System and method for monitoring growth environment based on radix ranunculi ternati

Abstract

The invention discloses a radix ranunculi ternati-based 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 pre-judgment decision module and a system updating and optimizing module. The method comprises the following steps: collecting radix ranunculi ternati growth related data and growth environment parameter data, constructing a growth stage model and an environment parameter change trend prediction model, dynamically adjusting a monitoring scheme, performing early warning on environment factors which do not conform to a growth standard, dividing risk levels, and generating a pre-intervention operation list; meanwhile, the crop recovery condition after early warning treatment is recorded, and system performance is evaluated and optimized; the system provides an innovative scheme for monitoring the planting environment of the radix ranunculi ternati, the monitoring and management level of the growth environment of the radix ranunculi ternati can be effectively improved, and healthy growth of the radix ranunculi ternati is guaranteed.

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 increasing demand for the quality of traditional Chinese medicinal materials, precise monitoring of the growth environment of cat's paw grass has become a key to ensuring 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 shortcomings in monitoring the growth environment of cat's paw grass, 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 ensure the efficient and high-quality growth of cat's paw grass.

[0003] Precise monitoring is of great significance to ensuring the healthy growth of crops and improving yield and quality. The existing technology for monitoring the growth environment of cat's paw grass has limitations. Most monitoring technologies can only obtain part of the environmental parameters, and cannot comprehensively cover the data of different growth stages, physiological state and growth environment parameters of cat's paw grass, lacking completeness and systematicness of data. 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 different growth stage data, physiological state data and growth environment parameter data of cat's paw grass;

[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 needs of the current growth stage of cat's paw grass;

[0007] A risk prediction decision module is configured to construct an environment parameter change trend prediction model based on the growth environment parameter data, to give a warning prompt and divide a risk level for the environment parameter that does not meet the cat's paw grass growth standard, and to generate a corresponding pre-intervention operation list.

[0008] A system updating and optimizing module is configured to record crop recovery after each pre-warning treatment, to evaluate system performance, to determine an optimization direction, and to update and optimize the system.

[0009] The second aspect of the application provides a method for executing a cat's paw grass growth environment monitoring system, comprising the following steps:

[0010] S2, physiological demand dynamic monitoring, configured to construct a growth stage model based on cat's paw grass physiological state data, to dynamically match the environment demand set of each growth cycle of cat's paw grass based on the growth stage, and to dynamically adjust the monitoring frequency and threshold value of the environment parameter, so as to obtain a monitoring scheme that meets the demand of the current growth stage of cat's paw grass;

[0011] S3, risk prediction decision, configured to construct an environment parameter change trend prediction model based on the growth environment parameter data, to give a warning prompt and divide a risk level for the environment parameter that does not meet the cat's paw grass growth standard, and to generate a corresponding pre-intervention operation list;

[0012] S4, system updating and optimizing, configured to record crop recovery after each pre-warning treatment, to evaluate system performance, to determine an optimization direction, and to update and optimize the system.

[0013] The beneficial effects of the application are as follows: 1. In the data collection module, the different growth stage data, physiological state data and growth environment parameter data of cat's paw grass are comprehensively collected, multi-dimensional information in the whole growth cycle is covered, rich and accurate data basis for in-depth understanding of the growth condition of cat's paw grass is provided, and targeted analysis and decision-making are facilitated in the subsequent stage.

[0014] 2. In the physiological demand dynamic monitoring module, a growth stage model is constructed based on the physiological state of cat's paw grass, the demand threshold value of different growth cycles is dynamically matched, the monitoring frequency and threshold value are adjusted, a monitoring scheme that meets the demand of the current growth stage is formed, the precision and intelligence of monitoring are realized, and the monitoring efficiency of the growth environment of cat's paw grass is improved.

[0015] 3. In the risk prediction decision module, the environmental parameter change trend prediction model is constructed based on the growth environment parameter data, which can provide early warning and risk level division for the environmental factors that do not meet the growth standards of catpaw grass, generate pre-intervention operation list, help the planting personnel to take measures in advance, reduce the influence of environmental risk on the growth of catpaw grass, ensure its healthy growth, and improve the planting benefit.

[0016] 4. In the system updating and optimization module, the recovery of the crops after the early warning disposal is recorded, the system performance is evaluated, the optimization direction is determined, and the system is updated and optimized, so that the system can continuously adapt to the changes of the growth environment of catpaw grass and the planting requirements, continuously improve the monitoring accuracy, early warning timeliness and other performances, and provide long-term stable and reliable support for the growth environment monitoring of catpaw grass. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

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

[0019] Figure 2 It is a schematic diagram of the execution method connection of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] Referring to Figure 1 The present application provides a catpaw grass growth environment monitoring system based on catpaw grass, which comprises a data collection module for collecting growth-related data of catpaw grass, wherein the related data includes catpaw grass data in different growth stages, physiological state data and growth environment parameter data.

[0022] In specific embodiments of the present application, the different growth stage data includes heat accumulation amount, photoperiod index, water balance value, calculated by the following measured data: daily maximum temperature, daily minimum temperature, daily total amount of photosynthetically active radiation, rainfall, irrigation amount, evapotranspiration amount; physiological state data includes plant height, stem diameter, flowering or not, chlorophyll content of cat grass at each growth stage; growth environment parameter data includes prediction time, predicted temperature, total rainfall in the next 24 hours, historical water storage coefficient, predicted sunshine duration.

[0023] The physiological demand dynamic monitoring module is used for constructing a growth stage model based on the physiological state data of cat grass, dynamically matching the demand threshold value of cat grass at different growth periods, dynamically adjusting the monitoring frequency and threshold value of the environmental parameters based on the demand threshold value of cat grass at different growth periods, and obtaining a monitoring scheme meeting the demand of cat grass at the current growth stage.

[0024] In specific embodiments of the present application, the growth stage model is constructed, which specifically includes: using statistical methods and data analysis techniques to associate the growth stage with the corresponding environmental parameter demand threshold value, and constructing the growth stage model: obtaining a growth stage index S = p, (GDD ∈ GDD p )∧(DLI∈DLI p )∧(WBI∈WBI p ), p = 1, 2, 3 represents three growth stages of cat grass, wherein S is the growth stage index, GDD is the heat accumulation amount, and T dmax is the daily maximum temperature, T dmin is the daily minimum temperature, T d ' is the base temperature; DLI is the photoperiod index, and DLI = E, wherein E is the daily total amount of photosynthetically active radiation; WBI is the water balance value, and WBI = (J + G) - B, wherein J is the rainfall, G is the irrigation amount, and B is the evapotranspiration amount; GDD p is the heat accumulation amount threshold value of cat grass seedling stage, flowering stage and tuber bulking stage stored in the database, DLI p is the photoperiod index threshold value of cat grass seedling stage, flowering stage and tuber bulking stage stored in the database, WBI p is the water balance value threshold value of cat grass seedling stage, flowering stage and tuber bulking stage stored in the database; when S = 1, it is the seedling stage of cat grass, when S = 2, it is the flowering stage of cat grass, and when S = 3, it is the tuber bulking stage of cat grass.

[0025] In specific embodiments of the present application, the dynamic matching catpaw grass each growth cycle environmental requirement set, and the dynamic adjustment environmental parameter monitoring frequency and threshold, which specifically includes: based on growth stage index, and according to growth index, the comparison of actual and standard plant height, actual and standard stem thickness, flowering rate, auxiliary judgment catpaw grass current growth stage, if plant morphology development is ahead of model, such as flowering rate > 50% but not reach the threshold value of accumulated temperature, then artificially forced upgrade growth stage and reduce model heat demand parameter, because reproductive growth priority is higher than environmental parameter; If plant morphology lags behind the model, such as accumulated temperature reaches the standard but the plant height is less than 80% of the standard value, then trigger three level root cause diagnosis-first check water balance value, second check chlorophyll content, < 1.5 mg / g determine nutrient deficiency, finally scan disease characteristics, stem base brown suggests standing disease, at the same time freeze stage conversion and generate diagnosis report; After conflict processing, the model needs to be calibrated: when morphology is ahead of schedule, adjust the accumulated temperature threshold, when morphology lags behind, add new limit conditions such as root health index ≥ 0.8, all conflict cases are automatically included in the database to realize closed loop optimization; This rule ensures that the environmental model and biological facts are mutually verified, among which flowering / fruiting fact is the highest priority evidence, followed by stem thickness / plant height, and chlorophyll and leaf number are auxiliary basis;

[0026] Determine the demand threshold of temperature, humidity, light and other environmental parameters corresponding to the stage, for the environmental parameters closely related to the demand of the current growth stage, increase the monitoring frequency; Extract the demand threshold and correlation degree of catpaw grass to each environmental parameter at each growth stage from the database, the correlation degree is a value between 0 and 1; Adjusted frequency = original frequency + correlation degree * frequency increase value corresponding to unit correlation degree.

[0027] Risk prediction decision module, for constructing environmental parameter change trend prediction model based on growth environment parameter data, warning and dividing risk grade for environmental factors not meeting catpaw grass growth standard, and generating corresponding pre-intervention operation list.

[0028] In specific embodiments of the present application, the specific steps of constructing the environmental parameter change trend prediction model include:

[0029] The risk of environmental change trend is divided into high temperature stress, waterlogging risk and insufficient light;

[0030] B1. Constructing high temperature stress prediction model, Wherein H is high temperature stress index, t is each hour after the present time, Tt is the forecast temperature at t hour, cri Tc is the high temperature critical value, D is the database preset drought correction coefficient, δ dry D is the drought flag;

[0031] B2. Constructing a waterlogging risk prediction model F=R total ×β hist ×(1+γ·δ wet ), wherein F is a waterlogging risk index, R total is the total rainfall expected in the next 24 hours, β hist is a historical waterlogging coefficient, γ is a waterlogging correction coefficient preset in the database, and δ wet is an over-wet flag;

[0032] B3. Constructing an insufficient light prediction model

[0033] d=1,2,...,n, n is any integer greater than 2, and d is the number of days of future prediction, wherein L is an insufficient light index, d is a future date, K d is the predicted sunshine duration on the dth day, K min is the minimum sunshine requirement for maintaining basic photosynthesis stored in the database, I d (x) is an indicator function that outputs 1 if the condition is met.

[0034] In specific embodiments of the present application, the environment factors that do not meet the cat's paw grass growth standards are warned and divided into risk levels, and the specific steps are as follows:

[0035] High temperature stress grading, when H min ≤H<H mid , it is a high temperature stress 1st warning, when H mid ≤H<H max , it is a high temperature stress 2nd warning, when H>H max , it is a high temperature stress 3rd warning, H min , H mid , H max are respectively the warning thresholds of the three levels of high temperature stress risk stored in the database;

[0036] Waterlogging risk grading, when F min ≤F<F mid , it is a waterlogging risk 1st warning, when F mid ≤F<F max , it is a waterlogging risk 2nd warning, when F>F max , it is a waterlogging risk 3rd warning, F min , F mid , F max are respectively the warning thresholds of the three levels of waterlogging risk risk stored in the database;

[0037] Insufficient light grading, when L min ≤L<L mid , it is a insufficient light 1st risk, when L mid ≤L<Lmax When L < L max , it is a three-level light deficiency warning, L min , L mid , L max are respectively the light deficiency warning threshold values of the three levels of risks stored in the database.

[0038] In specific embodiments of the present application, the corresponding pre-intervention operation list is generated, which specifically includes: high temperature stress pre-intervention operation: 1st level warning, closely monitor temperature changes, increase ventilation in planting area, and warn staff of high temperature work; 2nd level warning, turn on sunshade net, irrigate during 07:00-09:00 and 17:00-19:00, and the single irrigation amount is 5-8 liters per square meter to reduce temperature; 3rd level warning, turn on sunshade net, irrigate once every 2 hours, the single irrigation amount is 8-10 liters per square meter, and spray plant antitranspiration agent with a concentration of 0.1%-0.2% to reduce water loss of cat's claw grass;

[0039] Water accumulation risk pre-intervention operation: 1st level warning, check the drainage system, patrol low-lying points lower than the surrounding 0.3 meters and above, and ensure that the drainage channel is unobstructed; 2nd level warning, prepare drainage equipment in advance, dredge the blocked ditch, and pad the area with a height lower than the surrounding 0.5 meters and above to a height of 0.2-0.3 meters; 3rd level warning, start the drainage equipment, timely remove the accumulated water, simultaneously close the automatic irrigation system, spread wood ash, and use plastic film to cover and protect the cat's claw grass;

[0040] Light deficiency pre-intervention operation: 1st level warning, appropriately trim the surrounding obstructions to increase the light receiving area of the cat's claw grass; 2nd level warning, install light supplementing equipment and supplement light during the period when the daily light intensity is less than 2000 lux; 3rd level warning, strengthen light supplementation to maintain the daily light intensity at 5000-8000 lux, and adjust the planting layout of the cat's claw grass to transplant the plants with a horizontal distance of less than 1 meter from the surrounding obstructions to an area with better light with a horizontal distance of more than 3 meters from the obstructions.

[0041] The system updating and optimizing module is used to record the crop recovery after each warning treatment, evaluate the system performance, determine the optimization direction, update and optimize the system.

[0042] In specific embodiments of the present application, the recording of the crop recovery after each warning treatment and the evaluation of the system performance specifically include:

[0043] Through the combination of manual and automatic methods, cat paw grass growth and environmental data are collected at different time nodes, and cat paw grass recovery data after early warning and treatment are recorded; the system performance is evaluated according to the cat paw grass recovery data, the early warning accuracy rate = the number of correct early warnings / the total number of early warnings, and the target is greater than or equal to 85%; the treatment timeliness = the compliance rate of the operation completion time limit, and the target is greater than or equal to 90%; the recovery efficiency index = the benchmark recovery days / the actual recovery days, and the target is greater than or equal to 1.2; and the problem sources are analyzed according to the evaluation results.

[0044] It should be noted that the cat paw grass recovery data includes: physiological recovery days = the time required for leaf expansion / spot fading; growth lag rate = (non-affected plant height-affected plant height) / non-affected plant height*100%; yield compensation coefficient = yield in the affected area / yield in the normal area; and the evaluation system performance includes: early warning accuracy rate = the number of correct early warnings / the total number of early warnings, and the target is greater than or equal to 85%; treatment timeliness = the compliance rate of the operation completion time limit, and the target is greater than or equal to 90%; and recovery efficiency index = the benchmark recovery days / the actual recovery days, and the target is greater than or equal to 1.2, and the standard threshold is 1.0.

[0045] In specific embodiments of the present application, the determined optimization direction updates and optimizes the system, which specifically includes:

[0046] The optimization direction decision follows a three-level priority mechanism: based on the system performance evaluation results, if the early warning accuracy rate is lower than the standard threshold, threshold parameter optimization is started, the false alarm case is calculated to correct the coefficient, the new threshold = the original threshold*(1-the correction coefficient), the correction coefficient = the number of false alarms / the total number of times and the upper limit is 0.1, for example, when the high temperature false alarm rate is 20%, the critical temperature is lowered from 28℃ to 25.2℃; if the accuracy rate meets the standard but the treatment timeliness, i.e. the operation completion time limit compliance rate, is lower than the threshold, the operation process is reconstructed, such as simplifying "early warning→work order distribution→manual equipment collection" to "early warning→enable field emergency equipment package", shortening the response link; if the recovery efficiency index is less than the threshold for three consecutive times, the treatment scheme is upgraded, for example, changing "stop irrigation for 3 days" to stepwise irrigation after waterlogging, for example, 30% on the 4th day→60% on the 5th day→100% on the 6th day, to avoid physiological shock; according to the feedback, the system can be fine-tuned to accurately monitor, timely warn and effectively guide intervention, better meeting the environmental monitoring needs of cat paw grass planting and helping its healthy growth.

[0047] It should be noted that the present application also includes a database for storing reference raw data and various coefficients in calculation, including heat accumulation threshold, photoperiod index threshold, water balance value threshold, minimum sunshine requirement for maintaining basic photosynthesis, early warning accuracy rate threshold, treatment timeliness threshold, and recovery efficiency index threshold for cat paw grass seedling stage, flowering stage and tuber bulking stage, drought correction coefficient, and waterlogging correction coefficient, and the coefficients can be adjusted by the administrator according to the actual situation.

[0048] ReferenceFigure 2 As shown, the second aspect of the present application provides a catpaw grass growth environment monitoring method, comprising: S1, data collection, for collecting growth-related data of catpaw grass, the related data including catpaw grass different growth stage data, physiological state data and growth environment parameter data;

[0049] S2, physiological demand dynamic monitoring, for constructing a growth stage model based on catpaw grass physiological state data, dynamically matching the environment demand set of catpaw grass in each growth cycle based on the growth stage, and dynamically adjusting the monitoring frequency and threshold value of the environment parameter, to obtain a monitoring scheme meeting the demand of catpaw grass in the current growth stage;

[0050] S3, risk prediction decision, for constructing an environment parameter change trend prediction model based on the growth environment parameter data, warning and dividing risk levels for the environment parameters not meeting the growth standard of catpaw grass, and generating a corresponding pre-intervention operation list;

[0051] S4, system updating and optimization, for recording the crop recovery after each warning disposal, evaluating the system performance, determining the optimization direction, updating and optimizing 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 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 2, characterized in that, 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 threshold requirements, resulting in the growth stage index S = p,(GDD∈GDD). p )∧(DLI∈DLI p )∧(WBI∈WBI p ), p = 1, 2, 3 represent the three growth stages of cat's claw grass, where S is the growth stage index, GDD is the heat accumulation, and Where T dmax The highest temperature of the day, T dmin The lowest temperature of the day, T d ′ is the base temperature; DLI is the photoperiod index, and DLI = E, where E is the total daily photosynthetically active radiation; WBI is the water balance value, and WBI = (J + G) - B, where J is rainfall, G is irrigation, and B is evapotranspiration; GDD p DLI is the threshold for heat accumulation during the seedling, flowering, and tuber enlargement stages of cat's claw grass stored in the database. p WBI is used to store the photoperiod index thresholds for the seedling, flowering, and tuber enlargement stages of cat's claw grass in the database. p 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 for the seedling stage, S = 2 for the flowering stage, and S = 3 for the tuber enlargement stage.

4. The cat's claw grass growth environment monitoring system according to claim 3, characterized in that, 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 thresholds for environmental parameters such as temperature, humidity, and light corresponding to this stage. For environmental parameters closely related to the current growth stage, increase the monitoring frequency. Extract the required thresholds and correlations of cat's claw grass for each environmental parameter at each growth stage from the database. The correlation is a value between 0 and 1. Adjusted frequency = original frequency + correlation * frequency increase value corresponding to unit correlation.

5. A monitoring system for the growth environment of cat's claw grass according to claim 2, 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. Let T be the predicted temperature for hour t. cri This is the high-temperature critical value. The drought correction factor, δdr, is preset for the database. y This is a drought marker. B2. Construct a flood risk prediction model F=R total ×β hist ×(1+γ·δ wet ), where F is the flood risk index, R total To predict the total rainfall over the next 24 hours, β hist δ is the historical water accumulation coefficient, γ is the water accumulation correction coefficient preset in the database, and δ wet This is the over-humidity indicator. B3. Constructing a Prediction Model for Insufficient Lighting d = 1, 2, ..., n, where n is any integer greater than 2, d is the number of days in the future forecast, L is the insufficient light index, d is the future date, and K d For the predicted sunshine duration on day d, K min The database stores the minimum solar radiation requirements for maintaining basic photosynthesis. d (x) is an indicator function.

6. A monitoring system for the growth environment of cat's claw grass according to claim 6, 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 H min ≤H<H mid At that time, it was a Level 1 warning for high temperature stress. mid ≤H<H max At this time, it is a Level 2 warning for high temperature stress, when H > H max At that time, it was a Level 3 warning for high temperature stress, H min H mid H max These are the warning thresholds for three levels of high-temperature stress risk stored in the database; Flood risk classification, when F min ≤F<F mid At that time, it was a Level 1 warning for water accumulation risk. mid ≤F<F max At that time, it is a Level 2 warning for water accumulation risk, when F > F max At that time, it was a Level 3 warning for flood risk, F min F mid F max These are the warning thresholds for three levels of risk related to water accumulation in the database storage. Insufficient light grading, when L min ≤L<L mid At that time, it is considered a level 1 risk due to insufficient light. mid ≤L<L max When the light intensity is insufficient, it is a Level 2 warning; when L > L max At that time, it was a Level 3 warning for insufficient sunlight, L min L mid L max These are the warning thresholds for three levels of risk associated with insufficient lighting in the database storage.

7. A monitoring system for the growth environment of cat's claw grass according to claim 7, 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.

8. A monitoring system for the growth environment of cat's claw grass according to claim 8, 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 problems are analyzed based on the evaluation results.

9. A monitoring system for the growth environment of cat's claw grass according to claim 8, 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 less 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.

10. A method for implementing the cat's claw grass growth environment monitoring system according to any one of claims 1-9, 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.

Images