An AI-based intelligent building gardening maintenance system

Abstract

This invention discloses an AI-based intelligent building gardening maintenance system, comprising a core sensing module, a cloud server, an AI voice interaction terminal, a WeChat mini-program, and an actuator. The core sensing module collects soil temperature and humidity, pH value and acidity / alkalinity, and air temperature and humidity data, and uploads them via Wi-Fi. The cloud server generates maintenance instructions based on an AI dynamic decision-making model. The AI ​​voice interaction terminal and the WeChat mini-program respectively enable voice interaction, data visualization, remote control, and anomaly warnings. The actuator completes the maintenance operations and provides feedback on the results. This system is suitable for building gardening scenarios such as homes and office buildings.

Description

Technical Field ,

[0001] The present invention relates to the technical fields of intelligent gardening, Internet of Things monitoring, and artificial intelligence control, and particularly to an AI-based intelligent building gardening maintenance system applied to building scenarios such as homes and office buildings. The system integrates functions such as environmental data collection, wireless transmission, cloud analysis and decision-making, voice interaction, remote management via WeChat mini-program, and linkage control of actuators. Background Art

[0002] With the acceleration of urbanization and the improvement of residents' living quality, the demand for green plant maintenance continues to grow, and the application scenarios of green plants have gradually extended from traditional home potted plants to scenarios such as office buildings and indoor greening. In contrast, traditional green plant maintenance methods mainly rely on manual inspections and empirical judgments, and have problems such as low maintenance efficiency, high labor costs, large resource consumption, and unstable maintenance effects, making it difficult to meet the multi-point, continuous, and standardized maintenance requirements in the building gardening scenario.

[0003] Although existing intelligent gardening or smart agriculture solutions have introduced Internet of Things sensing, automatic control, and data analysis technologies, they still generally have the following deficiencies: First, many solutions rely on preset thresholds for rule-based control and lack the AI dynamic decision-making ability based on real-time multi-parameter data, making it difficult to adapt to the changes in complex small environments such as lighting, ventilation, and humidity inside buildings; Second, existing solutions mostly target large-scale scenarios such as farmland and greenhouses, and have not formed an adapted interaction method and management logic for fragmented and small-scale building gardening scenarios such as homes and office buildings; Third, some solutions have not effectively established a complete link of "data monitoring - analysis and decision-making - remote control - result feedback", making it difficult for users to obtain the maintenance status in a timely and intuitive manner and perform remote intervention.

[0004] Therefore, there is an urgent need to provide an AI-based intelligent building gardening maintenance system, which constructs a closed-loop intelligent maintenance solution suitable for the building gardening scenario through technical means such as multi-parameter perception, wireless transmission, AI dynamic decision-making, voice interaction, and remote management via WeChat mini-program, so as to improve maintenance efficiency, reduce resource consumption, and reduce the dependence on manual experience. Summary of the Invention

[0005] To solve the technical problems of the traditional green plant maintenance mode, such as low efficiency, high cost, dependence on manual experience, and difficulty in achieving standardized management, as well as the rigid decision-making, inconvenient interaction, and insufficient adaptability to building scenarios of existing intelligent gardening solutions, the present invention provides an AI-based intelligent building gardening maintenance system.

[0006] To achieve the above object, the present invention adopts the following technical solution: An AI-based intelligent building gardening maintenance system includes a core sensing module, a cloud server, an AI voice interaction terminal, a WeChat mini-program, and an actuator.

[0007] The invention is further characterized by:

[0008] The core sensing module is used to collect data such as soil temperature and humidity, soil pH value and acidity / alkalinity, and air temperature and humidity in the green plant maintenance environment, and upload the collected data to the cloud server via the WIFI module.

[0009] The cloud server is used to receive and store the collected data, call the green plant growth database, and analyze the collected data based on the AI ​​dynamic decision model to output corresponding maintenance instructions or early warning information.

[0010] The AI ​​voice interaction terminal is connected to the cloud server to receive user voice commands, parse user intent, retrieve environmental data and analysis results from the server, and return query results or maintenance execution results to the user.

[0011] The WeChat mini program communicates with the cloud server to enable environmental data visualization, remote management, threshold setting, historical record query, and anomaly warning.

[0012] The actuator is connected to a cloud server and / or an AI voice interaction terminal for control, and is used to perform maintenance operations such as watering and ventilation according to the maintenance instructions, and to feed back the operation results to the cloud server to form a closed-loop maintenance chain of "sensing - transmission - AI decision-making - interaction - control - feedback".

[0013] Furthermore, the core sensing module adopts an integrated circuit board structure, integrating soil temperature and humidity sensors, soil pH and acidity / alkalinity sensors, air temperature and humidity sensors, data processing circuits, and WIFI communication modules to improve the stability of data acquisition and transmission efficiency.

[0014] Furthermore, the cloud server is equipped with an AI dynamic decision-making model, preferably using a random forest algorithm model, and combines it with the suitable growth parameters of different plants stored in the green plant growth database to analyze the real-time collected data in order to generate personalized maintenance strategies.

[0015] Furthermore, the AI ​​voice interaction terminal uses a speech recognition engine and a natural language processing framework to achieve voice interaction, preferably using a natural language processing framework based on BERT model fine-tuning, in order to understand and parse gardening-related voice commands.

[0016] Furthermore, the WeChat mini program includes a data visualization module, a remote control module, and an anomaly warning module. The data visualization module is used to display data such as soil temperature and humidity, pH value, and air temperature and humidity, as well as their changing trends. The remote control module is used to trigger maintenance operations. The anomaly warning module is used to push warning information to users when environmental parameters exceed preset ranges.

[0017] Furthermore, the present invention can also optimize the corrosion resistance and environmental adaptability of the core sensing module, adapt the AI ​​voice interaction terminal to dialect recognition, optimize the interface and permission management of the WeChat mini program, and set up a data backup mechanism for the cloud server to improve the overall stability and applicability of the system.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects:

[0019] 1. By collecting multiple parameters in real time, such as soil temperature and humidity, pH value and acidity / alkalinity, as well as air temperature and humidity, the system can accurately perceive the growth environment of green plants.

[0020] 2. Real-time uploading, centralized storage, and analysis of environmental data are achieved through WIFI wireless transmission and cloud server processing;

[0021] 3. By analyzing sensor data through an AI dynamic decision-making model, targeted maintenance instructions can be generated based on different plant and environmental conditions, thereby improving the intelligence level of maintenance decisions;

[0022] 4. By working in synergy between the AI ​​voice interaction terminal and WeChat mini-program, voice interaction, visual display, remote management, and anomaly alerts can be achieved, improving user convenience.

[0023] 5. By establishing an execution mechanism and a results feedback mechanism, a complete closed-loop maintenance chain can be formed, which helps to improve maintenance efficiency in building gardening scenarios and reduce labor and resource consumption. Attached Figure Description

[0024] Figure 1 This is an overall architecture diagram of the AI-based intelligent building gardening maintenance system of the present invention;

[0025] Figure 2 This is a schematic diagram of the hardware structure of the core sensing module of the present invention;

[0026] Figure 3 This is a flowchart of the voice interaction process of the AI ​​voice interaction terminal of the present invention. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings. It should be understood that the following embodiments are only for explaining the present invention and are not intended to limit the scope of protection of the present invention.

[0028] like Figure 1 As shown, this embodiment provides an AI-based intelligent building gardening maintenance system, including a core sensing module, a cloud server, an AI voice interaction terminal, a WeChat mini-program, and actuators such as water pumps.

[0029] S1: Design and deployment of core sensing modules.

[0030] S101: Define the module's design area and non-design area. The design area is used to adapt to building greening carriers, such as smart flower pot slots or greening rack installation positions, and can be set as a rectangular area; the non-design area is the structural fixing part area at the edge of the module. By dividing the structural area, the orderly integration of sensors, communication modules, and data processing modules can be achieved, improving the overall deployment stability.

[0031] S102: Complete the integrated circuit board design and component soldering. For example... Figure 2 As shown, the core sensing module adopts a multi-layer PCB design, integrating a soil temperature and humidity sensor, a soil pH and acidity / alkalinity sensor, an air temperature and humidity sensor, a microcontroller data processing circuit, and a WIFI communication circuit. The soil temperature and humidity sensor collects soil moisture content data, the soil pH and acidity / alkalinity sensor collects soil acidity / alkalinity parameters, and the air temperature and humidity sensor collects ambient air environment parameters. The microcontroller processes the signals collected by each sensor, and the WIFI module enables wireless transmission of the collected data. Preferably, the soil temperature and humidity sensor can be an SHT30, the air temperature and humidity sensor can be an AHT20, the WIFI module can be an ESP8266, and the data processing chip can be an STM32F103C8T6 microcontroller.

[0032] S103: Set module operating parameters. The sampling frequency of each sensor is set via the program, and the collected data is cached. The WIFI module establishes a connection with the cloud server using the TCP / IP protocol to achieve real-time uploading of the collected data. Preferably, the sampling frequency is set to once every 5 minutes, the cache capacity is no less than 100 records, and the data can be retained for more than 24 hours after a power outage.

[0033] S2: Development and debugging of AI voice interaction terminal.

[0034] S201: Building the hardware and software framework of the AI ​​voice terminal. The AI ​​voice interaction terminal uses an embedded motherboard as its core and integrates a microphone array and speaker for voice acquisition and playback. On the software side, the terminal integrates a speech recognition engine and a natural language processing framework to achieve user voice command recognition and semantic understanding. Preferably, the iFlytek API can be used as the speech recognition engine, and a natural language processing framework based on BERT model fine-tuning can be adopted.

[0035] S202: Constructing interaction logic and AI decision-making models. For example... Figure 3As shown, users can initiate commands via voice, such as checking soil moisture, pH levels, or performing watering operations. The terminal first performs noise suppression and intent parsing on the voice signal, then accesses real-time sensor data and a plant growth database from the cloud server. The cloud server analyzes the current environment based on an AI dynamic decision-making model and determines whether maintenance operations are needed. When environmental parameters are detected to be abnormal or below the appropriate threshold for the corresponding plant, corresponding maintenance instructions or warning information are generated; when no operation is needed, query results or suggestions are returned. Preferably, the AI ​​dynamic decision-making model uses a random forest algorithm.

[0036] S203: Terminal Debugging and Optimization. Through simulated voice command testing, the voice recognition sensitivity, intent recognition logic, and decision response speed are optimized to ensure the terminal maintains relatively stable interactive performance even in common building noise environments. Furthermore, recognition adaptation for Mandarin and some dialects can be performed.

[0037] S3: Implementation of remote management function in WeChat mini program.

[0038] S301: Mini Program Architecture Design. WeChat Mini Programs are developed using a front-end and back-end separation model. The front-end is used for data display and user interaction, while the back-end connects to the cloud server and database to achieve data storage, command issuance, and state synchronization.

[0039] S302: Core Function Development.

[0040] 1. Data visualization module: Used to display data such as soil temperature and humidity, soil pH value, and air temperature and humidity in real time, and to show historical trends in the form of charts;

[0041] 2. Remote control module: Used to receive remote operation commands from users and remotely trigger maintenance operations such as irrigation and ventilation;

[0042] 3. Anomaly Warning Module: When the collected data exceeds the preset threshold, an alarm message containing abnormal parameters and suggested handling solutions will be pushed to the user;

[0043] 4. Historical Records Module: Used to store and query historical environmental data and maintenance execution records.

[0044] S303: User experience optimization. The mini-program interface supports switching between functions such as "View Data," "Control Devices," and "History," and is adapted to the screen sizes of mobile phones, tablets, and other devices. It also supports multi-user permission management to meet the shared control needs in home or office scenarios. The cloud database can be configured with a regular backup mechanism to improve data storage security.

[0045] S4: System integration and closed-loop operation.

[0046] The core sensing module, cloud server, AI voice interaction terminal, WeChat mini-program, and actuators such as water pumps are integrated and tested. Environmental data collected by the core sensing module is uploaded to the cloud server via WIFI; the cloud server outputs maintenance instructions based on the AI ​​dynamic decision-making model; the AI ​​voice interaction terminal and WeChat mini-program respectively provide feedback on the analysis results to the user and control the actuators to perform actions based on user instructions or system automatic decisions; after the actuators complete the corresponding maintenance operations, they return the execution results to the cloud server and synchronize them to the terminal and mini-program, thus forming a complete closed loop of "sensing—transmission—AI decision-making—interaction—control—feedback".

Claims

1. An AI-based intelligent building gardening maintenance system, characterized in that, The system includes a core sensing module, a cloud server, an AI voice interaction terminal, a WeChat mini-program, and an actuator. The core sensing module collects soil temperature and humidity, soil pH value and acidity / alkalinity, and air temperature and humidity data, and uploads the collected data to the cloud server via a Wi-Fi module. The cloud server receives and stores the collected data, accesses a plant growth database, and analyzes the collected data based on an AI dynamic decision-making model to generate corresponding maintenance instructions or early warning information. The AI ​​voice interaction terminal communicates with the cloud server and receives user voice commands, interprets user intent, and returns query results or maintenance execution results to the user. The WeChat mini-program communicates with the cloud server and visualizes the collected data, enabling remote control and anomaly warnings. The actuator is controlled by the cloud server and / or the AI ​​voice interaction terminal, and executes corresponding maintenance operations according to the maintenance instructions, feeding back the operation results to the cloud server to form a closed-loop maintenance chain.

2. The AI-based intelligent building gardening maintenance system according to claim 1, characterized in that, The core sensing module includes a multi-layer PCB board, a soil temperature and humidity sensor, a soil pH sensor or a soil acidity and alkalinity sensor, an air temperature and humidity sensor, a microcontroller data processing circuit, and a WIFI communication circuit. All components are integrated on the PCB board to form an integrated data acquisition and transmission module.

3. The AI-based intelligent building gardening maintenance system according to claim 1, characterized in that, The core sensing module collects and caches environmental data at a preset sampling frequency; the WIFI module establishes a connection with the cloud server using the TCP / IP protocol to achieve real-time uploading of the collected data; an AI dynamic decision-making model is deployed in the cloud server, which analyzes the collected data in conjunction with suitable environmental parameters for different green plants in the green plant growth database to generate corresponding maintenance instructions; the AI ​​dynamic decision-making model is a random forest algorithm model; the AI ​​voice interaction terminal includes an embedded motherboard, microphone array, speaker, speech recognition engine, and natural language processing framework; the AI ​​voice interaction terminal establishes bidirectional data interaction with the cloud server through a network interface to realize the recognition, parsing, and feedback of gardening-related voice commands.

4. The AI-based intelligent building gardening maintenance system according to claim 1, characterized in that, The natural language processing framework is a fine-tuned BERT model, and the AI ​​voice interaction terminal supports the recognition and processing of environmental query commands and maintenance control commands.

5. The AI-based intelligent building gardening maintenance system according to claim 1, characterized in that, The WeChat mini program includes a data visualization module, a remote control module, an anomaly warning module, and a historical record module. The data visualization module is used to display soil temperature and humidity, soil pH value, and air temperature and humidity data and their changing trends. The remote control module is used to trigger irrigation or ventilation operations. The anomaly warning module is used to push warning information to users when environmental parameters exceed preset thresholds.

6. The AI-based intelligent building gardening maintenance system according to claim 1, characterized in that, The actuator includes a water pump and / or a ventilation device for performing watering and / or ventilation operations according to the maintenance instructions; the cloud server is equipped with a data backup mechanism, and the system also includes anti-corrosion optimization for the core sensing module and dialect recognition adaptation for the AI ​​voice interaction terminal.

7. An AI-based intelligent building gardening maintenance method, characterized in that, The AI-based intelligent building gardening maintenance system according to any one of claims 1 to 6 includes the following steps: S1, collecting soil temperature and humidity, soil pH value and acidity / alkalinity, and air temperature and humidity data using a core sensing module, and uploading them to a cloud server via a WIFI module; S2, receiving query or control commands issued by users through an AI voice interaction terminal and / or a WeChat mini-program; S3, the cloud server calling the green plant growth database and analyzing the collected data based on an AI dynamic decision-making model to generate maintenance commands or early warning information; S4, the actuator performs the corresponding maintenance operation according to the maintenance instruction, and feeds back the execution result to the cloud server, and then the AI ​​voice interaction terminal and / or WeChat mini program return the processing result to the user.

8. The AI-based intelligent building gardening maintenance method according to claim 7, characterized in that, In step S2, the AI ​​voice interaction terminal performs noise suppression, speech recognition, and semantic parsing on the user's voice signal to extract the user's intent and complete the instruction classification; in step S3, the AI ​​dynamic decision-making model judges the current environmental status based on the suitable environmental parameter thresholds for different green plants. When the soil temperature and humidity are lower than the corresponding threshold or the soil pH value is in an abnormal range, it generates corresponding maintenance instructions or abnormal warning information. In step S4, after the actuator completes the maintenance operation, it stores the execution results and corresponding environmental parameters on the cloud server and synchronizes them to the history module of the WeChat mini program. At the same time, the AI ​​voice interaction terminal outputs voice feedback.

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