CN-122024413-A - Peony freeze injury monitoring diagnosis and disaster early warning method

CN122024413ACN 122024413 ACN122024413 ACN 122024413ACN-122024413-A

Abstract

The invention provides a peony freeze injury monitoring diagnosis and disaster early warning method, and relates to the technical field of agricultural disaster monitoring and intelligent early warning. The peony freeze injury monitoring, diagnosing and disaster early warning method comprises the following steps of S1, acquiring historical meteorological observation data and real-time meteorological observation data of a peony planting area, wherein the historical meteorological observation data comprise 1978-2024 year national basic meteorological station loading station observation data and 2005-2024 year regional meteorological station peony garden, small reservation, wu shop, huang, an Xing, royal and Dusi observation data of the peony planting area. By means of long-term continuous acquisition and combined application based on historical meteorological observation data and real-time meteorological observation data and combining data sources of multiple sites and multiple time scales in a specific area, space-time characteristics of low-temperature change in a peony growth environment are fully described, and a clear temperature threshold is introduced in a disaster recognition stage to define the frost damage process.

Inventors

ZHANG CUIYING
FAN XIANZHENG
LI NA
LI RUIYING
JIANG HUI
CHEN NAN
HAO XIAOLEI
Meng Ruijuan

Assignees

菏泽市气象局

Dates

Publication Date: 20260512
Application Date: 20260206

Claims (10)

1. A peony freeze injury monitoring diagnosis and disaster early warning method is characterized by comprising the following steps: s1, acquiring historical meteorological observation data in a peony planting area, wherein the historical meteorological observation data comprise 1978-2024 national basic weather station (lotus) data and 2005-2024 regional weather station (peony garden, small stay, wu shop, huang, an Xing, huangzhen and Dusi) data of the peony planting area, and the meteorological observation data at least comprise daily minimum air temperature, daily average air temperature and continuous air temperature change records; S2, screening disaster factors of the frost damage of the peony tree based on the historical meteorological observation data, identifying and dividing the frost damage process according to an air temperature threshold, defining that the frost damage process starts the day when the lowest air temperature is less than or equal to 4 ℃, defining that the frost damage process ends when the lowest air temperature is less than or equal to 4 ℃, and determining that the frost damage process is complete once; s3, based on the recognized frost damage process, extracting four weather disaster factors as model input variables, namely the lowest air temperature of the process, the duration of hours at low temperature, the harmful negative accumulated temperature and the temperature reduction amplitude of the process; S4, carrying out standardization treatment on the original values of the four meteorological disaster causing factors to eliminate the influence of different dimensions and orders of magnitude, wherein the calculation formula is as follows: ; In the formula, Is the standardized value of the ith process of a certain disaster causing factor, For the original value of the factor i-th process, An average value of the factor for n times of frost processes for many years, n being the total number of frost processes; s5, constructing a peony frost damage index model by adopting a weighted index summation method, inputting the standardized disaster causing factors into the model, and calculating a peony frost damage index CI, wherein the calculation formula is as follows: ; wherein CI is the frost damage index of peony; The weight coefficient of each disaster causing factor is respectively 0.4 of the lowest process air temperature, 0.3 of harmful negative accumulation temperature, 0.2 of continuous hours and 0.1 of process cooling amplitude; the disaster factor is the j th disaster factor after standardization; s6, substituting four disaster causing factors obtained in the current or predicted meteorological process into the peony frost damage index model to obtain a current peony frost damage index; And S7, grading the tree peony frost damage degree and outputting disaster early warning based on the tree peony frost damage index to realize tree peony frost damage monitoring diagnosis and disaster early warning.
2. The method for monitoring, diagnosing and warning disasters of peony freeze injury according to claim 1, wherein the meteorological observation data in S1 further comprises hour-by-hour minimum air temperature record, day maximum air temperature record, day average air temperature record, air temperature change rate data and continuous low-temperature period record, so as to enhance the definition degree of frost process identification and the integrity of model input data.
3. The method for monitoring, diagnosing and warning disasters of peony freeze injury according to claim 1, wherein the identifying of the frost injury process in S2 further comprises one or more of the following conditions: When the lowest air temperature is lower than 4 ℃ in 24 continuous hours and the air temperature is in a continuous descending trend, the effective start of the frost damage process is determined; When the lowest air temperature is higher than 4 ℃ in 24 continuous hours and no obvious cooling trend appears, the effective end of the frost damage process is determined, so that the accuracy and stability of the frost process identification are improved.
4. The method for monitoring, diagnosing and warning disasters of peony freeze injury according to claim 1, wherein the lowest air temperature in the process in S3 is the lowest air temperature value occurring during a frost injury process; The low-temperature continuous hours is an accumulated value of hours meeting the condition that the air temperature is lower than the critical air temperature (4 ℃) in the process of one frost damage; The harmful negative accumulated temperature is the accumulated quantity of the difference value between the air temperature and the critical air temperature at each moment when the air temperature of the hour is lower than the critical air temperature (4 ℃) in the process of one frost damage; The temperature reduction range of the process is the difference between the lowest temperature of the day from the day before the start of a frost damage process to the coldest day during the end of the process.
5. The method for monitoring, diagnosing and early warning disasters of peony freeze injury according to claim 1, wherein the standardization process in S4 adopts a zero-mean unit variance standardization method, and each disaster-causing factor meets the comparability requirement by carrying out mean normalization and variance scaling on each consistent disaster-causing factor, and eliminates the interference of different physical dimensions on a comprehensive index model.
6. The method for monitoring, diagnosing and early warning the frost damage of the peony according to claim 1, wherein the weight coefficient aj in S5 is obtained through a principal component analysis method or other multivariate statistical analysis methods, and the weight coefficient reflects the relative contribution degree of each disaster causing factor to the occurrence probability and the damage degree of the frost damage of the peony, so that the scientificity and the interpretation of the model are improved.
7. The method for monitoring, diagnosing and early warning disasters of peony frost damage according to claim 1, wherein the current or predicted meteorological process data obtained in S6 are derived from a real-time meteorological monitoring system, a numerical weather forecast system or a historical meteorological database, and are input into the peony frost damage index model after carrying out missing compensation, outlier rejection and time scale unified processing through a data preprocessing module.
8. The method for monitoring, diagnosing and warning disasters of peony frost damage according to claim 1, wherein the peony frost damage level in S7 is divided into at least three levels according to peony frost damage index CI: When 0.0 < CI < 0.3, a slight frost hazard is determined; When CI is more than or equal to 0.3 and less than or equal to 0.6, judging that the frost damage is moderate; When CI is more than or equal to 0.6 and less than or equal to 1.0, the frost damage is judged to be heavy.
9. The method for monitoring, diagnosing and warning disasters of peony freeze injury according to claim 1, wherein the method further comprises generating corresponding disaster warning information based on frost injury grades, and sending the warning information to peony planting management personnel, an agricultural production management system or a disaster emergency platform in at least one communication mode to realize advanced deployment of freeze injury prevention and control measures.
10. The method for monitoring, diagnosing and early warning disasters of peony freeze injury according to claim 1, wherein the method can be integrated into an agricultural meteorological service platform or an agricultural Internet of things system, and realizes continuous monitoring, dynamic diagnosis, real-time early warning and decision support of the peony freeze injury by cooperating with a sensor network, a data acquisition terminal, a data processing server and a user terminal.

Description

Peony freeze injury monitoring diagnosis and disaster early warning method Technical Field The invention relates to the technical field of agricultural disaster monitoring and intelligent early warning, in particular to a peony freeze injury monitoring diagnosis and disaster early warning method. Background The technical field of agricultural disaster monitoring and intelligent early warning refers to the comprehensive technical field of sensing, identifying, evaluating and early warning of various natural disaster risks in the agricultural production process. The method is a technical method for monitoring, diagnosing and warning the frost risk of the peony in real time, analyzing disaster factors, judging the frost degree and outputting warning information in the peony growth process, and is used for realizing early identification, dynamic diagnosis and grading warning of the peony frost risk, wherein the low-temperature disaster mainly occurs in the key object waiting period of peony growth in spring, the disaster form is mainly frost damage, the frost damage belongs to a typical expression form of the frost damage, and scientific frost prevention decision basis is provided for peony cultivation management, so that the frost damage loss is reduced, and the stability, ornamental value and economic value of peony production are guaranteed. In the prior art, the macroscopic perception and the general early warning of disaster information are focused in the actual operation, the disaster risk is often judged by depending on single-site weather data or short-term air temperature change conditions, the microclimate difference in the area and the background of many years weather are not considered sufficiently, and the problem that the early warning result is not matched with the actual damage degree easily occurs in the application of specific crops. In the prior art, single or small quantity of meteorological indexes are generally adopted for risk assessment, and factors such as low-temperature duration time, intensity of a cooling process, accumulated cold quantity and the like are lack of system integration, so that freeze injury identification stays on the aspect of whether low temperature occurs or not, and the difference of physiological influences of different low-temperature processes on crops is difficult to reflect. In actual production management, the technology is mostly judged by depending on an empirical threshold, and when meteorological conditions are close to a critical state, early warning lag or fuzzy grade conditions easily occur, so that the implementation time and the implementation strength of protective measures are affected. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a peony freeze injury monitoring diagnosis and disaster early warning method, which solves the problems that the prior art focuses on macroscopic perception and general early warning of disaster information in actual operation, often depends on single-site weather data or short-term air temperature change conditions to judge disaster risks, has insufficient consideration on microclimate differences in areas and multi-year weather background, and is easy to cause mismatching of early warning results and actual damage degree in specific crop application. In order to achieve the purposes, the invention is realized by the following technical scheme that the peony freeze injury monitoring diagnosis and disaster early warning method comprises the following steps: s1, acquiring historical meteorological observation data in a peony planting area, wherein the historical meteorological observation data comprise 1978-2024 national basic weather station (lotus) data and 2005-2024 regional weather station (peony garden, small stay, wu shop, huang, an Xing, huangzhen and Dusi) data of the peony planting area, and the meteorological observation data at least comprise daily minimum air temperature, daily average air temperature and continuous air temperature change records; S2, screening disaster factors of the frost damage of the peony tree based on the historical meteorological observation data, identifying and dividing the frost damage process according to an air temperature threshold, defining that the frost damage process starts the day when the lowest air temperature is less than or equal to 4 ℃, defining that the frost damage process ends when the lowest air temperature is less than or equal to 4 ℃, and determining that the frost damage process is complete once; s3, based on the recognized frost damage process, extracting four weather disaster factors as model input variables, namely the lowest air temperature of the process, the duration of hours at low temperature, the harmful negative accumulated temperature and the temperature reduction amplitude of the process; S4, carrying out standardization treatment on the original values of the four meteorological disaster causing factors to eli