A method for continuously optimizing the ergonomics of a landscaping process

Abstract

This invention proposes a method for continuously optimizing the efficiency of garden maintenance processes. The method includes: real-time data acquisition via handheld ground-based data acquisition devices and drones equipped with various data acquisition devices, including: hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps of plants; preprocessing the real-time acquired data and then fusing it into multidimensional data, constructing a garden maintenance process intelligent analysis model based on the fused data; optimizing the scheduling and resource allocation of garden maintenance plans based on the intelligent analysis model; optimizing the intelligent analysis model based on the real-time data collected during the garden maintenance process according to the garden maintenance plan in step S3, combined with the data continuously collected in real-time in step S1; automatically optimizing the garden maintenance plan based on the optimized intelligent analysis model, thereby improving garden maintenance efficiency, reducing maintenance costs, and improving garden maintenance quality.

Description

Technical Field

[0002] This invention belongs to the field of intelligent garden management technology, and more specifically, relates to a method for continuously optimizing garden technology efficiency. Background Technology

[0004] With the increasing workload of urban greening maintenance, the landscaping maintenance industry faces problems such as high construction costs and low work efficiency. Traditional landscaping maintenance techniques mainly rely on experience and manual judgment, neglecting factors such as weather, geographical environment, and plant varieties, making it difficult to achieve targeted optimization. Especially during construction, there is often a lack of real-time data support and scientific process optimization methods, leading to resource waste and human error, resulting in high costs and low efficiency.

[0005] Existing landscaping maintenance and management methods largely rely on manual operations and traditional management approaches. For example, many existing landscaping maintenance processes rely on manual inspections and experience-based judgments to determine the content and procedures for construction. However, this approach not only makes it difficult to achieve real-time dynamic adjustments, but also, due to a lack of sufficient analysis of environmental and plant characteristics, often fails to achieve effective cost control and efficiency improvement.

[0006] In addition, although some technologies have been introduced in recent years, such as data analysis and the Internet of Things (IoT), most of these technologies are limited to single-dimensional data monitoring, such as climate monitoring and soil moisture, and lack comprehensive analysis and optimization of multi-dimensional data, thus failing to systematically improve the efficiency of garden maintenance.

[0007] For example, existing technologies such as patent CN106925589A, "A Landscape Greening Maintenance Management System and Method," propose a landscape greening maintenance management system based on information technology, which uses meteorological data and sensors to collect information such as soil moisture and temperature for automated monitoring. However, this method still fails to fully integrate multi-dimensional environmental data (such as geographical environment and plant varieties), and also lacks the ability to systematically consider and dynamically adjust processes.

[0008] However, the existing technology has the following problems:

[0009] 1. Reliance on experience and manual judgment: Current landscaping maintenance techniques typically rely on manual inspections and experience-based judgment, lacking scientific and systematic data support. This makes it difficult to consider multi-dimensional factors (such as weather changes, geographical environment, plant varieties, etc.) during the decision-making process, easily leading to inaccurate or unsuitable process solutions, which in turn affects construction efficiency and quality;

[0010] 2. Lack of multi-dimensional data analysis: Existing technologies typically rely on a single data source (such as meteorological data, soil moisture, etc.) for garden maintenance and management, failing to comprehensively analyze multiple factors such as weather, geographical environment, and plant varieties. This makes it impossible to dynamically optimize maintenance processes according to the actual environment, easily leading to resource waste and process incompatibility;

[0011] 3. Poor dynamic adjustment capability: Existing technologies often cannot adjust and optimize processes in real time when dealing with rapidly changing environmental conditions (such as sudden weather changes). Therefore, garden maintenance work is easily affected by external factors, making it impossible to adjust maintenance plans in a timely manner to adapt to changing environmental conditions, thus reducing work efficiency;

[0012] 4. Lack of cost control and efficiency optimization: Existing landscaping maintenance and management methods cannot effectively control construction costs and improve work efficiency through scientific analysis. Traditional methods rely heavily on fixed construction plans and cannot be flexibly adjusted according to real-time data and environmental changes. Therefore, construction costs often cannot be effectively optimized.

[0013] 5. Data silos and difficulties in information integration: Although some existing technologies have introduced information management and data monitoring, the data sources in most systems are isolated and lack a unified integration and analysis platform, resulting in poor information flow and the inability to achieve overall process optimization and continuous improvement through systematic analysis.

[0014] 6. Limitations of technology application: Although some existing technologies have introduced sensors, Internet of Things (IoT) and other technologies, the application of these technologies is mainly limited to single data collection and monitoring. They lack comprehensive processing and intelligent optimization of multi-dimensional data, making it difficult to achieve the goal of continuously improving process efficiency.

[0015] Therefore, in view of the shortcomings of existing technologies, there is an urgent need for a method to continuously optimize the efficiency of garden technology. This method can be achieved by using multi-dimensional data analysis (including weather, geographical environment, plant varieties, etc.) to continuously optimize construction technology, reduce costs, improve efficiency, and fill the gaps in existing technologies. Summary of the Invention

[0017] Therefore, to solve the above-mentioned technical problems, this invention proposes a method for continuously optimizing the efficiency of garden maintenance processes. This method includes: real-time data acquisition using handheld ground-based data acquisition devices and drones equipped with various data acquisition devices, including: hyperspectral data of plants, multispectral data, laser point cloud data, and ground orthophoto maps; preprocessing the real-time acquired data and then fusing it into multidimensional data, constructing a garden maintenance process intelligent analysis model based on the fused data; optimizing the scheduling and resource allocation of garden maintenance plans based on the intelligent analysis model; optimizing the intelligent analysis model based on the real-time data collected during the garden maintenance process of the garden maintenance plan in step S3, combined with the data continuously collected in real-time in step S1; automatically optimizing the garden maintenance plan based on the optimized intelligent analysis model, thereby improving garden maintenance efficiency, reducing maintenance costs, improving garden maintenance quality, promoting sustainable development and environmental protection, simplifying management and operation, and achieving social and economic benefits.

[0018] A method for continuously optimizing the efficiency of garden craftsmanship includes the following steps:

[0019] Step S1: Real-time data collection is carried out using ground-based handheld data acquisition devices and drones equipped with various data acquisition devices. The data includes: plant hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps.

[0020] Step S2: After preprocessing the real-time collected data, multi-dimensional data fusion is performed, and the fused data is used to construct an intelligent analysis model for garden maintenance processes.

[0021] Step S3: Optimize and schedule the garden maintenance plan and allocate resources based on the intelligent analysis model of garden maintenance technology;

[0022] Step S4: Based on the real-time data collected during the garden maintenance process according to the garden maintenance plan in Step S3, and combined with the data continuously collected in real time in Step S1, optimize the intelligent analysis model of garden maintenance process, and automatically optimize the garden maintenance plan according to the optimized intelligent analysis model of garden maintenance process.

[0023] Furthermore, in step S1, the various information acquisition devices include multispectral sensors or hyperspectral sensors, aerial cameras and / or satellite remote sensors and lidar. The multispectral sensors collect data in different bands through reflectance spectral characteristics, monitor environmental indicators such as plant growth status and soil moisture in real time, and identify plant health status, nutrient deficiencies, and pests and diseases. The hyperspectral sensors provide more detailed spectral data, covering hundreds of bands, to help conduct more accurate plant health assessments, detect subtle changes in leaves, identify potential plant diseases, and provide early warnings. The aerial cameras collect geometrically corrected aerial images through orthophotos or satellite remote sensors, which can accurately reflect the spatial layout of the garden, obtain topographic information, and support accurate measurement of area, spatial distribution analysis, and subsequent maintenance planning. The lidar collects laser point cloud data, and the drone can collect three-dimensional point cloud data, reflecting information on elevation, tree height, and ground undulation within the garden area, providing a basis for accurate three-dimensional spatial analysis, tree distribution, and maintenance planning. All of these data are uploaded to the data processing center in a timely manner through the drone wireless transmission system to ensure the real-time nature of maintenance management and data consistency.

[0024] Furthermore, in step S1, if drones cannot be used in certain scenarios due to limited area, flight permit restrictions, or cost controls, ground sensors and handheld devices are used to collect data. These ground sensors and handheld devices include ground soil sensors, handheld hyperspectral imaging devices, and laser scanners.

[0025] Furthermore, in step S1, multiple fixed monitoring points are set up in the garden, and a sensor network is used to monitor various environmental parameters in real time, combined with local cameras or ground sensors to monitor the plant growth status.

[0026] Furthermore, in step S2, preprocessing involves cleaning, denoising, and removing missing values ​​from the collected raw data to ensure data quality. Standardization processes unify the format of multi-source data collected from different sensors and devices, facilitating subsequent analysis.

[0027] Furthermore, in step S2, multidimensional data fusion combines data collected by multispectral or hyperspectral sensors with laser point cloud data, which not only identifies the health status of plants but also accurately analyzes their spatial location and growth height.

[0028] Furthermore, in step S3, optimizing scheduling and resource allocation includes: arranging the work tasks of maintenance personnel and the priority of maintenance tasks to ensure that the necessary maintenance work is completed at the appropriate time.

[0029] Furthermore, maintenance personnel include both humans and automated garden maintenance robots.

[0030] Furthermore, in step S3, the optimized scheduling and resource allocation are transmitted to the integrated management platform to ensure that all maintenance-related personnel can monitor the execution status of landscaping maintenance tasks in real time.

[0031] Furthermore, the integrated management platform ensures that users at different levels can access relevant data and task information through cloud data sharing, thereby improving work efficiency. These users include administrators, technicians, and operators. The integrated management platform displays real-time garden maintenance data, analysis results, and optimization plans through charts, maps, and large screens, enabling managers and implementers to intuitively understand task progress and maintenance effects.

[0032] The beneficial effects of this invention are as follows: This invention proposes a method for continuously optimizing the efficiency of garden maintenance processes. The method includes: real-time data acquisition via handheld ground-based data acquisition devices and drones equipped with various data acquisition devices, including: hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps of plants; preprocessing the real-time acquired data and then fusing it into multidimensional data, constructing a garden maintenance process intelligent analysis model based on the fused data; optimizing the scheduling and resource allocation of garden maintenance plans based on the intelligent analysis model; optimizing the intelligent analysis model based on the real-time data collected during the garden maintenance process in step S3 and the continuously collected data in step S1; and automatically optimizing the garden maintenance plan based on the optimized intelligent analysis model. This method has the following beneficial effects:

[0033] 1. Improve garden maintenance efficiency; precise data collection and real-time monitoring: By using drones equipped with multispectral, hyperspectral, orthophoto, and laser point cloud sensors, this invention can comprehensively and in real-time collect various environmental data within the garden area. Compared with traditional manual inspections or single data collection methods, it greatly improves the efficiency and accuracy of data collection. Intelligent process optimization: Based on big data analysis and intelligent decision-making, this invention can optimize maintenance processes in real time and automatically adjust maintenance strategies according to real-time environmental changes (such as weather, soil moisture, etc.), thereby avoiding inefficient manual intervention in traditional maintenance methods and significantly improving work efficiency and accuracy.

[0034] 2. Reduce maintenance costs; optimize resource allocation: Through multi-dimensional data fusion and intelligent optimization algorithms, this invention can accurately predict garden maintenance needs and rationally allocate resources, avoiding the waste of maintenance resources in traditional methods. For example, in terms of irrigation, the system can automatically adjust the irrigation amount according to soil moisture and weather forecasts, reducing water waste and labor costs: Through automated data collection and maintenance scheduling, the need for a large number of manual inspections and adjustments in traditional garden maintenance is reduced, thus lowering labor costs.

[0035] 3. Improve the quality of garden maintenance; precise monitoring and early warning of diseases and pests: using hyperspectral and multispectral sensors, the system can identify early symptoms of plant diseases and pests and intervene in advance. Compared with traditional manual inspection, the system can accurately identify diseases in the early stage, reduce plant losses and unnecessary post-treatment, significantly improve the quality of maintenance, and optimize the plant growth environment: through comprehensive analysis of data on plant health status, soil moisture, climate change and other aspects, the system can accurately adjust the maintenance plan to ensure that plants are in the most suitable growth environment, thereby improving the overall quality and landscape effect of the garden;

[0036] 4. Promote sustainable development and environmental protection; energy conservation and resource saving: Intelligent irrigation, fertilization and other maintenance measures effectively avoid the waste of resources such as water, electricity and fertilizer, and realize a more energy-saving and environmentally friendly maintenance mode. For example, by precisely controlling the amount of irrigation and fertilization, the use of water resources and fertilizers is reduced, the burden on the environment is reduced, and the use of chemical substances is reduced. Through intelligent monitoring and early warning, the system can reduce the need for the large-scale use of pesticides and fertilizers in traditional maintenance, reduce the risk of environmental pollution, and promote green and environmentally friendly garden management.

[0037] 5. Simplified Management and Operation; Automation and Remote Control: The intelligent data analysis and process optimization system of this invention can be remotely managed and monitored through an integrated platform, simplifying the complex operations of traditional maintenance work. Managers can view the real-time status and data reports of garden maintenance at any time and make decisions based on the system's recommended optimization schemes, eliminating the need for frequent on-site inspections and greatly improving management efficiency. Enhanced data visualization and decision support: This invention provides powerful data visualization functions, helping managers intuitively understand the maintenance status through charts, maps, and other methods, providing support for decision-making. Compared to traditional reports and data tables, the system is more intuitive and easier to understand, reducing the difficulty of data analysis and the possibility of errors.

[0038] 6. Social and Economic Benefits; Promoting Smart City Construction: The intelligent garden maintenance system of this invention aligns with the trend of modern urban greening management, providing effective technical support for the construction of smart cities. Through data collection and intelligent management, garden greening maintenance becomes more scientific and efficient, contributing to the modernization of urban garden management and improving its overall level. Through intelligent optimization technology, the quality and efficiency of garden maintenance are significantly improved, enhancing the overall level of garden management. This is of great significance for improving the quality of the urban environment and enhancing the happiness of citizens. Attached Figure Description

[0040] Figure 1 This is a flowchart of a method for continuously optimizing the efficiency of garden technology according to the present invention.

[0041] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0043] The following embodiments are described to aid in understanding this application. These embodiments are not, and should not be, construed in any way as limiting the scope of protection of this application.

[0044] In the following description, those skilled in the art will recognize that throughout this discussion, components may be described as individual functional units (which may include subunits), but those skilled in the art will recognize that various components or portions thereof may be divided into individual components or may be integrated together (including integrated within a single system or component).

[0045] Furthermore, the connection between components or systems is not intended to be limited to a direct connection; on the contrary, data between these components may be modified, reformatted, or otherwise altered by intermediate components. Additionally, other or fewer connections may be used. It should also be noted that the terms "connection," "link," or "input" should be understood to include direct connections, indirect connections via one or more intermediate devices, and wireless connections.

[0046] Example 1

[0047] like Figure 1 The diagram shown is a flowchart of a method for continuously optimizing the efficiency of garden technology according to the present invention.

[0048] A method for continuously optimizing the efficiency of garden craftsmanship includes the following steps:

[0049] Step S1: Real-time data collection is carried out using ground-based handheld data acquisition devices and drones equipped with various data acquisition devices. The data includes: plant hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps.

[0050] Step S2: After preprocessing the real-time collected data, multi-dimensional data fusion is performed, and the fused data is used to construct an intelligent analysis model for garden maintenance processes.

[0051] Step S3: Optimize and schedule the garden maintenance plan and allocate resources based on the intelligent analysis model of garden maintenance technology;

[0052] Step S4: Based on the real-time data collected during the garden maintenance process according to the garden maintenance plan in Step S3, and combined with the data continuously collected in real time in Step S1, optimize the intelligent analysis model of garden maintenance process, and automatically optimize the garden maintenance plan according to the optimized intelligent analysis model of garden maintenance process.

[0053] In step S1, the various information acquisition devices include multispectral sensors or hyperspectral sensors, aerial cameras and / or satellite remote sensors and lidar. The multispectral sensors collect data in different bands through reflectance spectral characteristics, monitor environmental indicators such as plant growth status and soil moisture in real time, and identify plant health status, nutrient deficiencies, and pests and diseases. The hyperspectral sensors provide more detailed spectral data, covering hundreds of bands, to help conduct more accurate plant health assessments, detect subtle changes in leaves, identify potential plant diseases, and provide early warnings. The aerial cameras collect geometrically corrected aerial images through orthophotos or satellite remote sensors, which can accurately reflect the spatial layout of the garden, obtain topographic information, and support accurate measurement of area, spatial distribution analysis, and subsequent maintenance planning. The lidar collects laser point cloud data, and the drone can collect three-dimensional point cloud data, reflecting information on elevation, tree height, and ground undulation within the garden area, providing a basis for accurate three-dimensional spatial analysis, tree distribution, and maintenance planning. All of these data are uploaded to the data processing center in a timely manner through the drone wireless transmission system to ensure the real-time nature of maintenance management and data consistency.

[0054] In step S1, if drones cannot be used in certain scenarios due to limited area, flight permit restrictions, or cost controls, ground sensors and handheld devices are used to collect data. Ground sensors and handheld devices include ground soil sensors, handheld hyperspectral imaging devices, and laser scanners.

[0055] In step S1, multiple fixed monitoring points are set up in the garden, and a sensor network is used to monitor various environmental parameters in real time. The plant growth status is also monitored in combination with local cameras or ground sensors.

[0056] In step S2, preprocessing involves cleaning, denoising, and removing missing values ​​from the collected raw data to ensure data quality. Standardization processes unify the format of multi-source data collected from different sensors and devices, facilitating subsequent analysis.

[0057] In step S2, multidimensional data fusion combines data collected by multispectral or hyperspectral sensors with laser point cloud data, which can not only identify the health status of plants, but also accurately analyze their spatial location and growth height.

[0058] In step S3, optimizing scheduling and resource allocation includes: arranging the work tasks of maintenance personnel and the priority of maintenance tasks to ensure that the necessary maintenance work is completed at the appropriate time.

[0059] Maintenance personnel include both humans and automated garden maintenance robots.

[0060] In step S3, the optimized scheduling and resource allocation are transmitted to the integrated management platform to ensure that all maintenance-related personnel can keep abreast of the execution status of the landscaping maintenance tasks in real time.

[0061] The integrated management platform ensures that users at different levels can access relevant data and task information through cloud data sharing, thereby improving work efficiency. The users include administrators, technicians, and operators. The integrated management platform displays garden maintenance data, analysis results, and optimization plans in real time through charts, maps, and large screens, enabling managers and implementers to intuitively understand task progress and maintenance effects.

[0062] The beneficial effects of this invention are as follows: This invention proposes a method for continuously optimizing the efficiency of garden maintenance processes. The method includes: real-time data acquisition via handheld ground-based data acquisition devices and drones equipped with various data acquisition devices, including: hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps of plants; preprocessing the real-time acquired data and then fusing it into multidimensional data, constructing a garden maintenance process intelligent analysis model based on the fused data; optimizing the scheduling and resource allocation of garden maintenance plans based on the intelligent analysis model; optimizing the intelligent analysis model based on the real-time data collected during the garden maintenance process in step S3 and the continuously collected data in step S1; and automatically optimizing the garden maintenance plan based on the optimized intelligent analysis model. This method has the following beneficial effects:

[0063] 2. Improve garden maintenance efficiency; precise data collection and real-time monitoring: By using drones equipped with multispectral, hyperspectral, orthophoto, and laser point cloud sensors, this invention can comprehensively and in real-time collect various environmental data within the garden area. Compared with traditional manual inspections or single data collection methods, it greatly improves the efficiency and accuracy of data collection. Intelligent process optimization: Based on big data analysis and intelligent decision-making, this invention can optimize maintenance processes in real time and automatically adjust maintenance strategies according to real-time environmental changes (such as weather, soil moisture, etc.), thereby avoiding inefficient manual intervention in traditional maintenance methods and significantly improving work efficiency and accuracy.

[0064] 2. Reduced maintenance costs; optimized resource allocation: Through multi-dimensional data fusion and intelligent optimization algorithms, this invention can accurately predict garden maintenance needs and rationally allocate resources, avoiding the waste of maintenance resources in traditional methods. For example, in irrigation, the system can automatically adjust the irrigation amount based on soil moisture and weather forecasts, reducing water waste and labor costs: Through automated data collection and maintenance scheduling, the need for extensive manual inspections and adjustments in traditional garden maintenance is reduced, thus lowering labor costs.

[0065] 3. Improve the quality of garden maintenance; precise monitoring and early warning of diseases and pests: using hyperspectral and multispectral sensors, the system can identify early symptoms of plant diseases and pests and intervene in advance. Compared with traditional manual inspection, the system can accurately identify diseases in the early stage, reduce plant losses and unnecessary post-treatment, significantly improve the quality of maintenance, and optimize the plant growth environment: through comprehensive analysis of data on plant health status, soil moisture, climate change and other aspects, the system can accurately adjust the maintenance plan to ensure that plants are in the most suitable growth environment, thereby improving the overall quality and landscape effect of the garden;

[0066] 4. Promote sustainable development and environmental protection; energy conservation and resource saving: Intelligent irrigation, fertilization and other maintenance measures effectively avoid the waste of resources such as water, electricity and fertilizer, and realize a more energy-saving and environmentally friendly maintenance mode. For example, by precisely controlling the amount of irrigation and fertilization, the use of water resources and fertilizers is reduced, the burden on the environment is reduced, and the use of chemical substances is reduced. Through intelligent monitoring and early warning, the system can reduce the need for the large-scale use of pesticides and fertilizers in traditional maintenance, reduce the risk of environmental pollution, and promote green and environmentally friendly garden management.

[0067] 5. Simplified Management and Operation; Automation and Remote Control: The intelligent data analysis and process optimization system of this invention can be remotely managed and monitored through an integrated platform, simplifying the complex operations of traditional maintenance work. Managers can view the real-time status and data reports of garden maintenance at any time and make decisions based on the system's recommended optimization schemes, eliminating the need for frequent on-site inspections and greatly improving management efficiency. Enhanced data visualization and decision support: This invention provides powerful data visualization functions, helping managers intuitively understand the maintenance status through charts, maps, and other methods, providing support for decision-making. Compared to traditional reports and data tables, the system is more intuitive and easier to understand, reducing the difficulty of data analysis and the possibility of errors.

[0068] 6. Social and Economic Benefits; Promoting Smart City Construction: The intelligent garden maintenance system of this invention aligns with the trend of modern urban greening management, providing effective technical support for the construction of smart cities. Through data collection and intelligent management, garden greening maintenance becomes more scientific and efficient, contributing to the modernization of urban garden management and improving its overall level. Through intelligent optimization technology, the quality and efficiency of garden maintenance are significantly improved, enhancing the overall level of garden management. This is of great significance for improving the quality of the urban environment and enhancing the happiness of citizens.

[0069] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method of continuously optimizing the ergonomics of a landscaping process, characterized by: Includes the following steps: Step S1: Real-time data collection is carried out using ground-based handheld data acquisition devices and drones equipped with various data acquisition devices. The data includes: plant hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps. Step S2: After preprocessing the real-time collected data, multi-dimensional data fusion is performed, and the fused data is used to construct an intelligent analysis model for garden maintenance processes. Step S3: Optimize and schedule the garden maintenance plan and allocate resources based on the intelligent analysis model of garden maintenance technology; Step S4: Based on the real-time data collected during the garden maintenance process according to the garden maintenance plan in Step S3, and combined with the data continuously collected in real time in Step S1, optimize the intelligent analysis model of garden maintenance process, and automatically optimize the garden maintenance plan according to the optimized intelligent analysis model of garden maintenance process.

2. The method of continuously optimizing garden ergonomics of claim 1, wherein: In step S1, the various information acquisition devices include multispectral sensors or hyperspectral sensors, aerial cameras and / or satellite remote sensors and lidar. The multispectral sensors collect data in different bands through reflectance spectral characteristics, monitor environmental indicators such as plant growth status and soil moisture in real time, and identify plant health status, nutrient deficiencies, and pests and diseases. The hyperspectral sensors provide more detailed spectral data, covering hundreds of bands, to help conduct more accurate plant health assessments, detect subtle changes in leaves, identify potential plant diseases, and provide early warnings. The aerial cameras collect geometrically corrected aerial images through orthophotos or satellite remote sensors, which can accurately reflect the spatial layout of the garden, obtain topographic information, and support accurate measurement of area, spatial distribution analysis, and subsequent maintenance planning. The lidar collects laser point cloud data, and the drone can collect three-dimensional point cloud data, reflecting information on elevation, tree height, and ground undulation within the garden area, providing a basis for accurate three-dimensional spatial analysis, tree distribution, and maintenance planning. All of these data are uploaded to the data processing center in a timely manner through the drone wireless transmission system to ensure the real-time nature of maintenance management and data consistency.

3. The method of continuously optimizing garden ergonomics of claim 2, wherein: In step S1, if drones cannot be used in certain scenarios due to limited area, flight permit restrictions, or cost controls, ground sensors and handheld devices are used to collect data. Ground sensors and handheld devices include ground soil sensors, handheld hyperspectral imaging devices, and laser scanners.

4. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 3, characterized in that: In step S1, multiple fixed monitoring points are set up in the garden, and a sensor network is used to monitor various environmental parameters in real time. The plant growth status is also monitored in combination with local cameras or ground sensors.

5. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 1, characterized in that: In step S2, preprocessing involves cleaning, denoising, and removing missing values ​​from the collected raw data to ensure data quality. Standardization processes unify the format of multi-source data collected from different sensors and devices, facilitating subsequent analysis.

6. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 5, characterized in that: In step S2, multidimensional data fusion combines data collected by multispectral or hyperspectral sensors with laser point cloud data, which can not only identify the health status of plants, but also accurately analyze their spatial location and growth height.

7. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 1, characterized in that: In step S3, optimizing scheduling and resource allocation includes: arranging the work tasks of maintenance personnel and the priority of maintenance tasks to ensure that the necessary maintenance work is completed at the appropriate time.

8. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 7, characterized in that: Maintenance personnel include both humans and automated garden maintenance robots.

9. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 7, characterized in that: In step S3, the optimized scheduling and resource allocation are transmitted to the integrated management platform to ensure that all maintenance-related personnel can keep abreast of the execution status of the landscaping maintenance tasks in real time.

10. The method for continuously optimizing the efficiency of garden craftsmanship according to claim 9, characterized in that: The integrated management platform ensures that users at different levels can access relevant data and task information through cloud data sharing, thereby improving work efficiency. The users include administrators, technicians, and operators. The integrated management platform displays garden maintenance data, analysis results, and optimization plans in real time through charts, maps, and large screens, enabling managers and implementers to intuitively understand task progress and maintenance effects.

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