CN115762062A

CN115762062A - Kiwi fruit orchard meteorological disaster monitoring and early warning method and device

Info

Publication number: CN115762062A
Application number: CN202211393504.5A
Authority: CN
Inventors: 王景红; 张维敏; 李化龙; 柏秦凤; 郭建平
Original assignee: Shaanxi Provincial Agricultural Remote Sensing And Economic Crops Meteorological Service Center
Current assignee: Shaanxi Provincial Agricultural Remote Sensing And Economic Crops Meteorological Service Center
Priority date: 2022-11-08
Filing date: 2022-11-08
Publication date: 2023-03-07
Anticipated expiration: 2042-11-08
Also published as: CN115762062B

Abstract

The application provides a meteorological disaster monitoring and early warning method and device for a kiwi fruit orchard, wherein predicted meteorological data in a future preset time period, which are obtained by predicting through a meteorological observation station corresponding to a target area, are obtained at intervals of preset duration, so that the predicted meteorological data in the future preset time period are obtained according to the predicted meteorological data and a kiwi fruit canopy temperature prediction model, the predicted temperature data of a target position on a kiwi fruit tree in the future preset time period are obtained according to the predicted meteorological data and the kiwi fruit target position temperature prediction model, and the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area in the future preset time period is determined according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree. The method realizes timely and accurate prediction of the kiwi fruit meteorological disaster information.

Description

Kiwi fruit orchard meteorological disaster monitoring and early warning method and device

Technical Field

The application relates to the technical field of kiwi fruit planting, in particular to a method and a device for monitoring and early warning of meteorological disasters in a kiwi fruit orchard.

Background

The Kiwi fruit growing area in the northern foot of Qinling mountain of Shaanxi province and Qinba mountain area is the largest central superior production area in the whole country, the yield and the area both account for 1/3 of the total world, the yield of the Kiwi fruit in Shaanxi province reaches 129.43 ten thousand tons in 2021 year, the planting area is 97.94 ten thousand mu, and the Kiwi fruit industry is the leading industry of local income increasing and poverty deprivation and enrichment.

With the increase of climatic events such as climate warming and extreme weather, meteorological disasters such as high-temperature heat injury, freezing injury and the like frequently occur, and the method has the characteristics of wide influence range, long duration and the like, seriously influences the yield and the fruit quality of the kiwi fruits, and causes influence on the income increase of fruit growers. If the early warning and early warning are performed on the meteorological disasters and the early warning and proper defense are achieved, adverse meteorological factors can be reasonably avoided to the maximum extent during the growth and development of the kiwi fruits, and the aims of high yield, stable yield and high quality are achieved.

At present, fruit growers often judge whether the meteorological disasters occur or not by depending on experience and the like, and the method for judging whether the meteorological disasters occur or not according to the experience is not only inaccurate, but also difficult to determine the duration of the meteorological disasters, the influence degree of the meteorological disasters and the like, so that the fruit growers are difficult to prevent the meteorological disasters. Therefore, how to timely, accurately, normatively and efficiently make the kiwi fruit meteorological disaster early warning service, solve the key technical problem that kiwi fruit disaster early warning is inaccurate, are the key of meteorological disaster prevention and reduction relief work, and are effective measures for defending and lightening meteorological disaster loss.

Disclosure of Invention

The application provides a kiwi fruit orchard meteorological disaster monitoring and early warning method and device, and solves the key technical problem that kiwi fruit disaster early warning is inaccurate.

In a first aspect, the application provides a kiwi fruit orchard meteorological disaster monitoring and early warning method, which comprises the following steps:

acquiring predicted meteorological data, which is obtained by predicting from a meteorological observation station corresponding to a target area at preset time intervals, in a future preset time period, wherein the target area contains at least one kiwi fruit orchard;

acquiring kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and a kiwi fruit canopy temperature prediction model;

according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model, acquiring predicted temperature data of a target position on a kiwi fruit tree in a future preset time period, wherein the kiwi fruit target position temperature prediction model is at least one of the following: the method comprises the following steps of (1) a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model;

according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree, disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area in a future preset time period is determined, wherein the disaster information at least comprises one of the following items: disaster grade, disaster duration, and disaster start time.

Optionally, before obtaining kiwi fruit canopy temperature data in a future preset time period according to the forecast meteorological data and kiwi fruit canopy temperature forecast model, the method further includes:

obtaining historical meteorological data of a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where a sample kiwi fruit garden is located, and acquiring canopy historical temperature and at least one of the following items, which are actually detected by the preset sampling time point in the historical time period, of a kiwi fruit tree: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branches and historical temperature of root necks;

obtaining a kiwi fruit canopy temperature prediction model according to the historical meteorological data and the canopy historical temperature;

based on the canopy historical temperature and at least one of: and obtaining a kiwi fruit target position temperature prediction model by using the historical fruit surface temperature, the historical leaf surface temperature, the historical branch temperature and the historical root neck temperature.

Optionally, the obtaining of the predicted weather data, which is predicted by the weather observation station corresponding to the target area at each preset interval time and is in a future preset time period, includes:

acquiring predicted meteorological data of a plurality of grid points in the target area in a future preset time period, which is obtained by predicting by the meteorological station at intervals of preset duration;

according to forecast meteorological data and kiwi fruit canopy temperature prediction model, obtain kiwi fruit canopy temperature data in the future preset time quantum, include:

acquiring kiwi fruit canopy temperature data in a future preset time period corresponding to each grid point according to the predicted meteorological data and kiwi fruit canopy temperature prediction model of each grid point in the target area;

according to kiwi fruit canopy temperature data and kiwi fruit target location temperature prediction model, obtain in the future preset time quantum the prediction temperature data of last target location of kiwi fruit tree, include:

acquiring predicted temperature data of the target position on the kiwi fruit tree in a future preset time period corresponding to each lattice point according to the kiwi fruit canopy temperature data corresponding to each lattice point and a kiwi fruit target position temperature prediction model;

according to the forecast temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree, disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area in the future preset time period is determined, and the method comprises the following steps:

and determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point within a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree.

Optionally, the method further includes:

obtaining a disaster prediction distribution map of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area according to the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point;

and obtaining a disaster grade prediction distribution color mark map of the target position on the kiwi fruit tree in the kiwi fruit orchard of the target area according to the actual kiwi fruit orchard distribution map in the target area, the disaster prediction distribution map of the target position on the kiwi fruit tree in the kiwi fruit orchard of the target area and the color mark corresponding to the disaster grade of the target position on the kiwi fruit tree.

Optionally, historical meteorological data detected by a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where the sample kiwi fruit orchard is located is obtained, and canopy historical temperature actually detected by the preset sampling time point in the historical time period on the kiwi fruit tree and at least one of the following items: the historical temperature of fruit face, the historical temperature of leaf surface, the historical temperature of branch and the historical temperature of root neck include:

acquiring historical meteorological data corresponding to different weather types, actually detected canopy historical temperature on the kiwi fruit tree and at least one of the following items: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branch and historical temperature of root neck;

the obtaining of the kiwi fruit orchard canopy temperature prediction model according to the historical meteorological data and the historical canopy temperature on the kiwi fruit tree comprises:

acquiring kiwi fruit orchard canopy temperature prediction models corresponding to different weather types according to historical meteorological data corresponding to different weather types and actually detected canopy historical temperatures on kiwi fruit trees;

determining a predicted weather type corresponding to the predicted weather data according to the predicted weather data;

determining a kiwi fruit canopy temperature prediction model corresponding to the predicted weather type according to the predicted weather type;

and acquiring the kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model corresponding to the predicted weather type.

Optionally, in summer, the weather type is sunny, cloudy or rainy;

in winter, the weather types are sunny days and non-sunny days, and the non-sunny days comprise cloudy days, rainy days and snowy days.

Optionally, according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relationship between the disaster grade and the temperature range of the target position on the kiwi fruit tree, determining the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future, including:

determining at least one kiwi variety actually planted in the target area;

and determining the kiwi fruit orchard disaster information of the target position of each kiwi fruit variety in the target area within a preset time period in the future according to the actual planting of each kiwi fruit variety in the target area, the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade of the target position on the kiwi fruit tree associated with each kiwi fruit variety and the temperature range.

Optionally, the kiwi fruit canopy temperature prediction model is a unitary linear regression model, the kiwi fruit surface temperature prediction model and the kiwi fruit leaf surface temperature prediction model are both exponential models, the kiwi fruit branch temperature prediction model is a unitary linear regression model, and the kiwi fruit root neck prediction model is a unitary secondary regression model.

Optionally, in summer, the kiwi fruit target position temperature prediction model includes: a kiwi fruit surface temperature prediction model and/or a kiwi fruit surface temperature prediction model;

in winter, the kiwi fruit target position temperature prediction model includes: a kiwi fruit branch temperature prediction model and/or a kiwi fruit root neck temperature prediction model.

The second aspect, this application provides a kiwi fruit orchard meteorological disaster monitoring early warning device, includes:

the acquiring module is used for acquiring predicted meteorological data, which is predicted by a meteorological observation station corresponding to a target area within a future preset time period, at intervals of preset duration, wherein the target area comprises at least one kiwi fruit orchard;

the first prediction module is used for obtaining kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model;

the second prediction module is used for acquiring predicted temperature data of the target position on the kiwi fruit tree in a future preset time period according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model, wherein the kiwi fruit target position temperature prediction model is at least one of the following: the method comprises the following steps of (1) a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model;

the determining module is used for determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree, wherein the disaster information at least comprises one of the following items: disaster grade, disaster duration, and disaster start time.

Optionally, the method further includes: a model training module;

the model training module is used for the first processing module before obtaining the kiwi fruit canopy temperature data in the future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model:

obtaining historical meteorological data of a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where a sample kiwi fruit garden is located, and acquiring canopy historical temperature and at least one of the following items, which are actually detected by the preset sampling time point in the historical time period, of a kiwi fruit tree: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branch and historical temperature of root neck;

Optionally, when the obtaining module obtains predicted weather data, which is predicted by a weather observation station corresponding to the target area at preset time intervals and is within a future preset time period, the obtaining module is specifically configured to:

acquiring predicted meteorological data of a plurality of grid points in the target area in a future preset time period, which is obtained by predicting by the meteorological observation station at preset time intervals;

the first prediction module is used for specifically acquiring the kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model:

the second prediction module is used for specifically acquiring predicted temperature data of the target position on the kiwi fruit tree in a future preset time period according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model:

the determining module is used for determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree, and is specifically used for:

Optionally, the method further includes: a processing module;

the processing module is used for obtaining a disaster prediction distribution map of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area according to the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point;

Optionally, the model training module acquires historical meteorological data detected by a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where a sample kiwi fruit orchard is located, and the historical temperature of a canopy on a kiwi fruit tree actually detected by the preset sampling time point in the historical time period and at least one of the following items: the historical temperature of the fruit surface, the historical temperature of the leaf surface, the historical temperature of the branch and the historical temperature of the root neck are specifically used for:

acquiring historical meteorological data corresponding to different weather types, actually detected canopy historical temperature on the kiwi fruit tree and at least one of the following items: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branches and historical temperature of root necks;

the model training module is used for specifically obtaining the kiwi fruit orchard canopy temperature prediction model according to the historical meteorological data and the historical canopy temperature of the kiwi fruit trees:

the first prediction module is used for specifically obtaining the kiwi fruit canopy temperature data in the future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model:

Optionally, in summer, the weather type is sunny, cloudy or rainy;

in winter, the weather types are sunny days and non-sunny days, wherein the non-sunny days comprise cloudy days, rainy days and snowy days.

Optionally, the determining module is configured to determine disaster information of the target location on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future according to the predicted temperature data of the target location on the kiwi fruit tree and the corresponding relationship between the disaster level and the temperature range of the target location on the kiwi fruit tree, and is specifically configured to:

determining at least one kiwi variety actually planted in the target area;

In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor and a memory;

the memory stores computer-executable instructions; the at least one processor executes computer-executable instructions stored in the memory to perform the method of the first aspect of the embodiments of the present application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where program instructions are stored, and when the program instructions are executed by a processor, the method according to the preset item of the first aspect of the embodiment of the present invention is implemented.

In a fifth aspect, embodiments of the present application provide a program product, where the program product includes a computer program, where the computer program is stored in a readable storage medium, and at least one processor of an electronic device may read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the electronic device to implement the method according to the first aspect of the embodiments of the present application.

The beneficial effects of this application are as follows:

the application provides a kiwi fruit orchard meteorological disaster monitoring and early warning method and device, forecast meteorological data in a future preset time period, which are obtained by forecasting through a meteorological observation station corresponding to a target area, are obtained at intervals of preset duration, so that according to the forecast meteorological data and a kiwi fruit canopy temperature forecasting model, kiwi fruit canopy temperature data in the future preset time period are obtained, then according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature forecasting model, forecast temperature data of a target position on a kiwi fruit tree in the future preset time period are obtained, and according to the forecast temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree, disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area in the future preset time period is determined. Timely and accurate prediction of kiwi fruit meteorological disaster information is achieved, so that fruit growers can take preventive measures in advance, and losses caused by meteorological disasters are reduced.

In addition, in the embodiment, the kiwi fruit canopy temperature data is predicted by predicting meteorological data, and then the predicted temperature data of the target position on the kiwi fruit tree is obtained according to the kiwi fruit canopy temperature data, so that the kiwi fruit canopy temperature data is utilized to convert the external temperature data (namely the related temperature data detected by the meteorological observation station) into the small environment inside the kiwi fruit orchard, compared with the external temperature data, the predicted temperature data of the target position on the kiwi fruit tree is more strongly correlated with the kiwi fruit canopy temperature data in the small environment inside the kiwi fruit orchard, the kiwi fruit canopy height is generally 1.8 m, and compared with the leaf surface, fruit branch and root neck part, the correlation between the kiwi fruit canopy temperature and the external temperature is stronger, therefore, in the embodiment, the kiwi fruit canopy temperature is predicted by using the kiwi fruit canopy temperature prediction model and the predicted meteorological data, and the kiwi fruit canopy temperature prediction model and the kiwi fruit canopy temperature are obtained according to the kiwi fruit canopy temperature prediction model and the kiwi fruit tree canopy temperature, so that the predicted temperature data of the target position on the kiwi fruit tree is more accurate.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a kiwi fruit orchard meteorological disaster monitoring and early warning method according to an embodiment of the application;

FIG. 2 is a flowchart of a method for obtaining a temperature prediction model according to an embodiment of the present disclosure;

fig. 3 is a graph illustrating a relationship between a kiwi fruit surface temperature and a kiwi fruit canopy temperature according to an embodiment of the present application;

fig. 4 is a graph illustrating a relationship between a kiwi fruit leaf surface temperature and a kiwi fruit canopy temperature according to an embodiment of the present application;

fig. 5 is a flowchart of a kiwi fruit orchard meteorological disaster monitoring and early warning method according to another embodiment of the present application;

fig. 6 is a schematic structural diagram of a kiwi fruit orchard meteorological disaster monitoring and early warning device according to an embodiment of the application;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application are clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Fig. 1 is a flowchart of a method for monitoring and warning meteorological disasters in a kiwi fruit orchard according to an embodiment of the present application, where an execution subject of the method according to the present embodiment may be, for example, a server, as shown in fig. 1, the method according to the present embodiment includes:

s101, acquiring predicted meteorological data, which are obtained by predicting meteorological stations corresponding to a target area, in a future preset time period at preset time intervals.

Wherein, the target area contains at least one kiwi fruit orchard.

Specifically, the target area is illustrated by taking the Zhou-county of xi 'an city and the Mei-county of Bao Ji city in Shaanxi province as examples, wherein the Zhou-county of xi' an city and the Mei-county of Bao Ji city are kiwi fruit planting places.

For the week to county of the city of xi' an, the predicted weather data in the future preset time period predicted by the weather observation station in the week to county is acquired from the weather bureau, for example, the current time is 20 minutes at 17 days at 9 months and 20 days at 2022 years, the predicted weather data in the preset time period after 20 minutes at 17 days at 9 months and 20 days at 2022 years can be acquired, for example, the predicted weather data including the remaining time of the day at 20 months and the full time at 15 days in the future at 2022 years is acquired, or the predicted weather data at any one or more of the remaining time of the day at 9 months and 20 days at 2022 years and the time at 15 days in the future is selectively acquired.

The meteorological data is changed in real time, and therefore, the predicted meteorological data in a future preset time period needs to be acquired periodically, for example, every half hour, or every 1 hour.

And for the Mei county of the Baoji city, acquiring predicted meteorological data, which is obtained by predicting from the Mei county meteorological observation station of the Baoji city, in a future preset time period at preset time intervals.

S102, acquiring kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model.

Specifically, the kiwi fruit canopy temperature prediction model is a model of correlation between weather temperature data and kiwi fruit canopy temperature.

And inputting the obtained predicted meteorological data at one and/or more moments in a future preset time period into the kiwi fruit canopy temperature prediction model to obtain corresponding kiwi fruit canopy temperature data.

For example, when the current time is 2022 years, 9 months, 20 days and 8 days, the obtained predicted meteorological data of the 20 days left time of 2022 years, 9 months and 20 days and the whole time within 15 days in the future are input into the kiwi fruit canopy temperature prediction model, and the kiwi fruit canopy temperature data of the 20 days left time of 2022 years, 9 months and 20 days and the whole time within 15 days in the future are obtained.

Wherein, the inspector can select a specific time point as required to obtain the kiwi fruit canopy temperature data at the specific time point, for example, when the current time is 2022 years, 9 months, 20 days and 8 days, at this time, if the inspector wants to know the kiwi fruit canopy temperature at 2022 years, 9 months, 22 days and 8 days, the obtained temperature data in the predicted meteorological data at 2022 years, 9 months, 22 days and 8 days is input into the kiwi fruit canopy temperature prediction model, and the kiwi fruit canopy temperature at 2022 years, 9 months, 22 days and 8 days is obtained.

When the predicted meteorological data in the future preset time period are updated, the updated predicted meteorological data in the future preset time period are input into the kiwi fruit canopy temperature prediction model, corresponding kiwi fruit canopy temperature data are obtained, and therefore the obtained kiwi fruit canopy temperature data are updated.

By adopting the method, the canopy temperature of the kiwi fruits planted in Zhou Zhi county and the canopy temperature of the kiwi fruits planted in Mei county are obtained respectively.

S103, acquiring predicted temperature data of the target position on the kiwi fruit tree in a future preset time period according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model.

The kiwi fruit target position temperature prediction model is at least one of the following models: the system comprises a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model.

Specifically, the kiwi fruit surface temperature prediction model, the kiwi fruit leaf surface temperature prediction model, the kiwi fruit branch temperature prediction model and the kiwi fruit root neck temperature prediction model are relation models of kiwi fruit surface temperature, kiwi fruit leaf surface temperature, kiwi fruit branch temperature, kiwi fruit root neck temperature and kiwi fruit canopy temperature respectively.

The explanation is given by taking the predicted temperature data of the kiwi fruit as an example:

and respectively inputting the predicted kiwi fruit canopy temperature data of the 9-20-day remaining time in 2022 and the whole time in 15 days in the future into a kiwi fruit surface temperature prediction model to obtain the predicted kiwi fruit surface temperature data of the 9-20-day remaining time in 2022 and the whole time in 15 days in the future.

When monitoring personnel want to check the predicted temperature data of the kiwi fruit surface at any time point of 9, 22 and 22 days in 2022 years, the kiwi fruit canopy temperature data at the moment closest to the time point can be taken as the kiwi fruit canopy temperature data at the time point. For example, if the predicted temperature data of the kiwi fruit canopy at the integral point of 22 days at 9 and 9 months in 2022 is obtained, and the monitoring personnel want to check the predicted temperature data of the kiwi fruit surface at 25 minutes at 11 days at 22 days at 9 and 22 months in 2022, the predicted temperature data of the kiwi fruit surface at 25 minutes at 11 days at 22 days at 9 and 22 months at 2022 is selected to obtain the predicted temperature data of the kiwi fruit surface at 25 minutes at 11 days at 9 and 22 months at 2022.

By adopting the method, at least one of the following kiwi fruit orchards in Zhou Zhi county and Mei county is obtained respectively: kiwi fruit surface temperature, kiwi fruit leaf surface temperature, kiwi fruit branch temperature and kiwi fruit root neck temperature.

In summer, generally in 5-9 months, branches and leaves of the kiwi fruit grow out, and the kiwi fruit needs to monitor the fruit surface temperature and the leaf surface temperature of the kiwi fruit after fruiting, so that the kiwi fruit surface temperature prediction model and the kiwi fruit leaf surface temperature prediction model are selected as the kiwi fruit target position temperature prediction model in summer.

In winter, generally, the branches and leaves of the kiwi fruit fall off in 11 months to 2 months next year, only branches and roots and necks are generally reserved, and at the moment, the temperature of the branches and the temperature of the roots and necks of the kiwi fruit need to be monitored, so that the kiwi fruit branch temperature prediction model and the kiwi fruit root and neck temperature prediction model are selected as the kiwi fruit target position temperature prediction model in winter.

And S104, determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree.

Wherein the disaster information at least comprises one of the following items: disaster grade, disaster duration, and disaster start time.

Specifically, the different positions of kiwi fruit have different temperature bearing capacities, and therefore, the temperatures of the different positions of kiwi fruit correspond to the corresponding relationship between the disaster level and the temperature range. Wherein, tables 1-4 are the corresponding relationship between the disaster grade and the temperature range corresponding to the kiwi fruit leaf surface, kiwi fruit surface, kiwi fruit branch and kiwi fruit root and neck respectively. Table 3 shows the correspondence between the disaster level and the temperature range corresponding to the kiwi fruit branch temperature in the two kiwi fruit varieties of emerald green and Xu Xiang.

TABLE 1 correspondence between disaster level and temperature range for leaf surface temperature (LT)

Temperature range	Color of legend	Disaster rating
			LT≤30℃	Green colour	Is suitable for
30℃～39℃	Yellow colour	Mild heat damage
			39℃～45℃	Red colour	Moderate heat damage
LT＞45℃	Brown colour	Severe heat damage

TABLE 2 correspondence between disaster level and temperature range for fruit surface temperature (NT 1)

Temperature range	Color of legend	Disaster rating
			NT1≤47℃	Green colour	Is suitable for
47℃～49℃	Yellow colour	Light sunscald
			49℃～51℃	Red colour	Moderate sunburn
NT1＞51℃	Brown colour	Severe sunburn

TABLE 3 correlation between disaster grade and temperature range for branch temperature (NT 2)

TABLE 4 correspondence between disaster grade and temperature range for root Neck Temperature (NTs)

Temperature range	Color of legend	Disaster rating
			NTs≥-8℃	Green colour	Level 0
-11℃～-8℃	Light green	Level 1
			-13℃～-11℃	Yellow colour	Stage 2
-14℃～-13℃	Deep yellow	Grade 3
			-50℃～-14℃	Red colour	4 stage
NTs＞-15℃	Brown colour	Grade 5

Here, the disaster information of the fruit surface of kiwifruit is predicted as an example.

After the predicted temperature data of the kiwi fruit surfaces at the integral point moment in 22 days in 9 and 22 months in 2022 are obtained, the temperature interval of the predicted temperature data of each kiwi fruit surface in the corresponding relation between the disaster grade and the temperature range of each kiwi fruit surface is determined, and therefore the disaster grade corresponding to the predicted temperature data of each kiwi fruit surface is determined according to the disaster grade corresponding to the temperature interval.

Wherein, to calamity start time, because the prediction meteorological data of the future within the preset time quantum that acquires, consequently, the time point of the forecast temperature of kiwi fruit face that obtains at every turn is definite to can confirm that the kiwi fruit face reaches the start time of the calamity grade that this kiwi fruit face's forecast temperature corresponds, and calamity duration, thereby confirm the calamity information that the calamity temperature data of kiwi fruit face corresponds.

For example, in summer, the weather station predicts that the predicted temperature data of the kiwi fruit surface at 12 days at 7 months, 21 months and 12 months in 2022 is 46 ℃, the predicted temperature data of the kiwi fruit surface at 13 days at 7 months, 21 months and 21 months in 2022 is 47 ℃, which indicates that the temperature grade of the kiwi fruit surface is light sunscald at 13 days at 7 months, 21 months and 7 months in 2022;

the predicted temperature data of the kiwi fruit surfaces at 14 days 7, 21 and 7 of 2022, which is predicted by the meteorological station at 12 days 7, 19 and 7 of 2022, is 47.9 ℃, which indicates that the kiwi fruit surfaces are still lightly sunscald at 14 days 21 and 7 of 2022;

the predicted temperature data of the kiwi fruit surfaces at 2022, 7, 21, 15 and 7, which are predicted by the meteorological station at 2022, 7, 19, 12 and is 48.6 ℃, respectively, show that the kiwi fruit surfaces are still lightly sunscald at 2022, 7, 21, 15 and 7;

the predicted temperature data of the kiwi fruit surfaces at 16 days at 7 months, 19 months and 12 days in 2022 are respectively 49.2 ℃ which is predicted by the meteorological station at 7 months, 19 months and 16 days in 2022, which shows that the temperature grade of the kiwi fruit surfaces is moderate sunburn at 16 days at 7 months, 19 days in 2022, the temperature grade of the kiwi fruit surfaces is mild sunburn, the starting time of the mild sunburn is 13 days at 7 months, 19 days in 2022, 7 months, 19 days and 16 days in 2022, the time of the mild sunburn of the kiwi fruit surfaces is 3 hours, and the starting time of the mild sunburn of the kiwi fruit surfaces is 16 days at 7 months, 19 days and 7 months in 2022.

Wherein, because the interval is preserved when predicting meteorological forecast data and is carried out the renewal, consequently, when predicting meteorological forecast data and update, the kiwi fruit face temperature that the prediction obtained also can be updated.

After the disaster information is obtained, the disaster information can be broadcasted in the weather forecast of the target area, a short message containing the disaster information can be sent to fruit growers, the disaster information can be broadcasted in the kiwi fruit orchard, and the fruit growers can be informed timely, so that the fruit growers can take preventive measures in advance. The present embodiment does not limit the way of notifying fruit growers.

Optionally, the kiwi fruit has a plurality of varieties, and the same target position on different varieties has different capacities for bearing the temperature, so that the disaster grades of the same target position on different varieties may be different at the same temperature. Thus, one implementation of S104 includes S1041 and S1042:

s1041, determining at least one kiwi fruit variety actually planted in the target area.

Specifically, the variety of kiwifruit planted in each kiwifruit orchard in the target area is determined.

S1042, according to each kiwi variety actually planted in the target area, the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade of the target position on the kiwi fruit tree associated with each kiwi variety and the temperature range, determining the kiwi fruit orchard disaster information of the target position of each kiwi variety in the target area.

Specifically, the temperature of kiwi branches is taken as an example for explanation, as shown in table 3, table 3 shows disaster grades corresponding to different varieties of kiwi branch parts at the same temperature, when the disaster grade is determined, the variety of kiwi is determined, and then disaster information of the kiwi branch part is determined.

In the embodiment, the predicted meteorological data in the future preset time period, which is obtained by predicting the meteorological observation station corresponding to the target area, is obtained every preset time period, so that the kiwi fruit canopy temperature data in the future preset time period is obtained according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model, the predicted temperature data of the target position on the kiwi fruit tree in the future preset time period is obtained according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model, and the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area in the future preset time period is determined according to the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree. Timely and accurate prediction of kiwi fruit meteorological disaster information is achieved, so that fruit growers can make precautionary measures in advance, and loss caused by meteorological disasters is reduced.

In addition, in the embodiment, the kiwi fruit canopy temperature data is predicted by the predicted meteorological data, and then the predicted temperature data of the target position on the kiwi fruit tree is obtained according to the kiwi fruit canopy temperature data, so that the kiwi fruit canopy temperature data is utilized to convert the external temperature data (namely, the related temperature data detected by the meteorological observation station) into the small environment inside the kiwi fruit orchard, and in the small environment inside the kiwi fruit orchard, compared with the external temperature data, the predicted temperature data of the target position on the kiwi fruit tree is more strongly correlated with the kiwi fruit canopy temperature data, and the kiwi fruit canopy height is generally 1.8 m, and compared with the leaf surface, fruit branch and root neck parts, the correlation between the kiwi fruit canopy temperature and the external temperature is stronger, therefore, in the embodiment, the kiwi fruit canopy temperature is obtained by the kiwi fruit canopy temperature prediction model and the predicted meteorological data, and the kiwi fruit canopy temperature prediction model is obtained according to the kiwi fruit canopy temperature, so that the predicted temperature data of the target position on the kiwi fruit tree is more accurate.

Fig. 2 is a flowchart of a temperature prediction model obtaining method according to an embodiment of the present disclosure. The execution main body of the temperature prediction model obtaining method shown in fig. 2 may be the same as the execution main body of the kiwi fruit orchard meteorological disaster monitoring and early warning method shown in fig. 1, or the execution main body of the temperature prediction model obtaining method shown in fig. 2 is different from the execution main body of the kiwi fruit orchard meteorological disaster monitoring and early warning method shown in fig. 1.

As shown in fig. 2, the method shown in this embodiment includes:

s201, obtaining historical meteorological data detected by a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where a sample kiwi fruit orchard is located, and acquiring canopy historical temperature actually detected by the preset sampling time point in the historical time period on a kiwi fruit tree and at least one of the following items: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branches and historical temperature of root necks.

Specifically, the sample kiwi fruit orchard can be any kiwi fruit orchard in Zhou-Zhi-Xian or Mei-Xian, and can also be kiwi fruit orchards in other regions.

For example, weather data (i.e., historical weather data) detected by a weather observation station in the week to county at 8, 10, 12, 14, 16, and 17 hours per day, and canopy temperature (canopy historical temperature), fruit surface temperature (i.e., fruit surface historical temperature), and leaf surface temperature (i.e., leaf surface historical temperature) detected at 8, 10, 12, 14, 16, and 17 hours per day within 5-9 months per year in 2020 and 2021 years are acquired.

Meteorological data (i.e., historical meteorological data) detected by a meteorological station in Zhou-county at 0, 2, 4, 6, 8, 18, 20, and 22 hours a day, and canopy temperature (canopy historical temperature), branch temperature (i.e., branch historical temperature), and root neck temperature (i.e., root neck historical temperature) detected at 0, 2, 4, 6, 8, 18, 20, and 22 hours a day, within 12-2 months a year 2019 and 12-2 months a year 2020, are acquired.

The number of sampling time points and the interval duration between adjacent time sampling points are not limited, and only historical meteorological data, canopy historical temperature and at least one of the following are needed: and (4) enabling the historical temperature of the fruit surface, the historical temperature of the leaf surface, the historical temperature of branches and the historical temperature of root necks to be in one-to-one correspondence with each other. Namely historical meteorological data at 9, month, 1 and 8 in 2020, and canopy historical temperature at 9, month, 1 and 8 in 2020, and at least one of: the historical temperature of the fruit surface, the historical temperature of the leaf surface, the historical temperature of the branches and the historical temperature of the root necks are in one-to-one correspondence.

The kiwi fruits grow new leaves in about 5 months per year, and the kiwi fruits grow new leaves in about 6 months per year, so that in about 5 months to 9 months per year, historical meteorological data, canopy historical temperature, fruit surface historical temperature and leaf surface historical temperature in the period of 5 months to 9 months are obtained by adopting the method, and a kiwi fruit canopy temperature prediction model, a kiwi fruit surface temperature prediction model and a kiwi fruit leaf surface temperature prediction model corresponding to summer are obtained;

in about 10 months of each year, no kiwi fruit is generally available on a kiwi fruit tree, and leaves can fall off when the kiwi fruit tree enters winter, so that historical meteorological data, canopy historical temperature, branch historical temperature and root neck historical temperature in the period from 11 months to 2 months of the next year are obtained by the method, and a kiwi fruit canopy temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model corresponding to the winter are obtained.

S202, obtaining a kiwi fruit canopy temperature prediction model according to historical meteorological data and canopy historical temperature.

Specifically, historical meteorological data are used as independent variables, canopy historical temperatures are used as dependent variables, multiple groups of historical meteorological data and canopy historical temperatures are input into an initial kiwi fruit canopy temperature prediction model, and the kiwi fruit canopy temperature prediction model is obtained through training. Wherein, the kiwi fruit canopy temperature prediction model is a unary linear regression model.

Wherein, the kiwi fruit canopy temperature prediction model in summer is: ta =0.8817X +3.7152 ² ＝0.9098。

Wherein X is forecast weather data directly forecast weather data of the day detected by the weather observation station or the weather data of the day detected by the weather observation station.

R ² Indicates the reliability, R, of the kiwi fruit canopy temperature prediction model ² The closer the absolute value of the temperature value is to 1, the higher the reliability of the kiwi fruit canopy temperature prediction model is, and the closer the kiwi fruit canopy temperature obtained by prediction according to the kiwi fruit canopy temperature prediction model is to the real fruit surface temperature.

0.8817 and 3.7152 are parameters obtained by training an initial kiwi fruit canopy temperature prediction model from multiple sets of historical meteorological data and canopy historical temperatures.

In winter, the kiwi fruit canopy temperature prediction model is as follows: ta =1.0222X +0.369 ² ＝0.9148。

S203, according to the historical temperature of the canopy and at least one of the following items: and obtaining a kiwi fruit target position temperature prediction model by using the historical fruit surface temperature, the historical leaf surface temperature, the historical branch temperature and the historical root neck temperature.

Specifically, for example, the historical canopy temperature can be used as an independent variable, the historical fruit surface temperature can be used as a dependent variable, the historical canopy temperature and the historical fruit surface temperature can be input into an initial kiwi fruit surface temperature prediction model, and the kiwi fruit surface temperature prediction model is obtained through training, wherein the kiwi fruit surface temperature prediction model is an index model, and the kiwi fruit surface temperature prediction model is exemplarily: NT1=0.2486Ta ^1.4906 Wherein Ta is the temperature of the canopy at 180cm above the kiwi fruit tree. 0.2486 and 1.4906 are parameters obtained by training an initial kiwi fruit surface temperature prediction model according to the canopy historical temperature and the fruit surface historical temperature.

The relationship graph between the kiwi fruit surface temperature and the kiwi fruit canopy temperature shown by the kiwi fruit surface temperature prediction model is shown in fig. 3.

The method used for obtaining the kiwi fruit surface temperature prediction model is adopted to obtain the kiwi fruit surface temperature prediction model, the kiwi fruit branch temperature prediction model and the kiwi fruit root neck temperature prediction model, which are not repeated herein.

Wherein, kiwi fruit blade surface temperature prediction model is the index model, and is exemplary, kiwi fruit blade surface temperature prediction model is:LT＝0.725Ta ^1.1382 . Wherein, 0.725 and 1.1382 are parameters obtained by training an initial kiwi fruit leaf surface temperature prediction model according to the canopy historical temperature and the leaf surface historical temperature.

The kiwi fruit leaf surface temperature prediction model shows a graph of the relationship between kiwi fruit leaf surface temperature and kiwi fruit canopy temperature, which is shown in fig. 4.

The kiwi fruit branch temperature prediction model is an index model, and exemplarily, the kiwi fruit branch temperature prediction model is as follows: NT2=1.0311Ta-2.2821. 1.0311 and 2.2821 are parameters obtained by training an initial kiwi fruit branch temperature prediction model according to the canopy historical temperature and the branch historical temperature.

Wherein, kiwi fruit root neck temperature prediction model is the index model, and is exemplary, kiwi fruit root neck temperature prediction model is: NTS =1.6799Ta +3.48. 1.6799 and 3.48 are parameters obtained by training an initial kiwi fruit root neck temperature prediction model according to the canopy historical temperature and the root neck historical temperature.

Optionally, in order to improve the accuracy of the disaster information prediction, a kiwi fruit canopy temperature prediction model, a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model corresponding to different weather types can be obtained, namely, one possible implementation manner of S201 is as follows:

obtaining historical meteorological data, actually detecting the historical temperature of the canopy on the kiwi fruit tree, and at least one of the following items: fruit face historical temperature, leaf surface historical temperature, branch historical temperature and root neck historical temperature classify according to different weather types, acquire historical meteorological data, the actual detection that correspond with different weather types the canopy historical temperature and at least one as follows on the kiwi fruit tree: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branches and historical temperature of root necks.

Correspondingly, obtaining a kiwi fruit orchard canopy temperature prediction model corresponding to different weather types and at least one of the following: the system comprises a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model. The above embodiments can be referred to in the model obtaining method, and details are not repeated here.

Optionally, in summer, the weather types may be classified into sunny days, cloudy days or rainy days, and in winter, the weather types may be classified into sunny days and non-sunny days, and the non-sunny days may include cloudy days, rainy days and snowy days. Of course, the division of the weather type can be selected according to actual needs, and the application does not limit the division.

Therefore, when the disaster information of the target position on the kiwi fruit tree is predicted, the weather type is determined according to the predicted meteorological data, so that a kiwi fruit orchard canopy temperature prediction model corresponding to the weather type and at least one of the following are selected: the kiwi fruit surface temperature prediction model, the kiwi fruit leaf surface temperature prediction model, the kiwi fruit branch temperature prediction model and the kiwi fruit root neck temperature prediction model improve the prediction accuracy.

Fig. 5 is a flowchart of a method for monitoring and warning meteorological disasters in a kiwi fruit orchard according to another embodiment of the present application, and as shown in fig. 5, the method shown in this embodiment includes:

s501, obtaining predicted meteorological data of a plurality of grid points in a target area in a future preset time period, which is obtained by prediction of a meteorological station at intervals of preset time.

Specifically, the forecast meteorological data of grid points of the region where the Zhou-county is located is obtained from the meteorological bureau, wherein the grid points are obtained by grid-pointing the region where the Zhou-county is located, namely the region where the Zhou-county is located is divided into a plurality of grids, and the center of each grid is a grid point.

S502, obtaining kiwi fruit canopy temperature data in a future preset time period corresponding to each grid point according to the predicted meteorological data and kiwi fruit canopy temperature prediction model of each grid point in the target area.

Specifically, the predicted meteorological data of each grid point is input into the kiwi fruit canopy temperature prediction model, and kiwi fruit canopy temperature data corresponding to each grid point is obtained, and S202 may be referred to in the specific method, which is not described herein again.

S503, obtaining the predicted temperature data of the target position on the kiwi fruit tree in the future preset time period corresponding to each grid point according to the kiwi fruit canopy temperature data corresponding to each grid point and the kiwi fruit target position temperature prediction model.

Specifically, the kiwi fruit canopy temperature data corresponding to each lattice point is input into at least one of the following items: obtaining predicted temperature data of corresponding positions on the kiwi fruit tree from a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model, wherein S203 can be referred to in the concrete method, and details are not repeated here.

S504, determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each grid point in a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each grid point and the corresponding relation between the disaster grade and the temperature range of the target position on the kiwi fruit tree.

Specifically, disaster information of the target position on the kiwi fruit tree in the grid where the lattice is located is obtained according to the predicted temperature data of the target position on the kiwi fruit tree in each lattice, where reference may be made to S204 for a specific method, which is not described herein again.

It should be noted that, the region where the zhou-zhi county is located includes a region where no kiwi fruit is planted, so when the forecasted meteorological data on the grid point is obtained from the meteorological bureau, the forecasted meteorological data on the grid point is also obtained for the region where no kiwi fruit is planted, and the disaster information of the region where no kiwi fruit is planted can be preset disaster information, wherein the disaster level in the preset disaster information is 0 level, that is, the level of damage to the target position on the kiwi fruit tree is not caused.

The method comprises the steps of obtaining predicted meteorological data on each grid point in a target area, equivalently carrying out grid point processing on the target area, dividing the target area into a plurality of small grids, reducing errors of the predicted meteorological data on the area where the grids are located, and considering factors such as terrain, climate types and surrounding environments corresponding to the grid points when the meteorological data on the grid points are obtained in the prior art, so that the factors such as the terrain, the climate types and the surrounding environments corresponding to the grid points are considered, the predicted meteorological data of each grid point are further improved, and the accuracy of disaster information prediction of the target position on a kiwi fruit tree in a kiwi fruit orchard corresponding to the grid points is further improved.

Optionally, in order to more intuitively show disaster information of a target position on a kiwi fruit tree in a kiwi fruit orchard in a target area, on the basis of the above embodiment, the method of the present application may further include:

and S505, obtaining a disaster prediction distribution diagram of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area according to the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point.

Specifically, the number of grid points is determined, then grid point maps corresponding to a target area are manufactured by utilizing buffer software, the disaster level of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each grid point corresponds to the grid corresponding to the grid point on the grid point map obtained by grid-pointing the target area, and the duration and the starting time of the disaster of each grid point are associated with the grid corresponding to the grid point, so that the disaster prediction distribution map of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area is obtained.

S506, obtaining a disaster grade prediction distribution color scale map of the target position on the kiwi fruit tree in the kiwi fruit orchard of the target area according to the actual kiwi fruit orchard distribution map of the target area, the disaster prediction distribution map of the target position on the kiwi fruit tree in the kiwi fruit orchard of the target area and a color scale corresponding to the disaster grade of the target position on the kiwi fruit tree.

Specifically, the actual kiwi fruit orchard distribution map in the target area and the disaster prediction distribution map of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area are overlaid, so that disaster information corresponding to each kiwi fruit orchard in the target area can be determined. As shown in tables 1 to 4, the color scales corresponding to different disaster levels at different positions on the kiwi fruit trees are different, so that in order to more visually display the disaster levels corresponding to the kiwi fruit orchards at different positions in the target area, the color scales corresponding to different disaster levels at different positions on the kiwi fruit trees can be attached to the superimposed distribution diagram, and the disaster level prediction distribution color scale map of the target position on the kiwi fruit trees in the kiwi fruit orchard of the target area is obtained.

Fig. 6 is kiwi fruit orchard meteorological disaster monitoring early warning device's that this application embodiment provided structure schematic diagram, as shown in fig. 6, kiwi fruit orchard meteorological disaster monitoring early warning device of this embodiment includes: an acquisition module 601, a first prediction module 602, a second prediction module 603, and a determination module 604. Optionally, kiwi fruit orchard meteorological disaster monitoring early warning device can also include: a model training module 605. Optionally, kiwi fruit orchard meteorological disaster monitoring and early warning device can also include: a processing module 606.

The obtaining module 601 is configured to obtain predicted weather data within a future preset time period, which is obtained by predicting weather corresponding to a target area, every preset time interval, where the target area includes at least one kiwi fruit orchard;

the first prediction module 602 is configured to obtain kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model;

the second prediction module 603 is configured to obtain predicted temperature data of a target position on a kiwi fruit tree within a future preset time period according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model, where the kiwi fruit target position temperature prediction model is at least one of: the method comprises the following steps of (1) a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model;

a determining module 604, configured to determine disaster information of a target location on a kiwi fruit tree in a kiwi fruit orchard in a target area within a preset time period in the future according to predicted temperature data of the target location on the kiwi fruit tree and a corresponding relationship between a disaster level and a temperature range of the target location on the kiwi fruit tree, where the disaster information at least includes one of: disaster grade, disaster duration, and disaster start time.

Optionally, before the first processing module 602 obtains the kiwi fruit canopy temperature data in the future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model, the model training module 605 is configured to:

acquiring historical meteorological data of a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where a sample kiwi fruit garden is located, and acquiring canopy historical temperature actually detected by the preset sampling time point in the historical time period on kiwi fruit trees and at least one of the following items: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branch and historical temperature of root neck;

obtaining a kiwi fruit canopy temperature prediction model according to historical meteorological data and canopy historical temperature;

Optionally, when the obtaining module 601 obtains predicted weather data, which is predicted by a weather observation station corresponding to the target area at preset time intervals and is within a future preset time period, the obtaining module is specifically configured to:

the first prediction module 602 is specifically configured to, when obtaining kiwi fruit canopy temperature data within a future preset time period according to the predicted meteorological data and kiwi fruit canopy temperature prediction model:

acquiring kiwi fruit canopy temperature data within a future preset time period corresponding to each grid point according to the predicted meteorological data and kiwi fruit canopy temperature prediction model of each grid point in the target area;

the second prediction module 603 is specifically configured to, when obtaining the predicted temperature data of the target position on the kiwi fruit tree within the future preset time period according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model:

acquiring predicted temperature data of a target position on a kiwi fruit tree in a future preset time period corresponding to each grid point according to the kiwi fruit canopy temperature data corresponding to each grid point and a kiwi fruit target position temperature prediction model;

the determining module 604 is specifically configured to, when determining disaster information of a target position on a kiwi fruit tree in a kiwi fruit orchard in a target area within a preset time period in the future according to predicted temperature data of the target position on the kiwi fruit tree and a corresponding relationship between a disaster level and a temperature range of the target position on the kiwi fruit tree:

Optionally, the processing module 606 is configured to obtain a disaster prediction distribution map of a target position on a kiwi fruit tree in a kiwi fruit orchard in a target area according to disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each grid point;

Optionally, the model training module 605 acquires historical meteorological data detected by a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where the sample kiwi fruit garden is located, and a canopy historical temperature actually detected by the preset sampling time point in the historical time period on the kiwi fruit tree, and at least one of the following: the historical temperature of the fruit surface, the historical temperature of the leaf surface, the historical temperature of the branch and the historical temperature of the root neck are specifically used for:

acquiring historical meteorological data corresponding to different weather types, actually detected canopy historical temperature on a kiwi fruit tree and at least one of the following items: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branch and historical temperature of root neck;

model training module 605 is specifically used for, when obtaining kiwi fruit orchard canopy temperature prediction model according to historical meteorological data and canopy historical temperature on the kiwi fruit tree:

acquiring a kiwi fruit orchard canopy temperature prediction model corresponding to different weather types according to historical meteorological data corresponding to different weather types and actually detected canopy historical temperatures on kiwi fruit trees;

Optionally, in summer, the weather type is sunny, cloudy or rainy;

Optionally, the determining module 604 is configured to, when determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree and a corresponding relationship between the disaster level and the temperature range of the target position on the kiwi fruit tree, specifically:

determining at least one kiwi variety actually planted in the target area;

and determining the kiwi fruit orchard disaster information of the target position of each kiwi fruit variety in the target area within a preset time period in the future according to the actually planted kiwi fruit variety in the target area, the predicted temperature data of the target position on the kiwi fruit tree and the corresponding relation between the disaster grade of the target position on the kiwi fruit tree associated with each kiwi fruit variety and the temperature range.

in winter, the kiwi fruit target position temperature prediction model comprises: a kiwi fruit branch temperature prediction model and/or a kiwi fruit root neck temperature prediction model.

Kiwi fruit orchard meteorological disaster monitoring early warning device more than this embodiment can be used for carrying out the technical scheme in above-mentioned each method embodiment, and its theory of realization and technological effect are similar, and it is no longer repeated here.

Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application, where the electronic device is, for example, a server, and as shown in fig. 7, the electronic device according to this embodiment may include: at least one processor 701 and a memory 702. Fig. 7 shows an electronic device as an example of a processor. Wherein,

and a memory 702 for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory 702 may comprise a Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

The processor 701 is configured to execute the computer execution instruction stored in the memory 702 to implement the monitoring and early warning method for meteorological disasters in a kiwi fruit orchard according to any one of the embodiments.

The processor 701 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the embodiments of the present Application.

Optionally, in a specific implementation, if the memory 702 and the processor 701 are implemented independently, the memory 702 and the processor 701 may be connected to each other through the bus 703 and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The buses may be divided into address buses, data buses, control buses, etc., but do not represent only one bus or one type of bus.

Alternatively, in a specific implementation, if the memory 702 and the processor 701 are implemented by being integrated on a chip, the memory 702 and the processor 701 may complete the same communication through an internal interface.

The electronic device described above in this embodiment may be configured to execute the technical solutions in the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

The embodiment of the application also provides a computer-readable storage medium, wherein program instructions are stored in the computer-readable storage medium, and when being executed by a processor, the program instructions implement the monitoring and early warning method for the meteorological disasters in the kiwi fruit orchard.

The embodiment of the present application further provides a program product, where the program product includes a computer program, where the computer program is stored in a readable storage medium, and the computer program can be read from the readable storage medium by at least one processor of an electronic device, and the at least one processor executes the computer program to enable the electronic device to implement the kiwi fruit orchard meteorological disaster monitoring and early warning method according to any one of the above embodiments of the present application.

An embodiment of the present application provides a chip system, including: the processor executes the computer execution instructions stored in the memory, so that the processor executes the kiwi fruit orchard meteorological disaster monitoring and early warning method.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media capable of storing program codes, such as Read-Only Memory (ROM), random Access Memory (RAM), magnetic or optical disk, and the like.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. The kiwi fruit orchard meteorological disaster monitoring and early warning method is characterized by comprising the following steps:

according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model, obtaining predicted temperature data of the target position on the kiwi fruit tree in a future preset time period, wherein the kiwi fruit target position temperature prediction model is at least one of the following: the method comprises the following steps of (1) a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model;

2. The method of claim 1, wherein before obtaining the kiwi canopy temperature data within a predetermined time period in the future based on the predicted meteorological data and kiwi canopy temperature prediction model, the method further comprises:

3. The method of claim 1, wherein the obtaining of the predicted weather data, which is predicted by the weather observation station corresponding to the target area within the preset time period in the future at every preset interval of time comprises:

acquiring predicted temperature data of the target position on the kiwi fruit tree within a future preset time period corresponding to each lattice point according to the kiwi fruit canopy temperature data corresponding to each lattice point and a kiwi fruit target position temperature prediction model;

and determining disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point within a preset time period in the future according to the predicted temperature data of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point and the corresponding relation between the disaster grade of the target position on the kiwi fruit tree and the temperature range.

4. The method of claim 3, further comprising:

obtaining a disaster prediction distribution diagram of the target position on the kiwi fruit tree in the kiwi fruit orchard in the target area according to the disaster information of the target position on the kiwi fruit tree in the kiwi fruit orchard corresponding to each lattice point;

5. The method according to claim 2, wherein the historical meteorological data detected at a preset sampling time point in a historical time period of a meteorological observation station corresponding to an area where a sample kiwi fruit orchard is located, the historical canopy temperature of the kiwi fruit tree actually detected at the preset sampling time point in the historical time period, and at least one of the following are obtained: the historical temperature of fruit face, the historical temperature of leaf surface, the historical temperature of branch and the historical temperature of root neck include:

acquiring historical meteorological data corresponding to different weather types, actually monitored canopy historical temperature on the kiwi fruit tree and at least one of the following items: historical temperature of fruit surface, historical temperature of leaf surface, historical temperature of branch and historical temperature of root neck;

acquiring kiwi fruit orchard canopy temperature prediction models corresponding to different weather types according to historical meteorological data corresponding to different weather types and actually monitored canopy historical temperatures on kiwi fruit trees;

determining a predicted weather type corresponding to the predicted meteorological data according to the predicted meteorological data;

6. The method of claim 5, wherein the weather type is sunny, cloudy, or rainy in summer;

7. The method according to claim 1, wherein the determining disaster information of the target location on the kiwi fruit tree in the kiwi fruit orchard in the target area within a preset time period in the future according to the predicted temperature data of the target location on the kiwi fruit tree and the corresponding relationship between the disaster grade and the temperature range of the target location on the kiwi fruit tree comprises:

determining at least one kiwi variety actually planted in the target area;

8. The method according to any one of claims 1 to 7, wherein the kiwi canopy temperature prediction model is a univariate linear regression model, the kiwi fruit surface temperature prediction model and the kiwi leaf surface temperature prediction model are both exponential models, the kiwi branch temperature prediction model is a univariate linear regression model, and the kiwi root neck prediction model is a univariate quadratic regression model.

9. The method according to any one of claims 1-7, wherein the kiwi target site temperature prediction model comprises, during summer: a kiwi fruit surface temperature prediction model and/or a kiwi fruit surface temperature prediction model;

10. The utility model provides a kiwi fruit orchard meteorological disaster monitoring early warning device which characterized in that includes:

the first processing module is used for obtaining kiwi fruit canopy temperature data in a future preset time period according to the predicted meteorological data and the kiwi fruit canopy temperature prediction model;

the second processing module is used for acquiring predicted temperature data of the target position on the kiwi fruit tree in a future preset time period according to the kiwi fruit canopy temperature data and the kiwi fruit target position temperature prediction model, wherein the kiwi fruit target position temperature prediction model is at least one of the following: the method comprises the following steps of (1) a kiwi fruit surface temperature prediction model, a kiwi fruit leaf surface temperature prediction model, a kiwi fruit branch temperature prediction model and a kiwi fruit root neck temperature prediction model;