House planting component based on fourth-generation residence and control system

Abstract

The invention provides a house planting component based on a fourth-generation house and a control system, and relates to the technical field of intelligent gardening planting.The house planting component comprises a mounting plane, a planting area and a vine planting cavity opening, a bottom box is placed at the bottom of the inner back face of the mounting plane, and square supporting piles are clamped to one side and the middle of the top of the bottom box; a placement batten is connected between the supporting piles on the two sides in a mortise and tenon joint mode, embedded blocks are embedded in the two sides of the placement batten, mounting clamping sets are arranged on the side edges of the embedded blocks, and the mounting clamping sets are matched with the supporting piles. The planting efficiency and the user experience are obviously improved. Firstly, precise environment regulation and control and efficient resource management are realized; the environmental data acquisition engine monitors the environmental conditions of the planting area in real time by using high-precision temperature, humidity and illumination sensors.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent gardening planting, and in particular to a house planting component and a control system based on a fourth-generation house. Background Art

[0002] As an important functional space in modern residences, urban balconies have significant differences in usage requirements and actual functions. According to market research, users prioritize balcony functions in the following order: planting, reading, drinking tea, drying clothes, fitness, and meeting guests, while actual usage is mainly drying clothes, planting, storing sundries, reading, drinking tea, and meeting guests. Although planting is one of the core demands of users, it is limited by the small balcony space, unstable environmental conditions (such as insufficient light, temperature and humidity fluctuations), and insufficient user management time. Currently, balcony planting mostly relies on simple equipment, which makes it difficult to achieve refined and intelligent management, resulting in a planting experience that cannot meet the expectations of urban users for efficient and convenient rural life.

[0003] The fourth generation of housing (also known as three-dimensional garden ecological housing or urban forest garden building) takes "every household has a garden, every home has a courtyard" as its core concept, aiming to reproduce the traditional living style through green building design. However, in the systematic management of balcony planting, the control system faces the following key technical challenges: Insufficient control system integration: Most of the existing balcony planting equipment are independent modules (such as separate lighting devices or irrigation equipment), lacking a unified control system architecture, and unable to effectively integrate functions such as temperature, humidity, lighting, and pest control. The data between devices is isolated, making it difficult to coordinate and support the growth needs of diverse plants (such as leafy vegetables, fruits, and vines), resulting in users having to frequently switch operations and difficult maintenance.

[0004] Limited dynamic control capabilities: Current intelligent planting control systems are mostly based on fixed parameters (such as timed illumination or irrigation), and cannot adaptively adjust the spectrum, irrigation volume or ventilation strategy according to real-time environmental changes (such as seasonal temperature differences, sunshine duration) or plant growth stages (seedling stage, flowering stage). The control algorithm lacks an environmental feedback mechanism, and the planting effect depends on user experience, making it difficult to achieve precise management.

[0005] Low resource utilization efficiency: Existing irrigation and pest control control systems lack precise control capabilities. For example, irrigation systems often use fixed spraying patterns and do not dynamically optimize water volume in combination with soil moisture, resulting in about 30% water waste; pest control devices do not adjust liquid spraying according to pest activity probability or wind speed, resulting in low liquid use efficiency and increased environmental burden.

[0006] Insufficient remote monitoring and intelligence: The existing planting control system has limited remote monitoring functions, and data collection (such as plant health and environmental parameters) and user interaction (such as mobile phone applications) are often delayed or disconnected. Users need to frequently check the status of the equipment manually, which makes it difficult to achieve fully automated management, reducing the intelligent experience.

[0007] Therefore, a house planting component and control system based on the fourth-generation residence is needed to solve the above problems. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a house planting component and a control system based on the fourth-generation residence, which solves the problems mentioned in the above background technology.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a house planting component control system based on the fourth-generation house, the control system is arranged in a multi-source acquisition component, and the control system includes an environmental data acquisition engine, a temperature balance control engine, a water supply control engine, a photosynthesis optimization engine, a pest behavior regulation engine, a plant status remote diagnosis engine and a power resource allocation engine; The environmental data acquisition engine collects temperature data of the planting area through a temperature sensor, collects relative humidity data of the planting area through a humidity sensor, and collects light intensity data of the planting area through a light sensor; the collected temperature data, humidity data, and light intensity data are stored in a data buffer of the multi-source acquisition component, and transmitted to the temperature balance control engine, the water supply control engine, the photosynthesis optimization engine, and the pest behavior regulation engine through an internal communication bus; The temperature balance control engine receives the temperature data transmitted by the environmental data acquisition engine, adopts the temperature grid distribution optimization algorithm to divide the planting area into three-dimensional grids, calculates the temperature value of each grid point according to the geometric structure data of the planting area and the wall heat dissipation coefficient, and generates the speed control instruction of the hair dryer group by comparing the deviation between the target temperature and the actual temperature, so that the temperature deviation of the planting area is controlled within 0.5 degrees Celsius; the speed control instruction is transmitted to the hair dryer group through the communication bus, and the temperature balance state data is transmitted to the plant state remote diagnosis engine; The water supply control engine receives the humidity data and plant species data transmitted by the environmental data acquisition engine, and adopts a root water demand prediction algorithm. The algorithm calculates the instantaneous irrigation amount of each group of potted plants placed on the slats according to the soil humidity data and the absorption characteristics of the plant roots, generates an operating instruction for the water pump in the storage material box, and performs irrigation through the sprinkler assembly; the irrigation amount data and the soil humidity status are transmitted to the plant status remote diagnosis engine; The photosynthesis optimization engine receives the light intensity data and plant growth stage data transmitted by the environmental data acquisition engine, and adopts the spectral efficiency modulation algorithm. The algorithm calculates the light intensity and spectrum ratio of the light component according to the photosynthetic absorption characteristics of the plant, generates light intensity adjustment instructions and irradiation duration instructions, and controls the operation of the light component; the light status data is transmitted to the plant status remote diagnosis engine. The wall heat dissipation coefficient (default 0.1 watts / square meter / degree Celsius) is a thermal parameter of the rear plate and support piles of the planting area, and is preset in the multi-source acquisition component based on the material properties (waterproof composite board and steel).

[0010] Preferably, the pest behavior regulation engine receives temperature data, humidity data transmitted by the environmental data acquisition engine and pest activity data provided by an external database, and adopts a rhythmic behavior interference algorithm. The algorithm constructs a pest activity rhythm model based on the temperature and humidity data of the past 7 days, calculates the probability of pest activity per hour, and generates a spraying instruction for the insect repellent nozzle assembly when the probability exceeds 0.7, the instruction includes a frequency of spraying 0.1 ml of mosquito repellent liquid per minute and a duration of 2 minutes; the algorithm adjusts the concentration of the liquid according to the humidity data, and increases the concentration by 10% when the humidity is lower than 50%; the spraying instruction is transmitted to the high-efficiency water pump of the mosquito and insect repellent assembly through the communication bus to execute the liquid spraying; the spraying status data and the pest activity probability are transmitted to the plant status remote diagnosis engine for health assessment, and the pest activity data comes from an external cloud database and is regularly acquired through the Wi-Fi module (speed 150 megabits / second) of the multi-source acquisition assembly. The database is maintained by agricultural research institutions or pest monitoring platforms and contains data on the activity patterns of common pests (such as aphids and whiteflies), such as daily active time periods, temperature and humidity thresholds, including pest species, baseline values ​​for activity probability (for example, aphid baseline value 0.2), temperature influence factors (probability increases by 0.01 for every 1 degree Celsius increase) and humidity influence factors (probability increases by 0.02 for every 10% increase in humidity). These data are stored in JSON format. The pest behavior regulation engine combines 7 days of temperature and humidity data to calculate the hourly pest activity probability based on the rhythmic behavior interference algorithm, and the external database data is used as the initial parameters of the algorithm.

[0011] Preferably, the plant status remote diagnosis engine receives the temperature balance status data transmitted by the temperature balance control engine, the irrigation volume data and soil moisture status transmitted by the water supply control engine, the light status data transmitted by the photosynthesis optimization engine, and the high-resolution image data collected by the monitoring component, and adopts a leaf health fine evaluation algorithm. The algorithm divides the image into sub-regions of 128×128 pixels, extracts the leaf edge curvature, color distribution and texture features of each sub-region, and calculates the health score through a feature weighted fusion model. The weight is adjusted according to the plant species. The texture weight of leafy vegetables is 0.4, and the color weight is 0.3; when When the health score is less than 80 points, a report containing the abnormality type and adjustment suggestions is generated. The adjustment suggestions include increasing the irrigation volume by 20% and extending the light duration by 1 hour. The report is transmitted to the user's mobile phone application via a wireless network, and the user sends control instructions for the lighting component and the sprinkler component through the application. The health score and adjustment suggestions are stored in the multi-source acquisition component for subsequent analysis. The plant species data is manually entered by the user through the mobile phone application, or the image is automatically recognized by the image recognition module of the plant status remote diagnosis engine after the high-definition camera (resolution 1920×1080 pixels) of the monitoring component takes the plant image. The image recognition module uses a pre-trained convolutional neural network model (accuracy 95%) to support the classification of common plant species (such as lettuce, strawberry, and basil). The data includes plant names and growth characteristics, such as leafy vegetables (texture weight 0.4, color weight 0.3), and fruits (texture weight 0.3, color weight 0.4). The data is stored in the storage unit (32 megabytes of flash memory) of the multi-source acquisition component 3. The leaf health fine assessment algorithm adjusts the weights of the feature weighted fusion model according to the plant species data to generate a targeted health score.

[0012] Preferably, the power resource allocation engine receives the real-time power consumption data of each engine, adopts a load balancing optimization algorithm, and the algorithm constructs a power demand prediction model for the next hour based on the average and peak power consumption data of the past 7 days. The power allocation ratio of each engine is calculated by a linear programming method, and the temperature balance control engine is allocated 30%, and the photosynthesis optimization engine is allocated 25%, ensuring that the total power consumption does not exceed 90% of the rated power; the allocation ratio generates a power control instruction, which is transmitted to the power supply device through a communication bus; the power supply device has overvoltage protection, overcurrent protection, waterproof treatment and leakage protection functions, and the overvoltage protection adopts a combination circuit of a varistor and a transient suppression diode. When the voltage rises by more than 1.2 times the rated value, the circuit is turned on and shunted within 0.1 milliseconds; the power distribution status data is transmitted to the plant status remote diagnosis engine for system stability evaluation, and the real-time power consumption data is collected by a Hall effect power consumption sensor (accuracy 0.01 watt) embedded in the circuit of each engine. The sensor is distributed in the power supply interface of the temperature balance control engine (hair dryer group), the moisture supply control engine (high-efficiency water pump), the photosynthesis optimization engine (lighting component) and other modules. The power consumption sensor measures the current and voltage of each engine every minute to calculate the power (power = Voltage × current), the data is converted into a digital signal through an analog-to-digital converter and transmitted to the data processor of the multi-source acquisition component 3. Typical power consumption data is: Temperature balanced control engine: 100-300 watts; Photosynthesis-optimized engine: 50-150 watts; Moisture supply control engine: 30-50 watts.

[0013] Preferably, the spectral efficiency modulation algorithm of the photosynthesis optimization engine receives the light intensity data and the plant growth stage database transmitted by the environmental data acquisition engine, and the database contains the spectral requirements of the seedling stage, the vegetative growth stage and the flowering stage; the algorithm divides the spectrum into three bands of red light, blue light and green light, and calculates the weight of each band according to the photosynthetic absorption characteristics of the plant, the weight of red light in the seedling stage is 0.5, and the weight of blue light is 0.4; the algorithm adjusts the driving current of the lighting component through an iterative optimization method to control the light intensity deviation within 5 micromoles per square meter per second, and dynamically adjusts the irradiation time according to the sunshine time data, and extends it by 2 hours when the sunshine time is less than 6 hours; the light intensity adjustment instruction and the irradiation time instruction are transmitted to the lighting component through the communication bus; the spectral status data is transmitted to the plant status remote diagnosis engine for health assessment, and the plant growth stage database is pre-set in the storage unit of the multi-source acquisition component, provided by agricultural experts, and regularly updated from the cloud through the Wi-Fi module (update frequency once a month). The database covers the growth stages and spectral requirements of common plants (such as lettuce, strawberry, and basil). The database is stored in a table format, including plant species, growth stages (seedling stage, vegetative growth stage, and flowering stage) and corresponding spectral weights. The spectral efficiency modulation algorithm of the photosynthesis optimization engine calculates the weights of red light, blue light, and green light according to the plant growth stage database, and generates the light intensity and spectral ratio instructions of the lighting component 26. The sunshine time data is recorded by the light sensor (accuracy 1 micromol / m2 / s) for the duration of daily light intensity > 50 micromol / m2 / s, and is stored in the buffer of the multi-source acquisition component 3.

[0014] Preferably, the temperature data, humidity data and light intensity data collected by the environmental data acquisition engine through the temperature sensor, humidity sensor and light sensor are stored in the buffer of the multi-source acquisition component, and transmitted to the temperature balance control engine, the moisture supply control engine, the photosynthesis optimization engine and the pest behavior regulation engine through the internal communication bus; each engine processes the received data respectively to generate corresponding control instructions; the temperature balance control engine generates a speed control instruction for the hair dryer group, the moisture supply control engine generates a water pump operation instruction, the photosynthesis optimization engine generates a light intensity adjustment instruction for the lighting assembly, and the pest behavior regulation engine generates a spraying instruction for the insect repellent nozzle assembly; all control instructions are transmitted to the corresponding hardware through the communication bus for execution, and the execution status data is summarized to the plant status remote diagnosis engine to generate a comprehensive environmental regulation report.

[0015] Preferably, the rhythmic behavior interference algorithm of the pest behavior regulation engine receives the real-time wind speed data and humidity data transmitted by the environmental data acquisition engine, constructs a liquid medicine diffusion model, and the model calculates the coverage of the liquid medicine in the planting area according to the wind speed data. When the wind speed exceeds 2 meters per second, the spraying angle of the insect repellent nozzle assembly is adjusted to tilt upward by 15 degrees; when the humidity is lower than 40%, the liquid medicine spraying volume is increased by 0.05 ml per minute; the adjusted spraying instruction is transmitted to the high-efficiency water pump of the mosquito and insect repellent assembly through the communication bus; the liquid medicine diffusion state data is transmitted to the plant state remote diagnosis engine to provide environmental impact assessment, and the real-time wind speed data is collected by the hot wire wind speed sensor (accuracy 0.1 meters per second, response time 0.2 seconds) in the planting area. The sensor is installed near the hair dryer group, covering the planting area and the vine planting cavity. The wind speed sensor measures the air flow rate every minute, and the signal is processed by the amplification circuit and the analog-digital converter to generate a digital signal, which is stored in the buffer of the multi-source acquisition component 3. The typical wind speed range is 0-5 meters per second.

[0016] Preferably, the leaf health fine assessment algorithm of the plant state remote diagnosis engine receives the health score data of the past 7 days, constructs a plant growth trend model, and the model analyzes the slope of the health score by a sliding window method, with a window size of 7 days; when the slope is less than negative 2 points per day, it is judged as a potential abnormality, and an optimization instruction including increasing the light intensity by 10% and increasing the irrigation volume by 20% is generated; the optimization instruction is transmitted to the user's mobile phone application via a wireless network; the growth trend data and the optimization instruction are stored in the multi-source acquisition component to provide subsequent growth predictions. The health score data is generated by the leaf health fine assessment algorithm of the plant state remote diagnosis engine, and is calculated based on the high-definition camera image and environmental data (temperature, humidity, light) of the monitoring component. Each calculated health score (range 0-100 points) is stored in the 32-megabyte flash memory of the multi-source acquisition component 3 in the form of a timestamp, and the 7-day data occupies about 100 kilobytes. The plant status remote diagnosis engine queries the health score data of the past 7 days through the storage unit of the multi-source acquisition component 3, builds a sliding window (window size 7 days) to analyze the score slope, and the growth trend model judges the plant health trend based on the score slope (core formula: slope = (current score - score 7 days ago) / 7). When the slope is <-2 points / day, an optimization instruction is generated.

[0017] Preferably, the temperature grid distribution optimization algorithm of the temperature balance control engine receives the temperature data and the planting area geometry data transmitted by the environmental data acquisition engine, and constructs a three-dimensional heat flux distribution model. The model calculates the heat flux density of each grid point by the finite difference method. When the temperature of a grid point exceeds the target value by 1 degree Celsius, an instruction to increase the local air volume of the blower group by 10% is generated; the instruction is transmitted to the blower group through the communication bus; the heat flux distribution state data is transmitted to the plant state remote diagnosis engine to provide temperature uniformity evaluation, and the heat flux distribution state data is generated by the temperature grid distribution optimization algorithm of the temperature balance control engine. Based on the temperature data and the planting area geometry data transmitted by the environmental data acquisition engine, the algorithm calculates the heat flux density of each grid point in the three-dimensional grid in the planting area by the finite difference method, and the state matrix is ​​transmitted to the plant state remote diagnosis engine through the CAN bus for evaluating temperature uniformity. The geometry data is the physical parameters of the planting area 22, including length (2 meters), width (1 meter), height (1.5 meters) and grid spacing (5 centimeters), which are preset in the storage unit of the multi-source acquisition component and manually input when the system is installed.

[0018] The house planting component based on the fourth generation of housing includes an installation plane, a planting area and a vine planting cavity. A bottom box is placed at the bottom of the back side of the installation plane. Support piles are clamped on one side and the middle of the top of the bottom box. The support piles are square in shape. Placement strips are connected between the support piles on both sides by mortise and tenon joints. Both sides of the placement strips are embedded with embedded blocks. The sides of the embedded blocks are provided with installation card groups. The installation card groups and the support piles are adapted to each other. A rear plate is clamped on the back of the top of the bottom box. The planting area is composed of the rear plate and The bottom of the bottom box is composed of support piles on both sides, and five groups of placement strips are installed inside the planting area. A vine guard plate is clamped on the other side of the top of the bottom box, and the shape of the vine guard plate is L-shaped. The vine planting cavity is composed of the support piles and the vine guard plate in the middle. The back of the vine guard plate is provided with a mesh hole. The middle of the bottom end of the placement strip is snap-connected with a control bottom module. An insect repellent nozzle assembly is provided on one side of the bottom end of the control bottom module. A sprinkler assembly is provided in the middle of the bottom end of the control bottom module. A lighting component is installed on the other side, a drainage straight pipe is provided on one side close to the bottom box in the installation plane, an absorption pipe head is installed on the top of the drainage straight pipe, the absorption pipe head is bonded to the wall, the bottom of the drainage straight pipe is connected to the water outlet of the installation plane, one side of the drainage straight pipe is connected to a drainage subdivision pipe, there are five groups of drainage subdivision pipes, all of which are located at the bottom of the placement strip board, a connecting pipe end is provided on one side of the top of the drainage subdivision pipe, a drainage hole is opened on one side of the bottom of the placement strip board, a water injection pipe opening is opened on the other side of the bottom of the placement strip board, the end of the connecting pipe end passes through the drainage hole and is connected to the bottom of the potted plant on the placement strip board, a protective panel is clamped on the front of the bottom box, a mosquito and insect repellent component is placed on one side of the bottom box, and a storage material box is placed on the other side of the bottom box, a hair dryer group is installed on one side of the top in the installation plane, a monitoring and surveillance component is installed on the other side of the top in the installation plane, and a multi-source acquisition component is installed near the front of the monitoring and surveillance component in the installation plane.

[0019] The present invention provides a house planting component and control system based on the fourth generation of housing. It has the following beneficial effects: 1. Aiming at the limited space of urban balconies and the demand for intelligent planting, the present invention significantly improves the planting efficiency and user experience through modular component design and multi-engine collaborative control. First, precise environmental control and efficient resource management are achieved. The environmental data acquisition engine uses high-precision temperature, humidity, and light sensors to monitor the environmental conditions of the planting area in real time, and the data is quickly transmitted to each engine through the CAN bus of the multi-source acquisition component. The temperature balance control engine adopts a grid distribution optimization algorithm, combined with the geometric structure of the planting area and the waterproof composite board characteristics of the back plate, to drive the variable frequency motor and multi-directional air outlet of the blower group, accurately adjust the air flow, and ensure that the temperature in the planting area is uniform and stable. The water supply control engine analyzes the soil moisture and plant species of the potted plants through the root water demand prediction algorithm, controls the telescopic tube and multi-mode nozzle of the sprinkler assembly (supporting uniform spraying and drip irrigation), and accurately delivers clean water or nutrient solution from the storage material box to the potted plant root system to avoid water waste. The photosynthesis optimization engine adjusts the red, blue, and green LED spectrum ratio of the lighting assembly according to the plant growth stage (such as seedling stage and flowering stage), optimizes the photosynthetic efficiency through the spectrum switching mechanism, and provides the best lighting environment for the plants. These features work together to significantly improve the efficiency of water and light resource utilization, creating ideal growing conditions for a variety of plants.

[0020] 2. The precise pest control and intelligent plant health management in the present invention are greatly enhanced. The pest behavior regulation engine runs a rhythmic behavior interference algorithm based on 7-day environmental data and a cloud-based pest activity database to predict the peak of pest activity, drive the atomizing nozzle and angle adjustment motor of the insect repellent nozzle assembly, and accurately spray the liquid medicine. The liquid medicine diffusion model dynamically adjusts the spraying angle and concentration according to the wind speed and humidity to ensure that the liquid medicine covers the planting area and the vine planting cavity, reduces the amount of chemical agents, and protects plants and the environment. The plant status remote diagnosis engine uses the high-definition camera of the monitoring component to collect plant images, combines the leaf health fine assessment algorithm, analyzes the leaf edge, color and texture, and generates a health report. If an abnormality is detected (such as yellowing or wilting), the system sends adjustment suggestions (such as increasing irrigation or extending light) to the user's mobile phone application via Wi-Fi. The user can remotely operate the lighting component or the sprinkler component to achieve unattended intelligent management, improve the convenience of planting and the health level of plants.

[0021] 3. The stable operation of the system and the multi-functional integration of the balcony are optimized. The power resource allocation engine monitors the power consumption of each engine through the load balancing optimization algorithm, reasonably allocates power to the hair dryer group, lighting components and other equipment, and cooperates with the overvoltage protection circuit and waterproof shell of the power supply equipment to ensure the stable operation of the system in a complex environment. The house planting components use support piles and placement strips connected by mortise and tenon joints, combined with the mesh design of the L-shaped vine guard plate, to support the mixed planting of leafy vegetables, fruits and vines. The structure is compact and detachable, which is easy to install and maintain. The control module integrates insect repellent, irrigation, and lighting functions to reduce the space occupied by equipment. The drainage straight pipe and subdivided pipes efficiently handle the residual irrigation water, and are seamlessly compatible with balcony functions such as drying clothes and reading. The system provides users with real-time monitoring and operation interfaces through the wireless transmission module of the multi-source acquisition component, which perfectly fits the concept of "every household has a garden" in the fourth-generation residential buildings, and improves the utilization rate of balcony space and living comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the control system of the present invention; Figure 2 It is a data processing framework diagram of the control system of the present invention; Figure 3 It is the overall structure diagram of the present invention; Figure 4 It is the overall axonometric view of the present invention; Figure 5 It is a top view schematic diagram of the present invention; Figure 6 It is a component structure diagram of the present invention.

[0023] Legend: 1. Installation plane; 2. Blower assembly; 3. Multi-source collection assembly; 4. Monitoring and surveillance assembly; 5. Bottom box; 6. Support piles; 7. Vine planting cavity opening; 8. Placement strips; 9. Inserts; 10. Installation card group; 11. Control bottom module; 12. Drainage hole; 13. Water injection pipe; 14. Drainage subdivision pipeline; 15. Connecting pipe end; 16. Absorption pipe head; 17. Drainage straight pipe; 18. Protection panel; 19. Mosquito and insect repellent assembly; 20. Storage material box; 21. Back panel; 22. Planting area; 23. Mesh; 24. Insect repellent nozzle assembly; 25. Sprinkler assembly; 26. Light assembly; 27. Vine guard plate. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one: like Figure 1-Figure 6As shown, the present invention provides a house planting component based on the fourth generation of housing and a control system thereof, which is suitable for the inner side of a balcony and can also be used in an indoor garden or a roof terrace. The component achieves portability and flexibility through a detachable splicing structure, and all components adopt standardized interfaces. The support piles 6 and the placement strips 8 are connected by mortise and tenon joints and can be fixed with screws to enhance stability. The planting component includes an installation plane 1, a planting area 22 and a vine planting cavity 7. The installation plane 1 is a rectangular steel frame, and a bottom box 5 is placed at the bottom of the back. A square-section steel support pile 6 is clamped on one side and the middle of the top of the bottom box 5. Aluminum alloy placement strips 8 are connected between the support piles 6 on both sides by mortise and tenon joints. Blocks 9 are embedded on both sides of the placement strips 8, and mounting card groups 10 are provided on the sides of the blocks 9. The mounting card groups 10 are clamped with the support piles 6 The bottom box 5 is adapted to be compatible with the groove, the back of the top of the bottom box 5 is clamped with a waterproof composite board back plate 21, the back plate 21 and the support piles 6 on both sides form a planting area 22, and five groups of placement strips 8 with the same structure are installed in the planting area 22, each group of placement strips 8 carries potted plants, and the potted plants have built-in humidity sensors. The other side of the top of the bottom box 5 is clamped with an L-shaped steel grid plate vine guard plate 27, and the back of the vine guard plate 27 is provided with a mesh hole 23, which forms a vine planting cavity 7 with the middle support piles 6 for the growth of climbing plants. The middle part of the bottom end of the placement strip 8 is connected to the control bottom module 11 by a buckle, and the control bottom module 11 is composed of a microprocessor, a signal transceiver and a power supply interface. An insect repellent nozzle assembly 24 is provided on one side of the bottom end of the control bottom module 11. The insect repellent nozzle assembly 24 includes an atomizing nozzle, an angle adjustment motor and a particle size adjustment valve. The angle adjustment The motor drives the atomizing nozzle to rotate 0-45 degrees, and the particle size regulating valve controls the atomized particle size of the liquid medicine to 10-50 microns. A sprinkler assembly 25 is provided in the middle of the bottom end of the control bottom module 11. The sprinkler assembly 25 is composed of a telescopic tube, a multi-mode nozzle and a flow controller. The telescopic tube length adjustment range is 0-10 cm. The multi-mode nozzle supports uniform spraying and drip irrigation. A lighting assembly 26 is installed on the other side of the bottom end of the control bottom module 11. The lighting assembly 26 is composed of red, blue and green LED lamp beads, a spectrum switching mechanism and a heat sink. The spectrum switching mechanism adjusts the proportions of red light 620-630 nanometers, blue light 450-460 nanometers and green light 510-520 nanometers. The insect repellent nozzle assembly 24, the sprinkler assembly 25 and the lighting assembly 26 are for the plants on the bottom layer of the slat 8. It provides mosquito repellent, irrigation and supplementary lighting. A PVC drainage straight pipe 17 is arranged near the bottom box 5 in the installation plane 1. An absorption pipe head 16 is installed on the top of the drainage straight pipe 17. The absorption pipe head 16 is bonded to the wall by adhesive. The bottom of the drainage straight pipe 17 is connected to the drain outlet of the installation plane 1. Five groups of drainage subdivision pipes 14 are connected to the drainage straight pipe 17 and are located at the bottom of the placement strip 8. A connecting pipe end 15 is arranged on the top of the drainage subdivision pipe 14. A drainage hole 12 is opened on one side of the bottom of the placement strip 8, and a water injection pipe port 13 is opened on the other side. The connecting pipe end 15 penetrates the drainage hole 12 and is connected to the bottom of the potted plant. Excess irrigation water flows into the drainage straight pipe 17 through the drainage hole 12, the connecting pipe end 15, and the drainage subdivision pipe 14 and is discharged into the sewer. A transparent polycarbonate protective panel 18 is clamped on the front of the bottom box 5.A mosquito and insect repellent assembly 19 is placed on one side of the bottom box 5. The mosquito and insect repellent assembly 19 includes a liquid medicine storage tank and a high-efficiency water pump. The liquid medicine storage tank is connected to the bottom module 11 through a pipeline to control the insect repellent nozzle assembly 24. A storage material box 20 is placed on the other side of the bottom box 5. The storage material box 20 stores clean water or nutrient solution and is connected to the bottom module 11 through a pipeline to control the sprayer assembly 25. A hair dryer group 2 is installed on one side of the top of the installation plane 1. The hair dryer group 2 includes a variable frequency motor and a multi-directional air outlet. The multi-directional air outlet supports horizontal and vertical adjustments. A monitoring and surveillance assembly 4 is installed on the other side of the top. The monitoring and surveillance assembly 4 includes a high-definition camera with a resolution of 1920×1080 pixels and a wireless transmission module. A multi-source acquisition group is installed near the front of the monitoring and surveillance assembly 4 in the installation plane 1. Component 3, the multi-source acquisition component 3 includes a data processor, a storage unit and a communication interface. A temperature sensor is configured in the planting area 22. The power supply device is placed in the bottom box 5 or on the top of the balcony, including an overvoltage protection circuit, an overcurrent protection circuit, a waterproof sealed housing and a leakage protection switch to power all electronic devices. The control system is set in the multi-source acquisition component 3, including an environmental data acquisition engine, a temperature balance control engine, a water supply control engine, a photosynthesis optimization engine, a pest behavior regulation engine, a plant status remote diagnosis engine and a power resource allocation engine. The environmental data acquisition engine collects the temperature data of the planting area 22 through the temperature sensor, the humidity sensor in the pot collects the soil humidity data, and the light sensor collects the light intensity data. The data is stored in the multi-source acquisition component 3. The storage unit of the acquisition component 3 is transmitted to the temperature balance control engine, the water supply control engine, the photosynthesis optimization engine and the pest behavior regulation engine through the communication interface and the internal communication bus. The working principle is that the temperature sensor collects the ambient temperature of the planting area 22 every minute, the humidity sensor collects the soil humidity of the potted plants every minute, and the light sensor collects the light intensity every minute. The data is pre-processed by the data processor of the multi-source acquisition component 3, stored in the storage unit, and distributed through the communication bus. The temperature balance control engine receives the temperature data and uses the temperature grid distribution optimization algorithm to divide the planting area 22 into three-dimensional grids. According to the geometric structure data of the planting area 22 and the wall heat dissipation coefficient, the temperature value of each grid point is calculated, and the target temperature is compared with the actual temperature. Deviation, generate the frequency conversion motor speed control instruction of hair dryer group 2, control the multi-directional air outlet angle, make the temperature deviation within 0.5 degrees Celsius, the instruction is transmitted to hair dryer group 2 through the communication bus, hair dryer group 2 adjusts the frequency conversion motor speed and air outlet direction, accelerates air flow and reduces the temperature around the plant, and transmits the temperature equilibrium state data to the plant state remote diagnosis engine. The water supply control engine receives the soil moisture data and plant species data, adopts the root water demand prediction algorithm, calculates the instantaneous irrigation amount of each group of potted plants on the slats 8 according to the soil moisture data and the absorption characteristics of the plant roots, generates the high-efficiency water pump operation instruction in the storage material box 20, controls the telescopic tube length of the sprinkler assembly 25 and the operation of the multi-mode sprinkler, and selects the uniform spray or drip irrigation mode.The sprinkler assembly 25 adjusts the water flow through the flow controller, and the telescopic tube is adjusted to the optimal position according to the height of the potted plant. The clean water or nutrient solution is transported to the sprinkler assembly 25 through the pipeline and accurately sprinkled on the potted soil. The irrigation amount data and soil moisture status are transmitted to the plant status remote diagnosis engine. The photosynthesis optimization engine receives the light intensity data and plant growth stage data, and uses the spectral efficiency modulation algorithm to calculate the light intensity and spectrum ratio of the red, blue and green LED lamp beads of the lighting assembly 26 according to the photosynthetic absorption characteristics of the plant, and generates light intensity adjustment instructions and irradiation time instructions. The red light weight is adjusted by 0.5 and the blue light weight is adjusted by 0.4 through the spectral switching mechanism to control the operation of the lighting assembly 26 so that the light intensity deviation is controlled within 5 micromoles per square meter per second. The irradiation time is adjusted according to the time data. When the sunshine time is less than 6 hours, it is extended by 2 hours. The lighting component 26 maintains operational stability through the heat sink. The lighting status data is transmitted to the plant status remote diagnosis engine. The pest behavior control engine receives temperature data, humidity data and pest activity data, and adopts the rhythmic behavior interference algorithm. According to the temperature and humidity data of the past 7 days, a pest activity rhythm model is constructed to calculate the probability of pest activity per hour. When the probability exceeds 0.7, a spraying instruction is generated for the insect repellent nozzle component 24. The instruction includes a frequency of spraying 0.1 ml of mosquito repellent liquid per minute and a duration of 2 minutes. The concentration of the liquid is adjusted according to the humidity data. When the humidity is lower than 50%, the concentration is increased by 10%. The instruction is transmitted to the mosquito repellent component 19 through the communication bus. High-efficiency water pump, high-efficiency The water pump sucks the liquid medicine in the liquid medicine storage tank and transports it to the insect repellent nozzle assembly 24 through a pipeline. The atomizing nozzle sprays the liquid medicine. The particle size regulating valve optimizes the atomization effect. The angle regulating motor adjusts the spraying direction. The spraying status data and the probability of pest activity are transmitted to the plant status remote diagnosis engine. The plant status remote diagnosis engine receives the temperature balance status data, irrigation volume data, soil moisture status, light status data, spraying status data and the high-resolution image data collected by the monitoring component 4. The leaf health fine assessment algorithm is used to segment the image into 128×128 pixel sub-areas, extract the leaf edge curvature, color distribution and texture features, and calculate the health score through the feature weighted fusion model. The texture weight of leafy vegetables is 0.4, the color weight is 0.3, and when the health is When the score is lower than 80 points, a report containing abnormal types and adjustment suggestions is generated. The abnormal types include yellowing and wilting. The adjustment suggestions include increasing the irrigation volume by 20% and extending the light duration by 1 hour. The report is transmitted to the user's mobile phone application through the wireless transmission module. The user sends control instructions for the lighting component 26 and the sprinkler component 25 through the application. The health score and adjustment suggestions are stored in the multi-source acquisition component 3. The power resource allocation engine receives the real-time power consumption data of each engine, and uses the load balancing optimization algorithm to build a power demand prediction model for the next hour based on the average and peak power consumption data of the past 7 days. The power allocation ratio of each engine is calculated by the linear programming method. The temperature balance control engine is allocated 30%, and the photosynthesis optimization engine is allocated 25%.Ensure that the total power consumption does not exceed 90% of the rated power, generate power control instructions based on the distribution ratio, and transmit them to the power supply device through the communication bus. The power supply device is combined with an overvoltage protection circuit and a transient suppression diode. When the voltage rises by more than 1.2 times the rated value, the circuit is turned on and shunted within 0.1 milliseconds. The power distribution status data is transmitted to the plant status remote diagnosis engine. The rhythmic behavior interference algorithm of the pest behavior regulation engine receives real-time wind speed data and humidity data, builds a liquid medicine diffusion model, and calculates the liquid medicine coverage range according to the wind speed data. When the wind speed exceeds 2 meters per second, the spraying angle of the insect repellent nozzle assembly 24 is adjusted, tilted upward by 15 degrees, and the amount of liquid medicine sprayed per minute is increased by 0.05 ml when the humidity is lower than 40%. The adjusted spraying instruction is transmitted to the mosquito and insect repellent assembly 19 high-efficiency water pump through the communication bus. The liquid medicine diffusion status data is transmitted to the plant status remote diagnosis engine. The leaf health fine evaluation algorithm of the plant status remote diagnosis engine receives the health score data of the past 7 days, builds a plant growth trend model, and analyzes the health score slope through the sliding window method. The window size is 7 days, and it is judged as potential when the slope is lower than negative 2 points per day. Abnormal, generate optimization instructions including increasing light intensity by 10% and increasing irrigation volume by 20%, the optimization instructions are transmitted to the user's mobile phone application through the wireless transmission module, the growth trend data and optimization instructions are stored in the multi-source acquisition component 3, the temperature grid distribution optimization algorithm of the temperature balance control engine receives temperature data and planting area 22 geometric structure data, constructs a three-dimensional heat flux distribution model, and calculates the heat flux density of each grid point by the finite difference method. When the temperature of a certain grid point exceeds the target value by 1 degree Celsius, an instruction to increase the local air volume of the hair dryer group 2 by 10% is generated. The instruction is transmitted to the hair dryer group 2 through the communication bus, and the heat flux distribution state data is transmitted to the plant state remote diagnosis engine. This embodiment realizes portability through a detachable splicing structure, and the innovative sprinkler assembly 25 telescopic tube and insect repellent nozzle assembly 24 particle size regulating valve improve the accuracy of irrigation and prevention and control. The light assembly 26 spectrum switching mechanism optimizes photosynthesis efficiency. The control system realizes intelligent management through multi-engine collaboration and customized algorithms, significantly improving plant growth efficiency and resource utilization. Users can monitor and optimize the plant growth environment in real time through mobile phone applications. , Specific embodiment 2:

[0027] like Figure 1-Figure 6 As shown, the key algorithms mentioned in Example 1 are analyzed in detail below: The temperature balance control engine receives temperature data and uses a temperature grid distribution optimization algorithm. The formula is:

[0028] in, is the grid point In time The temperature, is the thermal conductivity coefficient, is the time step, is the grid spacing, is the convection heat dissipation coefficient, The wind speed of hair dryer group 2, is the ambient temperature, Reflects the rate at which heat is transferred from high-temperature grid points to low-temperature grid points. Is a summation symbol, indicating the sum of the current grid point The temperature difference of all neighboring grid points is accumulated. In the temperature grid distribution optimization algorithm of the temperature field balance engine, the planting area 22 is divided into three-dimensional grids (grid spacing cm). For each grid point , its "neighbors" refer to the grid points that are directly adjacent in three-dimensional space, that is, the grid points in six directions: up and down, front and back, and left and right (in three-dimensional grids, called 6 neighborhoods). It means traversing these neighboring grid points and calculating the sum of the temperature differences between them and the current grid point. The sum result is used to calculate the amount of heat transferred between grid points, reflecting the influence of neighboring grid points on the temperature of the current grid point. Indicates that a neighboring grid point is at time The temperature at the moment, for the current grid point , is one of its neighboring grid points (e.g. or ) at time The temperature data comes from the environmental data acquisition engine, and is collected in real time through temperature sensors (thermocouple type, accuracy 0.1 degrees Celsius) distributed in the planting area 22. The temperature sensor collects the temperature data of the planting area 22 once a minute, and the data is stored in the buffer of the multi-source acquisition component 3. After the temperature field balancing engine divides the planting area 22 into three-dimensional grids, it maps the sensor data to each grid point through an interpolation method (such as linear interpolation) to generate For example, if a grid point has no direct sensor coverage, the algorithm calculates its temperature based on the nearest sensor data and the grid spacing. With the current grid point temperature The difference is used to calculate the heat conduction term. The formula input is the real-time temperature collected by the temperature sensor, the grid division parameters of the geometric structure data of the planting area 22, and the wall heat dissipation coefficient. The calculation process divides the planting area 22 into a three-dimensional grid, iteratively calculates the temperature of each grid point based on the principles of heat conduction and convection, compares the target temperature and the actual temperature deviation, outputs the variable frequency motor speed control instruction of the hair dryer group 2, controls the multi-directional air outlet angle, and controls the temperature deviation within 0.5 degrees Celsius. The instruction is transmitted to the hair dryer group 2 through the communication bus. The hair dryer group 2 adjusts the speed of the variable frequency motor and the direction of the air outlet to accelerate the air flow and reduce the temperature around the plant. The temperature balance state data is transmitted to the plant status remote diagnosis engine. The algorithm solves the problem of uneven temperature in the planting area 22, such as local high temperature inhibiting plant growth. By dynamically adjusting the air volume and direction of the hair dryer group 2, the heat flow distribution is accurately controlled. The actual effect is that the temperature deviation is controlled within 0.5 degrees Celsius, which improves the stability of the plant growth environment.

[0029] The water supply control engine receives soil moisture data and plant species data and uses a root water demand prediction algorithm: The specific mathematical formula is:

[0030] in, For the The instantaneous irrigation volume of each potted plant, is the plant root absorption rate, is the target humidity, is the actual humidity, is the volume of potted soil. The formula input is the soil moisture collected by the humidity sensor, the plant species data in the storage unit and the root absorption characteristics. The calculation process estimates the water demand according to the plant species (such as the absorption rate of leafy vegetables is 0.02 ml per minute per cubic centimeter), compares the actual humidity with the target humidity, outputs the instantaneous irrigation amount of each group of potted plants placed on the slats 8, generates the high-efficiency water pump operation instructions in the storage material box 20, controls the telescopic tube length of the sprinkler assembly 25 and the operation of the multi-mode nozzle, and selects the uniform spray or drip irrigation mode. The sprinkler assembly 25 adjusts the water flow through the flow controller, and the telescopic tube is adjusted to the optimal position according to the height of the potted plant. The clean water or nutrient solution is transported to the sprinkler assembly 25 through the pipeline and accurately sprinkled on the potted soil. The irrigation amount data and soil moisture status are transmitted to the plant status remote diagnosis engine. This algorithm solves the problem of waste or shortage caused by inaccurate irrigation water. By dynamically calculating the irrigation amount, the actual effect is to reduce the waste of water resources by 20% and ensure that the plant roots fully absorb water and nutrients.

[0031] The photosynthesis optimization engine receives light intensity data and plant growth stage data and uses a spectral efficiency modulation algorithm. The core formula is:

[0032] in, For band The spectrum ratio of (red light R, blue light B, green light G), where R, B, and G appear in the calculation of the spectrum ratio, and refer to the spectrum bands of red light (R), blue light (B), and green light (G). Red light (R) refers to the visible light band with a wavelength range of 620-630 nanometers, which is mainly used to promote photosynthesis and flowering and fruiting of plants. In the spectral efficiency modulation algorithm of the photosynthesis optimization engine, the absorption coefficient of the red light band and the growth stage weight are used to calculate the proportion of red light in the output of the lighting component 26. Red light has a high absorption efficiency for plant chlorophyll a and chlorophyll b, especially in the vegetative growth period and flowering period, which plays an important role in promoting photosynthetic efficiency and fruit development. The wavelength range (620-630 nanometers) is based on the photosynthetic absorption characteristics of plants, and is preset by agricultural experts in the plant growth stage database and stored in the storage unit (32 megabyte flash memory) of the multi-source acquisition component 3. The red light absorption coefficient (about 0.8-0.9) and growth stage weight (e.g. 0.5 in the seedling stage, 0.7 in the flowering stage) are determined according to the plant species (such as lettuce, strawberry) and growth stage. After the algorithm calculates the red light ratio, it generates a light intensity adjustment instruction, and adjusts the output intensity of the red light LED lamp beads through the spectrum switching mechanism (PWM modulator) of the lighting component 26 to ensure that the light intensity deviation is controlled within 5 micromoles per square meter per second. Blue light (B) refers to the visible light band with a wavelength range of 450-460 nanometers, which is mainly used to promote the vegetative growth and leaf development of plants. The blue light band is used to calculate the blue light ratio in the spectral efficiency modulation algorithm, which has an important influence on the light morphology of plants (such as leaf expansion, stem elongation inhibition) and chlorophyll synthesis, especially in the seedling stage and vegetative growth period. The wavelength range (450-460 nanometers) is based on the photosynthetic absorption characteristics of plants, preset in the plant growth stage database, and stored in the multi-source acquisition component 3. The absorption coefficient of blue light (about 0.7-0.85) and the weight of the growth stage (e.g. 0.4 in the seedling stage and 0.3 in the vegetative growth stage) are determined according to the plant species and growth stage, and can be updated monthly through the Wi-Fi module. After the algorithm calculates the blue light ratio, it generates a light intensity adjustment instruction, and adjusts the output intensity of the blue light LED lamp beads through the PWM modulator of the lighting component 26 to support the morphological development needs of plants at different growth stages. Green light (G) refers to the visible light band with a wavelength range of 510-520 nanometers, which is mainly used to adjust the photosynthetic balance of plants and the light transmittance of leaves. The green light band is used to calculate the green light ratio in the spectral efficiency modulation algorithm. Although plants have a low absorption efficiency of green light (chlorophyll reflects green light), a proper amount of green light can penetrate the leaves, promote deep photosynthesis, and adjust the photosynthetic efficiency of plants, especially in high light intensity environments to prevent light inhibition. The wavelength range (510-520 nanometers) is based on the photosynthetic characteristics of plants, preset in the plant growth stage database, and stored in the multi-source acquisition component 3.The absorption coefficient of green light (approximately 0.2-0.3) and the growth stage weight (for example, 0.1-0.15 for the whole stage) are determined according to the plant species and growth stage. After the algorithm calculates the green light ratio, it generates a light intensity adjustment instruction and adjusts the output intensity of the green light LED lamp bead through the PWM modulator of the lighting component 26 to optimize the overall spectrum ratio and improve the efficiency of light energy utilization by coordinating with red light and blue light. is the photosynthetic absorption coefficient of the band, For growth stage The band weights (such as 0.5 for red light and 0.4 for blue light in the seedling stage) are used as the input of the formula, and the data of the plant growth stage collected by the light sensor and the storage unit. The calculation process divides the spectrum into 620-630 nanometers for red light, 450-460 nanometers for blue light, and 510-520 nanometers for green light. The proportion of each band is calculated according to the photosynthetic absorption characteristics of the plant, and the light intensity and spectrum ratio of the red, blue and green LED lamp beads of the lighting component 26 are output. The light intensity adjustment instruction and the irradiation time instruction are generated, and the spectrum output is adjusted through the spectrum switching mechanism to control the operation of the lighting component 26 so that the light intensity deviation is controlled within 5 micromoles per square meter per second. The irradiation time is adjusted according to the sunshine time data. When the sunshine time is less than 6 hours, it is extended by 2 hours. The lighting component 26 maintains operational stability through the heat sink, and the light status data is transmitted to the plant status remote diagnosis engine. This algorithm solves the problem of low photosynthesis efficiency caused by insufficient light or unsuitable spectrum. By dynamically adjusting the spectrum ratio and irradiation time, the actual effect is to increase the photosynthesis efficiency by 10% and promote plant growth. The pest behavior control engine receives temperature data, humidity data, and pest activity data, and uses a rhythmic behavior interference algorithm. The core formula is:

[0033] in, For time The probability of pest activity, and For the Temperature and humidity, is the weight (recent data has a higher weight), is the 24-hour period factor, is the baseline value of pest activity. The formula input is the 7-day data collected by the temperature sensor and humidity sensor, and the pest activity data in the storage unit. The calculation process builds a 24-hour pest activity rhythm model, and calculates the hourly activity probability based on the impact of temperature and humidity on pest behavior. When the probability exceeds 0.7, the spraying instruction of the insect repellent nozzle assembly 24 is output. The instruction includes a frequency of spraying 0.1 ml of insect repellent liquid per minute and a duration of 2 minutes. Adjust the concentration of the liquid according to the humidity data, and increase the concentration by 10% when the humidity is lower than 50%. The instruction is transmitted to the mosquito and insect repellent component 19 high-efficiency water pump through the communication bus. The high-efficiency water pump sucks the liquid in the liquid storage tank and transports it to the insect repellent nozzle assembly 24 through a pipeline. The atomizing nozzle sprays the liquid, the particle size regulating valve optimizes the atomization effect, and the angle adjustment motor adjusts the spraying direction. The spraying status data and the probability of pest activity are transmitted to the plant status remote diagnosis engine. The algorithm receives real-time wind speed data and humidity data to build a liquid diffusion model. The specific mathematical formula is:

[0034] in, is the area covered by the drug solution, is the diffusion coefficient, is the wind speed, is the spraying amount, The formula calculates the coverage of the liquid medicine. When the wind speed exceeds 2 meters per second, the spraying angle of the insect repellent nozzle assembly 24 is adjusted to tilt upward by 15 degrees. When the humidity is lower than 40%, the spraying amount of the liquid medicine is increased by 0.05 ml per minute. The adjusted spraying instruction is transmitted to the high-efficiency water pump, and the liquid medicine diffusion state data is transmitted to the plant state remote diagnosis engine. This algorithm solves the problem of liquid medicine waste caused by inaccurate pest control. It predicts the pest activity time and optimizes the liquid medicine distribution through probability model and diffusion model. The actual effect is to reduce the use of liquid medicine by 30% and form an effective protective barrier.

[0035] The plant status remote diagnosis engine receives temperature balance status data, irrigation amount data, soil moisture status, light status data, spraying status data and high-resolution image data collected by the monitoring component 4, and adopts a leaf health fine evaluation algorithm. The specific mathematical formula is:

[0036] in, is the health score (unitless), , , The edge curvature , Color distribution , Texture features The weight of (unitless, such as leaf texture weight 0.4, color weight 0.3). The formula input is the image data collected by the monitoring component 4 and the status data of other engines. The calculation process divides the image into 128×128 pixel sub-areas, extracts edge curvature, color distribution and texture features, and calculates the health score through a weighted fusion model. When the score is lower than 80 points, a report containing abnormal types (yellowing leaves, wilting) and adjustment suggestions (increasing irrigation volume by 20%, extending light duration by 1 hour) is generated. The report is transmitted to the user's mobile phone application through the wireless transmission module. The user sends control instructions for the lighting component 26 and the sprinkler component 25 through the application. The health score and adjustment suggestions are stored in the multi-source acquisition component 3. The algorithm further constructs a growth trend model. The specific mathematical formula is:

[0037] in, It is the slope of the 7-day health score (unit: points per day). When the slope is less than minus 2 points per day, it is judged as a potential abnormality, and an optimization instruction is generated (increase light intensity by 10%, irrigation volume by 20%). The instruction is transmitted to the mobile phone application, and the trend data is stored in the multi-source acquisition component 3. This algorithm solves the problem that the health status of plants is difficult to monitor in real time. It accurately identifies abnormalities through image feature analysis, provides accurate adjustment suggestions for actual effects, and improves user management efficiency.

[0038] The power resource allocation engine receives the real-time power consumption data of each engine and adopts the load balancing optimization algorithm. The core formula is:

[0039] in, For Engine The allocated power (in watts), is the current power demand (in watts), is the rated power (unit: watt), is the priority weight (unitless, such as 0.3 for temperature balance control engine, 0.25 for photosynthesis optimization engine), It is the predicted demand based on 7-day average and peak data (unit: watt). The formula input is real-time power consumption and historical data, calculates the 1-hour power demand forecast, optimizes the distribution to ensure that the total power consumption does not exceed 90% of the rated power, outputs power control instructions, and transmits them to the power supply device through the communication bus. The power supply device passes the overvoltage and overcurrent protection circuit. If the voltage exceeds the rated value by 1.2 times, the circuit will be turned on and diverted within 0.1 milliseconds. The power distribution status is transmitted to the plant status remote diagnosis engine. This algorithm solves the problem of low power utilization efficiency. Through dynamic load balancing, the actual effect is to reduce 15% of energy waste and ensure system stability. Specific embodiment three: like Figure 1-Figure 6As shown, the following is a description of the specific application logic steps of each engine and algorithm of the house planting component and control system based on the fourth-generation residential building: The temperature grid distribution optimization algorithm is applied in the temperature balance control engine to dynamically adjust the temperature distribution of the planting area and optimize the plant growth environment through the hair dryer group. Specific steps: Environmental data collection, the temperature sensor collects the temperature data of the planting area every minute and uploads the data to the control system. Temperature state evaluation, the system compares the actual temperature with the target temperature range of 20-25 degrees Celsius to determine whether it needs to be adjusted. Control strategy generation, based on temperature data and grid distribution optimization algorithm, the core formula is that the grid point temperature is equal to the temperature at the previous moment plus the time step multiplied by the ratio of the sum of the heat transfer coefficient and the temperature difference of the adjacent grid points, and then subtracts the air convection heat dissipation, and generates the speed and air outlet angle adjustment strategy of the hair dryer group. For example, when the temperature of a certain grid point exceeds 25 degrees Celsius, the wind speed is increased by 10%. Execute temperature control, the hair dryer group adjusts the speed through the variable frequency motor, and the multi-directional air outlet is adjusted to the target angle to accelerate air flow and reduce the temperature in the high temperature area. Feedback and self-learning, by real-time monitoring of temperature changes, optimize the next adjustment strategy to ensure that the temperature deviation is controlled within 0.5 degrees Celsius.

[0041] The root water demand prediction algorithm is applied in the water supply control engine to dynamically adjust the soil humidity of the potted plants and optimize the water supply through the sprinkler assembly. Specific steps: Environmental data collection, the humidity sensor collects the soil humidity data of the potted plants every minute and uploads it to the control system. Humidity status assessment, the system compares the actual humidity with the target humidity range of 40-60% to determine whether irrigation is needed. Control strategy generation, based on humidity data and the root water demand prediction algorithm, the core formula is that the instantaneous irrigation amount is equal to the plant root absorption rate multiplied by the difference between the soil humidity and the target humidity, and then multiplied by the volume of the potted soil, to generate efficient water pump operation instructions, such as starting the uniform spray mode when the humidity is lower than 40%. Execute irrigation control, the sprinkler assembly adjusts the water flow through the flow controller, and the telescopic tube is adjusted to the height of the potted plant to accurately sprinkle clean water or nutrient solution to the soil. Feedback and self-learning, by real-time monitoring of soil moisture changes, optimize the next irrigation amount, ensure accurate water supply, and reduce 20% water resource waste.

[0042] The spectral efficiency modulation algorithm is applied in the photosynthesis optimization engine to dynamically adjust the light intensity and spectrum ratio, and optimize the photosynthesis efficiency through the lighting components. Specific steps: Environmental data collection, the light sensor collects the light intensity data of the planting area every minute and uploads it to the control system. Light status evaluation, the system compares the actual light intensity with the target range of 100-200 micromoles per square meter per second to determine whether supplementary light is needed. Control strategy generation, based on the light data and the spectral efficiency modulation algorithm, the core formula is that the spectrum ratio is equal to the photosynthetic absorption coefficient of each band multiplied by the sum of the weights of the growth stage, divided by the total absorption coefficient, to generate the light intensity and spectrum ratio adjustment strategy of the red, blue and green LED lamp beads, such as the red light weight of 0.5 and the blue light weight of 0.4 in the seedling stage. Execute light regulation, the lighting component adjusts the spectrum output through the spectrum switching mechanism to control the light intensity deviation within 5 micromoles per square meter per second. If the sunshine is less than 6 hours, extend the exposure for 2 hours. Feedback and self-learning, by real-time monitoring of photosynthesis efficiency, optimize the next spectrum ratio and improve photosynthesis efficiency by 10%.

[0043] The rhythmic behavior interference algorithm is applied in the pest behavior control engine to dynamically adjust the spraying of insect repellent liquid and optimize the pest control effect through the insect repellent nozzle assembly. Specific steps: Environmental data collection, temperature sensor, humidity sensor and wind speed sensor collect temperature, humidity and wind speed data every minute and upload them to the control system. Pest activity evaluation, the system is based on 7 days of temperature and humidity data, combined with the rhythmic behavior interference algorithm, the core formula is that the probability of pest activity is equal to the weighted average of the temperature and humidity data of the past 7 days multiplied by the time period factor, plus the baseline value of pest activity, to calculate the probability of pest activity per hour. Control strategy generation, when the probability exceeds 0.7, the insect repellent nozzle assembly spraying instruction is generated, for example, spraying 0.1 ml of liquid per minute for 2 minutes; according to the liquid diffusion model formula, the liquid coverage area is equal to the diffusion coefficient multiplied by the wind speed, spraying volume and spraying angle cosine, adjust the spraying angle and liquid concentration, tilt 15 degrees when the wind speed exceeds 2 meters per second, and increase the spraying volume by 0.05 ml when the humidity is less than 40%. To perform insect repellent control, the efficient water pump sucks the liquid medicine, and the insect repellent nozzle assembly sprays the liquid medicine through the atomizing nozzle and the particle size regulating valve. Feedback and self-learning, through real-time monitoring of the probability of pest activity, optimize the next spraying strategy, and reduce the use of liquid medicine by 30%.

[0044] The leaf health fine assessment algorithm is applied in the plant status remote diagnosis engine to dynamically assess the plant health status and optimize user management through monitoring components. Specific steps: Environmental data collection, high-definition cameras collect plant image data every hour, temperature, humidity, and light sensors collect environmental data, and upload them to the control system. Health status assessment, the system divides the image data into 128×128 pixel sub-areas, extracts edge curvature, color distribution, and texture features, and calculates the health score according to the leaf health fine assessment algorithm. The core formula is that the health score is equal to the weighted sum of edge curvature, color distribution, and texture features, and the texture weight of leafy vegetables is 0.4 and the color weight is 0.3. Control strategy generation, when the score is less than 80 points, a report containing abnormal types (yellowing, wilting) and adjustment suggestions (increasing irrigation by 20%, extending light duration by 1 hour) is generated; based on the 7-day health score, the score slope is calculated, and optimization instructions are generated when the slope is less than negative 2 points per day, such as increasing light intensity by 10%. Execute health management, reports and instructions are sent to the user's mobile phone application through the wireless transmission module, and the user sends control instructions to the lighting components and sprinkler components. Feedback and self-learning, through real-time monitoring of health score changes, optimize the next adjustment suggestions and improve user management efficiency.

[0045] The load balancing optimization algorithm is applied in the power resource allocation engine to dynamically allocate power to each engine and optimize energy utilization through power supply equipment. Specific steps: Environmental data collection: Each engine collects real-time power consumption data every minute and uploads it to the control system. Power consumption status evaluation: The system compares the actual power consumption with 90% of the rated power to determine whether the allocation needs to be adjusted. Control strategy generation: Based on the power consumption data and the load balancing optimization algorithm, the core formula is that the engine power allocation is equal to the product of the total demand and the minimum value of 90% of the rated power, and then multiplied by the weighted ratio of the engine's predicted demand to the total predicted demand, to generate power allocation instructions, such as 30% for the temperature balancing control engine and 25% for the photosynthesis optimization engine. Execute power regulation: The power supply equipment allocates power through the overvoltage and overcurrent protection circuit. If the voltage exceeds 1.2 times the rated value, the circuit will conduct and shunt within 0.1 milliseconds. Feedback and self-learning: By monitoring power consumption changes in real time, the next allocation strategy is optimized to reduce energy waste by 15%. Specific embodiment four: like Figure 1-Figure 6 As shown, based on the above content, the following control system use cases are provided: Case 1: Planting leafy vegetables (lettuce) on the balcony: The user installed planting components on the inner side of the balcony of an urban apartment to plant lettuce, with the goal of maintaining a suitable growth environment and optimizing resource utilization. The temperature balance control engine detected through the temperature sensor that the temperature in the local area of ​​planting area 22 rose to 27 degrees Celsius due to direct sunlight, exceeding the target range of 20-25 degrees Celsius. The temperature grid distribution optimization algorithm is run. The core formula is that the grid point temperature is equal to the temperature at the previous moment plus the time step multiplied by the ratio of the heat transfer coefficient to the sum of the temperature differences of adjacent grid points, and then subtracted from the air convection heat dissipation. The heat flux distribution in the high-temperature area is calculated, and the instructions for increasing the speed of the variable frequency motor of the hair dryer group by 15% and tilting the multi-directional air outlet by 30 degrees are generated. The hair dryer group adjusts the wind speed and direction through the variable frequency motor (power 200 watts) and the servo motor, and reduces the temperature to 24.5 degrees Celsius within 3 minutes, with the deviation controlled within 0.5 degrees Celsius to avoid high temperature inhibiting the growth of lettuce. The water supply control engine detects that the soil humidity of the potted plant is 35%, which is lower than the target range of 40-60%. The root water demand prediction algorithm is run. The core formula is that the instantaneous irrigation volume is equal to the plant root absorption rate (0.02 ml per minute per cubic centimeter for lettuce) multiplied by the humidity difference and then multiplied by the soil volume (500 cubic centimeters), and the calculated irrigation volume is 5 ml. The high-efficiency water pump (flow rate 0.5 liters per minute) is started, and the sprinkler assembly is precisely irrigated in drip irrigation mode (flow rate 0.2 ml per second) through the telescopic tube (adjusted to 5 cm). It is completed within 5 seconds, and the humidity rises to 42%, reducing water waste by 20%. The photosynthesis optimization engine detects that the light intensity on a cloudy day is 80 micromoles per square meter per second, which is lower than the target of 100-200 micromoles per square meter per second. The spectral efficiency modulation algorithm is run. The core formula is that the spectral ratio is equal to the photosynthetic absorption coefficient of each band multiplied by the sum of the weights of the growth stage divided by the total absorption coefficient, generating red light (weight 0.5) and blue light (weight 0.4) ratio instructions. The lighting component uses a PWM modulator to adjust the output of LED lamp beads (red light 620-630 nanometers, blue light 450-460 nanometers), supplementing the light to 150 micromoles per square meter per second for 4 hours, increasing photosynthesis efficiency by 10%. Result: The growth cycle of lettuce is shortened by 5 days, the leaves are fresh and tender, and the yield is increased by 15%.

[0047] Case 2: Growing strawberries in an indoor garden: When users use planting components to grow strawberries in indoor gardens, they need to precisely control the environment to improve the quality of the fruit. The pest behavior control engine runs the rhythmic behavior interference algorithm through temperature (22 degrees Celsius), humidity (50%) and 7 days of historical data. The core formula is that the probability of pest activity is equal to the weighted average of the temperature and humidity data of the past 7 days multiplied by the time period factor plus the baseline value of pest activity. The probability of pest activity at 8 o'clock in the evening is calculated to be 0.75, which exceeds the threshold of 0.7. Combined with the liquid diffusion model (coverage area equals diffusion coefficient multiplied by wind speed, spraying volume and spraying angle cosine), due to the wind speed of 1.5 meters per second, a spraying instruction is generated (0.1 ml per minute, for 2 minutes, with an angle of 10 degrees). The high-efficiency water pump (power 30 watts) draws liquid from the liquid storage tank, and the insect repellent nozzle assembly sprays through the atomizing nozzle (particle size 20 microns), covering the planting area 22, reducing the use of liquid by 30%, and effectively preventing aphids. The water supply control engine detected that the humidity of the strawberry pot was 38%, ran the root water demand prediction algorithm, and calculated the irrigation volume as 6 ml. The sprinkler assembly irrigated the nutrient solution in a uniform spray pattern (particle size 80 microns) through a telescopic tube (adjusted to 7 cm), and the humidity rose to 45%, ensuring that the fruit was adequately hydrated. The plant status remote diagnosis engine collected strawberry leaf images through a high-definition camera (resolution 1920×1080 pixels) and ran a leaf health fine assessment algorithm. The core formula is that the health score is equal to the weighted sum of edge curvature, color distribution and texture features (texture weight 0.4, color weight 0.3), and the score is 85 points, which is within the normal range. The 7-day score slope is 0.5 points per day, and an optimization instruction is generated (increase light intensity by 5%). The user receives instructions through the mobile phone application and adjusts the lighting components to optimize the sweetness of the fruit. Result: The sugar content of strawberry fruit increased by 10%, there was no insect pest, and the yield increased by 20%.

[0048] Case 3: Planting vines (grapes) on the roof terrace: Users grow grapes on the roof terrace, using vine planting cavity 7, and need to deal with high temperatures and unstable light. The temperature balance control engine detects that the terrace reaches 30 degrees Celsius due to the high temperature in summer, runs the temperature grid distribution optimization algorithm, and generates instructions to increase the speed of the hair dryer group by 20% and the vertical 45 degrees of the air outlet. The hair dryer group accelerates the air flow through the variable frequency motor and multi-directional air outlet, and the temperature drops to 25 degrees Celsius within 5 minutes, maintaining a suitable environment for grape growth. The photosynthesis optimization engine detects that the light intensity in cloudy weather is 60 micromoles per square meter per second, runs the spectral efficiency modulation algorithm, generates red light (weight 0.6) and blue light (weight 0.3) ratio instructions, and the lighting components fill the light to 180 micromoles per square meter per second, extending the exposure for 3 hours to promote grape photosynthesis. The power resource allocation engine detects the power consumption of each engine through a power consumption sensor (accuracy 0.01 watt) and runs a load balancing optimization algorithm. The core formula is that the power allocated to the engine is equal to the product of the total demand and the minimum value of 90% of the rated power multiplied by the weighted ratio of the engine's predicted demand to the total predicted demand. 30% of the power is allocated to the temperature balance control engine and 25% of the power is allocated to the photosynthesis optimization engine. The power supply device adjusts the power through the overvoltage protection circuit (response 0.1 millisecond) to ensure that the total power consumption does not exceed 90% of the rated power, reducing energy waste by 15%. The plant status remote diagnosis engine detects that the grape leaf score is 78 points, identifies slight leaf yellowing, and generates a recommendation to increase irrigation by 15%. The user adjusts the sprinkler assembly through the mobile phone application to restore the healthy state. Result: The grape vines grow vigorously, the fruit matures 7 days earlier, and it is energy-efficient and efficient.

[0049] Case 4: Balcony planting herbs (basil): The user grows basil on the balcony and pays attention to pest control and health management. The pest behavior control engine calculates the probability of pest activity as 0.8 through humidity 45% and night data, and generates a spraying instruction (0.12 ml per minute, tilted at an angle of 20 degrees). The insect repellent nozzle assembly sprays the liquid (particle size 15 microns) to cover the basil plants and form a protective barrier. The plant status remote diagnosis engine collects images through the camera, scores 75 points, identifies wilting, and generates recommendations to increase irrigation by 20% and light duration by 1 hour. The user adjusts the sprinkler assembly and lighting assembly, and the score rises to 85 points within 3 days. The water supply control engine and the photosynthesis optimization engine coordinate and control to maintain a humidity of 50% and a light intensity of 150 micromoles per square meter per second. The power resource allocation engine optimizes power distribution to ensure stable system operation. Result: The basil leaves are lush, pest-free, fragrant, and the growth cycle is shortened by 4 days.

[0050] Case 5: Planting succulents indoors: Users need low water and stable light to grow succulents indoors. The water supply control engine detects 30% humidity and calculates 2 ml of irrigation. The sprinkler assembly irrigates precisely in drip mode (flow rate 0.1 ml per second) to keep the succulent roots healthy. The photosynthesis optimization engine detects a light intensity of 50 micromoles per square meter per second and generates a blue light (weight 0.5) proportional instruction. The light assembly fills the light to 120 micromoles per square meter per second to maintain the color of the succulents. The temperature balance control engine maintains a temperature of 23 degrees Celsius, and the plant status remote diagnosis engine scores 90 points, with no abnormalities. The power resource allocation engine reduces power consumption by 15%. Result: The succulents are full in shape, bright in color, and grow steadily.

[0051] The above use cases combine the temperature balance control engine, water supply control engine, photosynthesis optimization engine, pest behavior regulation engine, plant status remote diagnosis engine and power resource allocation engine to demonstrate the application of each engine in actual scenarios and reflect its functions and effects.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control system based on the fourth generation residential house planting components, characterized by: It includes an environmental data collection engine, a temperature balance control engine, a water supply control engine, a photosynthesis optimization engine, a pest behavior regulation engine, a plant status remote diagnosis engine, and a power resource allocation engine; The environmental data acquisition engine collects temperature data of the planting area through a temperature sensor, collects relative humidity data of the planting area through a humidity sensor, and collects light intensity data of the planting area through a light sensor, and transmits the collected data to the temperature balance control engine, the water supply control engine, the photosynthesis optimization engine and the pest behavior regulation engine through an internal communication bus. The temperature balance control engine receives the temperature data transmitted by the environmental data acquisition engine, and adopts a temperature grid distribution optimization algorithm to divide the planting area into three-dimensional grids. According to the geometric structure data of the planting area and the wall heat dissipation coefficient, the temperature value of each grid point is calculated, and the calculated temperature value is transmitted to the plant status remote diagnosis engine. The water supply control engine receives the humidity data and plant species data transmitted by the environmental data acquisition engine, and adopts a root water demand prediction algorithm. The algorithm calculates the required irrigation amount according to the soil moisture data and the absorption characteristics of the plant roots, and the irrigation amount data is transmitted to the plant status remote diagnosis engine. The photosynthesis optimization engine receives the light intensity data and plant growth stage data transmitted by the environmental data acquisition engine, and adopts a spectral efficiency modulation algorithm. The algorithm calculates the data of subsequent plant supplementary lighting according to the photosynthetic absorption characteristics of the plant.

2. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The pest behavior regulation engine receives temperature data, humidity data transmitted by the environmental data acquisition engine and pest activity data provided by an external database, and adopts a rhythmic behavior interference algorithm. The algorithm constructs a pest activity rhythm model based on the temperature and humidity data of the past 7 days and calculates the probability of pest activity per hour.

3. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The plant status remote diagnosis engine receives the temperature balance status data transmitted by the temperature balance control engine, the irrigation volume data and soil moisture status transmitted by the water supply control engine, and the light status data transmitted by the photosynthesis optimization engine, and adopts a leaf health fine assessment algorithm to calculate the health score through a feature weighted fusion model. The weight is adjusted according to the plant species, with the texture weight of leafy vegetables being 0.4 and the color weight being 0.

3. When the health score is lower than 80 points, a report is generated containing the abnormality type and adjustment suggestions, and the adjustment suggestions include increasing the irrigation volume by 20% and extending the light duration by 1 hour. The report is transmitted to the user's mobile phone application via a wireless network.

4. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The power resource allocation engine receives the real-time power consumption data of each engine and adopts a load balancing optimization algorithm. The algorithm builds a power demand prediction model for the next hour based on the average and peak power consumption data of the past 7 days, and calculates the power allocation ratio of each engine through a linear programming method, with the temperature balance control engine allocated 30% and the photosynthesis optimization engine allocated 25%, ensuring that the total power consumption does not exceed 90% of the rated power; the allocation ratio generates a power control instruction, which is transmitted to the power supply device through a communication bus; The power supply equipment has overvoltage protection, overcurrent protection, waterproof treatment and leakage protection functions. The overvoltage protection adopts a combination circuit of varistor and transient suppression diode. When the voltage rises by more than 1.2 times the rated value, the circuit will turn on and shunt within 0.1 milliseconds; the power distribution status data is transmitted to the plant status remote diagnosis engine for system stability assessment.

5. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The spectral efficiency modulation algorithm of the photosynthesis optimization engine receives the light intensity data and the plant growth stage database transmitted by the environmental data acquisition engine, and the database contains the spectral requirements of the seedling stage, the vegetative growth stage and the flowering stage; the algorithm divides the spectrum into three bands of red light, blue light and green light, and calculates the weight of each band according to the photosynthetic absorption characteristics of the plant, wherein the weight of red light in the seedling stage is 0.5, and the weight of blue light is 0.

4.

6. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The environmental data acquisition engine collects temperature data, humidity data and light intensity data through temperature sensors, humidity sensors and light sensors, and transmits them to the temperature balance control engine, water supply control engine, photosynthesis optimization engine and pest behavior regulation engine through an internal communication bus; each engine processes the received data and generates corresponding control instructions; All control instructions are transmitted to the corresponding hardware through the communication bus for execution, and the execution status data is summarized to the plant status remote diagnosis engine to generate a comprehensive environmental control report.

7. The control system of the house planting component based on the fourth generation residence according to claim 2 is characterized in that: The rhythmic behavior interference algorithm of the pest behavior regulation engine receives real-time wind speed data and humidity data transmitted by the environmental data acquisition engine, constructs a liquid medicine diffusion model, and the model calculates the coverage of the liquid medicine in the planting area based on the wind speed data. The liquid medicine diffusion model is transmitted to the plant status remote diagnosis engine to provide an environmental impact assessment.

8. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The leaf health fine-grained assessment algorithm of the plant status remote diagnosis engine receives health score data from the past 7 days and constructs a plant growth trend model. The model analyzes the slope of the health score through a sliding window method with a window size of 7 days. When the slope is lower than negative 2 points per day, it is judged as a potential abnormality and generates optimization instructions including increasing light intensity by 10% and increasing irrigation volume by 20%. The optimization instructions are transmitted to the user's mobile phone application via a wireless network.

9. The control system of the house planting component based on the fourth generation residence according to claim 1 is characterized in that: The temperature grid distribution optimization algorithm of the temperature balance control engine receives the temperature data and the planting area geometry data transmitted by the environmental data acquisition engine, and constructs a three-dimensional heat flux distribution model. The model calculates the heat flux density of each grid point by the finite difference method.

10. The control system of the house planting component based on the fourth generation residence according to any one of claims 1 to 9, wherein the house planting component based on the fourth generation residence corresponding to the control system is characterized in that: The invention comprises an installation plane, a planting area and a vine planting cavity, a bottom box is placed at the bottom of the back side in the installation plane, one side and the middle of the top of the bottom box are clamped with support piles, the shape of the support piles is square, and placement strips are connected by mortise and tenon joints between the support piles on both sides, both sides of the placement strips are embedded with embedded blocks, the sides of the embedded blocks are provided with installation card groups, the installation card groups and the support piles are adapted to each other, a back plate is clamped on the back side of the top of the bottom box, the planting area is composed of the back plate and the support piles on both sides, and the planting area is provided with a plurality of support piles on the bottom box. A total of five sets of placement strips are installed inside, and a vine guard plate is clamped on the other side of the top of the bottom box. The shape of the vine guard plate is L-shaped, and the vine planting cavity is composed of a support pile in the middle and a vine guard plate. A mesh hole is opened on the back of the vine guard plate. The middle of the bottom end of the placement strip is clamped and connected with a control bottom module. An insect repellent nozzle assembly is provided on one side of the bottom end of the control bottom module. A sprinkler assembly is provided in the middle of the bottom end of the control bottom module. A light assembly is installed on the other side of the bottom end of the control bottom module. The installation plane A straight drainage pipe is provided on one side of the inner surface near the bottom box, an absorption pipe head is installed on the top of the straight drainage pipe, the absorption pipe head is bonded to the wall, the bottom of the straight drainage pipe is connected to the installation plane drain outlet, one side of the straight drainage pipe is connected to a drainage subdivision pipe, there are five groups of drainage subdivision pipes, all of which are located at the bottom of the placement strip board, a connecting pipe end is provided on one side of the top of the drainage subdivision pipe, a drainage hole is opened on one side of the bottom of the placement strip board, a water injection pipe opening is opened on the other side of the bottom of the placement strip board, the end of the connecting pipe end passes through the drainage hole and is connected to the bottom of the potted plant on the placement strip board, a protective panel is clamped on the front of the bottom box, a mosquito and insect repellent component is placed on one side of the bottom box, and a storage material box is placed on the other side of the bottom box, a hair dryer group is installed on one side of the top in the installation plane, a monitoring and monitoring component is installed on the other side of the top in the installation plane, a multi-source collection component is installed near the front of the monitoring and monitoring component in the installation plane, and the control system is arranged in the multi-source collection component.

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