Fishing farmer microclimate information acquisition early warning method and system

Abstract

The invention discloses a fisherman micro-meteorological information acquisition early warning method and system, and relates to the technical field of micro-meteorological early warning, and the method comprises the steps: obtaining real-time first meteorological data from a meteorological bureau; monitoring and acquiring second meteorological data through a meteorological sensor; integrating the first meteorological data and the second meteorological data through the cloud platform; establishing a meteorological prediction model, processing the integrated first meteorological data and second meteorological data, and analyzing to generate a meteorological prediction result in a period of time in the future; and determining whether to trigger an alarm according to the weather prediction result, and notifying breeding management personnel. Based on micro-meteorological parameters required by aquaculture production, various automatic acquisition methods are adopted, and micro-meteorological early warning and disaster prevention and reduction are provided for aquaculture culturists. More accurate fishery micrometeorological data prediction and early warning are realized, farmers are helped to respond in time under severe weather or other dangerous conditions, and the safety and efficiency of fishery activities are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of micro-meteorological early warning technology, and more specifically to a method and system for collecting and early warning micro-meteorological information for fisheries and agriculture. Background Art

[0002] With the continuous development of aquaculture, the status of aquaculture in the world's livelihood and trade is becoming increasingly important. Climate change has a huge impact on the global fish ecosystem and has attracted much attention. Meteorological conditions are an important factor affecting aquaculture. Special climatic conditions have an important impact on the feeding, growth and development of aquatic animals. With the increasing scale of aquaculture production and the continuous improvement of the degree of intensification, it is increasingly affected by meteorological conditions. In recent years, many aquaculture industries have suffered frequent losses due to meteorological disasters.

[0003] Therefore, how to provide a method and system for collecting and warning micro-meteorological information for fisheries and agriculture, based on the micro-meteorological parameters required for aquaculture production, using a variety of automatic acquisition methods to provide micro-meteorological warnings for aquaculturists and disaster prevention and mitigation is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0004] In view of this, the present invention provides a method and system for collecting and warning micro-meteorological information for fisheries and agriculture. Based on the micro-meteorological parameters required for aquaculture production, a variety of automatic acquisition methods are used to provide micro-meteorological warnings for aquaculture farmers to prevent and reduce disasters. More accurate prediction and warning of micro-meteorological data in fisheries are achieved, helping farmers to respond in time in bad weather or other dangerous situations, and ensuring the safety and efficiency of fishery activities.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a method for collecting and warning micro-meteorological information for fisheries and agriculture, comprising:

[0006] Get real-time first-rate weather data from the weather bureau;

[0007] Deploy meteorological sensors around the fishing grounds, and monitor and obtain second meteorological data through the meteorological sensors;

[0008] Transmitting the first meteorological data and the second meteorological data to a cloud platform in real time;

[0009] Integrate the first meteorological data and the second meteorological data through a cloud platform;

[0010] Establishing a meteorological forecast model, processing the integrated first meteorological data and the second meteorological data, and analyzing and generating meteorological forecast results for a period of time in the future;

[0011] Determine whether to trigger an alarm based on the weather forecast results and notify the breeding management personnel.

[0012] Preferably, there are multiple meteorological sensors, and the numbers of the meteorological sensors are mapped with the meteorological sensor types, models, monitoring ranges, locations of the components to which they belong, and importance of the meteorological sensors to which they belong, forming a one-to-many mapping relationship, which is saved to the cloud platform.

[0013] Preferably, a meteorological forecast model is established to process the integrated first meteorological data and the second meteorological data, and analyze and generate meteorological forecast results for a period of time in the future, including:

[0014] Obtaining actual meteorological results and at least two first prediction models corresponding to historical meteorological data of the meteorological sensor and the meteorological bureau at the same time;

[0015] Inputting the actual meteorological results into each first prediction model for training, and obtaining a corresponding second prediction model;

[0016] Based on the meteorological sensor data and the meteorological bureau data corresponding to the actual meteorological results, each second prediction model performs prediction on the results of the same meteorological monitoring process to obtain an initial prediction result;

[0017] The monitoring values ​​of each second prediction model in the same meteorological monitoring process and the actual values ​​corresponding to the same meteorological monitoring process are spliced ​​to obtain multiple meteorological data sets;

[0018] The multiple meteorological data sets are input into a meteorological prediction model based on multiple decision tree regression algorithms for training to obtain a final meteorological prediction model, and the meteorological evolution in a future period of time is predicted based on the second prediction model and the final meteorological prediction model to obtain a meteorological prediction result.

[0019] Preferably, the meteorological prediction model based on multiple decision tree regression algorithms includes: dividing the meteorological data set into a training set and a test set according to a set ratio;

[0020] Construct a meteorological prediction model based on multiple decision tree regression algorithms; generate a new training set with the same number of decision tree regression algorithms according to the sample data in the training set; train the machine learning model with the independent variables of the sample data in the new training set as input and the response variables of the sample data in the training set as output to obtain a trained meteorological prediction model.

[0021] Preferably, the training process of the meteorological prediction model includes: determining the characteristics of the decision tree regression algorithm according to the number of independent variables of the sample data in the new training set; dividing the sample data in the new training set into data regions according to the characteristics of the decision tree regression algorithm to obtain data partition regions; obtaining the average value of the response variable of the sample data in the new training set and inputting the response variable value of the sample data in each data partition region; determining the loss function of the meteorological prediction model according to the response variable value and the average value of the response variable to complete the training of the meteorological prediction model.

[0022] Preferably, the at least two first prediction models are a variety of combinations including an autoregressive model, a moving average model, an autoregressive moving average model and an autoregressive difference moving average model.

[0023] Preferably, the method further includes: displaying the analyzed meteorological forecast results of the fishery to the aquaculture management personnel through a visual interface, and sending early warning information through a mobile phone App or text message.

[0024] Preferably, a fishery and agriculture micro-meteorological information collection and early warning system comprises:

[0025] A first meteorological data acquisition module, used to acquire real-time first meteorological data from the meteorological bureau;

[0026] A second meteorological data acquisition module is used to deploy meteorological sensors around the fishing ground and monitor and acquire second meteorological data through the meteorological sensors;

[0027] A data transmission module, used for transmitting the first meteorological data and the second meteorological data to a cloud platform in real time;

[0028] A data integration module, used for integrating the first meteorological data and the second meteorological data through a cloud platform;

[0029] A data processing module is used to establish a meteorological prediction model based on machine learning, process the integrated first meteorological data and the second meteorological data, and analyze and generate meteorological prediction results for a period of time in the future;

[0030] The early warning module is used to determine whether to trigger an alarm based on the weather forecast results and notify the breeding management personnel.

[0031] It can be known from the above technical scheme that compared with the prior art, the present invention discloses a method and system for collecting and warning micro-meteorological information of fisheries and agriculture, including: obtaining real-time first meteorological data from the Meteorological Bureau; deploying meteorological sensors around the fishing grounds, monitoring and obtaining second meteorological data through the meteorological sensors; transmitting the first meteorological data and the second meteorological data to the cloud platform in real time; integrating the first meteorological data and the second meteorological data through the cloud platform; establishing a meteorological prediction model, processing the integrated first meteorological data and the second meteorological data, analyzing and generating meteorological prediction results for a period of time in the future; determining whether to trigger an alarm based on the meteorological prediction results, and notifying the aquaculture management personnel. The present invention comprehensively utilizes the data of the Meteorological Bureau and the sensor data, combined with modern information technology, to establish a scientific, real-time, and efficient micro-meteorological information collection and warning system for fisheries and agriculture, providing decision support for aquaculture management, improving the ability to respond to meteorological changes, and ultimately ensuring the safety and stability of fishery production. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 The present invention provides a flow chart of a method for collecting and warning fishery and agricultural micro-meteorological information.

[0034] Figure 2 The present invention provides a structural schematic diagram of a fishery and agriculture micro-meteorological information collection and early warning system. DETAILED DESCRIPTION

[0035] 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.

[0036] The embodiment of the present invention discloses a method and system for collecting and warning micro-meteorological information for fisheries and agriculture, which combines the real-time meteorological data of the meteorological bureau with the meteorological sensor data deployed around the fishing ground to build a complete micro-meteorological information collection and warning system. The system not only provides real-time monitoring and prediction services, but also can issue an alarm in time when severe weather occurs to ensure the safety of fishery production.

[0037] In a specific embodiment of the present invention, a method for collecting and warning micro-meteorological information of fisheries and agriculture is provided. Figure 1 As shown, it includes: obtaining real-time first meteorological data from the Meteorological Bureau; such as temperature, humidity, wind speed, rainfall and other information;

[0038] Deploy meteorological sensors around the fishing grounds to monitor and obtain second meteorological data through the meteorological sensors; monitor more detailed meteorological data, such as wind direction, air pressure, etc. through the meteorological sensors;

[0039] Connecting the deployed meteorological sensors to the cloud platform via the Internet of Things, and transmitting the first meteorological data and the second meteorological data to the cloud platform in real time; ensuring real-time transmission and storage of data;

[0040] Integrate the first meteorological data and the second meteorological data through a cloud platform;

[0041] Establish a meteorological forecast model, process the integrated first meteorological data and second meteorological data, analyze and generate meteorological forecast results for a period of time in the future; combine the meteorological forecast model for analysis to generate more accurate fishing ground meteorological data;

[0042] Determine whether to trigger an alarm based on the weather forecast results and notify the breeding management personnel.

[0043] Establish a weather warning mechanism to automatically trigger an alarm and send a notification when weather forecasts indicate the possibility of severe weather or other dangerous conditions;

[0044] Micrometeorology refers to the small-scale climate characteristics in the near-ground atmosphere and upper soil caused by certain structural features of the underlying surface. Such small-scale climate characteristics are generally manifested in the numerical values ​​of individual meteorology, and sometimes in individual weather phenomena (such as wind, fog, frost, sleet, etc.). However, changes in such elements will not cause significant changes in the weather and climate characteristics determined by large-scale processes (advection, fronts).

[0045] Specifically, there are multiple meteorological sensors, including digital thermometers, humidity sensors, anemometers, wind direction sensors, rainfall sensors, etc. The numbers of the meteorological sensors are mapped with the types, models, monitoring ranges, locations of the components and importance of the meteorological sensors to form a one-to-many mapping relationship, which is saved on the cloud platform.

[0046] Specifically, a meteorological forecast model is established to process the integrated first meteorological data and the second meteorological data, and analyze and generate meteorological forecast results for a period of time in the future, including:

[0047] Obtaining actual meteorological results and at least two first prediction models corresponding to historical meteorological data of the meteorological sensor and the meteorological bureau at the same time;

[0048] Inputting the actual meteorological results into each first prediction model for training, and obtaining a corresponding second prediction model;

[0049] Based on the meteorological sensor data and the meteorological bureau data corresponding to the actual meteorological results, each second prediction model performs prediction on the results of the same meteorological monitoring process to obtain an initial prediction result;

[0050] The monitoring values ​​of each second prediction model in the same meteorological monitoring process and the actual values ​​corresponding to the same meteorological monitoring process are spliced ​​to obtain multiple meteorological data sets; the meteorological monitoring process is to use meteorological sensors to monitor or collect monitoring data from the meteorological bureau.

[0051] The multiple meteorological data sets are input into a meteorological prediction model based on multiple decision tree regression algorithms for training to obtain a final meteorological prediction model, and the meteorological evolution in a future period of time is predicted based on the second prediction model and the final meteorological prediction model to obtain a meteorological prediction result.

[0052] Specifically, a meteorological prediction model based on multiple decision tree regression algorithms includes: dividing the meteorological data set into a training set and a test set according to a set ratio; the meteorological data set includes a first meteorological data set and a second meteorological data set, each of which contains sample data of meteorological anomalies; the sample data includes: an independent variable and a response variable; the meteorological data set is the monitoring results and actual results corresponding to the monitoring results of the meteorological bureau and meteorological sensors and the actual meteorological results in the same time period, and the predicted values ​​preset in the time period, and the predicted values ​​are multi-dimensional features of the actual values ​​corresponding to each time period in the actual meteorological results;

[0053] Construct a meteorological prediction model based on multiple decision tree regression algorithms; generate a new training set with the same number of decision tree regression algorithms according to the sample data in the training set; train the machine learning model with the independent variables of the sample data in the new training set as input and the response variables of the sample data in the training set as output to obtain a trained meteorological prediction model.

[0054] Specifically, the training process of the meteorological prediction model includes: determining the characteristics of the decision tree regression algorithm according to the number of independent variables of the sample data in the new training set; dividing the sample data in the new training set into data regions according to the characteristics of the decision tree regression algorithm to obtain data partition regions; obtaining the average value of the response variable of the sample data in the new training set and inputting the response variable value of the sample data in each data partition region; determining the loss function of the meteorological prediction model according to the response variable value and the average value of the response variable to complete the training of the meteorological prediction model.

[0055] Specifically, the at least two first prediction models are a variety of combinations including an autoregressive model, a moving average model, an autoregressive moving average model and an autoregressive difference moving average model.

[0056] Specifically, it also includes: displaying the analyzed meteorological forecast results of the fishery to the aquaculture managers through a visual interface, and sending early warning information through mobile phone apps and text messages, helping aquaculture managers make better decisions, such as timely drainage, oxygenation, etc.

[0057] In a specific embodiment of the present invention, a fishery and agriculture micro-meteorological information collection and early warning system, such as Figure 2 As shown, including:

[0058] A first meteorological data acquisition module, used to acquire real-time first meteorological data from the meteorological bureau;

[0059] A second meteorological data acquisition module is used to deploy meteorological sensors around the fishing ground and monitor and acquire second meteorological data through the meteorological sensors;

[0060] A data transmission module, used for transmitting the first meteorological data and the second meteorological data to a cloud platform in real time;

[0061] A data integration module, used for integrating the first meteorological data and the second meteorological data through a cloud platform;

[0062] A data processing module is used to establish a meteorological prediction model based on machine learning, process the integrated first meteorological data and the second meteorological data, and analyze and generate meteorological prediction results for a period of time in the future;

[0063] The early warning module is used to determine whether to trigger an alarm based on the weather forecast results and notify the breeding management personnel.

[0064] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0065] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for collecting and warning micro-meteorological information for fisheries and agriculture, characterized in that: include: Get real-time first-rate weather data from the weather bureau; Deploy meteorological sensors around the fishing grounds, and monitor and obtain second meteorological data through the meteorological sensors; Transmitting the first meteorological data and the second meteorological data to a cloud platform in real time; Integrate the first meteorological data and the second meteorological data through a cloud platform; Establishing a meteorological forecast model, processing the integrated first meteorological data and the second meteorological data, and analyzing and generating meteorological forecast results for a period of time in the future; Determine whether to trigger an alarm based on the weather forecast results and notify the breeding management personnel.

2. A method for collecting and warning fishery and agricultural micro-meteorological information according to claim 1, characterized in that: There are multiple meteorological sensors, and the numbers of the meteorological sensors are mapped with the meteorological sensor types, models, monitoring ranges, locations of the components they belong to, and importance of the meteorological sensors they belong to, forming a one-to-many mapping relationship, which is saved in the cloud platform.

3. A method for collecting and warning fishery and agricultural micro-meteorological information according to claim 1, characterized in that: A meteorological forecast model is established to process the integrated first meteorological data and second meteorological data, and analyze and generate meteorological forecast results for a period of time in the future, including: Obtaining actual meteorological results and at least two first prediction models corresponding to historical meteorological data of the meteorological sensor and the meteorological bureau at the same time; Inputting the actual meteorological results into each first prediction model for training, and obtaining a corresponding second prediction model; Based on the meteorological sensor data and the meteorological bureau data corresponding to the actual meteorological results, each second prediction model performs prediction on the results of the same meteorological monitoring process to obtain an initial prediction result; The monitoring values ​​of each second prediction model in the same meteorological monitoring process and the actual values ​​corresponding to the same meteorological monitoring process are spliced ​​to obtain multiple meteorological data sets; The multiple meteorological data sets are input into a meteorological prediction model based on multiple decision tree regression algorithms for training to obtain a final meteorological prediction model, and the meteorological evolution in a future period of time is predicted based on the second prediction model and the final meteorological prediction model to obtain a meteorological prediction result.

4. A method for collecting and warning micro-meteorological information for fisheries and agriculture according to claim 3, characterized in that: A meteorological prediction model based on multiple decision tree regression algorithms includes: dividing the meteorological data set into a training set and a test set according to a set ratio; Construct a meteorological prediction model based on multiple decision tree regression algorithms; generate a new training set with the same number of decision tree regression algorithms according to the sample data in the training set; train the machine learning model with the independent variables of the sample data in the new training set as input and the response variables of the sample data in the training set as output to obtain a trained meteorological prediction model.

5. A method for collecting and warning micro-meteorological information for fisheries and agriculture according to claim 4, characterized in that: The training process of the meteorological prediction model includes: determining the characteristics of the decision tree regression algorithm according to the number of independent variables of the sample data in the new training set; dividing the sample data in the new training set into data regions according to the characteristics of the decision tree regression algorithm to obtain data partition regions; obtaining the average value of the response variable of the sample data in the new training set and inputting the response variable value of the sample data in each of the data partition regions; determining the loss function of the meteorological prediction model according to the response variable value and the average value of the response variable to complete the training of the meteorological prediction model.

6. A method for collecting and warning micro-meteorological information for fisheries and agriculture according to claim 3, characterized in that: The at least two first prediction models are a variety of combinations including an autoregressive model, a moving average model, an autoregressive moving average model and an autoregressive difference moving average model.

7. The method for collecting and warning micro-meteorological information for fisheries and agriculture according to claim 1 is characterized in that: Also includes: The analyzed fishery weather forecast results are displayed to aquaculture managers through a visual interface, and early warning information is sent via mobile phone apps and text messages.

8. A fishery and agriculture micro-meteorological information collection and early warning system, using a fishery and agriculture micro-meteorological information collection and early warning method according to any one of claims 1 to 7, characterized in that: include: A first meteorological data acquisition module, used to acquire real-time first meteorological data from the meteorological bureau; A second meteorological data acquisition module is used to deploy meteorological sensors around the fishing ground and monitor and acquire second meteorological data through the meteorological sensors; A data transmission module, used for transmitting the first meteorological data and the second meteorological data to a cloud platform in real time; A data integration module, used for integrating the first meteorological data and the second meteorological data through a cloud platform; A data processing module is used to establish a meteorological prediction model based on machine learning, process the integrated first meteorological data and the second meteorological data, and analyze and generate meteorological prediction results for a period of time in the future; The early warning module is used to determine whether to trigger an alarm based on the weather forecast results and notify the breeding management personnel.

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