A garden full-cycle intelligent maintenance method and system based on ecological adaptability

Abstract

This invention discloses a method and system for intelligent full-cycle garden maintenance based on ecological adaptability, belonging to the field of garden ecological maintenance technology. The invention first constructs a basic database containing ecological parameters of the target area, plant characteristics, and local ecological information, covering the entire process from planning and design, construction and cultivation, daily maintenance, and ecological restoration. Suitable plants are selected through ecological adaptability scoring, and intelligent maintenance control is achieved by combining an Internet of Things (IoT) sensing network, while simultaneously establishing a full-cycle data sharing system. This invention can increase plant survival rate by more than 20%, reduce overall maintenance costs by 15%, and reduce chemical pesticide usage by 30%. It can be widely applied to various garden scenarios such as coastal gardens, urban road greening, and municipal parks.

Description

Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of garden ecological maintenance and smart municipal engineering. Specifically, it relates to a garden full-cycle intelligent maintenance method and system based on ecological adaptability. It can be widely applied to the full-cycle maintenance and management of various garden scenarios such as coastal ecological gardens, urban road greening, and municipal parks. It can effectively adapt to the ecological characteristics of different regions and take into account the needs of maintenance cost control and the improvement of garden ecosystem stability. Background Technology

[0002] As the scale of urban ecological construction in my country continues to expand, the demand for garden maintenance is also rapidly increasing. Traditional garden maintenance models have long relied on human experience for decision-making, often employing standardized maintenance measures without fully considering the compatibility of garden plants with local climate, soil, and ecosystems. This generally results in low plant survival rates, high maintenance costs, and insufficient ecological stability. Furthermore, current maintenance work often focuses only on routine maintenance during the plant growth stage, severing the collaborative relationship between early planning and design, mid-term construction and cultivation, and later ecological restoration. This significantly increases the difficulty of subsequent maintenance due to problems left over from the initial configuration and construction phases.

[0003] Among the existing related technologies, Chinese patent application with publication number CN109726940A proposes a method and system for the maintenance and management of garden plants. It generates corresponding maintenance tasks by constructing a maintenance strategy database, which can realize the standardized scheduling of the maintenance process. However, this solution only uses preset time nodes as the basis for maintenance decisions, does not introduce ecological adaptability logic, and does not cover the planning, construction and ecological restoration stages, so it cannot fundamentally improve the stability of the garden ecosystem.

[0004] Chinese patent application CN112690163A proposes a smart maintenance solution based on the Internet of Things for seedlings. It collects seedling and environmental data through the Internet of Things to calculate maintenance solutions, which can improve the accuracy of maintenance decisions. However, the solution does not integrate ecological adaptation rules into the decision-making logic, nor does it form a closed-loop management mechanism for the entire cycle, and its adaptability to different ecological scenarios is insufficient.

[0005] Chinese patent application CN118235685A proposes a garden maintenance method and related equipment. It can optimize the accuracy of irrigation operations by collecting plant growth status and environmental parameters to correct irrigation timing. However, this solution only focuses on the optimization of a single irrigation link, does not cover all types of maintenance operations, and does not consider the long-term succession needs of garden ecosystems, resulting in strong limitations in application scenarios.

[0006] In summary, existing garden maintenance technologies generally suffer from insufficient ecological adaptability, lack of full-cycle management and control, and a single intelligent decision-making logic. They are unable to meet the dual needs of controlling maintenance costs and improving the stability of garden ecosystems, and cannot adapt to the differentiated maintenance needs of complex scenarios such as coastal saline-alkali land and urban roads. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a smart garden maintenance method and system based on ecological adaptability throughout the entire life cycle. This invention addresses the problems of insufficient ecological adaptability, lack of full-cycle management and control, single intelligent decision-making logic, and poor adaptability to complex scenarios in existing garden maintenance technologies. While reducing the cost of garden maintenance throughout the entire life cycle, it also improves the stability of the garden ecosystem and adapts to the differentiated maintenance needs of gardens of different regions and types.

[0008] To achieve the above objectives, the technical solution adopted by this invention includes a smart garden maintenance method based on ecological adaptability throughout its entire lifecycle, which specifically performs the following steps: S1. Construct a basic database of garden ecology, covering ecological environment parameters, plant species characteristics, and local ecosystem information for the target garden area. Ecological environment parameters should include at least soil type, soil pH, soil organic matter content, soil moisture, soil salinity, light intensity, precipitation, temperature, and wind speed. Plant species characteristics should include at least the ecological adaptability parameters of native and introduced plants, growth cycle, water and fertilizer requirements, disease and pest resistance, and interspecies symbiotic relationships. Local ecosystem information should include at least local species diversity, food chain structure, and typical ecological community configuration patterns.

[0009] S2. Conduct full-cycle ecological adaptability planning and design, based on the ecological database and combined with the functional positioning of the garden to complete the preliminary planning, which includes the following sub-steps: S21. Plant variety selection and adaptation: Priority will be given to local native plant varieties. An ecological adaptability scoring model will be used to score varieties from four dimensions: climate adaptability, soil adaptability, disease and pest resistance, and ecological synergy. Plant varieties with a score ≥85 will be selected. The weighting of each dimension in the ecological adaptability scoring model is as follows: climate adaptability 30%, soil adaptability 30%, disease and pest resistance 20%, and ecological synergy 20%. The scoring is based on a 100-point scale, and plant varieties with a single dimension score below 70 will be directly eliminated. If it is necessary to introduce alien plants, a species invasion risk index assessment will be used to ensure that the alien plant invasion risk index is below 0.3. The species invasion risk index assessment will be conducted from four dimensions: reproductive capacity, dispersal capacity, adaptability to local natural enemies, and ecological niche crowding capacity. The index value ranges from 0 to 1. Alien plants with an index ≥0.3 are strictly prohibited from being introduced. Specifically, the invasion risk index for alien plants introduced into coastal gardens or saline-alkali land gardens should be ≤0.2, and the invasion risk index for alien plants introduced into urban road greening should be ≤0.25.

[0010] S22. Community Structure Adaptation Configuration: Based on the typical local ecological community configuration pattern, construct multi-level symbiotic communities, reserving 15% to 20% ecological reserve area. The multi-level symbiotic community is a composite structure of trees, shrubs, herbs, and aquatic plants. Among them, coastal ecological parks reserve ≥20% ecological reserve area, while ecological reserve area can be omitted for urban road greening due to space constraints.

[0011] S23. Develop a maintenance plan in advance. Based on the selected plant varieties and community structure, develop a full-cycle maintenance benchmark plan, and clarify the phased goals and core parameters for irrigation, fertilization, pruning, and pest and disease control.

[0012] S3. Implement ecological adaptability management during the construction and cultivation phase. Based on the planned design scheme, conduct construction operations and collect real-time ecological parameters of the construction area using deployed intelligent monitoring equipment. Implement targeted soil improvement and plant planting control, automatically adjusting the construction plan when parameters deviate from preset thresholds. The intelligent monitoring equipment should include at least soil sensors, light sensors, and a weather station. Planting parameters should include at least planting density, planting depth, and planting time. During the construction phase, dynamically adjust the application rates of organic fertilizer and microbial agents based on real-time monitored soil parameters.

[0013] S4. Implement intelligent adaptive regulation during routine maintenance, constructing an IoT sensing network covering the entire garden area to collect real-time data on soil moisture, plant physiological status, pest and disease occurrence, and meteorological data. Compare the collected data with plant ecological requirement parameters in the ecological database, and use big data analysis models to determine maintenance needs, automatically executing adaptive maintenance operations. Adaptive maintenance operations prioritize ecological control methods combining biological and physical control to manage pests and diseases, precisely spraying environmentally friendly agents only when pest and disease outbreaks exceed preset thresholds. Irrigation and fertilization are completed using integrated water and fertilizer equipment, with operations avoiding peak pedestrian and vehicular traffic hours.

[0014] S5. Conduct adaptive regulation during the ecological restoration phase. For degraded areas of the garden ecosystem, assess the type and degree of degradation, develop and implement targeted adaptive ecological restoration plans, deploy long-term monitoring equipment to track restoration effects, and dynamically adjust restoration measures. For coastal saline-alkali areas, prioritize a combination of salt-tolerant native plant replanting and soil improvement; for eutrophic water areas, prioritize a water ecosystem self-purification construction plan involving submerged plant planting and aquatic animal release; and for degraded roadside greening areas, prioritize a combination of pollution-resistant plant replanting and human-induced interference isolation.

[0015] S6. Achieve full-cycle data management and sharing, summarizing ecological environment data, plant growth data, maintenance operation data, and restoration effect data throughout the entire garden lifecycle, constructing a full-cycle maintenance data archive, and achieving data sharing through a cloud platform. The full-cycle maintenance data shared on the cloud platform should be used for planning reference, maintenance cost prediction, seedling survival rate prediction, and garden ecological value assessment for at least similar garden projects.

[0016] This invention also includes an intelligent full-cycle garden maintenance system based on ecological adaptability, applying the aforementioned maintenance methods. Specifically, it includes a basic database module, a planning and design module, a construction control module, an intelligent maintenance module, an ecological restoration module, and a data sharing module. The basic database module stores ecological environment parameters, plant variety characteristics, and local ecosystem information. The planning and design module enables plant variety selection, community structure configuration, and pre-planning of maintenance schemes. The construction control module collects ecological parameters during the construction phase and adjusts the construction plan. The intelligent maintenance module includes an IoT sensing subunit, a data analysis subunit, and an automated operation subunit, used to automatically execute adaptive maintenance operations. The ecological restoration module assesses the state of degraded areas, generates adaptive restoration plans, and tracks restoration effects. The data sharing module stores and shares full-cycle maintenance data archives.

[0017] Due to the use of the above-described technical solution, the present invention has the following significant advantages over the prior art: 1. Significantly improved ecological adaptability of gardens. By selecting plant varieties through ecological adaptability scoring models and species invasion risk assessment mechanisms, priority is given to native plants adapted to the local ecology, and multi-level symbiotic communities that conform to local ecological laws can be constructed. This can effectively reduce the risk of invasion by alien species, enhance the self-regulation capacity of garden ecosystems, and increase the average survival rate of plants by more than 20%.

[0018] 2. Reduced overall costs through full-cycle collaborative management. The solution covers the entire process from planning and design, construction and cultivation, daily maintenance, and ecological restoration, breaking down information barriers between stages and avoiding the increase in subsequent maintenance costs due to problems left over from the early stages. The overall maintenance cost throughout the entire cycle can be reduced by more than 15%.

[0019] 3. Intelligent ecological maintenance reduces environmental impact. By collecting multi-dimensional data in real time through the Internet of Things and dynamically generating maintenance plans based on ecological adaptation rules, ecological pest and disease control methods are prioritized, which can reduce the use of chemical agents by more than 30%, balancing maintenance effectiveness with ecological and environmental protection needs.

[0020] 4. Strong adaptability to multiple scenarios. Different control parameters can be set for different types of garden scenarios such as coastal saline-alkali land, urban road greening, and municipal parks, which can adapt to the garden maintenance needs of different regions and functional positions, and have a wide range of applications. Attached Figure Description

[0021] This invention includes three accompanying figures, covering the entire process and system architecture of the core technical solution, facilitating understanding and verification of the technical solution: Figure 1 This is a schematic diagram of the overall process of the intelligent garden maintenance method based on ecological adaptability throughout the entire life cycle, as described in this invention. Figure 2 This is a schematic diagram of the sub-processes of the full-cycle ecological adaptability planning and design steps of the present invention; Figure 3 This is a schematic diagram of the architecture of the intelligent garden maintenance system based on ecological adaptability throughout the entire life cycle, as presented in this invention. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. The embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0023] Example 1: Intelligent full-cycle maintenance of the coastal landscape of Shenzhen Bay Park like Figure 1 and 2 As shown, this embodiment is applied to the coastal section of Shenzhen Bay Park, covering a total area of ​​120 acres. The bay-facing area has sandy soil, and due to the maritime climate, it experiences frequent high temperatures and humidity in summer, as well as typhoons. The initial soil salinity is 0.3% to 0.5%. The original garden was mainly composed of introduced palm trees, with a plant survival rate of only 68%. Coastal wind and sand caused frequent pests and diseases, with an annual maintenance cost of approximately 800 yuan per square meter. The nearshore waters also exhibit mild eutrophication. The maintenance method of this invention is adapted to the coastal ecological characteristics to carry out full-cycle management. The specific implementation process is as follows: S1. Construct a basic database of horticultural ecology. Combining drone aerial photography and field sampling, the pH value of sandy soil was measured to be 7.5 to 8.2, and the organic matter content was 0.8% to 1.2%. Data on Shenzhen's marine climate over the past 10 years was integrated, showing an average annual temperature of 22 to 26 degrees Celsius, annual precipitation of 1900 to 2200 mm, and an average of 3 to 5 typhoons per year. Ecological parameters of native coastal plants in Shenzhen were analyzed, including the water and fertilizer requirements, salt and wind resistance characteristics of species such as Casuarina equisetifolia, Hibiscus syriacus, Kandelia candel, and Piper kaempferia galanga. Information on the local coastal mudflat ecological chain structure and typical halophyte community configuration patterns was also recorded.

[0024] S2. Conduct full-cycle ecological adaptability planning and design, and complete the preliminary planning by combining the functional positioning of coastal ecological protection and landscape display.

[0025] S21. Plant Variety Selection and Adaptation. Local, salt-tolerant plant varieties were prioritized. An ecological adaptability scoring model was used, evaluating four dimensions: climate adaptability, soil adaptability, disease and pest resistance, and ecological synergy. The weightings for each dimension were 30% for climate adaptability, 30% for soil adaptability, 20% for disease and pest resistance, and 20% for ecological synergy. Scoring was based on a 100-point scale. Casuarina equisetifolia scored 92 points, Hibiscus syriacus 89 points, and Kandelia candel 90 points, all meeting the selection requirement of ≥85 points. The introduced exotic plant, Foxtail Palm, was included in the planting list after an invasion risk index assessment score of 0.15, below the entry threshold of 0.3, and meeting the requirement of an invasion risk index ≤0.2 for coastal gardens.

[0026] S22. Community Structure Adaptation Configuration. Based on the typical coastal mudflat ecological community configuration pattern, a multi-layered coastal windbreak and sand-fixing community composed of Casuarina equisetifolia, Hibiscus syriacus, and Thick Vine is constructed. An underwater ecological chain composed of Vallisneria natans, Hydrilla verticillata, shrimp, and razor clam is constructed in the nearshore waters. A 20% ecological reserve area is reserved to cope with the natural ecological succession after typhoons, meeting the requirement of reserving ≥20% ecological reserve area for coastal ecological gardens.

[0027] S23. Develop a maintenance plan in advance. Based on the selected plant varieties and community structure, develop a 20-year full-cycle maintenance benchmark plan, clarify the core maintenance principles of less fertilizer and more water, and pruning and thinning branches before typhoons, and set phased parameter thresholds for irrigation, fertilization, pruning, and pest and disease control.

[0028] S3. Implement ecological adaptability management during the construction and cultivation phase. Construction operations will be carried out based on the planned design scheme. 300 kg of humus-rich organic fertilizer per acre and salt-tolerant microbial agents will be applied to sandy soils to improve soil salinity. The planting density of Casuarina equisetifolia will be 5 to 6 plants per acre, with a planting depth of 100 to 120 cm. Planting will be completed in spring, avoiding the typhoon season. Thirty dual soil salinity and humidity sensors and 15 wind speed sensors will be deployed in the construction area to collect ecological parameters in real time. The construction plan will be automatically adjusted when parameters deviate from preset thresholds.

[0029] S4. Implement intelligent adaptive regulation during routine maintenance. A comprehensive IoT sensing network covering the entire park is constructed, collecting data on soil moisture, plant physiological status, pest and disease occurrence, and meteorological data every 20 minutes. This data is compared with plant ecological requirement parameters in the ecological database, and maintenance needs are assessed using a big data analysis model, automatically executing adaptive maintenance operations. Low-salinity water-soluble fertilizer is applied using integrated water and fertilizer equipment; when soil salinity exceeds 0.4%, the irrigation amount is automatically increased to 30 liters per plant for salt leaching. Before typhoons, intelligent pruning robots are used to thin branches of casuarina trees to reduce wind resistance. AI visual recognition detects a mild infestation of spider mites, releasing predatory mites for biological control; no chemical pesticides are used throughout the process. Irrigation and fertilization operations are carried out in the early morning to avoid peak daytime visitor traffic.

[0030] S5. Implement adaptive regulation during the ecological restoration phase. For eutrophic areas in nearshore waters, replant 60 *Vallisneria natans* plants per square meter and release 80 shrimp per square meter to establish a self-purification system. For areas where vegetation has collapsed after the typhoon, replant creeping plants such as *Impatiens balsamina* to strengthen soil stabilization. Deploy long-term monitoring equipment to track restoration progress quarterly and dynamically adjust restoration measures based on monitoring data.

[0031] S6. Achieve full-cycle data management and sharing. Summarize ecological environment data, plant growth data, maintenance operation data, and restoration effect data throughout the entire project lifecycle to construct a 20-year full-cycle maintenance data archive. Achieve data sharing through a cloud platform to provide adaptive maintenance references for other coastal park projects in Shenzhen.

[0032] After the implementation of this embodiment, the overall survival rate of plants increased from 68% to 98%, the annual irrigation water consumption decreased from 180 cubic meters per mu to 130 cubic meters per mu, the incidence of diseases and pests decreased from 45% to 12%, the annual maintenance cost decreased from 800 yuan per square meter to 520 yuan, the transparency of near-shore water increased from 0.8 meters to 1.5 meters, and the soil salinity in the windbreak and sand-fixing area remained stable in the range of 0.15% to 0.25%. All indicators were better than the traditional maintenance model.

[0033] Example 2: Intelligent Maintenance of Greenery Along Shennan Avenue in Shenzhen Throughout its Entire Lifecycle Please see Figure 1 and Figure 2 This embodiment is applied to the greening of a section of Shennan Avenue, with a total length of 5 kilometers and a width of 8 to 10 meters. The soil is urban construction backfill soil with a pH value of 6.0 to 7.8 and an organic matter content of 1.0% to 1.5%. It is significantly affected by traffic exhaust, high temperatures and direct sunlight, and human disturbance. The original greening plants were mainly roses and large-leaved banyan trees. Due to standardized maintenance measures, the survival rate was only 75% due to summer drought. Manual maintenance costs were high, approximately 650 yuan per square meter per year, and diseases and pests such as powdery mildew and longhorn beetles were frequent. The maintenance method of this invention is adapted to the ecological characteristics of urban roads to carry out full-cycle management. The specific implementation process is as follows: S1. Construct a basic database of landscape ecology. Collect soil parameters and traffic microclimate data for road areas. Summer temperatures around road surfaces can reach 40 degrees Celsius, and light intensity can reach 120,000 lux. Compile ecological parameters of native roadside greening plants in Shenzhen, including the pollution resistance, sun tolerance, and pruning tolerance characteristics of varieties such as Terminalia chebula, Loropetalum chinense, Zoysia japonica, and Bougainvillea. Simultaneously, input information on typical green community configuration patterns for local urban roads.

[0034] S2. Conduct full-cycle ecological adaptability planning and design, and complete the preliminary planning by combining the functional positioning of road greening landscape display and ecological protection.

[0035] S21. Plant Variety Selection and Adaptation. Local, pollution-resistant plant varieties were prioritized. An ecological adaptability scoring model was used to evaluate them across four dimensions. The selected varieties were: Terminalia catappa (88 points), Loropetalum chinense (87 points), and Bougainvillea (91 points), all meeting the selection requirement of ≥85 points. Jacaranda mimosifolia, an introduced plant due to landscape requirements, scored 0.2 on the species invasion risk index assessment, below the entry threshold of 0.3, and also met the requirement of an invasive species risk index ≤0.25 for urban roadside greening; therefore, it was included in the planting list.

[0036] S22. Community Structure Adaptation Configuration. Based on the typical urban road greening community configuration pattern in the local area, a multi-layered road greening community composed of Terminalia catappa, Loropetalum chinense, and Zoysia japonica was constructed. Bougainvillea was used for isolation strip greening due to its climbing characteristics. Due to the limited space in the road greening area, no ecological blank areas were set up.

[0037] S23. Develop a maintenance plan in advance. Based on the selected plant varieties and community structure, develop a 15-year full-cycle maintenance benchmark plan, clarify the core maintenance principles of morning and evening irrigation in summer, light pruning in winter, and suspension of fertilization during peak traffic periods, and set phased parameter thresholds for each operation.

[0038] S3. Implement ecological adaptation management during the construction and cultivation phase. Based on the planning and design scheme, construction work will be carried out, with 250 kg of organic fertilizer per acre and leaf mold applied to the backfill soil to improve soil organic matter content. The planting density of Terminalia catappa is 4 plants per acre, with a planting depth of 80-90 cm. Planting will be done at night to reduce the impact of high temperatures and direct sunlight. Forty soil moisture and nutrient sensors and 20 temperature sensors will be deployed in the construction area, and construction parameters will be adjusted in real time in conjunction with traffic monitoring data.

[0039] S4. Implement intelligent adaptive control during routine maintenance. A comprehensive IoT sensing network covering the entire road section is constructed, collecting various monitoring data every 15 minutes. Big data analysis models are used to determine maintenance needs and automatically execute adaptive maintenance operations. Integrated water and fertilizer equipment precisely irrigates during the low-temperature periods of early morning and late evening, applying 15 liters of fertilizer per plant and a pollution-resistant compound fertilizer with a nitrogen-phosphorus-potassium ratio of 2:1:3. Intelligent pruning robots control the pruning height of Loropetalum chinense to 0.5 to 0.8 meters according to landscape requirements, adjusting the pruning frequency to once a month, avoiding peak traffic hours. AI visual recognition detects mild powdery mildew and sprays Bacillus subtilis biological agents to replace traditional chemical agents.

[0040] S5. Implement adaptive regulation during the ecological restoration phase. For areas with severe traffic exhaust pollution, replant pollution-resistant plants such as bougainvillea. For areas where Zoysia japonica has degraded due to human trampling, restore it by replanting turf and using protective fencing. Deploy long-term monitoring equipment to track the restoration effect monthly and dynamically adjust restoration measures based on monitoring data.

[0041] S6. Achieve full-cycle data management and sharing. Summarize various maintenance data throughout the project's entire lifecycle, construct a 15-year full-cycle maintenance data archive, achieve data sharing through a cloud platform, and develop standardized and adaptable solutions for the maintenance of green spaces along Shenzhen's main urban roads.

[0042] After the implementation of this embodiment, the overall survival rate of plants increased from 75% to 96%, the proportion of annual maintenance labor costs decreased from 85% to 30%, the incidence of pests and diseases decreased from 40% to 10%, the annual maintenance cost decreased from 650 yuan per square meter to 420 yuan, and the annual irrigation water consumption decreased from 150 cubic meters per mu to 105 cubic meters. All indicators are better than the traditional maintenance model.

[0043] Maintenance system implementation architecture like Figure 3 As shown, the two embodiments described above employ an ecologically adaptable, full-cycle intelligent garden maintenance system, each comprising six functional modules. The basic database module stores all ecological environment parameters, plant variety characteristics, and local ecosystem information collected during the project. The planning and design module enables plant variety selection, community structure configuration, and pre-planning of maintenance schemes. The construction control module collects ecological parameters during the construction phase and automatically adjusts the construction plan. The intelligent maintenance module includes an IoT sensing subunit, a data analysis subunit, and an automated operation subunit. The IoT sensing subunit collects various monitoring data, the data analysis subunit assesses maintenance needs, and the automated operation subunit executes various maintenance operations. The ecological restoration module assesses the state of degraded areas, generates adaptive restoration plans, and tracks restoration effects. The data sharing module stores and shares full-cycle maintenance data archives.

[0044] Those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. If such modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include such modifications and variations.

Claims

1. A smart garden maintenance method based on ecological adaptability throughout its entire lifecycle, characterized in that, Includes the following steps: S1. Construct a basic database of garden ecology, which covers the ecological environment parameters, plant species characteristics and local ecosystem information of the target garden area; S2. Full-cycle ecological adaptability planning and design: Based on the aforementioned ecological database, and combined with the functional positioning of the garden, preliminary planning is completed, specifically including: S21. Plant variety selection: Local native plant varieties should be given priority. An ecological adaptability scoring model should be used to score the varieties from four dimensions: climate adaptability, soil adaptability, disease and pest resistance, and ecological synergy. Plant varieties with a score of ≥85 should be selected. If it is necessary to introduce alien plants, the species invasion risk index should be used to assess the alien plant invasion risk index to ensure that the alien plant invasion risk index is <0.

3. S22. Community structure adaptation configuration: Based on the configuration pattern of typical local ecological communities, construct multi-level symbiotic communities and reserve 15%-20% of ecological blank areas; S23. Pre-planning of maintenance: Based on the selected plant varieties and community structure, develop a full-cycle maintenance benchmark plan, and clarify the phased goals and core parameters for irrigation, fertilization, pruning, and pest and disease control. S3. Ecological Adaptability Management during Construction and Cultivation Phase: Based on the planning and design scheme, construction operations are carried out. Ecological parameters of the construction area are collected in real time through deployed intelligent monitoring equipment. Soil improvement and plant planting regulation are carried out in a targeted manner. When the parameters deviate from the preset threshold, the construction scheme is automatically adjusted. S4. Intelligent Adaptive Regulation in Routine Maintenance: Construct an Internet of Things sensing network covering the entire garden area to collect real-time data on soil moisture, plant physiological status, pest and disease occurrence, and meteorological data. Compare the collected data with plant ecological requirement parameters in the ecological basic database, use big data analysis models to determine maintenance needs, and automatically execute adaptive maintenance operations. S5. Adaptive regulation during the ecological restoration phase: For degraded areas of the garden ecosystem, assess the type and degree of degradation, develop and implement adaptive ecological restoration plans, deploy long-term monitoring equipment to track the restoration effect and dynamically adjust restoration measures; S6. Full-cycle data management and sharing: Summarize ecological environment data, plant growth data, maintenance operation data, and restoration effect data throughout the entire cycle of the garden, construct a full-cycle maintenance data archive, and realize data sharing through a cloud platform.

2. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S1, the ecological environment parameters include at least soil type, soil pH, soil organic matter content, soil moisture, soil salinity, light intensity, precipitation, air temperature, and wind speed; the plant variety characteristics include at least the ecological adaptability parameters of native and introduced plants, growth cycle, water and fertilizer requirements, disease and pest resistance, and interspecies symbiotic relationships; and the local ecosystem information includes at least local species diversity, ecological chain structure, and typical ecological community configuration patterns.

3. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S21, the weight percentages of each dimension of the ecological adaptability scoring model are as follows: climate adaptability 30%, soil adaptability 30%, disease and pest resistance 20%, and ecological synergy 20%. The scoring uses a 100-point scale, and plant varieties with a single-dimensional score below 70 are directly eliminated.

4. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S21, the species invasion risk index assessment is carried out from four dimensions: reproductive capacity, dispersal capacity, adaptability to local natural enemies, and niche crowding capacity. The index value ranges from 0 to 1, and alien plants with an index ≥ 0.3 are strictly prohibited from being introduced. Among them, the invasion risk index of alien plants introduced into coastal gardens / saline-alkali land gardens is ≤ 0.2, and the invasion risk index of alien plants introduced into urban road greening is ≤ 0.

25.

5. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S22, the multi-level symbiotic community is a composite structure of trees, shrubs, herbs, and aquatic plants; among them, the coastal ecological garden reserves ≥20% ecological blank area, while the ecological blank area can be cancelled for urban road greening due to space constraints.

6. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S3, the intelligent monitoring equipment includes at least a soil sensor, a light sensor, and a weather station; the planting parameters include at least planting density, planting depth, and planting time. During the construction phase, the amount of organic fertilizer and microbial agent applied is dynamically adjusted based on the real-time monitored soil parameters.

7. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S4, the adaptive maintenance operation prioritizes the use of ecological control methods that combine biological and physical control to deal with pests and diseases, and only sprays environmentally friendly agents precisely when the pests and diseases exceed the preset outbreak threshold; irrigation and fertilization operations are completed through integrated water and fertilizer equipment, and the operation time avoids peak hours of pedestrian and vehicle traffic.

8. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S5, the ecological restoration plan prioritizes a combination of salt-tolerant native plant replanting and soil improvement for coastal saline-alkali areas, a water ecological self-purification construction plan that prioritizes submerged plant planting and aquatic animal release for eutrophic water areas, and a combination of pollution-resistant plant replanting and human interference isolation for degraded roadside greening areas.

9. The intelligent garden maintenance method based on ecological adaptability throughout its entire lifecycle, as described in claim 1, is characterized in that... In step S6, the full-cycle maintenance data shared by the cloud platform is used at least for planning reference, maintenance cost prediction, seedling survival rate prediction, and ecological value assessment of similar garden projects.

10. A smart garden maintenance system based on ecological adaptability throughout its entire lifecycle, employing the maintenance method described in any one of claims 1-9, characterized in that, include: The basic database module is used to store ecological environment parameters, plant variety characteristics, and local ecosystem information; The planning and design module is used to realize the functions of plant variety selection, community structure configuration, and pre-formulation of maintenance plans; The construction management module is used to collect ecological parameters during the construction phase and adjust the construction plan. The intelligent maintenance module includes an IoT sensing subunit, a data analysis subunit, and an automated operation subunit, which are used to automatically perform adaptive maintenance operations. The ecological restoration module is used to assess the state of degraded areas, generate suitable restoration plans, and track restoration effects. The data sharing module is used to store and share maintenance data archives throughout the entire lifecycle.

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