Plant management and control method and device based on bubble mud

Abstract

The invention provides a plant management and control method and device based on bubble mud, and relates to the technical field of plant management and control technologies. Based on the plant variety information, matching the type, the proportion and the replacement period of the bubble mud; monitoring the actual growth state of the plant, and calculating deviation data of an actual growth curve and a standard growth curve; if the deviation data exceeds a preset threshold value, generating a bubble mud regulation and control scheme; adjusting or replacing the bubble mud, and guiding a user to carry out environment coordinated regulation and control; and collecting the operated plant state, evaluating the control effect, and dynamically adjusting the subsequent control strategy to make the plant growth state close to the standard growth curve. The plant management and control method and device based on the bubble mud have the advantages that bubble mud configuration is matched with plant requirements, bubble mud selection is more targeted, fertilizer loss and environmental pollution are reduced, bubble mud replacement and adjustment opportunity judgment is more accurate, and plant management and control are more effective.

Description

Technical Field

[0001] The present application relates to the field of plant control technology, and in particular to a plant control method and device based on bubble mud. Background Art

[0002] Home gardening, especially indoor potted ornamental plant cultivation, is increasingly popular among the public because of its aesthetics and air purification function. As an emerging soilless cultivation substrate, bubble mud has been widely used in the field of home gardening due to its cleanliness, good air permeability and excellent water retention. However, most bubble mud products currently sold on the market use a universal formula design to provide a uniform nutrient ratio for all plants. This universal formula model is difficult to accurately match the specific nutrient requirements of different types of ornamental plants during their growth.

[0003] Although a variety of bubble mud products have emerged on the market, it is often difficult for ordinary users to accurately judge and choose the products that best suit the characteristics of the plants they grow. When making a choice, users can usually only rely on vague product descriptions or their own relatively scarce botanical knowledge, which often leads to unsatisfactory plant growth. In order to make up for the insufficient nutrient supply of bubble mud, users usually need to apply additional liquid fertilizer in the later stages of plant growth. However, liquid fertilizers are very easy to be quickly lost from the bubble mud after application, resulting in low nutrient utilization and waste of resources, and may also cause potential pollution to the indoor environment.

[0004] In addition, different plants have significantly different nutrient requirements at different growth stages. Users often have difficulty accurately determining when to replace the bubble mud or when to add nutrients. There is a general lack of scientific and effective plant management methods, which often results in unsatisfactory results in plant maintenance and management.

[0005] In view of the above problems, the existing technology needs to be improved urgently. Summary of the invention

[0006] The purpose of this application is to provide a plant control method and device based on bubble mud, which has the advantages of matching the bubble mud configuration with plant needs, more targeted bubble mud selection, reducing fertilizer loss and environmental pollution, more accurate judgment of the timing of bubble mud replacement and adjustment, and more effective plant control.

[0007] In the first aspect, the present application provides a plant control method based on bubble mud, which is used in a household indoor potted plant planting scenario to configure bubble mud and regulate the growth state for specific plant species. The technical solution is as follows:

[0008] The method includes:

[0009] Obtain plant species information and establish a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption;

[0010] Match the bubble mud type, ratio, and replacement cycle based on plant species information;

[0011] Monitor the actual growth status of plants and calculate the deviation data between the actual growth curve and the standard growth curve;

[0012] If the deviation data exceeds the preset threshold, a bubble mud control plan including ingredient control, structure control, and replacement strategy is generated;

[0013] Perform bubble mud adjustment or replacement operations to guide users to coordinate environmental control;

[0014] Collect the status of plants after the operation, evaluate the control effect, and dynamically adjust the subsequent control strategy to make the plant growth status closer to the standard growth curve.

[0015] Further, in the present application, if the deviation data exceeds a preset threshold, the step of generating a bubble mud control scheme including ingredient control, structure control, and replacement strategy includes:

[0016] If the deviation data exceeds the preset threshold, the user's personalized planting needs are obtained, and a user expectation curve including plant height control goals and flowering promotion goals is established;

[0017] The standard growth curve is integrated with the user expectation curve to obtain the target growth curve;

[0018] Analyze the deviation data to determine the deviation type and degree between the actual growth status of the plant and the target growth curve;

[0019] According to the deviation type and degree, and plant species information, a control scheme is selected from the bubble mud component control library, the structure control library, and the replacement strategy library to generate a bubble mud control scheme including component control instructions, structure control instructions, and replacement strategy instructions;

[0020] Among them, ingredient regulation instructions include nutrient type, quantity, and release rate; structure regulation instructions include bubble mud density, layered structure, and air permeability; replacement strategy instructions include replacement timing, replacement ratio, and new bubble mud type.

[0021] Furthermore, in the present application, the step of analyzing the deviation data and determining the deviation type and degree of deviation between the actual growth state of the plant and the target growth curve includes:

[0022] Collecting multi-source data of plant growth status, wherein the multi-source data includes image data, sensor data and user input data;

[0023] For the multi-source data, data synchronization processing is performed based on timestamps to obtain synchronized multi-source data;

[0024] Preprocessing the synchronized multi-source data to obtain preprocessed multi-source data;

[0025] Based on the preprocessed multi-source data, the deviation data between the actual growth state of the plant and the target growth curve in terms of height growth rate, leaf number, color change and nutrient absorption are calculated;

[0026] According to the deviation data, the deviation type and degree of deviation between the actual growth state of the plant and the target growth curve are determined, wherein the deviation types include growth retardation, excessive growth, malnutrition and pests and diseases, and the deviation degrees include mild, moderate and severe.

[0027] Further, in the present application, the step of selecting a control scheme from a bubble mud component control library, a structure control library, and a replacement strategy library according to the deviation type and degree, and plant species information, and generating a bubble mud control scheme including component control instructions, structure control instructions, and replacement strategy instructions includes:

[0028] Constructing a bubble mud regulation knowledge graph, wherein the bubble mud regulation knowledge graph includes plant species nodes, deviation type nodes, deviation degree nodes, component regulation scheme nodes, structure regulation scheme nodes, replacement strategy nodes, and association relationships between nodes;

[0029] Based on the plant species information, the deviation type and the deviation degree, a multi-hop search is performed in the bubble mud regulation knowledge graph to obtain a set of candidate regulation schemes, wherein the multi-hop search includes: taking the plant species node as the starting node, performing a first-hop search according to the plant species information to obtain an associated deviation type node set and a deviation degree node set; taking the deviation type node set and the deviation degree node set as the starting node, performing a second-hop search to obtain an associated component regulation scheme node set, a structural regulation scheme node set and a replacement strategy node set;

[0030] Calculating a confidence score for each regulation scheme in the set of candidate regulation schemes, the confidence score comprehensively considering the strength of association between the regulation scheme and the plant species, the deviation type and the deviation degree, and the compatibility between the regulation schemes;

[0031] According to the confidence score, a control scheme with the highest confidence score is selected from the candidate control scheme set to generate a bubble mud control scheme including component control instructions, structure control instructions and replacement strategy instructions.

[0032] Furthermore, in the present application, the step of calculating the confidence score of each regulation scheme in the candidate regulation scheme set includes:

[0033] Obtaining association relationship data between nodes in the bubble mud regulation knowledge graph, wherein the association relationship data includes a first association strength between a plant species node and a component regulation scheme node, a second association strength between a deviation type node and a component regulation scheme node, a third association strength between a deviation degree node and a component regulation scheme node, and a compatibility score between component regulation scheme nodes;

[0034] For each control scheme in the candidate control scheme set, according to the component control scheme node contained therein, the weighted association strength between it and the plant species node, the deviation type node and the deviation degree node is calculated, wherein the weighted association strength is obtained by weighted summing the first association strength, the second association strength and the third association strength, and the weight coefficient is determined according to the plant species, the deviation type and the deviation degree;

[0035] Calculating the compatibility score of each control scheme in the candidate control scheme set, wherein the compatibility score is obtained by weighted average of the compatibility scores between the nodes of each component control scheme in the control scheme, and the weight coefficient is determined according to the effect size of each component control scheme node;

[0036] The weighted association strength and compatibility score are weightedly summed to obtain the confidence score of each regulation scheme in the candidate regulation scheme set, and the weight coefficient is determined according to the association strength and compatibility.

[0037] Further, in the present application, the step of matching the type, ratio, and replacement cycle of bubble mud based on plant species information includes:

[0038] Obtain environmental factor data on region, season, and light intensity, and build an environmental factor database;

[0039] Extract basic nutrient requirement data of plants based on plant species information;

[0040] According to the environmental factor data, the basic nutrient requirement data of the plant is corrected using the environmental factor impact model to obtain the target nutrient requirement data, wherein the environmental factor impact model is: target nutrient requirement data = basic nutrient requirement data * (1 + regional correction coefficient + seasonal correction coefficient + light correction coefficient);

[0041] According to the target nutrient demand data, select the type of bubble mud and determine the bubble mud ratio;

[0042] Predict the bubble mud replacement cycle. The bubble mud replacement cycle prediction takes into account the plant nutrient absorption rate, bubble mud nutrient release rate and environmental factors.

[0043] Furthermore, in the present application, the step of correcting the basic nutrient requirement data of the plant using the environmental factor impact model according to the environmental factor data to obtain the target nutrient requirement data includes:

[0044] Obtain the longitude and latitude coordinates of the plant planting area, query the historical meteorological data of the corresponding area, and obtain soil composition data, annual average precipitation data, and air humidity data;

[0045] Based on the soil composition data, the regional correction coefficient is calculated using the soil nutrient conversion model, which is: regional correction coefficient = α*(soil organic matter content / soil total nutrient content)+β*(soil pH value-7), where α and β are model parameters, which are adjusted according to the plant species;

[0046] Obtain the current date, determine the current season according to the preset season division rules, and calculate the season correction coefficient using the nonlinear season impact model in combination with the plant growth cycle data. The nonlinear season impact model is: season correction coefficient = γ*sin(2πt / T+φ), where γ is the seasonal impact amplitude, t is the current time, T is the plant growth cycle, φ is the phase offset, and γ, T and φ are adjusted according to the plant species and growth stage;

[0047] Obtain the light intensity data of the plant's location, obtain spectral data in combination with a spectral sensor, and use a photosynthetically active radiation model to calculate a light correction coefficient, wherein the photosynthetically active radiation model is: light correction coefficient = δ*PAR*(red light ratio / blue light ratio), where δ is a light influencing factor, PAR is photosynthetically active radiation intensity, the red light ratio and the blue light ratio are spectral component ratios, and δ is adjusted according to the plant species;

[0048] According to the regional correction factor, seasonal correction factor and light correction factor, the target nutrient requirement data is calculated using the environmental factor impact model.

[0049] Further, in the present application, if the deviation data exceeds a preset threshold, the step of generating a bubble mud control scheme including ingredient control, structure control, and replacement strategy includes:

[0050] If the deviation data exceeds the preset threshold, the humidity, conductivity and gas composition of the bubble mud are monitored to determine whether the bubble mud has the risk of hardening, nutrient imbalance and excessive odor.

[0051] If there is a risk of compaction, adjust the bubble mud formula, increase the fiber material content, reduce the proportion of sticky components, generate a loosening instruction, and prompt the user to loosen the soil to increase the air permeability of the bubble mud;

[0052] If there is a risk of nutrient imbalance, the bubble mud nutrients are analyzed to determine the types of missing nutrients, and a directional nutrient supplement instruction is generated to prompt the user to add the corresponding nutrients to balance the bubble mud nutrients;

[0053] If there is a risk of odor exceeding the standard, the bubble mud gas composition is detected, the type of odorous gas is determined, and an activated carbon adsorption instruction or a bubble mud replacement instruction is generated to prompt the user to perform activated carbon adsorption or replace the bubble mud to eliminate the odor.

[0054] Further, in the present application, the step of performing the bubble mud adjustment or replacement operation and guiding the user to perform environmental collaborative regulation includes:

[0055] Evaluate the adaptability of plant roots to the adjustment or replacement of bubble mud, adjust the light intensity, temperature and humidity according to the adaptability, and obtain the environmental factor regulation parameters;

[0056] According to the changes in plant transpiration rate, guide users to adjust indoor air humidity and ventilation to maintain plant water balance;

[0057] Monitor the light intensity of different parts of the plant in real time, and guide users to adjust the position of the plant or use supplementary lighting equipment to achieve uniform lighting for the entire plant.

[0058] Furthermore, in the present application, a plant control device based on bubble mud is also proposed, which is used for indoor potted plant planting scenes at home to configure bubble mud and regulate the growth state for specific plant species. The device includes:

[0059] The acquisition module is used to obtain plant species information and establish a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption;

[0060] A matching module is used to match the type, ratio, and replacement cycle of bubble mud based on plant species information;

[0061] A monitoring module is used to monitor the actual growth status of the plant and calculate the deviation data between the actual growth curve and the standard growth curve;

[0062] A generation module, used to generate a bubble mud control plan including component control, structure control, and replacement strategy if the deviation data exceeds a preset threshold;

[0063] An execution module is used to perform bubble mud adjustment or replacement operations and guide users to perform collaborative environmental control;

[0064] The adjustment module is used to collect the status of plants after operation, evaluate the control effect, and dynamically adjust the subsequent control strategy to make the plant growth status closer to the standard growth curve.

[0065] From the above, it can be seen that the present application provides a plant control method and device based on bubble mud, which configures bubble mud and regulates the growth status of different types of plants, so as to match the bubble mud configuration with plant needs, make the bubble mud selection more targeted, reduce fertilizer loss and environmental pollution, make the timing of bubble mud replacement and adjustment more accurate, and make plant control more effective. It has the characteristics of matching the bubble mud configuration with plant needs, making the bubble mud selection more targeted, reducing fertilizer loss and environmental pollution, making the timing of bubble mud replacement and adjustment more accurate, and making plant control more effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of the process of a plant control method based on bubble mud provided in this application.

[0067] Figure 2 A schematic structural diagram of a plant control device based on bubble mud provided in this application.

[0068] In the figure: 210, acquisition module; 220, matching module; 230, monitoring module; 240, generation module; 250, execution module; 260, adjustment module. DETAILED DESCRIPTION

[0069] The technical solutions in the present application will be clearly and completely described below in conjunction with the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0070] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0071] In the home indoor potted planting scene, bubble mud is widely used as an emerging soilless cultivation matrix. However, existing general-purpose bubble mud products are difficult to accurately match the specific nutrient requirements of different ornamental plants. When choosing bubble mud products, users often rely on vague product descriptions or limited botanical knowledge, resulting in poor plant growth. In addition, users lack scientific and effective plant management methods, and it is difficult to accurately judge when to replace bubble mud or add nutrients, which affects the effectiveness of plant maintenance and management. These problems urgently require a plant management method based on bubble mud to configure bubble mud and regulate growth status for specific plant species.

[0072] Specifically, in a home indoor potted planting scene, users may plant a variety of ornamental plants at the same time, such as green radish, ivy, and orchids. These plants have significant differences in their nutrient requirements at different growth stages. For example, green radish requires a higher proportion of nitrogen fertilizer in the early stages of growth to promote leaf growth, while orchids require more phosphorus and potassium fertilizers during the flowering period. However, existing general-purpose bubble mud products cannot meet these differentiated nutrient needs. Users may observe problems such as yellowing of green radish leaves, slow growth of ivy, or delayed flowering of orchids, but it is difficult to accurately judge whether the bubble mud needs to be replaced or specific nutrients need to be added.

[0073] In this regard, refer to Figure 1 This application proposes a plant control method based on bubble mud, which is used in a household indoor potted plant planting scenario to configure bubble mud and regulate the growth state of specific plant species. The method includes:

[0074] S110, obtaining plant species information, and establishing a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption;

[0075] S120, matching the type, proportion, and replacement cycle of bubble mud based on the plant species information;

[0076] S130, monitoring the actual growth state of the plant, and calculating the deviation data between the actual growth curve and the standard growth curve;

[0077] S140, if the deviation data exceeds a preset threshold, generating a bubble mud control scheme including component control, structure control, and replacement strategy;

[0078] S150, performing bubble mud adjustment or replacement operations to guide the user to perform environment collaborative regulation;

[0079] S160. Collect the status of plants after the operation, evaluate the control effect, and dynamically adjust the subsequent control strategy to make the plant growth status closer to the standard growth curve.

[0080] Among them, the standard growth curve refers to the ideal growth status curve established for a specific plant species, which includes indicators such as height growth rate, number of leaves, color changes, and nutrient absorption.

[0081] Among them, bubble mud types refer to bubble mud products with different ingredient ratios and structural characteristics. Specifically, a variety of raw material combinations can be used, such as fiber materials, water-retaining materials, nutrient-releasing materials, etc., to meet the growth needs of different plants.

[0082] The deviation data refers to the difference between the actual growth curve and the standard growth curve. Specifically, a weighted calculation method of multi-dimensional indicators can be used to quantify the degree of deviation between the plant growth state and the ideal state, providing a basis for the generation of subsequent control plans.

[0083] The bubble mud regulation scheme refers to the bubble mud adjustment strategy formulated for plant growth deviations, which can include ingredient regulation (such as adjusting nutrient ratios), structural regulation (such as changing density), and replacement strategies (such as replacing part or all of the bubble mud) to optimize the plant growth environment.

[0084] Among them, environmental coordinated control means adjusting the light, temperature, humidity and other environmental factors while adjusting the bubble mud. Specifically, it can be assisted by intelligent devices or manually operated by users to achieve comprehensive optimization of the plant growth environment.

[0085] The core innovation of this application is to establish a standard growth curve, match the bubble mud type, monitor the actual growth status, generate a control plan, perform adjustment operations and dynamically evaluate the effect, so as to achieve precise bubble mud configuration and growth status control for specific plant species. This method overcomes the problem that general bubble mud products are difficult to meet the specific nutrient needs of different plants, and at the same time provides users with a scientific and effective plant management method, improving the effect and user experience of indoor potted planting at home.

[0086] The working principle of this application is explained as follows:

[0087] First, by obtaining plant species information, a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption is established. This step uses a plant growth database or expert knowledge system to build an ideal growth state model for each plant.

[0088] Next, based on the plant species information, match the bubble mud type, ratio and replacement cycle. This step selects the most suitable type from the bubble mud product library by analyzing the physiological characteristics and growth requirements of the plants, and determines the initial ratio and estimated replacement cycle.

[0089] Then, the actual growth status of the plants is continuously monitored, including using image recognition technology to analyze the appearance of the plants and using sensors to detect the environmental parameters of the bubble mud. Through data processing algorithms, the deviation data between the actual growth curve and the standard growth curve is calculated.

[0090] When the deviation data exceeds the preset threshold, the regulation plan is triggered, taking into account the plant growth stage, environmental factors and historical regulation effects, and generating a bubble mud regulation plan that includes ingredient regulation, structure regulation and replacement strategies.

[0091] Then, the bubble mud adjustment or replacement operation is performed. For ingredient regulation, it may involve adding specific nutrients or adjusting the pH value; for structural regulation, it may include changing the density or layered structure of the bubble mud; for replacement strategy, it may be partially or completely replacing the bubble mud. At the same time, users will be guided to perform environmental coordinated regulation, such as adjusting light intensity, humidity, etc.

[0092] Finally, the plant status after the operation is collected, and the control effect is evaluated through image analysis and sensor data comparison. Based on the evaluation results, the system will dynamically adjust the subsequent control strategy, such as adjusting the threshold, optimizing the control scheme generation algorithm, etc., to form a closed-loop feedback system to continuously optimize the plant growth status.

[0093] As a preferred implementation, the present application can be applied to the scene of growing green radish indoors at home. The specific implementation steps are as follows:

[0094] First, the user enters the plant species information "Scindapsus" through the mobile app. The system retrieves the standard growth curve of Scindapsus from the database, including weekly height growth rate (such as 1.5-2cm / week), leaf number increase rate (such as 1-2 leaves / week), leaf color standard value (such as chlorophyll content index 40-50) and nutrient absorption curve.

[0095] Then, the system matches the type of bubble mud suitable for the growth of green radish, such as choosing a bubble mud product rich in nitrogen and with good water retention performance. The initial ratio may be 70% bubble mud matrix + 30% water retention particles, and the estimated replacement cycle is 3 months.

[0096] The cameras and sensors installed on the flower pots collect data on the growth of the green radish every day. For example, image processing technology is used to measure the height of the plant and the number of leaves, a colorimetric sensor is used to detect the color of the leaves, and a conductivity sensor is used to monitor the nutrient content in the bubble mud.

[0097] Assume that after 45 days of planting, the system detects that the actual growth curve of the green radish has deviated: the height growth rate has dropped to 1 cm / week, and the leaf color has turned yellow (the chlorophyll content index has dropped to 35). These deviation data exceed the preset 20% threshold.

[0098] System-generated regulation plan: Ingredient regulation: increase the nitrogen content and add 5g slow-release nitrogen fertilizer; Structure regulation: increase the proportion of water-retaining particles by 10% to improve moisture retention capacity; Replacement strategy: it is recommended to replace 25% of the bubble mud after 30 days.

[0099] The user follows the system's instructions to perform control operations. At the same time, the system recommends moving the green radish to a location with more sunlight and appropriately increasing the indoor humidity.

[0100] One week after implementing the control measures, the system collected data on the status of the green radish again. It was found that the height growth rate rebounded to 1.8cm / week and the leaf color returned to normal (chlorophyll content index reached 45). The system evaluated that the control effect was good and incorporated this successful experience into the algorithm optimization for the treatment of similar situations in the future.

[0101] Through this continuous monitoring, analysis, regulation and evaluation cycle, the system can keep the growth state of the green radish always close to the optimal curve, achieving precise plant control based on bubble mud.

[0102] In some of the above embodiments of the present application, it is proposed to generate a bubble mud regulation scheme including component regulation, structure regulation, and replacement strategy to deal with plant growth deviations. However, the following problems exist in this process: First, there is a lack of consideration for the personalized planting needs of users, and it is difficult to meet the specific expectations of different users for the plant growth status; second, there is no clear target growth curve, and it is impossible to accurately measure the deviation between the actual growth status of the plant and the expected status; third, there is a lack of detailed analysis of the type and degree of deviation, and it is difficult to formulate targeted regulation schemes; finally, the generation process of the regulation scheme lacks systematicity and comprehensiveness, which may lead to poor regulation effects.

[0103] In this regard, the present application further proposes that if the deviation data exceeds a preset threshold, the user's personalized planting needs are obtained, and a user expectation curve including plant height control targets and flowering promotion targets is established; the standard growth curve and the user expectation curve are integrated to obtain the target growth curve; the deviation data is analyzed to determine the deviation type and degree of deviation between the actual growth state of the plant and the target growth curve; according to the deviation type and degree, and plant species information, a control scheme is selected from the bubble mud component control library, the structure control library, and the replacement strategy library to generate a bubble mud control scheme including component control instructions, structure control instructions, and replacement strategy instructions; wherein, the component control instructions include nutrient type, quantity, and release rate; the structure control instructions include bubble mud density, layered structure, and air permeability; and the replacement strategy instructions include replacement timing, replacement ratio, and new bubble mud type.

[0104] The technical solution of this application is aimed at the scene of indoor potted plant cultivation at home. Considering that different users have different expectations for plants, the general maintenance guidance is difficult to meet the customized needs of users, and a customized maintenance guidance method is provided. Specifically, the method includes the following main steps:

[0105] First, the user's personalized planting needs are obtained, and a user expectation curve including plant height control goals and flowering promotion goals is established. This step can be achieved in many ways. For example, a user interaction interface can be designed to allow users to input their specific expectations for plant height and flowering time. In addition, the user expectation curve can be automatically generated by analyzing the user's historical planting data and preferences.

[0106] Secondly, the standard growth curve and the user expectation curve are integrated to obtain the target growth curve. This step can use the weighted average method to integrate the standard growth curve and the user expectation curve. The weight can be dynamically adjusted according to the characteristics of the plant species and the rationality of the user's expectations. For example, for some plants with relatively fixed growth characteristics, a higher weight can be given to the standard growth curve; and for some ornamental plants with more flexible growth, the weight of the user expectation curve can be appropriately increased.

[0107] Next, the deviation data is analyzed to determine the type and degree of deviation between the actual growth status of the plant and the target growth curve. This step can be achieved by establishing a multi-dimensional deviation assessment model. The model can consider multiple indicators such as plant height, number of leaves, color, flowering status, etc., and calculate the deviation value of each dimension by comparing with the target growth curve. Then, according to the preset threshold, the deviation is classified into different types (such as slow growth, too fast growth, etc.) and degrees (such as mild, moderate, severe).

[0108] Finally, according to the deviation type and degree, and plant species information, a control scheme is selected from the bubble mud component control library, the structure control library, and the replacement strategy library to generate a bubble mud control scheme containing component control instructions, structure control instructions, and replacement strategy instructions. This step can be achieved by establishing a decision tree or a rule library. For example, in the case of slow growth, a scheme to increase the nutrient release rate may be selected from the component control library; in the case of too fast growth, a scheme to increase the density of the bubble mud may be selected from the structure control library. At the same time, the compatibility between different control schemes needs to be considered to ensure that the generated comprehensive control scheme is feasible and efficient.

[0109] Through the above steps, the technical solution of the present application can achieve refined and personalized management of indoor potted plants in the home.

[0110] As a specific embodiment, consider an indoor ornamental plant, such as a planting scenario of green radish. The user hopes that the green radish will reach a height of 50 cm within 3 months and new branches will begin to appear after 2 months. Based on these requirements, the system first establishes a user expectation curve. Then, the system merges this user expectation curve with the standard growth curve of green radish to obtain a target growth curve.

[0111] During the planting process, the system uses image recognition technology to monitor the actual growth status of the green radish every day. Suppose one month after planting, the system finds that the actual height of the green radish is 15cm, while the height on the target growth curve should be 20cm, so it determines this deviation as "slow growth" and the degree of deviation is "moderate".

[0113] Based on this judgment, we selected a solution to increase the release rate of nitrogen from the bubble mud component control library, and a solution to increase the air permeability of the bubble mud from the structure control library. Specifically, the component control instructions include: increasing the release rate of nitrogen from the original 2mg / day to 3mg / day; the structure control instructions include: increasing the density of the bubble mud from the original 0.3g / cm 3 Reduced to 0.25g / cm 3 , to increase breathability.

[0114] At the same time, a replacement strategy instruction is also generated: if the growth rate of the green radish still does not reach the target within the next two weeks, the user is advised to replace 25% of the existing bubble mud with new bubble mud with higher nutrient content.

[0115] In this way, the technical solution of the present application can provide users with refined and personalized plant management solutions, effectively improving the success rate and user satisfaction of indoor potted plant cultivation at home.

[0116] In some of the above embodiments of the present application, a step of analyzing deviation data and determining the deviation type and degree of deviation between the actual growth state of the plant and the target growth curve is proposed to determine the difference between the plant growth state and the expected target. However, in this process, there are problems such as incomplete data collection, inaccurate data processing, and in-depth deviation analysis. This may lead to inaccurate assessment of the plant growth state, thereby affecting the formulation and implementation of subsequent bubble mud control plans.

[0117] In this regard, the present application further proposes to collect multi-source data on the growth status of plants, the multi-source data including image data, sensor data and user input data; for the multi-source data, perform data synchronization processing based on timestamps to obtain synchronized multi-source data; preprocess the synchronized multi-source data to obtain preprocessed multi-source data; based on the preprocessed multi-source data, calculate the deviation data between the actual growth status of the plant and the target growth curve in terms of height growth rate, number of leaves, color change and nutrient absorption; according to the deviation data, determine the deviation type and degree of deviation between the actual growth status of the plant and the target growth curve, the deviation types include slow growth, excessive growth, malnutrition and pests and diseases, and the deviation degrees include mild, moderate and severe.

[0118] This application collects multi-source data on the growth status of plants, including image data, sensor data, and user input data. Image data can be obtained through high-definition cameras installed around the plants to capture the appearance characteristics of the plants, such as plant height, number of leaves, and color changes. Sensor data can be collected through multifunctional sensors implanted in bubble mud, including parameters such as temperature, humidity, pH value, and conductivity. User input data is collected through mobile applications or web interfaces, including users' subjective evaluations and observation records of plant growth conditions.

[0119] For multi-source data, this application performs data synchronization based on timestamps. Specifically, a unified data collection cycle can be set, such as collecting data once an hour. At each collection, the same timestamp is marked for data from all data sources. In this way, data from different sources are ensured to be consistent in time, providing a reliable data basis for subsequent analysis.

[0120] The synchronized multi-source data is preprocessed to obtain preprocessed multi-source data. The preprocessing steps include image data denoising, sensor data calibration, and user input data cleaning. Image data denoising can use algorithms such as Gaussian filtering or median filtering to eliminate random noise in the image. Sensor data calibration establishes a calibration curve and corrects the system error of the sensor by comparing it with standard equipment. User input data cleaning eliminates obviously wrong or abnormal inputs by setting a reasonable value range and logic checking.

[0121] Based on the pre-processed multi-source data, this application calculates the deviation data between the actual growth state of the plant and the target growth curve in terms of height growth rate, number of leaves, color change and nutrient absorption. The height growth rate can be obtained through continuous image data analysis, the number of leaves is automatically counted by image recognition algorithm, and the color change is quantified by comparing the RGB value changes of the plant leaves in the image. Nutrient absorption can be indirectly evaluated by analyzing the conductivity changes in the sensor data.

[0122] Based on the calculated deviation data, the present application determines the deviation type and degree of deviation between the actual growth state of the plant and the target growth curve. Deviation types include growth retardation, excessive growth, malnutrition, and pests and diseases. For example, when the height growth rate is significantly lower than the target curve, it can be judged as growth retardation; when the number of leaves decreases sharply or the color is abnormal, it may indicate malnutrition or pest and disease problems. The degree of deviation is divided into three levels: mild, moderate, and severe, which can be divided by setting different thresholds. For example, for the height growth rate, a deviation from the target curve of less than 10% can be defined as mild, 10%-30% as moderate, and greater than 30% as severe.

[0123] By adopting the method of multi-source data collection and synchronous processing, this application significantly improves the comprehensiveness and accuracy of the data. The fusion of multi-source data makes the evaluation of plant growth status more comprehensive and objective, avoiding the one-sidedness that may be caused by a single data source. Timestamp synchronization ensures the temporal consistency of data from different sources and provides a reliable data basis for subsequent analysis. The data preprocessing step further improves the data quality and reduces the impact of noise and errors on the analysis results.

[0124] Based on high-quality multi-source data, this application can more accurately calculate the deviation between the actual growth status of plants and the target growth curve. By comprehensively considering multiple aspects such as height growth rate, number of leaves, color changes and nutrient absorption, a comprehensive assessment of plant growth status is conducted. This multi-dimensional deviation analysis method can identify abnormalities in plant growth earlier and more accurately.

[0125] Furthermore, this application provides a more refined and targeted basis for the subsequent formulation of bubble mud regulation plans by combining the deviation type and the deviation degree. For example, for plants that are judged to be moderately malnourished, a corresponding bubble mud nutrient supplementation strategy can be formulated; and for mild overgrowth, the structure of the bubble mud may need to be adjusted to appropriately control the growth rate.

[0126] As a preferred embodiment, the present application can be implemented in a home smart plant growth system. The system includes a multifunctional sensor module installed near the plant pot, which integrates a high-definition camera, a temperature and humidity sensor, a pH sensor, and a conductivity sensor. The system is also equipped with a central processing unit responsible for data acquisition, synchronization, and preprocessing. Users can interact with the system through a smartphone application, input observation records, and view analysis results.

[0127] Specifically, the system automatically collects multi-source data once an hour. The image data is captured by a high-definition camera with a resolution of 4K, ensuring that the subtle changes of the plants can be clearly captured. User input data is collected through a structured form in the application, including plant appearance scores (1-5 points) and descriptions of abnormal phenomena.

[0128] Data synchronization processing uses NTP (Network Time Protocol) to ensure the time consistency of all devices. In the preprocessing stage, the image data is denoised using a 3x3 Gaussian filter, the sensor data is corrected using a pre-established quadratic polynomial calibration curve, and the user input data is cleaned of abnormal input by setting a reasonable value range (such as a score of 1-5 points) and keyword detection.

[0129] Plant height and leaf count information were extracted from the images using machine learning algorithms such as convolutional neural networks. Color changes were quantified by comparing the average HSV values ​​of leaf areas in consecutive images. Nutrient absorption was assessed by analyzing the trend of electrical conductivity.

[0130] The determination of the type and degree of deviation is based on a preset decision tree model. For example, when the height growth rate is lower than 80% of the target curve for three consecutive days, it is judged as mild growth retardation; when it is lower than 60%, it is judged as moderate growth retardation; and when it is lower than 40%, it is judged as severe growth retardation. Similarly, corresponding thresholds are set for leaf number, color change, and nutrient absorption indicators.

[0131] In this way, the system can generate a detailed plant growth status report every day, including the specific values ​​of various indicators, the type and degree of deviation, and the results of the determination. This information provides users and the automatic bubble mud control system with an accurate basis for decision-making, significantly improving the scientificity and success rate of home plant cultivation.

[0132] In some of the above-mentioned embodiments of the present application, it is proposed to select a control scheme from a bubble mud component control library, a structure control library, and a replacement strategy library according to the deviation type and the deviation degree, plant species information, and the step of generating a bubble mud control scheme comprising a component control instruction, a structure control instruction, and a replacement strategy instruction is used to generate the bubble mud control scheme. However, in this process, how to effectively integrate multidimensional information such as plant species, deviation type and deviation degree, and quickly and accurately select the most suitable scheme from a large amount of control schemes is still a challenge. In addition, how to ensure that the selected control scheme is not only effective for a single factor, but also can improve the plant growth state in a coordinated manner in many aspects, is also a problem that needs to be solved.

[0133] In this regard, the present application further proposes a technical solution to construct a bubble mud regulation knowledge graph, conduct multi-hop search based on plant species information, deviation type and deviation degree, calculate the confidence score of candidate regulation schemes, and select the regulation scheme with the highest confidence score to generate a bubble mud regulation scheme.

[0134] This application constructs a bubble mud regulation knowledge graph, which contains plant species nodes, deviation type nodes, deviation degree nodes, component regulation scheme nodes, structural regulation scheme nodes, replacement strategy nodes, and the relationship between nodes. This structured knowledge representation method can effectively integrate and associate multidimensional information, providing a comprehensive knowledge basis for the subsequent regulation scheme selection.

[0135] Based on the constructed knowledge graph, the present application adopts a multi-hop search method to obtain a set of candidate control schemes. Specifically, taking the plant species node as the starting node, a first-hop search is performed based on the plant species information to obtain an associated deviation type node set and a deviation degree node set. Then, taking these nodes as the starting nodes, a second-hop search is performed to obtain an associated component control scheme node set, a structural control scheme node set, and a replacement strategy node set. This multi-hop search method can effectively utilize the association relationships in the knowledge graph and quickly locate the control scheme related to the current plant condition.

[0136] In order to select the most suitable scheme from the candidate regulation schemes, this application introduces a confidence scoring mechanism. The confidence scoring comprehensively considers the correlation strength between the regulation scheme and the plant species, the deviation type and degree, and the compatibility between the regulation schemes. This multi-dimensional scoring mechanism can comprehensively evaluate the applicability and effectiveness of each regulation scheme.

[0137] Finally, the present application selects the control scheme with the highest confidence score from the candidate control scheme set according to the calculated confidence score, and generates a bubble mud control scheme including component control instructions, structure control instructions, and replacement strategy instructions. This scoring-based selection method can ensure that the selected control scheme can improve the plant growth state in a coordinated manner in multiple aspects.

[0138] This application constructs a bubble mud regulation knowledge graph to achieve a structured representation of the complex relationship between plant species, deviation types, deviation degrees, and regulation schemes. This structured knowledge representation provides a comprehensive knowledge basis for the subsequent selection of regulation schemes, avoiding selection difficulties caused by insufficient information.

[0139] Based on the knowledge graph, this application uses a multi-hop search method to obtain a set of candidate regulatory solutions. This search method can make full use of the associations in the knowledge graph and quickly locate the regulatory solutions related to the current plant conditions. Compared with the traditional one-to-one matching or rule query method, multi-hop search can handle complex associations more efficiently, greatly improving the efficiency of regulatory solution retrieval.

[0140] As a specific implementation method, the bubble mud regulation knowledge graph of the present application can be implemented using a graph database. In this graph database, the plant species node can contain attributes such as "species name", "growth cycle", "suitable temperature range", etc.; the deviation type node can contain attributes such as "deviation name", "influence degree", etc.; the deviation degree node can contain attributes such as "degree level", "value range", etc.; the regulation scheme node (including ingredient regulation scheme, structure regulation scheme and replacement strategy) can contain attributes such as "scheme name", "applicable conditions", "expected effect", etc.

[0141] When performing a multi-hop search, you can use the Cypher query language to implement it. For example, for a rose that is growing slowly, the Cypher query for the first hop search may be as follows:

[0142] ```

[0143] MATCH(p:PlantSpecies{name:'Rose'})-[:HAS_DEVIATION]->(d:DeviationType{name:'Growth retardation'})

[0144] RETURN p,d

[0145] ```

[0146] The Cypher query for the second hop search might be as follows:

[0147] ```

[0148] MATCH(d:DeviationType{name:'Growth retardation'})-[:HAS_SOLUTION]->(s:Solution)

[0149] WHERE s:CompositionControl OR s:StructureControl OR s:ReplacementStrategy

[0150] RETURN s

[0151] ```

[0152] When calculating the confidence score, a weighted summation method can be used. For example, for a control plan s, its confidence score can be expressed as:

[0153] Score(s)=w1*R(s,p)+w2*R(s,d)+w3*C(s);

[0154] Among them, R(s,p) represents the association strength between regulation scheme s and plant species p, R(s,d) represents the association strength between regulation scheme s and deviation type d, C(s) represents the compatibility score of regulation scheme s, and w1, w2, and w3 are weight coefficients.

[0155] In this way, the present application can provide customized bubble mud control solutions for different plant species and growth conditions, significantly improving the use effect of bubble mud in home indoor potted planting scenarios. At the same time, this method based on knowledge graph and multi-dimensional scoring also has good scalability and can continuously optimize the control effect as knowledge accumulates.

[0156] In some embodiments of the present application, a step of calculating the confidence score of each control scheme in the candidate control scheme set is proposed to select the most suitable control scheme from the candidate control scheme set. However, in this process, how to comprehensively consider the correlation strength of the control scheme and plant species, deviation type and deviation degree, and the compatibility between the control schemes, thereby calculating an accurate confidence score, still remains a challenge. Simply carrying out linear weighting of each factor may not fully reflect the complex correlation relationship, and it is also difficult to ensure the effectiveness of the selected control scheme in practical applications.

[0157] In this regard, the present application further proposes to obtain the association relationship data between each node in the bubble mud regulation knowledge graph, the association relationship data includes the first association strength between the plant species node and the component regulation scheme node, the second association strength between the deviation type node and the component regulation scheme node, the third association strength between the deviation degree node and the component regulation scheme node, and the compatibility score between the component regulation scheme nodes; for each regulation scheme in the candidate regulation scheme set, according to the component regulation scheme node it contains, calculate the weighted association strength between it and the plant species node, the deviation type node and the deviation degree node, the weighted association strength is obtained by weighted summing the first association strength, the second association strength and the third association strength, and the weight coefficient is determined according to the plant species, the deviation type and the deviation degree; calculate the compatibility score of each regulation scheme in the candidate regulation scheme set, the compatibility score is obtained by weighted averaging the compatibility scores between the component regulation scheme nodes in the regulation scheme, and the weight coefficient is determined according to the role of each component regulation scheme node; weighted sum the weighted association strength and the compatibility score to obtain the confidence score of each regulation scheme in the candidate regulation scheme set, and the weight coefficient is determined according to the association strength and compatibility.

[0158] The technical solution proposed in this application first obtains the association relationship data between each node in the bubble mud regulation knowledge graph. These data include the first association strength between the plant species node and the component regulation scheme node, the second association strength between the deviation type node and the component regulation scheme node, the third association strength between the deviation degree node and the component regulation scheme node, and the compatibility score between the component regulation scheme nodes. By obtaining these association relationship data, a comprehensive data basis is provided for the subsequent confidence score calculation.

[0159] Next, for each control scheme in the candidate control scheme set, the weighted association strength between it and the plant species node, the deviation type node and the deviation degree node is calculated. This step is achieved by weighted summing the first association strength, the second association strength and the third association strength. The weight coefficient is determined based on the plant species, the deviation type and the deviation degree, so that the weight can be dynamically adjusted according to the importance of different factors to improve the accuracy of the evaluation. For example, for some plant species, the impact of the deviation type may be more emphasized, and the weight of the second association strength can be increased; while for other plant species, the degree of deviation may be more concerned, and the weight of the third association strength can be increased accordingly.

[0160] Then, the compatibility score of each regulation scheme in the candidate regulation scheme set is calculated. This step is achieved by taking a weighted average of the compatibility scores between the nodes of each component regulation scheme in the regulation scheme. The weight coefficient is determined according to the role of each component regulation scheme node, which can more accurately reflect the importance of different components in the overall regulation scheme. For example, for some regulation schemes, a specific component may play a key role, so the weight of the node corresponding to the component can be increased; for other regulation schemes, multiple components may play a role together, so the weight can be distributed relatively evenly.

[0161] Finally, the weighted association strength and compatibility score are weighted and summed to obtain the confidence score of each regulation scheme in the candidate regulation scheme set. The weight coefficient is determined according to the importance of association strength and compatibility, so that the correlation between the regulation scheme and the plant growth state and the compatibility of the components within the regulation scheme can be balanced in the final score.

[0162] In this way, the technical solution of the present application can more comprehensively and accurately evaluate the effectiveness of candidate regulatory schemes. The calculation of weighted correlation strength takes into account the degree of correlation between the regulatory scheme and the plant species, deviation type and degree of deviation, ensuring that the selected regulatory scheme can accurately regulate the growth conditions of specific plants. The calculation of the compatibility score takes into account the interaction between the components within the regulatory scheme to avoid conflicts or mutual cancellation of components in the selected regulatory scheme in actual application.

[0163] The final confidence score can better reflect the overall performance of the regulation scheme by comprehensively considering the weighted association strength and compatibility score. This scoring method not only considers the matching degree between the regulation scheme and the plant growth state, but also considers the feasibility of the regulation scheme itself, thereby improving the accuracy of selecting the most suitable regulation scheme.

[0164] As a specific embodiment, the following scenario can be considered: Assuming there is a slow-growing indoor ornamental plant, the deviation type has been determined to be "slow growth" and the deviation degree is "moderate" through the above steps. Now it is necessary to select the most suitable regulation scheme from the candidate regulation scheme set.

[0165] First, we obtain the association relationship data from the bubble mud regulation knowledge graph. For example, for the plant species "indoor ornamental plants", the first association strength with the ingredient regulation scheme "increase nitrogen fertilizer" is 0.8, the second association strength with the deviation type "slow growth" is 0.9, and the third association strength with the deviation degree "moderate" is 0.7. At the same time, the compatibility score of "increase nitrogen fertilizer" and "increase phosphorus fertilizer" is 0.85.

[0166] Then, the weighted association strength is calculated. Assume that according to the current situation, the weights of plant species, deviation type, and deviation degree are determined to be 0.3, 0.4, and 0.3 respectively. Then, for the control scheme including "increasing nitrogen fertilizer", its weighted association strength is: 0.8*0.3+0.9*0.4+0.7*0.3=0.81.

[0167] Next, calculate the compatibility score. Assume that the control plan includes two components, "increase nitrogen fertilizer" and "increase phosphorus fertilizer", and their effect weights are 0.6 and 0.4 respectively. Then, the compatibility score of the control plan is: 0.85*0.6+0.85*0.4=0.85.

[0168] Finally, the confidence score is calculated. Assuming that the weights of the weighted association strength and compatibility score are 0.7 and 0.3 respectively, then the final confidence score of the regulation scheme is: 0.81*0.7+0.85*0.3=0.822.

[0169] In this way, a confidence score can be calculated for each of the candidate control schemes, and the scheme with the highest score can be selected as the final bubble mud control scheme. This scoring method can comprehensively consider the matching degree between the control scheme and the plant growth conditions and the feasibility of the control scheme itself, thereby improving the accuracy of selecting the most suitable control scheme and thus improving the effect of plant control.

[0170] In some of the above-mentioned embodiments of the present application, a step of matching the type, proportion and replacement cycle of bubble mud based on plant species information is proposed to provide a suitable growth environment for different plants. However, in this process, relying solely on plant species information may not fully take into account the impact of environmental factors on plant growth. Under different regions, seasons and lighting conditions, even the same plant may show different nutrient requirements. Therefore, a more accurate and dynamic method is needed to determine the configuration parameters of bubble mud to adapt to the actual growth needs of plants under different environments.

[0171] In this regard, the present application further proposes to obtain environmental factor data on region, season, and light intensity, and construct an environmental factor database; extract basic nutrient requirement data of plants based on plant species information; according to the environmental factor data, use the environmental factor impact model to correct the basic nutrient requirement data of plants to obtain target nutrient requirement data, and the environmental factor impact model is: target nutrient requirement data = basic nutrient requirement data * (1 + regional correction coefficient + seasonal correction coefficient + light correction coefficient); according to the target nutrient requirement data, select the bubble mud type and determine the bubble mud ratio; predict the bubble mud replacement cycle, and the bubble mud replacement cycle prediction takes into account the plant nutrient absorption rate, the bubble mud nutrient release rate and the influence of environmental factors.

[0172] The technical solution proposed in this application first constructs an environmental factor database by acquiring environmental factor data of region, season, and light intensity. This step provides an environmental parameter basis for the subsequent correction of plant nutrient requirements, and effectively solves the problem of unreasonable bubble mud configuration caused by ignoring environmental factors. The construction of the environmental factor database realizes the centralized management and rapid query of environmental factor data, ensuring the efficiency of data acquisition.

[0173] The second key step of this program is to extract the basic nutrient requirement data of plants based on plant species information. This provides basic data for subsequent nutrient requirement corrections and solves the problem of inaccurate bubble mud configuration due to lack of plant nutrient requirement information. Different plant species have different requirements for nutrients such as nitrogen, phosphorus, and potassium. This step provides a basic basis for bubble mud configuration.

[0174] The introduction of the environmental factor impact model is the core innovation of this solution. Through the formula: target nutrient demand data = basic nutrient demand data * (1 + regional correction coefficient + seasonal correction coefficient + light correction coefficient), dynamic adjustment of plant nutrient demand is achieved. This solves the problem of inapplicability of bubble mud configuration due to environmental changes. The use of correction coefficients quantitatively evaluates the impact of environmental factors and ensures the accuracy of the correction results.

[0175] The implementation steps of this program are to select the type of bubble mud and determine the ratio according to the target nutrient demand data. This achieves accurate selection and ratio of bubble mud types, and solves the problem of poor plant growth due to mismatched bubble mud types or improper ratios. Different bubble mud types contain different nutrient combinations and contents. This step ensures that the selected bubble mud can meet the growth needs of plants.

[0176] Finally, this scheme takes into account the prediction of the bubble mud replacement cycle. By comprehensively considering the plant nutrient absorption rate, the bubble mud nutrient release rate and the influence of environmental factors, a reasonable prediction of the bubble mud replacement time is achieved. This solves the problem of affecting plant growth due to untimely or frequent replacement, and ensures the accuracy of the prediction results.

[0177] As a preferred implementation, the environmental factor impact model can be further refined. For example, the calculation of the regional correction coefficient can adopt the soil nutrient conversion model: regional correction coefficient = α*(soil organic matter content / soil total nutrient content) + β*(soil pH value-7). Among them, α and β are model parameters, which are adjusted according to the plant species. This method takes into account the effects of soil organic matter content, total nutrient content and pH value on plant nutrient absorption, making regional correction more accurate.

[0178] The seasonal correction coefficient can adopt a nonlinear seasonal impact model: seasonal correction coefficient = γ*sin(2πt / T+φ). Among them, γ is the seasonal impact amplitude, t is the current time, T is the plant growth cycle, and φ is the phase offset. γ, T and φ are adjusted according to the plant species and growth stage. This periodic function can better simulate the changes in plant nutrient requirements at different growth stages.

[0179] The light correction coefficient can adopt the photosynthetically active radiation model: light correction coefficient = δ*PAR*(red light ratio / blue light ratio). Among them, δ is the light influencing factor, PAR is the photosynthetically active radiation intensity, and the red light ratio and blue light ratio are the spectral component ratios. δ is adjusted according to the plant species. This method not only takes into account the light intensity, but also the influence of spectral components on plant growth, making the light correction more comprehensive.

[0180] In practical applications, the technical solution of this application can be implemented through the following steps:

[0181] First, obtain the longitude and latitude coordinates of the plant planting area, query the historical meteorological data of the corresponding area, and obtain the soil composition data, annual average precipitation data, and air humidity data. For example, for an indoor foliage plant in a certain area, the data that may be obtained are: soil organic matter content 2.5%, soil total nutrient content 3%, soil pH value 7.2, annual average precipitation 540mm, and average relative humidity 50%.

[0182] Then, based on these data, the regional correction factor was calculated using the soil nutrient conversion model. Assuming α=0.5, β=0.2, the regional correction factor=0.5*(2.5% / 3%)+0.2*(7.2-7)=0.457.

[0183] Next, the current date is obtained and the current season is determined according to the preset season division rules. Assuming that it is spring and the plants are in the early growth stage, γ = 0.3, T = 365 days, φ = π / 2, then the season correction coefficient = 0.3*sin(2π*60 / 365+π / 2)≈0.295.

[0184] Furthermore, obtain the light intensity data and spectrum data of the plant location. Assume that the PAR value is 200 μmol / (m 2 ·s), the proportion of red light is 60%, the proportion of blue light is 30%, δ=0.001, then the illumination correction coefficient=0.001*200*(60% / 30%)=0.4.

[0185] Finally, the target nutrient requirement data is calculated according to the environmental factor impact model. Assuming that the basic nutrient requirement data is 100 units, the target nutrient requirement data = 100*(1+0.457+0.295+0.4) = 215.2 units.

[0186] Based on this target nutrient requirement data, you can choose the appropriate type of bubble mud and determine the ratio. For example, you may choose a type of bubble mud with high nitrogen, medium phosphorus, and low potassium, with a ratio of 70% basic bubble mud + 20% nitrogen fertilizer bubble mud + 10% water retention bubble mud.

[0187] In some embodiments, since soil may be available for purchase online, the regional correction factor may be removed.

[0188] In some of the above-mentioned embodiments of the present application, it is proposed to correct the basic nutrient requirement data of the plant using the environmental factor impact model according to the environmental factor data, and obtain the step of target nutrient requirement data to adjust the nutrient requirement of the plant. However, in this process, there is a lack of accurate consideration and quantitative processing of specific environmental factors, and it is difficult to accurately reflect the impact of different regions, seasons and light conditions on the nutrient requirements of plants. This may cause the calculation of the target nutrient requirement data to be inaccurate, thereby affecting the adjustment of the subsequent bubble mud formula and the effect of plant growth management.

[0189] In this regard, the present application further proposes obtaining the longitude and latitude coordinates of the plant planting area, querying the historical meteorological data of the corresponding area, and obtaining soil composition data, annual average precipitation data, and air humidity data; based on the soil composition data, using the soil nutrient conversion model to calculate the regional correction coefficient; obtaining the current date, determining the current season according to the preset season division rules, and combining the plant growth cycle data to calculate the season correction coefficient using the nonlinear seasonal impact model; obtaining the light intensity data of the plant's location, combining the spectral sensor to obtain the spectral data, and using the photosynthetic active radiation model to calculate the light correction coefficient; based on the regional correction coefficient, the seasonal correction coefficient and the light correction coefficient, using the environmental factor impact model to calculate the target nutrient demand data.

[0190] The technical solution of the present application achieves refined regulation of plant nutrient requirements by introducing multiple accurate environmental factor models. Specifically, the soil nutrient conversion model takes into account the effects of soil organic matter content, total nutrient content and pH value on plant nutrient absorption. These parameters directly reflect the soil fertility and nutrient effectiveness, and are key factors affecting plant nutrient absorption. By adjusting the model parameters α and β, personalized adjustments can be made for different plant species to improve the accuracy and applicability of the regional correction coefficient.

[0191] The nonlinear seasonal impact model uses a sine function to simulate the impact of seasonal changes on plants. The advantage of this method is that it can better reflect the impact of seasonal cycle changes on plant growth. The parameters γ, T and φ in the model can be adjusted according to the plant species and growth stage, so that the seasonal correction coefficient can more accurately reflect the seasonal demand changes of different plants at different growth stages.

[0192] The photosynthetically active radiation model takes into account the effects of light intensity and spectral components on plant nutrient requirements. The model not only takes into account the photosynthetically active radiation intensity (PAR), but also introduces the red light ratio and the blue light ratio, which reflects the different effects of different spectral components on plant growth. By adjusting the light impact factor δ, personalized adjustments can be made for different plant species to improve the accuracy of the light correction coefficient.

[0193] The combined use of these three models enables this application to fully consider the impact of key environmental factors such as region, season and light on plant nutrient requirements. Through the environmental factor impact model, these correction coefficients are integrated into the calculation of target nutrient requirement data, achieving accurate correction of plant nutrient requirements.

[0194] The technical solution of this application achieves more accurate bubble mud formula adjustment and plant growth management by accurately quantifying the impact of environmental factors on plant nutrient requirements. This method first obtains the precise geographical location information of the plant planting area, and obtains key environmental data such as soil composition, precipitation, and air humidity by querying historical meteorological data. Then, the regional correction coefficient is calculated using the soil nutrient conversion model, which takes into account the effects of soil organic matter content, total nutrient content, and pH value, which are directly related to the plant's nutrient absorption capacity.

[0195] Next, the application considers the impact of seasonal factors on plant growth. The current season is determined by obtaining the current date and combining it with the preset season division rules. Combined with the plant growth cycle data, a nonlinear seasonal impact model is used to calculate the seasonal correction coefficient. This model uses a sine function to simulate seasonal changes, which can better reflect the periodic characteristics of plant growth.

[0196] Light conditions are another important factor affecting plant growth and are also taken into consideration. This application obtains the light intensity data of the plant's location, and combines the spectral data obtained by the spectral sensor to calculate the light correction coefficient using the photosynthetically active radiation model. This model not only takes into account the light intensity, but also the spectral composition, especially the ratio of red light to blue light, which has an important impact on the photosynthesis and growth and development of plants.

[0197] Finally, this application takes into account the regional correction factor, seasonal correction factor and light correction factor, and uses the environmental factor impact model to calculate the final target nutrient requirement data. This method of comprehensively considering multiple environmental factors can more accurately reflect the actual nutrient requirements of plants in a specific environment.

[0198] Through this refined environmental factor analysis and quantitative processing, the present application can provide more accurate bubble mud formulas for different types of plants, in different geographical locations, different seasons and lighting conditions. This method not only improves the pertinence of the bubble mud formula, but also better meets the nutrient needs of plants at different growth stages, thereby promoting the healthy growth of plants and improving the success rate of home gardening.

[0199] In some of the above embodiments of the present application, it is proposed that if the deviation data exceeds a preset threshold, a bubble mud control scheme including component control, structural control, and replacement strategy is generated to control the plant growth state. However, in this process, some problems of the bubble mud itself may occur, such as hardening, nutrient imbalance, and excessive odor, etc. These problems may affect the normal growth of plants and are difficult to solve by simple component control, structural control, or replacement strategy. Therefore, a more comprehensive and sophisticated bubble mud control scheme is needed to deal with these potential problems.

[0200] In this regard, the present application further proposes that if the deviation data exceeds a preset threshold, the humidity, conductivity and gas composition of the bubble mud are monitored to determine whether the bubble mud is at risk of compaction, nutrient imbalance and excessive odor; if there is a risk of compaction, the bubble mud formula is adjusted to increase the fiber material content, reduce the proportion of sticky components, generate soil loosening instructions, and prompt the user to loosen the soil to increase the air permeability of the bubble mud; if there is a risk of nutrient imbalance, the bubble mud nutrients are analyzed to determine the types of missing nutrients, generate targeted nutrient supplement instructions, and prompt the user to add corresponding nutrients to balance the bubble mud nutrients; if there is a risk of excessive odor, the bubble mud gas composition is detected to determine the types of odorous gas, generate activated carbon adsorption instructions or bubble mud replacement instructions, and prompt the user to perform activated carbon adsorption or replace the bubble mud to eliminate the odor.

[0201] The technical solution of this application involves the steps of generating a bubble mud control scheme, and provides a bubble mud control scheme for the problems of bubble mud being easy to harden, nutrient imbalance, and odor in the home indoor potted planting scene after long-term use. Specifically, the scheme includes the following main steps:

[0202] First, monitor the humidity, conductivity, and gas composition of the bubble mud. This step can be achieved in a variety of ways. For example, a humidity sensor can be used to measure the moisture content of the bubble mud, a conductivity sensor can be used to measure the conductivity of the bubble mud, and a gas sensor can be used to detect the gas composition released by the bubble mud. These sensors can be integrated into a smart monitoring device to collect data periodically or in real time.

[0203] Secondly, based on the monitoring data, determine whether the bubble mud has the risk of hardening, nutrient imbalance, and excessive odor. The judgment criteria can be based on preset thresholds. For example, when the humidity is below a certain threshold, there may be a risk of hardening; when the conductivity exceeds a certain range, there may be a risk of nutrient imbalance; when the concentration of certain gas components exceeds the standard, there may be a risk of odor. These thresholds can be dynamically adjusted according to different plant species and growth stages.

[0204] Furthermore, in response to different risks, this application proposes corresponding regulatory measures:

[0205] For the risk of compaction, adjust the bubble mud formula, increase the fiber content, and reduce the proportion of sticky components. Specifically, increase the proportion of loose materials such as coconut husks and vermiculite, and reduce the proportion of sticky materials such as clay. At the same time, generate a loosening instruction to prompt the user to loosen the soil. Loosening can be achieved through special loosening tools or simple turning to increase the air permeability of the bubble mud.

[0206] For the risk of nutrient imbalance, analyze the nutrients in the bubble mud to determine the types of missing nutrients. This can be done through a portable nutrient detector or laboratory analysis. Based on the analysis results, generate targeted nutrient supplement instructions to prompt the user to add the corresponding nutrients. For example, if insufficient nitrogen is detected, the user can be guided to add fertilizer with a high nitrogen content.

[0207] For the risk of excessive odor, the gas composition of the bubble mud is tested to determine the type of odorous gas. This can be done with equipment such as gas chromatography-mass spectrometry. Based on the test results, an activated carbon adsorption instruction or a bubble mud replacement instruction is generated. Activated carbon adsorption can be achieved by sprinkling a layer of activated carbon particles on the surface of the bubble mud, while replacing the bubble mud requires the user to replace all or part of the bubble mud according to the instructions.

[0208] Therefore, the technical solution of the present application can solve possible problems by comprehensively monitoring the state of the bubble mud, thereby ensuring the normal growth of plants. Compared with simple ingredient regulation or replacement strategies, this method is more sophisticated and targeted, and can better adapt to the growth needs and environmental changes of different plants.

[0209] In some of the above embodiments of the present application, it is proposed to perform bubble mud adjustment or replacement operations to guide users to coordinate environmental regulation to adapt to plant growth needs. However, the following problems may exist in this process: the plant roots may adapt to the bubble mud adjustment or replacement to different degrees, which may lead to unstable growth; changes in plant transpiration rate may cause water imbalance; uneven light intensity in different parts of the plant may affect overall growth. If these problems are not effectively solved, they may affect the healthy growth and ornamental effect of the plant.

[0210] In this regard, the present application further proposes to evaluate the degree of adaptability of plant roots to the adjustment or replacement of bubble mud, adjust the light intensity, temperature and humidity according to the degree of adaptation, and obtain the environmental factor control parameters; guide users to adjust the indoor air humidity and ventilation volume according to changes in plant transpiration rate to maintain plant water balance; monitor the light intensity of different parts of the plant in real time, guide users to adjust the placement of the plant or use supplementary lighting equipment to achieve uniform overall lighting of the plant.

[0211] The present application involves executing bubble mud adjustment or replacement operations, and guiding users to perform environmental coordinated regulation steps. In the home indoor potted planting scenario, the adaptability of plant roots to the new environment varies. Factors such as uneven indoor light distribution and poor air circulation will aggravate the instability of plant growth status, and an environmental coordinated regulation solution is provided.

[0212] Evaluate the degree of adaptation of the plant roots to the adjustment or replacement of the bubble mud, adjust the light intensity, temperature and humidity according to the degree of adaptation, and obtain the environmental factor control parameters. This step is aimed at the home indoor potted planting scenario, where the adaptability of the plant roots to the new bubble mud environment is uncertain, and direct adjustment of the environment may cause plant discomfort. By evaluating the degree of adaptation of the plant roots to the adjustment or replacement of the bubble mud, quantifying the plant's acceptance of the new environment, adjusting the light intensity, temperature and humidity according to the degree of adaptation, the environmental factor control parameters are matched with the adaptability of the plant roots, avoiding adverse effects on plants caused by sudden environmental changes, and ensuring a smooth transition of plants.

[0213] In specific implementation, a variety of methods can be used to evaluate the degree of adaptation of plant roots. For example, the growth status of plant roots can be observed, such as the color, length, distribution density and other indicators of the roots. Another method is to use electrical impedance measurement technology to determine the degree of adaptation by measuring the change in resistance between the roots and the bubble mud. Based on the evaluation results, a gradual adjustment strategy can be adopted to gradually change the light intensity and temperature and humidity so that the plants can gradually adapt to the new environment.

[0214] According to the changes in plant transpiration rate, guide users to adjust indoor air humidity and ventilation to maintain plant water balance. This step is aimed at the problem that in the home indoor potted planting scenario, the plant transpiration rate may change after the bubble mud is adjusted or replaced, affecting the plant water balance. By reflecting the plant water needs in real time according to the changes in plant transpiration rate, adjusting indoor air humidity and ventilation, the environmental factors are coordinated with the plant transpiration rate, maintaining the plant water balance, and avoiding plant growth stagnation due to lack of water or excessive water.

[0215] To implement this step, leaf temperature sensors and ambient humidity sensors can be used to monitor the transpiration rate of plants. When a change in the transpiration rate is detected, the system can adjust the indoor air humidity through a smart humidifier or dehumidifier, while controlling smart windows or fans to adjust the ventilation. For example, when the transpiration rate increases, the system may recommend that users increase indoor humidity and reduce ventilation to reduce water loss; conversely, when the transpiration rate decreases, it may recommend reducing humidity and increasing ventilation to prevent excessive moisture accumulation.

[0216] Monitor the light intensity of different parts of the plant in real time, and guide users to adjust the position of the plant or use supplementary lighting equipment to achieve uniform lighting for the whole plant. This step is aimed at the problem of uneven indoor light distribution and large differences in light intensity in different parts of the plant in the home indoor potted planting scene, which affects the overall growth of the plant. By using indoor light sensors and image recognition technology, the light intensity of different parts of the plant is monitored in real time, the light conditions of each part of the plant are quantified, and users are guided to adjust the position of the plant or use supplementary lighting equipment to ensure uniform lighting for the whole plant and promote balanced growth of the plant.

[0217] In practical applications, multiple light sensors or a movable light sensor can be used to measure the light intensity of different parts of the plant. Combined with image recognition technology, the system can identify the leaf distribution of the plant and associate the light data with the leaf position. Based on this data, the system can generate a heat map of the light distribution of the plant, intuitively showing areas of uneven light.

[0218] Depending on the light distribution, the system can provide specific adjustment suggestions. For example, if it detects that there is sufficient light on the top of the plant but insufficient light at the bottom, the system may suggest that the user rotate the plant to a certain angle or add a fill light device at the bottom. For large plants, the system may recommend using multiple low-power LED fill lights distributed around the plant to achieve more uniform lighting.

[0219] In this way, the technical solution of the present application can achieve fine-grained regulation of the plant growth environment. Assessing the degree of adaptation of the plant root system and adjusting the environmental factors accordingly can reduce the stress response of the plant after the bubble mud is replaced or adjusted, and improve the survival rate and growth quality of the plant. Adjusting indoor humidity and ventilation according to changes in transpiration rate can better maintain the water balance of the plant and reduce growth disorders caused by water problems. Real-time monitoring and adjustment of light distribution can ensure that all parts of the plant receive uniform light, promote the overall balanced growth of the plant, and improve the ornamental value.

[0220] Second, refer to Figure 2The present application also proposes a plant control device based on bubble mud, which is used in a household indoor potted plant planting scenario to configure bubble mud and control the growth state of specific plant species. The device includes:

[0221] The acquisition module 210 is used to obtain plant species information and establish a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption;

[0222] A matching module 220, for matching the type, proportion, and replacement cycle of bubble mud based on plant species information;

[0223] The monitoring module 230 is used to monitor the actual growth state of the plant and calculate the deviation data between the actual growth curve and the standard growth curve;

[0224] A generation module 240 is used to generate a bubble mud control scheme including component control, structure control, and replacement strategy if the deviation data exceeds a preset threshold;

[0225] An execution module 250 is used to execute bubble mud adjustment or replacement operations and guide the user to perform environment collaborative regulation;

[0226] The adjustment module 260 is used to collect the plant status after the operation, evaluate the control effect, and dynamically adjust the subsequent control strategy to make the plant growth status closer to the standard growth curve.

[0227] By configuring bubble mud and regulating the growth status of different types of plants, the bubble mud configuration can be matched with plant needs, the bubble mud selection can be more targeted, fertilizer loss and environmental pollution can be reduced, the timing of bubble mud replacement and adjustment can be more accurate, and plant management and control can be more effective. The bubble mud configuration can be matched with plant needs, the bubble mud selection can be more targeted, fertilizer loss and environmental pollution can be reduced, the timing of bubble mud replacement and adjustment can be more accurate, and plant management and control can be more effective.

[0228] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A plant control method based on bubble mud, which is used in a household indoor potted plant planting scenario to configure bubble mud and regulate the growth state of specific plant species, the method comprising: Obtain plant species information and establish a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption; Match the type, ratio, and replacement cycle of bubble mud based on plant species information; Monitor the actual growth status of plants and calculate the deviation data between the actual growth curve and the standard growth curve; If the deviation data exceeds the preset threshold, a bubble mud control plan including ingredient control, structure control, and replacement strategy is generated; Perform bubble mud adjustment or replacement operations to guide users to coordinate environmental control; Collect the status of plants after the operation, evaluate the control effect, and dynamically adjust the subsequent control strategy to make the plant growth status closer to the standard growth curve.

2. A plant control method based on bubble mud according to claim 1, characterized in that: If the deviation data exceeds a preset threshold, the step of generating a bubble mud control scheme including component control, structure control, and replacement strategy includes: If the deviation data exceeds the preset threshold, the user's personalized planting needs are obtained, and a user expectation curve including plant height control goals and flowering promotion goals is established; The standard growth curve is integrated with the user expectation curve to obtain the target growth curve; Analyze the deviation data to determine the deviation type and degree between the actual growth status of the plant and the target growth curve; According to the deviation type and degree, and plant species information, a control scheme is selected from the bubble mud component control library, the structure control library, and the replacement strategy library to generate a bubble mud control scheme including component control instructions, structure control instructions, and replacement strategy instructions; Among them, ingredient regulation instructions include nutrient type, quantity, and release rate; structure regulation instructions include bubble mud density, layered structure, and air permeability; replacement strategy instructions include replacement timing, replacement ratio, and new bubble mud type.

3. A plant control method based on bubble mud according to claim 2, characterized in that: The step of analyzing the deviation data to determine the deviation type and degree of the deviation between the actual growth state of the plant and the target growth curve includes: Collecting multi-source data of plant growth status, wherein the multi-source data includes image data, sensor data and user input data; For the multi-source data, data synchronization processing is performed based on timestamps to obtain synchronized multi-source data; Preprocessing the synchronized multi-source data to obtain preprocessed multi-source data; Based on the preprocessed multi-source data, the deviation data between the actual growth state of the plant and the target growth curve in terms of height growth rate, leaf number, color change and nutrient absorption are calculated; According to the deviation data, the deviation type and degree of deviation between the actual growth state of the plant and the target growth curve are determined, wherein the deviation types include growth retardation, excessive growth, malnutrition and pests and diseases, and the deviation degrees include mild, moderate and severe.

4. A plant control method based on bubble mud according to claim 2, characterized in that: The step of selecting a control scheme from a bubble mud component control library, a structure control library, and a replacement strategy library according to the deviation type and degree and plant species information to generate a bubble mud control scheme including component control instructions, structure control instructions, and replacement strategy instructions comprises: Constructing a bubble mud regulation knowledge graph, wherein the bubble mud regulation knowledge graph includes plant species nodes, deviation type nodes, deviation degree nodes, component regulation scheme nodes, structure regulation scheme nodes, replacement strategy nodes, and association relationships between nodes; Based on the plant species information, the deviation type and the deviation degree, a multi-hop search is performed in the bubble mud regulation knowledge graph to obtain a set of candidate regulation schemes, wherein the multi-hop search includes: taking the plant species node as the starting node, performing a first-hop search according to the plant species information to obtain an associated deviation type node set and a deviation degree node set; taking the deviation type node set and the deviation degree node set as the starting node, performing a second-hop search to obtain an associated component regulation scheme node set, a structural regulation scheme node set and a replacement strategy node set; Calculating a confidence score for each regulation scheme in the set of candidate regulation schemes, the confidence score comprehensively considering the strength of association between the regulation scheme and the plant species, the deviation type and the deviation degree, and the compatibility between the regulation schemes; According to the confidence score, a control scheme with the highest confidence score is selected from the candidate control scheme set to generate a bubble mud control scheme including component control instructions, structure control instructions and replacement strategy instructions.

5. A plant control method based on bubble mud according to claim 4, characterized in that: The step of calculating the confidence score of each control scheme in the candidate control scheme set includes: Obtaining association relationship data between nodes in the bubble mud regulation knowledge graph, wherein the association relationship data includes a first association strength between a plant species node and a component regulation scheme node, a second association strength between a deviation type node and a component regulation scheme node, a third association strength between a deviation degree node and a component regulation scheme node, and a compatibility score between component regulation scheme nodes; For each control scheme in the candidate control scheme set, according to the component control scheme node contained therein, the weighted association strength between it and the plant species node, the deviation type node and the deviation degree node is calculated, wherein the weighted association strength is obtained by weighted summing the first association strength, the second association strength and the third association strength, and the weight coefficient is determined according to the plant species, the deviation type and the deviation degree; Calculating the compatibility score of each control scheme in the candidate control scheme set, wherein the compatibility score is obtained by weighted average of the compatibility scores between the nodes of each component control scheme in the control scheme, and the weight coefficient is determined according to the effect size of each component control scheme node; The weighted association strength and compatibility score are weightedly summed to obtain the confidence score of each regulation scheme in the candidate regulation scheme set, and the weight coefficient is determined according to the association strength and compatibility.

6. A plant control method based on bubble mud according to claim 1, characterized in that: The step of matching the type, proportion, and replacement cycle of bubble mud based on the plant species information includes: Obtain environmental factor data on region, season, and light intensity, and build an environmental factor database; Extract basic nutrient requirement data of plants based on plant species information; According to the environmental factor data, the basic nutrient requirement data of the plant is corrected using the environmental factor impact model to obtain the target nutrient requirement data, wherein the environmental factor impact model is: target nutrient requirement data = basic nutrient requirement data * (1 + regional correction coefficient + seasonal correction coefficient + light correction coefficient); According to the target nutrient demand data, select the type of bubble mud and determine the bubble mud ratio; Predict the bubble mud replacement cycle. The bubble mud replacement cycle prediction takes into account the plant nutrient absorption rate, bubble mud nutrient release rate and environmental factors.

7. A plant control method based on bubble mud according to claim 6, characterized in that: The step of correcting the basic nutrient requirement data of the plant using the environmental factor impact model according to the environmental factor data to obtain the target nutrient requirement data includes: Obtain the longitude and latitude coordinates of the plant planting area, query the historical meteorological data of the corresponding area, and obtain soil composition data, annual average precipitation data, and air humidity data; Based on the soil composition data, the regional correction coefficient is calculated using the soil nutrient conversion model, which is: regional correction coefficient = α*(soil organic matter content / soil total nutrient content)+β*(soil pH value-7), where α and β are model parameters, which are adjusted according to the plant species; Obtain the current date, determine the current season according to the preset season division rules, and calculate the season correction coefficient using the nonlinear season impact model in combination with the plant growth cycle data. The nonlinear season impact model is: season correction coefficient = γ*sin(2πt / T+φ), where γ is the seasonal impact amplitude, t is the current time, T is the plant growth cycle, φ is the phase offset, and γ, T and φ are adjusted according to the plant species and growth stage; Obtain the light intensity data of the plant's location, obtain spectral data in combination with a spectral sensor, and use a photosynthetically active radiation model to calculate a light correction coefficient, wherein the photosynthetically active radiation model is: light correction coefficient = δ*PAR*(red light ratio / blue light ratio), where δ is a light influencing factor, PAR is photosynthetically active radiation intensity, the red light ratio and the blue light ratio are spectral component ratios, and δ is adjusted according to the plant species; According to the regional correction factor, seasonal correction factor and light correction factor, the target nutrient requirement data is calculated using the environmental factor impact model.

8. The plant control method based on bubble mud according to claim 1, characterized in that: If the deviation data exceeds a preset threshold, the step of generating a bubble mud control scheme including component control, structure control, and replacement strategy includes: If the deviation data exceeds the preset threshold, the humidity, conductivity and gas composition of the bubble mud are monitored to determine whether the bubble mud has the risk of hardening, nutrient imbalance and excessive odor. If there is a risk of compaction, adjust the bubble mud formula, increase the fiber material content, reduce the proportion of sticky components, generate a loosening instruction, and prompt the user to loosen the soil to increase the air permeability of the bubble mud; If there is a risk of nutrient imbalance, the bubble mud nutrients are analyzed to determine the types of missing nutrients, and a directional nutrient supplement instruction is generated to prompt the user to add the corresponding nutrients to balance the bubble mud nutrients; If there is a risk of odor exceeding the standard, the bubble mud gas composition is detected, the type of odorous gas is determined, and an activated carbon adsorption instruction or a bubble mud replacement instruction is generated to prompt the user to perform activated carbon adsorption or replace the bubble mud to eliminate the odor.

9. The plant control method based on bubble mud according to claim 1, characterized in that: The step of performing the bubble mud adjustment or replacement operation and guiding the user to perform environmental collaborative regulation includes: Evaluate the adaptability of plant roots to the adjustment or replacement of bubble mud, adjust the light intensity, temperature and humidity according to the adaptability, and obtain the environmental factor regulation parameters; According to the changes in plant transpiration rate, guide users to adjust indoor air humidity and ventilation to maintain plant water balance; Monitor the light intensity of different parts of the plant in real time, and guide users to adjust the position of the plant or use supplementary lighting equipment to achieve uniform lighting for the entire plant.

10. A plant control device based on bubble mud, used in a household indoor potted plant planting scenario, for configuring bubble mud and regulating the growth state of specific plant species, characterized in that: The device includes: The acquisition module is used to obtain plant species information and establish a standard growth curve including height growth rate, leaf number, color change, and nutrient absorption; A matching module is used to match the type, ratio, and replacement cycle of bubble mud based on plant species information; A monitoring module is used to monitor the actual growth status of the plant and calculate the deviation data between the actual growth curve and the standard growth curve; A generation module, used to generate a bubble mud control plan including component control, structure control, and replacement strategy if the deviation data exceeds a preset threshold; An execution module is used to perform bubble mud adjustment or replacement operations and guide users to perform collaborative environmental control; The adjustment module is used to collect the status of plants after operation, evaluate the control effect, and dynamically adjust the subsequent control strategy to make the plant growth status closer to the standard growth curve.

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