Catpaw grass growth environment monitoring system and method

Abstract

The application discloses a cat's paw grass growth environment monitoring system and method, and relates to the field of plant growth environment monitoring.The system comprises a data collection module, a physiological demand dynamic monitoring module, a risk prediction decision module and a system updating and optimization module; by collecting cat's paw grass growth related data and growth environment parameter data, a growth stage model and an environment parameter change trend prediction model are constructed, a monitoring scheme is dynamically adjusted, environment factors that do not meet the growth standard are early warned and risk grades are divided, and a pre-intervention operation list is generated; meanwhile, crop recovery conditions after early warning disposal are recorded, system performance is evaluated and optimization is carried out; the system provides an innovative scheme for cat's paw grass planting environment monitoring, can effectively improve cat's paw grass growth environment monitoring and management level, and guarantees healthy growth of cat's paw grass.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of plant growth environment monitoring, in particular to a cat's paw grass growth environment monitoring system and method. BACKGROUND

[0002] With the development of agricultural modernization and the continuous improvement of the quality requirements of traditional Chinese medicinal materials, precise monitoring of cat's paw grass growth environment has become a key to guaranteeing its healthy growth and improving yield and quality. As an important traditional Chinese medicinal material, cat's paw grass is extremely sensitive to environmental conditions. However, the existing technology has many deficiencies in cat's paw grass growth environment monitoring, and it is difficult to meet the needs of real-time and precise monitoring. Therefore, it is urgent to build an intelligent and precise monitoring system and method, to comprehensively and real-time obtain multi-source data using advanced technology, to deeply analyze the growth environment of cat's paw grass, to realize scientific evaluation and effective early warning of its growth environment risk, to improve the ability of cat's paw grass planting to respond to environmental changes, and to guarantee the efficient and high-quality growth of cat's paw grass.

[0003] Precise monitoring is of great significance to guaranteeing the healthy growth of crops and improving yield and quality. The cat's paw grass growth environment monitoring in the prior art has limitations, and most monitoring technologies can only obtain part of the environmental parameters, and cannot comprehensively cover the data of different growth stages, physiological state data and growth environment parameter data of cat's paw grass, and the completeness and systematicness of the data are missing. It lacks the ability to dynamically adjust the monitoring scheme according to the physiological state and growth cycle of cat's paw grass, and it is difficult to accurately match the needs of different growth stages. In the risk prediction link, the existing technology cannot effectively build an environmental parameter change trend prediction model based on historical environmental data and short-term weather forecast data, and it is difficult to timely and accurately warn and classify the risk level of environmental factors that do not meet the growth standards of cat's paw grass, and the pre-intervention operation also lacks pertinence and timeliness. SUMMARY

[0004] The present application aims to provide a cat's paw grass growth environment monitoring system and method, which solves the problems in the background art.

[0005] To solve the above technical problems, the present application adopts the following technical scheme: the present application provides a cat's paw grass growth environment monitoring system in the first aspect, comprising: a data collection module for collecting growth-related data of cat's paw grass, the related data including cat's paw grass different growth stage data, physiological state data and growth environment parameter data;

[0006] A physiological demand dynamic monitoring module is used to build a growth stage model based on the physiological state data of cat's paw grass, dynamically match the environmental demand set of each growth cycle of cat's paw grass based on the growth stage, and dynamically adjust the monitoring frequency and threshold value of the environmental parameters to obtain a monitoring scheme that meets the demand of the current growth stage of cat's paw grass;

[0007] The risk prediction and decision-making module is used to build a predictive model of environmental parameter change trends based on growth environment parameter data, provide early warnings and classify risk levels for environmental parameters that do not meet the growth standards of cat's claw grass, and generate a corresponding pre-intervention operation list.

[0008] The system update and optimization module is used to record the crop recovery status after each early warning response, evaluate system performance, determine optimization directions, and update and optimize the system.

[0009] The second aspect of the present invention provides a method for performing a monitoring system based on the growth environment of cat's claw grass, comprising: S1, data collection, for collecting growth-related data of cat's claw grass, wherein the related data includes data on different growth stages of cat's claw grass, physiological state data and growth environment parameter data;

[0010] S2. Dynamic monitoring of physiological needs is used to construct a growth stage model based on the physiological state data of cat's claw grass, dynamically match the environmental needs set of cat's claw grass in each growth cycle based on the growth stage, and dynamically adjust the monitoring frequency and threshold of environmental parameters to obtain a monitoring scheme that meets the needs of cat's claw grass in the current growth stage.

[0011] S3, Risk Prediction and Decision Making, is used to build a predictive model of environmental parameter change trends based on growth environment parameter data, provide early warnings and risk level classifications for environmental parameters that do not meet the growth standards of cat's claw grass, and generate a corresponding pre-intervention operation list.

[0012] S4. System Update and Optimization: This function records crop recovery after each early warning response, assesses system performance, identifies optimization directions, and updates and optimizes the system.

[0013] The beneficial effects of this invention are as follows: 1. In the data collection module, this invention comprehensively collects data on different growth stages, physiological states, and growth environment parameters of cat's claw grass, covering multi-dimensional information throughout the entire growth cycle. This provides a rich and accurate data foundation for a deeper understanding of the growth status of cat's claw grass, facilitating subsequent targeted analysis and decision-making.

[0014] 2. In the physiological demand dynamic monitoring module of this invention, a growth stage model is constructed based on the physiological state of cat's claw grass. The demand thresholds of different growth cycles are dynamically matched and the monitoring frequency and thresholds are adjusted to form a monitoring scheme that fits the current growth stage demand, thereby achieving accurate and intelligent monitoring and improving the monitoring efficiency of cat's claw grass growth environment.

[0015] 3. In the risk prediction and decision-making module, this invention constructs an environmental parameter change trend prediction model based on growth environment parameter data. It can provide early warnings and classify risk levels for environmental factors that do not meet the growth standards of cat's claw grass, and generate a pre-intervention operation list to help growers take measures in advance to reduce the impact of environmental risks on the growth of cat's claw grass, ensure its healthy growth, and improve planting efficiency.

[0016] 4. In the system update and optimization module, this invention records the crop recovery status after early warning and treatment, evaluates system performance, determines optimization direction, and updates and optimizes the system, enabling the system to continuously adapt to changes in the growth environment and planting needs of cat's claw grass, continuously improving monitoring accuracy, early warning timeliness, and other performance, providing long-term, stable, and reliable support for monitoring the growth environment of cat's claw grass. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0019] Figure 2 This is a schematic diagram of the connection of the execution method of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 As shown, the present invention provides a cat's claw grass growth environment monitoring system, including: a data collection module for collecting growth-related data of cat's claw grass, the related data including data of different growth stages of cat's claw grass, physiological state data and growth environment parameter data.

[0022] In a specific embodiment of the present invention, the data for different growth stages include heat accumulation, photoperiod index, and water balance value, which are calculated using the following measurement data: daily maximum temperature, daily minimum temperature, daily total photosynthetically active radiation, rainfall, irrigation amount, and evapotranspiration; physiological state data include plant height, stem diameter, flowering status, and chlorophyll content of cat's claw grass at each growth stage; and growth environment parameter data include prediction time, forecast temperature, total rainfall in the next 24 hours, historical water accumulation coefficient, and forecast sunshine duration.

[0023] The physiological demand dynamic monitoring module is used to construct a growth stage model based on the physiological state data of cat's claw grass, and dynamically match the demand thresholds of cat's claw grass at different growth stages. Based on the demand thresholds of cat's claw grass at different growth stages, the monitoring frequency and thresholds of environmental parameters are dynamically adjusted to obtain a monitoring scheme that meets the needs of cat's claw grass at the current growth stage.

[0024] In a specific embodiment of the present invention, the construction of the growth stage model specifically includes: using statistical methods and data analysis techniques to correlate growth stages with corresponding environmental parameter demand thresholds to construct a growth stage model and obtain a growth stage index. , This represents the three growth stages of cat's claw plant, where S is the growth stage index and GDD is the heat accumulation. ,in The highest temperature of the day. This was the lowest temperature of the day. The base temperature; DLI is the photoperiod index, and Where E is the total daily photosynthetically active radiation; WBI is the water balance value, and Where J is rainfall, G is irrigation, and B is evapotranspiration; The threshold values ​​for heat accumulation during the seedling, flowering, and tuber enlargement stages of cat's claw grass are stored in the database. The photoperiod index thresholds for the seedling, flowering, and tuber enlargement stages of cat's claw grass are stored in the database. The threshold values ​​for water balance during the seedling, flowering, and tuber enlargement stages of cat's claw grass stored in the database are: S=1 represents the seedling stage, S=2 represents the flowering stage, and S=3 represents the tuber enlargement stage.

[0025] In a specific embodiment of the present invention, the dynamic matching of the environmental requirements set for each growth cycle of *Cat's Claw* and the dynamic adjustment of the monitoring frequency and threshold of environmental parameters specifically include: based on the growth stage index, and according to the growth indicators, comparing the actual and standard plant height, actual and standard stem diameter, and flowering rate, to help determine the current growth stage of *Cat's Claw*. If the plant morphology development is ahead of the model, such as not reaching the accumulated temperature threshold but with a flowering rate >50%, then the growth stage is artificially forcibly upgraded and the model's heat requirement parameters are lowered, because reproductive growth evidence has higher priority than environmental parameters; if the plant morphology lags behind the model, such as reaching the accumulated temperature threshold but with a plant height below the standard value. If 80% of cases are detected, a three-tiered root cause diagnosis is triggered—first, the water balance is checked; then, the chlorophyll content is examined (<1.5 mg / g indicates nutrient deficiency); finally, disease characteristics are identified, and stem base browning suggests damping-off. Simultaneously, the freezing stage is converted and a diagnostic report is generated. After conflict resolution, the model needs to be calibrated: if morphology is ahead, the accumulated temperature threshold is corrected; if morphology is behind, new constraints are added, such as adding a root health index ≥0.8. All conflict cases are automatically included in the database to achieve closed-loop optimization. This rule ensures that the environmental model and biological facts mutually validate each other, with flowering / fruiting facts being the highest priority evidence, followed by stem diameter / plant height, and chlorophyll and leaf number as auxiliary evidence.

[0026] Determine the required threshold values ​​for temperature, humidity, and light environmental parameters corresponding to the current stage. For environmental parameters closely related to the current growth stage, increase the monitoring frequency. Extract the required threshold values ​​and correlations of cat's claw grass for each environmental parameter at each growth stage from the database. The correlation value is a value between 0 and 1. Adjusted frequency = original frequency + correlation value * frequency increase value corresponding to unit correlation value.

[0027] The risk prediction and decision-making module is used to build a predictive model of environmental parameter change trends based on growth environment parameter data, provide early warnings and classify risk levels for environmental factors that do not meet the growth standards of cat's claw grass, and generate a corresponding pre-intervention operation list.

[0028] In a specific embodiment of the present invention, the steps for constructing the environmental parameter change trend prediction model include:

[0029] Environmental change trend risks are categorized into high temperature stress, water accumulation risk, and insufficient sunlight.

[0030] B1. Construct a high-temperature stress prediction model. , The time is defined as H, where H is the high-temperature stress index and t represents each hour after the current time. Forecast temperature for hour t, This is the high-temperature critical value. The drought correction factor preset for the database. This is a drought marker.

[0031] B2. Constructing a flood risk prediction model Where F is the flood risk index, To predict the total rainfall for the next 24 hours, Historical waterlogging coefficient, The preset water accumulation correction factor for the database, This is the over-humidity indicator.

[0032] B3. Constructing a Prediction Model for Insufficient Lighting , , , It is any integer greater than 2. Let L be the number of days in the future, where L is the insufficiency index and d is the future date. Forecast sunshine duration for day d. The database stores the minimum solar radiation requirements for maintaining basic photosynthesis. The output is 1 if the condition of the indicator function is true.

[0033] In a specific embodiment of the present invention, the steps for issuing early warnings and classifying risk levels for environmental factors that do not meet the growth standards of cat's claw grass are as follows:

[0034] High temperature stress classification, when At that time, it was a Level 1 warning for high temperature stress. At that time, it was a Level 2 warning for high temperature stress. At that time, it was a Level 3 warning for high temperature stress. These are the warning thresholds for three levels of high-temperature stress risk stored in the database;

[0035] Flood risk classification, when At that time, it was a Level 1 warning for the risk of flooding. At that time, it was a Level 2 warning for flood risk. At that time, it was a Level 3 warning for flood risk. These are the warning thresholds for three levels of risk related to water accumulation in the database storage.

[0036] Insufficient light is graded as follows: At that time, it was classified as a level 1 risk due to insufficient light. At that time, it was a Level 2 warning for insufficient sunlight. At that time, it was a Level 3 warning for insufficient sunlight. These are the warning thresholds for three levels of risk associated with insufficient lighting in the database storage.

[0037] In a specific embodiment of the present invention, the generation of the corresponding pre-intervention operation list specifically includes: high temperature stress pre-intervention operations: during a Level 1 warning, closely monitor temperature changes, increase ventilation in the planting area, and warn staff to work in high temperatures; during a Level 2 warning, turn on the shade net and irrigate between 07:00-09:00 and 17:00-19:00, with a single irrigation volume of 5-8 liters per square meter to lower the temperature; during a Level 3 warning, turn on the shade net and irrigate every 2 hours, with a single irrigation volume of 8-10 liters per square meter, while simultaneously spraying a plant antitranspirant at a concentration of 0.1%-0.2% to reduce water loss from the cat's claw grass;

[0038] Pre-intervention procedures for waterlogging risk: For Level 1 warnings, check the drainage system and inspect low-lying areas that are 0.3 meters or more below the surrounding area to ensure drainage channels are clear and free of water. For Level 2 warnings, prepare drainage equipment in advance, clear blocked ditches, and raise areas 0.5 meters or more below the surrounding area by 0.2-0.3 meters. For Level 3 warnings, activate drainage equipment to promptly remove waterlogging, simultaneously shut down the automatic irrigation system, apply wood ash, and cover and protect the cat's claw grass with plastic film.

[0039] Pre-intervention measures for insufficient light: During a Level 1 warning, trim surrounding obstructions appropriately to increase the area of ​​cat's claw grass receiving light; during a Level 2 warning, install supplemental lighting equipment and provide supplemental lighting during periods when the daily light intensity is below 2000 lux; during a Level 3 warning, strengthen supplemental lighting to maintain the daily light intensity at 5000-8000 lux, and at the same time adjust the planting layout of cat's claw grass, transplanting some plants that are less than 1 meter away from surrounding obstructions to areas with better light that are more than 3 meters away from obstructions.

[0040] The system update and optimization module is used to record the crop recovery status after each early warning response, evaluate system performance, determine optimization directions, and update and optimize the system.

[0041] In a specific embodiment of the present invention, the recording of crop recovery status after each early warning response and the evaluation of system performance specifically include:

[0042] By combining manual and automated methods, data on the growth and environment of cat's claw grass are collected at different time points, and the recovery status of cat's claw grass after early warning and treatment is recorded. The system performance is evaluated based on the recovery data of cat's claw grass: early warning accuracy = number of correct early warnings / total number of early warnings, with a target of ≥85%; treatment timeliness = operation completion time limit compliance rate, with a target of ≥90%; recovery efficiency index = baseline recovery days / actual recovery days, with a target of ≥1.2; and the root causes of the problems are analyzed based on the evaluation results.

[0043] It should be noted that the recovery data for cat's claw grass includes: physiological recovery days = time required for leaf unfolding / spot fading; growth lag rate = (height of unaffected plants - height of affected plants) / height of unaffected plants × 100%; yield compensation coefficient = yield of affected area / yield of normal area; the evaluation system performance includes: early warning accuracy = number of correct early warnings / total number of early warnings, target ≥ 85%; timeliness of treatment = compliance rate of operation completion time limit, target ≥ 90%; recovery efficiency index = baseline recovery days / actual recovery days, target ≥ 1.2, standard threshold is 1.0.

[0044] In a specific embodiment of the present invention, the determined optimization direction for updating and optimizing the system specifically includes:

[0045] The optimization decision-making follows a three-tier priority mechanism: Based on system performance evaluation results, if the early warning accuracy rate is lower than the standard threshold, threshold parameter optimization is initiated. A correction coefficient is calculated using false alarm cases. The new threshold = original threshold × (1 - correction coefficient), and the correction coefficient = number of false alarms / total number of false alarms with an upper limit of 0.1. For example, when the false alarm rate for high temperature is 20%, the critical temperature is lowered from 28℃ to 25.2℃. If the accuracy rate meets the standard but the timeliness of the response, i.e., the compliance rate of the operation completion time limit, is lower than the threshold, the operation process is restructured. For example, the process of "early warning → work order dispatch → manual equipment collection" is simplified to "early warning → activation of field emergency equipment package" to shorten the response time. If the recovery efficiency index is less than the threshold for three consecutive times, the response plan is upgraded. For example, the "stop irrigation for 3 days" after waterlogging is changed to stepped re-irrigation, such as 30% on the 4th day → 60% on the 5th day → 100% on the 6th day, to avoid physiological shock. Fine-tuning is performed based on feedback to ensure that the system can accurately monitor, provide timely early warnings, and effectively guide interventions, better meeting the environmental monitoring needs of cat's claw grass cultivation and helping it grow healthily.

[0046] It should be noted that the present invention also includes a database for storing reference to the original data and various coefficients used in the calculation, including the thresholds for heat accumulation during the seedling, flowering, and tuber enlargement stages of cat's claw grass, the threshold for photoperiod index, the threshold for water balance value, the minimum daylight requirement for maintaining basic photosynthesis, the threshold for early warning accuracy, the threshold for timely treatment, the threshold for recovery efficiency index, drought correction coefficient, and waterlogging correction coefficient, and the coefficients can be adjusted by the administrator according to the actual situation.

[0047] Reference Figure 2 As shown, the second aspect of the present invention provides a method for monitoring the growth environment of cat's claw grass, including: S1, data collection, for collecting growth-related data of cat's claw grass, the related data including data of different growth stages of cat's claw grass, physiological state data and growth environment parameter data;

[0048] S2. Dynamic monitoring of physiological needs is used to construct a growth stage model based on the physiological state data of cat's claw grass, dynamically match the environmental needs set of cat's claw grass in each growth cycle based on the growth stage, and dynamically adjust the monitoring frequency and threshold of environmental parameters to obtain a monitoring scheme that meets the needs of cat's claw grass in the current growth stage.

[0049] S3, Risk Prediction and Decision Making, is used to build a predictive model of environmental parameter change trends based on growth environment parameter data, provide early warnings and risk level classifications for environmental parameters that do not meet the growth standards of cat's claw grass, and generate a corresponding pre-intervention operation list.

[0050] S4. System Update and Optimization: This function records crop recovery after each early warning response, assesses system performance, identifies optimization directions, and updates and optimizes the system.

Claims

1. A monitoring system for the growth environment of cat's claw grass, characterized in that, include: The data collection module is used to collect growth-related data of cat's claw grass, including data on different growth stages of cat's claw grass, physiological state data, and growth environment parameter data. The physiological demand dynamic monitoring module is used to construct a growth stage model based on the physiological state data of cat's claw grass, dynamically match the environmental demand set of cat's claw grass in each growth cycle based on the growth stage, and dynamically adjust the monitoring frequency and threshold of environmental parameters to obtain a monitoring scheme that meets the needs of cat's claw grass in the current growth stage. The construction of the growth stage model specifically includes: Using statistical methods and data analysis techniques, a growth stage model is constructed by correlating growth stages with corresponding environmental parameter requirement thresholds, resulting in a growth stage index. , This represents the three growth stages of cat's claw plant, where S is the growth stage index and GDD is the heat accumulation. ,in The highest temperature of the day, This was the lowest temperature of the day. The base temperature; DLI is the photoperiod index, and Where E is the total daily photosynthetically active radiation; WBI is the water balance value, and Where J is rainfall, G is irrigation, and B is evapotranspiration; The threshold values ​​for heat accumulation during the seedling, flowering, and tuber enlargement stages of cat's claw grass are stored in the database. The photoperiod index thresholds for the seedling, flowering, and tuber enlargement stages of cat's claw grass are stored in the database. The threshold values ​​for water balance during the seedling, flowering, and tuber enlargement stages of cat's claw grass stored in the database are: S=1 represents the seedling stage, S=2 represents the flowering stage, and S=3 represents the tuber enlargement stage. The dynamic matching of environmental requirements for each growth cycle of *Cat's Claw* and the dynamic adjustment of the monitoring frequency and thresholds of environmental parameters specifically include: Based on the growth stage index and by comparing actual and standard plant height, actual and standard stem diameter, and flowering rate among the growth indicators, the current growth stage of *Corydalis edulis* is determined. If the plant morphology development is ahead of the model, the growth stage is artificially upgraded and the model's heat requirement parameters are lowered, as reproductive growth evidence takes precedence over environmental parameters. If the plant morphology lags behind the model, a three-level root cause diagnosis is triggered—first checking the water balance value, then checking the chlorophyll content, and finally scanning for disease characteristics. At the same time, the stage is frozen and a diagnostic report is generated. After conflict resolution, the model needs to be calibrated: when the morphology is ahead, the accumulated temperature threshold is corrected; when the morphology is behind, new constraints are added. All conflict cases are automatically included in the database to achieve closed-loop optimization. This rule ensures that the environmental model and biological facts are mutually verified, with flowering / fruiting facts being the highest priority evidence, followed by stem diameter / plant height, and chlorophyll and leaf number as auxiliary evidence. Determine the required threshold values ​​for temperature, humidity, and light environmental parameters corresponding to the current stage. For environmental parameters closely related to the current growth stage, increase the monitoring frequency. Extract the required threshold values ​​and correlations of cat's claw grass for each environmental parameter at each growth stage from the database. The correlation value is a value between 0 and 1. Adjusted frequency = original frequency + correlation value * frequency increase value corresponding to unit correlation value. The risk prediction and decision-making module is used to build a predictive model of environmental parameter change trends based on growth environment parameter data, provide early warnings and classify risk levels for environmental parameters that do not meet the growth standards of cat's claw grass, and generate a corresponding pre-intervention operation list. The system update and optimization module is used to record the crop recovery status after each early warning response, evaluate system performance, determine optimization directions, and update and optimize the system.

2. The cat's claw grass growth environment monitoring system according to claim 1, characterized in that, The data for different growth stages include heat accumulation, photoperiod index, and water balance value, which are calculated from the following measurements: daily maximum temperature, daily minimum temperature, daily total photosynthetically active radiation, rainfall, irrigation, and evapotranspiration; physiological status data include plant height, stem diameter, flowering status, and chlorophyll content of cat's claw grass at each growth stage; and growth environment parameters include forecast time, forecast temperature, total rainfall in the next 24 hours, historical water accumulation coefficient, and forecast sunshine duration.

3. The cat's claw grass growth environment monitoring system according to claim 1, characterized in that, The specific steps for constructing the environmental parameter change trend prediction model include: B1. Construct a high-temperature stress prediction model. , The time is defined as H, where H is the high-temperature stress index and t represents each hour after the current time. Forecast temperature for hour t, This is the high-temperature critical value. The drought correction factor preset for the database. This is a drought marker. B2. Constructing a flood risk prediction model Where F is the flood risk index, To predict the total rainfall for the next 24 hours, Historical waterlogging coefficient, The preset water accumulation correction factor for the database, This is the over-humidity indicator. B3. Constructing a Prediction Model for Insufficient Lighting , , , It is any integer greater than 2. Let L be the number of days in the future, where L is the insufficiency index and d is the future date. Forecast sunshine duration for day d. The database stores the minimum solar radiation requirements for maintaining basic photosynthesis. This is an indicator function.

4. The cat's claw grass growth environment monitoring system according to claim 3, characterized in that, The specific steps for issuing early warnings and classifying risk levels for environmental factors that do not meet the growth standards of cat's claw grass are as follows: High temperature stress classification, when At that time, it was a Level 1 warning for high temperature stress. At that time, it was a Level 2 warning for high temperature stress. At that time, it was a Level 3 warning for high temperature stress. These are the warning thresholds for three levels of high-temperature stress risk stored in the database; Flood risk classification, when At that time, it was a Level 1 warning for the risk of flooding. At that time, it was a Level 2 warning for flood risk. At that time, it was a Level 3 warning for flood risk. The warning thresholds are for three levels of risk of water accumulation in the database storage. Insufficient light is graded as follows: At that time, it was classified as a level 1 risk due to insufficient light. At that time, it was a Level 2 warning for insufficient sunlight. At that time, it was a Level 3 warning for insufficient sunlight. These are the warning thresholds for three levels of risk associated with insufficient lighting in the database storage.

5. A monitoring system for the growth environment of cat's claw grass according to claim 4, characterized in that, The generation of the corresponding pre-intervention operation list specifically includes: High-temperature stress pre-intervention operations: During a Level 1 warning, closely monitor temperature changes, increase ventilation in the planting area, and warn staff to work in high temperatures; during a Level 2 warning, turn on the shade net and irrigate between 07:00-09:00 and 17:00-19:00, with a single irrigation volume of 5-8 liters per square meter to lower the temperature; during a Level 3 warning, turn on the shade net and irrigate every 2 hours, with a single irrigation volume of 8-10 liters per square meter, while simultaneously spraying a plant antitranspirant at a concentration of 0.1%-0.2% to reduce water loss from the cat's claw grass; Pre-intervention procedures for waterlogging risk: For Level 1 warnings, check the drainage system and inspect low-lying areas that are 0.3 meters or more below the surrounding area to ensure drainage channels are clear and free of water. For Level 2 warnings, prepare drainage equipment in advance, clear blocked ditches, and raise areas 0.5 meters or more below the surrounding area by 0.2-0.3 meters. For Level 3 warnings, activate drainage equipment to promptly remove waterlogging, simultaneously shut down the automatic irrigation system, apply wood ash, and cover and protect the cat's claw grass with plastic film. Pre-intervention measures for insufficient light: During a Level 1 warning, trim surrounding obstructions appropriately to increase the area of ​​cat's claw grass receiving light; during a Level 2 warning, install supplemental lighting equipment and provide supplemental lighting during periods when the daily light intensity is below 2000 lux; during a Level 3 warning, strengthen supplemental lighting to maintain the daily light intensity at 5000-8000 lux, and at the same time adjust the planting layout of cat's claw grass, transplanting some plants that are less than 1 meter away from surrounding obstructions to areas with better light that are more than 3 meters away from obstructions.

6. The cat's claw grass growth environment monitoring system according to claim 1, characterized in that, The recording of crop recovery status after each early warning response and the evaluation of system performance specifically include: By combining manual and automated methods, data on the growth and environment of cat's claw grass are collected at different time points, and the recovery status of cat's claw grass after early warning and treatment is recorded. The system performance is evaluated based on the recovery data of cat's claw grass: early warning accuracy = number of correct early warnings / total number of early warnings, with a target of ≥85%; treatment timeliness = operation completion time limit compliance rate, with a target of ≥90%; recovery efficiency index = baseline recovery days / actual recovery days, with a target of ≥1.2; and the root causes of the problems are analyzed based on the evaluation results.

7. A monitoring system for the growth environment of cat's claw grass according to claim 6, characterized in that, The determined optimization direction involves updating and optimizing the system, specifically including: The optimization decision-making follows a three-level priority mechanism: Based on the system performance evaluation results, if the early warning accuracy is lower than the standard threshold, the threshold parameter optimization is initiated. The correction coefficient is calculated through false alarm cases. The new threshold = original threshold × (1 - correction coefficient), and the correction coefficient = number of false alarms / total number of false alarms with an upper limit of 0.

1. If the accuracy meets the standard but the timeliness of handling is lower than the threshold, the operation process is restructured to shorten the response time. If the recovery efficiency index is lower than the threshold for three consecutive times, the handling plan is upgraded to avoid physiological shock. Fine-tuning is performed based on feedback to ensure that the system can accurately monitor, provide timely early warnings, and effectively guide intervention.

8. A method for implementing the cat's claw grass growth environment monitoring system according to any one of claims 1-7, comprising: S1. Data collection, used to collect growth-related data of cat's claw grass, including data on different growth stages of cat's claw grass, physiological state data, and growth environment parameter data; S2. Dynamic monitoring of physiological needs is used to construct a growth stage model based on the physiological state data of cat's claw grass, dynamically match the environmental needs set of cat's claw grass in each growth cycle based on the growth stage, and dynamically adjust the monitoring frequency and threshold of environmental parameters to obtain a monitoring scheme that meets the needs of cat's claw grass in the current growth stage. S3, Risk Prediction and Decision Making, is used to build a predictive model of environmental parameter change trends based on growth environment parameter data, provide early warnings and risk level classifications for environmental parameters that do not meet the growth standards of cat's claw grass, and generate a corresponding pre-intervention operation list. S4. System Update and Optimization: This function records crop recovery after each early warning response, assesses system performance, identifies optimization directions, and updates and optimizes the system.

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