Comprehensive intelligent analysis system for plant cultivation state

Abstract

The invention discloses a plant cultivation state comprehensive intelligent analysis system, and relates to the technical field of plant cultivation analysis, and the system comprises a data collection unit which collects multi-dimensional data of a plant physiological state; the state quantification unit is used for receiving the multi-dimensional data and solving a comprehensive plant stress index; the dynamic prediction unit is used for receiving the comprehensive plant stress index and predicting a future trajectory; and the decision intervention unit is used for receiving the future trajectory, comparing the future trajectory with a preset intervention threshold value, generating an early warning signal when the future trajectory exceeds the preset intervention threshold value in a preset time window, and generating a normal state signal when the future trajectory does not exceed the preset intervention threshold value in the preset time window. According to the invention, in-situ continuous monitoring of the plant health state is realized, multi-source heterogeneous data is fused into a unified quantitative index, the future change trend of the multi-source heterogeneous data can be predicted, and a key technical support is provided for realizing prospective cultivation management and early warning.

Description

Technical Field

[0001] This invention relates to the field of plant cultivation analysis technology, and more specifically to a comprehensive intelligent analysis system for plant cultivation status. Background Technology

[0002] In plant cultivation, especially the cultivation of high-value-added economic crops, precise monitoring of plant life activities to cope with stresses caused by changes in environmental factors such as temperature, light, and water is a crucial link in ensuring the final yield and quality of crops. Traditional methods for assessing plant health status typically rely on biochemical analysis or molecular biological testing conducted by professionals in laboratories. These methods determine the physiological state of the plant by sampling plant tissues such as leaves and measuring indicators such as the content of specific secondary metabolites, pigments, or the activity of photosynthetic enzymes. For example, the plant's resistance can be assessed by detecting the content of phenolic and flavonoid compounds related to stress responses, or the efficiency of the photosynthetic system can be assessed by measuring chlorophyll fluorescence parameters. These classic detection methods, after long-term development, can provide relatively in-depth and accurate physiological and biochemical information, forming the foundation for plant stress physiology research and traditional agronomic practices.

[0003] However, the inherent shortcomings of these existing technologies become increasingly apparent when applied to real-time, dynamic agricultural production practices. These detection methods heavily rely on offline sampling and analysis, which not only causes irreversible damage to the plants themselves but also involves a cumbersome and time-consuming analytical process, completely failing to meet the needs for in-situ, continuous monitoring of plant conditions, and exhibiting significant lag in the analytical results. Secondly, the plant response to environmental stress is a complex and comprehensive process involving multiple levels, including internal chemical composition, macroscopic physical morphology, and core physiological functions. Existing technologies often only allow for isolated, single-dimensional analysis, lacking effective technical means to fuse multi-source, heterogeneous data from different sensors, making it difficult to form a comprehensive and unified quantitative assessment of plant conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a comprehensive intelligent analysis system for plant cultivation status, which solves the problems existing in the background technology.

[0005] To address the aforementioned technical problems, this invention provides a comprehensive intelligent analysis system for plant cultivation status, comprising: a data acquisition unit for acquiring multi-dimensional data on plant physiological status; The state quantization unit is used to receive multi-dimensional data and calculate the comprehensive plant stress index; The dynamic prediction unit is used to receive the comprehensive plant stress index and predict future trajectories; The decision intervention unit is used to receive future trajectories and compare them with preset intervention thresholds. When the future trajectory is about to exceed the preset intervention threshold within a preset time window, an early warning signal is generated. When the future trajectory does not exceed the preset intervention threshold within a preset time window, a normal status signal is generated.

[0006] Preferably, the data acquisition unit includes: Miniature Raman spectroscopy probes are used to obtain vibrational information of secondary metabolite molecules; Hyperspectral imaging unit for capturing canopy pigment and moisture status; Pulse-modulated chlorophyll fluorometer, used to measure photosynthetic efficiency.

[0007] Preferably, the state quantification unit calculates the comprehensive plant stress index by performing the following steps: Determine the normalized comprehensive environmental factors; transform multidimensional data into multiple standardized stress components; and perform weighted summation of all stress components to generate a comprehensive plant stress index.

[0008] Preferably, the standardized stress components are determined by acquiring real-time measurements of each physiological data point and normalizing them based on a preset reference baseline value under healthy conditions and a preset adverse threshold value under extreme stress.

[0009] Preferably, the normalized comprehensive environmental factor is determined by acquiring the real-time measurement values ​​of each environmental sensor and performing normalized weighted calculations based on preset weights, suitability values, and stress thresholds.

[0010] Preferably, the dynamic prediction unit predicts future trajectories by performing the following steps: A first-order dynamic system model was adopted, which describes the relaxation process tending towards equilibrium. The model was numerically solved to predict the future trajectory of the comprehensive plant stress index.

[0011] Preferably, the first-order dynamic system model includes the plant's intrinsic resilience coefficient, equilibrium stress index, and environmental stress sensitivity coefficient; The equilibrium stress index is calculated using a linear saturation model based on comprehensive environmental factors and preset calibration coefficients.

[0012] Preferably, the plant intrinsic resilience coefficient and environmental stress sensitivity coefficient are obtained by applying dynamically changing environmental stress to the plant during the calibration phase, recording the complete response curves of the comprehensive plant stress index and comprehensive environmental factors, and fitting the experimental data using the parameter estimation method in the identification theory.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, by deploying non-invasive, multimodal sensing units at the cultivation site, enables high-frequency, multi-dimensional acquisition of continuous data streams of internal chemical components, macroscopic physical morphology, and core physiological functions of plants without interfering with their normal growth. This fundamentally changes the previous detection paradigm that relied on discrete, destructive sampling, laying the prerequisite for accurate dynamic analysis and realizing in-situ, continuous monitoring of plant life activities.

[0014] 2. By establishing a rigorous internal quantitative model, this invention successfully solves the problem of fusing multi-source heterogeneous data. It can transform complex data streams from different physical sensors with different dimensions into a standardized single numerical indicator that accurately reflects the overall health status of plants through normalization and weighted fusion. This makes the previously vague concept of growth clear and comparable, greatly improving the objectivity, accuracy and interpretability of plant status assessment.

[0015] 3. This invention introduces a dynamic system model calibrated by experimental data. The system is no longer limited to describing the current state of the plant, but can scientifically deduce the evolution trajectory of plant health indicators over a period of time based on the current state and environmental changes. This forward-looking predictive ability can provide managers with early warning before irreversible physiological damage occurs, thereby gaining a valuable window of opportunity for decision-making and intervention, and realizing a deep leap in analysis from static assessment to dynamic prediction. Attached Figure Description

[0016] 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. Figure 1 This is a logic block diagram of a comprehensive intelligent analysis system for plant cultivation status according to the present invention. Detailed Implementation

[0017] 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.

[0018] Example 1: Please see Figure 1The present invention provides a comprehensive intelligent analysis system for plant cultivation status, comprising: a data acquisition unit for acquiring multi-dimensional data on plant physiological status; The state quantization unit is used to receive multi-dimensional data and calculate the comprehensive plant stress index; The dynamic prediction unit is used to receive the comprehensive plant stress index and predict future trajectories; The decision intervention unit is used to receive the future trajectory and compare it with the preset intervention threshold. When the future trajectory will exceed the preset intervention threshold within the preset time window, an early warning signal is generated. When the future trajectory does not exceed the preset intervention threshold within the preset time window, a normal status signal is generated. Its purpose is to provide real-time, quantitative and predictive analysis of the state of plant life activities in order to cope with the stress posed by sudden environmental changes to plants. In this embodiment, the system is constructed as a closed-loop analysis system that includes four functional units: data acquisition, state quantification, dynamic prediction and decision intervention, aiming to overcome the limitations of traditional offline detection methods in terms of timeliness and continuity. The data acquisition unit is designed to acquire multi-dimensional information reflecting the real-time physiological state of plants without loss. In this embodiment, the unit is deployed in the cultivation environment of lilac plants and continuously acquires multi-source heterogeneous physiological data of plants at a preset synchronization time frequency. The state quantification unit functions to transform and fuse the aforementioned multi-dimensional, heterogeneous data streams into a unified, standardized index through a quantification process involving multiple mathematical transformations and data fusion. In this embodiment, the unit receives real-time data from the data acquisition unit, normalizes external environmental factors and internal physiological indicators respectively, and finally performs weighted fusion to calculate a dimensionless scalar, namely the comprehensive plant stress index. This index is used as the core state variable to quantify the degree of comprehensive stress currently experienced by the plant. The dynamic prediction unit is designed to infer the future evolutionary trend of a plant based on its current state. In this embodiment, the unit receives the comprehensive plant stress index and its time series generated by the state quantification unit, and uses a mathematical model describing the dynamic response of the system to numerically solve and predict the trajectory of the index over a future period. The decision intervention unit functions to provide a basis for decision-making before potential damage occurs, based on future predictions. In this embodiment, the unit receives the future trajectory output by the dynamic prediction unit. The preset intervention threshold is a critical stress index value corresponding to a specific proportion (e.g., 5%) of irreversible yield or quality loss in plants, calculated based on a large amount of agronomic experimental data. The unit continuously compares the predicted future trajectory with this preset intervention threshold. When it is determined that the future trajectory will reach or exceed the threshold within a preset time window, the system automatically generates an early warning signal to prompt the manager to take intervention measures. Conversely, when the predicted trajectory remains below the threshold, the system generates a normal state signal, indicating that the plant is in a safe state. This embodiment, through the collaborative work of the aforementioned units, constitutes a complete intelligent analysis closed loop from in-situ sensing to state assessment and future prediction. It solves the technical problem that existing technologies cannot perform in-situ, real-time, and multi-dimensional monitoring of plant environmental stress responses, and realizes precise, dynamic, and forward-looking management of plant health status. It can provide early warning before irreversible physiological damage occurs, providing key technical support for precision agriculture and smart horticulture.

[0019] Example 2: The data acquisition unit includes: Miniature Raman spectroscopy probes are used to obtain vibrational information of secondary metabolite molecules; Hyperspectral imaging unit for capturing canopy pigment and moisture status; Pulse-modulated chlorophyll fluorometer, used to measure photosynthetic efficiency.

[0020] The data acquisition unit has been further specified; its purpose is to obtain more comprehensive and in-depth plant physiological indicators by integrating multiple complementary sensors, thereby improving the accuracy and reliability of subsequent analysis. The data acquisition unit specifically includes: The miniature Raman spectroscopy probe is used to detect molecular-level information of secondary metabolites in plant tissues in real time. In this embodiment, the probe is used to obtain the molecular vibrational spectra of specific phenolic and flavonoid compounds that are directly related to plant stress response. Changes in the content of these substances are a direct biochemical manifestation of plant resistance to external stress, so the probe provides in-depth chemical information. The hyperspectral imaging unit is used to macroscopically capture the overall health status of the plant canopy. In this embodiment, the unit is used to measure the reflectance of plant leaves to different wavelengths of light, thereby retrieving key indicators such as pigment content and tissue water content. Changes in these indicators directly reflect the overall photosynthetic capacity and water balance of the plant. The pulse-modulated chlorophyll fluorometer is used to accurately assess the operational efficiency of the plant photosynthetic system. In this embodiment, the instrument is used to measure the quantum yield of light energy conversion during photosynthesis, which is a parameter that is extremely sensitive to environmental stress and can reveal the extent of damage to photosynthetic function before visible symptoms appear in plants. By combining Raman spectroscopy, which provides molecular information, hyperspectral imaging, which provides macroscopic state information, and chlorophyll fluorescence technology, which provides photosynthetic efficiency information, the data acquisition unit of this embodiment can simultaneously construct a data profile of plant state from three dimensions: chemical, physical morphology, and physiological function. Compared with a single sensor, this multimodal data acquisition method greatly enriches the dimensions and levels of information, lays a solid data foundation for the accurate evaluation of the subsequent state quantification unit, and significantly improves the accuracy and robustness of the analysis results of the entire system.

[0021] Example 3: The state quantification unit calculates the comprehensive plant stress index by performing the following steps: Determine the normalized comprehensive environmental factors; transform multidimensional data into multiple standardized stress components; and perform weighted summation of all stress components to generate a comprehensive plant stress index.

[0022] The standardized stress components are determined by acquiring real-time measurements of each physiological data point and normalizing them based on a preset reference baseline value under healthy conditions and a preset adverse threshold value under extreme stress.

[0023] The normalized comprehensive environmental factors are determined by acquiring real-time measurements from each environmental sensor and performing normalized weighted calculations based on preset weights, suitability values, and stress thresholds.

[0024] The specific implementation method of calculating the comprehensive plant stress index by the state quantization unit is further clarified; it transforms multi-source, heterogeneous raw sensor data into a single stress measure with clear physical meaning through a series of mathematical transformations and fusions; the inherent logic of this process lies in: A normalized comprehensive environmental factor is determined to quantify the overall stress exerted on plants by the external environment. This factor is determined by acquiring real-time measurements from each environmental sensor and normalizing them according to a pre-defined weighted summation model. In this embodiment, the environmental sensors may include, but are not limited to, sensors for measuring air temperature and humidity, light intensity, carbon dioxide concentration, and soil moisture. To ensure that the quantification of environmental stress accurately reflects the severity of deviations from the suitable range and to avoid the cancellation of stresses from different directions in the calculation, the normalization model fully considers the bidirectional nature of stress. Specifically, a non-directional, always positive stress component is obtained by calculating the absolute value of the difference between the measured value and the suitable value and comparing it with the absolute value of the difference between the stress threshold and the suitable value. The model formula is: ; The normalized comprehensive environmental factor is dimensionless. For indexing environmental sensors; This represents the total number of environmental sensors. For the first Weighting coefficients for various environmental sensors; For the first The measured values ​​collected in real time by various environmental sensors; For the first Suitable values ​​(pre-calibrated) for various environmental factors; For the first Stress thresholds for various environmental factors (pre-calibrated); Weight here Suitable value With stress threshold All parameters were pre-calibrated through experimental means before system deployment, based on the differences in the sensitivity of specific plant varieties to different environmental stresses. This formula calculates environmental changes, whether above or below the optimal value, as a positive stress accumulation, thus more accurately reflecting the overall environmental pressure experienced by plants. Multidimensional data is transformed into multiple standardized stress components to unify the changes of each internal physiological indicator onto the same dimensionless scale for measurement. A standardized stress component is determined by acquiring real-time measurements of each physiological sensor and processing them according to a pre-defined normalization formula. This formula calculates the relative stress level corresponding to the current measurement by introducing a reference baseline under healthy conditions and an adverse threshold under extreme stress. The formula is as follows: ; The coercive component; Index for physiological sensors; The measured value is acquired in real time by the i-th physiological sensor; For health reference values; This is the limit threshold; The preset reference baseline value for the health status here. Preset adverse threshold under extreme stress All of these were obtained during the calibration phase by applying known levels of stress to the plants and using the destructive biochemical test results as a reference true value, and then employing statistical optimization methods to solve the problem in reverse; for example, the maximum photochemical efficiency measured by the pulse-modulated chlorophyll fluorometer was used to calibrate the plant. The limit threshold of ) It can apply progressively increasing drought stress to the sample plants; at each stress gradient, it can simultaneously measure... The value was determined, and its net photosynthetic rate was measured using a gas exchange analyzer. ;when When the level drops to 50% of the healthy state (e.g., before stress), the corresponding value at that moment will be... The measured value is defined as the limit threshold of the sensor. Similarly, to calibrate the limiting threshold of a micro Raman spectroscopy probe, the intensity change of its specific spectral peak can be correlated with the concentration of key metabolites with stress-resistant functions (such as abscisic acid ABA) determined by gold standard methods such as liquid chromatography. All stress components are weighted and summed to merge multiple independent physiological stress components into a final index that represents the overall stress state of the plant. This fusion process is achieved through a weighted summation model, the formula of which is: ; The comprehensive stress index; Index for physiological sensors; This represents the total number of sensors; These are the weighting coefficients; The coercive component; Weighting coefficients here Similarly, during the calibration phase, based on the varying importance of different physiological indicators to the overall health of the plant, statistical optimization methods were used to determine the comprehensive plant stress index, with the sum of all weight coefficients being 1. It is a scalar that changes over time, forming the basis for subsequent dynamic predictive analysis; As one specific implementation method, the weighting coefficient (as well as The determination of the weighting factors can be achieved using the analytic hierarchy process (AHP). This method involves inviting multiple agronomic or plant physiology experts to conduct pairwise comparisons of the importance of different physiological indicators (e.g., changes in secondary metabolites, chlorophyll fluorescence efficiency, canopy water content, etc.) to the overall health of the plant, constructing a judgment matrix. Subsequently, the normalized weighting coefficients are obtained by calculating the maximum eigenvalue of this matrix and its corresponding eigenvector. This method transforms qualitative expert experience into quantitative weight assignment, ensuring the scientific nature and reproducibility of weight allocation. Alternatively, objective weighting methods such as data-driven entropy weighting can also be used to determine the weights. Through the above steps, the state quantization unit of this embodiment successfully solves the technical challenges of fusing heterogeneous data, different dimensions, and different directions of change. It transforms complex, multidimensional raw data streams into a standardized, comparable, and precise single quantitative indicator that accurately reflects the overall health status of plants through a series of mathematical models with clear physical and biological significance. This not only improves the accuracy and interpretability of the assessment results, but also provides high-quality, standardized input for achieving accurate dynamic prediction.

[0025] Example 4: The dynamic prediction unit predicts future trajectories by performing the following steps: A first-order dynamic system model was adopted, which describes the relaxation process tending towards equilibrium. The model was numerically solved to predict the future trajectory of the comprehensive plant stress index.

[0026] The first-order dynamic system model includes the plant's intrinsic resilience coefficient, equilibrium stress index, and environmental stress sensitivity coefficient; The equilibrium stress index is calculated using a linear saturation model based on comprehensive environmental factors and preset calibration coefficients.

[0027] The plant intrinsic resilience coefficient and environmental stress sensitivity coefficient were obtained by applying dynamically changing environmental stress to the plants during the calibration phase, recording the complete response curves of the comprehensive plant stress index and comprehensive environmental factors, and fitting the experimental data using parameter estimation methods in identification theory.

[0028] The specific implementation method of the dynamic prediction unit predicting future trajectories is further clarified; in order to endow the system with the ability to make forward predictions, this embodiment establishes a dynamic model that can describe how the plant stress state evolves with time and environmental changes.

[0029] This prediction function is implemented in the following way: To describe the comprehensive plant stress index To illustrate the dynamic response process of plants, this embodiment employs a first-order linear dynamic system model that more realistically depicts the accumulation and decay of stress. This model decomposes the plant's stress response into two core components: a convergent adaptation process to the current environmental state and an instantaneous shock response to the rate of environmental change. This model is described by the following differential equation: ; To comprehensively measure plant stress index Regarding time The rate of change of , whose dimension is the reciprocal of time. ; This is the dimensionless comprehensive plant stress index calculated by the state quantification unit at the current moment; To balance the stress index, representing the current environmental factors The steady-state stress level that plants will eventually reach under sustained stabilizing effects; this index is dimensionless. For the current moment, the dimensionless normalized comprehensive environmental factors; For comprehensive environmental factors Regarding time The rate of change represents the degree of drastic change in the environment, and its dimension is the reciprocal of time. ; The intrinsic resilience coefficient of a plant represents the resilience under actual stress index. Deviating from equilibrium state The rate at which a plant relies on its own physiological regulation to restore itself to equilibrium is a coefficient that reflects the plant's self-repair ability, and its dimension is the reciprocal of time. ; The environmental stress sensitivity coefficient represents the instantaneous response intensity of a plant to drastic environmental changes. This coefficient is a dimensionless parameter that reflects the plant's sensitivity to sudden environmental changes. The physical meaning of this model (Equation B) is that the rate of change of the stress exponent is determined by two parts; the first term It is a relaxation term that describes the plant's intrinsic recovery mechanism: when actual stress occurs... Balance stress determined by the environment When inconsistency occurs, the system will self-recover or deteriorate at a rate proportional to the deviation, with a rate constant of . ; Second item It is an impact term that describes the system's direct response to sudden environmental changes: when the environment changes drastically (i.e., when the system experiences a sudden change in environment). A large absolute value will cause an instantaneous shock to the stress index that is proportional to the rate of change. The shock intensity is determined by the sensitivity coefficient. Decide; The equilibrium stress index in the model It is calculated using a linear saturation model to characterize the nonlinear effect of environmental stress from zero to saturation. The calculation method is as follows: ; To balance the stress index, representing the current environmental factors The steady-state stress level that plants will eventually reach under sustained stabilizing effects; this index is dimensionless. For the current moment, the dimensionless normalized comprehensive environmental factors; This is a dimensionless calibration coefficient determined during the calibration phase, representing the proportion of external environmental stress that is converted into internal homeostatic stress in the plant; The key parameter in the model is the plant's intrinsic resilience coefficient. Environmental stress sensitivity coefficient and calibration coefficients The acquisition method is crucial to the feasibility of the scheme; the values ​​of these parameters are not theoretically preset, but obtained through a rigorous experimental calibration process: in the calibration stage before system deployment, a series of dynamically changing environmental stresses of known patterns (e.g., gradual temperature increase or light abrupt change) are applied to the sample plants, while the data acquisition and state quantification unit of this invention is used to fully record the comprehensive environmental factors throughout the process. With the comprehensive plant stress index The continuous response curve is obtained; based on the experimental data of this response curve, parameter estimation methods in system identification theory (e.g., least squares method or Kalman filtering method) are used to fit the model (Equation B), thereby identifying the parameter that best describes the plant's response characteristics. , and value; During system operation, the dynamic prediction unit can numerically solve the differential equations of the first-order dynamic system model (Equation B) (e.g., using the Euler method or the Runge-Kutta method) based on current measurements. Value and Real-time changing trends, predict The evolutionary trajectory over the next period of time; This embodiment introduces a dynamic model with clear physical meaning, calibrated using experimental data, upgrading the entire analysis system from a static evaluation system to a dynamic prediction system; it can not only assess the current state but also predict future trends; through the analysis of key model parameters... and The empirical identification ensures the accuracy of predictions and a high degree of fit to the physiological characteristics of specific plant varieties; this predictive ability is the core of achieving forward-looking and preventive management, and can issue early warnings before catastrophic stresses cause substantial damage to plants, thereby gaining a valuable time window for intervention measures, resulting in significant technical benefits.

[0030] The beneficial effects and technological advancements demonstrated by this technical solution are rooted in its systematic architecture and inherent mathematical model. Its difference from existing technologies lies in three aspects: monitoring dimensions, analysis depth, and decision-making timeliness. The mathematical model in the scheme is not an isolated calculation step, but a logically progressive cognitive process. Its core lies in transforming the overall health status of plants, which cannot be directly observed, into a calculable and predictable physical quantity. The proposed comprehensive plant stress index This is not a formula derived from fundamental physical laws, but rather an engineering definition built upon multi-criteria decision theory. Its practical significance lies in creating a benchmark to measure the degree to which plants deviate from their ideal healthy state. Its logical basis is that, through a preliminary normalization step, multi-dimensional data from different physical dimensions, such as those from miniature Raman spectroscopy probes, hyperspectral imaging units, and pulse-modulated chlorophyll fluorometers, are uniformly mapped to a dimensionless [database / structure]. Within the interval; this ensures that the stress components from different sources are additive, while the weighting coefficients... The introduction of this concept reflects the different understandings of the importance of different physiological indicators to the overall health of plants; the entire calculation process is clearly described in the instruction manual, and is logically consistent, laying a solid quantitative foundation for subsequent dynamic analysis. Furthermore, the first-order dynamic system model used in the dynamic prediction unit describes a relaxation process driven by external forces and containing internal negative feedback; this formula is reasonably explained in the specification, rather than being generated out of thin air; the first term on the right... It describes the intrinsic recovery mechanism of plants: when the actual stress index Deviation from the equilibrium stress index determined by the current environment When the system recovers, it will automatically recover at a rate proportional to the deviation, and the recovery rate is determined by the plant's intrinsic resilience coefficient. Decision; Second item This describes the system's direct response to sudden environmental shocks, and its strength is determined by the environmental stress sensitivity coefficient. The model decomposes plant stress response into two basic processes: convergent adaptation to environmental states and transient perturbations to environmental changes. Its parameters... and The model was obtained by fitting experimental data, ensuring the fidelity of the model to specific plant physiological characteristics.

[0031] The beneficial effects of this solution are the inevitable result of the synergistic effect of the aforementioned technical features, specifically reflected in: First, it achieves a paradigm shift from discrete, destructive sampling to in-situ, continuous monitoring. Existing technologies generally rely on offline molecular biology detection, which is cumbersome, time-consuming, and causes damage to the plants themselves. This solution, by deploying a data acquisition unit consisting of a miniature Raman spectroscopy probe, a hyperspectral imaging unit, and a pulse-modulated chlorophyll fluorescence spectrometer, can acquire continuous data streams of internal chemical composition, macroscopic morphology, and core physiological functions at high frequency and in multiple dimensions without interfering with the normal growth of the plants. This fundamental change in monitoring methods is a prerequisite for achieving all subsequent accurate analysis and prediction. Secondly, it achieves the quantitative fusion of multi-source heterogeneous data into a single standardized state index. Faced with sensor data from multiple sources and of different dimensions, existing technologies are often ineffective or can only perform isolated analyses. The state quantification unit in this scheme, through normalization and weighted summation of environmental and physiological data, ultimately calculates the comprehensive plant stress index. The significance of this process lies in the fact that it provides a unified and clear evaluation benchmark, transforming the vague notion of good or poor plant growth into a precise and verifiable standard. Comparing values ​​within an interval greatly improves the objectivity and accuracy of the assessment; for example, when the calculated... Value At that time, it clearly indicates that the plant is in a healthy state of low stress; while when it rises to When this occurs, it indicates that the plant is under significant stress, even if no visible symptoms have yet appeared. Third, it achieves a significant leap in analytical depth, moving from passive response to proactive prediction. Existing technologies can at most inform managers of the plant's current state, and decisions often lag behind the occurrence of damage. This solution's dynamic prediction unit, utilizing an experimentally calibrated first-order dynamic system model, can deduce the future trajectory of the stress index based on current stress levels and environmental changes. For example, the system in… Constantly monitored At the same time, environmental factors It began to deteriorate rapidly, and the model could predict that... Hours later, The future trajectory will exceed the preset intervention threshold (e.g.) At this point, the decision-making intervention unit will generate an early warning signal; this predictive ability provides managers with a valuable window of opportunity for intervention, enabling them to take remedial measures (such as adjusting water and fertilizer, shading) before irreversible damage occurs to the plants, which is something that existing technologies cannot achieve at all; this shift from looking at the present to looking at the future is the core technological advancement of this solution.

[0032] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A comprehensive intelligent analysis system for plant cultivation status, characterized in that, include: The data acquisition unit is used to collect multi-dimensional data on the physiological state of plants; The state quantization unit is used to receive multi-dimensional data and calculate the comprehensive plant stress index; The dynamic prediction unit is used to receive the comprehensive plant stress index and predict future trajectories; The decision intervention unit is used to receive future trajectories and compare them with preset intervention thresholds. When the future trajectory is about to exceed the preset intervention threshold within a preset time window, an early warning signal is generated. When the future trajectory does not exceed the preset intervention threshold within a preset time window, a normal status signal is generated.

2. The comprehensive intelligent analysis system for plant cultivation status according to claim 1, characterized in that, The data acquisition unit includes: Miniature Raman spectroscopy probes are used to obtain vibrational information of secondary metabolite molecules; Hyperspectral imaging unit for capturing canopy pigment and moisture status; Pulse-modulated chlorophyll fluorometer, used to measure photosynthetic efficiency.

3. The comprehensive intelligent analysis system for plant cultivation status according to claim 1, characterized in that, The state quantification unit calculates the comprehensive plant stress index by performing the following steps: Determine the normalized comprehensive environmental factors; transform multidimensional data into multiple standardized stress components; and perform weighted summation of all stress components to generate a comprehensive plant stress index.

4. The comprehensive intelligent analysis system for plant cultivation status according to claim 3, characterized in that, The standardized stress components are determined by acquiring real-time measurements of each physiological data point and normalizing them based on a preset reference baseline value under healthy conditions and a preset adverse threshold value under extreme stress.

5. The comprehensive intelligent analysis system for plant cultivation status according to claim 3, characterized in that, The normalized comprehensive environmental factors are determined by acquiring real-time measurements from each environmental sensor and performing normalized weighted calculations based on preset weights, suitability values, and stress thresholds.

6. The comprehensive intelligent analysis system for plant cultivation status according to claim 1, characterized in that, The dynamic prediction unit predicts future trajectories by performing the following steps: A first-order dynamic system model was adopted, which describes the relaxation process tending towards equilibrium. The model was numerically solved to predict the future trajectory of the comprehensive plant stress index.

7. The comprehensive intelligent analysis system for plant cultivation status according to claim 6, characterized in that, The first-order dynamic system model includes the plant's intrinsic resilience coefficient, equilibrium stress index, and environmental stress sensitivity coefficient; The equilibrium stress index is calculated using a linear saturation model based on comprehensive environmental factors and preset calibration coefficients.

8. The comprehensive intelligent analysis system for plant cultivation status according to claim 7, characterized in that, The plant intrinsic resilience coefficient and environmental stress sensitivity coefficient were obtained by applying dynamically changing environmental stress to the plants during the calibration phase, recording the complete response curves of the comprehensive plant stress index and comprehensive environmental factors, and fitting the experimental data using parameter estimation methods in identification theory.

Images