WO2018232845A1 - 一种智能农业管理方法和系统 - Google Patents

一种智能农业管理方法和系统 Download PDF

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Publication number
WO2018232845A1
WO2018232845A1 PCT/CN2017/096022 CN2017096022W WO2018232845A1 WO 2018232845 A1 WO2018232845 A1 WO 2018232845A1 CN 2017096022 W CN2017096022 W CN 2017096022W WO 2018232845 A1 WO2018232845 A1 WO 2018232845A1
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data
price
crop
nutrient
moisture
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French (fr)
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杜光东
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Shenzhen Shenglu IoT Communication Technology Co Ltd
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Shenzhen Shenglu IoT Communication Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining

Definitions

  • the present invention relates to the field of Internet of Things, and in particular to an intelligent agricultural management method and system.
  • the present invention provides an intelligent agricultural management method and system.
  • the present invention provides an intelligent agricultural management method, the method specifically comprising the steps of:
  • the invention provides an intelligent agricultural management method, which monitors soil nutrient data and moisture data in real time and compares with preset standard values of nutrient content and moisture content, and outputs an alarm when the nutrient data is less than the standard value of nutrient content.
  • the alarm is output, and the amount of fertilizer and the amount of irrigation can be adjusted in time. Control nutrients and moisture in the most suitable range for crop growth, It is more scientific and reliable than empirically controlling the amount of fertilizer applied and the amount of irrigation. It saves fertilizer and saves water, and is more conducive to crop growth.
  • the crop price trend is predicted by a large enough big data base, and the time of harvesting crops is controlled according to the crop price trend forecast, and the crop is controlled in the price curve.
  • the method further includes controlling the adjustment of the recent irrigation amount according to the predicted weather data, and specifically includes the following steps:
  • Obtained predicted weather data for the region including predicted rainfall
  • the water vapor content and predicted weather data in the air are quantified into irrigation amounts
  • the amount of irrigation is adjusted in conjunction with the quantitative irrigation amount and soil moisture data.
  • the moisture data monitored in real time when the moisture data monitored in real time is low, it can be judged that the crop needs irrigation to ensure water absorption, and the water vapor content and the predicted rainfall in the air are obtained before the irrigation, and the water vapor content in the air and the predicted rainfall are obtained.
  • the amount of analysis is quantified into the amount of irrigation, combined with the quantitative irrigation amount and the moisture data monitored in real time, and the irrigation amount is appropriately adjusted.
  • Both water vapour content and predicted rainfall in the air may provide potential irrigation.
  • irrigation can be reduced, when water vapour content is low and/or weather data is predicted. If there is no rain in the near future, it can be irrigated normally. In this way, we can make full use of nature, save water resources, and achieve efficient irrigation.
  • the present invention provides an intelligent agricultural management system comprising:
  • a soil monitoring module for monitoring soil nutrient data and moisture data in real time
  • a weather information acquisition module configured to obtain historical weather data and predicted weather data of the region through the Internet
  • a price information acquisition module for obtaining historical price data of crops via the Internet
  • the control center is pre-set with a standard value of nutrient content and a standard value of moisture content, the control center receives nutrient data and moisture data of the soil; the control center compares the nutrient data and the nutrient content standard value, when the nutrient data is less than the standard value of the nutrient content When the alarm information is output, the farmers are reminded to adjust the amount of fertilizer; the control center compares the water data and the water content standard value, and outputs an alarm message when the water data is less than the standard value of the moisture content, reminding the farmers to adjust the irrigation amount;
  • the control center is connected to the soil monitoring module, the weather information acquisition module and the price information acquisition module respectively;
  • the control center processes the historical price data of the analyzed crops and the historical weather data of the region, and combines the predicted weather data to obtain the crop price trend prediction result, and controls the time of harvesting the crop according to the crop price trend prediction result.
  • the invention provides an intelligent agricultural management system, which comprises a soil monitoring module and weather information acquisition. Take the module, the price information acquisition module and the control center.
  • the soil monitoring module, the weather information acquisition module, and the price information acquisition module respectively obtain nutrient data and moisture data of the soil, historical weather data and predicted weather data of the region, and historical price data of the crop, and the control center determines whether it needs to be adjusted based on the above data.
  • the amount of fertilizer applied and the amount of irrigation is to analyze the forecast results of crop price trends, control the time of harvesting crops, and control the timing of crops entering the market through scientific methods, thereby increasing farmers' income.
  • the plurality of soil monitoring modules are evenly distributed in the soil of the monitored area, and the soil monitoring module and the control center are wirelessly networked through Bluetooth or ZigBee or WiFi, and the soil monitoring module only transmits the collected data.
  • the soil monitoring module is powered by battery or solar energy; the control center simultaneously receives nutrient data and moisture data sent by a plurality of soil monitoring modules and performs unified analysis and processing.
  • a plurality of soil monitoring modules are uniformly disposed in the soil of the monitored area.
  • the control center analyzes the nutrient data and moisture data based on the data collected by the individual soil monitoring modules, but the data collected by all soil monitoring modules.
  • a soil monitoring module stops working, data is faulty, and transmission is not smooth, the large data collected by other soil monitoring modules can still be comprehensively collected, so the data of a single soil monitoring module does not affect the entire system. Make an impact.
  • due to the large number of soil monitoring modules if the network is wired, it will undoubtedly increase the cost greatly.
  • the wireless monitoring network between the soil monitoring module and the control center simplifies the system and reduces the system. Outgoing support is conducive to large-scale deployment of soil monitoring modules.
  • the wireless networking will bring power problems and cannot be directly charged through the power line. Therefore, the soil monitoring module is powered by battery or solar energy, so that the soil monitoring module can perform data collection more independently without relying on system power supply. .
  • FIG. 1 is an architectural diagram of an intelligent agricultural management system of the present invention
  • FIG. 2 is a structural diagram of a network of a control center and a soil monitoring module of the present invention
  • FIG. 3 is a schematic flow chart of an intelligent agricultural management method according to the present invention.
  • FIG. 4 is a schematic diagram of interaction between nutrient data and moisture data for monitoring soil according to the present invention.
  • Figure 5 is a schematic flow chart of precise control of fertilization amount and irrigation amount according to the present invention.
  • FIG. 6 is a schematic flow chart of obtaining a crop price trend prediction result according to the present invention.
  • FIG. 7 is a schematic flow chart of modifying the rainfall correlation factor ⁇ , the wind level correlation factor ⁇ , and the temperature correlation factor ⁇ according to the present invention.
  • FIG. 1 is an architectural diagram of an intelligent agricultural management system according to the present invention.
  • An intelligent agricultural management system includes a control center 1, a soil monitoring module 2, a weather information acquisition module 3, and a price information acquisition module 4.
  • the control center 1 is connected to the soil monitoring module 2, the weather information acquiring module 3, and the price information acquiring module 4, respectively, and the meaning of the above "connected” is understood to include the physical connection and the transmission direction of the signal.
  • the soil monitoring module 2 is configured to monitor nutrient data and moisture data of the soil in real time, that is, nutrient data and moisture data of the collected and sent soil;
  • the weather information acquiring module 3 is configured to obtain historical weather data of the region through the Internet and Forecasting weather data;
  • the price information obtaining module 4 is configured to obtain historical price data of the crop through the Internet;
  • the control center 1 is pre-set with a nutrient content standard value and a moisture content standard value, the control center 1 receives nutrient data and moisture data of the soil; the control center 1 compares the nutrient data and the nutrient content standard value, when the nutrient data is smaller than When the nutrient content standard value is output, an alarm is issued to remind the farmers to adjust the fertilization amount; the control center 1 compares the water data and the water content standard value, and outputs an alarm when the moisture data is smaller than the water content standard value, reminding the farmers to adjust the irrigation amount.
  • the control center 1 is connected to the soil monitoring module 2, the weather information acquiring module 3, and the price information acquiring module 4, respectively;
  • the control center 1 processes the historical price data of the analyzed crops and the historical weather data of the region, and combines the predicted weather data to obtain the crop price trend prediction result, and controls the time of harvesting the crop according to the crop price trend prediction result.
  • FIG. 3 is a schematic flowchart of a smart agricultural management method according to the present invention.
  • An intelligent agricultural management method corresponding to an intelligent agricultural management system, comprising the steps of:
  • the principle of the intelligent agricultural management method is:
  • the soil monitoring module 2 monitors the nutrient data and the moisture data reflecting the real-time condition of the soil according to a certain sampling frequency, acquires the historical weather data and the predicted weather data of the region through the weather information acquiring module 3, and acquires the history of the crop through the price information acquiring module 4.
  • Price data which is implemented in parallel in three steps, aims to collect data that is closely related to agricultural management.
  • the idea of intellectual property management of intelligent agriculture is embodied in three aspects: 1.
  • the standard value of nutrient content and the standard value of moisture content are preset in the control center 1, and the soil monitoring module 2 obtains nutrient data and moisture data of the soil.
  • the control center 1 compares the nutrient data and the nutrient content standard value, compares the water data and the moisture content standard value, when the nutrient data is less than the nutrient content standard value and when the moisture data is less than the moisture content standard value, the alarm is output, the farmers according to The alarm controls the amount of fertilizer applied and the amount of irrigation.
  • the Control Center 1 analyzes the predicted weather data of the area, and outputs an alarm when weather warning occurs, and applies the meteorological information issued by the Meteorological Bureau directly to the system.
  • the control center 1 analyzes the historical price data of the crops and the historical weather data of the region, and outputs the forecast results of the crop price trends.
  • the farmers can control the time of harvesting the crops according to the forecast results of the crop price trends, and issue information when the crop prices are in an upward trend. Remind farmers to harvest in time, try to make the crops enter the market at the highest point of the predicted price; when the crop price is in a downward trend, it can send a message to remind the farmers to suspend the harvest, try to avoid the crop entering the market at the low price of the forecast price, from the perspective of price Best harvest time. It should be noted that the growth of the crop is a natural process, and the present embodiment only analyzes the time for controlling the harvested crop from an economic point of view.
  • control center 1 the most important function of the control center 1 is to collect historical price data of crops and historical weather data of the region and perform processing analysis to predict future crop price trends and output crop price trend prediction results.
  • farmers can grasp the harvest time of crops according to crop maturity and crop price trend forecasting results, control crops to enter the market at the highest point of the price curve, and increase farmers' income through scientific management methods.
  • the control center 1 is a device with data analysis and processing functions, intelligent decision-making and intelligent control.
  • the soil monitoring module 2 has only data acquisition and transmission functions.
  • the nutrient data and the moisture data of the soil are monitored in real time by a device having only data acquisition and transmission functions.
  • the soil monitoring module 2 monitors the nutrient data and moisture data of the soil in real time through sensors.
  • the sensors in the soil monitoring module 2 can sense nutrient and moisture information in the soil and output nutrient data and moisture data in the form of signals. Due to the large number of soil monitoring modules 2, and the various soil monitoring modules 2 have very simple and efficient transmission and reception requirements, only a small amount of data is needed to reflect the real-time conditions of the soil.
  • the weather information acquisition module 3 is configured to acquire historical weather data, real-time weather data, and predicted weather data of the region.
  • the weather information acquisition module 3 does not have to have an analysis or storage function, and only needs to obtain data of the weather bureau through the Internet.
  • the weather information acquisition module 3 is set to be the same smart device as the control center 1.
  • the weather information acquisition module 3 can be a soft device implemented by a computer program, and the purpose thereof is only to take historical weather data, real-time weather data and predicted weather data of the local area through the Internet.
  • Historical weather data is the weather information of the past year or years of the region, including the date and daily corresponding rainfall, wind size, temperature, etc.; real-time weather data is mainly used to combine real-time crop prices, and the rainfall-related factor ⁇ in the later period.
  • the wind level correlation factor ⁇ and the air temperature correlation factor ⁇ are used as a reference for correction; and the predicted weather data is used to obtain crop price trend prediction results according to the correlation, for example, the predicted weather data in the next half month includes the daily date and daily Corresponding rainfall, wind power, temperature, etc., according to the relevant relationship, can wait until the corresponding forecasted crop price per day, and further obtain the crop price trend forecast result.
  • the price of crops is susceptible to weather factors, and the weather, seasonality, rainfall, wind size, temperature, etc. have the most significant impact on crop prices.
  • the weather information acquisition module 3 obtains the historical weather data of the region, and then obtains the price weather correlation relationship through the control center 1 analysis, and further combines the predicted weather data and the price weather correlation relationship to obtain the crop price trend prediction result.
  • the innovation of this intelligent agricultural management system is that it has more attention than basic functions such as real-time monitoring of crop growth and maturity.
  • the price trend of crops Through the analysis of the main factors affecting crop prices, the correlation between crop prices and various factors is sought to predict the price trend of crops.
  • the traditional agricultural Internet of Things system can only produce crops better. This intelligent agricultural management system can also help farmers control harvest time and increase income.
  • this intelligent agricultural management method and system can accurately control the amount of fertilizer and irrigation.
  • the specific steps are:
  • the control center 1 is pre-set with a nutrient content standard value and a moisture content standard value, and the control center 1 receives nutrient data and moisture data of the soil;
  • the control center 1 compares the real-time nutrient data and the nutrient content standard value, and outputs alarm information when the real-time nutrient data is less than the nutrient content standard value, prompting the farmers to adjust the fertilization amount;
  • the control center 1 compares the real-time moisture data and the moisture content standard value, and outputs an alarm message when the real-time moisture data is smaller than the water content standard value, reminding the farmers to adjust the irrigation amount.
  • the above steps are mainly embodied in S4, pre-set the standard value of nutrient content and the standard value of moisture content, compare the nutrient data and the standard value of nutrient content, and output alarm information when the nutrient data is less than the standard value of nutrient content, and compare the standard values of moisture data and moisture content. Outputs an alarm message when the moisture data is less than the moisture content standard value.
  • the difference between the nutrient data and the nutrient content standard value and the difference between the moisture data and the moisture content standard value are also calculated, according to
  • the difference values are the amount of fertilization and the amount of irrigation, respectively, and the time limit for fertilization and the time limit for irrigation are separately output.
  • the control center compares nutrient data and nutrient content
  • the difference between the nutrient data and the nutrient content standard value and the difference between the moisture data and the water content standard value are also calculated, and the fertilization amount and the irrigation amount are respectively output according to the difference, and respectively Output fertilization time limit and irrigation time limit.
  • the nutrient data is slightly smaller than the standard value of the nutrient content, a smaller amount of fertilization is output, and a larger fertilization time limit is output; when the nutrient data is much smaller than the standard value of the nutrient content, a larger amount of fertilization is output, and the output is smaller. Fertilization time limit.
  • Each crop has its own unique characteristics, and the demand for nutrients and water varies from crop to crop.
  • a standard value of nutrient content and a standard value of moisture content are set in the control center 1 in advance.
  • grapes as an example, different nutrients have different nutrient content standard values. Grapes have a greater need for phosphorus and less nitrogen.
  • the standard value of phosphorus is set in the control center 1 in advance. 0.35, the standard value of nitrogen content is 0.15; at the same time, the grape has higher requirements on moisture, and the standard value of moisture content in the control center 1 is 0.28 in advance.
  • the control center 1 receives the nutrient data and moisture data of the soil.
  • the output alarm alerts the farmers to supplement the phosphate fertilizer in time; when the nutrient data of nitrogen is lower than 0.15, the output alarm reminds the farmers to supplement the nitrogen fertilizer in time; When the data is below 0.28, an alarm is output to remind farmers to irrigate in time.
  • the intelligent agriculture management method and system can also provide weather warning.
  • the control center 1 receives the predicted weather data acquired by the weather information acquisition module 3, and outputs an alarm when a weather warning occurs.
  • Weather factors are one of the most influential factors in agriculture. If the weather changes cannot be accurately controlled and prepared in time, agricultural production will be largely controlled by the weather. Taking rice as an example, typhoon has a great influence on rice, especially near the harvested rice. When the typhoon hits, the rice ear will be knocked down. Therefore, it is extremely important for agricultural production to keep an eye on the weather.
  • the weather information acquisition module 3 obtains the predicted weather data of the region from the weather bureau, and outputs an alarm in time when the typhoon warning occurs, so as to obtain more preparation time for the farmers.
  • FIG. 2 is a structural diagram of the network of the control center 1 and the soil monitoring module 2 of the present invention.
  • the intelligent agriculture management system further includes a plurality of forwarding nodes 5, the soil monitoring module 2 includes a soil nutrient sensor for monitoring soil nutrient data in real time, and the soil moisture sensor for real time Monitor soil moisture data.
  • the soil nutrient sensor and the soil moisture sensor are buried under the soil beside the crop root system, so that the environment of the soil monitoring module 2 is as close as possible to the actual environment of the crop, and the data collected by the soil monitoring module 2 can more accurately reflect the crop. status.
  • the soil monitoring module 2 is connected to the control center 1 through the forwarding node 5, and the soil nutrient sensor and the soil moisture sensor periodically send data to the forwarding node 5, and the forwarding node 5 receives the nutrient data of the soil nutrient sensor and the moisture data of the soil moisture sensor. And send the nutrient data and moisture data to the control center 1.
  • the soil monitoring module 2, the forwarding node 5 and the control center 1 respectively constitute a three-tier architecture of the agricultural Internet of Things.
  • the soil monitoring module 2 is the front-end data acquisition device of the agricultural Internet of Things, which has a large number, a simple data structure and is not suitable for the traditional Internet protocol. In the entire agricultural IoT architecture, the soil monitoring module 2 only needs to continuously collect and send nutrient data and moisture data. The accuracy and security of data required by traditional Internet protocols can be ignored in the agricultural Internet of Things. More attention is paid to the number of samples sampled, ie the number of soil monitoring modules 2 and whether the entire area is covered. Since the soil monitoring module 2 is not suitable for the traditional Internet protocol, the current Internet of Things is still based on the traditional Internet to varying degrees.
  • the forwarding node 5 and the control center 1 still follow the traditional Internet protocol.
  • the forwarding node 5 provides the data transmission and gateway functions of the traditional Internet, that is, performs protocol conversion, and the networking protocol of the front-end soil monitoring module 2 and the networking protocol of the traditional Internet.
  • the Control Center 1 provides data analysis, integrated control, and human-machine interface functions. In this way, a bridge between the agricultural Internet of Things and the traditional Internet can be established to provide a basis for the realization of the agricultural Internet of Things.
  • the soil monitoring module 2 as the front end of the system, only needs to have data acquisition and transmission functions, and does not need to have data receiving and processing functions. This is due to the large number of soil monitoring modules 2, which are only used to monitor the nutrient data and moisture data of the soil in real time, so the cost of configuring the data receiving or processing function of the soil monitoring module 2 is much greater than the effect.
  • the front end of the system does not need to have data receiving and processing functions, and there are disadvantages to a certain extent, because this will greatly increase the pressure of the subsequent control center 1 to process data.
  • a forwarding node 5 having the ability to receive, process and forward data, which can be forwarded before the nutrient data and moisture data are forwarded to the control center 1.
  • Pretreatment is performed. Pre-processing includes intelligent closure and cropping of nutrient data and moisture data, checking for complete data, discarding corrupted data and redundant data, and specifying the direction of data transfer.
  • the setting of the forwarding node 5 optimizes the structure of the system, so that the front end and the back end are more closely connected, and the efficiency of data collection, transmission, and processing is improved.
  • FIG. 4 is a schematic diagram of interaction between nutrient data and moisture data for monitoring soil according to the present invention.
  • real-time monitoring of soil nutrient data and moisture data specifically includes:
  • the first layer network device 20 collects nutrient data and moisture data of the soil, and transmits the collected nutrient data and moisture data to the second layer network device 50 in a broadcast form;
  • the second layer network device 50 preprocesses the data after receiving the nutrient data and the moisture data, and transmits the preprocessed nutrient data and moisture data to the layer 3 network device 10.
  • the first layer network device 20 corresponds to the soil monitoring module 2
  • the second layer network device 50 corresponds to the forwarding node 5
  • the third layer network device 10 corresponds to the control center 1.
  • real-time monitoring of soil nutrient data and moisture data specifically includes:
  • the soil monitoring module 2 collects nutrient data and moisture data of the soil and broadcasts it to the form.
  • the forwarding node 5 sends the collected nutrient data and moisture data; the soil monitoring module 2 does not have the data processing function, and can only perform simple data collection and transmission; and the soil monitoring module 2 passes between the collection and forwarding node 5 and the control center 1 In the wireless mode networking, after collecting the nutrient data and the moisture data, the soil monitoring module 2 transmits the collected data in the form of a broadcast.
  • the functions of the forwarding node 5 include forwarding data and pre-processing the data, specifically This includes intelligent closure and cropping of nutrient data and moisture data, checking for complete data, discarding corrupted data and redundant data, and specifying the direction of data transfer. Since the soil monitoring module 2 does not have the data processing function, if all the data collection work is performed by the control center 1, the management center 1 will be greatly burdened, and the data processing efficiency is low. Therefore, the nutrient data and the moisture data are put. Before being sent to the control center 1, the forwarding node 5 performs pre-processing to help share the pressure of the control center 1 and improve the data processing efficiency of the entire system.
  • the control center 1 receives and processes the pre-processed nutrient data and moisture data, and can control the forwarding node 5 to receive the nutrient data and the moisture data collected by the specific soil monitoring module 2 as needed; for example, when it is necessary to pay attention to the real-time condition of the soil in a certain area
  • the nutrient data and moisture data of the soil collected by the soil monitoring module 2 in the region may be marked as interested.
  • the layer 3 network device 10 receives the nutrient data and moisture data of interest through the layer 2 network device.
  • the forwarding node 5 receives the nutrient data and moisture data collected by the specific soil monitoring module 2 while ignoring the nutrient data and moisture data collected by the other soil monitoring modules 2. Only accept the data of interest, can process the data more strongly, and improve the efficiency of data processing.
  • the soil monitoring module 2 transmits the nutrient data and the moisture data collected to the soil by means of broadcasting, and can be transferred to other forwarding nodes 5 that are working normally when some forwarding nodes 5 cannot transmit data for various reasons. Guarantee the reliability of the system.
  • the data frame of the soil monitoring module 2 is very small and carries only the most important information, the use of the broadcast form does not affect the operation and maintenance cost of the entire system.
  • the soil nutrient sensor and the soil moisture sensor monitor the soil nutrient data and moisture data according to a specific sampling frequency, that is, the soil nutrient sensor and the soil moisture sensor continuously work continuously, and continuously monitor the soil nutrient data and moisture data.
  • the specific sampling frequency specifically refers to data collection every one hour or two hours, and then the collected nutrient data and moisture data are sent.
  • the specific sampling frequency is set for the soil nutrient sensor and the soil moisture sensor to balance cost and data validity. Soil nutrient data and moisture data do not change from time to time. If soil nutrient sensors and soil moisture sensors are working at all times, the data is of low effectiveness and the cost of equipment is high. As the number increases, such conditions become more and more obvious. Therefore, data collection is performed every hour or two, which is a good balance between cost and data availability.
  • the soil nutrient sensor and the soil moisture sensor are disposed in the monitored farmland, and the monitored farmland is divided into a plurality of small areas.
  • the data frames collected by the soil nutrient sensor and the soil moisture sensor include a transmission pointing code, an address code, and sensing data.
  • the number of front-end devices in the Internet of Things is huge. Considering the overhead and efficiency of data, soil nutrient sensors and soil moisture sensors only carry the most useful information for this system.
  • the above data frame can meet the basic requirements of the agricultural Internet of Things, wherein the transmission pointing code records the data transmission direction, that is, which forwarding node 5 sends data; the address code records the location of the soil nutrient sensor or the soil moisture sensor; Then, it is nutrient data or moisture data, such as nutrient data of phosphorus and nitrogen in the above embodiment, and the nutrient data and moisture data corresponding to each location can be visually analyzed by combining the address code and the sensing data.
  • FIG. 5 is a schematic flow chart of accurately controlling the amount of fertilizer applied and the amount of irrigation according to the present invention.
  • the monitored farmland is divided into multiple small areas and the data frames collected by the soil nutrient sensor and the soil moisture sensor include the transmission pointing code, the address code and the sensing data, which can be used to precisely control the amount of fertilizer and the amount of irrigation, including:
  • the monitored farmland is divided into several small areas; the soil nutrient sensor and the soil moisture sensor both have a certain monitoring radius, so the small area division of the monitored farmland can be realized by setting the soil nutrient sensor and the soil moisture sensor at intervals. .
  • the control center 1 can display the nutrient and water status of each small area to the farmers through maps, such as using the form of rainfall distribution map in the weather forecast to intuitively realize human-computer interaction, and the farmers obtain information based on the information obtained from the control center 1. Fertilize or irrigate to achieve precise control of fertilization and irrigation.
  • the Internet of Things has a similar place to the traditional Internet, but in many ways, there is still a fundamental difference between the two.
  • the traditional Internet is concerned with the accuracy and reliability of data, while the Internet of Things is a lossy, intermittent network with low requirements for accuracy and reliability.
  • the number of samples is high, and there is no need to sacrifice high cost to maintain accurate data.
  • Sex In the field of Internet of Things, in most cases, the number of samples is high, and there is no need to sacrifice high cost to maintain accurate data. Sex.
  • the data collected by each soil nutrient sensor and soil moisture sensor is "small data”
  • the data collected by all soil nutrient sensors and soil moisture sensors is "big data", when a single "small data” When there is a timeout or packet loss, it will not affect "big data”.
  • the "inaccuracy” and "unreliability" of the Internet of Things data can be overcome.
  • the method further comprises controlling the adjustment of the recent irrigation amount according to the predicted weather data, specifically comprising the following steps:
  • Obtained predicted weather data for the region including predicted rainfall
  • the water vapor content and predicted weather data in the air are quantified into irrigation amounts
  • Adjust the amount of irrigation by combining the amount of irrigation and the moisture data of the soil.
  • FIG. 6 is a schematic flow chart of obtaining a crop price trend prediction result according to the present invention.
  • the process of obtaining the crop price trend prediction result specifically includes:
  • the present invention analyzes crop price trend prediction results based on historical price data of crops and historical weather data in the region. Firstly obtain historical price data and historical weather data, establish a price-related model through mathematical modeling, and then analyze the rainfall correlation factor ⁇ , wind level correlation factor ⁇ and temperature-related factor ⁇ according to multiple linear regression analysis methods. The relationship between crop prices and weather, combined with forecasting weather data, can be used to predict crop prices. Through the above steps, the relationship between price and weather is mathematically expressed, which ensures the accuracy of the prediction results and provides a scientific basis for agricultural management.
  • the soft system corresponding to the above-described processing analysis process for the historical price data of the crop and the historical weather data of the region includes the price data unit 11, the weather data unit 12, the model building unit 13, the price forecasting unit 14, the trend predicting unit 15, and the correction. Unit 16.
  • the price data unit 11 is for organizing historical price data of the crop, the historical price data of the crop including the crop price Y of each day in the past period of time.
  • Crop historical price data For the basis of price forecasting, the specific time period in the past is specifically the past few months or years. The greater the time span of a particular time period, the more accurate the subsequent crop price trend predictions.
  • the format of the historical price data of the crop is (date, price). Taking the price of corn as an example, the historical price data of corn can be (2013.03.02, 10).
  • the weather data unit 12 is for processing historical weather data of the local area, including historical rainfall A, wind level B, and temperature C for each day in the past period of time.
  • the control center 1 also has means for processing real-time weather data and predicted weather data, the principle of which is the same as the weather data unit 12 for processing historical weather data.
  • the format of historical weather data is (date, rainfall, wind level, temperature), such as (2013.03.02,150,3,22).
  • the mathematical model is used to construct a price-related model, combined with historical price data (such as corn) and historical weather data of a certain crop, and the rainfall correlation factor ⁇ , wind level correlation factor ⁇ and temperature correlation are analyzed according to multiple linear regression analysis methods.
  • the factor ⁇ determines the correlation between crop prices and weather. For example, the obtained rainfall correlation factor ⁇ is 0.02, the wind level correlation factor ⁇ is 1.5, and the temperature correlation factor ⁇ is 0.8.
  • FIG. 7 is a schematic flow chart of the modified rainfall correlation factor ⁇ , the wind level correlation factor ⁇ , and the temperature correlation factor ⁇ according to the present invention.
  • the real-time crop price and the real-time weather data are first acquired, the real-time weather data is the weather data of the day, the real-time crop price is the crop price of the day, and the rainfall is continuously correlated according to the predicted crop price and the actual crop price.
  • the factor ⁇ , the wind level correlation factor ⁇ and the air temperature correlation factor ⁇ are corrected, and the correction process specifically includes:
  • the traversal calculation yields the difference between the predicted crop price and the actual crop price per day over the past period of time. And take Absolute value As a set of revised reference numbers;
  • the trend prediction unit 15 is configured to compare the predicted crop price with the crop price of each day in the past period of time, and output the crop price trend prediction result. Using the above method to sequentially determine the predicted crop price over a period of time, the crop price trend prediction result can be obtained.
  • the correction unit 16 continuously corrects the rainfall correlation factor ⁇ , the wind level correlation factor ⁇ , and the temperature correlation factor ⁇ according to the predicted crop price and the real-time crop price, and the correction unit 16 specifically includes a price comparator, a traverser, an averager, Judger and iterator.
  • the price comparator is used to obtain predicted crop prices and actual crop prices for each day in the past; for example, the predicted crop price is 19.5 and the real-time crop price is 20.
  • the traversal is used to traverse the difference between the predicted crop price and the actual crop price for each day in the past period of time. And take Absolute value As a set of revised reference numbers; forecast crop price is 19.5, real-time crop price is 20, For 0.5, the corrected reference number for the day is 0.5.
  • the averager is used to calculate the average value ⁇ of all modified reference numbers; the value of ⁇ is obtained by a weighted average method, for example, the corrected reference numbers of 5 days are 0.5, 0.3, 0.2, 0.4, and 0.6, respectively, and the value of ⁇ is 0.4. .
  • the determiner is used to compare the absolute value of the difference between the predicted crop price and the real-time crop price of the day.
  • 2 ⁇ when When less than 2 ⁇ , the forecasted crop price on the day is believed to be credible, otherwise the forecasted crop price on the day is deemed untrustworthy; for example, the absolute value of the difference between the predicted crop price and the real-time crop price of a certain day
  • the forecasted crop price for this day is believed to be credible, when the absolute value of the difference between the predicted crop price and the real-time crop price for one day When it is 0.8 or more, it is determined that the predicted crop price of this day is not credible.
  • the iterator is used to adopt or discard the real-time crop price and real-time weather data of the day.
  • the forecast crop price of the day is credible, it is determined that the real-time crop price of the day is highly correlated with the real-time weather data, and the real-time crop price and real time of the day are
  • the weather data is included in the price-related model and reanalyzed the rainfall correlation factor ⁇ , the wind level correlation factor ⁇ and the temperature-related factor ⁇ ; when the forecasted crop price on the day is not credible, it is determined that the correlation between the real-time crop price and the real-time weather data on that day is weak.
  • the real-time crop price and real-time weather data of the day are discarded, and the original rainfall correlation factor ⁇ , the wind level correlation factor ⁇ , and the temperature correlation factor ⁇ are retained. It is simply understood that when the predicted crop price and the real-time crop price are too different, it is considered that the price data and weather data of that day have no reference value, are not retained and are not used for subsequent price forecasting; when the forecast crop price and the real-time crop price are not much different At that time, it was considered that the price data and weather data of that day had reference value, which was retained and used as historical data for subsequent price forecasting.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of cells is only a logical function division.
  • multiple units or components may be combined or integrated. Go to another system, or some features can be ignored or not executed.
  • the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • An integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, can be stored in a computer readable storage medium.
  • the technical solution of the present invention contributes in essence or to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

本发明涉及一种智能农业管理方法和系统，方法包括：监测土壤的养分数据和水分数据；获取历史天气数据和预测天气数据；获取历史价格数据；对比养分数据和养分含量标准值，对比水分数据和水分含量标准值；处理分析作物的历史价格数据和历史天气数据，结合预测天气数据得到作物价格趋势预测结果；根据作物价格趋势预测结果控制收获作物的时间。系统包括：管控中心、土壤监测模块、天气信息获取模块和价格信息获取模块。本发明除了监测作物自身的情况外，更关注作物的价格趋势，通过分析影响作物价格的主要因素，寻找作物价格与各因素之间的相关关系，从而对作物的价格趋势进行预测，在此基础帮助农民控制收获时间。

Description

一种智能农业管理方法和系统 技术领域
本发明涉及物联网领域，具体涉及一种智能农业管理方法和系统。
背景技术
改革开放以来，中国社会不断向前发展，经济、军事、科技等领域一直保持着良好的增长态势。中国作为世界第一人口大国，随着人口的攀升，保守估计如今的人口数量已经突破14亿，如此庞大的人口数量为社会建设提供了生产力后盾，但也带来了巨大的挑战。解决了占全球1/5人口的温饱问题，是一切发展的基础。
随着科技的在农业上的应用，农业逐渐从小规模农田种植发展到大规模农场种植。现有技术中，不管是小规模种植还是大规模农场种植，大多数都是由农民决定如何管理，如作物的种类、施肥量和收获时机等等均由农民根据主观判断来控制，这也使得收益在很大程度上依赖于农民的经验。但是，主观的经验不可避免地会存在不够严谨的地方，为了对农业生产进行更加科学的管理，业界亟需寻找另一种农业管理模式对农业生产进行科学管理，使农民不再“看天吃饭”。
发明内容
为解决上述技术问题，本发明提供了一种智能农业管理方法和系统。
第一方面，本发明提供了一种智能农业管理方法，该方法具体包括步骤：
S1.实时监测土壤的养分数据和水分数据；
S2.获取本地区的历史天气数据和预测天气数据；
S3.获取作物的历史价格数据；
S4.预先设置养分含量标准值和水分含量标准值，对比养分数据和养分含量标准值，当养分数据小于养分含量标准值时输出警报信息，对比水分数据和水分含量标准值，当水分数据小于水分含量标准值时输出警报信息；
S5.处理分析作物的历史价格数据和历史天气数据，结合预测天气数据得到作物价格趋势预测结果；
S6.根据作物价格趋势预测结果控制收获作物的时间。
本发明提供的一种智能农业管理方法，实时监测土壤的养分数据和水分数据并与预先设置的养分含量标准值和水分含量标准值对比，当养分数据小于养分含量标准值时输出警报，当水分数据小于水分含量标准值时输出警报，便可及时调整施肥量和灌溉量。把养分和水分控制在最适合作物成长的范围， 比通过经验控制施肥量和灌溉量更加科学可靠，节省肥料和节约用水的同时更有利于作物成长。另外，获取本地区的历史天气数据和作物的历史价格数据作为基础，通过足够庞大的大数据基础对作物价格趋势进行预测，再根据作物价格趋势预测结果控制收获作物的时间，控制作物在价格曲线的最高点进入市场，通过科学管理方法使农民摆脱“看天吃饭”的宿命。
进一步的，在获取的本地区的预测天气数据后，所述方法还包括根据预测天气数据控制调整近期的灌溉量，具体包括以下步骤：
实时监测土壤的水分数据；
实时监测空气中的水汽含量；
获取的本地区的预测天气数据，所述预测天气数据包括预测降雨量；
把空气中的水汽含量和预测天气数据解析量化成灌溉量；
结合所述量化灌溉量和土壤的水分数据，调整灌溉量。
上述实施例中，当实时监测到的水分数据偏低即可判断作物需要灌溉以保证水分吸收，在灌溉之前先获取空气中的水汽含量和预测降雨量，并把空气中的水汽含量和预测降雨量解析量化成灌溉量，结合量化灌溉量和实时监测到的水分数据，适当调整灌溉量。空气中的水汽含量和预测降雨量都可能带来潜在灌溉，当水汽含量较高和/或预测天气数据显示近期内有降雨时，可以减少灌溉量，当水汽含量较低和/或预测天气数据显示近期不会有降雨时，则可以正常灌溉。通过这样的方式来充分利用大自然，节约水资源，实现高效灌溉。
第二方面，本发明提供了一种智能农业管理系统，包括：
土壤监测模块，用于实时监测土壤的养分数据和水分数据；
天气信息获取模块，用于通过互联网获取本地区的历史天气数据和预测天气数据；
价格信息获取模块，用于通过互联网获取作物的历史价格数据；
管控中心，预先设置有养分含量标准值和水分含量标准值，所述管控中心接收土壤的养分数据和水分数据；所述管控中心对比养分数据和养分含量标准值，当养分数据小于养分含量标准值时输出警报信息，提醒农民调整施肥量；所述管控中心对比水分数据和水分含量标准值，当水分数据小于水分含量标准值时输出警报信息，提醒农民调整灌溉量；
所述管控中心分别与土壤监测模块、天气信息获取模块和价格信息获取模块相连；
所述管控中心处理分析作物的历史价格数据和本地区的历史天气数据，结合预测天气数据得到作物价格趋势预测结果，并根据作物价格趋势预测结果控制收获作物的时间。
本发明提供的一种智能农业管理系统，包括土壤监测模块、天气信息获 取模块、价格信息获取模块和管控中心。土壤监测模块、天气信息获取模块、价格信息获取模块分别获取土壤的养分数据和水分数据、本地区的历史天气数据和预测天气数据和作物的历史价格数据，管控中心再基于上述数据判断是否需要调整施肥量和灌溉量，更重要的是分析出作物价格趋势预测结果，控制收获作物的时间，通过科学的方法控制作物进入市场的时机，从而提高农民的收入。
进一步的，多个土壤监测模块均匀分布在被监控区域的土壤内，所述土壤监测模块与管控中心之间通过蓝牙或ZigBee或者WiFi的形式无线组网，所述土壤监测模块只发送采集到的养分数据和水分数据而不接收任何信息，所述土壤监测模块通过电池或者太阳能的方式供电；所述管控中心同时接收多个土壤监测模块所发送的养分数据和水分数据并进行统一分析处理。
上述实施例中，为了准确高效地实时监测土壤的养分数据和水分数据，在被监控区域的土壤内均匀设置多个土壤监测模块。管控中心分析处理养分数据和水分数据的依据不是单个土壤监测模块所采集的数据，而是所有土壤监测模块所采集的数据。当某个土壤监测模块出现停止工作、数据出错和传输不畅通等问题时，其他土壤监测模块所采集的庞大数据仍能全面地进行数据采集，因此单个土壤监测模块的数据并不会对整个系统造成影响。其次，由于土壤监测模块的数量庞大，如果通过有线的方式进行组网，无疑会极大增加成本，因此，土壤监测模块与管控中心之间通过无线组网，简化系统的同时也减少了系统的出支，有利于大规模布置土壤监测模块。再次，无线组网会带来供电问题，无法直接通过输电线取电，因此，土壤监测模块通过电池或者太阳能的方式供电，使得土壤监测模块能够不依赖于系统供电而是更加独立地进行数据采集。
附图说明
图1为本发明一种智能农业管理系统的架构图；
图2为本发明管控中心与土壤监测模块组网的架构图；
图3为本发明一种智能农业管理方法的流程示意图；
图4为本发明监测土壤的养分数据和水分数据的交互示意图；
图5为本发明精确控制施肥量和灌溉量的流程示意图；
图6为本发明得到作物价格趋势预测结果的流程示意图；
图7为本发明修正降雨量相关因子α、风力等级相关因子β和气温相关因子γ的流程示意图。
具体实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、接 口、技术之类的具体细节，以便透切理解本发明。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本发明。在其它情况中，省略对众所周知的系统、电路以及方法的详细说明，以免不必要的细节妨碍本发明的描述。
如图1所示，图1为本发明一种智能农业管理系统的架构图。一种智能农业管理系统，包括管控中心1、土壤监测模块2、天气信息获取模块3和价格信息获取模块4。所述管控中心1分别与土壤监测模块2、天气信息获取模块3和价格信息获取模块4相连，上述“相连”的含义应理解为包括物理连接和信号的传输方向。
各个模块的作用分别是：
所述土壤监测模块2用于实时监测土壤的养分数据和水分数据，即采集和上送土壤的养分数据和水分数据；所述天气信息获取模块3用于通过互联网获取本地区的历史天气数据和预测天气数据；所述价格信息获取模块4用于通过互联网获取作物的历史价格数据；
所述管控中心1预先设置有养分含量标准值和水分含量标准值，所述管控中心1接收土壤的养分数据和水分数据；所述管控中心1对比养分数据和养分含量标准值，当养分数据小于养分含量标准值时输出警报，提醒农民调整施肥量；所述管控中心1对比水分数据和水分含量标准值，当水分数据小于水分含量标准值时输出警报，提醒农民调整灌溉量。
所述管控中心1分别与土壤监测模块2、天气信息获取模块3和价格信息获取模块4相连；
所述管控中心1处理分析作物的历史价格数据和本地区的历史天气数据，结合预测天气数据得到作物价格趋势预测结果，并根据作物价格趋势预测结果控制收获作物的时间。
如图3所示，图3为本发明一种智能农业管理方法的流程示意图。与一种智能农业管理系统对应的一种智能农业管理方法，具体包括步骤：
S1.实时监测土壤的养分数据和水分数据；
S2.获取本地区的历史天气数据和预测天气数据；
S3.获取作物的历史价格数据；
S4.预先设置养分含量标准值和水分含量标准值，对比养分数据和养分含量标准值，当养分数据小于养分含量标准值时输出警报信息，对比水分数据和水分含量标准值，当水分数据小于水分含量标准值时输出警报信息；
S5.处理分析作物的历史价格数据和历史天气数据，结合预测天气数据得到作物价格趋势预测结果；
S6.根据作物价格趋势预测结果控制收获作物的时间。
在本实施例中，结合智能农业管理系统，本智能农业管理方法的原理为： 通过土壤监测模块2按照一定的采样频率监测反映土壤实时状况的养分数据和水分数据、通过天气信息获取模块3获取本地区的历史天气数据和预测天气数据、通过价格信息获取模块4获取作物的历史价格数据，这三步并行执行，目的在于收集与农业管理密切相关的数据。在本发明中，智能农业管理的物联网思想体现在三个方面：一、管控中心1内预先设置养分含量标准值和水分含量标准值，土壤监测模块2获取土壤的养分数据和水分数据后上送到管控中心1，管控中心1对比养分数据和养分含量标准值，对比水分数据和水分含量标准值，当养分数据小于养分含量标准值和当水分数据小于水分含量标准值时输出警报，农民根据警报控制施肥量和灌溉量。二、管控中心1分析本地区的预测天气数据，当出现天气预警时输出警报，把气象局发布的气象信息直接应用在本系统中。三、管控中心1处理分析作物的历史价格数据和本地区的历史天气数据，输出作物价格趋势预测结果，农民可根据作物价格趋势预测结果控制收获作物的时间，当作物价格处于上升趋势时发出信息提醒农民及时收获，尽量使得作物在预测价格的最高点进入市场；而当作物价格处于下降趋势时则可发出信息提醒农民暂缓收获，尽量避免作物在预测价格的低位进入市场，从价格的角度分析最佳收获时间。需要说明的是，作物的生长是一个自然过程，本实施例仅仅是从经济的角度分析控制收获作物的时间。
在本实施例中，管控中心1最为重要的功能是收集作物的历史价格数据和本地区的历史天气数据并进行处理分析，对未来的作物价格趋势进行预测，输出作物价格趋势预测结果。农民可根据作物的成熟度和作物价格趋势预测结果掌握作物的收获时间，控制作物在价格曲线的最高点进入市场，通过科学管理方法增加农民的收入。
管控中心1为具有数据分析处理功能、智能决策和智能控制的设备，与管控中心1不同，土壤监测模块2为只具有数据采集和发送功能。对应地，所述S1中，通过只具有数据采集和发送功能的设备来实时监测土壤的养分数据和水分数据。在农业物联网中，并不是所有的设备都是如管控中心1一样的智能设备，更多的是设置在边缘的数据采集设备。在本实施例中，土壤监测模块2通过传感器来实时监测土壤的养分数据和水分数据。作为一种监测装置，土壤监测模块2内的传感器能感受土壤中的养分和水分信息，并把养分数据和水分数据以信号的形式输出。由于土壤监测模块2的数量庞大，而且各个土壤监测模块2都有着非常简单且高效的收发需求，只需要提供很小的数据量就可以反映出土壤的实时状况。
天气信息获取模块3用于获取本地区的历史天气数据、实时天气数据和预测天气数据。在本实施例中，天气信息获取模块3不必具有分析或储存功能，仅需要通过互联网获取气象局的数据即可。当然，在某些情况下，也可 以把天气信息获取模块3设置为与管控中心1一样的智能设备。天气信息获取模块3可以为通过计算机程序实现的软设备，其目的仅仅是通过互联网取本地区的历史天气数据、实时天气数据和预测天气数据。历史天气数据为本地区过去一年或者数年的天气信息，包括日期和每天对应的降雨量、风力大小、气温等；实时天气数据主要用于结合实时作物价格，在后期对降雨量相关因子α、风力等级相关因子β和气温相关因子γ进行修正时提供参考；而预测天气数据则用于根据相关关系获得作物价格趋势预测结果，例如，未来半个月内预测天气数据包括每天的日期和每天对应的降雨量、风力大小、气温等，根据相关关系即可等到每天对应的预测作物价格，进一步获得作物价格趋势预测结果。
作物的价格易受天气因素影响，而天气因素中，季节、降雨量、风力大小、气温等对作物价格的影响最为显著。通过天气信息获取模块3获取本地区的历史天气数据，再通过管控中心1分析得到价格天气相关关系，进一步结合预测天气数据和价格天气相关关系便可得到作物价格趋势预测结果。
与其它只关注作物的生长情况和成熟度的传统的农业物联网系统相比，本智能农业管理系统的创新之处在于：除了具备实时监测作物的生长情况和成熟度等基本功能外，更关注的是作物的价格趋势。通过对影响作物价格的主要因素进行分析，寻找作物价格与各因素之间的相关关系，从而对作物的价格趋势进行预测。传统的农业物联网系统只能让作物更好地生成，本智能农业管理系统还能在此基础帮助农民控制收获时间，增加收入。
另外，本智能农业管理方法和系统还能精确控制施肥量和灌溉量。具体步骤为：
所述管控中心1内预先设置有养分含量标准值和水分含量标准值，所述管控中心1接收土壤的养分数据和水分数据；
所述管控中心1对比实时的养分数据和养分含量标准值，当实时的养分数据小于养分含量标准值时输出警报信息，提醒农民调整施肥量；
所述管控中心1对比实时的水分数据和水分含量标准值，当实时的水分数据小于水分含量标准值时输出警报信息，提醒农民调整灌溉量。
上述步骤主要体现在S4中，预先设置养分含量标准值和水分含量标准值，对比养分数据和养分含量标准值，当养分数据小于养分含量标准值时输出警报信息，对比水分数据和水分含量标准值，当水分数据小于水分含量标准值时输出警报信息。
进一步，在S4中，对比养分数据和养分含量标准值和对比水分数据和水分含量标准值时，还计算养分数据与养分含量标准值的差值和水分数据与水分含量标准值的差值，根据差值分别输出施肥量和灌溉量，同时分别输出施肥时限和灌溉时限。对应地，所述管控中心在对比养分数据和养分含量标 准值和对比水分数据和水分含量标准值时，还计算养分数据与养分含量标准值的差值和水分数据与水分含量标准值的差值，根据差值分别输出施肥量和灌溉量，同时分别输出施肥时限和灌溉时限。例如，当养分数据略小于养分含量标准值时，输出较小的施肥量，同时输出较大的施肥时限；当养分数据远小于养分含量标准值时，输出较大的施肥量，同时输出较小的施肥时限。
每种作物都有其特有的特性，不同作物对养分和水分的需求也各不相同。利用大数据的方法把作物对养分和水分的需求量化。针对特定的作物，预先在管控中心1内设置有养分含量标准值和水分含量标准值。以葡萄为例，不同的养分有不同的养分含量标准值，葡萄对磷的需要较大而对氮的需要较小，把这些需求量化后，预先在管控中心1内设置磷的含量标准值为0.35，氮的含量标准值为0.15；同时葡萄对水分的要求较高，预先在管控中心1内设置水分含量标准值为0.28。管控中心1接收土壤的养分数据和水分数据，当磷的养分数据低于0.35时，输出警报提醒农民及时补充磷肥；当氮的养分数据低于0.15时，输出警报提醒农民及时补充氮肥；当水分数据低于0.28时，输出警报提醒农民及时灌溉。根据不同作物的特性科学界定不同的含量标准值和水分含量标准值，实时采集土壤中的养分数据和水分数据，便可精确控制土壤中的养分含量和水分含量。
另外，本智能农业管理方法和系统还能提供天气预警。管控中心1接收天气信息获取模块3所获取的预测天气数据，当出现天气预警时输出警报。天气因素是对农业影响最大的因素之一，如果不能准确把控天气变化并及时作好应对准备，农业生产将很大程度上受制于天气。以稻谷为例，台风对稻谷的影响很大，特别是临近收获的稻谷，台风来袭时会把稻穗打倒，因此，时刻关注天气的动向对农业生产极为重要。在实施例中，天气信息获取模块3从气象局获取本地区的预测天气数据，当出现台风预警时及时输出警报，为农民争取更多的准备时间。
如图2所示，图2为本发明管控中心1与土壤监测模块2组网的架构图。本智能农业管理系统还包括多个转发节点5，所述土壤监测模块2包括土壤养分传感器和土壤水分传感器，所述土壤养分传感器用于实时监测土壤的养分数据，所述土壤水分传感器用于实时监测土壤的水分数据。
所述土壤养分传感器和土壤水分传感器埋设在作物根系的旁边的土壤下，使得土壤监测模块2所处环境与作物的实际环境尽可能接近，土壤监测模块2所采集的数据能够更真实地反映作物的状态。
土壤监测模块2通过转发节点5与管控中心1相连，所述土壤养分传感器和土壤水分传感器定时向转发节点5发送数据，所述转发节点5接收土壤养分传感器的养分数据和土壤水分传感器的水分数据，并把养分数据和水分数据发送到管控中心1。
土壤监测模块2、转发节点5和管控中心1分别构成农业物联网的三层架构。其中土壤监测模块2为农业物联网最前端的数据采集设备，其数量庞大、数据结构简单且不适用于传统的互联网协议。在整个农业物联网架构中，土壤监测模块2只需要不断的采集和发送养分数据和水分数据，传统互联网协议所要求的数据的精确性和安全性等，在农业物联网可以忽略，农业物联网更关注采样的样本数，即土壤监测模块2的数量以及是否覆盖了全区域。由于土壤监测模块2不适用于传统的互联网协议，而现今的物联网在不同程度上还是要基于传统互联网，因此，转发节点5和管控中心1仍然遵循传统的互联网协议。转发节点5提供传统互联网的数据传输和网关功能，即进行协议转换，对接前端土壤监测模块2的组网协议与传统互联网的组网协议。管控中心1则提供数据分析、综合控制以及人机接口功能。通过这样，就可以在农业物联网和传统互联网之间架起一座桥梁，为农业物联网的实现提供基础。
在本智能农业管理系统的网络架构中，土壤监测模块2作为系统的最前端，只需要具有数据采集和发送功能，而不需要具备数据接收和处理功能。这是由于土壤监测模块2数量庞大，其作用仅在于实时监测土壤的养分数据和水分数据，因此土壤监测模块2配置数据接收或处理功能的成本远大于其带来的效果。另一方面，系统的最前端不需要具备数据接收和处理功能，在一定程度上也存在弊端，因为这将大大增加后面的管控中心1处理数据的压力。因此，在管控中心1和土壤监测模块2之间通过转发节点5连接起来，这些转发节点5具有接收、处理和转发数据的能力，能够在把养分数据和水分数据转发到管控中心1之前对其进行预处理。预处理包括对养分数据和水分数据进行智能化封闭和裁剪、检查数据是否完整、丢弃受损数据和冗余数据和指定数据的传输方向等等。在本实施例中，转发节点5的设置，优化了系统的结构，使得前端与后端的衔接更加密切，提高了数据采集、传输和处理的效率。
如图4所示，图4为本发明监测土壤的养分数据和水分数据的交互示意图。所述S1中，实时监测土壤的养分数据和水分数据具体包括：
第一层网络设备20采集土壤的养分数据和水分数据，并通过广播的形式向第二层网络设备50发送采集到的养分数据和水分数据；
第二层网络设备50接收到的养分数据和水分数据后对数据进行预处理，并向第三层网络设备10发送经过预处理的养分数据和水分数据。
第一层网络设备20对应的是土壤监测模块2，第二层网络设备50对应的是转发节点5，第三层网络设备10对应的是管控中心1。在本实施例中，实时监测土壤的养分数据和水分数据具体包括：
土壤监测模块2采集土壤的养分数据和水分数据，并通过广播的形式向 转发节点5发送采集到的养分数据和水分数据；土壤监测模块2不具备数据处理功能，只能进行简单的数据采集和发送；而且土壤监测模块2采集与转发节点5、管控中心1之间通过无线的方式组网，在采集到养分数据和水分数据后，土壤监测模块2会通过广播的形式发送所采集到的数据。
转发节点5接收到的养分数据和水分数据后对数据进行预处理，并向管控中心1发送经过预处理的养分数据和水分数据；转发节点5的功能包括转发数据和对数据进行预处理，具体包括对养分数据和水分数据进行智能化封闭和裁剪、检查数据是否完整、丢弃受损数据和冗余数据和指定数据的传输方向等等。由于土壤监测模块2不具备数据处理功能，而如果所有的数据采集工作都由管控中心1执行则会对管控中心1造成很大的负担，数据处理效率低，因此，在把养分数据和水分数据发送到管控中心1之前，先由转发节点5进行预处理，帮助分担管控中心1的压力，提高整个系统的数据处理效率。
管控中心1接收并处理经过预处理的养分数据和水分数据，并可根据需要控制转发节点5接收特定土壤监测模块2所采集的养分数据和水分数据；例如需要关注某个区域的土壤实时状况时，可以把该区域土壤监测模块2所采集土壤的养分数据和水分数据标注为感兴趣。如图4所示，第三层网络设备10通过第二层网络设备接收感兴趣的养分数据和水分数据。
转发节点5接收特定土壤监测模块2所采集的养分数据和水分数据而忽略其它土壤监测模块2所采集的养分数据和水分数据。只接受感兴趣的数据，能够针对性更强地处理数据，提高数据处理效率。
需要特别说明的是，土壤监测模块2通过广播的形式发送采集到土壤的养分数据和水分数据，能够在某些转发节点5由于各种原因而无法传输数据时转向其它正常工作的转发节点5，保证系统的可靠性。另外，由于土壤监测模块2的数据帧非常小，仅携带了最重要的信息，因此采用广播的形式也不会影响整个系统的运维成本。
所述土壤养分传感器和土壤水分传感器按照特定的采样频率监测土壤的养分数据和水分数据，即土壤养分传感器和土壤水分传感器不间断地持续工作，不停地监测土壤的养分数据和水分数据。特定的采样频率具体是指每隔一小时或者两小时则进行一次数据采集，然后把采集到的养分数据和水分数据上送。为土壤养分传感器和土壤水分传感器设置特定的采样频率是为了平衡成本和数据的有效性。土壤的养分数据和水分数据不会时刻变化，如果土壤养分传感器和土壤水分传感器时刻都在工作，一是数据的有效性低，二是设备的运行的成本高。随着数量的增多，这样的状况越来越明显。因此，每隔一小时或者两小时则进行一次数据采集，能够很好地平衡成本和数据的有效性。
优选地，所述土壤养分传感器和土壤水分传感器设置在受监测的农田内，所述受监测的农田划分为多个小区域。土壤养分传感器和土壤水分传感器所采集的数据帧包括传输指向码、地址码和感应数据。物联网的前端设备数量庞大，考虑到数据的开销和效率，土壤养分传感器和土壤水分传感器仅携带对本系统最有用的信息。
上述数据帧能够符合农业物联网的基本要求，其中传输指向码记载了数据的传输方向，即为通过哪个转发节点5上送数据；地址码记载了土壤养分传感器或土壤水分传感器的位置；感应数据则是养分数据或水分数据，如上述实施例中磷和氮的养分数据等，结合地址码和感应数据便可直观分析出每个地点对应的养分数据和水分数据。
如图5所示，图5为本发明精确控制施肥量和灌溉量的流程示意图。受监测的农田划分为多个小区域且土壤养分传感器和土壤水分传感器所采集的数据帧包括传输指向码、地址码和感应数据，可用于精确控制施肥量和灌溉量，具体包括：
S401.把受监测的农田划分为多个小区域；土壤养分传感器和土壤水分传感器均具有一定的监测半径，因此可以通过间隔设置土壤养分传感器和土壤水分传感器即可实现受监测农田的小区域划分。
S402.实时监测多个小区域内土壤的养分数据和水分数据；各个土壤养分传感器和土壤水分传感器采集数据的过程即为监测各个小区域内土壤的养分数据和水分数据的过程。
S403.对比多个小区域的养分数据和养分含量标准值，对比多个小区域的水分数据和水分含量标准值；一般来说，养分和水分都是顺着土壤蔓延分布的，所以当某个地方的养分数据或者水分数据不在可接受区间时，通常这个地方临近的一片区域的养分数据或者水分数据同样不在可接受区间。结合土壤养分传感器和土壤水分传感器的地址码和感应数据，管控中心1可以分析出哪个区域需要施肥或者灌溉。
S404.筛选出养分数据小于养分含量标准值的小区域，筛选出水分数据小于水分含量标准值的小区域；
S405.输出警报，精确控制各个小区域的施肥量和灌溉量。管控中心1可以通过地图的方式把各个小区域的养分和水分状况展现给农民，如采用天气预报中的降雨量分布图的形式，直观地实现人机交互，农民根据从管控中心1取得的信息施肥或者灌溉，完成精确控制施肥量和灌溉量。
物联网与传统互联网有类似的地方，但在很多方面上，二者的仍有本质上的区别。传统互联网关注的是数据的精确性和可靠性，而物联网是有损的、间歇性的网络，对精确性和可靠性的要求不高。在物联网领域，多数情况下都是对样本数量的要求较高，而没有必要牺牲高昂的成本来维持数据的准确 性。在本实施例中，每个土壤养分传感器和土壤水分传感器所采集的数据为“小数据”，而所有土壤养分传感器和土壤水分传感器所采集的数据为“大数据”，当单个“小数据”出现超时或者丢包时，并不会对“大数据”造成影响，只要通过增加样品就可以克服物联网数据的“不精确性”和“不可靠性”。
优选地，在获取的本地区的预测天气数据后，所述方法还包括根据预测天气数据控制调整近期的灌溉量，具体包括以下步骤：
实时监测土壤的水分数据；
实时监测空气中的水汽含量；
获取的本地区的预测天气数据，所述预测天气数据包括预测降雨量；
把空气中的水汽含量和预测天气数据解析量化成灌溉量；
结合量化灌溉量和土壤的水分数据，调整灌溉量。
如图6所示，图6为本发明得到作物价格趋势预测结果的流程示意图。所述S5中，得到作物价格趋势预测结果的过程具体包括：
S501.整理作物的历史价格数据，所述作物的历史价格数据包括过去一段时间内每一天的作物价格Y；
S502.获取本地区的历史天气数据，所述历史天气数据包括过去一段时间内每一天的降雨量A、风力等级B和气温C；
S503.组建价格相关模型，并根据价格相关模型分析出降雨量相关因子α、风力等级相关因子β和气温相关因子γ，得到价格天气相关关系Y＝αA+βB+γC；
S504.获取本地区的预测天气数据，并结合价格天气相关关系Y＝αA+βB+γC对作物的价格进行预测，得到预测作物价格；
S505.对比预测作物价格与过去一段时间内每一天的作物价格，输出作物价格趋势预测结果。
天气是影响作物价格的关键因素，因此，本发明基于作物的历史价格数据和本地区的历史天气数据对作物价格趋势预测结果进行分析。先获取历史价格数据和历史天气数据，通过数学建模的方法组建价格相关模型，再根据多元线性回归分析方法分析得到降雨量相关因子α、风力等级相关因子β和气温相关因子γ，就可确定作物价格与天气的相关关系，再结合预测天气数据便可得到预测作物价格。通过上述步骤，用数学方法表述价格与天气的关系，保证了预测结果的准确性，为农业管理提供了科学的根据。
与上述对作物的历史价格数据和本地区的历史天气数据进行处理分析过程对应的软系统包括价格数据单元11、天气数据单元12、模型组建单元13、价格预测单元14、趋势预测单元15和修正单元16。
所述价格数据单元11用于整理作物的历史价格数据，所述作物的历史价格数据包括过去一段时间内每一天的作物价格Y。作物的历史价格数据作 为价格预测的基础，以往特定时段具体为过去数月或者数年。特定时段的时间跨度越大，后续的作物价格趋势预测结果越准确。作物的历史价格数据的格式为(日期，价格)，以玉米的价格为例，玉米的历史价格数据可以为(2013.03.02，10)。
所述天气数据单元12用于处理本地区的历史天气数据，所述历史天气数据包括过去一段时间内每一天的降雨量A、风力等级B和气温C。同样地，管控中心1中也具有对实时天气数据和预测天气数据进行处理的单元，其原理与用于处理历史天气数据的天气数据单元12相同。历史天气数据的格式为(日期，降雨量，风力等级，气温)，如(2013.03.02，150，3，22)。
所述模型组建单元13用于组建价格相关模型，并根据价格相关模型分析出降雨量相关因子α、风力等级相关因子β和气温相关因子γ，得到价格天气相关关系Y＝αA+βB+γC。通过数学建模的方法组建价格相关模型，结合某种作物的历史价格数据(如玉米)和历史天气数据，根据多元线性回归分析方法分析得到降雨量相关因子α、风力等级相关因子β和气温相关因子γ，就可确定作物价格与天气的相关关系。例如，求得的降雨量相关因子α为0.02，风力等级相关因子β为1.5，气温相关因子γ为0.8，此时可以确定玉米价格与天气的相关关系为Y＝0.02*A+1.5*B+0.8*C。所述价格预测单元14用于根据本地区的预测天气数据，并结合作物的价格天气相关关系Y＝0.02*A+1.5*B+0.8*C对作物的价格进行预测，得到预测作物价格。如预测某天的天气数据为(201X.XX.XX，100，1，20)，对应的A为100，B为1，C为20，结合玉米的价格天气相关关系Y＝0.02*A+1.5*B+0.8*C，可得到预测三天后玉米的价格为19.5。
如图7所示，图7为本发明修正降雨量相关因子α、风力等级相关因子β和气温相关因子γ的流程示意图。所述S504中，先获取实时作物价格和实时天气数据，所述实时天气数据为当天的天气数据，所述实时作物价格为当天的作物价格，根据预测作物价格和实际作物价格不断对降雨量相关因子α、风力等级相关因子β和气温相关因子γ进行修正，修正的过程具体包括：
S5041.获取过去一段时间内每天的预测作物价格和实际作物价格；
S5042.遍历计算得到过去一段时间内每天的预测作物价格和实际作物价格之差
Figure PCTCN2017096022-appb-000001
并取
Figure PCTCN2017096022-appb-000002
的绝对值
Figure PCTCN2017096022-appb-000003
作为一组修正参考数；
S5043.计算所有修正参考数的平均值σ；
S5044.对比当天的预测作物价格和实时作物价格之差的绝对值
Figure PCTCN2017096022-appb-000004
与2σ，当
Figure PCTCN2017096022-appb-000005
小于2σ时认定当天的预测作物价格可信，否则认定当天的预测作物价格不可信。
S5045.当当天的预测作物价格可信时，认定当天的实时作物价格和实时天气数据相关性强，把当天的实时作物价格和实时天气数据纳入价格相关模 型并重新分析降雨量相关因子α、风力等级相关因子β和气温相关因子γ；当当天的预测作物价格不可信，认定当天的实时作物价格和实时天气数据相关性弱，则丢弃当天的实时作物价格和实时天气数据，保留原来的降雨量相关因子α、风力等级相关因子β和气温相关因子γ。
所述趋势预测单元15用于对比预测作物价格与过去一段时间内每一天的作物价格，输出作物价格趋势预测结果。用以上方法依次求出一段时间内的预测作物价格，即可得到作物价格趋势预测结果。
所述修正单元16根据预测作物价格与实时作物价格不断对降雨量相关因子α、风力等级相关因子β和气温相关因子γ进行修正，所述修正单元16具体包括比价器、遍历器、平均器、判断器和迭代器。
所述比价器用于用于获取过去一段时间内每天的预测作物价格和实际作物价格；如预测作物价格为19.5、实时作物价格为20时。
所述遍历器用于遍历计算得到过去一段时间内每天的预测作物价格和实际作物价格之差
Figure PCTCN2017096022-appb-000006
并取
Figure PCTCN2017096022-appb-000007
的绝对值
Figure PCTCN2017096022-appb-000008
作为一组修正参考数；预测作物价格为19.5、实时作物价格为20时，
Figure PCTCN2017096022-appb-000009
为0.5，当天的修正参考数为0.5。
所述平均器用于计算所有修正参考数的平均值σ；通过加权平均的方法取得σ的值，例如5天的修正参考数分别为0.5、0.3、0.2、0.4和0.6，则σ的值为0.4。
所述判断器用于对比当天的预测作物价格和实时作物价格之差的绝对值
Figure PCTCN2017096022-appb-000010
与2σ，当
Figure PCTCN2017096022-appb-000011
小于2σ时认定当天的预测作物价格可信，否则认定当天的预测作物价格不可信；例如某天的预测作物价格和实时作物价格之差的绝对值
Figure PCTCN2017096022-appb-000012
为0.7时，认定这天的的预测作物价格可信，当某天的预测作物价格和实时作物价格之差的绝对值
Figure PCTCN2017096022-appb-000013
为0.8以上时，认定这天的预测作物价格不可信。
所述迭代器用于采用或者丢弃当天的实时作物价格和实时天气数据，当当天的预测作物价格可信时，认定当天的实时作物价格和实时天气数据相关性强，把当天的实时作物价格和实时天气数据纳入价格相关模型并重新分析降雨量相关因子α、风力等级相关因子β和气温相关因子γ；当当天的预测作物价格不可信，认定当天的实时作物价格和实时天气数据相关性弱，则丢弃当天的实时作物价格和实时天气数据，保留原来的降雨量相关因子α、风力等级相关因子β和气温相关因子γ。简单理解为当预测作物价格和实时作物价格相差过大时，认为那天的价格数据与天气数据没有参考价值，不予保留且不用于后续的价格预测；当预测作物价格和实时作物价格相差不大时，认为那天的价格数据与天气数据具有参考价值，予以保留且作为历史数据继续用于后续的价格预测中。
读者应理解，在本说明书的描述中，参考术语“一个实施例”、“一些 实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，上述描述的装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本发明实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分，或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。

Claims (10)

  1. 一种智能农业管理方法，其特征在于，具体包括步骤：
    S1.实时监测土壤的养分数据和水分数据；
    S2.获取本地区的历史天气数据和预测天气数据；
    S3.获取作物的历史价格数据；
    S4.预先设置养分含量标准值和水分含量标准值，对比所述养分数据和所述养分含量标准值，当所述养分数据小于所述养分含量标准值时输出警报信息，对比所述水分数据和所述水分含量标准值，当所述水分数据小于所述水分含量标准值时输出警报信息；
    S5.处理分析所述作物的历史价格数据和所述历史天气数据，结合所述预测天气数据得到作物价格趋势预测结果；
    S6.根据所述作物价格趋势预测结果控制收获作物的时间。
  2. 根据权利要求1所述的一种智能农业管理方法，其特征在于，所述S1中，实时监测土壤的养分数据和水分数据具体包括：
    S101.采集土壤的养分数据和水分数据；
    S102通过广播的形式发送所述养分数据和水分数据；
    S103.对所述养分数据和水分数据进行预处理，所述预处理包括对所述养分数据和水分数据进行智能化封闭和裁剪、检查数据是否完整、丢弃受损数据、丢弃冗余数据和指定传输方向。
  3. 根据权利要求1所述的一种智能农业管理方法，其特征在于，所述S4中还包括精确控制施肥量和灌溉量，具体包括：
    S401.把受监测的农田划分为多个小区域；
    S402.实时监测所述多个小区域内土壤的养分数据和水分数据；
    S403.对比所述多个小区域的养分数据和养分含量标准值，对比所述多个小区域的水分数据和水分含量标准值；
    S404.筛选出所述养分数据小于所述养分含量标准值的小区域，筛选出所述水分数据小于所述水分含量标准值的小区域；
    S405.输出警报，精确控制各个小区域的施肥量和灌溉量。
  4. 根据权利要求1-3中任一项所述的一种智能农业管理方法，其特征在于，所述S5中，得到作物价格趋势预测结果的过程具体包括：
    S501.整理所述作物的历史价格数据，所述作物的历史价格数据包括过去一段时间内每一天的作物价格Y；
    S502.获取本地区的历史天气数据，所述历史天气数据包括过去一段时间内每一天的降雨量A、风力等级B和气温C；
    S503.组建价格相关模型，并根据所述价格相关模型分析出降雨量相关因子α、风力等级相关因子β和气温相关因子γ，得到价格天气相关关系 Y＝αA+βB+γC；
    S504.获取本地区的预测天气数据，并结合所述价格天气相关关系Y＝αA+βB+γC对作物的价格进行预测，得到预测作物价格；
    S505.对比预测作物价格与过去一段时间内每一天的作物价格，输出作物价格趋势预测结果。
  5. 根据权利要求4所述的一种智能农业管理方法，其特征在于，所述S504中，先获取实时作物价格和实时天气数据，所述实时天气数据为当天的天气数据，所述实时作物价格为当天的作物价格，根据预测作物价格和实际作物价格不断对降雨量相关因子α、风力等级相关因子β和气温相关因子γ进行修正，修正的过程具体包括：
    S5041.获取过去一段时间内每天的预测作物价格和实际作物价格；
    S5042.遍历计算得到过去一段时间内每天的预测作物价格和实际作物价格之差
    Figure PCTCN2017096022-appb-100001
    并取
    Figure PCTCN2017096022-appb-100002
    的绝对值
    Figure PCTCN2017096022-appb-100003
    作为一组修正参考数；
    S5043.计算所有修正参考数的平均值σ；
    S5044.对比当天的预测作物价格和实时作物价格之差的绝对值
    Figure PCTCN2017096022-appb-100004
    与2σ，当
    Figure PCTCN2017096022-appb-100005
    小于2σ时认定当天的预测作物价格可信，否则认定当天的预测作物价格不可信；
    S5045.当当天的预测作物价格可信时，认定当天的实时作物价格和实时天气数据相关性强，把当天的实时作物价格和实时天气数据纳入价格相关模型并重新分析降雨量相关因子α、风力等级相关因子β和气温相关因子γ；当当天的预测作物价格不可信，认定当天的实时作物价格和实时天气数据相关性弱，则丢弃当天的实时作物价格和实时天气数据，保留原来的降雨量相关因子α、风力等级相关因子β和气温相关因子γ。
  6. 一种智能农业管理系统，其特征在于，包括：
    土壤监测模块，用于实时监测土壤的养分数据和水分数据；
    天气信息获取模块，用于通过互联网获取本地区的历史天气数据和预测天气数据；
    价格信息获取模块，用于通过互联网获取作物的历史价格数据；
    管控中心，预先设置有养分含量标准值和水分含量标准值，所述管控中心接收所述土壤的养分数据和水分数据；所述管控中心对比所述养分数据和所述养分含量标准值，当所述养分数据小于所述养分含量标准值时输出警报信息，提醒农民调整施肥量；所述管控中心对比所述水分数据和所述水分含量标准值，当所述水分数据小于所述水分含量标准值时输出警报信息，提醒农民调整灌溉量；
    所述管控中心分别与所述土壤监测模块、所述天气信息获取模块和所述价格信息获取模块相连；
    所述管控中心处理分析所述作物的历史价格数据和所述本地区的历史天气数据，结合所述预测天气数据得到作物价格趋势预测结果，并根据所述作物价格趋势预测结果控制收获作物的时间。
  7. 根据权利要求6所述的一种智能农业管理系统，其特征在于，还包括多个转发节点，所述土壤监测模块包括土壤养分传感器和土壤水分传感器，所述土壤养分传感器用于实时监测土壤的养分数据，所述土壤水分传感器用于实时监测土壤的水分数据；
    所述土壤养分传感器和所述土壤水分传感器埋设在作物根系的旁边的土壤下；
    所述土壤养分传感器和所述土壤水分传感器通过所述转发节点与所述管控中心相连；
    所述土壤养分传感器和所述土壤水分传感器按照特定的采样频率监测所述土壤的养分数据和水分数据；
    所述土壤养分传感器和所述土壤水分传感器定时向所述转发节点发送数据，所述转发节点接收所述土壤养分传感器的养分数据和所述土壤水分传感器的水分数据，并把所述养分数据和所述水分数据发送到所述管控中心；
    所述土壤监测模块实时监测所述土壤的养分数据和水分数据具体包括：
    所述土壤养分传感器和所述土壤水分传感器分别采集土壤的养分数据和水分数据；
    所述土壤监测模块通过广播的形式把所述养分数据和水分数据发送到所述转发节点；
    所述转发节点对所述养分数据和水分数据进行预处理，所述预处理包括对所述养分数据和水分数据进行智能化封闭和裁剪、检查数据是否完整、丢弃受损数据、丢弃冗余数据和指定传输方向。
  8. 根据权利要求6所述的一种智能农业管理系统，其特征在于，所述监测的农田被划分为多个小区域，通过小区域精确控制施肥量和灌溉量，具体包括：
    所述土壤监测模块实时监测所述多个小区域内土壤的养分数据和水分数据；
    所述管控中心对比所述多个小区域的养分数据和养分含量标准值，对比所述多个小区域的水分数据和水分含量标准值；
    所述管控中心筛选出所述养分数据小于所述养分含量标准值的小区域，筛选出所述水分数据小于所述水分含量标准值的小区域；
    所述管制中心输出警报，精确控制各个小区域的施肥量和灌溉量。
  9. 根据权利要求6-9中任一项所述的一种智能农业管理系统，其特征在于，所述管控中心包括：
    价格数据单元，用于整理所述作物的历史价格数据，所述作物的历史价格数据包括过去一段时间内每一天的作物价格Y；
    天气数据单元，用于处理本地区的历史天气数据，所述历史天气数据包括过去一段时间内每一天的降雨量A、风力等级B和气温C；
    模型组建单元，用于组建价格相关模型，并根据所述价格相关模型分析出降雨量相关因子α、风力等级相关因子β和气温相关因子γ，得到价格天气相关关系Y＝αA+βB+γC；
    价格预测单元，用于获取本地区的预测天气数据，并结合所述价格天气相关关系Y＝αA+βB+γC对作物的价格进行预测，得到预测作物价格；
    趋势预测单元，用于对比预测作物价格与过去一段时间内每一天的作物价格，输出作物价格趋势预测结果。
  10. 根据权利要求9所述的一种智能农业管理系统，其特征在于，所述价格信息获取模块获取实时作物价格，所述天气信息获取模块获取本地区的实时天气数据，所述实时天气数据为当天的天气数据，所述管控中心还包括修正单元，所述修正单元根据预测作物价格和实际作物价格不断对降雨量相关因子α、风力等级相关因子β和气温相关因子γ进行修正，所述修正单元具体包括：比价器、遍历器、平均器、判断器和迭代器；
    比价器，用于获取过去一段时间内每天的预测作物价格和实际作物价格；
    遍历器，用于遍历计算得到过去一段时间内每天的预测作物价格和实际作物价格之差
    Figure PCTCN2017096022-appb-100006
    并取
    Figure PCTCN2017096022-appb-100007
    的绝对值
    Figure PCTCN2017096022-appb-100008
    作为一组修正参考数；
    平均器，用于计算所有修正参考数的平均值σ；
    判断器，用于对比当天的预测作物价格和实时作物价格之差的绝对值
    Figure PCTCN2017096022-appb-100009
    与2σ，当
    Figure PCTCN2017096022-appb-100010
    小于2σ时认定当天的预测作物价格可信，否则认定当天的预测作物价格不可信；
    迭代器，用于采用或者丢弃当天的实时作物价格和实时天气数据，当当天的预测作物价格可信时，认定当天的实时作物价格和实时天气数据相关性强，把当天的实时作物价格和实时天气数据纳入价格相关模型并重新分析降雨量相关因子α、风力等级相关因子β和气温相关因子γ；当当天的预测作物价格不可信，认定当天的实时作物价格和实时天气数据相关性弱，则丢弃当天的实时作物价格和实时天气数据，保留原来的降雨量相关因子α、风力等级相关因子β和气温相关因子γ。
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