CN112215716A

CN112215716A - Crop growth intervention method, device, equipment and storage medium

Info

Publication number: CN112215716A
Application number: CN202011088861.1A
Authority: CN
Inventors: 肖晶晶; 张育慧; 姚益平; 李正泉; 郭芬芬; 王治海
Original assignee: Climate Center Of Zhejiang Province
Current assignee: Climate Center Of Zhejiang Province
Priority date: 2020-10-13
Filing date: 2020-10-13
Publication date: 2021-01-12

Abstract

The invention relates to the technical field of agricultural meteorological monitoring, and discloses a crop growth intervention method, device, equipment and storage medium. The method comprises the following steps: acquiring farming data and target meteorological data corresponding to crops to be intervened; determining a first meteorological condition corresponding to the crop under a preset fitness value according to the growth stage of the crop; evaluating the target meteorological data and the first meteorological conditions by a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops grow properly under a second meteorological condition; and if not, generating meteorological disaster early warning information, sending the meteorological disaster early warning information to a business unit, and prompting to intervene crops. The technical problems that the crop growth is greatly influenced by weather conditions and cannot be interfered are solved, the crop growth is interfered in advance, and the crop yield is improved.

Description

Crop growth intervention method, device, equipment and storage medium

Technical Field

The invention relates to the technical field of agricultural meteorological monitoring, in particular to a crop growth intervention method, device, equipment and storage medium.

Background

The agricultural products are the basis of production and life of people and are the self-sufficient basis of a country. The crop growth condition, the plant diseases and insect pests or the crop nutrition condition can be known in time by monitoring the growth condition of the crops, so that people are guided to take corresponding management measures, and the normal growth of the crops is further ensured.

The crops are one of the main grain and oil crops in China. At present, the crop planting area, the yield and the export quantity of China are always in the leading position of the world. There are many factors that affect the growth of crops, mainly including sunlight intensity, temperature, humidity, etc. The growth of crops is greatly influenced by climate, but the existing farming activities are more traditional and cannot intervene in advance on the growth of crops. In view of the above, the invention provides a crop growth intervention method aiming at climate factors, quantitatively evaluating the influence of climate on crop growth, analyzing the temperature, rainfall, sunshine and the like required by crop production based on the actual growth data of crops, and further performing real-time intervention on the growth condition of crops.

Disclosure of Invention

The invention mainly aims to solve the technical problems that the growth of crops is greatly influenced by climate and cannot be subjected to pre-drying, and can intervene in advance on the growth of crops to improve the yield of the crops.

In a first aspect, the present invention provides a method for intervening in the growth of crops, comprising:

acquiring farming data and target meteorological data corresponding to crops to be intervened, wherein the target meteorological data is meteorological data in a certain time period;

determining a growth stage of crops to be intervened, and determining a first meteorological condition corresponding to the crops to be intervened under a preset fitness value according to the growth stage;

evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, wherein the evaluation result is a disaster early warning level of the crops to be intervened under a second meteorological condition corresponding to the target meteorological data;

comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops to be intervened are suitable for growing under the second meteorological condition;

and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene the crops to be intervened, wherein the meteorological disaster early warning information at least comprises the geographical position, the early warning time, the disaster type and the early warning grade of an early warning area.

Optionally, in a first implementation manner of the first aspect of the present invention, before the acquiring the farming data and the target meteorological data corresponding to the crop to be intervened, the method includes:

acquiring an agricultural data set, and labeling agricultural data in the agricultural data set according to a preset labeling rule, wherein the agricultural data comprises agricultural data or meteorological data;

establishing a database object for the marked agricultural data, wherein the database object comprises one of but not limited to a data table, a view, a trigger and a storage process;

and storing the database object according to a preset data entry format to obtain a basic database.

Optionally, in a second implementation manner of the first aspect of the present invention, the acquiring the farming data and the target meteorological data corresponding to the crop to be intervened includes:

acquiring farming data and historical weather data corresponding to crops to be intervened from a preset basic database;

and inputting the historical weather data into the weather forecast model to obtain target weather data corresponding to the crops to be intervened.

Optionally, in a third implementation manner of the first aspect of the present invention, the obtaining the evaluation result of the target meteorological data by evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model includes:

inputting the target meteorological data into the weather forecast model to obtain a second meteorological condition corresponding to the target meteorological data;

comparing the second meteorological conditions with the first meteorological conditions to determine the factors causing the meteorological disasters, and determining early warning factors required by model construction according to the factors causing the meteorological disasters;

calculating the weight of the early warning factor by using a preset AHP algorithm to obtain the weight value of the early warning factor, and determining a target early warning factor according to the weight value of the early warning factor;

and determining a division standard of an early warning grade, and performing a control test based on the weighted value of the target early warning factor and the division standard of the early warning grade to obtain an evaluation result of the target meteorological data.

Optionally, in a fourth implementation form of the first aspect of the present invention, the farming data includes one of farm climate data, crop observation data, agricultural statistical data, and geographic information data;

the historical weather data comprises historical observation data, historical standard meteorological data and historical meteorological observation data corresponding to the crops to be intervened;

the historical meteorological observation data comprise temperature, precipitation, humidity and sunshine.

Optionally, in a fifth implementation manner of the first aspect of the present invention, the crop growth intervention method further includes:

determining the current growth and development characteristics of the crops according to the historical meteorological observation data and the crop observation data;

predicting the next growth and development characteristics of the crops according to the target meteorological data and historical agricultural statistical data;

and screening at least one agricultural meteorological condition affecting the current growth development characteristic and the next development characteristic from a preset agricultural meteorological condition set as an agricultural meteorological index.

A second aspect of the invention provides a crop growth intervention device comprising:

the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring farming data and target meteorological data corresponding to crops to be intervened, and the target meteorological data is meteorological data in a certain time period;

the first determining module is used for determining a growth stage of a crop to be intervened and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

the evaluation module is used for evaluating the target meteorological data and the first meteorological conditions through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, wherein the evaluation result is a disaster early warning level of the crops to be intervened under a second meteorological condition corresponding to the target meteorological data;

the comparison module is used for comparing the evaluation result with a preset meteorological disaster early warning standard and judging whether the crops to be intervened are suitable for growing under the second meteorological condition;

and the sending module is used for generating meteorological disaster early warning information when the crops grow unsuitably under the second meteorological condition and sending the meteorological disaster early warning information to a business unit to intervene the crops to be intervened, wherein the meteorological disaster early warning information at least comprises the geographical position of an early warning area, early warning time, disaster type and early warning grade.

Optionally, in a first implementation manner of the second aspect of the present invention, the crop growth intervening device further comprises:

the marking module is used for acquiring agricultural data and marking the agricultural data according to a preset marking rule, wherein the agricultural data comprises agricultural data or meteorological data;

the construction module is used for constructing a database object for the marked agricultural data, wherein the database object comprises but is not limited to one of a data table, a view, a trigger and a storage process;

and the storage module is used for storing the database object according to a preset data entry format to obtain a basic database.

Optionally, in a second implementation manner of the second aspect of the present invention, the obtaining module includes:

the system comprises an acquisition unit, a pre-setting unit and a control unit, wherein the acquisition unit is used for acquiring farming data and historical weather data corresponding to crops to be intervened from a preset basic database;

and the input unit is used for inputting the historical weather data into the weather forecast model to obtain target weather data corresponding to the crops to be intervened.

Optionally, in a third implementation form of the second aspect of the invention, the evaluation module is specifically configured to:

Optionally, in a fourth implementation form of the second aspect of the present invention, the farming data includes farm climate data, crop observation data, agricultural statistical data, and geographic information data;

Optionally, in a fifth implementation manner of the second aspect of the present invention, the crop growth intervening device further includes:

the second determining module is used for determining the current growth and development characteristics of the crops according to the historical meteorological observation data and the crop observation data;

the prediction module is used for predicting the next growth and development characteristics of the crops according to the target meteorological data and historical agricultural statistical data;

and the screening module is used for screening at least one agricultural meteorological condition which influences the current growth and development characteristics and the next development characteristics from a preset agricultural meteorological condition set to serve as an agricultural meteorological index.

A third aspect of the invention provides a crop growth intervention apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;

the at least one processor invokes the instructions in the memory to cause the crop growth intervention device to perform the crop growth intervention method described above.

A fourth aspect of the present invention provides a computer readable storage medium having stored therein instructions which, when run on a computer, cause the computer to perform the above-described crop growth intervention method.

According to the technical scheme provided by the invention, the farming data and the target meteorological data corresponding to the crops to be intervened are obtained; determining a growth stage of the crop, and determining a first meteorological condition corresponding to the crop under a preset fitness value according to the growth stage; evaluating the target meteorological data and the first meteorological conditions by a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops grow properly under a second meteorological condition; and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to the service unit to intervene the crops. The technical problems that the growth of crops is greatly influenced by climate and cannot be interfered are solved, the growth of the crops can be interfered in advance, and the yield of the crops is improved.

Drawings

FIG. 1 is a schematic view of a first embodiment of the crop growth intervention method of the present invention;

FIG. 2 is a schematic view of a second embodiment of the crop growth intervention method of the present invention;

FIG. 3 is a schematic view of a third embodiment of the crop growth intervention method of the present invention;

FIG. 4 is a schematic view of a fourth embodiment of the crop growth intervention method of the present invention;

FIG. 5 is a schematic view of a fifth embodiment of the crop growth intervention method of the present invention;

FIG. 6 is a schematic view of a first embodiment of the crop growth intervening device of the present invention;

FIG. 7 is a schematic view of a second embodiment of the crop growth intervening device of the invention;

fig. 8 is a schematic view of an embodiment of the crop growth intervening apparatus of the present invention.

Detailed Description

The embodiment of the invention provides a crop growth intervention method, a device, equipment and a storage medium, wherein in the technical scheme of the invention, agricultural data and target meteorological data corresponding to crops to be intervened are obtained firstly; determining a growth stage of the crop, and determining a first meteorological condition corresponding to the crop under a preset fitness value according to the growth stage; evaluating the target meteorological data and the first meteorological conditions by a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops grow properly under a second meteorological condition; and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene the crops. The technical problem that the crop growth is greatly influenced by climate and cannot be interfered is solved.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

For ease of understanding, a specific flow chart of an embodiment of the present invention is described below, with reference to fig. 1, a first embodiment of a method for crop growth intervention in an embodiment of the present invention includes:

101. acquiring farming data and target meteorological data corresponding to crops to be intervened, wherein the target meteorological data is meteorological data in a certain time period;

it is to be understood that the implementation subject of the present invention may be a weather index prediction device of a farming activity, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.

In this embodiment, before carrying out agricultural weather detection, need establish basic database earlier, can use SQLServer2008 database management platform to establish as the development platform, collect data and include: collecting and sorting agricultural social and economic databases of crop layout, yield, disaster and the like; collecting and sorting main agricultural meteorological disaster indexes, and integrating a meteorological disaster index library of main crops and agricultural activities by combining the research results of predecessors. Extracting different meteorological disaster grade indexes by using a statistical method based on years of disaster data, social statistical data and the like; collecting and sorting a main agricultural weather forecast index library used by the business service, and constructing the suitability index of the agricultural weather forecast in the growth season and the key development period of main crops; and constructing an index system of a main crop growth and development dynamic diagnosis model based on a crop model, and constructing an agricultural weather information library comprising real-time weather information, weather forecast data, farmland microclimate information, agricultural weather indexes and the like.

In this embodiment, the farm data includes, but is not limited to, one of farmland climate data, crop observation data, agricultural statistical data, and geographic information data, and the weather data includes, but is not limited to, one of weather observation data and weather forecast data, where the farmland climate data includes observation elements such as farmland microclimates, soil moisture, solar radiation, and special element observation, and includes data such as temperatures, wind speeds, and relative humidities of different canopy heights of a plurality of farmland microclimate observation stations; observation data of a plurality of automatic farmland soil moisture observation stations comprise relative humidity of 0-100cm of soil depth, volume water content of soil and weight water content of soil; the 9 solar radiation data comprise total solar radiation, scattered radiation and effective radiation; the 18 characteristic elements are mainly used for observing field CO₂Concentration of, including CO₂The maximum, minimum and average values of the concentration, and the time at which the most occurred. The crop observation data comprises data of crop development period, phenological condition, disaster and soil moisture observation stations of a plurality of agricultural meteorological observation stations. The agricultural statistical data includes crop yield, area, disaster data and the like (including disaster-causing area and disaster-forming area such as typhoon, drought, flood and the like). The geographic information comprises basic data such as an administrative map, a water body, DEM elevation and the like of a certain area, and comprises an administrative region map, a first-level river, a second-level river, city marks, agricultural meteorological site distribution and basic background map display with names. Weather (meteorology)The observation data comprises observation data of a plurality of conventional automatic weather stations and a plurality of regional automatic weather stations time by time, and mainly comprises a plurality of weather elements such as air temperature, precipitation, relative humidity, sunshine duration and the like. The weather forecast data comprises forecast data based on the elements such as the average temperature, the highest temperature, the lowest temperature, the precipitation and the like of a weather station multi-mode 5km multiplied by 5km grid in a certain future time period; refined town forecast data in a certain period of time in the future comprise a plurality of meteorological elements such as air pressure, average air temperature, highest air temperature, lowest air temperature, precipitation, wind speed, relative humidity and the like of a plurality of town representative stations. The agricultural meteorological index data comprises an industrial meteorological disaster index system, including various agricultural meteorological disasters of various crops such as spring coldness, tea frost, citrus frost damage and the like; the agricultural weather forecast index system comprises agricultural forecast indexes of crops and various agricultural affair key periods, wherein the agricultural forecast indexes comprise double-season early rice sowing, double-season early rice harvesting, late rice harvesting, orange harvesting, tea harvesting, waxberry harvesting, rape harvesting and the like; an agricultural climate suitability evaluation model system comprises a suitability evaluation model of single meteorological factors such as temperature, humidity and sunshine and comprehensive climate factors.

102. Determining a growth stage of the crop to be intervened, and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

in the embodiment, the weather data is input into the agricultural meteorological condition evaluation model, so that the fitness value of the crop to be predicted under the current meteorological condition can be obtained.

In this embodiment, taking crops as tea trees as an example, the influence of weather conditions on the growth and development of tea trees, the quality of tea leaves and the yield of tea leaves is expressed as the comprehensive effect of a plurality of meteorological elements, and a fuzzy mathematical analysis method is applied to respectively establish a climate suitability model based on temperature, moisture and sunlight and a comprehensive climate suitability model, and quantitatively evaluate the influence of weather conditions on the growth of tea leaves. The quantitative index of the evaluation result of the fitness model of each meteorological element uniformly defines a domain value of 0, 1]Namely: is suitable for being 1 and is not suitable for being 0, taking a temperature suitability model as an example, the temperature influences the sprouting of tea buds and the growth speed of new shoots,even whether the tea tree can survive. The change of temperature directly influences the normal growth of the young shoots of the tea trees, the quality of the tea leaves and the yield of the tea leaves. As with other crops, tea leaves have three base point temperatures, namely an optimum temperature, a minimum temperature and a maximum temperature, at different growth stages. Under the optimum temperature, the tea leaves grow and develop rapidly and well; at the highest and lowest temperatures, tea plants cease to grow and develop, but remain viable. If the tea tree is continuously lifted or lowered, the tea tree can be damaged to different degrees until death occurs. The temperature suitability (P) for tea growth is established by fuzzy mathematical method_T) Model, the calculation formula is as follows:

wherein T is the average temperature in the growing season of the tea leaves; t is₁、T₂、T₀Respectively the lowest temperature, the highest temperature and the most suitable temperature for the growth of the tea in different time periods. When T is equal to T₁Or T ═ T₂When is, P_T0; when T is equal to T₀When is, P_T＝1。

And determining a first meteorological condition corresponding to the tea tree under a preset fitness value according to the current growth stage of the tea tree.

103. Evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data;

in this embodiment, taking a fine monitoring and early warning model of tea tree frost damage as an example, the tea tree frost damage grade index includes three contents: meteorological indexes, tea tree damage symptoms and new shoot and bud damage rates. Wherein the meteorological indexes refer to the minimum air temperature and the duration hours of each day during the growth period of the spring tea young shoots. The frost damage grades of the tea trees are divided into four grades, namely mild frost, moderate frost, severe frost and extra-severe frost. Based on the method, by applying observation data of the lowest air temperature in the county level and by means of a GIS technology, a county level tea frost damage fine monitoring model is established.

The weather index specified in the standard is the lowest hourly temperature, the lowest temperature of the actual OCF forecast data is only the daily value, and no small value exists. According to the local standard of the frost damage grade of tea trees, the daily minimum temperature of early warning meteorological indexes of the frost damage of tea leaves is determined by combining forecast products and tea production practice, and the standard is divided into four grades of mild, moderate, severe and extra-severe. And fusing OCF forecast data, establishing a frost damage refined early warning model, taking villages and towns and 5km grids as units in space, and obtaining early warning time effectiveness of 8 days.

In the embodiment, the agricultural meteorological disasters are various in types, and the occurrence time period, the damage mechanism and the indexes are different. From the disaster occurrence mechanism, agricultural drought, waterlogging and the like belong to accumulation type; rainstorm, strong wind, hail and the like belong to sudden onset; the influence caused by some disasters is dominant, and the influence can be visually judged through external morphological characteristics after the disasters occur, such as flooding, strong wind, hail and the like; some disasters are recessive, such as cold damage, heat damage, cold dew and the like, and the time of the damage symptoms is delayed. From the aspect of influencing the crop species, the crops mainly comprise field crops (rice, rape, barley and wheat and the like) and economic forest and fruit crops (tea, citrus, waxberry and the like). Taking tea, the most important economic crop in Zhejiang province as an example, quantitative monitoring, early warning and evaluation of agricultural meteorological disasters are analyzed. By means of a control test, response changes of physiological and biochemical indexes of crops to a stress environment are analyzed, disaster meteorological indexes are screened out by combining production practice, and a disaster monitoring and early warning evaluation model is constructed by means of a mathematical method. And the agricultural meteorological disaster monitoring, early warning and evaluation service is developed by combining the data of meteorological observation, weather forecast and the like.

104. Comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops to be intervened are suitable for growing under a second meteorological condition;

in this embodiment, the evaluation result is the disaster early warning level of the crop to be intervened under the second meteorological condition corresponding to the target meteorological data.

In this embodiment, taking the late rice low-temperature freezing injury in autumn as an example, the late rice low-temperature freezing injury grade index in autumn includes three contents: meteorological indexes, late rice damage symptoms and rice heading influence rate. Wherein the meteorological index refers to the minimum air temperature and the duration hours of each day during the heading and flowering period of the indica rice. The low-temperature meteorological disaster grade of late rice in autumn is divided into two levels, namely mild low-temperature freezing damage and severe low-temperature freezing damage.

The weather index specified in the standard is the lowest hourly temperature, the lowest temperature of the actual OCF forecast data is only the daily value, and no small value exists. According to the local standard of the low-temperature freezing injury grade of late rice in autumn, the lowest daily temperature of early warning meteorological indexes of the low-temperature meteorological disasters in late rice in autumn is determined by combining forecast products and late rice production practice, and the standard is divided into mild and severe. For example, when the average daily temperature is less than or equal to 22 ℃ and the duration is equal to 2 days, the freezing injury belongs to mild freezing injury; when the daily average temperature is less than or equal to 22 ℃ and the duration is more than or equal to 3 days, the freeze injury belongs to severe freeze injury.

105. And if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene crops to be intervened, wherein the meteorological disaster early warning information at least comprises the geographical position, early warning time, disaster type and early warning grade of an early warning area.

In this embodiment, it is possible to know what suitability value and disaster early warning level the weather condition of whatever is corresponding to through the calculated suitability value and disaster early warning level, and through such a correspondence system, classification of the agricultural weather index is realized, for example, the temperature suitability model and the frost damage refinement monitoring early warning model both use the temperature as a calculation basis, and when the agricultural weather index is the daily average temperature, when the suitability value of the temperature condition in the temperature suitability model is higher, and the degree in the frost damage refinement monitoring early warning model is low, the temperature condition is set to be suitable.

In the embodiment of the invention, the farming data and the target meteorological data corresponding to the crops to be intervened are obtained; determining a growth stage of the crop, and determining a first meteorological condition corresponding to the crop under a preset fitness value according to the growth stage; evaluating the target meteorological data and the first meteorological conditions by a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops are suitable for growing under a second meteorological condition; and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene the crops. The technical problems that the growth of crops is greatly influenced by climate and cannot be interfered are solved, the growth of the crops can be interfered in advance, and the yield of the crops is improved.

Referring to fig. 2, a second embodiment of the method for intervening crop growth according to the present invention comprises:

201. acquiring agricultural data and marking the agricultural data according to a preset marking rule, wherein the agricultural data comprises agricultural data or meteorological data;

in this embodiment, the collected data includes, but is not limited to, one of farmland climate data, crop observation data, agricultural statistical data, geographic information, meteorological observation data, weather forecast data, and agricultural meteorological index data, the data is obtained by observing the data mainly through some hardware devices, for example, the meteorological observation data is observed time by time through a conventional automatic weather station and a regional automatic weather station, the farmland climate data is observed through a farmland microclimate observation station and an automatic farmland soil moisture observation station, the crop observation data is observed through an agricultural weather observation station, the agricultural statistical data mainly counts the obtained crop yield, area, disaster, and the like, the weather forecast data is forecast data of elements such as average temperature, maximum temperature, minimum temperature, precipitation, and the like in 1-8 days in the future, the geographic data is a previously detected administrative map, water body, DEM elevation and other basic data.

202. Establishing a database object for the marked agricultural data;

in this embodiment, the database object includes a data table, a view, a trigger, a storage process, and the like.

203. Storing the database object according to a preset data entry format to obtain a basic database;

in this embodiment, after the data is stored according to the set entry format, the obtained data in the basic database needs to be updated, after the hardware device continuously observes and obtains new data, that is, the updated data is converted, the conversion indicates that the text data is converted into field values in the database, and the conversion is realized by automatically updating a process script, where the automatically updating process script first obtains the updated data, directionally decomposes the data according to the given data type and corresponding parameters, then loads the data into a memory, and simultaneously connects to a background database, and automatically imports the updated data in the memory into specific fields in a specific table in the background database. The automatic updating method of the database data can add the preprocessed data into the database periodically through an automatic process, so that the content of the database is continuously supplemented and corrected.

In this embodiment, the database includes, but is not limited to: (1) and (4) meteorological observation data. The observation data of 71 conventional automatic weather stations and 2259 regional automatic weather stations in the whole province comprises 35 weather elements such as air temperature, precipitation, relative humidity, sunshine duration and the like; (2) microclimate of farmland. The farmland microclimate data mainly comprises observation elements such as farmland microclimate, soil moisture, solar radiation, special total element observation and the like, and comprises data such as temperature, wind speed, relative humidity and the like of different canopy heights of 52 farmland microclimate observation stations; observation data of 31 automatic farmland soil moisture observation stations comprise the relative humidity of 0-100cm of soil depth, the volume water content of soil and the weight water content of soil; the 9 solar radiation data comprise total solar radiation, scattered radiation and effective radiation; the 18 characteristic elements are mainly observed field CO2 concentration, including maximum, minimum and average values of CO2 concentration and the time of occurrence of the maximum value; (3) and (5) observing crops. The system comprises data [ training sets ] of observation stations for crop development period, phenology, disaster and soil moisture of 13 agricultural meteorological observation stations; (4) and (5) performing agricultural statistics. Including crop yield, area, disasters (including typhoon, drought, flood and other disaster causing areas and disaster areas) in 1971-2016; (5) and (4) weather forecast. Forecasting data based on the elements such as the average temperature, the highest temperature, the lowest temperature, the precipitation and the like of the Zhejiang province gas station multi-mode 5km multiplied by 5km grid in the future 1-8 days; refining town forecast data 1-8 days in the future, wherein the town forecast data comprises 19 meteorological elements such as air pressure, average air temperature, highest air temperature, lowest air temperature, precipitation, wind speed, relative humidity and the like of 1314 representative stations of villages and towns in the whole province; (6) geographic information. Basic data such as administrative maps of Zhejiang province, city, county and towns, water body, DEM elevation and the like; the method comprises the following steps: 5 thousands of Zhejiang city and county administrative division diagrams, second-level river flows, city labels and agricultural meteorological site distribution and names are displayed as basic background diagrams, and when the display proportion is enlarged to a certain degree, the rural administrative division diagrams in a ratio of 1:10000 are displayed. DEM total province is 1:250000 spatial resolution; (7) agricultural weather indicator data. Comprises an agricultural meteorological disaster index system, which comprises 14 agricultural meteorological disasters of 8 crops such as spring cold, tea frost, citrus freeze and the like; the agricultural weather forecast index system comprises forecast indexes of 6 crops and 7 agricultural crop key periods, wherein the forecast indexes comprise double-season early rice sowing, double-season early rice harvesting, late rice harvesting, orange harvesting, tea harvesting, waxberry harvesting, rape harvesting and the like; an agricultural climate suitability evaluation model system comprises a suitability evaluation model of single meteorological factors such as temperature, humidity and sunshine and comprehensive climate factors.

204. Acquiring farming data and target meteorological data corresponding to crops to be intervened, wherein the target meteorological data is meteorological data in a certain time period;

205. determining a growth stage of the crop to be intervened, and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

206. evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data;

207. comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops to be intervened are suitable for growing under a second meteorological condition;

208. and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene crops to be intervened, wherein the meteorological disaster early warning information at least comprises the geographical position, early warning time, disaster type and early warning grade of an early warning area.

The steps 204-208 in the present embodiment are similar to the steps 101-105 in the first embodiment, and are not described herein again.

Referring to fig. 3, a third embodiment of the method for intervening crop growth according to the present invention includes:

301. acquiring farming data and historical weather data corresponding to crops to be intervened from a preset basic database;

in this embodiment, before carrying out agricultural weather detection, need establish basic database earlier, can use SQLServer2008 database management platform to establish as the development platform, and main data collection includes: collecting and sorting agricultural social and economic databases of crop layout, yield, disaster and the like; collecting and sorting main agricultural meteorological disaster indexes, and integrating a meteorological disaster index library of main crops and agricultural activities by combining the research results of predecessors. Extracting different meteorological disaster grade indexes by using a statistical method based on years of disaster data, social statistical data and the like; collecting and sorting a main agricultural weather forecast index library used by the business service, and constructing the suitability indexes of agricultural weather forecast in the main crop growth season and the key development period; and constructing an index system of a main crop growth and development dynamic diagnosis model based on a crop model, and constructing a modern agricultural meteorological information library comprising real-time meteorological information, weather forecast data, farmland microclimate information, agricultural meteorological indexes and the like.

In this embodiment, the farm affair data includes but is not limited to one of farmland climate data, crop observation data, agricultural statistical data and geographic information data, and the weather data includes but is not limited to one of weather observation data and weather forecast data, where the farmland climate data includes observation elements such as farmland microclimate, soil moisture, solar radiation, and special total element observation, and includes data such as temperatures, wind speeds, and relative humidity of different canopy heights of a plurality of farmland microclimate observation stations; observation data of a plurality of automatic farmland soil moisture observation stations comprise relative humidity of 0-100cm of soil depth, volume water content of soil and weight water content of soil; the 9 solar radiation data comprise total solar radiation, scattered radiation and effective radiation; the 18 characteristic elements are mainly used for observing field CO₂Concentration of, including CO₂The maximum, minimum and average values of the concentration, and the time at which the most occurred. The crop observation data comprises data of crop development period, phenological condition, disaster and soil moisture observation stations of a plurality of agricultural meteorological observation stations. The agricultural statistical data comprises crop yield, area and disasters (including disaster-causing areas and disaster-forming areas such as typhoons, drought, flooding and the like) in a time period. The geographic information comprises basic data such as an administrative map, a water body and DEM elevations of a certain area. The method comprises the steps of displaying an administrative division diagram, a first-level river, a second-level river, city labels and agricultural meteorological site distribution and names as basic background diagrams. The weather forecast data comprises forecast data of elements such as average temperature, highest temperature, lowest temperature, precipitation and the like in a certain time period in the future based on a weather station multi-mode 5km multiplied by 5km grid; refined town forecast data and packet in certain time period in futureThe method comprises a plurality of meteorological elements such as air pressure, average air temperature, highest air temperature, lowest air temperature, precipitation, wind speed, relative humidity and the like of a plurality of representative stations of villages and towns. The agricultural meteorological index data comprises an industrial meteorological disaster index system, including various agricultural meteorological disasters of various crops such as spring coldness, tea frost, citrus frost damage and the like; the agricultural weather forecast index system comprises agricultural forecast indexes of key periods of various farming events, such as double-season early rice sowing, double-season early rice harvesting, late rice harvesting, orange picking, tea picking, waxberry picking, rape harvesting and the like; an agricultural climate suitability evaluation model system comprises a suitability evaluation model of single meteorological factors such as temperature, humidity and sunshine and comprehensive climate factors.

For example, in this embodiment, the crop selected is early season rice, the service development period includes a seeding period and a harvesting period, and the requirements of the early season rice on the surrounding climate environment are different at different periods. The early rice in Zhejiang province is mainly planted in the middle-south part, a small amount of early rice is also planted in the north part, seeding generally starts in the late 3 th of month and ends in the middle 4 th of month, and the moderate degree of seeding is directly influenced by the condition of the air temperature, so that the average daily temperature in the meteorological factors of the temperature is selected as the agricultural meteorological index when the obtained agricultural data and weather data are consistent with the agricultural data and weather data starting in the late 3 th of month and ending in the middle 4 th of month. The selected crops are spring tea, the picking of the fresh leaves is preferably carried out before and after sunrise in the early morning under normal conditions, and the temperature is lower and the humidity is higher, so that the tenderness of the fresh leaves and the tender shoots of the new shoots is kept. In rainy days or high-temperature days, fresh leaves are not suitable for picking, otherwise, the quality of the tea leaves is influenced, so the daily rainfall is selected as a meteorological index for picking the tea leaves.

302. Inputting historical weather data into a preset weather forecast model to obtain target weather data corresponding to crops to be intervened;

in this embodiment, the step of performing weather prediction by using the weather forecast model includes: and forecasting the influence suitability of the future weather on the agriculture by combining the agricultural weather indexes. Such as: the water is reduced by more than 10mm in 24 hours in the future, and the weather forecast suitability for late rice harvesting is not suitable.

303. Determining a growth stage of the crop to be intervened, and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

304. evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data;

305. comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops to be intervened are suitable for growing under a second meteorological condition;

306. and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene crops to be intervened, wherein the meteorological disaster early warning information at least comprises the geographical position, early warning time, disaster type and early warning grade of an early warning area.

The steps 303-306 in the present embodiment are similar to the steps 102-105 in the first embodiment, and are not described herein again.

Referring to fig. 4, a fourth embodiment of the method for intervening crop growth according to the present invention includes:

401. acquiring farming data and target meteorological data corresponding to crops to be intervened, wherein the target meteorological data is meteorological data in a certain time period;

402. determining a growth stage of the crop to be intervened, and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

403. inputting the target meteorological data into a weather forecasting model to obtain a second meteorological condition corresponding to the target meteorological data;

in the embodiment, the future weather is predicted to obtain a weather prediction result, and the influence suitability of the future weather on agriculture is predicted by combining with the agricultural weather indexes. Such as: the water is reduced by more than 10mm in 24 hours in the future, and the weather forecast suitability for late rice harvesting is not suitable.

404. Comparing the second meteorological conditions with the first meteorological conditions to determine factors causing meteorological disasters, and determining early warning factors required by model construction according to the factors causing meteorological disasters;

in the embodiment, agricultural social and economic databases such as crop layout, yield, disaster and the like are collected and sorted; collecting and sorting main agricultural meteorological disaster indexes, and integrating a meteorological disaster index library of main agricultural crops and agricultural activities by combining the research results of predecessors. Based on years of disaster data, social statistical data and the like, extracting different meteorological disaster factors by using a statistical method, collecting and sorting a main agricultural weather forecast index library used by business service, and constructing suitability indexes of agricultural weather forecast in main crop growth seasons and key development periods; a main crop growth dynamic diagnosis model index system based on a crop simulation model is established, and a modern agricultural meteorological information base including real-time meteorological information, weather forecast data, farmland microclimate information, agricultural meteorological indexes and the like is established to determine an early warning factor required by model establishment.

405. Calculating the weight of the early warning factor by using a preset AHP algorithm to obtain the weight value of the early warning factor, and determining a target early warning factor according to the weight value of the early warning factor;

in this embodiment, the AHP algorithm is also called an Analytic Hierarchy Process (AHP), which is a systematic and hierarchical analysis method combining qualitative analysis and quantitative analysis. The method is characterized in that on the basis of deeply researching the essence, influencing factors, internal relations and the like of the complex decision problem, the thinking process of the decision is made to be mathematical by using less quantitative information, so that the simple decision method is provided for the complex decision problem with multiple targets, multiple criteria or no structural characteristics. Are models and methods for making decisions on complex systems that are difficult to quantify completely.

The analytic hierarchy process decomposes the problem into different component factors according to the nature of the problem and the general target to be achieved, and combines the factors according to the mutual correlation influence and membership relation among the factors in different levels to form a multi-level analytic structure model, thereby finally leading the problem to be summarized into the determination of the relative important weight value of the lowest level (scheme, measure and the like for decision making) relative to the highest level (general target) or the scheduling of the relative priority order.

Early warning grade, the frost damage of tea trees is taken as an example in this embodiment, and the tea tree frost damage grade index includes three parts: meteorological indexes, tea tree damage symptoms and new shoot and bud damage rates. Wherein the meteorological indexes refer to the minimum air temperature and the duration hours of each day during the growth period of the spring tea young shoots. The frost damage grades of the tea trees are divided into four grades, namely mild frost, moderate frost, severe frost and extra-severe frost. The weather index specified in the standard is the minimum hourly temperature, the minimum atmospheric temperature of the actual OCF forecast data is only the daily value, and no small value exists. According to the local standard of the frost damage grade of tea trees, the daily minimum temperature of early warning meteorological indexes of the frost damage of tea leaves is determined by combining forecast products and tea production practice, and the standard is divided into four grades of mild, moderate, severe and extra-severe.

406. Determining a division standard of an early warning grade, and performing a control test based on a weighted value of a target early warning factor and the division standard of the early warning grade to obtain an evaluation result of the target meteorological data;

in this embodiment, an evaluation result of the target weather data can be obtained by a threshold of the warning factor and a division standard of the warning level, a disaster warning level corresponding to the second weather condition corresponding to the target weather data is known, and the grading of the agricultural weather indexes is realized by such a corresponding relationship, for example, the temperature suitability model and the fine frost damage monitoring and warning model both use the temperature as a calculation basis, and when the agricultural weather indexes are daily average temperatures, the temperature condition is set to be suitable when the suitability value of the temperature condition in the temperature suitability model is higher and the suitability of the temperature condition in the fine frost damage monitoring and warning model is slight.

In the embodiment, taking tea trees as an example, based on biological characteristics of the tea trees, combining information such as a frost damage grade index and disaster situations, screening out a disaster-causing factor of the frost damage of the tea leaves, standardizing index data, and establishing a tea leaf frost damage evaluation index model for quantitatively evaluating the influence of a primary frost damage process on tea production.

(1) Disaster-causing factor

When the lowest temperature is less than or equal to 4 ℃ in the day, the previous day is defined as the beginning of the frost damage process; when the lowest temperature is more than or equal to 4 ℃ in the day, the frost damage process is finished, and a primary frost process is defined. Comprehensively considering the actual tea production, frost damage situations and synchronous meteorological data, and screening 4 disaster-causing factors of the frost damage of the tea, namely the lowest temperature in the process, the harmful negative accumulated temperature, the duration hours and the process cooling amplitude.

Defining the lowest air temperature in the process as the lowest air temperature value occurring in one process period.

Continuous hours: defined as the cumulative number of hours during a single process to meet a minimum hourly air temperature less than the critical air temperature (< 4 ℃).

③ harmful negative accumulated temperature (accumulated cold): defined as the accumulation of air temperatures that meet below the critical air temperature during a process. The approximate formula (see the following equation) is used to solve:

in the formula, X_{Process for producing a metal oxide}The negative accumulated temperature (accumulated cold) is harmful in the process, i is the number of days for which the process is continued, T_minThe lowest daily temperature (. degree. C.), T_mThe daily average temperature (. degree. C.), T_cIs the critical air temperature of frost injury of tea trees. T (t) is the instantaneous air temperature (. degree. C.).

The process cooling range is as follows: defined as the decrease in daily average gas temperature occurring from 48 hours before the end of a process.

(2) Normalization process

The data standardization processing is carried out on the original values of the 4 disaster factors, and the calculation formula is as follows:

in the formula, X_iA normalized value of a certain disaster-causing factor in the ith process; x is the number of_iIs an original value of a certain disaster factor ith process; x is the average value of the n processes of the corresponding disaster factors for a plurality of years; n is the total number of processes (generally not less than 30 processes).

(3) Frost damage index of tea tree

And establishing a tea frost damage index model based on 4 disaster-causing factors after standardized treatment by applying a weighted index summation method. The calculation formula is as follows:

in the formula: CI is the frost damage index of the tea tree; a is_jThe weight influence coefficient of the disaster-causing factor can be obtained by a principal component analysis method or a hierarchical analysis method and the like; x_jIs a disaster-causing factor; wherein j represents 1, 2, 3, 4 respectively: x₁The lowest temperature in the process; x₂Harmful negative accumulated temperature; x₃Duration hours; x₄The temperature reduction range of the process.

407. Comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops to be intervened are suitable for growing under a second meteorological condition;

408. and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene crops to be intervened.

The steps 401-.

Referring to fig. 5, a fifth embodiment of the method for intervening crop growth according to the present invention includes:

501. acquiring farming data and target meteorological data corresponding to crops to be intervened;

502. determining a growth stage of the crop to be intervened, and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

503. evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data;

504. comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops to be intervened are suitable for growing under a second meteorological condition;

505. if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene crops to be intervened;

506. determining the current growth and development characteristics of crops according to historical meteorological observation data and crop observation data;

in this embodiment, for example, the crops to be intervened are spring tea, the meteorological observation data includes observation data of the automatic meteorological station for each hour, and mainly includes a plurality of meteorological elements such as air temperature, precipitation, relative humidity, sunshine duration, and the like, the crop observation data includes data of the climatic observation station for climate, disaster, and soil moisture, and the current agricultural season is determined by the meteorological observation data through the data, and the seeding period or harvesting period of the crops to be intervened is determined according to the crop condition observed by the crop observation data.

507. And predicting the next growth and development characteristic of the crops according to the target meteorological data and the historical agricultural statistical data, and intensively screening at least one agricultural meteorological condition influencing the current growth and development characteristic and the next development characteristic from preset agricultural meteorological conditions to serve as an agricultural meteorological index.

In this embodiment, after the current development characteristic is determined, the next development characteristic of the crop in a short period can be further determined according to the weather forecast data of a period of time, the next development characteristic of the crop to be predicted in a longer period of time is determined according to the agricultural statistical data, the agricultural weather indexes of different times are determined in the subsequent stage, and different farming suggestions are output according to the different agricultural weather indexes.

The steps 501-505 in this embodiment are similar to the steps 101-105 in the first embodiment, and will not be described in detail here.

In the embodiment of the invention, the farming data and the target meteorological data corresponding to the crops to be intervened are obtained; determining a growth stage of the crop, and determining a first meteorological condition corresponding to the crop under a preset fitness value according to the growth stage; evaluating the target meteorological data and the first meteorological conditions by a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, comparing the evaluation result with a preset meteorological disaster early warning standard, and judging whether the crops grow properly under a second meteorological condition; and if not, generating meteorological disaster early warning information, and sending the meteorological disaster early warning information to a business unit to intervene crops. The technical problems that the growth of crops is greatly influenced by climate and the crops cannot be interfered are solved, the growth of the crops can be interfered in advance, and the yield of the crops is improved.

With reference to fig. 6, the method for intervening in crop growth according to the embodiment of the present invention is described above, and the crop growth intervening device according to the embodiment of the present invention is described below, where a first embodiment of the crop growth intervening device according to the embodiment of the present invention includes:

the system comprises an acquisition module 601, a data processing module and a data processing module, wherein the acquisition module 601 is used for acquiring farming data and target meteorological data corresponding to crops to be intervened, and the target meteorological data is meteorological data within a certain time period;

a first determining module 602, configured to determine a growth phase of a crop to be intervened, and determine, according to the growth phase, a first weather condition corresponding to the crop under a preset fitness value;

the evaluation module 603 is configured to perform evaluation processing on the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, where the evaluation result is a disaster early warning level of the crop to be intervened under a second meteorological condition corresponding to the target meteorological data;

a comparison module 604, configured to compare the evaluation result with a preset meteorological disaster early warning standard, and determine whether the crop is suitable for growth under the second meteorological condition;

a sending module 605, configured to generate weather disaster early warning information when the crop is not suitable for growth under the second weather condition, and send the weather disaster early warning information to a business unit to intervene on the crop, where the weather disaster early warning information at least includes a geographical location of an early warning area, early warning time, a disaster type, and an early warning level.

Referring to fig. 7, a second embodiment of the crop growth intervening device according to the embodiment of the present invention specifically includes:

a sending module 605, configured to generate weather disaster early warning information when the crop is not suitable for growth under the second weather condition, and send the weather disaster early warning information to a business unit to intervene on the crop, where the weather disaster early warning information at least includes a geographical location of an early warning area, early warning time, a disaster type, and an early warning level;

in this embodiment, the crop growth intervening device further includes:

the labeling module 606 is configured to acquire an agricultural data set and label agricultural data in the agricultural data set according to a preset labeling rule, where the agricultural data includes farm data or meteorological data;

a building module 607 for building a database object for the annotated agricultural data, wherein the database object includes but is not limited to one of a data table, a view, a trigger, and a storage process;

the storage module 608 is configured to store the database object according to a preset data entry format, so as to obtain a basic database.

In this embodiment, the obtaining module 601 includes:

an obtaining unit 6011, configured to obtain, from a preset basic database, pesticide data and historical weather data corresponding to crops to be intervened;

an input unit 6012, configured to input the historical weather data into a preset weather forecast model, so as to obtain target weather data corresponding to the crop to be intervened.

In this embodiment, the evaluation module 603 is specifically configured to:

inputting the target meteorological data into a preset weather forecast model to obtain a second meteorological condition corresponding to the target meteorological data;

In this embodiment, the crop growth intervening device further includes:

a second determining module 609, configured to determine a current growth and development characteristic of the crop according to the historical meteorological observation data and the crop observation data;

the predicting module 610 is used for predicting the next growth and development characteristics of the crops according to the target meteorological data and historical agricultural statistical data;

the screening module 611 is configured to collectively screen at least one agricultural weather condition affecting the current growth and development characteristic and the next growth characteristic from preset agricultural weather conditions as an agricultural weather indicator.

The crop growth intervention device in the embodiment of the present invention is described in detail in the above fig. 6 and fig. 7 from the perspective of the modular functional entity, and the crop growth intervention device in the embodiment of the present invention is described in detail in the following from the perspective of the hardware processing.

Fig. 8 is a schematic structural diagram of a crop growth intervention device 800 according to an embodiment of the present invention, where the crop growth intervention device 800 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) storing an application 833 or data 832. Storage 820 and storage media 830 may be, among other things, transient or persistent storage. The program stored in the storage medium 830 may include one or more modules (not shown), each of which may include a series of instruction operations for the crop growth intervention apparatus 800. Still further, the processor 810 may be configured to communicate with the storage medium 830 and execute a series of instruction operations in the storage medium 830 on the crop growth intervention device 800 to implement the steps of the crop growth intervention method provided by the above-described method embodiments.

The crop growth intervention device 800 may also include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input-output interfaces 860, and/or one or more operating systems 831, such as Windows service, Mac OS X, Unix, Linux, FreeBSD, and the like. It will be appreciated by those skilled in the art that the crop growth intervention equipment configuration illustrated in fig. 8 does not constitute a limitation of the crop growth intervention equipment provided herein, and may include more or fewer components than illustrated, or some components in combination, or a different arrangement of components.

The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and which may also be a volatile computer readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the above-mentioned crop growth intervention method.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity of the information (anti-counterfeiting) and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or other various media capable of storing program codes.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A crop growth intervention method, the crop growth intervention method comprising:

determining a growth stage of a crop to be intervened, and determining a first meteorological condition corresponding to the crop to be intervened under a preset fitness value according to the growth stage;

evaluating the target meteorological data and the first meteorological conditions through a preset agricultural meteorological monitoring and early warning evaluation model to obtain an evaluation result of the target meteorological data, wherein the evaluation result is a disaster early warning level of the crops to be intervened under a second meteorological condition corresponding to the target meteorological data;

2. The crop growth intervention method of claim 1, prior to the obtaining of the farming data and the target meteorological data corresponding to the crop to be intervened, comprising:

acquiring agricultural data and marking the agricultural data according to a preset marking rule, wherein the agricultural data comprises agricultural data or meteorological data;

establishing a database object for the marked agricultural data, wherein the database object comprises but is not limited to one of a data table, a view, a trigger and a storage process;

3. The crop growth intervention method of claim 2, wherein the obtaining of the farming data and the target meteorological data corresponding to the crop to be intervened comprises:

4. The crop growth intervention method of any one of claims 1-3, wherein the evaluating the target meteorological data and the first meteorological condition through a preset agricultural meteorological monitoring and warning evaluation model to obtain the evaluation result of the target meteorological data comprises:

determining a division standard of an early warning grade, and performing a control test based on the weighted value of the target early warning factor and the division standard of the early warning grade to obtain an evaluation result of the target meteorological data.

5. The crop growth intervention method of claim 1, wherein the farming data comprises farm climate data, crop observation data, agricultural statistics data, and geographic information data;

the historical weather data comprises historical observation data, historical standard weather data and historical weather observation data corresponding to the crops to be intervened;

6. The crop growth intervention method of claim 4, further comprising:

7. A crop growth intervention device, the crop growth intervention device comprising:

and the sending module is used for generating meteorological disaster early warning information when the crops grow unsuitably under the second meteorological condition and sending the meteorological disaster early warning information to a business unit to intervene the crops to be intervened, wherein the meteorological disaster early warning information at least comprises the geographical position, the early warning time, the disaster type and the early warning grade of an early warning area.

8. The crop growth intervention device of claim 7, further comprising:

9. A crop growth intervention apparatus, the crop growth intervention apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;

the at least one processor invokes the instructions in the memory to cause the crop growth intervention device to perform the crop growth intervention method of any of claims 1-6.

10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a crop growth intervention method as claimed in any one of claims 1-6.