network structure, increase the depth of the hidden layer, change the activation function, etc. to optimize. At the same time, you can also draw images such as confusion matrix and ROC curve to understand the model performance more intuitively until the test results meet the test requirements. The test requirements refer to the control model corresponding to the test results that meet the requirements. After the corresponding control method is output, the result after the control method is controlled can meet the use requirements, that is, it means the accuracy of the control model after use. For example, if the effect achieved after using the control scheme is 90%, 90% is used as the test requirement. The trained control model is used to predict the future state of the fishery, and the corresponding control strategy is formulated accordingly. For example, if we find that the water quality parameters (such as dissolved oxygen and pH value) in the water quality predicted by the model are close to or below the threshold, we need to change the water in time to restore the water quality.

[0057] S2: Obtain the fishery information of the current category, extract the current fishery information features according to the fishery information of the current category; match the corresponding control model based on the category to which the current fishery information features belong to obtain the target control scheme;

[0058] The step of obtaining a target control scheme by matching a corresponding control model based on the category to which the current fishing ground information feature belongs includes: obtaining an output port of a database and a plurality of control models, and constructing a matching model based on the output port of the database and the plurality of control models; obtaining a use category corresponding to the current fishing ground information feature to obtain a target use category, and starting a control model corresponding to the target use category based on the matching model to obtain a target control model; and inputting the fishing ground information corresponding to the current fishing ground information feature into the target control model to obtain a target control scheme;

[0059] Specifically, by constructing a matching model, according to the category to which the fishery information characteristics belong, the information point corresponding to the fishery information characteristics is determined as the target information point, and the control model on the model point corresponding to the target information point is started according to the association chain, and the fishery information of the current category is input into the control model to obtain the target control plan. The corresponding control model can be adopted according to the fishery information characteristics, and different control models can be adopted for different categories of fishery information, so as to classify and manage the fisheries and improve the management effect;

[0060] The steps of constructing a matching model based on the output port of a database and a plurality of control models include: obtaining a plurality of control models, and each control model corresponds to a model point, and the plurality of model points are arranged in a ring to obtain a model ring; obtaining the usage category of the fishery information characteristics corresponding to each control model, and the fishery information characteristics of each usage category correspond to an information point, and the plurality of information points are arranged in a ring to obtain an information ring; setting the information ring inside the model ring, and the information ring coincides with the center point of the model ring, and the center point of the information ring is set as a common point, and an information channel is provided between the common point and each information point, and the information point and the model point are bound through an association chain; obtaining the output port of the database, connecting the output port to the common point through a communication channel, and obtaining a matching model;

[0061] It should be noted that the fishery information features corresponding to each control model and arranged in a circular manner means that each control model corresponds to a type of fishery information, and the features of each category of fishery information are extracted to obtain the fishery information features, wherein the fishery information features must be consistent with the features of the fishery information required by the control model, and the fishery information features corresponding to the control model are obtained;

[0062] Specifically, the matching model is provided with model points, association chains, information points, information channels, public points, transmission channels, and data output ports. The current fishing ground information that needs to be regulated is transmitted to the public point via the transmission channel through the database port, and the current fishing ground information features are extracted. The current fishing ground information features are compared with the fishing ground information features at the fishing ground information points, and the information points corresponding to the fishing ground information features with the highest similarity are found as the target information points. Then, the regulation model at the model point corresponding to the target information point is started via the association chain, and the current fishing ground information corresponding to the fishing ground information features at the target information point is input into the regulation model, and a regulation plan corresponding to the current fishing ground information is automatically generated, and the fishing ground is regulated and managed according to the regulation plan.

[0063] The use category corresponding to the current fishery information feature is obtained to obtain the target use category, and the control model corresponding to the target use category is started based on the matching model. The steps of obtaining the target control model include:

[0064] Obtaining the usage category corresponding to the current fishing ground information feature, extracting the fishing ground information feature of the current fishing ground information corresponding to the usage category to obtain the current fishing ground information feature;

[0065] Obtaining the fishing ground information features at each information point to obtain the benchmark fishing ground information features, and comparing the current fishing ground information features with multiple benchmark fishing ground information features to obtain multiple feature similarities;

[0066] The information point where the benchmark fishing ground information feature corresponding to the highest feature similarity value is located is taken as the target information point, and the control model corresponding to the target information point is started based on the association chain to obtain the target control model;

[0067] It should be noted that the feature similarity is obtained by comparing the current fishing ground information features with multiple benchmark fishing ground information features, and the current fishing ground information features are compared with each benchmark fishing ground information feature in turn to evaluate the similarity. The current fishing ground information features and each benchmark fishing ground information feature are presented as a set of sub-features. The evaluation formula corresponding to the feature similarity is: Where T represents the set of current fishing ground information features, Ti′ represents the set of i-th benchmark fishing ground information features, when both sets T and Ti′ are 0, Defined as 1, it should be noted that The larger the value of , the higher the similarity between the two.

[0068] Specifically, by comparing two information features, the information point corresponds to an information feature of a category, and the fishery information features of the current category are compared with the benchmark fishery information features at each information point, and the Jaccard similarity coefficient is used to compare the similarities and differences between limited sample sets. The larger the Jaccard coefficient value, the higher the sample similarity. Since there are multiple benchmark bathing ground information features, there are multiple feature similarities. The feature similarities are arranged in descending order, and the point where the benchmark fishery information feature corresponding to the feature similarity with the highest value is located is used as the target information point, and the current fishery information corresponding to the current fishery information feature is transmitted to the target information point through the information channel, which can facilitate matching the corresponding regulation model according to the fishery information, thereby improving the accuracy of matching;

[0069] S3: predicting multiple backup fishing ground information according to the sub-item characteristics of the target control plan, and formulating backup target control plans one by one according to the backup fishing ground information;

[0070] The steps of predicting a plurality of backup fishing ground information corresponding to the sub-item characteristics of the target control plan and formulating the backup target control plan one by one according to the backup fishing ground information include: obtaining the sub-item characteristics of the target control plan, predicting the trend information of the sub-item characteristics one by one, wherein the trend information is the possible consequence information of the sub-item characteristics; formulating a plurality of backup fishing ground information corresponding to the trend information, and formulating the backup target control plan according to the backup fishing ground information;

[0071] Specifically, the sub-item feature means that, assuming that the target control scheme is a control scheme for water quality categories, the water quality categories include dissolved oxygen, pH value, and water temperature as sub-item information of water quality categories, and the sub-item feature is obtained by extracting the features of the sub-item information. For example, the sub-item feature corresponding to the water temperature information is the water temperature, and the possible consequence information is that the water temperature may continue to rise or continue to fall, etc.;

[0072] S4: updating the real-time fishing ground information to obtain the changed fishing ground information in real time. If the changed fishing ground information is the backup fishing ground information, starting the backup target control plan. If the changed fishing ground information is not the backup fishing ground information, re-matching the control model to obtain the target control plan.

[0073] The steps of updating the real-time fishing ground information in real time to obtain the changed fishing ground information, starting the backup target control scheme if the changed fishing ground information is the backup fishing ground information, and re-matching the control model to obtain the target control scheme if the changed fishing ground information is not the backup fishing ground information include: collecting the real-time fishing ground information based on the monitoring points, obtaining the difference information between the real-time fishing ground information and the current fishing ground information as the changed fishing ground information; obtaining the sub-item features of the changed fishing ground information, starting the backup target control scheme if the sub-item features of the changed fishing ground information are the trend information of the backup fishing ground information, and re-matching the control model to obtain the target control scheme if the sub-item features of the changed fishing ground information are not the trend information of the backup fishing ground information;

[0074] Specifically, the distinguishing information refers to the information that is different from the current fishing ground information and the real-time fishing ground information. For example, the water temperature in the current fishing ground information is 10 degrees Celsius. Assuming that it meets the requirements of the target fish for breeding, but when adjusting the dissolved oxygen or pH value of the water quality, the water temperature also changes. Assuming that the water temperature rises to 15 degrees Celsius, the real-time fishing ground information at this time is that the water temperature is 15 degrees Celsius, and the water temperature of the current fishing ground information is 10 degrees Celsius. The water temperatures of the two are different, which means that the water temperature information is different, and the increase of 5 degrees Celsius is used as the distinguishing information to obtain the changed fishing ground information. The sub-item feature corresponding to the changed fishing ground information is water temperature. It is judged whether the water temperature increases by 5 degrees Celsius. If the trend information contains the changed fishing ground information, the backup target control plan corresponding to the trend information is adopted. If there is no corresponding changed fishing ground information in the trend information, the changed fishing ground information is input into the corresponding control model to redetermine the control plan.

[0075] A smart fishery integrated management system, applied to any of the above-mentioned fishery integrated management methods, comprising:

[0076] A construction module is used to obtain and classify fishing ground information to obtain multiple categories of fishing ground information, wherein the multiple categories of fishing ground information include water quality information and feeding information; obtain each category of historical fishing ground information and extract information features corresponding to each category of historical fishing ground information, and obtain multiple historical fishing ground information features based on multiple categories of historical fishing ground information; and construct multiple control models based on multiple historical fishing ground information features;

[0077] The information collection module is used to obtain the current category of fishing ground information, extract the current fishing ground information features according to the current category of fishing ground information; match the corresponding control model based on the category to which the current fishing ground information features belong to obtain the target control plan;

[0078] A plan formulation module is used to predict multiple backup fishing ground information corresponding to the sub-item characteristics of the target control plan, and formulate backup target control plans one by one according to the backup fishing ground information;

[0079] The control management module is used to update the real-time fishing ground information in real time to obtain the changed fishing ground information. If the changed fishing ground information is the backup fishing ground information, the backup target control plan is started. If the changed fishing ground information is not the backup fishing ground information, the control model is re-matched to obtain the target control plan.

[0080] The present invention, by constructing a comprehensive management platform, trains multiple models according to historical information, obtains relevant data of the fishery, automatically matches corresponding models according to the relevant data of the fishery, obtains corresponding control schemes, preliminarily applies the control schemes, obtains fishery information in real time, predicts based on the fishery information as trend information, obtains backup control schemes based on the trend information using corresponding models, judges the similarity between the real-time information and the trend information, determines whether to modify the backup control schemes and the main control schemes according to whether the similarity meets preset conditions, and if modification is required, uses the modified control schemes to control the fishery as a management result.

[0081] By setting up monitoring points, the fishing ground information of the fishing ground area is fully obtained, and the fishing ground information is classified according to the use category to obtain multiple categories of fishing ground information, which can improve the comprehensiveness of the information and provide convenience for subsequent processing and regulation; based on multiple historical fishing ground information features, multiple regulation models are constructed using neural network models. These models can automatically match the target regulation plan according to the current fishing ground information characteristics, realizing the intelligent management of the fishing ground; according to the sudden changes in the fishing ground environment, by predicting the trend information of the sub-item characteristics, multiple backup fishing ground information and backup target regulation plans are formulated, which improves the predictability and flexibility of the regulation plan; it can monitor and manage the fishing ground environment in real time, discover problems in time and take corresponding regulation measures, thereby improving the efficiency of fishery production. At the same time, intelligent management means also reduce the frequency and cost of manual intervention. By collecting and analyzing historical fishing ground information and characteristics, a personalized regulation model suitable for different types of data in the fishing ground is constructed. The model can automatically generate corresponding regulation strategies according to different types of fishing ground information, thereby realizing intelligent, efficient and optimized management.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0083] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart fishery integrated management method, characterized in that: The following steps are involved: S1: Obtaining fishing ground information and classifying the fishing ground information to obtain multiple categories of fishing ground information, wherein the multiple categories of fishing ground information include water quality information and feeding information; obtaining each category of historical fishing ground information and extracting information features corresponding to each category of historical fishing ground information, and obtaining multiple historical fishing ground information features based on multiple categories of historical fishing ground information; and constructing multiple control models based on multiple historical fishing ground information features; S2: Obtain the fishery information of the current category, extract the current fishery information features according to the fishery information of the current category; match the corresponding control model based on the category to which the current fishery information features belong to obtain the target control scheme; S3: predicting multiple backup fishing ground information according to the sub-item characteristics of the target control plan, and formulating backup target control plans one by one according to the backup fishing ground information; S4: updating the real-time fishing ground information to obtain the changed fishing ground information; if the changed fishing ground information is the backup fishing ground information, starting the backup target control plan; if the changed fishing ground information is not the backup fishing ground information, re-matching the control model to obtain the target control plan.

2. The method for integrated management of intelligent fisheries according to claim 1, characterized in that: The steps of obtaining the fishing ground information and classifying the fishing ground information to obtain multiple categories of fishing ground information include: Obtain the fishery area information to be managed, divide the fishery area information and deploy monitoring points; Obtaining the fishery information of the fishery area based on the monitoring points and uploading it to the database, obtaining the usage category of each fishery information in the database, wherein the usage category includes water quality category and feeding category; A plurality of fishing ground information is classified based on usage categories to obtain a plurality of categories of fishing ground information.

3. The intelligent fishery integrated management method according to claim 1, characterized in that: The step of constructing multiple control models based on multiple historical fishery information features includes: Obtaining historical fishery information features of multiple categories, and dividing the historical fishery information features of each category into a training set and a validation set; Construct a neural network model, and train the neural network model based on the training set to obtain a preliminary control model; The preliminary control model is tested based on the validation set, and the preliminary control model that meets the test requirements is used as the target control model; Multiple regulation models are obtained based on multiple historical fishery information characteristics.

4. The intelligent fishery integrated management method according to claim 1, characterized in that: The step of obtaining a target control scheme by matching a corresponding control model based on the category to which the current fishery information feature belongs comprises: Obtaining an output port of a database and multiple control models, and building a matching model based on the output port of the database and the multiple control models; Obtain the use category corresponding to the current fishery information feature to obtain the target use category, start the regulation model corresponding to the target use category based on the matching model, and obtain the target regulation model; The fishing ground information corresponding to the current fishing ground information characteristics is input into the target control model to obtain the target control plan.

5. A smart fishery integrated management method according to claim 4, characterized in that: The step of constructing a matching model based on the output port of the database and multiple control models includes: Acquire multiple control models, each of which corresponds to a model point, and arrange the multiple model points in a ring to obtain a model ring; Obtaining the usage category of the fishery information features corresponding to each control model, each usage category of the fishery information features corresponds to an information point, and arranging multiple information points in a ring to obtain an information ring; The information ring is set inside the model ring, and the center points of the information ring and the model ring coincide with each other. The center point of the information ring is set as a common point. An information channel is set between the common point and each information point. The information point and the model point are bound through an association chain. The output port of the database is obtained, and the output port is connected to the common point through a communication channel to obtain a matching model.

6. A smart fishery integrated management method according to claim 5, characterized in that: The use category corresponding to the current fishery information feature is obtained to obtain the target use category, and the control model corresponding to the target use category is started based on the matching model. The steps of obtaining the target control model include: Obtaining the usage category corresponding to the current fishing ground information feature, extracting the fishing ground information feature of the current fishing ground information corresponding to the usage category to obtain the current fishing ground information feature; Obtaining the fishing ground information features at each information point to obtain the benchmark fishing ground information features, and comparing the current fishing ground information features with multiple benchmark fishing ground information features to obtain multiple feature similarities; The information point where the benchmark fishing ground information feature corresponding to the highest feature similarity value is located is taken as the target information point, and the regulation model corresponding to the target information point is started based on the association chain to obtain the target regulation model.

7. The intelligent fishery integrated management method according to claim 1, characterized in that: The steps of predicting a plurality of backup fishing grounds information according to the sub-item characteristics of the corresponding target control plan and formulating the backup target control plan one by one according to the backup fishing ground information include: obtaining the sub-item characteristics of the target control plan, predicting the trend information of the sub-item characteristics one by one, wherein the trend information is the possible consequence information of the sub-item characteristics; formulating a plurality of backup fishing grounds information according to the trend information, and formulating the backup target control plan according to the backup fishing ground information.

8. The intelligent fishery integrated management method according to claim 1, characterized in that: The step of updating the real-time fishing ground information in real time to obtain the changed fishing ground information, starting the backup target control scheme if the changed fishing ground information is the backup fishing ground information, and re-matching the control model to obtain the target control scheme if the changed fishing ground information is not the backup fishing ground information includes: Collecting real-time fishing ground information based on monitoring points, obtaining difference information between the real-time fishing ground information and the current fishing ground information as changed fishing ground information; Obtaining sub-item features of the changed fishing ground information, and activating the backup target control plan if the sub-item features of the changed fishing ground information are trend information of the backup fishing ground information; If the sub-item characteristics of the changed fishing ground information are not the trend information of the backup fishing ground information, the control model is re-matched to obtain the target control plan.

9. A smart fishery integrated management system, applied to the fishery integrated management method according to any one of claims 1 to 8, characterized in that: include: A construction module is used to obtain and classify fishing ground information to obtain multiple categories of fishing ground information, wherein the multiple categories of fishing ground information include water quality information and feeding information; obtain each category of historical fishing ground information and extract information features corresponding to each category of historical fishing ground information, and obtain multiple historical fishing ground information features based on multiple categories of historical fishing ground information; and construct multiple control models based on multiple historical fishing ground information features; The information collection module is used to obtain the current category of fishing ground information, extract the current fishing ground information features according to the current category of fishing ground information; match the corresponding control model based on the category to which the current fishing ground information features belong to obtain the target control plan; A plan formulation module is used to predict multiple backup fishing ground information corresponding to the sub-item characteristics of the target control plan, and formulate backup target control plans one by one according to the backup fishing ground information; The control management module is used to update the real-time fishing ground information in real time to obtain the changed fishing ground information. If the changed fishing ground information is the backup fishing ground information, the backup target control plan is started. If the changed fishing ground information is not the backup fishing ground information, the control model is re-matched to obtain the target control plan.

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