Garden plant maintenance accurate decision-making system based on big data analysis

Abstract

The invention relates to the technical field of garden maintenance, and discloses a garden plant maintenance accurate decision-making system based on big data analysis, which comprises a sensor acquisition layer, a data transmission layer, a data analysis layer, a decision-making application layer and a terminal display layer, the sensor acquisition layer is used for acquiring environment information and plant growth state data in a garden, drawing a three-dimensional space model of the garden and marking the plant information into the three-dimensional space model. Collected environment information and plant growth state data are stored in a database, and the data are analyzed and compared through a big data analysis technology, so that the growth state and the development trend of the plant are obtained; the method comprises the following steps: automatically generating a maintenance scheme, analyzing and deciding the maintenance scheme, determining water demand, fertilizer demand and pest control key points and difficulties of plants, and formulating corresponding control strategies, thereby ensuring reduction of pest and disease damage, improvement of greening quality and reduction of capital investment; and precise maintenance of garden plants is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of garden maintenance, and in particular to a precise decision-making system for garden plant maintenance based on big data analysis. Background Art

[0002] With the continuous development of science and technology and people's increasing attention to environmental protection, garden management has become an indispensable part of modern urban construction; and in the maintenance and management of garden plants, the application of intelligent big data technology is increasing. Through big data analysis, plant growth status data is obtained, thereby realizing automatic plant maintenance. While ensuring the quality of plant growth, it reduces the waste of resources caused by manual management and improves the efficiency of garden management. This not only improves the management efficiency of gardens and green spaces, but also improves the quality of life of urban residents.

[0003] A Chinese patent discloses a big data-based garden maintenance plan optimization system and method (Announcement No. CN118230250A). This patented technology extracts features from garden plant growth monitoring videos over a predetermined time period to understand plant growth within that time period. It also extracts features from soil monitoring indicator data at multiple predetermined time points within that time period to understand soil nutrient levels. Finally, a fertilization plan is generated based on the soil nutrient levels and plant growth. This approach allows for more effective garden plant maintenance and management, but it doesn't guarantee the reduction of pests and diseases, improve greening quality, or reduce capital investment, nor does it achieve precise maintenance. Summary of the Invention

[0004] The purpose of the present invention is to provide a precise decision-making system for garden plant maintenance based on big data analysis to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The garden plant maintenance precision decision-making system based on big data analysis includes sensor acquisition layer, data transmission layer, data analysis layer, decision application layer and terminal display layer; among them, The sensor collection layer is used to collect environmental information and plant growth status data in the garden, draw a three-dimensional spatial model of the garden, and mark the plant information in the three-dimensional spatial model; The data transmission layer is used to transmit the collected data to the data analysis layer; The data analysis layer is used to store the collected data in a database and analyze and compare the data using big data analysis technology to obtain the growth status and development trend of the plants; The decision application layer is used to automatically generate a maintenance plan based on the growth status of the plant, analyze and decide on the maintenance plan, and maintain the plant through manual or automated equipment; The terminal display layer is used to evaluate and optimize the maintenance plan; and to display various information in the garden area through the terminal module.

[0006] As a further solution of the present invention: the hardware equipment in the sensor acquisition layer includes temperature and humidity sensors, light sensors, carbon dioxide sensors, conductivity sensors, soil salinity sensors and high-definition cameras, wherein the high-definition cameras include fixed cameras and mobile cameras; the temperature and humidity in the soil and air are monitored by temperature and humidity sensors; the illumination time and light intensity of plants are monitored by light sensors; the carbon dioxide content in the air is monitored by carbon dioxide sensors; the content of soluble substances in the soil and the conductivity of the soil are monitored by conductivity sensors; the salt content in the soil is monitored by soil salt sensors; the growth status of plants and the infection of pests and diseases are monitored by fixed cameras; images within the garden are collected by mobile cameras, a three-dimensional spatial model of the garden is drawn, and plant information is annotated into the three-dimensional spatial model.

[0007] As a further solution of the present invention: the communication protocols in the data transmission layer include serial communication protocol, UDP protocol and HTTP protocol, among which the serial communication protocol is used to realize data transmission between various sensor data and the control center, the UDP protocol is used to realize data transmission between the control center and the server, and the HTTP protocol realizes data transmission between the server and the client.

[0008] As a further solution of the present invention: in the data analysis layer, the database includes a basic database, an operation database, an algorithm model library and a knowledge base, wherein: The basic database is used to store data on the current status of garden plants, garden plant planning data, garden plant coverage data, garden ancient and famous tree data, and personnel information data; The operation database is used to store garden plant resource information, inspection and rectification information data, maintenance operation record data and greening project progress data; The algorithm model library is used to store various mathematical models for data analysis; The knowledge base is used to store historical data of garden plants.

[0009] As a further solution of the present invention: in the data analysis layer, big data analysis technology uses machine learning and data mining algorithms to analyze and process the collected data; wherein, the data mining algorithms include classification algorithms, clustering algorithms, association rule algorithms, regression analysis algorithms, neural network algorithms, support vector machine algorithms and decision tree algorithms.

[0010] As a further solution of the present invention: in the decision application layer, the maintenance plan is generated as follows: S1. Use big data analysis technology to predict plant growth trends and the likelihood of pest and disease development, and determine intervention and prevention measures; S2. Setting upper and lower thresholds for plant growth status data; then judging the plant growth status by comparing the real-time collected data with preset indicators; S3. Quantify and analyze historical data, capital investment, maintenance costs, and greening effects, comprehensively consider indicators such as saving water, reducing pests and diseases, improving greening quality, and reducing capital investment, and determine the optimal plant maintenance plan.

[0011] As a further solution of the present invention: in the terminal display layer, the terminal module includes a mobile APP module and a background management web module. Through the mobile APP module or the background management web module, remote real-time management and remote control of garden maintenance can be realized, and information within the forest area can be viewed in real time.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention stores the collected environmental information and plant growth status data in a database, and analyzes and compares the data through big data analysis technology to obtain the growth status and development trend of the plants; thereby automatically generating a maintenance plan, and then analyzing and deciding on the maintenance plan, determining the water and fertilizer requirements of the plants, as well as the key points and difficulties of pest and disease control, and formulating corresponding prevention and control strategies, thereby ensuring the reduction of pests and diseases, improving the quality of greening, and reducing capital investment; and realizing the precise maintenance of garden plants. DETAILED DESCRIPTION

[0013] In the embodiment of the present invention, the garden plant maintenance precision decision-making system based on big data analysis includes a sensor acquisition layer, a data transmission layer, a data analysis layer, a decision application layer and a terminal display layer; wherein, The sensor collection layer is used to collect environmental information and plant growth status data in the garden, draw a three-dimensional spatial model of the garden, and mark the plant information in the three-dimensional spatial model; The data transmission layer is used to transmit the collected data to the data analysis layer; The data analysis layer is used to store the collected data in a database and analyze and compare the data using big data analysis technology to obtain the growth status and development trend of the plants; The decision-making application layer is used to automatically generate maintenance plans based on the growth status of plants, analyze and decide on the maintenance plans, and maintain the plants through manual or automated equipment. For example, the water requirement is determined based on soil moisture and the plant species, and the irrigation amount is automatically adjusted through the intelligent irrigation system to achieve water-saving irrigation. Based on the occurrence patterns and trends of pests and diseases, future prevention and control priorities and difficulties are predicted, and corresponding prevention and control strategies are formulated. The terminal display layer is used to evaluate and optimize the maintenance plan; and to display various information in the garden area through the terminal module; such as environmental information, plant growth status data, plant location and corresponding type.

[0014] Preferably, the hardware devices in the sensor collection layer include temperature and humidity sensors, light sensors, carbon dioxide sensors, conductivity sensors, soil salinity sensors, and high-definition cameras, wherein the high-definition cameras include fixed cameras and mobile cameras, such as fixed cameras installed on streetlight poles and trees, and mobile cameras installed on cars, robots, and drones; The temperature and humidity in the soil and air are monitored through temperature and humidity sensors; the illumination time and intensity of plants are monitored through light sensors; the carbon dioxide content in the air is monitored through carbon dioxide sensors; the content of soluble substances in the soil and the conductivity of the soil are monitored through conductivity sensors, thereby providing information on soil fertility and moisture conditions; the salt content in the soil is monitored through soil salt sensors, thereby providing information on the degree of soil salinization; plant growth conditions and pest infection are monitored through fixed cameras; mobile cameras are used to collect images within the garden, draw a three-dimensional spatial model of the garden, and annotate plant information into the three-dimensional spatial model.

[0015] Preferably, the communication protocols in the data transmission layer include serial communication protocol, UDP protocol and HTTP protocol, wherein the serial communication protocol is used to realize the data transmission between various sensor data and the control center. For example, the control center adopts a microprocessor to convert the electrical signal collected by the sensor into a digital signal through the microprocessor. The UDP protocol is used to realize the data transmission between the control center and the server, and the HTTP protocol realizes the data transmission between the server and the client.

[0016] Preferably, in the data analysis layer, the database includes a basic database, an operation database, an algorithm model library and a knowledge base, wherein: The basic database is used to store data on the current status of garden plants, garden plant planning data, garden plant coverage data, garden ancient and famous tree data, and personnel information data; among which, the current status data of garden plants includes plant resource information, plant species name, location, and ecological characteristics; the personnel information data includes maintenance management unit information, maintenance personnel information, and maintenance status information; The operational database is used to store garden plant resource information, inspection and rectification information data, maintenance operation record data, and greening project progress data. For example, by matching garden plant information with actual plant resources, the plant inventory status can be monitored in real time, so that plant supply can be rationally allocated and plants can be replanted and replanted immediately during the maintenance process. The algorithm model library is used to store various mathematical models for data analysis, such as the vegetation coverage calculation formula and the green space ratio calculation method, which can help managers better understand the current situation and predict future trends; The knowledge base is used to store historical data on garden plants, such as historical irrigation and fertilization records, historical disease and pest infection conditions, and historical disease and pest control records; thus, it can provide accurate reference for garden maintenance work; for example, by querying the knowledge base, one can understand the growth habits and stress resistance characteristics of various plants in the garden, so as to adopt appropriate maintenance strategies to improve the adaptability and survival rate of garden plants.

[0017] Preferably, in the data analysis layer, big data analysis technology uses machine learning and data mining algorithms to analyze and process the collected data, thereby reducing dependence on manual intervention, improving maintenance efficiency, and realizing refined maintenance operations; wherein, data mining algorithms include classification algorithms, clustering algorithms, association rule algorithms, regression analysis algorithms, neural network algorithms, support vector machine algorithms, and decision tree algorithms.

[0018] Preferably, in the decision application layer, the maintenance plan is generated as follows: S1. Use big data analysis technology to predict plant growth trends and the likelihood of pest and disease development, and determine intervention and prevention measures; S2. Setting upper and lower thresholds for plant growth status data; then judging the plant growth status by comparing the real-time collected data with preset indicators; S3. Quantify and analyze historical data, capital investment, maintenance costs, and greening effects, comprehensively consider indicators such as saving water, reducing pests and diseases, improving greening quality, and reducing capital investment, and determine the optimal plant maintenance plan.

[0019] Preferably, in the terminal display layer, the terminal module includes a mobile APP module and a background management web module. Through the mobile APP module or the background management web module, remote real-time management and remote control of garden maintenance can be achieved, and information within the garden area can be viewed in real time.

[0020] The above is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A precise decision-making system for garden plant maintenance based on big data analysis, characterized by: It includes sensor acquisition layer, data transmission layer, data analysis layer, decision application layer and terminal display layer; among them, The sensor collection layer is used to collect environmental information and plant growth status data in the garden, draw a three-dimensional spatial model of the garden, and mark the plant information in the three-dimensional spatial model; The data transmission layer is used to transmit the collected data to the data analysis layer; The data analysis layer is used to store the collected data in a database and analyze and compare the data using big data analysis technology to obtain the growth status and development trend of the plants; The decision application layer is used to automatically generate a maintenance plan based on the growth status of the plant, analyze and decide on the maintenance plan, and maintain the plant through manual or automated equipment; The terminal display layer is used to evaluate and optimize the maintenance plan; and to display various information in the garden area through the terminal module.

2. The garden plant maintenance precision decision-making system based on big data analysis according to claim 1 is characterized in that: The hardware devices in the sensor acquisition layer include temperature and humidity sensors, light sensors, carbon dioxide sensors, conductivity sensors, soil salinity sensors and high-definition cameras, among which the high-definition cameras include fixed cameras and mobile cameras; the temperature and humidity in the soil and air are monitored by temperature and humidity sensors; the light exposure time and light intensity of plants are monitored by light sensors; the carbon dioxide content in the air is monitored by carbon dioxide sensors; the content of soluble substances in the soil and the conductivity of the soil are monitored by conductivity sensors; the salt content in the soil is monitored by soil salt sensors; the growth status of plants and the infection of pests and diseases are monitored by fixed cameras; images within the garden are collected by mobile cameras, a three-dimensional spatial model of the garden is drawn, and plant information is annotated into the three-dimensional spatial model.

3. The garden plant maintenance precision decision-making system based on big data analysis according to claim 1 is characterized in that: The communication protocols in the data transmission layer include serial communication protocol, UDP protocol and HTTP protocol. Among them, the serial communication protocol is used to realize the data transmission between various sensor data and the control center, the UDP protocol is used to realize the data transmission between the control center and the server, and the HTTP protocol realizes the data transmission between the server and the client.

4. The garden plant maintenance precision decision-making system based on big data analysis according to claim 1 is characterized in that: In the data analysis layer, the database includes a basic database, an operational database, an algorithm model library and a knowledge base, wherein: The basic database is used to store data on the current status of garden plants, garden plant planning data, garden plant coverage data, garden ancient and famous tree data, and personnel information data; The operation database is used to store garden plant resource information, inspection and rectification information data, maintenance operation record data and greening project progress data; The algorithm model library is used to store various mathematical models for data analysis; The knowledge base is used to store historical data of garden plants.

5. The garden plant maintenance precision decision-making system based on big data analysis according to claim 1 is characterized in that: In the data analysis layer, big data analysis technology uses machine learning and data mining algorithms to analyze and process the collected data; among them, data mining algorithms include classification algorithms, clustering algorithms, association rule algorithms, regression analysis algorithms, neural network algorithms, support vector machine algorithms, and decision tree algorithms.

6. The garden plant maintenance precision decision-making system based on big data analysis according to claim 1 is characterized in that: In the decision application layer, the maintenance plan is generated as follows: S1. Use big data analysis technology to predict plant growth trends and the likelihood of pest and disease development, and determine intervention and prevention measures; S2. Setting upper and lower thresholds for plant growth status data; then judging the plant growth status by comparing the real-time collected data with preset indicators; S3. Quantify and analyze historical data, capital investment, maintenance costs, and greening effects, comprehensively consider indicators such as saving water, reducing pests and diseases, improving greening quality, and reducing capital investment, and determine the optimal plant maintenance plan.

7. The garden plant maintenance precision decision-making system based on big data analysis according to claim 1 is characterized in that: In the terminal display layer, the terminal module includes a mobile APP module and a background management web module. Through the mobile APP module or the background management web module, remote real-time management and remote control of garden maintenance can be achieved, and information within the garden area can be viewed in real time.