Garden heritage plant landscape monitoring management system and method

Abstract

The invention discloses a garden heritage plant landscape monitoring management system and method. The monitoring management system comprises a data acquisition module, a feature extraction module, a monitoring calculation module and a storage and visualization module. The data acquisition module is used for acquiring the current situation of the garden heritage plant landscape and processing the data; the feature extraction module is used for extracting spatial scale information and disease data information of the garden heritage plant landscape; the monitoring calculation module is used for carrying out spatial scale and vision field monitoring calculation and disease monitoring calculation on the garden heritage plant landscape; and the storage and visualization module is used for storing monitoring data and analysis results of the garden heritage plant landscape, and analyzing spatial scale and vision field monitoring results and disease analysis results. According to the invention, digital acquisition of garden heritage plant landscape information, automatic feature data extraction, quantitative evaluation and analysis, and digital information storage and presentation in a complex site environment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of garden protection, and in particular to a garden heritage plant landscape monitoring management system and method. Background Art

[0002] Chinese classical gardens are representatives and models of world garden art, with extremely high historical, cultural and artistic values. Garden heritage is an important material relic of Chinese classical gardens and an important part of the world cultural heritage. The unique plant landscape is an important feature that distinguishes Chinese garden heritage from other world cultural heritages, and is therefore one of the focuses of Chinese garden heritage protection. In garden heritage, unlike buildings, rockery, water bodies and other elements that have been maintained in a relatively stable state for a long time, plant landscape is the only vital element that is in constant growth and change. Its rise and fall directly affects the authenticity and authenticity of garden heritage. However, with the development of time, the trees in the garden heritage have experienced more than a hundred years of continuous growth, and many problems such as scale imbalance, frequent diseases, and restricted growth have emerged. These phenomena have caused the plant landscape in the garden heritage to lose its original historical style, and the authenticity and authenticity of the garden heritage have been damaged. The heritage status is not optimistic and needs to be effectively monitored and protected.

[0003] The existing monitoring and management of garden heritage plant landscapes relies on human observation and recording, and decision-making relies on the personal experience and subjective judgment of managers. The results of decisions are highly related to the knowledge background of decision-makers. There are the following problems: On the one hand, the number of plants in garden heritage is huge and the site conditions are complex. The collection of existing monitoring data relies on human observation and recording, with backward means, low degree of informatization, and low work efficiency. On the other hand, garden heritage plant landscape monitoring is a multi-dimensional and complex decision. The existing subjective analysis and decision-making are difficult to comprehensively and objectively evaluate the current situation and obtain accurate intervention and control ranges. Therefore, the existing technology lacks a quantitative evaluation system and method for garden heritage plant landscape monitoring and management. Summary of the invention

[0004] Purpose of the invention: To make up for the shortcomings and deficiencies of the prior art, a garden heritage plant landscape monitoring and management system and method are provided to improve the efficiency of monitoring and management; to ensure the comprehensiveness and objectivity of the analysis, to analyze and calculate the current status of the garden heritage plant landscape in a systematic and objective way, and to determine the protection level, intervention measures and control scope of the plant landscape.

[0005] Technical solution: A garden heritage plant landscape monitoring and management system, including a data acquisition module, a feature extraction module, a monitoring and calculation module, and a storage and visualization module;

[0006] The data acquisition module is used to collect the current status of the garden heritage plant landscape and process the data, including a front-end acquisition module and a data matching module; the front-end acquisition module is used to collect spatial information and current status photos of the garden heritage plant landscape in a non-destructive and non-contact manner, including a ground acquisition module and an aerial acquisition module; the data matching module is used to match the point cloud data and photos collected by the ground acquisition module and the aerial acquisition module;

[0007] The feature extraction module is used to extract the spatial scale information and disease data information of the garden heritage plant landscape, including a spatial data extraction module and a disease data extraction module; the spatial data extraction module is used to identify and segment individual trees in the point cloud model and extract spatial scale data; the disease data extraction module is used to identify and classify tree disease types and perform area calculation;

[0008] The monitoring and calculation module is used to perform spatial scale and visual field monitoring and calculation, and disease monitoring and calculation on the garden heritage plant landscape;

[0009] The storage and visualization module includes a storage module and a visualization module. The storage module is used to store the monitoring data and analysis results of the garden heritage plant landscape; the visualization module is used to integrate the spatial scale and visual area monitoring results and disease analysis results.

[0010] Further, the spatial data extraction module includes a group identification module, a monomer segmentation module, a spatial feature calculation module and a spatial data output module;

[0011] The cluster recognition module is used to identify plant landscape clusters from the overall model. By using a point cloud extraction method based on surface segmentation, the curvature of the local surface area of ​​the point cloud is first calculated to identify areas with high curvature or complex shapes; then the point cloud with complex curvature characteristics is segmented as the target cluster; finally, the point cloud model of the plant landscape cluster is obtained by identifying the color and texture information of the image and cleaning non-tree objects;

[0012] The monomer segmentation module is used to segment tree monomers from the plant landscape group. By using the Hough transform technology based on shape detection to extract point clouds, the geometric shape parameters of the trunk and crown are first defined; then the midpoints of the point cloud are mapped to the parameter space to find the point cloud matching the target parameters; finally, the point cloud that meets the characteristics of the trunk and crown is identified and separated;

[0013] The spatial feature calculation module is used to extract the spatial scale data of the garden heritage and the scale data of the tree monomer, including a tree height calculation unit, a crown calculation unit, a breast diameter calculation unit, and a canopy volume calculation unit; the tree height calculation unit first detects the Z coordinates of the highest point and the lowest point; then calculates the tree height; the crown calculation unit first projects the tree point cloud to the XY plane; then uses the convex hull algorithm to calculate the boundary of the crown on the XY plane; finally calculates the maximum extension distance of the projection in different directions; the breast diameter calculation unit first extracts the point cloud data at 1.3 meters according to the tree height; then uses RANSAC or the least squares fitting method to fit the circular cross-section of these point clouds; finally calculates the trunk diameter by the radius of the fitted circle; the canopy volume calculation unit first extracts the crown; then uses a sphere or ellipsoid model to fit the point cloud data, and the fitting parameters include the radius and height of the crown; finally calculates the crown volume according to the geometric model;

[0014] The spatial data output unit is used to output the calculation results into a suitable storage format.

[0015] Furthermore, the disease data extraction module is used to identify and classify tree disease types and calculate the area; it includes a disease characterization recognition module, a disease feature calculation module and a disease data output unit;

[0016] The disease characterization recognition module is used to identify the disease characterization type of the trees in the model, and includes an input unit and an algorithm unit; the input unit is used to pre-process the collected pictures, use enhanced image technology to highlight the diseased area; and use a geometric correction method to correct the image with angle distortion;

[0017] The algorithm unit is used to batch identify the location and type of disease representations, and use image segmentation technology to segment disease areas with heterogeneous colors, textures, and shapes from images; use a pre-trained convolutional neural network classification model to identify and classify the types of disease representations in the image;

[0018] The disease feature calculation module is used to calculate the size of the disease representation, including a projection unit, a length and width calculation unit and an area calculation unit; the projection unit is used to project the segmented disease photos onto the point cloud model, convert the two-dimensional image information into a point cloud with three-dimensional coordinates, and project photos of the same disease area taken at different angles onto the point cloud model through a perspective projection model; the length and width calculation unit calculates the maximum expansion range of the disease area in different directions, projects the disease point cloud on the XY plane, and uses a boundary detection method to define the minimum circumscribed rectangle and calculate the maximum expansion length; the area calculation unit calculates the surface area by performing three-dimensional convex hull fitting on the disease point cloud to obtain the disease area;

[0019] The disease data output unit outputs the type and size information of the disease.

[0020] Furthermore, the monitoring and calculation module includes a spatial scale and visual field monitoring module and a disease monitoring module;

[0021] The spatial scale and viewshed monitoring module is used to analyze the spatial scale and viewshed of the garden heritage plant landscape unit, determine the plant landscape height, width and viewshed control range, calculate the intervention value, determine whether the garden heritage plant landscape needs to be adjusted by intervention measures, and the adjustment standards; it includes a spatial division module, a scale calculation module, a viewshed calculation module and an overlay calculation module;

[0022] The space division module is used to identify the plant landscape and the space unit where it is located, and divide the garden heritage into different space units. The space unit is based on the courtyard, which contains the plant landscape; the space unit is bounded by buildings, corridors or walls; the boundaries of the space unit and the scope of the plant landscape are marked in the plan;

[0023] The scale calculation module is used to define the reasonable range values ​​of the crown width level ratio and the height level ratio, and determine whether the current ratio is within the reasonable range. If not, intervention is required; it includes a crown width level analysis module and a height level analysis module; the crown width level analysis module is used to compare the current data with the historical data, analyze whether the current crown width level ratio conforms to the crown width level ratio in the initial stage of garden construction, calculate a reasonable ratio range, and provide reference data for pruning and supplementing plant landscapes; the height level analysis module is used to compare the current data with the historical data, analyze whether the current height level ratio conforms to the height level ratio in the initial stage of garden construction, calculate a reasonable ratio range, and provide reference data for pruning and increasing and decreasing plant landscapes;

[0024] The view calculation module is used to define the reasonable range values ​​of the horizontal view and the vertical view, and determine whether the current range is within the reasonable range. If not, intervention is required; the horizontal view analysis module is used to compare the current data with the historical data, analyze whether the current horizontal view range conforms to the horizontal view range at the initial stage of the garden construction, calculate the reasonable range, and provide reference data for the pruning and increase and decrease of the plant landscape; the vertical view analysis module is used to compare the current data with the historical data, analyze whether the current vertical view range conforms to the vertical view range at the initial stage of the garden construction, calculate the reasonable range, and provide reference data for the pruning and increase and decrease of the plant landscape;

[0025] The superposition calculation module superimposes the calculation results of the scale calculation module and the viewshed calculation module.

[0026] Furthermore, the crown width level analysis module includes a first level division unit, a first calculation unit and a first determination unit;

[0027] The first level division unit uses space units as units to perform crown width level division and quantity statistics on plant landscapes, and the division standards are: 0-4 meters is a small crown width, 4-8 meters is a small and medium crown width, 8-12 meters is a medium crown width, 12-16 meters is a large crown width, and more than 16 meters is an extra-large crown width;

[0028] The first calculation unit calculates the historical crown width level ratio CLH(H)=A1:B1:C1:D1:E1, where A1 is the number of historical small crown widths, B1 is the number of historical small and medium crown widths, C1 is the number of historical medium crown widths, D1 is the number of historical large crown widths, and E1 is the amount of historical extra-large crown width data;

[0029] The first determination unit calculates the current crown width level ratio CLH(C)=A2:B2:C2:D2:E2, where A2 is the current number of small crown widths, B2 is the current number of small and medium crown widths, C2 is the current number of medium crown widths, D2 is the current number of large crown widths, and E2 is the current number of extra-large crown widths; determines whether the CLH(C) result is within a reasonable range of crown widths:

[0030] If it meets the requirements, the intervention value is 0 and no adjustment is required;

[0031] If it does not meet the requirements, the intervention value is 1, and artificial intervention is required on the existing plant landscape to adjust CLH(C) to the historical crown width ratio value.

[0032] Further, the height level analysis module includes a second level division unit, a second calculation unit and a second determination unit;

[0033] The second level division unit uses space units as units to divide the plant landscape into different levels and count the number of plants. The division standard is: 0-5 meters is low-level, 5-10 meters is low-middle-level, 10-15 meters is medium-high-level, 15-20 meters is high-level, and above 20 meters is extra-high-level.

[0034] The second calculation unit calculates the historical height level ratio HSR(H)=a1:b1:c1:d1:e1, where a1 is the number of historical low-rises, b1 is the number of historical middle-low-rises, c1 is the number of historical middle-high-rises, d1 is the number of historical high-rises, and e1 is the number of historical extra-high-rises;

[0035] The second determination unit calculates the current height level ratio HSR(C)=a2:b2:c2:d2:e2, where a2 is the current number of low-rises, b2 is the current number of low-middle-rises, c2 is the current number of high-middle-rises, d2 is the current number of high-rises, and e2 is the current number of extra-high-rises; determines whether the HSR(C) result is within a reasonable height range:

[0036] If it meets the requirements, the intervention value is 0 and no adjustment is required;

[0037] If it does not meet the requirements, the intervention value is 1, and it is necessary to use HSR(H)=a2:b2:c2:d2:e2 as the standard to intervene in the garden heritage plant landscape so that the height level ratio is within the reasonable ratio value.

[0038] Further, the horizontal visual range analysis module includes a third visual range identification unit, a third type classification unit and a third determination unit;

[0039] The third visual field recognition unit takes the plant landscape as the target object and the main viewpoint as the viewing point, respectively defines the range of the historical horizontal visual field and the current horizontal visual field, and calculates the visual field angle;

[0040] The third type division unit defines the types of historical horizontal viewing area and current horizontal viewing area respectively, wherein: 1° to 10° is the best horizontal viewing area, 10° to 20° is the instantaneous horizontal viewing area, 21° to 30° is the effective horizontal viewing area, 31° to 60° is the comfortable horizontal viewing area, and 61° to 120° is the maximum horizontal viewing area;

[0041] The third determination unit determines whether the types of the historical horizontal view and the current horizontal view are at the same level:

[0042] If they are at the same level, the intervention value is 0, and no intervention is required;

[0043] If they are not at the same level, the intervention value is 1, and the type and angle of the historical horizontal view are used as standard values ​​to intervene in the existing plant landscape.

[0044] Further, the horizontal visual range analysis module includes a fourth visual range identification unit, a fourth type classification unit and a fourth determination unit;

[0045] The fourth visual field recognition unit takes the plant landscape as the target object and the main viewpoint as the viewing point, respectively defines the range of the historical vertical visual field and the current vertical visual field, and calculates the visual field angle;

[0046] The fourth type division unit defines the types of the historical vertical viewing area and the current vertical viewing area, respectively, wherein: 0 to 27 degrees is the best vertical viewing area, 28 degrees to 45 degrees is the effective vertical viewing area, and greater than 45 degrees is the maximum vertical viewing area;

[0047] The fourth determination unit determines whether the types of the historical vertical view and the current vertical view are at the same level: if they are at the same level, the intervention value is 0, and no intervention is required;

[0048] If they are not at the same level, the intervention value is 1, and the type, angle, and D / H of the historical vertical view are used as standard values ​​to intervene in the existing plant landscape, where D is the horizontal distance from the viewpoint to the center of the plant landscape, and H is the height of the plant landscape.

[0049] The method for implementing the garden heritage plant landscape monitoring and management system described in any of the above items comprises the following steps:

[0050] S1, the data acquisition module collects information losslessly on the target garden heritage plant landscape, collects ground point cloud and image information through cameras and sensors, and collects aerial video and image information through drones; performs data preprocessing and multi-source information matching, and imports the matching results into the feature extraction module;

[0051] S2, the feature extraction module receives the matching result and sends the matching result to the spatial data extraction module and the disease data extraction module; the spatial data extraction module and the disease data extraction module respectively perform feature recognition, segmentation and data calculation, and output the result and import it into the monitoring calculation module;

[0052] S3, the monitoring and calculation module receives the result of feature extraction, sends the spatial data extraction result to the spatial scale and viewshed monitoring module, and sends the disease data extraction result to the disease monitoring module; the spatial scale and viewshed monitoring module and the disease monitoring module respectively analyze to obtain the spatial scale and viewshed assessment level and disease assessment level of the target garden heritage plant landscape; and inputs the analysis results into the storage and visualization module;

[0053] S4, the storage and visualization module receives the monitoring calculation results, matches the calculation results with the geographic information, and converts them into visual images, which are displayed on the map; and stores the data of the data acquisition module, feature extraction module, and monitoring calculation module as a heritage archive.

[0054] Further, the implementation steps of disease monitoring are as follows:

[0055] S51, calculate the disease index;

[0056] The disease index D of plant landscape is calculated using the formula:

[0057] D=0.5*D 树干 +0.2*D 树冠 +0.3*D 树基

[0058] Among them, D 树干 Scoring is based on the ratio of disease width to trunk circumference, 0-20% for 1 point, 20-50% for 2 points, 50-99% for 3 points;

[0059] D 树冠 The score is calculated based on the frequency of occurrence, with 1 point for each occurrence;

[0060] D 树基Points are awarded based on the proportion of the disease width to the tree base circumference, 0-20% for 1 point, 20-50% for 2 points, 50-99% for 3 points;

[0061] S52, to classify the disease level;

[0062] Risk classification is carried out according to the calculation results, and the classification criteria are as follows:

[0063] 0 points is level 1, no risk;

[0064] 0.2-0.5 is level 2, low risk;

[0065] 0.6-0.9 is level 3, medium risk;

[0066] >0.9 is level 4, high risk;

[0067] S53, formulate maintenance strategies according to risk levels;

[0068] Among them, level 1 risk-free trees do not require intervention; level 2 low-risk trees require regular inspections and enhanced protection during daily management and maintenance; level 3 medium-risk and level 4 high-risk trees require key inspections, targeted analysis of the causes of the disease and prevention and control, among which level 4 high-risk trees require special attention to their root and trunk lesions.

[0069] Compared with the prior art, the present invention has the following significant effects:

[0070] 1. The garden heritage plant landscape monitoring and management system of the present invention collects the current status of the garden heritage plant landscape through the data collection module, extracts the spatial scale information and disease data information of the garden heritage plant landscape through the feature extraction module, and the monitoring and calculation module is used to perform spatial scale and visual field monitoring and calculation, and disease monitoring and calculation on the garden heritage plant landscape, thereby realizing the digital collection of garden heritage plant landscape information under complex site environments, automatic feature data extraction, quantitative evaluation and analysis, and digital information storage and presentation, solving the problems of low efficiency, insufficient accuracy, and difficulty in data sharing in previous monitoring methods that rely on manual text recording;

[0071] 2. The present invention integrates two requirements for the protection of garden heritage plant landscapes: spatial scale and field of view monitoring, and disease monitoring. It designs a feature data extraction module and a monitoring calculation module to convert the textual descriptions of specific features in previous monitoring into quantifiable indicators and data. The monitoring results obtained using the developed quantitative calculation method can accurately analyze and evaluate the spatial scale and field of view changes and disease status of the plant landscape, determine the intervention level, and solve the problem that the garden heritage plant landscape was highly subjective and difficult to quantify. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1It is a structural block diagram of the present invention;

[0073] Figure 2 It is the structural block diagram of the front-end acquisition module;

[0074] Figure 3 It is the structural block diagram of the data matching module;

[0075] Figure 4 It is the structural block diagram of the spatial data extraction module;

[0076] Figure 5 This is the structural block diagram of the disease data extraction module;

[0077] Figure 6 It is the structural block diagram of the spatial scale and viewshed monitoring module;

[0078] Figure 7 This is the structural block diagram of the disease monitoring module. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0080] like Figure 1 As shown, the garden heritage plant landscape monitoring and management system of the present invention includes a data acquisition module, a feature extraction module, a monitoring and calculation module, and a storage and visualization module.

[0081] like Figure 2 , Figure 3 As shown, the data acquisition module is used to collect the current status of the garden heritage plant landscape and process the data, including a front-end acquisition module and a data matching module.

[0082] Front-end acquisition module: used to collect spatial information and current status photos of garden heritage plant landscapes in a non-destructive and non-contact manner, including ground acquisition module and aerial acquisition module.

[0083] Ground acquisition module: Built-in 3D laser scanning device, which consists of laser rangefinder, reflective prism, digital camera, power supply and auxiliary equipment. The device projects laser onto the surface of the object to be measured based on the principle of laser ranging, and then reflects back to the built-in sensor of the scanning lens along the same path to calculate the distance from the scanning lens to the object to be measured. According to the horizontal and vertical angles, the relative spatial position of the target point can be calculated. The built-in camera of the device can take photos of the current status of the garden heritage plant landscape.

[0084] Aerial collection module: Use drone equipment to take photos of garden heritage. Its flight route is S-shaped, and the photo overlap rate is 70%.

[0085] Data matching module: used to match the point cloud data and photos collected by the ground acquisition module and the aerial acquisition module, including a preprocessing module, a point cloud matching module and a photo matching module.

[0086] Preprocessing module: used to process the data collected by the ground acquisition module into a point cloud model PC1, and to process the photos collected by the drone in the aerial acquisition module into a three-dimensional point cloud model PC2.

[0087] Point cloud matching module: used to match the ground point cloud model and the aerial point cloud model. This module includes a ground positioning unit and an aerial positioning unit. The ground positioning unit is used to set the garden heritage landmarks, which are the building corners of the garden heritage and are not blocked by plants. The aerial positioning unit is used for positioning the drone model according to the measured difference of the ground positioning landmarks.

[0088] Photo matching module: used to match the point cloud matching module and the tree photo set to ensure that they are in the same coordinate system. This module uses the feature point matching method ICP (Iterative Closest Point) algorithm to achieve matching between point clouds and images.

[0089] like Figure 4 , Figure 5 As shown, the feature extraction module is used to extract the spatial scale information and disease data information of the garden heritage plant landscape, including a spatial data extraction module and a disease data extraction module.

[0090] Spatial data extraction module: used to identify and segment individual trees in the point cloud model and extract spatial scale data; includes group recognition module, individual segmentation module, spatial feature calculation module and spatial data output module.

[0091] Cluster identification module: used to identify plant landscape clusters from the overall model. This module uses a point cloud extraction method based on surface segmentation. First, the surface curvature is calculated: the curvature is calculated for the local surface area of ​​the point cloud to identify areas with high curvature or complex shapes; then surface segmentation is performed: point clouds with complex curvature features are segmented as target clusters; finally, data cleaning is performed: by identifying image color and texture information, non-tree objects are cleaned to obtain a point cloud model of plant landscape clusters.

[0092] Single-body segmentation module: used to segment tree single bodies from plant landscape groups. This module extracts point clouds by using the Hough Transform technology based on shape detection. First, the geometric shape parameters of the trunk and crown are defined; then the midpoints of the point cloud are mapped to the parameter space to find point clouds that match the target parameters; point clouds that meet the characteristics of the trunk and crown are identified and separated.

[0093] Spatial feature calculation module: used to extract the spatial scale data of garden heritage and the scale data of tree monomers. It includes tree height calculation unit, crown calculation unit, breast diameter calculation unit, and canopy volume calculation unit.

[0094] Tree height calculation unit: The height of a tree is determined by the distance between the highest point of the tree and the ground. First, the Z coordinates of the highest and lowest points are detected; then the tree height is calculated, and the calculation formula is: tree height = tree top Z coordinate - ground Z coordinate.

[0095] Crown calculation unit: The crown width is the maximum extension width of the tree crown on the horizontal plane, which is determined by calculating the projection area of ​​the point cloud on the XY plane. First, the tree point cloud is projected to the XY plane; then the convex hull algorithm is used to calculate the boundary of the crown on the XY plane; finally, the maximum extension distance of the projection in different directions is calculated, that is, the crown width.

[0096] DBH calculation unit: DBH is the trunk diameter at 1.3 meters above the ground. First, extract the point cloud data at 1.3 meters according to the tree height; then use RANSAC or least squares fitting method to fit the circular cross-section of these point clouds; finally, calculate the trunk diameter (DBH = 2*radius) by the radius of the fitted circle.

[0097] Canopy volume calculation unit: The canopy volume is estimated by fitting a geometric model. First, the tree crown is extracted; then a sphere or ellipsoid model is used to fit the point cloud data, and the fitting parameters include the radius and height of the tree crown; finally, the canopy volume is calculated based on the geometric model.

[0098] Spatial data output unit: used to output the calculation results into a suitable storage format, such as tables and point cloud models, for management and spatial scale analysis.

[0099] Disease data extraction module: used to identify and classify tree disease types and calculate the area, including a disease characterization recognition module, a disease feature calculation module and a disease data output unit.

[0100] Disease representation identification module: used to identify the disease representation type of trees in the model. This module includes an input unit and an algorithm unit.

[0101] Input unit: This unit is used to pre-process the collected images. This unit uses image enhancement technology to highlight the diseased area and adopts geometric correction method to correct the images with angular distortion.

[0102] Algorithm unit: The algorithm unit is used to identify the location and type of disease representations in batches. This unit uses image segmentation technology to segment disease areas with heterogeneous colors, textures, and shapes from images; and uses a pre-trained convolutional neural network classification model to identify and classify the types of disease representations in images.

[0103] Disease feature calculation module: used to calculate the size (length, width, area) of disease representation. This module includes a projection unit, a length and width calculation unit, and an area calculation unit.

[0104] Projection unit: The projection unit is used to project the segmented disease photos onto the point cloud model, converting the two-dimensional image information into a point cloud with three-dimensional coordinates. This unit projects photos of the same disease area taken at different angles onto the point cloud model through a perspective projection model.

[0105] Length and width calculation unit: calculates the maximum extension range of the diseased area in different directions. This unit projects the diseased point cloud on the XY plane, uses the boundary detection method to define the minimum circumscribed rectangle, and calculates the maximum extension length.

[0106] Area calculation unit: This unit calculates the surface area by performing three-dimensional convex hull fitting on the defect point cloud to obtain the defect area.

[0107] Disease data output unit: Output the type and size information of the disease for management and disease analysis.

[0108] like Figure 6 , Figure 7 As shown in the figure, the monitoring and calculation module is used to perform spatial scale and viewshed monitoring and calculation, and disease monitoring and calculation on the garden heritage plant landscape. It includes a spatial scale and viewshed monitoring module and a disease monitoring module.

[0109] The spatial scale and viewshed monitoring module is used to analyze the spatial scale and viewshed of the garden heritage plant landscape unit, determine the height, width and viewshed control range of the plant landscape, calculate the intervention value, determine whether the garden heritage plant landscape needs to be adjusted by intervention measures, and the adjustment standards; it includes a spatial division module, a scale calculation module, a viewshed calculation module and an overlay calculation module.

[0110] Spatial division module: used to identify plant landscapes and the spatial units in which they are located. The garden heritage is divided into different spatial units. The division is based on the following principles: the spatial unit is based on the courtyard, which contains the plant landscape; the spatial unit is bounded by buildings, corridors or walls. The boundaries of the spatial unit and the scope of the plant landscape are marked in the plan.

[0111] Scale calculation module: used to define the reasonable range of crown width level ratio and height level ratio, and determine whether the current ratio is within the reasonable range. If not, intervention is required; including crown width level analysis module and height level analysis module.

[0112] Crown width level analysis module: used to compare current data with historical data, analyze whether the current crown width level ratio conforms to the crown width level ratio in the initial stage of garden construction, calculate a reasonable ratio range, and provide reference data for pruning and supplementing plant landscapes; includes the first level division unit, the first calculation unit and the first judgment unit.

[0113] The first level division unit: taking the space unit as the unit, the crown width of the plant landscape is divided and the quantity is counted. The division standard is 0-4 meters for small crown width (X); 4-8 meters for small and medium crown width (ZX); 8-12 meters for medium crown width (Z); 12-16 meters for large crown width (Z); more than 16 meters for extra large crown width (TD).

[0114] The first calculation unit: calculate the historical crown width layer ratio CLH (H), CLH (H) = A1: B1: C1: D1: E1, where A1 is the number of historical small crown widths, B1 is the number of historical small and medium crown widths, C1 is the number of historical medium crown widths, D1 is the number of historical large crown widths, and E1 is the number of historical extra-large crown widths. The reasonable ranges of the crown widths of each layer are:

[0115] Small crown width (X)∈[A1-5%,A1+5%];

[0116] Small and medium crown width (ZX)∈[B1-5%,B1+5%];

[0117] Middle crown width (Z)∈[C1-5%,C1+5%];

[0118] Middle crown width (Z)∈[D1-5%,D1+5%];

[0119] Extra large crown width (TD)∈[E1-5%,E1+5%];

[0120] The first judgment unit: calculate the current crown width level ratio CLH(C), CLH(C) = A2: B2: C2: D2: E2, where A2 is the current number of small crown widths, B2 is the current number of small and medium crown widths, C2 is the current number of medium crown widths, D2 is the current number of large crown widths, and E2 is the current number of extra-large crown widths. Determine whether the CLH(C) result is within the reasonable range of crown width:

[0121] If it meets the requirements, the intervention value is 0, which means no adjustment is required;

[0122] If it does not meet the requirements, the intervention value is 1, and artificial intervention is required on the existing plant landscape to adjust CLH(C) to the historical level ratio value, that is, the calculation result in the first calculation unit: A1:B1:C1:D1:E1.

[0123] Height level analysis module: used to compare current data with historical data, analyze whether the current height level ratio is consistent with the height level ratio in the initial stage of garden construction, calculate a reasonable ratio range, and provide reference data for pruning and increasing or decreasing plant landscapes; includes a second level division unit, a second calculation unit, and a second determination unit.

[0124] The second level division unit: taking the space unit as the unit, the plant landscape is divided into different levels and the number is counted. The division standard is 0-5 meters as low level (D); 5-10 meters as medium-low level (ZD); 10-15 meters as medium-high level (ZG); 15-20 meters as high level (G); and above 20 meters as extra high level (TG).

[0125] Second calculation unit: calculate the historical height level ratio HSR(H), HSR(H) = a1:b1:c1:d1:e1, where a1 is the number of historical low-rises, b1 is the number of historical middle-low-rises, c1 is the number of historical middle-high-rises, d1 is the number of historical high-rises, and e1 is the number of historical super-high-rises. The reasonable range of heights of each layer is:

[0126] Low level (D)∈[a1-5%,a1+5%];

[0127] Middle and lower level (ZD)∈[b1-5%,b1+5%];

[0128] Middle and high level (ZG)∈[c1-5%,c1+5%];

[0129] High level (G) ∈ [d1-5%, d1+5%];

[0130] Extra high layer (TG)∈[e1-5%,e1+5%].

[0131] Second judgment unit: calculate the current height level ratio HSR(C), HSR(C) = a2:b2:c2:d2:e2, where a2 is the current number of low-rise buildings, b2 is the current number of low- and medium-rise buildings, c2 is the current number of medium- and high-rise buildings, d2 is the current number of high-rise buildings, and e2 is the current number of super-high-rise buildings. Determine whether the HSR(C) result is within the reasonable height range:

[0132] If it meets the requirements, the intervention value is 0, which means no adjustment is required;

[0133] If it does not meet the requirements, the intervention value is 1, that is, it is necessary to use HSR(H)=a2:b2:c2:d2:e2 as the standard to intervene in the garden heritage plant landscape so that the height level ratio is within the reasonable ratio value.

[0134] View calculation module: used to delimit the reasonable range values of the horizontal view and the vertical view, and determine whether the current range is within the reasonable interval. If not, intervention is required. It includes a horizontal view analysis module A and a vertical view analysis module.

[0135] Horizontal view analysis module: used to compare the current data with the historical data, analyze whether the current horizontal view range conforms to the horizontal view range at the initial stage of garden construction, calculate the reasonable range, and provide reference data for the pruning, addition, and reduction of plant landscapes.

[0136] Third view recognition unit: taking the plant landscape as the target object and the main viewing point as the viewing point, delimit the ranges of the historical horizontal view and the current horizontal view respectively, and calculate the view angle.

[0137] Third type division unit: delimit the types of the historical horizontal view area HVA(H) and the current horizontal view area HVA(C) respectively, where: 1° - 10° is the best horizontal view area (level 1); 10° - 20° is the fleeting horizontal view area (level 2); 21° - 30° is the effective horizontal view area (level 3); 31° - 60° is the comfortable horizontal view area (level 4); 61° - 120° is the maximum horizontal view area (level 5).

[0138] Third determination unit: determine whether the types of HVA(H) and HVA(C) are at the same level:

[0139] If they are at the same level, the intervention value is 0, that is, no intervention is required;

[0140] If they are not at the same level, the intervention value is 1, and it is necessary to take the type and angle of HVA(H) as the standard value to intervene in the current plant landscape.

[0141] Vertical view analysis module: used to compare the current data with the historical data, analyze whether the current vertical view range conforms to the vertical view range at the initial stage of garden construction, calculate the reasonable range, and provide reference data for the pruning, addition, and reduction of plant landscapes.

[0142] Fourth view recognition unit: taking the plant landscape as the target object and the main viewing point as the viewing point, delimit the ranges of the historical vertical view and the current vertical view respectively, and calculate the view angle.

[0143] Fourth type division unit: delimit the types of the historical vertical view area VVA(H) and the current vertical view area VVA(C) respectively, where 0 - 27° is the best vertical view area, that is, D / H < 1 (level 1); 28° - 45° is the effective vertical view area, that is, 1 < D / H < 2 (level 2); greater than 45° is the maximum vertical view area, that is, D / H > 2 (level 3). Where D is the horizontal distance from the viewing point to the center point of the plant landscape, and H is the height of the plant landscape.

[0144] The fourth determination unit: determines whether the types of VVA (H) and VVA (C) are at the same level. If they are at the same level, the intervention value is 0, that is, no intervention is required; if they are not at the same level, the intervention value is 1, and the type, angle, and D / H of VVA (H) are used as standard values ​​to intervene in the existing plant landscape.

[0145] Overlay calculation module: The calculation results of the scale calculation module and the viewshed calculation module are superimposed. The total intervention value = 0, that is, there is no need to intervene in the existing plant landscape; the total intervention value = 1~4, that is, it is necessary to intervene in the existing plant landscape. The higher the intervention value, the more urgent the measures to be taken.

[0146] Disease monitoring module: used to calculate the disease index of individual trees in the garden heritage, define the disease level, and formulate targeted inspection and management strategies. The implementation steps of disease monitoring are as follows:

[0147] Step 51, calculating the disease index;

[0148] The formula used to calculate the disease index D of plant landscape is:

[0149] D=0.5*D 树干 +0.2*D 树冠 +0.3*D 树基

[0150] D 树干 Points are scored based on the ratio of the disease width to the trunk circumference: 0-20% is 1 point; 20%-50% is 2 points; 50%-99% is 3 points.

[0151] D 树冠 Points are awarded based on the frequency of occurrence, with 1 point awarded for each occurrence.

[0152] D 树基 Points are scored based on the ratio of the disease width to the tree base circumference: 0-20% is 1 point; 20%-50% is 2 points; 50%-99% is 3 points.

[0153] Step 52, disease grade determination;

[0154] Risk classification is carried out according to the calculation results, and the classification criteria are as follows:

[0155] 0 points is level 1, no risk;

[0156] 0.2-0.5 is level 2, low risk;

[0157] 0.6-0.9 is level 3, medium risk;

[0158] >0.9 is level 4, high risk.

[0159] Step 53, disease inspection and prevention strategy;

[0160] Maintenance strategies are formulated based on risk levels. Level 1 risk-free trees do not require intervention; Level 2 low-risk trees require regular inspections and enhanced protection during daily management and maintenance; Level 3 medium-risk and Level 4 high-risk trees require focused inspections, targeted analysis of the causes of diseases and prevention and control. Level 4 high-risk trees require special attention to root and trunk lesions, and external support should be provided when necessary to enhance their overall stability.

[0161] The storage and visualization module includes a storage module and a visualization module.

[0162] The storage module is used to store the monitoring data and analysis results of the garden heritage plant landscape, including the basic data module, the characteristic data module and the monitoring result module.

[0163] Basic data module: used to store the basic data collected in the data acquisition module.

[0164] Feature data module: used to store the feature data extracted in the feature extraction module.

[0165] Monitoring result module: used to store the monitoring results analyzed in the monitoring calculation module.

[0166] The visualization module is used to integrate the spatial scale with the visual field monitoring results and disease analysis results, including a positioning matching module and a map display module.

[0167] Positioning matching module: used to match the analysis results and geographic information stored in the storage module.

[0168] Map display module: used to convert geographic information data into graphics or images and display them on the map.

[0169] The present invention also provides a method for monitoring and managing garden heritage plant landscape, and the specific implementation steps are as follows:

[0170] Step 1: The data acquisition module collects information losslessly on the target garden heritage plant landscape. The camera and sensor collect ground point cloud and image information, and the drone collects aerial video and image information. It also performs data preprocessing and multi-source information matching, and imports the matching results into the feature extraction module.

[0171] Step 2: The feature extraction module receives the matching results and sends them to the spatial data extraction module and the disease data extraction module; feature recognition, segmentation and data calculation are performed in the two modules respectively, and the results are output and imported into the monitoring calculation module.

[0172] Step 3: The monitoring and calculation module receives the feature extraction results, sends the spatial data extraction results to the spatial scale and viewshed monitoring module, and sends the disease data extraction results to the disease monitoring module; respectively analyze and obtain the spatial scale and viewshed assessment level and disease assessment level of the target garden heritage plant landscape. Input the analysis results into the storage and visualization module.

[0173] Step 4: The storage and visualization module receives the monitoring calculation results, matches the calculation results with the geographic information, and converts them into visual images, which are displayed on the map; and stores the data from the data acquisition module, feature extraction module, and monitoring calculation module as a heritage archive.

[0174] In summary, the garden heritage plant landscape monitoring and management system constructed by the present invention realizes the digital efficient management and multi-party sharing of multi-source and multi-type data of the garden heritage plant landscape, solves the problems of low efficiency, time-consuming and labor-intensive traditional monitoring and management, and provides a new tool for the monitoring and management of the garden heritage plant landscape. The garden heritage plant landscape analysis method developed by the present invention carries out systematic and quantitative calculation and analysis of its current status, solves the problems of strong subjectivity in previous analysis and evaluation, high knowledge reserve requirements for heritage management personnel, and lack of systematic and objective analysis methods, and provides a more systematic and scientific method for the protection of garden heritage plant landscape.

Claims

1. A garden heritage plant landscape monitoring and management system, characterized in that: It includes data acquisition module, feature extraction module, monitoring calculation module, storage and visualization module; The data acquisition module is used to collect the current status of the garden heritage plant landscape and process the data, including a front-end acquisition module and a data matching module; the front-end acquisition module is used to collect spatial information and current status photos of the garden heritage plant landscape in a non-destructive and non-contact manner, including a ground acquisition module and an aerial acquisition module; the data matching module is used to match the point cloud data and photos collected by the ground acquisition module and the aerial acquisition module; The feature extraction module is used to extract the spatial scale information and disease data information of the garden heritage plant landscape, including a spatial data extraction module and a disease data extraction module; the spatial data extraction module is used to identify and segment individual trees in the point cloud model and extract spatial scale data; the disease data extraction module is used to identify and classify tree disease types and perform area calculation; The monitoring and calculation module is used to perform spatial scale and visual field monitoring and calculation, and disease monitoring and calculation on the garden heritage plant landscape; The storage and visualization module includes a storage module and a visualization module. The storage module is used to store the monitoring data and analysis results of the garden heritage plant landscape; the visualization module is used to integrate the spatial scale and visual area monitoring results and disease analysis results.

2. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The spatial data extraction module includes a group identification module, a monomer segmentation module, a spatial feature calculation module and a spatial data output module; The cluster recognition module is used to identify plant landscape clusters from the overall model. By using a point cloud extraction method based on surface segmentation, the curvature of the local surface area of ​​the point cloud is first calculated to identify areas with high curvature or complex shapes; then the point cloud with complex curvature characteristics is segmented as the target cluster; finally, the point cloud model of the plant landscape cluster is obtained by identifying the color and texture information of the image and cleaning non-tree objects; The monomer segmentation module is used to segment tree monomers from the plant landscape group. By using the Hough transform technology based on shape detection to extract point clouds, the geometric shape parameters of the trunk and crown are first defined; then the midpoints of the point cloud are mapped to the parameter space to find the point cloud matching the target parameters; finally, the point cloud that meets the characteristics of the trunk and crown is identified and separated; The spatial feature calculation module is used to extract the spatial scale data of the garden heritage and the scale data of the tree monomer, including a tree height calculation unit, a tree crown calculation unit, a breast diameter calculation unit, and a canopy volume calculation unit; the tree height calculation unit first detects the Z coordinates of the highest point and the lowest point; and then calculates the tree height; The tree crown calculation unit first projects the tree point cloud onto the XY plane; then uses the convex hull algorithm to calculate the boundary of the tree crown on the XY plane; and finally calculates the maximum extension distance of the projection in different directions; the diameter at breast height calculation unit first extracts the point cloud data at 1.3 meters according to the tree height; then uses RANSAC or the least squares fitting method to fit the circular cross-section of these point clouds; and finally calculates the trunk diameter by the radius of the fitted circle; the canopy volume calculation unit first extracts the tree crown; then uses a sphere or ellipsoid model to fit the point cloud data, and the fitting parameters include the radius and height of the tree crown; and finally calculates the tree crown volume according to the geometric model; The spatial data output unit is used to output the calculation results into a suitable storage format.

3. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The disease data extraction module is used to identify and classify tree disease types and calculate the area; it includes a disease characterization recognition module, a disease feature calculation module and a disease data output unit; The disease characterization recognition module is used to identify the disease characterization type of the trees in the model, and includes an input unit and an algorithm unit; the input unit is used to pre-process the collected pictures, use enhanced image technology to highlight the diseased area; and use a geometric correction method to correct the image with angle distortion; The algorithm unit is used to batch identify the location and type of disease representations, and use image segmentation technology to segment disease areas with heterogeneous colors, textures, and shapes from images; use a pre-trained convolutional neural network classification model to identify and classify the types of disease representations in the image; The disease feature calculation module is used to calculate the size of the disease representation, including a projection unit, a length and width calculation unit and an area calculation unit; the projection unit is used to project the segmented disease photos onto the point cloud model, convert the two-dimensional image information into a point cloud with three-dimensional coordinates, and project photos of the same disease area taken at different angles onto the point cloud model through a perspective projection model; the length and width calculation unit calculates the maximum expansion range of the disease area in different directions, projects the disease point cloud on the XY plane, and uses a boundary detection method to define the minimum circumscribed rectangle and calculate the maximum expansion length; the area calculation unit calculates the surface area by performing three-dimensional convex hull fitting on the disease point cloud to obtain the disease area; The disease data output unit outputs the type and size information of the disease.

4. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The monitoring and calculation module includes a spatial scale and visual field monitoring module and a disease monitoring module; The spatial scale and viewshed monitoring module is used to analyze the spatial scale and viewshed of the garden heritage plant landscape unit, determine the plant landscape height, width and viewshed control range, calculate the intervention value, and determine whether the garden heritage plant landscape needs to be adjusted by intervention measures, as well as the adjustment standards; It includes space division module, scale calculation module, viewshed calculation module and overlay calculation module; The space division module is used to identify the plant landscape and the space unit where it is located, and divide the garden heritage into different space units. The space unit is based on the courtyard, which contains the plant landscape; the space unit is bounded by buildings, corridors or walls; the boundaries of the space unit and the scope of the plant landscape are marked in the plan; The scale calculation module is used to define the reasonable range values ​​of the crown width level ratio and the height level ratio, and determine whether the current ratio is within the reasonable range. If not, intervention is required; it includes a crown width level analysis module and a height level analysis module; the crown width level analysis module is used to compare the current data with the historical data, analyze whether the current crown width level ratio conforms to the crown width level ratio in the initial stage of garden construction, calculate a reasonable ratio range, and provide reference data for pruning and supplementing plant landscapes; the height level analysis module is used to compare the current data with the historical data, analyze whether the current height level ratio conforms to the height level ratio in the initial stage of garden construction, calculate a reasonable ratio range, and provide reference data for pruning and increasing and decreasing plant landscapes; The view calculation module is used to define the reasonable range values ​​of the horizontal view and the vertical view, and determine whether the current range is within the reasonable range. If not, intervention is required; the horizontal view analysis module is used to compare the current data with the historical data, analyze whether the current horizontal view range conforms to the horizontal view range at the initial stage of the garden construction, calculate the reasonable range, and provide reference data for the pruning and increase and decrease of the plant landscape; the vertical view analysis module is used to compare the current data with the historical data, analyze whether the current vertical view range conforms to the vertical view range at the initial stage of the garden construction, calculate the reasonable range, and provide reference data for the pruning and increase and decrease of the plant landscape; The superposition calculation module superimposes the calculation results of the scale calculation module and the viewshed calculation module.

5. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The crown width level analysis module includes a first level division unit, a first calculation unit and a first determination unit; The first level division unit uses space units as units to perform crown width level division and quantity statistics on plant landscapes, and the division standards are: 0-4 meters is a small crown width, 4-8 meters is a small and medium crown width, 8-12 meters is a medium crown width, 12-16 meters is a large crown width, and more than 16 meters is an extra-large crown width; The first calculation unit calculates the historical crown width level ratio CLH(H)=A1:B1:C1:D1:E1, where A1 is the number of historical small crown widths, B1 is the number of historical small and medium crown widths, C1 is the number of historical medium crown widths, D1 is the number of historical large crown widths, and E1 is the amount of historical extra-large crown width data; The first determination unit calculates the current crown width level ratio CLH(C)=A2:B2:C2:D2:E2, where A2 is the current number of small crown widths, B2 is the current number of small and medium crown widths, C2 is the current number of medium crown widths, D2 is the current number of large crown widths, and E2 is the current number of extra-large crown widths; determines whether the CLH(C) result is within a reasonable range of crown widths: If it meets the requirements, the intervention value is 0 and no adjustment is required; If it does not meet the requirements, the intervention value is 1, and artificial intervention is required on the existing plant landscape to adjust CLH(C) to the historical crown width ratio value.

6. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The height level analysis module includes a second level division unit, a second calculation unit and a second determination unit; The second level division unit uses space units as units to divide the plant landscape into different levels and count the number of plants. The division standard is: 0-5 meters is low-level, 5-10 meters is low-middle-level, 10-15 meters is medium-high-level, 15-20 meters is high-level, and above 20 meters is extra-high-level. The second calculation unit calculates the historical height level ratio HSR(H)=a1:b1:c1:d1:e1, where a1 is the number of historical low-rises, b1 is the number of historical middle-low-rises, c1 is the number of historical middle-high-rises, d1 is the number of historical high-rises, and e1 is the number of historical extra-high-rises; The second determination unit calculates the current height level ratio HSR(C)=a2:b2:c2:d2:e2, where a2 is the current number of low-rises, b2 is the current number of low-middle-rises, c2 is the current number of high-middle-rises, d2 is the current number of high-rises, and e2 is the current number of extra-high-rises; determines whether the HSR(C) result is within a reasonable height range: If it meets the requirements, the intervention value is 0 and no adjustment is required; If it does not meet the requirements, the intervention value is 1, and it is necessary to use HSR(H)=a2:b2:c2:d2:e2 as the standard to intervene in the garden heritage plant landscape so that the height level ratio is within the reasonable ratio value.

7. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The horizontal visual range analysis module includes a third visual range identification unit, a third type classification unit and a third determination unit; The third visual field recognition unit takes the plant landscape as the target object and the main viewpoint as the viewing point, respectively defines the range of the historical horizontal visual field and the current horizontal visual field, and calculates the visual field angle; The third type division unit defines the types of historical horizontal viewing area and current horizontal viewing area respectively, wherein: 1° to 10° is the best horizontal viewing area, 10° to 20° is the instantaneous horizontal viewing area, 21° to 30° is the effective horizontal viewing area, 31° to 60° is the comfortable horizontal viewing area, and 61° to 120° is the maximum horizontal viewing area; The third determination unit determines whether the types of the historical horizontal view and the current horizontal view are at the same level: If they are at the same level, the intervention value is 0, and no intervention is required; If they are not at the same level, the intervention value is 1, and the type and angle of the historical horizontal view are used as standard values ​​to intervene in the existing plant landscape.

8. The garden heritage plant landscape monitoring and management system according to claim 1 is characterized in that: The horizontal visual range analysis module includes a fourth visual range identification unit, a fourth type classification unit and a fourth determination unit; The fourth visual field recognition unit takes the plant landscape as the target object and the main viewpoint as the viewing point, respectively defines the range of the historical vertical visual field and the current vertical visual field, and calculates the visual field angle; The fourth type division unit defines the types of the historical vertical viewing area and the current vertical viewing area, respectively, wherein: 0 to 27 degrees is the best vertical viewing area, 28 degrees to 45 degrees is the effective vertical viewing area, and greater than 45 degrees is the maximum vertical viewing area; The fourth determination unit determines whether the types of the historical vertical view and the current vertical view are at the same level: if they are at the same level, the intervention value is 0, and no intervention is required; If they are not at the same level, the intervention value is 1, and the type, angle, and D / H of the historical vertical view are used as standard values ​​to intervene in the existing plant landscape, where D is the horizontal distance from the viewpoint to the center of the plant landscape, and H is the height of the plant landscape.

9. The method for implementing the garden heritage plant landscape monitoring and management system according to any one of claims 1 to 8, characterized in that: The steps include: S1, the data acquisition module collects information losslessly on the target garden heritage plant landscape, collects ground point cloud and image information through cameras and sensors, and collects aerial video and image information through drones; performs data preprocessing and multi-source information matching, and imports the matching results into the feature extraction module; S2, the feature extraction module receives the matching result and sends the matching result to the spatial data extraction module and the disease data extraction module; the spatial data extraction module and the disease data extraction module respectively perform feature recognition, segmentation and data calculation, and output the result and import it into the monitoring calculation module; S3, the monitoring and calculation module receives the result of feature extraction, the spatial data extraction result is sent to the spatial scale and visual field monitoring module, and the disease data extraction result is sent to the disease monitoring module; The spatial scale and viewshed monitoring module and the disease monitoring module respectively analyze and obtain the spatial scale and viewshed assessment level and disease assessment level of the target garden heritage plant landscape; the analysis results are input into the storage and visualization module; S4, the storage and visualization module receives the monitoring calculation results, matches the calculation results with the geographic information, converts them into a visual image, and displays them on a map; The data from the data acquisition module, feature extraction module, and monitoring calculation module are stored as heritage archives.

10. The method for implementing the garden heritage plant landscape monitoring and management system according to claim 9 is characterized in that: The implementation steps of disease monitoring are as follows: S51, calculate the disease index; The disease index D of plant landscape is calculated using the formula: D=0.5*D 树干 +0.2*D 树冠 +0.3*D 树基 Among them, D 树干 Scoring is based on the ratio of disease width to trunk circumference, 0-20% for 1 point, 20-50% for 2 points, 50-99% for 3 points; D 树冠 The score is calculated based on the frequency of occurrence, with 1 point for each occurrence; D 树基 Points are awarded based on the proportion of the disease width to the tree base circumference, 0-20% for 1 point, 20-50% for 2 points, 50-99% for 3 points; S52, to classify the disease level; Risk classification is carried out according to the calculation results, and the classification criteria are as follows: 0 points is level 1, no risk; 0.2-0.5 is level 2, low risk; 0.6-0.9 is level 3, medium risk; >0.9 is level 4, high risk; S53, formulate maintenance strategies according to risk levels; Among them, level 1 risk-free trees do not require intervention; level 2 low-risk trees require regular inspections and enhanced protection during daily management and maintenance; level 3 medium-risk and level 4 high-risk trees require key inspections, targeted analysis of the causes of the disease and prevention and control, among which level 4 high-risk trees require special attention to their root and trunk lesions.

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