Flower cuttage breeding growth cultivation monitoring system and method

Abstract

The invention discloses a growth and cultivation monitoring system and method for flower cuttage breeding, and belongs to the field of flower cuttage breeding, and the method comprises the steps: installing a multi-mode sensor array in a flower cuttage breeding region, collecting the related data of the breeding region, and transmitting the collected data to an edge gateway; a comprehensive maintenance algorithm is set in the background system, and a nutrient solution blending scheme and an irrigation control scheme are generated; the background system sends the generated nutrient solution blending scheme and the irrigation control scheme to an on-site irrigation control system; after irrigation is completed, the field control system stops irrigation operation, records irrigation data and uploads the data to the background system for analysis. According to the method, intelligent regulation and control of water supply in the cuttage breeding process are achieved, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of flower cutting propagation, and particularly to a growth cultivation monitoring system and method for flower cutting propagation. Background Art

[0002] Flower cutting propagation is a common plant propagation method. By inserting branches, leaves, etc. of plants into suitable substrates, they can take root and germinate, thus multiplying new plants. The traditional monitoring and management method of flower cutting propagation mainly relies on manual observation and empirical judgment, with problems such as single monitoring means, inability to monitor multiple environmental parameters and plant physiological states in real time, and inability to dynamically adjust the nutrient solution ratio and irrigation strategy according to real-time data, resulting in low cutting survival rate and low growth efficiency.

[0003] With the development of emerging technologies such as the Internet of Things and artificial intelligence, a flower cutting propagation monitoring system based on a multi-modal sensor array, edge computing, and cloud intelligent algorithms has emerged. This system can collect various environmental parameters such as soil temperature and humidity, air temperature and humidity, light intensity, carbon dioxide concentration, and soil conductivity in the cutting propagation area in real time, and perform data preprocessing in combination with plant physiological characteristics to provide accurate input data for the background intelligent algorithm. The background system generates targeted nutrient solution formulas and irrigation strategies through dynamic environmental quality index algorithms, nutrient solution formulation optimization algorithms, and irrigation control algorithms, and issues them to the on-site execution system to achieve intelligent control of the cutting propagation process.

[0004] However, the existing flower cutting propagation monitoring systems still need to be further optimized and improved in terms of sensor layout, data preprocessing, algorithm design, etc., to improve the adaptability, accuracy, and intelligent level of the system, so as to better meet the growth needs of different varieties of flowers and improve the cutting survival rate and growth quality. Summary of the Invention

[0005] One object of the present invention is to provide a growth cultivation monitoring method for flower cutting propagation, so as to solve the problem in the prior art that the nutrient solution ratio and irrigation strategy cannot be dynamically adjusted according to real-time monitoring data, resulting in the inability to meet the growth needs of different varieties of flowers.

[0006] The present invention is achieved through the following technical solutions. A method for monitoring the growth and cultivation of flower cuttings includes the following steps: S100. Install a multi-modal sensor array in the flower cutting and breeding area to collect relevant data in the breeding area and send the collected data to the edge gateway. The edge gateway preprocesses the collected data and sends the processed data to the background system; S200. A comprehensive maintenance algorithm is set in the background system. The comprehensive maintenance algorithm generates a nutrient solution preparation plan and an irrigation control plan based on the data sent by the edge gateway; S300. The background system sends the generated nutrient solution preparation plan and irrigation control plan to the on-site irrigation control system. After receiving the plans, the on-site control system adjusts the ratio of the nutrient solution according to the nutrient solution preparation plan and performs corresponding irrigation operations according to the irrigation control plan; S400. When the irrigation is completed, the on-site control system stops the irrigation operation, records the data of this irrigation, and uploads the data to the background system for analysis.

[0007] Further, the multi-modal sensor array integrates a soil temperature and humidity sensor, an air temperature and humidity sensor, a light intensity sensor, a CO2 concentration sensor, and a soil conductivity sensor. The above sensors are integrated into a multi-modal sensor array through the edge gateway.

[0008] Further, the preprocessing includes: S110. Split the data collected by the multi-modal sensor array and independently process each sensor through the following formula to standardize the sensor data:

[0009] , where is the standardized data of the i-th sensor, is the original data of the sensor, is the sign function of, is the sensor correlation base value, is the upper limit of the sensing range; S120. After the preprocessing of the sensor data, perform in-depth data processing according to the physiological requirements of specific plant varieties based on the already standardized data, and realize the extraction and mapping of specific variety characteristics through variety characteristic-level standardization. The variety characteristic-level standardization is shown in the following formula:

[0010] , where is the metabolic equivalent coefficient, α and β are variety photosynthetic type parameters, is the optimal photosynthetic temperature, is the variety osmotic characteristic value.

[0011] Furthermore, the comprehensive maintenance algorithm includes: a dynamic environmental quality index sub-algorithm, a nutrient solution formulation optimization sub-algorithm, and an irrigation control sub-algorithm. Among them, the dynamic environmental quality index sub-algorithm is used to calculate the variable factors in the cultivation area based on the received standardized sensor data, improving the accuracy and consistency of environmental state assessment; the nutrient solution formulation optimization sub-algorithm is used to optimize the nutrient solution formula according to the evaluation result of the dynamic environmental quality and in combination with the plant metabolic requirements characterized by the metabolic equivalent coefficient; the goal of the irrigation control sub-algorithm is to minimize the water potential gradient, thereby obtaining the optimal irrigation strategy.

[0012] Furthermore, the dynamic environmental quality index sub-algorithm is shown as follows:

[0013] , where is the dynamic environmental quality index at time t, is the environmental state vector at time t, is the time-varying weight function, is the wet-ion composite index, is the wet-ion composite index of the environmental state vector, is the maximum value of the wet-ion composite index.

[0014] Furthermore, the nutrient solution formulation optimization sub-algorithm includes: a state space equation part and a hard and soft constraint part. Among them, the state space equation is:

[0015] , where is the change rate of the nutrient solution concentration, a is the proportionality coefficient, is the actually measured conductivity, is the set target conductivity, is the proportionality coefficient, is the change rate of the root water potential; is the root water potential function, is the variety-specific parameter, is the volume of the plant's woody part in the target flower cultivation area is the area of the plant's roots in the target flower cultivation area; the hard constraint is , where is the conductivity, is the lowest allowable value of the conductivity, is the highest allowable value of the conductivity; the soft constraint is , where is the conductivity change rate, is the threshold of the safe conductivity change rate to prevent the EC from changing too quickly and causing an impact on the plant.

[0016] Further, the specific form of the irrigation control sub-algorithm is shown as follows:

[0017] , where is the overall value function, E is the expectation operator, is the discount factor, is the discount rate, is the quadratic term of the water potential difference, is the weight coefficient, is the quadratic term of the control action, u is the irrigation amount, is the weight coefficient.

[0018] Further, the wet-ion composite index is expressed by the following formula:

[0019] , where is the degree field amplification, is the humidity oscillation coefficient, is the EC variability.

[0020] On the other hand, the present invention provides a growth cultivation monitoring system for flower cutting propagation, and the growth cultivation monitoring system includes: a processor; a memory storing a computer program, which when executed by the processor, implements the growth cultivation monitoring method for flower cutting propagation as described above.

[0021] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0022] 1. By arranging a multi-modal sensor array in the cutting propagation area, the present invention can realize real-time monitoring of various environmental parameters such as soil temperature and humidity, air temperature and humidity, light intensity, carbon dioxide concentration, soil conductivity, etc., accurately grasp the physiological state of the plants, and provide data support for accurately controlling the cutting propagation environment.

[0023] 2. By standardizing the sensor data and extracting the variety characteristics through the edge gateway, and introducing the metabolic equivalent coefficient to quantify and standardize the physiological characteristics of different varieties of flowers, the present invention provides accurate input data for the background intelligent algorithm, and improves the adaptability and intelligent level of the system.

[0024] 3. Through the comprehensive maintenance algorithm, the present invention dynamically generates targeted nutrient solution formulations and irrigation strategies according to the real-time monitoring data, meets the growth requirements of different varieties of flowers, improves the cutting survival rate and growth quality, realizes intelligent control of water supply during the cutting propagation process through the obtained optimal irrigation strategy, and improves the resource utilization efficiency. Description of the Drawings

[0025] The accompanying drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0026] Figure 1 It is a flowchart of the method provided in Embodiment 1 of the present invention. Specific embodiments

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0028] Embodiment 1

[0029] Figure 1 It shows a flowchart of the growth and cultivation monitoring method for flower cuttage propagation in this embodiment. It can be seen from the figure that this embodiment includes the following steps:

[0030] Step 1: First, install a multi-modal sensor array in the area for cultivating flower cuttage propagation to collect relevant data in the propagation area.

[0031] Specifically, in this embodiment, the multi-modal sensor array is integrated with a soil temperature and humidity sensor, an air temperature and humidity sensor, a light intensity sensor, a CO2 concentration sensor, and a soil conductivity sensor.

[0032] These sensors are respectively installed at different positions in the propagation area and integrated into a multi-modal sensor array through an edge gateway. The edge gateway processes the data collected by the sensors and sends the processed data to the background processing system.

[0033] It should be noted that when arranging sensors in the park, it is necessary to arrange them specifically according to the different varieties of the cultivated flowers. For different flowers, different arrangement methods can be selected according to the cultivation characteristics.

[0034] For example, for orchids (such as Phalaenopsis / Dendrobium officinale, etc.), it is necessary to strengthen the monitoring of the air layer. Three-level temperature and humidity sensors can be arranged at 5 cm from the substrate / middle of the canopy leaf curtain / top of the facility to collect data in a ladder manner; install the CO2 sensor in the photosynthetic active area of the leaves (10 cm from the new leaves); considering the vulnerability of the roots of orchids, a high-frequency capacitive soil sensor can be used to avoid damaging the aerial root structure, and the EC sensor is arranged 2-3 cm below the substrate surface. An ultraviolet wavelength sensor (in the 325-380 nm band) can also be added to monitor the light quality composition.

[0035] For roses / Chinese roses, a soil temperature and humidity sensor consisting of a vertical probe array buried in three layers of 5 / 15 / 25 cm in the substrate soil can be used to track the sites where callus formation occurs; the EC sensors are centrally arranged around the base of the cuttings (a circular array with a radius of 5 cm).

[0036] In addition, it should be noted that in this embodiment, considering that the sensor data comes from different types of sensors and the data of various types vary greatly. To ensure the rapidity of data transmission and subsequent data processing, the data collected by the multi-modal sensor array is preprocessed in the edge gateway, thereby reducing the difficulty of subsequent data processing.

[0037] Specifically, in this embodiment, the preprocessing of the data collected by the multi-modal sensor array in the edge gateway includes the following steps:

[0038] 1) Split the data collected by the multi-modal sensor array, and then independently process each sensor through the following formula to eliminate the difference in physical dimensions:

[0039] ,

[0040] where, is the standardized data of the i-th sensor, is the original data of the sensor, is the sign function of, is the sensor correlation base value, which is used to remove the differences between sensors (for example, the orchid EC base value is 0.8, and the rose is 1.2), is the upper limit of the sensing range.

[0041] 2) After the preprocessing of the sensor data, on the basis of the standardized data, further in-depth data processing is carried out according to the physiological needs of specific plant varieties. Through variety characteristic-level standardization, specific variety characteristics are extracted and mapped. The standardized data can be further processed through the following formula:

[0042] ,

[0043] where, is the metabolic equivalent coefficient, α and β are variety photosynthetic type parameters, which are used to distinguish the weights of different photosynthetic types according to different flower varieties, is the optimal photosynthetic temperature, which reflects the most suitable temperature for cultivating different flowers, is the variety osmotic characteristic value, which reflects the pressure of the cell osmotic balance point when different varieties of flowers are cuttaged and propagated, and helps to reflect the metabolic requirements of plants under different osmotic pressures.

[0044] It should be noted that by extracting and mapping variety characteristics, it is possible to quantify and standardize the physiological characteristics of different plant varieties, providing a unified metabolic equivalent coefficient M k , and this coefficient can be used to optimize environmental control and nutrient management strategies to make them more in line with the actual needs of plants, thereby improving the growth efficiency and health status of plants.

[0045] Step 2: The edge gateway sends the preprocessed data to the background system, and a comprehensive maintenance algorithm is set in the background system. The comprehensive maintenance algorithm generates a nutrient solution preparation plan and an irrigation control plan based on the data sent by the edge gateway.

[0046] Specifically, in this embodiment, the comprehensive maintenance algorithm consists of a dynamic environmental quality index sub-algorithm, a nutrient solution preparation optimization sub-algorithm, and an irrigation control sub-algorithm.

[0047] Among them, the dynamic environmental quality index sub-algorithm is used to calculate the variable factors (such as temperature, humidity, EC, etc.) in the cultivation area according to the received standardized sensor data. Uniformly inputting the standardized data of the edge gateway into the dynamic environmental quality index algorithm model can improve the accuracy and consistency of environmental state evaluation.

[0048] By using the standardized data to calculate the dynamic environmental quality index, the suitability of the environment for flower cutting propagation during this period is evaluated through the dynamic environmental quality.

[0049] Specifically, the dynamic environmental quality index sub-algorithm can be shown as the following formula:

[0050] ,

[0051] Among them, is the dynamic environmental quality index at time t, is the environmental state vector at time t, and this environmental state vector is composed of multiple normalized sensor data. This environmental state vector is obtained by combining all the normalized sensor data at time t, , where, here represents the normalized data of the i-th sensor at time t.

[0052] is a time-varying weight function, which is used to calculate the influence degree of a certain variable at a historical moment on the current moment. The weight value will decay over time, and this function can usually be defined using a decay function or empirical data. For example, it can be defined as: , where, is the attenuation coefficient, which determines the relative importance of data at different time points. e is the natural constant, t is the current moment, is the past moment.

[0053] is the wet-ion composite index, which is calculated based on the environmental state vector and can be expressed by the following formula:

[0054] ,

[0055] where, is the degree field amplification, representing the change in environmental temperature, indicating the degree of change in environmental temperature over a period of time. is the humidity oscillation coefficient, representing the fluctuation amplitude of environmental humidity. is the EC variability, used to represent the change in salt concentration. Here, , and are all normalized data provided by at time .

[0056] is the wet-ion composite index of the environmental state vector, specifically representing an index comprehensively calculated from temperature, humidity, and EC, used to evaluate the environmental quality. is the maximum value of the wet-ion composite index, making the dynamic environmental quality index value fluctuate within a fixed range.

[0057] It should be noted that during the construction of the dynamic environmental quality index sub-algorithm

[0058] is actually the combination of multiple sensor data at time after standardization (i.e., ). These normalized data are combined into the environmental state vector , and the dynamic environmental quality index is obtained through calculating the wet-ion composite index and weighted calculation.

[0059] The nutrient solution formulation optimization sub-algorithm optimizes the nutrient solution formula according to the evaluation result of the dynamic environmental quality and combines the plant metabolic requirements characterized by the metabolic equivalent coefficient.

[0060] Specifically, the nutrient solution formulation optimization sub-algorithm can include a state space equation part and a hard and soft constraint part as shown in the following formula:

[0061] The state space equation is,

[0062] ,

[0063] The hard constraint is,​

[0064] ,

[0065] The soft constraint is that

[0066] ,

[0067] wherein, is the change rate of nutrient solution concentration, a is a proportionality coefficient used to describe the relationship between the actual EC and the set EC, is the actually measured conductivity, is the set target conductivity, is a proportionality coefficient describing the relationship between the change in root water potential and the nutrient solution concentration, is the change rate of root water potential.

[0068] is the root water potential function describing the water absorption capacity of plant roots, is a variety-specific parameter describing the water potential requirement characteristics of cuttings. It is affected by the metabolic equivalent coefficient. The variety-specific parameter in the root water potential function is determined by the specific requirements of the plant, and this requirement is directly affected by the metabolic equivalent coefficient. In the metabolic equivalent coefficient, reflects the osmotic characteristics of the plant, and the osmotic characteristics affect the specific value. Plants with a large variety osmotic characteristic value require a higher value to meet more water and nutrient requirements; through the calculation of the metabolic equivalent coefficient, the value is adjusted to an appropriate level for calculating the root water potential. is the volume of the plant's woody part in the target flower cultivation area, that is, the volume of the cutting cultivation area, is the area of the plant's roots in the target flower cultivation area, that is, the volume under the soil in the cutting cultivation area.

[0069] is the conductivity, is the lowest allowable value of the conductivity. Too low will affect plant growth, is the highest allowable value of the conductivity. Too high will cause root burns to the plant, is the conductivity change rate, is the threshold value of the safe conductivity change rate to prevent the impact on plants caused by too rapid EC change.

[0070] It should be noted that in the nutrient solution preparation optimization sub-algorithm formula shown in this embodiment, the metabolic equivalent coefficient is used as the key parameter. The metabolic equivalent coefficient quantifies the physiological requirements of the plant into a comprehensive attribute, and this attribute can be directly used in the nutrient solution preparation optimization process. Specifically, the metabolic equivalent coefficient Mk Provides the specific demand parameters of the plant for the nutrient solution formulation optimization algorithm, and guides the concentration and ingredient ratio of the nutrient solution through these parameters. Specifically, the optimal photosynthesis temperature Determines the optimal temperature for plant photosynthesis, and the nutrient solution formulation optimization sub-algorithm adjusts according to the difference between the current environmental temperature and the optimal photosynthesis temperature The value of, so that the nutrient solution concentration adapts to the optimal metabolic requirements of the plant at this temperature. When the environmental temperature deviates from , the supply ratio of nutrient elements can be adjusted to compensate for the impact of temperature on the plant's metabolic rate. And the variety osmotic characteristic value Determines that the root water potential parameter affects the calculation of the root water potential function . The osmotic characteristic value affects the calculation of the root water potential function through variety-specific parameters and the ratio of xylem to root volume, and further affects the nutrient solution formulation strategy. The nutrient solution formulation optimization algorithm will adjust the nutrient solution concentration in real time according to the difference between the actual sensor data and the set target. In this process, the set target is dynamically adjusted, and this adjustment depends on the metabolic equivalent coefficient M k The parameters provided.

[0071] That is to say, by measuring and quantifying the key parameters of plant growth through the metabolic equivalent coefficient, the nutrient solution formulation optimization sub-algorithm can achieve precise, dynamic and intelligent nutrient solution formulation based on the quantified final parameters. This calculation relationship ensures that the algorithm has adaptability in the face of diverse plant needs, thereby improving the efficiency and growth quality of plant cuttings.

[0072] The goal of the irrigation control sub-algorithm is to minimize the water potential gradient. In this embodiment, the integrated performance achieved by the irrigation control strategy is evaluated through a value function. Its specific form is shown in the following formula:

[0073] ,

[0074] Among them, Is the overall value function, which is the expected Integrates the discounted benefits / losses from time t from zero to T,

[0075] Is the expectation operator, which reflects the randomness and uncertainty in the consideration process. The irrigation system is usually affected by various factors, such as weather changes, fluctuations in soil moisture, etc. Therefore, we use the expected value to describe the impact of this uncertainty on the objective function.

[0076] Is the discount factor, is the discount rate, representing the weight for discounting over time. The longer the time, the more the discounting, and the greater the weight of earlier impacts.

[0077] is the quadratic term of the water potential difference, representing the loss or cost brought by the water potential fluctuation effect. is the weight coefficient. is the quadratic term of the control action, representing the energy or resource consumption brought by the irrigation control action. Here, u is the irrigation amount. is the weight coefficient.

[0078] It should be noted that the dynamic environmental quality index sub - algorithm determines the water demand of plants, which affects the water potential difference in the irrigation control sub - algorithm. The nutrient solution formulation optimization sub - algorithm adjusts the irrigation amount and nutrient solution ratio according to the water demand and nutrient demand of plants, affecting the cost of the irrigation amount. The irrigation control sub - algorithm synthesizes the water potential difference and irrigation cost, promoting the system to make the optimal irrigation decision in a dynamic environment.

[0079] These three sub - algorithms are integrated into a dynamic feedback framework through a closed - loop control system. Specifically: The system first evaluates the current environmental state according to the dynamic environmental quality index sub - algorithm. Then, it adjusts the irrigation amount u(t) and the ratio of the nutrient solution at each moment through the nutrient solution formulation optimization sub - algorithm to ensure that the growth needs of plants are met. Finally, the system minimizes the resource consumption and plant water potential difference in the entire irrigation process by optimizing the value function of the irrigation control sub - algorithm, thereby obtaining the optimal irrigation strategy. Through this coupling mechanism, the entire monitoring and control system can adapt to environmental changes, dynamically adjust the irrigation strategy, and minimize resource waste.

[0080] Step 3: The background system generates a nutrient solution formulation plan and an irrigation control plan according to the growth needs of the flowers and sends them to the on - site irrigation control system. After receiving the plans, the on - site control system adjusts the ratio of the nutrient solution according to the nutrient solution formulation plan and simultaneously performs corresponding irrigation operations according to the irrigation control plan to ensure that the flowers obtain appropriate water and nutrients during the cutting and breeding process.

[0081] Step 4: After the irrigation is completed, the on - site control system stops the irrigation operation and records the data of this irrigation (such as irrigation amount, time, humidity change, specific ratio of the nutrient solution, etc.). These data can be analyzed by the background system to optimize future irrigation control plans.

[0082] Example 2

[0083] In this example, a growth cultivation monitoring system for flower cutting and breeding is disclosed.

[0084] The growth and cultivation monitoring system for flower cutting propagation in this embodiment includes: a processor and a memory. A computer program is stored in the memory. When the computer program is executed by the processor, it can implement the growth and cultivation monitoring method for flower cutting propagation as in Embodiment 1.

[0085] The specific implementation manners described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A growth cultivation monitoring method for flower cuttage propagation, characterized in that, The described growth and cultivation monitoring method includes: S100. Install a multi-modal sensor array in the flower cutting and breeding area to collect relevant data in the breeding area and send the collected data to the edge gateway. The edge gateway preprocesses the collected data and sends the processed data to the background system. S200. There is a comprehensive maintenance algorithm set in the background system. The comprehensive maintenance algorithm generates a nutrient solution formulation plan and an irrigation control plan based on the data sent by the edge gateway. S300. The background system sends the generated nutrient solution formulation plan and irrigation control plan to the on-site irrigation control system. After receiving the plan, the on-site control system adjusts the ratio of the nutrient solution according to the nutrient solution formulation plan and simultaneously performs corresponding irrigation operations according to the irrigation control plan. S400. When the irrigation is completed, the on-site control system stops the irrigation operation, records the data of this irrigation, and uploads the data to the background system for analysis.

2. The growth and cultivation monitoring method for flower cuttage breeding according to claim 1, characterized in that The multi-modal sensor array integrates a soil temperature and humidity sensor, an air temperature and humidity sensor, a light intensity sensor, a CO2 concentration sensor, and a soil conductivity sensor. The above sensors are integrated into a multi-modal sensor array through the edge gateway.

3. The growth and cultivation monitoring method for flower cutting propagation according to claim 1, characterized in that The preprocessing includes: S110. Split the data collected by the multi-modal sensor array and independently process each sensor through the following formula to realize the standardization of sensor data: ， Among them, is the normalized data of the i-th sensor, is the original data of the sensor, is the sign function of is the sensor correlation base value, is the upper limit of the sensing range; S120. After the preprocessing of the sensor data, perform in-depth data processing according to the physiological requirements of specific plant varieties based on the already standardized data, and realize the extraction and mapping of specific variety characteristics through variety characteristic-level standardization. The variety characteristic-level standardization is shown in the following formula: ， Among them, is the metabolic equivalent coefficient, and α and β are the variety photosynthetic type parameters, is the optimal photosynthetic temperature, is the variety osmotic characteristic value.

4. The growth and cultivation monitoring method for flower cutting propagation according to claim 1, characterized in that, The comprehensive maintenance algorithm includes: A dynamic environmental quality index sub-algorithm, a nutrient solution formulation optimization sub-algorithm, and an irrigation control sub-algorithm. Among them, The dynamic environmental quality index sub-algorithm is used to calculate the variable factors in the cultivation area based on the received standardized sensor data to improve the accuracy and consistency of the environmental state assessment. The nutrient solution formulation optimization sub-algorithm is used to optimize the formula of the nutrient solution according to the evaluation result of the dynamic environmental quality and in combination with the plant metabolic requirements characterized by the metabolic equivalent coefficient. The goal of the irrigation control sub-algorithm is to minimize the water potential gradient, and then obtain the optimal irrigation strategy.

5. The growth and cultivation monitoring method for flower cuttage propagation according to claim 4, characterized in that, The dynamic environmental quality index sub-algorithm is shown in the following formula: ， wherein, is the dynamic environmental quality index at time t, is the environmental state vector at time time, is the time-varying weight function, is the wet-ion composite index, is the wet-ion composite index of the environmental state vector, is the maximum value of the wet-ion composite index.

6. The growth and cultivation monitoring method for flower cutting propagation according to claim 4, characterized in that, The nutrient solution formulation optimization sub-algorithm includes a state space equation part and a hard and soft constraint part. Among them, The state space equation is: ， wherein, is the change rate of the nutrient solution concentration, a is the proportionality coefficient, is the actually measured conductivity, is the set target conductivity, is the proportionality coefficient, is the change rate of the root water potential; is the root water potential function, is the variety-specific parameter, is the volume of the plant's woody part in the target flower cultivation area is the area of the plant's roots in the target flower cultivation area; The hard constraint is, ， Wherein, is the conductivity, is the minimum allowable value of the conductivity, is the maximum allowable value of the conductivity; The soft constraint is, ， Among them, is the rate of change of conductivity, is the threshold value of the rate of change of safe conductivity to prevent the rapid change of EC from impacting plants.

7. The growth and cultivation monitoring method for flower cuttage breeding according to claim 4, characterized in that The specific form of the irrigation control sub-algorithm is shown in the following formula: ， Among them, is the overall value function, E is the expectation operator, is the discount factor, is the discount rate, is the quadratic term of the water potential difference, is the weight coefficient, is the quadratic term of the control action, u is the irrigation amount, is the weight coefficient.

8. The growth and cultivation monitoring method for flower cutting propagation according to claim 4, characterized in that, The wet-ion composite index is represented by the following formula: ， Among them, is the degree field amplification, is the humidity oscillation coefficient, is the EC variability.

9. A growth cultivation monitoring system for flower cuttage propagation, characterized in that, The growth and cultivation monitoring system includes: A processor; A memory storing a computer program, which when executed by the processor, implements the growth and cultivation monitoring method for flower cutting and breeding as described in any one of claims 1 to 8.

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