A landscaping partition maintenance method and system

Abstract

This invention discloses a method and system for zoned maintenance of garden green spaces, relating to the field of garden green space management technology. The method includes dividing a garden area into multiple grid units to construct a multi-dimensional zoning dataset; establishing a multi-factor similarity calculation model based on the multi-dimensional zoning dataset to calculate multi-factor similarity values; setting a similarity threshold and clustering adjacent grid units with multi-factor similarity values ​​greater than the threshold to form multiple basic zoning areas; optimizing and adjusting the basic zoning areas according to plant phenological characteristics and seasonal maintenance requirements to generate multiple maintenance zoning areas; and matching corresponding maintenance schemes from a preset maintenance strategy library based on the characteristics of each maintenance zoning area to perform differentiated maintenance operations on each maintenance zoning area. This invention realizes a transformation from traditional extensive management to intelligent and precise maintenance, improving resource allocation efficiency and maintenance effectiveness through a data-driven zoning strategy.

Description

Technical Field

[0001] This invention relates to the field of landscape greening management technology, and in particular to a method and system for zoned maintenance of landscape greening. Background Technology

[0002] Landscape and green space maintenance is a crucial component of urban ecological construction, directly impacting plant growth quality and the overall urban environment. Traditional landscape maintenance primarily relies on manual inspections and experience-based judgment, applying uniform maintenance standards and methods across the entire landscape area. With the development of sensor and Internet of Things (IoT) technologies, existing landscape maintenance systems are gradually incorporating environmental monitoring functions, using parameters such as soil moisture and temperature to guide maintenance operations such as irrigation and fertilization.

[0003] However, existing technologies have the following shortcomings in garden maintenance: maintenance operations lack specificity and do not adequately consider the differences in plant species and growth environments in different areas; the scope of data utilization is limited, mainly based on single parameters such as soil moisture and temperature, and lacks comprehensive analysis of multiple factors such as plant distribution, terrain features, and seasonal changes; the zoning management method is simple, mostly using manual division or geometric division, and lacks scientific zoning basis and dynamic adjustment mechanism. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for zoning maintenance of garden greening, which solves the technical problem of lack of scientific zoning management and precise maintenance strategies in garden maintenance.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for zoned maintenance of garden green spaces, comprising:

[0008] The garden area was divided into multiple grid units, and environmental monitoring data, plant species distribution data and topographic elevation data of each grid unit were obtained to construct a multidimensional partitioned dataset.

[0009] A multi-factor similarity calculation model is established based on a multi-dimensional partitioned dataset to calculate the multi-factor similarity values ​​between adjacent grid cells;

[0010] Set a similarity threshold, and cluster and merge adjacent grid cells whose multi-factor similarity values ​​are greater than the similarity threshold to form multiple basic partitions;

[0011] The basic zones are optimized and adjusted based on plant phenological characteristics and seasonal maintenance requirements to generate multiple maintenance zones;

[0012] Based on the characteristics of the maintenance zones, the corresponding maintenance plan is matched from the preset maintenance strategy library, and differentiated maintenance operations are carried out for each maintenance zone.

[0013] Monitor the plant growth status of each maintenance zone, and initiate local zone adjustment or zone-by-zone gradual optimization processes based on the number of abnormal zones.

[0014] As a preferred embodiment of the garden greening zoning maintenance method of the present invention, the method of dividing the garden area into multiple grid units includes:

[0015] Obtain the geographic coordinate boundary data and total area data of the garden area, and calculate the length and width of the garden area;

[0016] Based on the total area of ​​the garden area and the preset unit density parameters, combined with the length-to-width ratio of the garden area and the complexity of plant distribution, the side length of the grid unit is determined;

[0017] Using the geometric center of the garden area as the origin of the coordinate system, grid lines are sequentially divided along the long axis of the garden area according to the side length of the grid unit to generate a grid matrix.

[0018] Calculate the overlap ratio between the grid cells and the boundary of the garden area, and merge the boundary grid cells according to the overlap ratio;

[0019] Assign a unique identifier to each grid cell, record the center coordinates and boundary coordinates of each grid cell, and establish a direct adjacency matrix of the grid cells.

[0020] As a preferred embodiment of the garden greening zoning maintenance method of the present invention, the establishment of a multi-factor similarity calculation model includes:

[0021] Preprocess the multidimensional partitioned dataset;

[0022] Based on environmental monitoring data, the weighted Euclidean distance is used to calculate the similarity of environmental factors between adjacent grid cells;

[0023] Based on plant species distribution data, the similarity of plant factors between adjacent grid cells is calculated using the Jaccard similarity coefficient.

[0024] Based on topographic elevation data, the standardized Euclidean distance algorithm is used to calculate the similarity of topographic factors between adjacent grid cells;

[0025] A weighted allocation mechanism was established to calculate the weighted similarity of environmental factors, plant factors, and topographic factors, and to obtain the multi-factor similarity value.

[0026] As a preferred embodiment of the garden greening zoning maintenance method of the present invention, the formation of multiple basic zones includes:

[0027] The similarity threshold range is set based on the characteristics of the garden area, and the optimal similarity threshold is determined by an adaptive threshold determination algorithm.

[0028] Construct a grid adjacency matrix, traverse all adjacent grid cell pairs, and compare the multi-factor similarity values ​​with the optimal similarity threshold to make a judgment;

[0029] Adjacent grid cells with multi-factor similarity values ​​greater than the optimal similarity threshold are marked as pending merging. A disjoint-set data structure is used to perform clustering and merging operations to form a candidate spatial cell set.

[0030] The candidate spatial unit set is partitioned for effectiveness evaluation. Spatial units that do not meet the minimum management size condition are merged into adjacent spatial units according to the nearest neighbor similarity principle to form an effective spatial unit set.

[0031] Based on the set of effective spatial units, a basic partition identifier code is generated, and a mapping relationship between grid units and basic partitions is established to form multiple basic partitions.

[0032] As a preferred embodiment of the landscape greening zoning maintenance method of the present invention, the optimization and adjustment of the basic zoning based on plant phenological characteristics and seasonal maintenance requirements includes:

[0033] Acquire phenological characteristic data for each plant species within the basic zoning area, and determine seasonal maintenance requirements based on seasonal climate changes;

[0034] Analyze the differences in phenological characteristics among different plant species within the same basic zone to identify basic zones with conflicting phenological periods;

[0035] Based on the results of phenological conflict identification, conflicting plant species were redistributed and partition boundaries were adjusted to form partitions with consistent phenological characteristics.

[0036] Based on seasonal maintenance requirements, seasonal adjustments are made to zones with consistent phenological characteristics;

[0037] The zones that have completed seasonal adaptation adjustments are numbered and labeled to generate multiple maintenance zones.

[0038] As a preferred embodiment of the garden greening zoning maintenance method of the present invention, the method includes: matching the corresponding maintenance plan from a preset maintenance strategy library according to the characteristics of the maintenance zoning, which includes:

[0039] Extract the grid cell data of each maintenance zone, obtain the feature parameters through aggregation calculation, and combine the feature parameters to construct the maintenance zone feature vector;

[0040] Calculate the matching degree between the feature vector of the maintenance zone and the feature vector of each strategy template in the preset maintenance strategy library, and select the strategy template with a matching degree less than the preset matching degree threshold as the candidate maintenance scheme.

[0041] A cost-benefit assessment is conducted on the candidate maintenance schemes, and the maintenance scheme with the best cost-benefit ratio is selected as the final maintenance scheme.

[0042] As a preferred embodiment of the garden greening zone maintenance method of the present invention, the differentiated maintenance operations for each maintenance zone include:

[0043] The maintenance parameter configuration is determined based on the final maintenance plan. The maintenance parameter configuration is dynamically adjusted in combination with the seasonal characteristics of the maintenance zone and the plant growth status to generate a set of maintenance parameters.

[0044] Analyze the set of maintenance parameters and generate a set of zone-specific maintenance instructions based on various maintenance parameters;

[0045] Distribute the zoned differentiated maintenance instruction set to the corresponding maintenance equipment and maintenance personnel terminals;

[0046] Maintenance equipment and personnel perform differentiated maintenance operations on maintenance zones according to the received instructions;

[0047] Obtain the operation status of each maintenance zone and record the operation completion status.

[0048] Secondly, the present invention provides a zoned maintenance system for landscaping, comprising:

[0049] The dataset construction module is used to divide the garden area into multiple grid units, obtain environmental monitoring data, plant species distribution data and topographic elevation data of each grid unit, and construct a multidimensional partitioned dataset.

[0050] The similarity calculation module is used to build a multi-factor similarity calculation model based on a multi-dimensional partitioned dataset and calculate the multi-factor similarity values ​​between adjacent grid cells.

[0051] The clustering and merging module is used to set a similarity threshold and cluster and merge adjacent grid cells whose multi-factor similarity values ​​are greater than the similarity threshold to form multiple basic partitions.

[0052] The zoning optimization module is used to optimize and adjust the basic zoning based on plant phenological characteristics and seasonal maintenance requirements, generating multiple maintenance zoning zones;

[0053] The maintenance operation module is used to match the corresponding maintenance plan from the preset maintenance strategy library according to the characteristics of the maintenance zone, and to carry out differentiated maintenance operations for each maintenance zone.

[0054] The monitoring and adjustment module is used to monitor the plant growth status of each maintenance zone and initiate local zone adjustment or zone-by-zone gradual optimization processes based on the number of abnormal zones.

[0055] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the garden greening zoning maintenance method of the first aspect of the present invention.

[0056] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the garden greening zoning maintenance method of the first aspect of the present invention.

[0057] The beneficial effects of this invention are as follows: By employing refined grid division, multi-factor similarity metric calculation, and intelligent clustering and merging, this invention achieves a transformation from traditional extensive zoning to precise scientific zoning, improving the accuracy of garden zoning and the targeted nature of maintenance management. Through optimization of phenological characteristics consistency, seasonal adaptability adjustment, and zoning scale control, it achieves the scientific, economical, and stable management of maintenance zoning, improving the level of refined management and overall operational efficiency of garden maintenance. By establishing a complete technical chain from data collection, intelligent analysis, scheme generation to execution monitoring, it achieves the scientific selection of maintenance schemes, the dynamic configuration of parameters, and the standardization of operation execution, thereby improving maintenance quality and management level. Attached Figure Description

[0058] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart illustrating the zoning maintenance methods for landscaping.

[0060] Figure 2 A flowchart for calculating the multi-factor similarity value of the zoning maintenance method for garden greening.

[0061] Figure 3 A flowchart illustrating the basic zoning process for the zoning maintenance method of garden greening.

[0062] Figure 4 This is a module connection diagram for the zoning maintenance system of garden greening. Detailed Implementation

[0063] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0064] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0066] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for zoned maintenance of garden greening, the flowchart of which is shown below. Figure 1 As shown, the method includes the following steps:

[0067] S1: Divide the garden area into multiple grid units, obtain environmental monitoring data, plant species distribution data and topographic elevation data of each grid unit, and construct a multidimensional partitioned dataset.

[0068] Specifically, step S1 includes:

[0069] S1.1: Obtain the geographic coordinate boundary data and total area data of the garden area, and calculate the length and width of the garden area.

[0070] S1.2: Determine the side length of the grid unit based on the total area of ​​the garden area and the preset unit density parameters, combined with the length-to-width ratio of the garden area and the complexity of plant distribution.

[0071] In one embodiment, the initial grid cell side length is calculated based on a preset cell density parameter and the total area of ​​the garden area; the density of plant species and the degree of terrain variation within the garden area are statistically analyzed to determine the complexity of plant distribution; an adjustment coefficient is set based on the aspect ratio of the garden area and the complexity of plant distribution to adjust the initial grid cell side length, thus determining the final grid cell side length. The preset cell density parameter refers to the number of grid cells set per square meter.

[0072] S1.3: Using the geometric center point of the garden area as the origin of the coordinate system, grid lines are sequentially divided along the long axis of the garden area according to the side length of the grid unit to generate a grid matrix.

[0073] Among them, the long axis direction of the garden area refers to the direction of the maximum side length of the garden area.

[0074] S1.4: Calculate the overlap ratio between the grid cells and the boundary of the garden area, and merge the boundary grid cells according to the overlap ratio.

[0075] In one embodiment, the overlap area ratio is calculated as the ratio of the effective coverage area of ​​the boundary grid cell within the garden area to the total area of ​​the grid cells. When the overlap area ratio is less than a preset threshold, the boundary grid cell is merged with the adjacent internal grid cell.

[0076] Specifically, the preset ratio threshold is determined based on the ratio of the standard area of ​​the grid cell to the minimum effective management area, and the merging process is implemented through the minimum area difference selection algorithm.

[0077] S1.5: Assign a unique identifier to each grid cell, record the center coordinates and boundary coordinates of each grid cell, and establish a direct adjacency matrix of the grid cells.

[0078] S1.6: Obtain environmental monitoring data, plant species distribution data, and terrain elevation data for each grid cell to construct a multidimensional partitioned dataset.

[0079] In this embodiment, the environmental monitoring data includes soil moisture data, soil pH data, soil nutrient content data, light intensity data, and ambient temperature data. Specifically, soil moisture, pH, and nutrient content data reflect soil conditions and influence plant growth and fertilization strategies; light intensity and ambient temperature data determine plant photosynthesis and physiological activities; plant species distribution data reflect the community characteristics of different areas; and topographic elevation data affects drainage conditions and local climate. The integration of multidimensional environmental data can comprehensively reflect the ecological differences between garden zones.

[0080] Ideally, by combining multi-factor adaptive grid division and boundary grid merging, the grid division accuracy can be automatically adjusted according to the actual characteristics of different garden areas, effectively solving the problem of irregular grid unit area in boundary areas, and realizing intelligent grid division and comprehensive data collection of garden areas.

[0081] S2: Establish a multi-factor similarity calculation model based on a multi-dimensional partitioned dataset to calculate the multi-factor similarity values ​​between adjacent grid cells.

[0082] Specifically, the flowchart for calculating multi-factor similarity values ​​is as follows: Figure 2 As shown, it includes:

[0083] S2.1: Preprocess the multidimensional partitioned dataset.

[0084] In one embodiment, different types of data are processed using corresponding preprocessing strategies, including but not limited to missing value handling, outlier detection, and data integrity verification.

[0085] S2.2: Based on environmental monitoring data, the similarity of environmental factors between adjacent grid cells is calculated using weighted Euclidean distance.

[0086] It should be noted that environmental monitoring data is multidimensional and continuous. The weighted Euclidean distance algorithm can effectively process this type of data and reflect the differences in importance of each indicator.

[0087] Specifically, Z-score standardization is performed on environmental monitoring data; environmental feature vectors are constructed based on the standardized environmental monitoring data of each grid unit; the weights of environmental indicators are determined by principal component analysis or expert evaluation methods according to the degree of influence of each environmental monitoring indicator on the growth of garden plants; the weighted distance is calculated for the environmental feature vectors of adjacent grid units, and the weighted Euclidean distance value is converted into environmental factor similarity value using the inverse distance conversion method.

[0088] S2.3: Based on plant species distribution data, the similarity of plant factors between adjacent grid cells is calculated using the Jaccard similarity coefficient.

[0089] It should be noted that plant species distribution data have both aggregate and discrete characteristics, and the Jaccard similarity coefficient can accurately measure the degree of similarity between different aggregates.

[0090] In plant factor similarity calculation, the plant species in each grid cell are treated as a set. The plant factor similarity value is calculated by counting the number of plant species shared by adjacent grid cells and the total number of plant species contained in two grid cells. When the plant species distribution data includes cover information, the similarity calculation is optimized by combining plant cover data to more accurately reflect the degree of similarity of plant communities.

[0091] S2.4: Based on the terrain elevation data, the terrain factor similarity between adjacent grid cells is calculated using the standardized Euclidean distance algorithm.

[0092] It should be noted that terrain elevation data has continuous and numerical characteristics, and the standardized Euclidean distance algorithm can effectively quantify the degree of terrain difference.

[0093] Specifically, the terrain elevation data is normalized; a terrain feature vector is constructed based on the terrain elevation data (such as slope and aspect) of each grid cell; the terrain feature vectors of adjacent grid cells are standardized and Euclidean distance is calculated; and the distance value is converted into a terrain factor similarity value using an inverse distance conversion method.

[0094] S2.5: Establish a weight allocation mechanism to perform weighted comprehensive calculation of environmental factor similarity, plant factor similarity, and topographic factor similarity to obtain a multi-factor similarity value.

[0095] In one embodiment, a combined subjective and objective weighting method is employed. Subjective weights based on expert knowledge are obtained using the Analytic Hierarchy Process (AHP), while objective weights based on data variability are calculated using the entropy weighting method. A linear weighted combination is then used to obtain the comprehensive weight coefficient. From the perspective of influence, environmental factors directly affect the survival and growth status of plants, and the calculation results typically show their relatively high weights; plant factors reflect the ecological harmony and landscape consistency of species configuration, and their weights are moderate; topographic factors mainly affect the convenience of maintenance operations, and their weights are relatively low.

[0096] Ideally, by establishing a multi-factor similarity calculation model, a quantitative assessment and comprehensive comparison of environmental conditions, plant communities, and topographic features among garden grid units can be achieved. This model employs targeted similarity calculation methods to process different types of zoning data and performs comprehensive calculations through a weight allocation mechanism. This enables accurate identification of adjacent grid units with similar ecological characteristics and maintenance needs, improving the accuracy and rationality of garden zoning.

[0097] S3: Set a similarity threshold, and cluster and merge adjacent grid cells whose multi-factor similarity values ​​are greater than the similarity threshold to form multiple basic partitions.

[0098] Specifically, the flowchart for the formation of basic partitions is as follows: Figure 3 As shown, it includes:

[0099] S3.1: Set a similarity threshold range based on the characteristics of the garden area, and use an adaptive threshold determination algorithm to determine the optimal similarity threshold.

[0100] Preferably, the adaptive threshold determination algorithm iteratively optimizes within a similarity threshold range based on constraints on the number of grid cells and management cost. Specifically, an initial threshold and iteration step size are set within the similarity threshold range; the similarity threshold is adjusted and the corresponding number of partitions and management cost are calculated; an upper limit for cost constraints and a range for the number of partitions are set; when the constraints are not met, the threshold is adjusted and recalculated; through multiple rounds of iterative comparison, the similarity threshold that satisfies the constraints and has the optimal management cost is selected as the optimal similarity threshold.

[0101] Furthermore, the management cost calculation includes personnel allocation costs and equipment investment costs; the iterative optimization process sets a maximum iteration limit to ensure algorithm convergence; the similarity threshold range is determined based on the complexity of plant distribution and terrain variation characteristics in the garden area. By employing the above adaptive threshold determination algorithm, the subjectivity of manually setting fixed thresholds is avoided, improving the rationality and adaptability of similarity threshold setting.

[0102] S3.2: Construct a grid adjacency matrix, traverse all adjacent grid cell pairs, and compare the multi-factor similarity values ​​with the optimal similarity threshold to make a judgment.

[0103] Preferably, constructing the grid adjacency matrix includes: initializing the grid adjacency matrix, using a coordinate mapping algorithm to determine the spatial positional relationship of each grid cell; identifying grid cell pairs that are directly adjacent vertically and horizontally based on the four-neighbor adjacency rule, and identifying grid cell pairs that are adjacent diagonally based on the eight-neighbor adjacency rule; calculating the distance between grid cells based on the extended neighborhood adjacency rule with distance weight and determining the extended adjacency relationship according to a preset distance threshold, wherein the preset distance threshold is determined according to the grid cell size.

[0104] In one embodiment, the extended neighborhood adjacency is assigned using a distance-weighted approach, with closer neighbors receiving a higher weight. Grid cells exceeding a distance threshold are not established as adjacencies. Compared to a single neighborhood relationship identification method, this approach can more accurately determine the spatial adjacency between grid cells and reduce the problem of discontinuous partitioning.

[0105] S3.3: Mark adjacent grid cells with multi-factor similarity values ​​greater than the optimal similarity threshold as pending merging states, and use a disjoint-set data structure to perform clustering and merging operations to form a candidate spatial cell set.

[0106] Specifically, the disjoint-set data structure is initialized, treating each grid cell in the state to be merged as an independent node. All adjacent grid cell pairs in the states to be merged are traversed, and a merge operation is performed to group adjacent grid cells into the same set. This merge operation is repeated until all grid cell pairs meeting the conditions are clustered. Each connected component in the disjoint-set data structure is output as the merged region cell. The merged region cells are then combined with grid cells in independent states (i.e., grid cells not marked as being in the state to be merged) to form a candidate spatial cell set. Using a disjoint-set data structure for clustering and merging offers higher computational efficiency and better space complexity control compared to traditional recursive search methods.

[0107] S3.4: Evaluate the effectiveness of partitioning the candidate spatial unit set, and merge spatial units that do not meet the minimum management scale condition into adjacent spatial units according to the nearest neighbor similarity principle to form an effective spatial unit set.

[0108] Further, the candidate spatial unit set is traversed to determine whether each spatial unit meets the minimum management scale condition. The minimum management scale condition means that the geometric feature parameters (such as area, perimeter, etc.) of the spatial unit all reach the corresponding minimum management scale threshold. For spatial units that do not meet the condition, the nearest neighbor similarity principle is used to calculate their multi-factor similarity values ​​with adjacent spatial units, and the adjacent unit with the highest similarity value is selected as the merging target. The merging operation is performed, and the boundary and attribute information of the spatial units are updated. The above process is repeated until all spatial units meet the minimum management scale condition, and the effective spatial unit set is output. Among them, the minimum management scale threshold is determined based on the needs of garden management operations and statistical analysis of historical management data.

[0109] S3.5: Generate basic partition identifier codes based on the set of effective spatial units, establish the mapping relationship between grid units and basic partitions, and form multiple basic partitions.

[0110] S4: Optimize and adjust the basic zones according to plant phenological characteristics and seasonal maintenance requirements to generate multiple maintenance zones.

[0111] It should be noted that the entire optimization and adjustment process includes at least one of partition merging and partition splitting, and sets a lower limit for partition area and an adjustment time interval constraint. Among them, the lower limit constraint for partition area ensures the rationality of partition size and the economy of maintenance operations, while the adjustment time interval constraint is mainly used to control the frequency of triggered adjustments in S6, avoiding overly sensitive adjustments to partitions due to minor changes in environmental data.

[0112] Specifically, step S4 includes:

[0113] S4.1: Obtain phenological characteristic data of each plant species within the basic zone, and determine seasonal maintenance requirements based on seasonal climate changes.

[0114] Among them, phenological characteristic data includes time node data for each phenological period and corresponding demand data; seasonal maintenance requirements refer to the maintenance operation time windows and resource allocation standards formulated for different seasons based on the changes in climate conditions in each season.

[0115] S4.2: Analyze the differences in phenological characteristics among different plant species within the same basic zone, and identify basic zones with conflicting phenological periods.

[0116] It should be noted that phenological conflict refers to a situation where the differences in phenological characteristics among plant species within the same basic zone exceed the corresponding preset phenological difference threshold. These differences include variations in bud break time and water requirements, and the corresponding preset phenological difference threshold can be set based on plant physiological characteristics, the feasibility of maintenance operations, and garden management experience.

[0117] S4.3: Based on the results of phenological conflict identification, conflicting plant species are redistributed and partition boundaries are adjusted to form partitions with consistent phenological characteristics.

[0118] Specifically, the phenological characteristics of each plant species within the base zone where phenological period conflicts exist are analyzed. The similarity of phenological characteristics between each plant species and those in adjacent zones is calculated, and the conflicting plant species are reassigned to the adjacent zones with the most similar phenological characteristics. The boundaries of the reassigned zones are adjusted through grid cell assignment changes to ensure that each zone meets the preset lower limit requirement for zone area after adjustment. The consistency of phenological characteristics of plant species and the compliance of zone size within each zone after adjustment are verified to ensure that there are no longer any conflicts exceeding the phenological difference threshold within the zones.

[0119] S4.4: In accordance with seasonal maintenance requirements, make seasonal adaptive adjustments to zones with consistent phenological characteristics.

[0120] Furthermore, based on the maintenance operation time windows and resource allocation standards for each season, the matching degree of maintenance operation arrangements for each zone in different seasons is analyzed. Adjacent zones with highly overlapping maintenance operation time windows are merged, while zones with significant differences in resource allocation needs are split. A seasonal adaptability evaluation index for each zone is established to ensure that the adjusted zones can achieve efficient and intensive management under unified seasonal maintenance requirements.

[0121] S4.5: Number and label the zones that have completed seasonal adaptation adjustments to generate multiple maintenance zones.

[0122] Ideally, through multi-level optimization and adjustment, a transformation from extensive basic zoning to refined maintenance zoning was achieved, improving the scientific and economic efficiency of garden maintenance. By identifying and redistributing phenological characteristic conflicts, the problem of mismatched plant maintenance needs within the same zone was eliminated, avoiding maintenance operation conflicts and resource waste. Adaptive adjustments based on seasonal maintenance requirements achieved precise matching between maintenance zones and the time windows for maintenance operations in each season, ensuring that plants within the same zone can complete maintenance operations within a unified time window, improving the efficiency of maintenance resource allocation. The established lower limit constraint on zone area effectively solved the technical defects of excessive zoning, ensuring the stability and economy of maintenance zoning.

[0123] S5: Match the corresponding maintenance plan from the preset maintenance strategy library according to the characteristics of the maintenance zone, and carry out differentiated maintenance operations for each maintenance zone.

[0124] Specifically, step S5 includes:

[0125] S5.1: Match the corresponding maintenance plan from the preset maintenance strategy library according to the maintenance zoning characteristics.

[0126] Specifically, grid cell data for each maintenance zone is extracted, and feature parameters are obtained through aggregation calculation. These feature parameters are then combined to construct a feature vector for the maintenance zone. The aggregation calculation employs an area-weighted average algorithm. The matching degree between the maintenance zone feature vector and the feature vectors of each strategy template in the preset maintenance strategy library is calculated. Strategy templates with a matching degree less than a preset matching degree threshold are selected as candidate maintenance schemes. The matching degree is calculated using the Euclidean distance algorithm (the smaller the distance, the higher the similarity). The preset matching degree threshold is determined through statistical analysis of historical validation data. The preset maintenance strategy library is established based on the experience of landscape maintenance experts, historical maintenance data, and cost-benefit analysis.

[0127] Furthermore, a cost-benefit assessment is conducted on the candidate maintenance schemes, and the maintenance scheme with the best cost-benefit ratio is selected as the final maintenance scheme. The steps are as follows: calculate the estimated cost of each candidate maintenance scheme in the current zone, including irrigation costs, fertilizer costs, and labor costs; evaluate the expected effects of each candidate maintenance scheme, including plant growth quality and green landscape effect; calculate the comprehensive score of each candidate scheme using a weighted comprehensive evaluation method; and select the candidate scheme with the highest score as the final maintenance scheme based on the ranking of comprehensive scores.

[0128] S5.2: Generate a set of maintenance parameters based on the final maintenance plan, and carry out differentiated maintenance operations for each maintenance zone.

[0129] Specifically, the basic parameter configuration is extracted based on the final maintenance plan, and the basic parameter configuration is dynamically adjusted in combination with the seasonal characteristics of the maintenance zone and the plant growth status to generate a set of maintenance parameters. The steps are as follows: extract the basic parameter configuration from the final maintenance plan; make seasonal adjustments to the basic parameters according to the current seasonal characteristics of the maintenance zone, and make intensity adjustments to the seasonally adjusted parameters according to the plant growth status; integrate the adjusted parameters to generate a complete set of maintenance parameters.

[0130] It should be noted that the set of maintenance parameters includes irrigation parameters, fertilization parameters, pruning parameters, and pest and disease control parameters. For example, irrigation parameters include irrigation frequency and duration, fertilization parameters include fertilizer type and amount, pruning parameters include pruning intensity and location, and pest and disease control parameters include pesticide type and concentration.

[0131] Furthermore, the maintenance parameter set is analyzed, and a zone-specific maintenance instruction set is generated based on various maintenance parameters. The steps are as follows: The parameter set is classified and analyzed according to the maintenance parameter type to form irrigation parameter tables, fertilization parameter tables, pruning parameter tables, and pest and disease control parameter tables; Combining the equipment configuration information and personnel configuration information of each maintenance zone, various parameters are converted into equipment executable instructions and personnel operation instructions; The instructions are optimized and sorted, taking into account operation priority, equipment scheduling, and personnel arrangement, to generate a time-sequential maintenance instruction sequence; The instruction sequences of each zone are integrated to form a complete zone-specific maintenance instruction set.

[0132] Furthermore, the differentiated maintenance instruction set for each zone is distributed to the corresponding maintenance equipment and personnel terminals. The maintenance equipment and personnel then perform differentiated maintenance operations on the maintenance zones according to the received instructions. The operation execution status of each maintenance zone is obtained, and the operation completion status is recorded.

[0133] Ideally, this step constructs a complete intelligent landscape maintenance management system, forming a closed-loop management process from scheme selection to parameter generation and operation execution. Intelligent selection of maintenance schemes is achieved through feature vector matching, optimal resource allocation is ensured by cost-benefit assessment, a dual dynamic adjustment mechanism ensures maintenance parameters accurately adapt to actual needs, and standardized operation execution is achieved through differentiated instruction generation. The entire process works synergistically, systematically improving the intelligence level and management efficiency of landscape maintenance.

[0134] S6: Monitor the plant growth status of each maintenance zone and initiate local zone adjustment or zone-by-zone gradual optimization processes based on the number of abnormal zones.

[0135] Specifically, an abnormal zoning refers to a situation where the deviation value of any key growth indicator continuously exceeds the corresponding preset deviation threshold for a preset duration. Key growth indicators include, but are not limited to, plant height, chlorophyll content, and growth rate. The deviation value is calculated using the deviation analysis method, which determines the deviation based on the degree of difference between the average value of a certain indicator within the zoning and the corresponding benchmark value. The benchmark value can be determined using historical normal data, reference values ​​for similar zoning, or relevant standard values. This method has the advantages of strong comparability and accurate judgment, and is suitable for assessing the degree of abnormality of different indicators. The quantification method of the degree of difference can be adjusted according to actual application needs and monitoring accuracy requirements.

[0136] Furthermore, when the number of abnormal partitions is less than or equal to a preset threshold, a local partition adjustment is initiated; when the number of abnormal partitions exceeds the preset threshold, a progressive partition optimization process is initiated. Local partition adjustment includes: fine-tuning the boundaries of abnormal partitions, optimizing maintenance parameters, or small-scale merging and splitting them with adjacent normal partitions. The progressive partition optimization process includes: analyzing the spatial distribution patterns of abnormal partitions, identifying adjacent abnormal partitions and assessing their similarity, merging and reconstructing adjacent abnormal partitions with high similarity, fine-tuning the boundaries of isolated abnormal partitions, and updating maintenance requirement files and corresponding maintenance plans.

[0137] It should be noted that the preset deviation threshold is set based on a comprehensive consideration of plant species characteristics, historical maintenance data, and expert experience. The preset duration is determined based on plant physiological characteristics, seasonal variation patterns, and the garden management cycle. Different indicators have different durations; rapid response indicators such as chlorophyll content have relatively shorter durations, while stable indicators such as plant height have relatively longer durations. Specific values ​​can be adjusted according to plant species, climate conditions, and management precision requirements. The preset quantity threshold is set based on the proportion of the total number of garden zones (e.g., 20%) and maintenance management capabilities. It can be dynamically adjusted in conjunction with seasonal changes. When the number of abnormal zones exceeds 50% of the total number of zones, an emergency management mode is activated.

[0138] Ideally, through intelligent monitoring and adaptive adjustment mechanisms, precise and automated optimization of plant maintenance management is achieved. When there are few abnormal zones, local fine-tuning is performed; when there are many abnormal zones, a systemic reconstruction is initiated, effectively improving the efficiency of garden maintenance and the quality of plant growth.

[0139] This embodiment also provides a garden greening zone maintenance system, the module connection diagram of which is shown below. Figure 4 As shown, the system includes:

[0140] The dataset construction module is used to divide the garden area into multiple grid units, obtain environmental monitoring data, plant species distribution data and topographic elevation data of each grid unit, and construct a multidimensional partitioned dataset.

[0141] The similarity calculation module is used to build a multi-factor similarity calculation model based on a multi-dimensional partitioned dataset and calculate the multi-factor similarity values ​​between adjacent grid cells.

[0142] The clustering and merging module is used to set a similarity threshold and cluster and merge adjacent grid cells whose multi-factor similarity values ​​are greater than the similarity threshold to form multiple basic partitions.

[0143] The zoning optimization module is used to optimize and adjust the basic zoning based on plant phenological characteristics and seasonal maintenance requirements, generating multiple maintenance zoning zones;

[0144] The maintenance operation module is used to match the corresponding maintenance plan from the preset maintenance strategy library according to the characteristics of the maintenance zone, and to carry out differentiated maintenance operations for each maintenance zone.

[0145] The monitoring and adjustment module is used to monitor the plant growth status of each maintenance zone and initiate local zone adjustment or zone-by-zone gradual optimization processes based on the number of abnormal zones.

[0146] This embodiment also provides a computer device applicable to the landscape greening zoning maintenance method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the landscape greening zoning maintenance method proposed in the above embodiment.

[0147] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0148] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for implementing zoned maintenance of garden greening proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0149] In summary, this invention achieves a transformation from traditional extensive zoning to precise scientific zoning through refined grid division, multi-factor similarity metric calculation, and intelligent clustering and merging, thereby improving the accuracy of garden zoning and the targeted nature of maintenance management. By optimizing phenological characteristics, adjusting seasonal adaptability, and controlling zoning scale, it achieves scientific rigor, economy, and management stability in maintenance zoning, improving the level of refined management and overall operational efficiency of garden maintenance. By establishing a complete technical chain from data collection, intelligent analysis, scheme generation to execution monitoring, it achieves scientific selection of maintenance schemes, dynamic parameter configuration, and standardized operation execution, thus improving maintenance quality and management level.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for zoned maintenance of landscaping, characterized in that: include: The garden area was divided into multiple grid units, and environmental monitoring data, plant species distribution data and topographic elevation data of each grid unit were obtained to construct a multidimensional partitioned dataset. A multi-factor similarity calculation model is established based on the multi-dimensional partitioned dataset to calculate the multi-factor similarity values ​​between adjacent grid cells; A similarity threshold is set, and adjacent grid cells with multi-factor similarity values ​​greater than the similarity threshold are clustered and merged to form multiple basic partitions; The basic zones are optimized and adjusted according to plant phenological characteristics and seasonal maintenance requirements to generate multiple maintenance zones; Based on the characteristics of the maintenance zones, the corresponding maintenance plan is matched from the preset maintenance strategy library, and differentiated maintenance operations are carried out for each maintenance zone. Monitor the plant growth status of each maintenance zone, and initiate local zone adjustment or zone-by-zone gradual optimization processes based on the number of abnormal zones; The formation of multiple basic partitions includes: The similarity threshold range is set based on the characteristics of the garden area, and the optimal similarity threshold is determined by an adaptive threshold determination algorithm. Construct a grid adjacency matrix, traverse all adjacent grid cell pairs, and compare the multi-factor similarity values ​​with the optimal similarity threshold to determine the result; Adjacent grid cells with multi-factor similarity values ​​greater than the optimal similarity threshold are marked as pending merging. A disjoint-set data structure is used to perform clustering and merging operations to form a candidate spatial cell set. The candidate spatial unit set is partitioned for effectiveness evaluation. Spatial units that do not meet the minimum management size condition are merged into adjacent spatial units according to the nearest neighbor similarity principle to form an effective spatial unit set. Based on the set of effective spatial units, a basic partition identifier code is generated, and a mapping relationship between grid units and basic partitions is established to form multiple basic partitions; The optimization and adjustment of the basic zoning based on plant phenological characteristics and seasonal maintenance requirements includes: Acquire phenological characteristic data for each plant species within the basic zoning area, and determine seasonal maintenance requirements based on seasonal climate changes; Analyze the differences in phenological characteristics among different plant species within the same basic zone to identify basic zones with conflicting phenological periods; Based on the results of phenological conflict identification, conflicting plant species were redistributed and partition boundaries were adjusted to form partitions with consistent phenological characteristics. Based on the aforementioned seasonal maintenance requirements, seasonal adjustments are made to zones with consistent phenological characteristics; The zones that have completed seasonal adaptation adjustments are numbered and labeled to generate multiple maintenance zones.

2. The method for maintaining a garden landscape division according to claim 1, wherein: The process of dividing the garden area into multiple grid units includes: Obtain the geographic coordinate boundary data and total area data of the garden area, and calculate the length and width of the garden area; Based on the total area of ​​the garden area and the preset unit density parameters, combined with the length-to-width ratio of the garden area and the complexity of plant distribution, the side length of the grid unit is determined; Using the geometric center point of the garden area as the origin of the coordinate system, grid lines are sequentially divided along the long axis of the garden area according to the side length of the grid unit to generate a grid matrix. Calculate the overlap ratio between the grid cells and the boundary of the garden area, and merge the boundary grid cells according to the overlap ratio; Assign a unique identifier to each grid cell, record the center coordinates and boundary coordinates of each grid cell, and establish a direct adjacency matrix of the grid cells.

3. The method of claim 1, wherein: The establishment of the multi-factor similarity calculation model includes: Preprocess the multidimensional partitioned dataset; Based on environmental monitoring data, the weighted Euclidean distance is used to calculate the similarity of environmental factors between adjacent grid cells; Based on plant species distribution data, the similarity of plant factors between adjacent grid cells is calculated using the Jaccard similarity coefficient. Based on topographic elevation data, the standardized Euclidean distance algorithm is used to calculate the similarity of topographic factors between adjacent grid cells; A weighted allocation mechanism was established to calculate the weighted similarity of environmental factors, plant factors, and topographic factors, and to obtain the multi-factor similarity value.

4. The method for zoned maintenance of landscaping as described in claim 1, characterized in that: The step of matching the corresponding maintenance plan from the preset maintenance strategy library based on the maintenance zoning characteristics includes: Extract the grid cell data of each maintenance zone, obtain feature parameters through aggregation calculation, and combine the feature parameters to construct the maintenance zone feature vector; Calculate the matching degree between the feature vector of the maintenance zone and the feature vector of each strategy template in the preset maintenance strategy library, and select the strategy template with a matching degree less than the preset matching degree threshold as the candidate maintenance scheme. The candidate maintenance schemes are evaluated for cost-effectiveness, and the maintenance scheme with the best cost-effectiveness ratio is selected as the final maintenance scheme.

5. The method of claim 1, wherein: The differentiated maintenance operations for each maintenance zone include: The maintenance parameter configuration is determined based on the final maintenance plan, and the maintenance parameter configuration is dynamically adjusted in combination with the seasonal characteristics of the maintenance zone and the plant growth status to generate a set of maintenance parameters. The set of maintenance parameters is analyzed, and a set of zone-specific maintenance instructions is generated based on the various maintenance parameters. Distribute the differentiated maintenance instruction set for each zone to the corresponding maintenance equipment and maintenance personnel terminals; Maintenance equipment and personnel perform differentiated maintenance operations on maintenance zones according to the received instructions; Obtain the operation status of each maintenance zone and record the operation completion status.

6. A garden greening subarea maintenance system based on the garden greening subarea maintenance method according to any one of claims 1-5, characterized in that: include: The dataset construction module is used to divide the garden area into multiple grid units, obtain environmental monitoring data, plant species distribution data and topographic elevation data of each grid unit, and construct a multidimensional partitioned dataset. The similarity calculation module is used to establish a multi-factor similarity calculation model based on the multi-dimensional partitioned dataset and calculate the multi-factor similarity values ​​between adjacent grid cells. The clustering and merging module is used to set a similarity threshold and cluster and merge adjacent grid cells whose multi-factor similarity values ​​are greater than the similarity threshold to form multiple basic partitions. The zoning optimization module is used to optimize and adjust the basic zoning according to plant phenological characteristics and seasonal maintenance requirements, generating multiple maintenance zoning zones; The maintenance operation module is used to match the corresponding maintenance plan from the preset maintenance strategy library according to the characteristics of the maintenance zone, and to carry out differentiated maintenance operations for each maintenance zone. The monitoring and adjustment module is used to monitor the plant growth status of each maintenance zone and initiate local zone adjustment or zone-by-zone gradual optimization processes based on the number of abnormal zones.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the garden greening zoning maintenance method according to any one of claims 1 to 5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by a processor to realize the steps of the garden greening partition maintenance method in any one of claims 1-5.

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