Crop growth adaptive agrometeorological observation system

Abstract

This invention discloses a crop-adaptive agricultural meteorological observation system, belonging to the field of agricultural meteorological observation technology. It integrates multiple devices to collaboratively measure various meteorological variables to indicate weather conditions, deploys them in a grid-like layout, and collects and transmits data regularly. It collects key growth parameters according to crop growth stages and synchronously correlates them with meteorological data; optimizes data and accurately matches it to construct a fusion dataset; establishes a correlation model to identify key meteorological factors; adjusts observation parameters according to crop needs; sets two-level early warnings and pushes information through multiple channels; retrieves data from multiple dimensions and securely stores it; and provides visual operation and parameter setting functions. This invention improves the accuracy of meteorological data through multi-variable collaborative observation, dynamically adapts to the needs of different crop growth stages and varieties, provides comprehensive regional collaborative observation coverage, timely and targeted early warnings, supports linkage with external devices, promotes the refinement and intelligence of agricultural meteorological observation and planting management, and helps ensure crop growth.

Description

Technical Field

[0001] This invention relates to the field of agricultural meteorological observation technology, and in particular to an agricultural meteorological observation system adapted to crop growth. Background Technology

[0002] Agricultural production is closely related to meteorological conditions. Changes in meteorological parameters such as sunshine, temperature, humidity, rainfall, and wind speed directly affect the entire growth process of crops, including germination, growth, flowering, and fruiting. Different crops and different growth stages of the same crop have significantly different requirements for meteorological conditions. Accurately capturing changes in meteorological parameters and relating them to crop growth status is key to achieving refined planting management and improving crop yield and quality. With the development of smart agriculture, traditional agricultural meteorological observation methods are no longer sufficient to meet the precision and personalized needs of modern planting, becoming a significant bottleneck restricting the improvement of agricultural production efficiency.

[0003] Traditional agricultural meteorological observations rely heavily on single-variable measuring instruments, such as independent thermometers, hygrometers, and rain gauges. While these instruments can acquire individual meteorological data, they lack the ability to perform multi-variable collaborative measurement and integrated analysis, failing to comprehensively reflect the combined impact of weather conditions on crop growth. These observation devices typically collect data at fixed frequencies, without dynamically adjusting observation parameters based on crop growth stages. This results in insufficient accuracy of meteorological data collection during critical growth periods and redundant data during regular growth periods. Furthermore, the dispersed and uncoordinated deployment of observation equipment makes it difficult for single-point observation data to cover the meteorological differences across large planting areas, hindering the formation of regionalized, three-dimensional meteorological observation networks and the accurate capture of spatial heterogeneity in field microclimates. In addition, traditional observation systems fail to establish an effective correlation between meteorological data and crop growth status, providing only basic meteorological data and unable to offer targeted planting and management suggestions for specific crop varieties and growth stages, significantly diminishing the application value of meteorological data.

[0004] While some existing agricultural meteorological observation systems attempt to integrate multiple types of sensors, numerous shortcomings remain. The data processing stage lacks an effective cross-calibration mechanism, and satellite meteorological data and ground observation data fail to fully complement and verify each other, resulting in insufficient accuracy in meteorological parameter measurements and difficulty in supporting precise crop-meteorological correlation analysis. The systems lack adaptability to crop variety characteristics; the same observation logic applies to all crops, failing to meet the differentiated meteorological requirements of different varieties. Early warning mechanisms are mostly based on fixed threshold triggers, failing to consider the combined impact of meteorological disaster intensity, duration, and crop growth stage sensitivity, leading to insufficient accuracy and timeliness in early warnings. Furthermore, issues such as energy consumption management and remote operation and maintenance of field observation equipment remain unresolved, resulting in high equipment failure rates and difficulty in ensuring long-term stable operation. These problems prevent existing systems from achieving dynamic matching between meteorological observations and crop growth needs, hindering the provision of scientific and accurate decision support for growers and restricting the intelligent and efficient development of agricultural production. Summary of the Invention

[0005] The present invention proposes a crop growth-adaptive agricultural meteorological observation system to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a crop growth-adapted agricultural meteorological observation system, comprising the following modules: The meteorological parameter full-domain acquisition module integrates a sunshine duration recorder, a high-precision rain gauge, an atmospheric potential difference meter, a temperature and humidity sensor, a wind speed and direction monitor, and a soil moisture sensor. It is laid out in a grid according to crop planting areas, covering the core planting area, the marginal growth area, and the irrigation impact area. It collaboratively measures multiple meteorological variables such as sunshine duration, rainfall, atmospheric potential difference, temperature and humidity, wind speed and direction, and soil moisture. All devices automatically collect data at 15-minute intervals and transmit it wirelessly to the data processing center. It is suitable for two main agricultural scenarios: open field and greenhouse planting. The crop growth status monitoring module is equipped with high-resolution image acquisition equipment, spectral sensors, and plant height measurement devices. It collects key growth parameters for different growth stages of grain crops such as wheat, corn, and rice, as well as cash crops such as fruits and vegetables. The image acquisition equipment adjusts the shooting angle and frequency according to the crop growth cycle, collecting data 3 times a day during the critical growth period and once a day during the normal growth period. The spectral sensor captures the crop reflectance spectral characteristics to invert the growth status. The plant height measurement device achieves non-contact and accurate measurement through laser ranging. All growth parameters are synchronously linked with multivariate meteorological data according to timestamps. The multi-source data fusion processing module performs outlier removal, noise reduction, smoothing, and format standardization on multivariate meteorological parameter data. It corrects measurement biases of dynamic variables using the Kalman filter algorithm, performs image segmentation, feature extraction, and data normalization transformation on crop growth parameters, and uses a spatiotemporal alignment algorithm to accurately match multivariate meteorological data and crop growth data according to the collection location and time dimension. It constructs a multi-dimensional fusion dataset containing multiple meteorological variables, crop growth status, and planting area environment, providing high-quality data support for subsequent correlation analysis. The crop-meteorological correlation analysis module has a built-in database of the growth characteristics of different crops, covering the adaptation thresholds of various crops to multivariate meteorological parameters at key growth stages. It establishes a nonlinear correlation model between crop growth status and weather conditions indicated by multiple meteorological variables through deep learning algorithms, analyzes the degree of impact of weather condition changes on crop growth, identifies key meteorological variables that promote or inhibit crop growth, and generates a crop-meteorological adaptability analysis report. The dynamic adaptation and adjustment module automatically adjusts the acquisition parameters of the multivariate meteorological observation equipment based on the correlation analysis results. When crops enter the critical growth period, the acquisition frequency of core meteorological variables is increased to once every 10 minutes. When the weather conditions indicated by the multivariate meteorological parameters exceed the crop adaptation threshold, the acquisition range of relevant auxiliary variables is expanded. At the same time, targeted field management suggestions are pushed to the planting management end. Irrigation plans are recommended when drought warnings are issued, and shading protection measures are recommended when high temperature warnings are issued, so as to achieve dynamic matching between the observation system and the crop growth needs. The early warning push module, based on the correlation analysis results and crop growth adaptation thresholds, sets up a two-level early warning mechanism: meteorological anomaly early warning and crop growth stress early warning. The meteorological anomaly early warning targets extreme weather events indicated by the synergistic indication of multivariate meteorological parameters, while the crop growth stress early warning targets situations caused by the continuous deviation of multivariate meteorological parameters from the adaptation thresholds. Early warning information is pushed through multiple channels, and the warning type, scope of impact, duration, and suggested countermeasures are marked. The data storage management module uses a distributed database to store multivariate meteorological data, crop growth parameters, fusion processing results, correlation analysis reports, early warning records and field management logs. It supports multi-dimensional retrieval, and the data retention period meets the requirements of agricultural production archive management. It has timed automatic backup and anomaly recovery functions to achieve long-term secure data storage and complete traceability. The human-computer interaction module provides a visual operation interface, supports trend chart display of multivariate meteorological data and crop growth data, visualization of crop-meteorological correlation, historical query of early warning information, allows users to set crop type, growth period parameters, early warning threshold and collection frequency of multivariate meteorological observation equipment, and export crop growth analysis reports and meteorological observation statistical reports to meet the operation needs of different users.

[0007] Furthermore, it also includes a crop growth period meteorological demand adaptation coefficient calculation unit. By quantifying the degree of adaptation of multiple meteorological variables to a specific crop growth period, it provides data support for the generation of observation parameter adjustment and management recommendations. The calculation formula is as follows: in This is the crop growth period meteorological demand adaptation coefficient, with a value ranging from 0 to 1. The closer the value is to 1, the better the weather conditions indicated by the multivariate meteorological parameter are suited to the crop's current growth stage requirements. The weighting coefficient for solar radiation adaptation ranges from 0.2 to 0.3. The weighting coefficient for temperature and humidity adaptation ranges from 0.3 to 0.4. The weighting coefficient for rainfall is set to a range of 0.2 to 0.3. The wind speed adaptation weighting coefficient ranges from 0.1 to 0.2, and + + + =1, The sunshine suitability value ranges from 0 to 1, and is determined by the ratio of the actual sunshine duration to the suitable sunshine duration for the current growth stage of the crop. The temperature and humidity compatibility value ranges from 0 to 1, and is calculated based on the degree of fit between the actual temperature and humidity and the suitable temperature and humidity range. The rainfall fit score ranges from 0 to 1, and is determined by the ratio of actual rainfall to the crop's current water requirement during its growth stage. The wind speed adaptability value ranges from 0 to 1, and is derived from the matching between the actual wind speed and the suitable wind speed range for crops. Through quantitative calculation, the degree of adaptability between multivariate meteorological conditions and crop growth needs can be accurately assessed.

[0008] Furthermore, it also includes a multi-source data cross-calibration module, which improves the accuracy of meteorological parameter measurements through complementary verification of satellite meteorological data and ground multivariate meteorological observation data. Satellite meteorological data is obtained through public meteorological satellite data interfaces, covering macro-meteorological information at the regional scale, while ground observation data consists of micro-multivariate meteorological parameters collected locally by the system. A weighted fusion algorithm is used to calibrate the two types of data. During the calibration process, weights are assigned according to data accuracy and spatiotemporal resolution. Satellite data focuses on regional trend calibration, while ground data focuses on single-point precision correction. A calibration error model is established by combining historical observation data, and calibration parameters are dynamically adjusted to achieve consistency and accuracy of multivariate meteorological observation data at different spatiotemporal scales, providing a reliable data foundation for crop-meteorological correlation analysis.

[0009] Furthermore, it also includes a regional meteorological collaborative observation module, which supports the exchange and collaborative work of multivariate meteorological data from multiple observation stations. Observation units are divided according to the distribution of planting areas. Each observation unit is equipped with one main station and 3-5 auxiliary stations. The main station is responsible for the overall collection and analysis of multivariate meteorological parameters and crop growth data in the region. The auxiliary stations focus on the precise observation of specific areas. The main station and auxiliary stations achieve real-time data synchronization through wireless communication. When a station detects extreme meteorological events or abnormal crop growth indicated by multivariate meteorological parameters, it automatically triggers the collaborative observation mechanism of surrounding stations, increases the observation frequency and parameter collection range in the region, and forms a regionalized and three-dimensional observation network, breaking through the limitations of single-point observation.

[0010] Furthermore, it also includes a crop variety adaptation and optimization module, which has a built-in meteorological adaptability database for different crop varieties. This database covers the tolerance thresholds and growth preference differences of different varieties of the same crop to multivariate meteorological factors. Users can select the corresponding adaptation model according to the specific variety they are planting. The system automatically adjusts the meteorological adaptation thresholds and correlation analysis parameters. The rainfall adaptation thresholds for drought-resistant varieties are different from those for conventional varieties, and the sunshine duration requirements for early-maturing varieties are different from those for late-maturing varieties. By accurately matching crop variety characteristics with multivariate meteorological observation and analysis logic, it provides more precise support for targeted planting management.

[0011] Furthermore, it also includes a meteorological disaster risk prediction module, which constructs a meteorological disaster risk assessment model based on historical multivariate meteorological data, real-time observation data, and crop growth status. The calculation formula is as follows: in This represents the risk value of meteorological disasters to crop growth, ranging from 0 to 1. The closer the value is to 1, the higher the risk level. The disaster intensity weighting coefficient ranges from 0.3 to 0.4. The weighting coefficient for disaster duration ranges from 0.2 to 0.3. The weighting coefficient for crop growth period sensitivity ranges from 0.2 to 0.3. The weighting coefficient for crop stress resistance ranges from 0.1 to 0.2, and + + + =1, The quantitative value for meteorological disaster intensity level ranges from 0 to 1, and the disaster type and intensity are classified based on the collaborative determination of multivariate meteorological parameters. The quantified value for the duration of a disaster ranges from 0 to 1, and is determined by the ratio of the expected duration of the disaster to the crop's tolerance time. The sensitivity quantification value for crop growth period ranges from 0 to 1, with the value for critical growth period being higher than that for normal growth period. The value of crop stress resistance is quantified from 0 to 1, which is determined according to the disaster tolerance of crop varieties. This calculation can accurately predict the risk of meteorological disasters affecting crop growth, trigger early warnings, and push out defensive measures.

[0012] Furthermore, it also includes an energy consumption optimization and management module, which integrates a solar power supply unit and an energy consumption monitoring unit to meet the power supply needs of field observation equipment. The solar power supply unit collects solar energy through photovoltaic panels and stores it in batteries. The energy consumption monitoring unit monitors the power consumption status of various multivariable meteorological observation equipment in real time and dynamically adjusts the power supply strategy according to the importance of the equipment and the crop growth stage. During non-critical growth periods, the power supply of non-core equipment is reduced, and in extreme weather, priority is given to ensuring the power supply of core meteorological observation equipment and early warning devices. It has a low battery warning function and automatically sends a power supply maintenance reminder when the battery power is lower than the threshold, so as to achieve long-term stable operation of the system in the field environment.

[0013] Furthermore, it also includes a historical data tracing and trend prediction module, which supports querying multivariate meteorological data, crop growth parameters and correlation analysis results for the same period in previous years. Through comparative analysis, it presents the long-term trend of meteorological changes and crop growth. Based on historical multivariate meteorological data and machine learning algorithms, it constructs a short-term meteorological trend prediction model to predict the changing trend of key meteorological parameters in the next 7-15 days. Combined with the current growth status of crops, it predicts the future growth trend of crops, providing data support for medium- and long-term agricultural production decisions.

[0014] Furthermore, it also includes a remote operation and maintenance and fault diagnosis module, which monitors the operating status of various multivariable meteorological observation equipment, sensors, and communication modules in real time, collects key operation and maintenance parameters of the equipment, identifies abnormal equipment conditions through data analysis, automatically locates the fault location and fault type, pushes fault warnings and maintenance suggestions to operation and maintenance personnel, and supports remote adjustment of equipment operating parameters and restarting faulty equipment.

[0015] Furthermore, it also includes an expansion interface module, providing standardized data interfaces and device access interfaces, supporting access to external agricultural production-related equipment, realizing the linkage control of multivariate meteorological observation, crop monitoring and field operation equipment, automatically triggering the operation of irrigation equipment based on soil moisture and multivariate meteorological forecast data, adjusting fertilization plans according to crop growth status and meteorological conditions, supporting data interoperability with agricultural big data platforms and smart agricultural management systems, and realizing intelligent collaborative management of the entire agricultural production process.

[0016] Compared with existing technologies, the beneficial effects of this invention are: In terms of meteorological observation accuracy, the system achieves comprehensive and accurate weather condition indications through multi-variable collaborative measurement and data cross-calibration. It integrates multiple devices such as sunshine duration recorders, rain gauges, and temperature and humidity sensors to simultaneously collect multiple meteorological variables, including sunshine duration, rainfall, temperature and humidity, and wind speed and direction, collaboratively reflecting field weather conditions and overcoming the limitations of traditional single-variable observations. Through weighted fusion calibration of satellite meteorological data and ground observation data, and by establishing an error model based on historical data to dynamically adjust parameters, the system ensures the consistency and accuracy of meteorological data across different spatiotemporal scales, providing a high-quality data foundation for subsequent correlation analysis.

[0017] In terms of crop adaptability and dynamic adjustment, the system achieves a deep integration of meteorological observations with crop growth needs. It incorporates a database of growth characteristics for different crops and varieties, covering the adaptation thresholds of meteorological parameters for each growth stage. Through deep learning algorithms, it establishes a nonlinear correlation model between crop growth status and meteorological variables, accurately identifying key meteorological factors affecting crop growth. The system dynamically adjusts the observation frequency and parameter collection range based on changes in crop growth stages, increasing the collection density of core meteorological variables during critical growth stages and expanding the collection of auxiliary variables when meteorological parameters exceed adaptation thresholds. This enables the observation system to respond to and dynamically match crop growth needs in real time, enhancing the relevance and application value of the observation data.

[0018] In terms of regional coverage and early warning management, the system constructs a regionalized collaborative observation network to improve the comprehensiveness of meteorological monitoring and the effectiveness of early warning. Observation units are divided according to planting areas, and a three-dimensional observation network is formed through data exchange and collaboration between main and auxiliary stations. This effectively covers the core planting area, marginal growth area, and irrigation-affected area, avoiding the spatial limitations of single-point observation. A two-level early warning mechanism is established, combining multivariate meteorological parameters to collaboratively determine the risks of extreme weather events and crop growth stress. Early warning information and targeted management suggestions are disseminated through multiple channels, giving growers sufficient time to respond and reducing the impact of meteorological disasters on crop growth.

[0019] In terms of system practicality and scalability, the system boasts advantages such as stable operation, convenient maintenance, and wide compatibility. It integrates solar power supply and energy consumption optimization management modules, dynamically adjusting power supply strategies to ensure long-term stable operation of field observation equipment and reduce maintenance costs. The remote maintenance and fault diagnosis module can monitor equipment status in real time, automatically locate faults, and push maintenance suggestions, reducing the frequency of on-site maintenance. The expansion interface module supports the connection of external agricultural equipment such as irrigation and fertilization systems, enabling coordinated control of meteorological observation, crop monitoring, and field operations. It can also interoperate with agricultural big data platforms, adapting to different scenarios such as open fields and greenhouse cultivation, meeting the needs of different users including growers, agricultural technicians, and management departments, and providing strong support for intelligent collaborative management of the entire agricultural production process.

[0020] Overall, the system deeply integrates meteorological observation with crop growth through innovative designs such as multivariate collaborative observation, crop dynamic adaptation, regional collaborative linkage, and precise early warning delivery. This promotes the transformation of agricultural meteorological observation from "data collection" to "precision service," enhances the refinement and intelligence of agricultural planting management, and provides important technical support for ensuring crop yield and quality and promoting efficient and sustainable agricultural development. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of the crop growth-adaptive agricultural meteorological observation system proposed in this invention; Figure 2 A line graph showing the changes in the frequency of core meteorological variables collected during different growth stages of wheat; Figure 3 A bar chart comparing the weather adaptability of different tomato varieties; Figure 4 Radar chart showing the impact of key meteorological factors on maize growth; Figure 5 A scatter plot showing the correlation between temperature and humidity inside the greenhouse and cucumber fruit set rate; Figure 6 This is a heat map showing the spatial distribution of rainfall in the Daejeon area. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0025] Reference Figures 1 to 6 A crop growth-adaptive agricultural meteorological observation system, comprising the following modules: The meteorological parameter acquisition module integrates a sunshine duration recorder, a high-precision rain gauge, an atmospheric potential difference meter, a temperature and humidity sensor, a wind speed and direction monitor, and a soil moisture sensor. It is laid out in a grid according to crop planting areas, covering the core planting area, the marginal growth area, and the irrigation impact area. By coordinating the measurement of multiple meteorological variables such as sunshine duration, rainfall, atmospheric potential difference, temperature and humidity, wind speed and direction, and soil moisture, it accurately indicates the weather conditions in the field. The sunshine duration recorder uses photoelectric sensing principle to record the effective sunshine duration in real time. The rain gauge accurately captures the total amount and intensity of rainfall through weighing measurement. The atmospheric potential difference meter monitors the potential change between the cloud layer and the ground. The temperature and humidity sensor simultaneously collects the temperature and humidity of the air and the soil surface. The wind speed and direction monitor obtains airflow data at a height of 1-2 meters near the ground. All devices automatically collect data at 15-minute intervals and transmit it wirelessly to the data processing center. It is suitable for two main agricultural scenarios: open field and greenhouse planting. The crop growth status monitoring module is equipped with high-resolution image acquisition equipment, spectral sensors, and plant height measurement devices. It collects key growth parameters such as plant height, leaf area index, chlorophyll content, flowering rate, and fruit set rate for different growth stages of grain crops such as wheat, corn, and rice, as well as cash crops such as fruits and vegetables. The image acquisition equipment adjusts the shooting angle and frequency according to the crop growth cycle, collecting data 3 times a day during critical growth stages and once a day during the normal growth stage. The spectral sensor captures the crop reflectance spectral characteristics to invert the growth status. The plant height measurement device achieves non-contact and accurate measurement through laser ranging. All growth parameters are synchronously linked with multivariate meteorological data collected by timestamp. The multi-source data fusion processing module performs outlier removal, noise reduction, smoothing, and format standardization on multivariate meteorological parameter data. It corrects measurement biases of dynamic variables such as temperature, humidity, and wind speed using the Kalman filter algorithm. It performs image segmentation, feature extraction, and data normalization transformation on crop growth parameters. It uses a spatiotemporal alignment algorithm to accurately match multivariate meteorological data and crop growth data according to the collection location and time dimension, and constructs a multi-dimensional fusion dataset containing multiple meteorological variables, crop growth status, and planting area environment, providing high-quality data support for subsequent correlation analysis. The crop-meteorological correlation analysis module has a built-in database of the growth characteristics of different crops, covering the adaptation thresholds of various crops to multivariate meteorological parameters at key growth stages such as germination, seedling, jointing, flowering, and maturity. It establishes a nonlinear correlation model between crop growth status and weather conditions indicated by multiple meteorological variables through deep learning algorithms, analyzes the degree of impact of weather condition changes on crop growth, identifies key meteorological variables that promote or inhibit crop growth, and generates a crop-meteorological adaptability analysis report. The dynamic adaptation and adjustment module automatically adjusts the acquisition parameters of the multivariate meteorological observation equipment based on the correlation analysis results. When crops enter the critical growth period, the acquisition frequency of core meteorological variables such as sunshine and rainfall is increased to once every 10 minutes. When the weather conditions indicated by the multivariate meteorological parameters exceed the crop adaptation threshold, the acquisition range of relevant auxiliary variables is expanded. At the same time, targeted field management suggestions are pushed to the planting management end. Irrigation plans are recommended when drought warnings are issued and shading protection measures are recommended when high temperature warnings are issued, so as to achieve dynamic matching between the observation system and the crop growth needs. The early warning push module, based on the correlation analysis results and crop growth adaptation thresholds, sets up a two-level early warning mechanism: meteorological anomaly early warning and crop growth stress early warning. The meteorological anomaly early warning targets extreme meteorological events such as rainstorms, strong winds, high temperatures and droughts, which are indicated by the synergistic relationship of multivariate meteorological parameters. The crop growth stress early warning targets situations such as slow growth and poor development caused by the continuous deviation of multivariate meteorological parameters from the adaptation thresholds. Early warning information is pushed through multiple channels such as mobile APP, SMS, and field sound and light alarms, clearly indicating the early warning type, scope of impact, duration and corresponding response measures. The data storage management module uses a distributed database to store multivariate meteorological data, crop growth parameters, fusion processing results, correlation analysis reports, early warning records and field management logs. It supports multi-dimensional retrieval by crop type, growth stage, meteorological variables, time range and other dimensions. The data retention period meets the requirements of agricultural production archive management. It has timed automatic backup and anomaly recovery functions to achieve long-term secure data storage and complete traceability. The human-computer interaction module provides a visual operation interface, supports trend chart display of multivariate meteorological data and crop growth data, visualization of crop-meteorological correlation, and historical query of early warning information. It allows users to set crop type, growth period parameters, early warning thresholds and collection frequency of multivariate meteorological observation equipment, and export crop growth analysis reports and meteorological observation statistical reports, adapting to the operation needs of different users such as growers, agricultural technicians, and agricultural management departments.

[0026] This invention also includes a crop growth period meteorological demand adaptation coefficient calculation unit. By quantifying the degree of adaptation of multiple meteorological variables to a specific crop growth period, it provides data support for the generation of observation parameter adjustment and management suggestions. The calculation formula is as follows: in This is the crop growth period meteorological demand adaptation coefficient, with a value ranging from 0 to 1. The closer the value is to 1, the better the weather conditions indicated by the multivariate meteorological parameter are suited to the crop's current growth stage requirements. The weighting coefficient for solar radiation adaptation ranges from 0.2 to 0.3. The weighting coefficient for temperature and humidity adaptation ranges from 0.3 to 0.4. The weighting coefficient for rainfall is set to a range of 0.2 to 0.3. The wind speed adaptation weighting coefficient ranges from 0.1 to 0.2, and + + + =1, The sunshine suitability value ranges from 0 to 1, and is determined by the ratio of the actual sunshine duration to the suitable sunshine duration for the current growth stage of the crop. The temperature and humidity compatibility value ranges from 0 to 1, and is calculated based on the degree of fit between the actual temperature and humidity and the suitable temperature and humidity range. The rainfall fit score ranges from 0 to 1, and is determined by the ratio of actual rainfall to the crop's current water requirement during its growth stage. The wind speed adaptability value ranges from 0 to 1, and is derived from the matching between the actual wind speed and the suitable wind speed range for crops. Through quantitative calculation, the degree of adaptability between multivariate meteorological conditions and crop growth needs can be accurately assessed.

[0027] This invention also includes a multi-source data cross-calibration module, which improves the accuracy of meteorological parameter measurements through complementary verification of satellite meteorological data and ground multivariate meteorological observation data. The satellite meteorological data is obtained through a public meteorological satellite data interface and covers macro-meteorological information such as regional scale temperature, humidity, rainfall, and cloud cover. The ground observation data consists of micro-multivariate meteorological parameters collected locally by the system. A weighted fusion algorithm is used to calibrate the two types of data. During the calibration process, weights are assigned according to data accuracy and spatiotemporal resolution. Satellite data focuses on regional trend calibration, while ground data focuses on single-point precision correction. A calibration error model is established by combining historical observation data, and calibration parameters are dynamically adjusted to achieve consistency and accuracy of multivariate meteorological observation data at different spatiotemporal scales, providing a reliable data foundation for crop-meteorological correlation analysis.

[0028] This invention also includes a regional meteorological collaborative observation module, which supports the exchange and collaborative work of multivariate meteorological data from multiple observation stations. Observation units are divided according to the distribution of planting areas. Each observation unit is equipped with one main station and 3-5 auxiliary stations. The main station is responsible for the overall collection and analysis of multivariate meteorological parameters and crop growth data in the region. The auxiliary stations focus on the precise observation of specific areas. The main station and auxiliary stations achieve real-time data synchronization through wireless communication. When a station detects extreme meteorological events or abnormal crop growth indicated by multivariate meteorological parameters, it automatically triggers the collaborative observation mechanism of surrounding stations, increases the observation frequency and parameter collection range of the region, and forms a regionalized and three-dimensional observation network. This breaks through the limitations of single-point observation and improves the comprehensiveness of monitoring meteorological and crop growth status in large-area planting areas.

[0029] This invention also includes a crop variety adaptation and optimization module, which has a built-in meteorological adaptability database for different crop varieties. This database covers the tolerance thresholds and growth preference differences of different varieties of the same crop to multivariate meteorological factors. Users can select the corresponding adaptation model according to the specific variety being planted. The system automatically adjusts the meteorological adaptation thresholds and correlation analysis parameters. The rainfall adaptation thresholds for drought-resistant varieties are different from those for conventional varieties, and the sunshine duration requirements for early-maturing varieties are different from those for late-maturing varieties. By accurately matching the characteristics of crop varieties with the multivariate meteorological observation and analysis logic, the system's ability to adapt to the growth needs of different crop varieties is improved, providing more precise support for targeted planting management.

[0030] This invention also includes a meteorological disaster risk prediction module, which constructs a meteorological disaster risk assessment model based on historical multivariate meteorological data, real-time observation data, and crop growth status. The calculation formula is as follows: in This represents the risk value of meteorological disasters to crop growth, ranging from 0 to 1. The closer the value is to 1, the higher the risk level. The disaster intensity weighting coefficient ranges from 0.3 to 0.4. The weighting coefficient for disaster duration ranges from 0.2 to 0.3. The weighting coefficient for crop growth period sensitivity ranges from 0.2 to 0.3. The weighting coefficient for crop stress resistance ranges from 0.1 to 0.2, and + + + =1, The quantitative value for meteorological disaster intensity level ranges from 0 to 1, and the disaster type and intensity are classified based on the collaborative determination of multivariate meteorological parameters. The quantified value for the duration of a disaster ranges from 0 to 1, and is determined by the ratio of the expected duration of the disaster to the crop's tolerance time. The sensitivity quantification value for crop growth period ranges from 0 to 1, with the value for critical growth period being higher than that for normal growth period. The value of crop stress resistance is quantified from 0 to 1, which is determined according to the disaster tolerance of crop varieties. This calculation can accurately predict the risk of meteorological disasters affecting crop growth, trigger early warnings, and push out defensive measures.

[0031] This invention also includes an energy consumption optimization management module, which integrates a solar power supply unit and an energy consumption monitoring unit to meet the power supply needs of field observation equipment. The solar power supply unit collects solar energy through photovoltaic panels and stores it in a battery. The energy consumption monitoring unit monitors the power consumption status of various multivariable meteorological observation equipment in real time and dynamically adjusts the power supply strategy according to the importance of the equipment and the crop growth stage. During non-critical growth periods, the power supply of non-core equipment is reduced, and in extreme weather, priority is given to ensuring the power supply of core meteorological observation equipment and early warning devices. It has a low battery warning function and automatically sends a power supply maintenance reminder when the battery power is below a threshold, so as to realize the long-term stable operation of the system in the field environment and reduce the operation and maintenance costs.

[0032] This invention also includes a historical data tracing and trend prediction module, which supports querying multivariate meteorological data, crop growth parameters and correlation analysis results for the same period in previous years. Through comparative analysis, it presents the long-term trend of meteorological changes and crop growth. Based on historical multivariate meteorological data and machine learning algorithms, it constructs a short-term meteorological trend prediction model to predict the changing trends of key meteorological parameters such as sunshine, rainfall, temperature and humidity in the next 7-15 days. Combined with the current growth status of crops, it predicts the future growth trend of crops, providing data support for medium- and long-term agricultural production decisions such as planting planning adjustments and agricultural input preparation.

[0033] This invention also includes a remote operation and maintenance and fault diagnosis module, which monitors the operating status of various multivariable meteorological observation equipment, sensors, and communication modules in real time, collects key operation and maintenance parameters such as equipment power supply voltage, data transmission rate, and operating temperature, identifies abnormal situations such as equipment offline, sensor failure, and communication interruption through data analysis, automatically locates the fault location and fault type, pushes fault warnings and maintenance suggestions to operation and maintenance personnel, supports remote adjustment of equipment operating parameters and restart of faulty equipment, reduces the frequency of on-site operation and maintenance, improves system operation and maintenance efficiency, and ensures the continuity of multivariable meteorological observation work.

[0034] This invention also includes an expansion interface module, providing standardized data and device access interfaces to support the connection of external agricultural production-related equipment such as soil nutrient sensors, irrigation equipment controllers, and fertilization equipment. This enables the coordinated control of multivariate meteorological observation, crop monitoring, and field operation equipment. It can automatically trigger the operation of irrigation equipment based on soil moisture and multivariate meteorological forecast data, adjust fertilization plans according to crop growth status and meteorological conditions, and support data exchange with agricultural big data platforms and smart agricultural management systems. This achieves intelligent collaborative management of the entire agricultural production process and improves the system's scalability and application scenario coverage.

[0035] The following two examples further illustrate specific embodiments of the present invention: Example 1 Application of wheat-maize rotation in northern field This embodiment is applied to a large-scale wheat-maize rotation planting area in the northern plains, covering an area of ​​5,000 mu (approximately 333 hectares), using a large-scale mechanized planting model. The region has four distinct seasons: dry and windy springs, hot and rainy summers, sunny and dry autumns, and cold and snowless winters. Crop growth is significantly affected by sunshine duration, rainfall, temperature, humidity, and strong winds. The wheat growth period encompasses winter dormancy, spring jointing, and summer maturity, while the maize growth period is concentrated in summer sowing and autumn harvest. Therefore, it is crucial to adapt to the meteorological requirements of the two crops' different growth stages, while also considering regional microclimate differences and the stability of large-scale observation.

[0036] I. Core Implementation Details 1. Comprehensive Meteorological Parameter Collection and Deployment: Observation equipment is deployed in a grid pattern according to crop planting areas, with one observation point set up for every 50 mu (approximately 3.3 hectares), for a total of 100 observation points, comprehensively covering the core planting area, marginal growth area, and irrigation-affected area. Each observation point integrates a sunshine duration recorder, a high-precision rain gauge, an atmospheric potential difference meter, a temperature and humidity sensor, a wind speed and direction monitor, and a soil moisture sensor, collaboratively measuring six meteorological variables to indicate weather conditions. The sunshine duration recorder is installed on an unobstructed bracket at a height of 1.5 meters above the ground; the rain gauge is fixed in an open area to avoid obstruction by trees or buildings; the temperature and humidity sensor simultaneously collects temperature and humidity data at a height of 2 meters above the ground and at a depth of 10 centimeters in the soil surface; the wind speed and direction monitor acquires airflow data at a height of 1.5 meters near the ground. All equipment automatically collects data at 15-minute intervals and transmits it wirelessly to the data processing center, adapting to the complex environment of open field planting, including wind, sun exposure, and rainfall.

[0037] 2. Crop Growth Status Monitoring Implementation: Equipped with high-resolution image acquisition equipment, spectral sensors, and plant height measurement devices, monitoring strategies are adjusted for different growth stages of wheat and corn. During the germination and seedling stages of wheat, plant height and chlorophyll content are collected once daily; during the jointing, flowering, and maturity stages, this is increased to three times daily, with simultaneous collection of leaf area index, flowering rate, and seed setting rate. For corn, the focus is on monitoring emergence rate after sowing; during the jointing and grain-filling stages, plant height, chlorophyll content, and fruit setting rate are collected intensified. The image acquisition equipment is mounted on a height-adjustable bracket, adjusting the shooting angle according to the crop's growth height to avoid leaf obstruction; the spectral sensor captures the crop reflectance spectrum in the 400-900 nm band to invert growth status; the plant height measurement device achieves non-contact measurement via laser ranging, taking the average of three points per measurement. All growth parameters are synchronously correlated with multivariate meteorological data using timestamps to ensure data consistency.

[0038] 3. Multi-source data fusion and correlation analysis: Outlier removal is performed on multivariate meteorological parameter data. The Laida criterion is used to remove data exceeding three standard deviations. Noise reduction and smoothing are achieved using the moving average method. Data from different devices are uniformly converted to a standardized format. Kalman filtering is used to correct measurement biases in dynamic variables such as temperature, humidity, and wind speed, improving data accuracy. Crop growth parameters are image segmented, crop region features are extracted and normalized, and a spatiotemporal alignment algorithm is used to accurately match multivariate meteorological data with crop growth data according to collection location and time dimension, constructing a multi-dimensional fused dataset. The built-in wheat-maize growth characteristic database is utilized, and a nonlinear correlation model between crop growth status and meteorological variables is established using deep learning algorithms. This analyzes the impact of insufficient sunshine duration on wheat jointing and the inhibitory effect of high temperature and drought on maize grain filling, identifying key meteorological factors and generating an adaptation analysis report.

[0039] 4. Dynamic Adjustment and Regional Collaborative Observation: When wheat enters the jointing stage or corn enters the grain-filling stage, the system automatically increases the collection frequency of core meteorological variables such as sunshine, rainfall, temperature, and humidity to once every 10 minutes. When rainfall is detected to be below the crop adaptation threshold, the collection range of auxiliary variables such as soil moisture and wind speed is expanded. The system is divided into 10 observation units according to planting areas, with one main station and three auxiliary stations in each unit. The main station coordinates the data aggregation and analysis within the region, while the auxiliary stations focus on precise observation of specific areas. The main station and auxiliary stations synchronize data in real time via wireless communication. When an auxiliary station detects strong winds or abnormal crop growth, it automatically triggers the collaborative observation mechanism of the three surrounding stations, increasing the observation frequency in that area to once every 5 minutes and expanding the collection of parameters such as atmospheric potential difference and soil moisture, forming a regionalized, three-dimensional observation network.

[0040] 5. Early Warning Push and Linked Control: Based on correlation analysis results and crop growth adaptation thresholds, a two-level early warning system is triggered. When multivariate meteorological parameters collaboratively indicate an impending rainstorm, a meteorological anomaly warning is pushed to growers and agricultural technicians via mobile app and SMS, simultaneously activating field audible and visual alarms, clearly indicating the warning type, affected area, duration, and drainage and flood prevention recommendations. When temperature and humidity continuously deviate from the adaptation threshold, causing slow wheat growth, a crop growth stress warning pushes targeted management recommendations such as shading and irrigation. An extended interface module connects to the field irrigation equipment controller, automatically triggering irrigation equipment operation based on soil moisture and weather forecast data, adjusting irrigation duration and water volume, and achieving linked control between meteorological observation and field operations.

[0041] 6. System Operation and Energy Management: The system integrates a solar power supply unit and an energy consumption monitoring unit. Each observation point is equipped with a photovoltaic panel and a battery. The photovoltaic panel converts solar energy into electrical energy, which is then stored in the battery. The energy consumption monitoring unit monitors the power consumption status of each observation device in real time. During non-critical growth periods for wheat and corn, the power supply to the soil moisture sensor and atmospheric potential difference meter is reduced. In extreme weather conditions, priority is given to ensuring the power supply to the sunshine duration recorder, rain gauge, and early warning device. When the battery charge is below 30%, a power supply maintenance reminder is automatically sent. The remote operation and maintenance and fault diagnosis module monitors the equipment's power supply voltage, data transmission rate, and operating temperature in real time. When a sensor fault or communication interruption is detected, the system automatically locates the fault location and type, pushes maintenance suggestions to maintenance personnel, and supports remote restarting of faulty equipment.

[0042] Table 1 Comparison of Application Effects of the Observation System for Wheat-Maize Rotation Scenarios in Northern Fields Evaluation indicators Traditional observation methods This invention system Comprehensiveness of meteorological observation Single surface Comprehensive and complete Crop growth period suitability Poor adaptability Highly adaptable Accuracy of early warning information Low accuracy Accurate and reliable Long-term stability of the system Poor stability Stable and reliable Regional meteorological coverage effect Uneven coverage Full coverage Table 1 clearly demonstrates the advantages of this invention in the application of field crops in northern China. Traditional observation methods can only measure single meteorological variables, failing to comprehensively reflect the combined impact of weather conditions on crop growth. Furthermore, fixed observation modes are poorly adapted to crop growth stages, early warnings rely heavily on experience-based judgments with low accuracy, equipment is susceptible to the harsh environment of the field and lacks stability, and single-point observations lead to uneven regional coverage. This invention's system comprehensively captures weather conditions through multi-variable collaborative observation, achieves high adaptability by dynamically adjusting observation strategies based on crop growth stages, improves early warning accuracy through multi-source data correlation analysis, ensures long-term stable operation through energy consumption optimization and remote maintenance, and achieves uniform coverage across the entire region through a regional collaborative observation network, perfectly meeting the planting needs of large-scale crop rotation in northern fields.

[0043] Example 2 Application of greenhouse tomato-cucumber rotation in southern China This embodiment is applied to a greenhouse tomato-cucumber rotation planting base in the hilly area of ​​southern China. A total of 120 standard greenhouses were constructed, each with an area of ​​300 square meters, adopting a facility-based cultivation model. This region experiences high temperature and humidity in summer, concentrated rainfall during the rainy season, and frequent typhoons. The meteorological conditions such as temperature, humidity, and light inside the greenhouses differ significantly from those in the open environment. Furthermore, tomatoes and cucumbers have different requirements for meteorological parameters. Tomatoes require sufficient light and suitable temperature and humidity during the flowering and fruiting period, while cucumbers are more tolerant of humidity during the fruiting period but are sensitive to wind speed. It is necessary to focus on adapting to the microclimate observation inside the greenhouses and the differentiated needs of crop varieties.

[0044] I. Core Implementation Details 1. Comprehensive Meteorological Parameter Collection and Deployment: Observation equipment is deployed in a grid pattern according to the greenhouse distribution, with one observation point set up in each greenhouse. Observation points in adjacent greenhouses form a collaborative network, covering the core planting area inside the greenhouse, the edge area of ​​the greenhouse, and the irrigation-affected area. Each observation point integrates a sunshine duration recorder, a high-precision rain gauge, an atmospheric potential difference meter, a temperature and humidity sensor, a wind speed and direction monitor, and a soil moisture sensor, collaboratively measuring six meteorological variables to indicate the weather conditions inside and outside the greenhouse. The sunshine duration recorder is installed at the ventilation opening at the top of the greenhouse to avoid the greenhouse film's reflection affecting the measurement accuracy; the temperature and humidity sensors are installed at the height of the crop canopy and 10 cm above the soil surface in the middle of the greenhouse, respectively; the rain gauge is deployed in the open area outside the greenhouse to simultaneously monitor outdoor rainfall; the wind speed and direction monitor is installed at the greenhouse entrance to monitor air circulation inside and outside the greenhouse. All equipment automatically collects data at 15-minute intervals and transmits it to the data processing center via a combination of wired and wireless methods, adapting to the high temperature, high humidity, and enclosed environment of the greenhouse.

[0045] 2. Crop Growth Status Monitoring Implementation: Equipped with high-resolution image acquisition equipment, spectral sensors, and plant height measurement devices, monitoring strategies are adjusted for different growth stages and varietal characteristics of tomatoes and cucumbers. During the tomato planting stage, plant height and chlorophyll content data are collected daily during germination and seedling stages, and three times daily during flowering and fruit setting, focusing on monitoring flowering rate, fruit setting rate, and fruit development status. During the cucumber planting stage, plant height data is collected daily after emergence, increasing to three times daily during the fruiting period, while leaf area index and fruit set rate are collected simultaneously. The image acquisition equipment is installed on a support frame inside the greenhouse, with the shooting angle adjusted to avoid obstruction by the greenhouse frame and film. The spectral sensor captures the crop's reflectance spectral characteristics to infer the nutritional status and growth status of tomatoes and cucumbers. The plant height measurement device achieves non-contact measurement through laser ranging, avoiding damage to the crop. All growth parameters are synchronously correlated with multivariate meteorological data according to timestamps to ensure data consistency.

[0046] 3. Multi-source data fusion and variety adaptation: Outlier removal, noise reduction, smoothing, and format standardization are performed on multivariate meteorological parameter data. Kalman filtering is used to correct measurement biases of dynamic variables such as temperature, humidity, and wind speed within the greenhouse. A spatiotemporal alignment algorithm is employed to accurately match micro-meteorological data from within the greenhouse with regional macro-meteorological data acquired by satellite, constructing a multi-dimensional fused dataset. The multi-source data cross-calibration module integrates the two types of data using a weighted fusion algorithm. Satellite data focuses on regional meteorological trend calibration, while greenhouse observation data focuses on precise single-point correction. A calibration error model is established based on historical observation data to dynamically adjust parameters. The crop variety adaptation optimization module has a built-in meteorological adaptability database for different tomato and cucumber varieties. After the user selects a variety, the system automatically adjusts the meteorological adaptation threshold and correlation analysis parameters. For example, the rainfall adaptation threshold for moisture-tolerant cucumber varieties is higher than that for conventional varieties, and the sunshine duration requirements for early-maturing tomato varieties differ from those for late-maturing varieties.

[0047] 4. Dynamic Adjustment and Disaster Prediction: Based on the correlation analysis results, the system automatically adjusts observation parameters. During key growth stages such as tomato flowering and fruit setting and cucumber fruiting, the collection frequency of core meteorological variables such as sunshine, temperature, and humidity is increased to once every 10 minutes. When the temperature and humidity inside the greenhouse exceed the crop's adaptation threshold, the collection range of auxiliary variables such as atmospheric potential difference and soil moisture is expanded. The meteorological disaster risk prediction module constructs a risk assessment model based on historical multivariate meteorological data, real-time observation data, and crop growth status. For disaster types such as high temperature and humidity and the outer impact of typhoons, it comprehensively analyzes the disaster intensity, duration, and crop growth stage sensitivity to accurately predict disaster risks. The early warning push module pushes early warning information through a mobile APP and greenhouse sound and light alarms. During high temperature and humidity warnings, it recommends turning on ventilation equipment, and during typhoon warnings, it pushes suggestions for greenhouse reinforcement and windproofing measures.

[0048] 5. Regional Collaboration and联动 Control: The regional meteorological collaborative observation module divides 120 greenhouses into 10 observation units. Each unit sets 1 main station and 11 auxiliary stations. The main station is responsible for overall analysis of meteorological parameters and crop growth data within the unit, while the auxiliary stations focus on precise observation inside the greenhouses. The main station and auxiliary stations achieve real-time data synchronization through wireless communication. When abnormal crop growth or sudden changes in meteorological parameters are detected in a certain greenhouse, the collaborative observation mechanism of 3 surrounding greenhouse observation points is automatically triggered, improving the observation frequency and parameter collection range in this area. The extended interface module supports access to irrigation equipment controllers, fertilization equipment, and ventilation equipment inside the greenhouses, automatically triggers the operation of irrigation equipment according to soil moisture and meteorological forecast data, adjusts the fertilization plan in combination with crop growth status and meteorological conditions, and regulates the temperature, humidity, and air circulation inside the greenhouses through ventilation equipment, realizing the联动 control of meteorological observation, crop monitoring, and field operations.

[0049] 6. System Operation and Maintenance and Energy Consumption Management: Integrate a solar power supply unit and an energy consumption monitoring unit. Each greenhouse observation point is equipped with a small photovoltaic panel and a storage battery. The photovoltaic panel is installed in the idle area on the top of the greenhouse to collect solar energy and store it in the storage battery. The energy consumption monitoring unit monitors the power consumption status of each observation device in real time. During the non-critical growth periods of tomatoes and cucumbers, reduce the power supply of the atmospheric potential difference measuring instrument and the wind speed and direction monitor. In extreme weather, prioritize ensuring the power supply of temperature and humidity sensors, sunshine time recorders, and warning devices. The remote operation and maintenance and fault diagnosis module monitors the power supply voltage, data transmission rate, and operating temperature of the devices in real time. When abnormal situations such as sensor failures and communication interruptions are identified, it automatically locates the fault location and type, pushes fault warnings and maintenance suggestions to the operation and maintenance personnel, supports remotely adjusting the device operation parameters and restarting the faulty device, reducing the frequency of on-site operation and maintenance.

[0050] Table 2 Comparison Table of Application Effects of the Observation System in the Southern Greenhouse Tomato-Cucumber Rotation Scenario Evaluation indicators Traditional observation methods This invention system Accuracy of meteorological observation Low accuracy Accurate and reliable Crop variety suitability Poor adaptability Highly adaptable Timeliness of disaster early warning Response lag Timely and efficient System environmental adaptability Poor adaptability Highly adaptable Effectiveness of linkage control Lack of coordination High-efficiency collaboration The data in Table 2 highlight the application value of the present invention in the southern greenhouse scenario. Traditional observation methods mostly use single sensors to measure the temperature and humidity inside the greenhouses, unable to comprehensively capture multi-variable meteorological conditions and weather conditions, and not considering the poor adaptability to the differentiated needs of tomato and cucumber varieties. Disaster warnings rely on manual observation with a lag in response. The equipment is difficult to adapt to the high-temperature and high-humidity environment inside the greenhouses with insufficient stability, and lacks the联动 control with field operation equipment. The system of the present invention improves the observation accuracy through multi-variable collaborative observation and data cross-calibration, realizes high adaptability by building a variety database, ensures timely and efficient warnings based on multi-dimensional analysis, improves environmental adaptability through energy consumption optimization and targeted deployment, and realizes efficient联动 between observation and operation through extended interfaces, perfectly adapting to the planting needs of southern greenhouse facility cultivation.

[0051] Refer to Figure 2This line graph visually presents the dynamic adjustment pattern of the collection frequency of key meteorological variables at different growth stages of wheat, aligning with the core design logic of dynamic adaptation and adjustment of the system. During the germination and seedling stages, wheat growth is slow and less sensitive to meteorological changes; the system maintains a basic collection frequency of 15 minutes per instance, meeting data requirements while reducing redundant collections. The jointing and flowering stages are critical phases for wheat's vegetative and reproductive growth; meteorological conditions such as temperature, humidity, and sunshine directly affect the number of grains per ear and the seed setting rate. The system automatically increases the collection frequency to 10 minutes per instance to ensure accurate capture of subtle changes in key variables. During the maturity stage, wheat growth tends to stabilize, and the collection frequency drops back to 15 minutes per instance. This graph clearly verifies the system's advantage of "adjusting as needed," ensuring data density during critical growth stages while reducing equipment energy consumption and data processing pressure during non-critical periods, avoiding the drawbacks of traditional fixed-frequency observations.

[0052] Reference Figure 3 This bar chart clearly compares the meteorological adaptability of different tomato varieties, intuitively reflecting the application effect of the system's crop variety adaptation optimization module. Drought-resistant varieties have the highest adaptability (0.88), because the system precisely adjusted the adaptation thresholds for rainfall and soil moisture to perfectly match their drought-resistant characteristics; late-maturing varieties have the second highest adaptability (0.85), due to the system recognizing and adapting to their need for long days; moisture-tolerant varieties have a relatively low adaptability (0.78), because the rainfall in the observation area fluctuates greatly during the rainy season, and although the system has adjusted the thresholds, it is still affected by environmental fluctuations; conventional varieties have a moderate adaptability (0.80). This chart breaks through the traditional "one-size-fits-all" adaptation mode of observation systems, helping growers clearly understand the adaptability performance of different varieties under local meteorological conditions, and providing direct data support for variety selection.

[0053] Reference Figure 4 This radar chart presents the impact of key meteorological factors on maize growth from multiple dimensions, aligning with the core function of the system's crop-meteorological correlation analysis module. Sunshine duration has the highest impact (0.90), as maize is a typical light-loving crop, and insufficient sunshine during the grain-filling stage directly leads to a decrease in thousand-grain weight; rainfall is the second highest (0.88), as maize requires a large amount of water during the jointing stage, and fluctuations in rainfall significantly affect its growth; temperature and humidity have an impact of 0.85, as a suitable temperature and humidity range is a core condition for successful maize pollination; wind speed has an impact of only 0.60, primarily causing lodging in strong winds, with limited daily impact; atmospheric potential difference has the lowest impact (0.45), serving only as an auxiliary reference for extreme weather warnings. This chart helps technicians focus on core observation parameters, optimize field management strategies, and verify that the system's correlation analysis model can accurately quantify the impact weights of different meteorological factors.

[0054] Reference Figure 5This scatter plot visually presents the non-linear correlation between the combined temperature and humidity values ​​inside the greenhouse and the cucumber fruit set rate, demonstrating the value of the system's multi-source data fusion analysis. When the combined temperature and humidity value is 0.75, the cucumber fruit set rate reaches its peak of 85%, indicating that this range represents the optimal temperature and humidity range for cucumber fruiting. When the temperature and humidity value is below 0.75, the fruit set rate increases with rising temperature and humidity, as low temperature and low humidity inhibit cucumber pollination and fruit development. When the temperature and humidity value is above 0.75, the fruit set rate decreases, because high temperature and high humidity easily lead to diseases, affecting fruit setting and development. Based on this pattern, the system can adjust the warning threshold. When the combined temperature and humidity value deviates from the core range of 0.75, it promptly pushes management suggestions such as ventilation and temperature control, helping growers to accurately regulate the greenhouse environment and effectively increase cucumber yield.

[0055] Reference Figure 6 This heatmap clearly shows the spatial distribution of weekly rainfall in five observation areas of the field, demonstrating the effectiveness of the system's regional meteorological collaborative observation module. Area A4 received the highest rainfall (35 mm / week), located near irrigation canals with high field humidity; Area A3 received the lowest rainfall (20 mm / week), situated on higher ground with faster water loss; the remaining areas received moderate rainfall of 25-30 mm / week. Traditional single-point observations tend to overlook this spatial heterogeneity, while the system collects data through a gridded layout of observation points and visualizes regional differences using a heatmap, helping farmers to implement targeted irrigation management—increasing irrigation in Area A3 and strengthening drainage and flood prevention measures in Area A4. This map verifies the comprehensiveness of the system's regional collaborative observation, overcoming the limitations of single-point observations and providing intuitive spatial data support for refined field management.

[0056] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A crop-adaptive agricultural meteorological observation system, characterized in that, Includes the following modules: The meteorological parameter full-domain acquisition module integrates a sunshine duration recorder, a high-precision rain gauge, an atmospheric potential difference meter, a temperature and humidity sensor, a wind speed and direction monitor, and a soil moisture sensor. It is laid out in a grid according to crop planting areas, covering the core planting area, the marginal growth area, and the irrigation impact area. It collaboratively measures multiple meteorological variables such as sunshine duration, rainfall, atmospheric potential difference, temperature and humidity, wind speed and direction, and soil moisture. All devices automatically collect data at 15-minute intervals and transmit it wirelessly to the data processing center. It is suitable for two main agricultural scenarios: open field and greenhouse planting. The crop growth status monitoring module is equipped with high-resolution image acquisition equipment, spectral sensors, and plant height measurement devices. It collects key growth parameters for different growth stages of grain crops such as wheat, corn, and rice, as well as cash crops such as fruits and vegetables. The image acquisition equipment adjusts the shooting angle and frequency according to the crop growth cycle, collecting data 3 times a day during the critical growth period and once a day during the normal growth period. The spectral sensor captures the crop reflectance spectral characteristics to invert the growth status. The plant height measurement device achieves non-contact and accurate measurement through laser ranging. All growth parameters are synchronously linked with multivariate meteorological data according to timestamps. The multi-source data fusion processing module performs outlier removal, noise reduction, smoothing, and format standardization on multivariate meteorological parameter data. It corrects measurement biases of dynamic variables using the Kalman filter algorithm, performs image segmentation, feature extraction, and data normalization transformation on crop growth parameters, and uses a spatiotemporal alignment algorithm to accurately match multivariate meteorological data and crop growth data according to the collection location and time dimension. It constructs a multi-dimensional fusion dataset containing multiple meteorological variables, crop growth status, and planting area environment, providing high-quality data support for subsequent correlation analysis. The crop-meteorological correlation analysis module has a built-in database of the growth characteristics of different crops, covering the adaptation thresholds of various crops to multivariate meteorological parameters at key growth stages. It establishes a nonlinear correlation model between crop growth status and weather conditions indicated by multiple meteorological variables through deep learning algorithms, analyzes the degree of impact of weather condition changes on crop growth, identifies key meteorological variables that promote or inhibit crop growth, and generates a crop-meteorological adaptability analysis report. The dynamic adaptation and adjustment module automatically adjusts the acquisition parameters of the multivariate meteorological observation equipment based on the correlation analysis results. When crops enter the critical growth period, the acquisition frequency of core meteorological variables is increased to once every 10 minutes. When the weather conditions indicated by the multivariate meteorological parameters exceed the crop adaptation threshold, the acquisition range of relevant auxiliary variables is expanded. At the same time, targeted field management suggestions are pushed to the planting management end. Irrigation plans are recommended when drought warnings are issued, and shading protection measures are recommended when high temperature warnings are issued, so as to achieve dynamic matching between the observation system and the crop growth needs. The early warning push module, based on the correlation analysis results and crop growth adaptation thresholds, sets up a two-level early warning mechanism: meteorological anomaly early warning and crop growth stress early warning. The meteorological anomaly early warning targets extreme weather events indicated by the synergistic indication of multivariate meteorological parameters, while the crop growth stress early warning targets situations caused by the continuous deviation of multivariate meteorological parameters from the adaptation thresholds. Early warning information is pushed through multiple channels, and the warning type, scope of impact, duration, and suggested countermeasures are marked. The data storage management module uses a distributed database to store multivariate meteorological data, crop growth parameters, fusion processing results, correlation analysis reports, early warning records and field management logs. It supports multi-dimensional retrieval, and the data retention period meets the requirements of agricultural production archive management. It has timed automatic backup and anomaly recovery functions to achieve long-term secure data storage and complete traceability. The human-computer interaction module provides a visual operation interface, supports trend chart display of multivariate meteorological data and crop growth data, visualization of crop-meteorological correlation, historical query of early warning information, allows users to set crop type, growth period parameters, early warning threshold and collection frequency of multivariate meteorological observation equipment, and export crop growth analysis reports and meteorological observation statistical reports to meet the operation needs of different users.

2. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a crop growth period meteorological demand adaptation coefficient calculation unit, which quantifies the degree of adaptation of multiple meteorological variables to a specific crop growth period, providing data support for the generation of observation parameter adjustment and management suggestions. The calculation formula is as follows: in This is the crop growth period meteorological demand adaptation coefficient, with a value ranging from 0 to 1. The closer the value is to 1, the better the weather conditions indicated by the multivariate meteorological parameter are suited to the crop's current growth stage requirements. The weighting coefficient for solar radiation adaptation ranges from 0.2 to 0.

3. The weighting coefficient for temperature and humidity adaptation ranges from 0.3 to 0.

4. The weighting coefficient for rainfall is set to a range of 0.2 to 0.

3. The wind speed adaptation weighting coefficient ranges from 0.1 to 0.2, and + + + =1, The sunshine suitability value ranges from 0 to 1, and is determined by the ratio of the actual sunshine duration to the suitable sunshine duration for the current growth stage of the crop. The temperature and humidity compatibility value ranges from 0 to 1, and is calculated based on the degree of fit between the actual temperature and humidity and the suitable temperature and humidity range. The rainfall fit score ranges from 0 to 1, and is determined by the ratio of actual rainfall to the crop's current water requirement during its growth stage. The wind speed adaptability value ranges from 0 to 1, and is derived from the matching between the actual wind speed and the suitable wind speed range for crops. Through quantitative calculation, the degree of adaptability between multivariate meteorological conditions and crop growth needs can be accurately assessed.

3. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a multi-source data cross-calibration module, which improves the accuracy of meteorological parameter measurements through complementary verification of satellite meteorological data and ground multivariate meteorological observation data. Satellite meteorological data is obtained through public meteorological satellite data interfaces and covers macro-meteorological information at the regional scale. Ground observation data consists of micro-multivariate meteorological parameters collected locally by the system. A weighted fusion algorithm is used to calibrate the two types of data. During the calibration process, weights are assigned according to data accuracy and spatiotemporal resolution. Satellite data focuses on regional trend calibration, while ground data focuses on single-point precision correction. A calibration error model is established by combining historical observation data and the calibration parameters are dynamically adjusted to achieve consistency and accuracy of multivariate meteorological observation data at different spatiotemporal scales, providing a reliable data foundation for crop-meteorological correlation analysis.

4. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a regional meteorological collaborative observation module, which supports the exchange and collaborative work of multivariate meteorological data from multiple observation stations. The observation units are divided according to the distribution of planting areas. Each observation unit is equipped with one main station and 3-5 auxiliary stations. The main station is responsible for the overall collection and analysis of multivariate meteorological parameters and crop growth data in the region. The auxiliary stations focus on the precise observation of specific areas. The main station and auxiliary stations achieve real-time data synchronization through wireless communication. When a station detects extreme meteorological events or abnormal crop growth indicated by multivariate meteorological parameters, the collaborative observation mechanism of surrounding stations is automatically triggered to increase the observation frequency and parameter collection range in the region, forming a regionalized and three-dimensional observation network, breaking through the limitations of single-point observation.

5. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a crop variety adaptation and optimization module, which has a built-in meteorological adaptability database for different crop varieties. This database covers the tolerance thresholds and growth preference differences of different varieties of the same crop to multivariate meteorological factors. Users can select the corresponding adaptation model according to the specific variety they are planting. The system automatically adjusts the meteorological adaptation thresholds and correlation analysis parameters. The rainfall adaptation thresholds for drought-resistant varieties are different from those for conventional varieties, and the sunshine duration requirements for early-maturing varieties are different from those for late-maturing varieties. By accurately matching crop variety characteristics with multivariate meteorological observation and analysis logic, it provides more precise support for targeted planting management.

6. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a meteorological disaster risk prediction module, which constructs a meteorological disaster risk assessment model based on historical multivariate meteorological data, real-time observation data, and crop growth status. The calculation formula is as follows: in This represents the risk value of meteorological disasters to crop growth, ranging from 0 to 1. The closer the value is to 1, the higher the risk level. The disaster intensity weighting coefficient ranges from 0.3 to 0.

4. The weighting coefficient for disaster duration ranges from 0.2 to 0.

3. The weighting coefficient for crop growth period sensitivity ranges from 0.2 to 0.

3. The weighting coefficient for crop stress resistance ranges from 0.1 to 0.2, and + + + =1, The quantitative value for meteorological disaster intensity level ranges from 0 to 1, and the disaster type and intensity are classified based on the collaborative determination of multivariate meteorological parameters. The quantified value for the duration of a disaster ranges from 0 to 1, and is determined by the ratio of the expected duration of the disaster to the crop's tolerance time. The sensitivity quantification value for crop growth period ranges from 0 to 1, with the value for critical growth period being higher than that for normal growth period. The value of crop stress resistance is quantified from 0 to 1, which is determined according to the disaster tolerance of crop varieties. This calculation can accurately predict the risk of meteorological disasters affecting crop growth, trigger early warnings, and push out defensive measures.

7. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes an energy consumption optimization and management module, which integrates a solar power supply unit and an energy consumption monitoring unit to meet the power supply needs of field observation equipment. The solar power supply unit collects solar energy through photovoltaic panels and stores it in batteries. The energy consumption monitoring unit monitors the power consumption status of various multivariable meteorological observation equipment in real time and dynamically adjusts the power supply strategy according to the importance of the equipment and the crop growth stage. During non-critical growth periods, the power supply of non-core equipment is reduced, and in extreme weather, priority is given to ensuring the power supply of core meteorological observation equipment and early warning devices. It has a low battery warning function and automatically sends a power supply maintenance reminder when the battery power is lower than the threshold, so as to achieve long-term stable operation of the system in the field environment.

8. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a historical data tracing and trend prediction module, which supports querying multivariate meteorological data, crop growth parameters and correlation analysis results for the same period in previous years. Through comparative analysis, it presents the long-term trend of meteorological changes and crop growth. Based on historical multivariate meteorological data and machine learning algorithms, it builds a short-term meteorological trend prediction model to predict the changing trend of key meteorological parameters in the next 7-15 days. Combined with the current growth status of crops, it predicts the future crop growth trend and provides data support for medium- and long-term agricultural production decisions.

9. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes a remote operation and maintenance and fault diagnosis module, which monitors the operating status of various multivariable meteorological observation equipment, sensors and communication modules in real time, collects key operation and maintenance parameters of the equipment, identifies abnormal equipment conditions through data analysis, automatically locates the fault location and fault type, pushes fault warnings and maintenance suggestions to operation and maintenance personnel, and supports remote adjustment of equipment operating parameters and restarting faulty equipment.

10. The crop growth-adaptive agricultural meteorological observation system according to claim 1, characterized in that, It also includes an expansion interface module, providing standardized data interfaces and device access interfaces, supporting access to external agricultural production-related equipment, realizing the linkage control of multivariate meteorological observation, crop monitoring and field operation equipment, automatically triggering the operation of irrigation equipment based on soil moisture and multivariate meteorological forecast data, adjusting fertilization plans according to crop growth status and meteorological conditions, supporting data interoperability with agricultural big data platforms and smart agricultural management systems, and realizing intelligent collaborative management of the entire agricultural production process.

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