CN115693909A - Renewable energy source fusion type intelligent farm comprehensive management system - Google Patents
Renewable energy source fusion type intelligent farm comprehensive management system Download PDFInfo
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Abstract
The invention relates to a renewable energy source integrated intelligent farm integrated management system. An integrated management system for improving energy operation efficiency of an intelligent farm according to an embodiment includes: the renewable energy fusion system comprises a solar power generation equipment monitoring system and a geothermal monitoring system; and an environmental data monitoring system, and the integrated management system collects and analyzes two or more kinds of renewable energy fusion data and intelligent farm internal and external environmental data to perform integrated management of the intelligent farm. In addition, a series of data collected from the intelligent farm is converted into a database, and the data change trend of each region of the intelligent farm is analyzed, so that the energy demand can be predicted. Meanwhile, in the embodiment, energy is supplied to the intelligent farm through the fusion of solar heat and geothermal energy, so that the energy consumption efficiency of the intelligent farm is improved.
Description
Technical Field
The present disclosure relates to a system for integrating two or more kinds of renewable energy that can be collected in an intelligent farm and comprehensively managing devices constructed in the intelligent farm, and more particularly, to an intelligent farm comprehensive management system that integrates solar power generation devices, a geothermal system, and an environmental sensor constructed in the intelligent farm to collect monitoring data and integrates renewable energy including sunlight and geothermal heat to provide, thereby achieving an increase in energy efficiency and a prediction of energy demand.
Background
Unless otherwise indicated herein, what is described in this section is not prior art to the claims in this application and is not admitted to be prior art by inclusion in this section.
An intelligent Farm (Smart Farm) is an intelligent agricultural system combining Information Communication Technology (ICT) in the production, processing and circulation links of agriculture, forestry, livestock and aquatic products, and is a technology which properly maintains the breeding environment of crops, livestock, aquatic products and the like by utilizing the technologies of Internet of things, big data, artificial intelligence and the like and automatically manages remotely through a PC, a Smart phone and the like. In the related art, by using the smart farm technology of the ICT technology, it is possible to perform precise management and prediction at each breeding stage based on accurate data for environmental information such as temperature, relative humidity, light quantity, carbon dioxide, soil, etc. and breeding information, and to improve yield and quality to improve profitability.
In particular, when the solar power generation device monitoring system, the geothermal device monitoring system, and the environmental data monitoring system are combined as one, the operational risk of facility horticulture can be minimized, and the energy efficiency consumed within the facility horticulture can be improved. However, in the case of current intelligent farm management, since it is used separately in units of environment and equipment systems, improvement of user operation efficiency and utilization rate are reduced, and a system for renewable energy equipment is also present alone. Therefore, there is a need for a system that can comprehensively manage built energy sources when two or more kinds of renewable energy sources are built.
In addition, in the prior art, since equipment manufacturers such as renewable energy equipment manufacturers and environmental sensors are different from one another, a system for serializing and integrating data has not been developed.
Disclosure of Invention
Technical problem to be solved
An integrated management system for improving energy operation efficiency of an intelligent farm according to an embodiment includes: the renewable energy fusion system comprises a solar power generation equipment monitoring system and a geothermal monitoring system; and an environmental data monitoring system, and the integrated management system collects and analyzes two or more kinds of renewable energy fusion data and internal and external environmental data of the intelligent farm to perform integrated management of the intelligent farm.
In addition, a database is built based on a series of data collected from the intelligent farm, and data change trends of each area of the intelligent farm are analyzed, so that energy demand can be predicted. Meanwhile, in the embodiment, energy is supplied to the intelligent farm through the fusion of solar heat and geothermal energy, thereby improving the energy consumption efficiency of the intelligent farm.
(II) technical scheme
The intelligent farm integrated management system according to the embodiment comprises: a data collection module collecting sensing data from sensors installed in each area of the intelligent farm; a data conversion module for constructing a database using the collected sensing data and converting and classifying data by time by using a calendaring of metadata of the collected data; a visualization module that distinguishes the classified and converted data according to the classification criteria and the metadata to visualize; the data analysis module is used for analyzing the trend of the collected data according to the collection area and the time change; and an energy fusion module fusing solar energy, thermal energy, and geothermal energy collected from the intelligent farm.
(III) advantageous effects
According to the renewable energy fusion integrated management system for improving the energy operation efficiency of the intelligent farm, in the intelligent farm comprising the glass greenhouse and the plastic greenhouse, the crop growth operation plan is made by monitoring the environmental data and the renewable energy data, so that the optimal operation scheme can be obtained.
In addition, the equipment maintenance plan is made through continuous monitoring of the equipment, so that the service life of the equipment is prolonged, and the maintenance cost can be reduced.
In addition, by collecting and monitoring environmental data in real time, the system can immediately respond to the abnormal crop environment, so that stable farm operation can be realized.
The effects of the present invention are not limited to the above-described effects, and should be understood to include all effects that can be inferred from the structure of the invention described in the detailed description of the present invention or the claims.
Drawings
Fig. 1 is a diagram illustrating an intelligent farm system according to an embodiment.
Fig. 2 is a diagram illustrating a data processing block of the intelligent farm integrated management system according to the embodiment.
Fig. 3 is a diagram illustrating an energy-autonomous intelligent farm system according to an embodiment.
Fig. 4 is a diagram for explaining a sunlight equipment measurement data extraction process of the intelligent farm integration system according to the embodiment.
Fig. 5 is a diagram illustrating a data format of a solar light device extracted according to an embodiment.
FIG. 6 is a diagram illustrating a geothermal device measurement embodiment.
Fig. 7 is a diagram illustrating a device measurement data format according to an embodiment.
Fig. 8 is a diagram showing installed sensors and a form of collected data in the intelligent farm according to the embodiment.
Fig. 9 is a diagram showing data forms of environmental sensors and measurements provided in the intelligent farm according to the embodiment.
Detailed Description
Advantages and features of the present invention and methods of accomplishing the same will become apparent by reference to the drawings and detailed description of the embodiments that follow. The present invention is not limited to the embodiments disclosed below, but may be embodied in various forms, which are provided only for the purpose of completeness of disclosure and to fully explain the scope of the invention to a person having ordinary skill in the art to which the present invention pertains, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.
In describing the embodiments of the present invention, if it is judged that detailed description on a known function or structure may obscure the gist of the present invention, detailed description thereof will be omitted. In addition, the terms described below are terms defined in consideration of functions in the embodiments of the present invention, and may vary according to intentions or conventions of users and operators. Therefore, it should be defined according to the entire contents of the present specification.
Fig. 1 is a diagram illustrating an intelligent farm system according to an embodiment.
Referring to fig. 1, the intelligent farm system may include: the system comprises a communication network, a sensor network comprising an internal environment sensor and an external environment sensor, a data collection device, a server platform, a solar power generation device, a geothermal device, a solar energy monitoring device, a solar equipment monitoring device, a geothermal energy monitoring device, a geothermal equipment monitoring device, an intelligent farm integrated management system and the like.
In an embodiment, a communication network built in the intelligent farm collects data of solar power generation devices, geothermal devices, and the like. The sensor network comprises a weather, temperature, humidity, insolation amount, rainfall, wind direction, wind speed and other sensor networks for measuring the external environment information of the intelligent farm and a weather, temperature and humidity sensor network for measuring the internal environment information of the intelligent farm. The data collection device collects data for transmitting and receiving data through the sensor network and the communication network. The big data processing unit processes and stores data collected through a communication network based on the cloud. The intelligent farm database stores data collected from the sensor network, the communication network, and the cloud-based big data processing unit.
In an embodiment, the location information system based on geographical information performs integrated management of the intelligent farm, and the intelligent farm integrated management system integrates the collected data to visualize. In addition, intelligent farm integrated management system includes: and the engine can extract the environmental data and the renewable energy source fusion data to search the environmental data and the renewable energy source fusion data of the intelligent farm.
The intelligent farm integrated management system according to the embodiment uses a data collection device of an authenticated RTU level, which can be linked with the korean energy agency REMS, to realize serialization of data with respect to renewable energy device data. In addition, the sensor network may mark sensor nodes for each area of the intelligent farm and perform error detection by comparing with data of other sensors inside the intelligent farm.
In addition, the intelligent farm integrated management system according to the embodiment provides a service of confirming the location of the corresponding intelligent farm in real time through the GIS-based module and can confirm the environmental data and the renewable energy fusion data for the first time. The intelligent farm integrated management system according to the embodiment provides a history management of collected data and a statistical analysis service that can report it. Meanwhile, the intelligent farm integrated management system includes an API that can respond to a request for sharing of data collected in the database.
Fig. 2 is a diagram illustrating a data processing block of the intelligent farm integrated management system according to the embodiment.
Referring to fig. 2, the intelligent farm integrated management system according to the embodiment may include: a data collection module 110, a data conversion module 120, a visualization module 130, a data analysis module 140, an energy demand prediction module 150, an energy fusion module 160, and an energy supply module 170. The term "module" used in this specification should be construed to include software, hardware, or a combination thereof, according to the context in which the term is used. For example, the software may be machine language, firmware, embedded code, and application software. As another example, the hardware can be circuitry, a processor, a computer, an integrated circuit core, a sensor, a Micro-Electro-Mechanical System (MEMS), a passive device, or a combination thereof.
The data collection module 110 collects sensing data from sensors provided in each area of the intelligent farm. For example, the data collection module 110 identifies a local data collection area by a sensor tag provided in the intelligent farm, and monitors a trend of variation of sensor data and whether or not the sensor operation state is abnormal. Specifically, the data collection module 110 may collect environmental sensing data such as temperature, humidity, and energy data such as absorbed heat energy, geothermal energy, etc., crop growth information, etc., from sensors disposed in a farm area, a land area, a greenhouse interior area, a sunlight area, etc., of the intelligent farm. In addition, the data collection module 110 may set a threshold value for each sensor value to perform collection data error detection, and may perform daily average, maximum, minimum, and sum calculations for each sensor value.
In addition, the data collection module 110 according to an embodiment extracts features of the continuously rapidly changing sensed data using machine learning techniques and models them through appropriate algorithms to detect abnormal data. For example, the data collection module 110 groups the collected data by similar values. Then, regions are set and boundary modeling (boundary modeling) is performed on the data to divide the data into normal data and abnormal data. Specifically, in an embodiment, the range of normal data may be set using a quartile. The quartile is a variable dividing the data into quarters, and is a variable from as few as 1/4, 1/2, 3/4 positions when the statistical variables are arranged into a degree distribution. The quartile number is a combination of values obtained by dividing the probability distribution into four equal parts on an arbitrary probability variable axis, and may be a combination of values obtained by dividing the probability distribution into four equal parts on an arbitrary probability variable axis. In an embodiment, a quartile may be set for measurement data including temperature, humidity, energy, current, voltage, and abnormal data of a malfunction or communication failure may be detected.
In addition, in an embodiment, important features are automatically extracted and the data is preprocessed, and then the data of the extracted important elements is transferred through a machine learning model learning module. Thereafter, a machine learning model is generated using various modeling techniques, and an appropriate model is selected by evaluation. The preprocessed data learned machine learning model will be used to detect anomalous data.
Specifically, the data collection module 110 obtains feature importance suitable for machine learning according to the classification criteria after inputting and analyzing the collected data to the AutoML. Thereafter, learning data of the machine learning model is generated using the result value of the AutoML. The data collection module 110 performs basic discretization, null removal, error removal, and other pre-processing on the data prior to utilizing AutoML. After that, the data is input to the AutoML. The data is again modified based on the significance of the features extracted by the AutoML.
The data conversion module 120 constructs a database using the collected data, and converts and classifies the data by time by using calendaring of metadata of the collected data. For example, the data transformation module 120 constructs a database through decentralized parallel pre-processing of the collected data, and distinguishes metadata including timing and regions from the constructed database to enable visualization thereof. In this way, the collected data can be calendared by day, month, and year, so that energy and environmental data can be searched for each period. In addition, energy demand prediction can be achieved through energy environment data search and data utilization for each power generation area based on GIS. In an embodiment, an energy application scheme of the intelligent farm is derived through energy demand prediction, so that the energy efficiency of the intelligent farm can be improved.
The visualization module 130 distinguishes the classified and transformed data according to classification criteria and metadata to visualize.
The data analysis module 140 analyzes a trend of the collected data according to the collection area and time.
The data analysis module 140 according to embodiments provides an efficient sensor data processing technique for real-time situation awareness in USN based monitoring applications such as fire monitoring systems, water quality management systems, crop planting systems, etc. Streaming data, such as sensor data, has the characteristic that the collected data stream changes rapidly and continuously over time. In an embodiment, for real-time outlier detection of data and multi-dimensional management of data, a real-time acquisition, processing, storage, and management method of a large volume of continuous sensing data is proposed and constructed, and based on this, a multi-dimensional analysis challenge from an application service is effectively performed. To this end, a data cube is used which is an On-Line Analytical Processing (OLAP) data model in existing databases and data warehouse systems. However, when a problem to be considered in generating the data cube occurs due to the continuous and large-volume characteristic of the sensor data, the problem is solved by a heuristic access method based on a weight value in the embodiment. For this purpose, applications that actually need to support multidimensional analytical challenges are selected for study by USN-based application model analysis. In addition, efficient multi-dimensional analysis challenges are supported through flow data feature analysis, compression techniques, and data storage models for supporting multi-dimensional analysis challenges.
In addition, in the embodiment, a progressive learning-based real-time outlier detection method may be used as the error data detection method. Outliers generated in USN based monitoring applications can sometimes lead to serious consequences. Therefore, the sensor data processing system should support management personnel and systems to make accurate decisions about outliers. In particular, the possibility that the judgment criterion of large-capacity continuous data such as sensor data changes with time is high, and there is a problem that real-time decision making is required. Accordingly, a method of detecting outliers in real time using an integrated (Ensemble) classifier is provided in an embodiment. The proposed method provides an outlier detection method for constructing a learning-based model for supporting real-time decision of stream data in an adaptive data cube model and efficiently classifying periodically input sensor data using a classifier, and a sensor data processing system applying the same.
Namely, a model of sensor data in a normal range is constructed through learning, and the sensor data in an abnormal range is monitored in real time, so that information about abnormal events in USN application can be actively provided. In addition, in order to classify the flow data, the model needs to be updated according to time. A method using an integrated classifier is adopted for this purpose. The method trains sets of integrators or classifiers from successive segments of the data stream. That is, a new classifier is built each time a new data stream arrives. Accordingly, it is possible by an embodiment to provide a sensor data processing system which provides an outlier detection function by using an integrated classifier to detect an outlier based on learning and thereby intelligently assist a decision on each sensor data periodically added and provide high dimensional information. Meanwhile, the sensor data processing system provided in the embodiment progressively reflects the changed determination criterion, so that a fast response speed can be supported.
The energy demand prediction module 150 predicts the energy demand of each region of the intelligent farm according to the trend of the collection region and the time variation by analyzing the collected data.
The energy fusion module 160 fuses solar energy, solar thermal energy, and geothermal energy collected from the intelligent farm. In an embodiment, the energy fusion module 160 may include a PhotoVoltaic heat-Thermal (PVT) Thermal collector, a solar heat and geothermal heat pump system that simultaneously generates heat and electricity using a radiant energy source of the sun.
The energy supply module 170 supplies the merged renewable energy to each region of the intelligent farm according to the result of the prediction of the energy demand. In an embodiment, the energy supply module 170 uses a seasonal thermal storage tank system that uses integrated application tank thermal storage (TTES) and underground thermal storage (BTES) to equalize the inter-seasonal load.
Fig. 3 is a diagram illustrating an energy-autonomous intelligent farm system according to an embodiment. The intelligent farm integration system according to the embodiment is an environment-friendly and efficient future-type greenhouse that replaces energy used in facilities such as vinyl houses or facility farms where mechanical devices are installed with various new renewable energy such as solar heat, geothermal heat, etc. from fossil fuel to store and supply energy in order to realize an energy-independent intelligent farm. Referring to fig. 3, a renewable energy fusion complex system intelligent farm using a compound quarterly regenerative solar heat and geothermal heat source heat pump according to an embodiment is provided. In embodiments, there is a disadvantage of an imbalance in seasonal energy production and consumption in a solar thermal system that generates heat using solar radiant energy. The heat collector generates the most heat in summer, but the consumption of heating and hot water in winter is the highest. The seasonal heat storage system is a system for storing heat generated from spring to autumn and supplying the heat in winter in order to solve the imbalance between seasons.
In an embodiment, energy autonomy of the intelligent farm is achieved through energy fusion. Specifically, the embodiment can provide a renewable energy source and fusion composite system which can be responsible for 80% of warm air load and more than 50% of cold air load and applies a composite quarterly heat accumulating type solar heat and geothermal source heat pump. In addition, the comprehensive energy system reduces the annual energy cost by more than 70%.
Fig. 4 is a diagram for explaining a sunlight equipment measurement data extraction process of the intelligent farm integration system according to the embodiment.
Referring to fig. 4, in the embodiment, renewable energy fusion data is provided, and a data protocol for each renewable energy device of the intelligent farm is provided for light weight of transmission and reception data. As shown in fig. 4, in the embodiment, for the measurement of the solar light device, the solar light device data may be extracted by setting the input and output terminals of the inverter as the measurement points.
Fig. 5 is a diagram illustrating a data format of a solar light device extracted according to an embodiment.
Referring to fig. 5, a measurement point and a data form of each energy source are set in the embodiment, and single-phase data, three-phase data, and fault status data are extracted from input/output terminals of the inverter. In an embodiment, the data measured at the input and output of the inverter may include voltage, current, system voltage, system current, inverter temperature, system overvoltage, and the like. After measuring the data, the extracted data may be converted into a single-phase data form (a), a three-phase data form (b), and a fault status data form (c).
Fig. 6 is a diagram showing an example of geothermal device measurement, and fig. 7 is a diagram showing a device measurement data form according to the example.
Referring to fig. 6, the intelligent farm integrated management system according to the embodiment sets the input and output ends of the heat pump and the heat storage tank as the geothermal device measuring points for the measurement of the geothermal devices, and measures the data on the geothermal devices at the geothermal device measuring points. In an embodiment, geothermal water inlet and outlet temperatures and cold and warm water inlet and outlet temperatures are measured at a geothermal equipment measurement point, and electricity meters, heat pumps, cold and warm air side-inlet and outlet temperatures, and hot water supply side-inlet and outlet temperatures may be measured. In an embodiment, as shown in fig. 7, after the measurement of the geothermal device, the measurement data may be converted into a geothermal device measurement data form (a) and a fault status data form (b) according to a set protocol.
According to the renewable energy fusion integrated management system for improving the energy operation efficiency of the intelligent farm, in the intelligent farm comprising the glass greenhouse and the plastic greenhouse, the crop growth operation plan is made by monitoring the environmental data and the renewable energy data, so that the optimal operation scheme can be obtained.
Fig. 8 is a diagram illustrating installed sensors and a form of collected data in an intelligent farm according to an embodiment.
Referring to fig. 8, in an embodiment, a sensor (a) for measuring the growth state and internal environment of crops may be provided inside the smart farm facilities horticulture. The sensor (a) arranged inside the facility gardening can be used as an internal meteorological environment sensor for sensing the contents of oxygen and nitrogen in the atmosphere, the temperature and the humidity and the like. In addition, as shown in (b) of fig. 8, a valid data range according to a measurement value of each sensor may be preset to deliver a reliable abnormality notification to an administrator when abnormal data is detected.
Fig. 9 is a diagram showing data forms of environmental sensors and measurements provided in the intelligent farm according to the embodiment.
Referring to fig. 9, the external environment sensor (a) according to the embodiment is disposed outside the smart farm and senses external environment information including outdoor temperature and humidity, instantaneous wind speed, daily/weekly/monthly/annual rainfall, solar radiation amount, ultraviolet index, sensible temperature, and the like. As shown in (b) of fig. 9, in the embodiment, a valid data range for each measured value may be preset to regard the sensed external environment information as an abnormal signal and inform an administrator when it is not included in the valid data range, so that farm loss due to natural disasters may be minimized.
According to the intelligent farm integrated management system of the embodiment, the solar power generation devices, the geothermal system and the environmental sensor constructed in the intelligent farm are interconnected to collect monitoring data, and renewable energy including sunlight and terrestrial heat is fused and provided, thereby realizing increase of energy efficiency and prediction of energy demand. In addition, maintenance schedules are made through continuous monitoring of the equipment to increase the service life of the equipment, so that maintenance cost can be reduced. Meanwhile, by collecting and monitoring environmental data in real time, the system can immediately deal with the abnormal crop environment, so that stable farm operation can be realized.
The disclosure is intended as an example only, and various modifications may be made by those skilled in the art without departing from the spirit of the claims as hereinafter claimed, and the scope of the disclosure is not intended to be limited to the particular embodiments described.
Claims (7)
1. An intelligent farm integrated management system comprising:
a data collection module collecting sensing data from sensors installed in each area of the intelligent farm;
a data conversion module for constructing a database using the collected sensing data, and converting and classifying data by time by calendaring using metadata of the collected data;
a visualization module that distinguishes the classified and converted data according to the classification criteria and the metadata to visualize;
a data analysis module for analyzing a trend of the collected data according to a collection area and time; and
and the energy fusion module fuses sunlight energy, solar thermal energy and geothermal energy collected from the intelligent farm.
2. The intelligent farm integrated management system according to claim 1,
the intelligent farm integrated management system further comprises:
and the energy demand forecasting module is used for forecasting the energy demand of each area of the intelligent farm according to the trend of the collected area and time change by analyzing the collected data.
3. The intelligent farm integrated management system according to claim 1,
the intelligent farm integrated management system further comprises:
and the energy supply module is used for supplying the fused renewable energy to each region of the intelligent farm according to the prediction result of the energy demand.
4. The intelligent farm integrated management system according to claim 1,
the data collection module identifies a local data collection area through a sensor tag provided in the intelligent farm, and monitors a variation trend of sensor data and whether a sensor operation state is abnormal.
5. The intelligent farm integrated management system according to claim 4,
the data collection module sets a threshold for each sensor value to perform collected data error detection and performs daily average, maximum, minimum, and sum calculations for each sensor value.
6. The intelligent farm integrated management system according to claim 1,
the energy fusion module comprises a photovoltaic panel, solar heat and a geothermal heat pump system which utilize sunlight energy to generate electricity.
7. The intelligent farm integrated management system according to claim 3,
the energy supply module uses a seasonal heat storage tank system that averages the load between seasons using comprehensive application tank heat storage and underground heat storage.
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