Method and system for construction acceptance of arbor and shrub specifications in landscaping engineering

Abstract

The present application relates to the technical field of plant specification measurement and acceptance, and in particular to a construction and acceptance method and system for the specification of arbor and shrub plants in landscaping engineering, comprising an acceptance task generation module, a standard acquisition module, a plant measurement module and a report generation module; plant attribute information is extracted based on a design file, and corresponding acceptance tasks are generated according to the plant attribute information; plant tags are scanned based on the acceptance tasks, and corresponding standard specification thresholds are called up; the diameter at breast height, crown width, tree height and branch points of the plants are measured and entered, and automatic compliance judgment is performed and associated with corresponding image data; an acceptance report is generated based on the measurement results and judgment results, and unqualified plants are marked, and a plant growth prediction model is constructed, plant prediction data is output based on current acceptance data for evaluation and early warning, and the accuracy and efficiency of plant construction and acceptance are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plant specification measurement and acceptance, and particularly relates to a construction acceptance method and system for arbor and shrub plant specifications in landscaping engineering. BACKGROUND

[0002] Landscaping engineering is an engineering of constructing landscape green land. Landscaping is to provide a good place for people to rest, cultural entertainment, close to nature, and meet people's desire to return to nature, and is an important measure to protect the ecological environment and improve the urban living environment. The design and application of plants play a crucial role in landscaping engineering. As an important link after design and construction, the acceptance of plants is limited by technical conditions, and the diameter at breast height of arbor is accepted as the basis, and the measurement data of shrub density and height are also rough. At the same time, the plant specifications provided by the construction party often do not meet the requirements of the design scheme, but there is a lack of quick comparison tool during acceptance, which leads to deviation of the design intention. In addition, relevant personnel often rely on their own experience to judge, and lack reliable basis. In addition, the traditional method cannot continuously track the growth state of the planted plants, and it is difficult to evaluate the long-term construction quality, which has a great influence on the accuracy and efficiency of the construction acceptance of plants. SUMMARY

[0003] The present application aims to provide a construction acceptance method and system for arbor and shrub plant specifications in landscaping engineering, and improve the accuracy and efficiency of plant construction acceptance.

[0004] To achieve the above-mentioned purpose, in a first aspect, the present application provides a construction acceptance method for arbor and shrub plant specifications in landscaping engineering, comprising the following steps:

[0005] Extracting plant attribute information based on a design file, and generating a corresponding acceptance task according to the plant attribute information;

[0006] Scanning a plant label and calling a corresponding standard specification threshold based on the acceptance task;

[0007] Measuring and inputting the diameter at breast height, crown width, tree height and branch point of the plant, and automatically judging compliance and associating with corresponding image data;

[0008] Generating an acceptance report based on the measurement results and judgment results, and marking unqualified plants.

[0009] Among them, the plant attribute information is extracted based on the design file, and the corresponding acceptance task is generated according to the plant attribute information, comprising:

[0010] The obtained design file is parsed and layer and legend recognition is performed to extract the attribute information embedded in the plant model object.

[0011] Generating a standard specification threshold range based on the garden plant specification acceptance specification rule base and the corresponding attribute information;

[0012] Obtaining user acceptance information, combining plant attribute information or coordinate distribution to generate an acceptance task.

[0013] The method further comprises:

[0014] Obtaining the plane coordinates of each plant legend, which is used to determine the position distribution in the acceptance task and to count the number of plants of the same type.

[0015] The method further comprises:

[0016] Based on the function division in the acceptance process, the corresponding viewing permission is obtained.

[0017] Based on the acceptance task, scanning the plant label and calling the corresponding standard specification threshold, comprising:

[0018] Based on the acceptance task, using the periodic emission of RFID to obtain the plant ID and signal strength, and combining GPS and Bluetooth for auxiliary positioning;

[0019] Scanning the two-dimensional code signboard and comparing it with the plant ID read by RFID;

[0020] After successful comparison, the corresponding plant attribute information and standard specification threshold range are called.

[0021] Wherein, the diameter at breast height, crown width, tree height and branch point of the plant are measured and entered, and automatic compliance judgment and association with corresponding image data are performed, comprising:

[0022] Measuring the diameter at breast height, crown width, tree height and branch point of the plant;

[0023] After binding all the measurement results with the corresponding plant ID, uploading is performed;

[0024] Comparing the received measurement results with the corresponding standard specification threshold range, and associating all the collected data with the corresponding image data.

[0025] Based on the measurement results and the judgment results, an acceptance report is generated, and unqualified plants are marked, comprising:

[0026] Obtaining the globally unique plant ID, all measured values of measurement items, compliance determination results, time stamps, geographic positions, acceptance personnel IDs and associated image evidence, and performing data cleaning and structured processing;

[0027] Generate an acceptance report based on the obtained qualified rate data and deviation data, and mark the unqualified plants.

[0028] The method further comprises:

[0029] A plant growth prediction model is constructed, and plant prediction data is output based on current acceptance data for evaluation and early warning.

[0030] The plant growth prediction model is constructed, and plant prediction data is output based on current acceptance data for evaluation and early warning, including:

[0031] A regression algorithm is used to generate corresponding growth prediction models for different plants based on stored historical data;

[0032] The current acceptance data is input into the growth prediction model to output corresponding plant prediction data, and each time the acceptance data is compared with the corresponding plant prediction data to evaluate and warn the plant growth.

[0033] In a second aspect, the present application provides a construction acceptance system for the size of arbor and shrub plants in landscaping engineering, which is applied to the construction acceptance method for the size of arbor and shrub plants in landscaping engineering provided in the first aspect. The construction acceptance system for the size of arbor and shrub plants in landscaping engineering comprises an acceptance task generation module, a standard acquisition module, a plant measurement module, and a report generation module.

[0034] The acceptance task generation module is configured to extract plant attribute information based on a design file and generate corresponding acceptance tasks according to the plant attribute information.

[0035] The standard acquisition module is configured to scan plant labels and call corresponding standard size thresholds based on the acceptance tasks.

[0036] The plant measurement module is configured to measure and input the diameter at breast height, crown width, tree height, and branch points of the plants, and automatically perform compliance judgment and association with corresponding image data.

[0037] The report generation module is configured to generate an acceptance report based on the measurement results and judgment results, mark unqualified plants, construct a plant growth prediction model, and output plant prediction data based on current acceptance data for evaluation and early warning.

[0038] This invention discloses a construction acceptance method and system for the specifications of trees and shrubs in landscaping projects. The construction acceptance system for the specifications of trees and shrubs in landscaping projects includes an acceptance task generation module, a standard acquisition module, a plant measurement module, and a report generation module. It extracts plant attribute information based on design documents and generates corresponding acceptance tasks based on the plant attribute information. Based on the acceptance tasks, it scans plant tags and retrieves corresponding standard specification thresholds. It measures and records the plant's diameter at breast height (DBH), crown width, tree height, and branching points, and automatically performs compliance judgments and associates them with corresponding image data. Based on the measurement and judgment results, it generates an acceptance report, marks unqualified plants, constructs a plant growth prediction model, and outputs plant prediction data based on the current acceptance data for evaluation and early warning, thereby improving the accuracy and efficiency of plant construction acceptance. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0040] Figure 1 This is a schematic diagram illustrating the steps of a construction and acceptance method for the specifications of trees and shrubs in a landscaping project, according to the first embodiment of the present invention.

[0041] Figure 2 This is a flowchart illustrating a construction and acceptance method for the specifications of trees and shrubs in landscaping projects, provided by this invention.

[0042] Figure 3 This is a flowchart illustrating step S104 provided by the present invention.

[0043] Figure 4 This is a structural schematic diagram of a construction and acceptance system for the specifications of trees and shrubs in a landscaping project, according to the second embodiment of the present invention.

[0044] Figure 5 This is a schematic diagram of the electronic device of the present invention.

[0045] In the diagram: 101 - Acceptance Task Generation Module, 102 - Standard Acquisition Module, 103 - Plant Measurement Module, 104 - Report Generation Module. Detailed Implementation

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0047] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting thereof. As used in this application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0048] It should be understood that, although the terms first, second, third, etc. can be used herein to describe various information, the information should not be limited to these terms. These terms are only used to differentiate one piece of information from another piece of information. For example, a first information can also be called a second information without departing from the scope of the application, and similarly, a second information can also be called a first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon determination" or "in response to a determination".

[0049] The first embodiment of the present application is:

[0050] Please refer to Figures 1-3 The present application provides a method for construction and acceptance of tree and shrub specifications in landscaping engineering, comprising the following steps:

[0051] S101, extracting plant attribute information based on the design file, and generating a corresponding acceptance task according to the plant attribute information.

[0052] Specifically, first, the design file of the landscaping engineering is obtained, wherein the format of the design file includes DWG / DXF format of CAD, BIM model file, PDF construction drawing, etc. The design file is parsed by using a special green design software, such as LandsCAD, SketchUp landscape plug-in, etc.

[0053] The system has a built-in garden plant legend recognition library that can automatically identify symbols, blocks or specific annotations in the design drawing that represent different arbor and shrub plants. For example, a circular symbol represents an arbor, a triangular symbol represents a shrub, and their legend descriptions are associated. By analyzing the layer structure of the design file, such as "arbor layer", "shrub layer", "plant annotation layer", the plant legends and text annotations placed on the corresponding layers are extracted. The garden plant legend recognition library is based on the industry standard "Garden Greening Engineering Design Drawing Standard" (CJJ / T 67-2015). Arbor legends are usually represented by circular symbols (diameter about 2-4mm), and shrub legends are represented by triangular symbols (edge length about 2-3mm), with legend description text (such as 'arbor''shrub'). The system identifies the symbol shape through a shape matching algorithm (such as Hough transform) and verifies it with the layer name (such as 'arbor layer''shrub layer'). For complex legends, the system supports user-defined rule mapping.

[0054] When extracting attribute information, it is necessary to determine whether the design file is a parameterized file. For parameterized design files (such as BIM models), directly extract the attribute information embedded in the plant model object, including plant species, scientific name, diameter design value, crown width design value, tree height design value, etc. For non-parameterized files (such as ordinary CAD drawings), use a combination of OCR (Optical Character Recognition) and rule matching technology: identify the text annotations near the plant legend, such as "Cinnamomum camphora, Φ18-20cm, H500-550cm, P350-400cm". Analyze the text string through a predefined plant specification keyword rule library (such as "Φ" or "diameter" for diameter, "H" or "tree height" for height, "P" or "crown width" for crown width), and extract the design values of each specification. The OCR keyword rule library defines the priority and context rules of the specification keywords. For example, 'H' is preferentially parsed as tree height in plant specification annotations, but when there are multiple meanings in the annotations (such as 'H' may represent humidity), the system eliminates ambiguity through context analysis (such as adjacent keywords 'diameter' 'crown width'). The rule library supports multi-language keyword mapping (such as 'Height' 'tree height'), and can be dynamically updated based on user feedback.

[0055] At the same time, the planar coordinates (X, Y) of each plant legend are extracted, which are used to determine the approximate location distribution in the acceptance plan and to count the number of the same type of plants.

[0056] The system has a built-in specification acceptance rule library for landscape plants, which integrates national, industry and local standards (such as "Landscaping Engineering Construction and Acceptance Specification" CJJ82-2012). When the design specification value of a certain plant is extracted, the corresponding allowable deviation range in the rule library is automatically matched, that is, the standard specification threshold range. For example, the rule library defines: for trees with a diameter at breast height (DBH) greater than 15 cm, the allowable deviation of DBH is ±5% of the design value; the allowable deviation of crown width is ±10% of the design value. If the design value is 20 cm, the system automatically calculates and generates the DBH threshold range of this plant as 19 cm to 21 cm.

[0057] To increase the flexibility of the system, users (such as owners or supervisors) are also allowed to fine-tune the deviation range based on the specific project characteristics on the basis of the rule library. The adjusted rules will serve as the acceptance criteria unique to this project.

[0058] All extracted and calculated plant attribute information (species, design value, threshold range, planned quantity, location coordinates) is stored in the system database in a structured manner, with each record representing an acceptance standard for a plant and assigned a unique standard plant ID.

[0059] Obtain user input acceptance information, which includes at least task name, associated project, calculation acceptance date, and acceptance section / area, etc. There are two ways to divide the acceptance range: way one (according to design coordinates): the system displays the coordinate distribution of the imported plants on the map interface (integrated GIS or based on the design map base map). Users can determine the plant range that needs to be accepted by frame selection, polygon selection or by green partition selection. The selected plant list will be automatically added to the acceptance task. Way two (according to plant species): users filter by plant species (such as all ginkgo, all osmanthus) from the imported plant list, and add all or part of the selected species to the acceptance task. The acceptance standard is to automatically bind the standard specification threshold range of the selected plants to the acceptance task as the criterion for this acceptance.

[0060] To ensure data security, according to the role of personnel in the acceptance process, the system permissions are divided into acceptance administrator, acceptance clerk, construction representative and supervisor, etc. Different viewing permissions and data operation permissions are divided according to different roles.

[0061] According to the obtained acceptance information, combined with the selected plant list and corresponding plant attribute information, standard specification threshold range and other data, an acceptance task is generated, and a unique two-dimensional code or task number is generated. When the acceptance task is created, the acceptance task is published to the account of the designated acceptance person, so as to facilitate timely acquisition of the acceptance task and timely start of the acceptance process, and there is no worry about leakage of relevant confidential information, and the acceptance task contains all the required information, and the acceptance person does not need to check the design file to perform acceptance, realizing digital and standardized conversion from design intention to executable acceptance task, and laying a solid foundation for subsequent efficient and accurate on-site acceptance.

[0062] S102, scanning the plant label based on the acceptance task and calling the corresponding standard specification threshold.

[0063] Specifically, before scanning the plant label, it is necessary to ensure that each plant has a corresponding plant identity label, that is, each arbor and shrub must be firmly bound with a plant identity sign on the main trunk or main branch when the nursery is out of the nursery or on-site planting. The sign is made of waterproof, corrosion-resistant and weather-resistant materials (such as ABS engineering plastic or anodized aluminum), with an ultra-high frequency RFID chip inside and a laser-engraved or printed two-dimensional code on the surface. The RFID chip and two-dimensional code in the sign store a globally unique plant ID (GUID). The ID coding rule is: project code (6 digits) + tree species code (4 digits) + planting area code (4 digits) + serial number (6 digits). For example: "PRJ001-MAQS-A001-000001".

[0064] According to the obtained acceptance task, the acceptance person holds a terminal integrated with an RFID read-write module to enter the acceptance area, and periodically transmits a wake-up signal through the terminal. In an unobstructed environment, the ultra-high frequency RFID read-write distance is 5-8m; in a densely planted area, the distance may be reduced to 2-3m, and it is recommended to use Impinj R700 reader. The special App on the terminal screen will display the list of nearby awakened plant IDs and their approximate signal strength (used for auxiliary coarse positioning). For densely planted areas, the App can combine the terminal's GPS and Bluetooth iBeacon (pre-deployed at key points on site) for auxiliary positioning, and highlight the plants to be accepted near the current position on the electronic map.

[0065] After the acceptance personnel approaches the target plant, the high-definition camera of the terminal scans the two-dimensional code signboard. This has two purposes: one is to ensure that the ID read by the RFID is consistent with the two-dimensional code ID, preventing RFID misreading or string reading. Two, the camera captures the image of the signboard, and the OCR identifies the basic information such as the Chinese name and scientific name of the plant marked on the signboard, which is used for the acceptance personnel to verify by naked eye to ensure that the plant is not selected incorrectly. After scanning and comparing, through 4G / 5G or pre-downloaded offline data package, the system immediately retrieves all the information corresponding to the globally unique plant ID: including plant species, design specification value, standard specification threshold range, expected position in the design drawing, etc., and displays them on the acceptance interface of the terminal App.

[0066] S103, measure and input the diameter at breast height, crown width, tree height and branch point of the plant, and automatically judge the compliance and associate with the corresponding image data.

[0067] Specifically, the measuring instrument needed has a laser radar scanner, a high-resolution optical camera, a high-precision GPS and an IMU (inertial measurement unit), a touch screen and a large-capacity battery. The laser radar can use RIEGL VZ-400i laser scanner with a measurement accuracy of ±5mm, or equivalent performance equipment (such as FARO Focus series). First, measure the diameter at breast height:

[0068] 1. Data collection:

[0069] Laser radar scanning: the operator aims the multifunctional plant measuring instrument at the breast height of the tree trunk (1.3 meters above the ground). The built-in laser radar emitter of the instrument rotates to emit laser beams, which are captured by the receiver after being reflected by the tree trunk surface.

[0070] Point cloud generation: by calculating the time of flight (ToF) of the laser, the distance and angle between each laser and the instrument are obtained, and the three-dimensional coordinates (X, Y, Z) of thousands of points on the cross section of the tree trunk are obtained, forming a high-density "point cloud".

[0071] 2. Data processing and calculation:

[0072] Point cloud preprocessing: the algorithm first filters the original point cloud to remove obvious noise points (such as tree leaves and branches).

[0073] Cross section extraction: due to the inevitable slight shaking during measurement, the algorithm filters the point cloud within a narrow range (e.g. 1.29m to 1.31m) in the Z-axis (height) direction to ensure that the points analyzed are at approximately the same height.

[0074] Cylinder fitting: a cylinder model is fitted using the random sample consensus algorithm (RANSAC).

[0075] Randomly sample three points from the point cloud and compute a cylinder model determined by them.

[0076] Compute the distance of all other points to the surface of this cylinder model and count the number of points within a pre-defined threshold (e.g. 1mm), called "inliers".

[0077] Repeat the above process for multiple iterations and finally select the cylinder model with the most "inliers" as the optimal fit. In the chest diameter measurement, the RANSAC algorithm is used to fit a cylinder model with a distance threshold set to 1-5mm, the specific value is adjusted according to the point cloud density: for high-density point cloud (point spacing <1cm), the threshold is set to 1mm; for low-density point cloud (point spacing ≥1cm), the threshold is set to 5mm. The number of iterations is set to 100-500 to ensure the fitting accuracy.

[0078] Diameter calculation: The diameter of the optimal fitted cylinder model is the calculated chest diameter value. This value is directly displayed on the screen and automatically recorded.

[0079] Ground diameter measurement: The process is exactly the same, just adjust the measurement height to 5-10 centimeters from the ground.

[0080] Upload: The measurement results (including possible partial close-up images) are automatically uploaded to the system via Bluetooth, and the App binds them with the current plant's globally unique plant ID.

[0081] Crown width measurement:

[0082] Standing posture calibration: The person stands about 2-3 meters away from the edge of the tree crown projection, with the measuring instrument lens facing the tree crown.

[0083] Data collection: SLAM scanning: The operator starts the crown width measurement mode and slowly walks around the plant with the handheld device. The LiDAR and IMU on the device are continuously working, the LiDAR obtains the point cloud of the surrounding environment, and the IMU records the movement and attitude changes of the device. Real-time modeling: The simultaneous localization and mapping (SLAM) algorithm fuses LiDAR and IMU data in real time, estimates the device's own motion trajectory, and gradually builds a high-precision three-dimensional point cloud model of the entire tree crown and its surrounding environment.

[0084] Data processing and calculation:

[0085] Point cloud segmentation: After scanning, the algorithm first separates the point cloud belonging to the target tree from the background (such as the ground, other plants, and buildings) using Euclidean clustering. In canopy measurement, the distance threshold for Euclidean clustering is set to 0.1-0.5m. For plants with dense canopies (such as shrubs), the threshold is set to 0.1m; for plants with sparse canopies (such as trees), the threshold is set to 0.5m. The minimum number of cluster points is 50 to filter noise.

[0086] Main trunk positioning: Locate the part of the canopy point cloud that connects to the ground, and determine the position of the root of the trunk (X0, Y0).

[0087] Projection and bounding box calculation:

[0088] All three-dimensional points of the tree canopy are vertically projected onto a horizontal plane to form a two-dimensional planar projection.

[0089] Calculate the maximum and minimum coordinates of the projected point set in the east-west (EW) and north-south (SN) directions.

[0090] East-west crown width = Xmax - Xmin;

[0091] North-South Crown Width = Ymax - Ymin;

[0092] Average crown width = (east-west crown width + north-south crown width) / 2.

[0093] Calculation of projected area:

[0094] Calculate the convex hull of a two-dimensional projected point set, which is the smallest convex polygon that can enclose all points.

[0095] The area of ​​the convex polygon is calculated using the shoelace formula, which is the projected area of ​​the tree canopy.

[0096] Data association: Results are automatically uploaded to the terminal app and bound to the plant ID.

[0097] Tree height and branching point height measurement:

[0098] Aiming: Align the laser point of the measuring instrument with the highest point of the treetop.

[0099] Calculation process: The device fuses two types of data:

[0100] Laser ranging: measuring the straight-line distance (S) from the instrument to the treetop.

[0101] IMU tilt angle: The angle (θ) between the optical axis of the measuring instrument and the horizontal plane.

[0102] Tree height calculation: According to trigonometric functions, tree height (H) consists of two parts:

[0103] H = H1 + H2.

[0104] H1 (height difference between instrument and tree base): Generally, the instrument height (H_device, known) minus the operator eye height (known) can be approximated as H1, but a more accurate approach is to use both laser measurements of the instrument to tree base distance and angle to recalculate.

[0105] H2 (height difference between tree base and tree top): H2 = S * sin(0);

[0106] Therefore, the tree height H ≈ H_device + S * sin(0).

[0107] 2. Branching point height measurement:

[0108] Data collection: In the complete tree point cloud obtained by LiDAR scanning, the information of the trunk and branches is already included.

[0109] Calculation process:

[0110] Point cloud clustering analysis: The algorithm clusters the point cloud to identify the trunk point cloud cluster and each level of branch point cloud cluster.

[0111] Trunk extraction: By analyzing the connectivity and vertical continuity of the point cloud, the trunk line from the ground to the tree top is extracted.

[0112] First branch identification: Scan from bottom to top along the trunk line, detect the first position where there is a significant increase in point cloud density or a significant deviation from the trunk center line, which is the first branching point.

[0113] Height calculation: Calculate the three-dimensional coordinates (X_b, Y_b, Z_b) of the branching point. The branching point height is the Z_b coordinate value (relative to the elevation reference surface set by the instrument at the time of measurement).

[0114] Data packet generation: All height data is automatically packaged and uploaded.

[0115] After the system receives any measurement data, it immediately compares it with the standard specification threshold range corresponding to the plant ID. If it is qualified: the screen displays a green check mark with a prompt sound. If it is not qualified: the screen displays a red cross with an alarm sound. At the same time, clearly display the over-standard items, design value, measured value and deviation percentage, such as: "diameter over standard: design value 20 cm, measured value 18 cm, deviation -10%".

[0116] During the above measurement process, the high-resolution camera of the multifunctional plant measurement instrument automatically triggers photo shooting at key steps: when scanning and identifying the plant identity sign, a panoramic photo containing the sign and the overall plant environment is automatically taken. When the breast height / ground diameter laser scanning is performed, a close-up photo of the measurement site is automatically taken. After the crown width scanning is completed, a tree crown form photo is automatically taken from the best angle. And the following metadata is automatically written in the EXIF information of each photo: global unique plant ID (PlantID), latitude and longitude coordinates (from GPS coordinates (GPSLatitude, GPSLongitude)), timestamp (DateTime), measurement result (such as qualified / unqualified status) (MeasurementStatus), acceptance task ID (TaskID), inspector ID (InspectorID).

[0117] The system automatically associates all the above images with the measurement data records of this acceptance, forming an indivisible data package. When the on-site network condition allows (or through the built-in SIM card of the terminal), the data package is automatically synchronized to the cloud system. If the network is interrupted, the data is temporarily stored locally, and after the network is restored, it is automatically resumed. For all records determined as "unqualified", the system will automatically generate a hash value for the complete data package (including all images and measurement data) associated with it, and upload it to the blockchain network for notarization, ensuring its non-tamperability, as key evidence for later settlement or dispute resolution. This minimizes manual intervention and subjective judgment, ensuring the objectivity, accuracy and efficiency of data collection, while forming a complete and reliable evidence chain.

[0118] S104, generating an acceptance report based on the measurement results and judgment results, and marking the unqualified plants.

[0119] Specifically, the system continuously receives on-site acceptance data packets uploaded from each intelligent acceptance terminal. Each data packet contains: a globally unique plant ID, measured values of all measurement items, compliance determination results, a timestamp, a geographic location, an acceptance personnel ID, and associated image evidence. First, the received raw data is automatically cleaned (such as removing obviously abnormal GPS points), and then the data is sorted by acceptance task ID and globally unique plant ID and stored in a structured project database. Then the overall pass rate of this acceptance task, the pass rate by plant species, and the pass rate by acceptance area are automatically calculated. For unqualified items, the distribution of deviation values is automatically counted, such as “diameter deviation small accounts for 70% of unqualified items”. The report engine extracts data from the database and automatically fills in the corresponding positions of the template, including: tables: generate a detailed plant acceptance list table, listing the design value, measured value, deviation, and determination result of each plant. Charts: automatically generate histograms (showing specification value distribution), pie charts (showing qualified / unqualified ratio), scatter plots (showing deviation situation), etc. Image evidence embedding: automatically embed the close-up photos of unqualified plants in the corresponding positions of the report with a text description.

[0120] After the report is generated, the system automatically converts it to PDF format. Users can set automatic distribution rules, such as sending the report to the project manager, the supervising party, and the owner's responsible person immediately after completion by email. All versions of the report are archived in the system and can be traced and reviewed.

[0121] Unqualified plants are marked as “to be rectified”, and the construction party reinitiates the acceptance application after rectification, and the system records the whole process trace.

[0122] In order to facilitate long-term acceptance and avoid omissions, a plant growth prediction model is constructed to output plant prediction data for evaluation and early warning based on current acceptance data, specifically:

[0123] First, the system creates an independent electronic file for each plant with a globally unique plant ID. This file not only contains the initial data at the time of acceptance, but also becomes a continuously updated data log, recording key data throughout the plant's life cycle:

[0124] Basic data: tree species, scientific name, planting time, initial design specifications, acceptance data.

[0125] Regular monitoring data: the system automatically generates a long-term re-measurement plan (such as 3, 6, 12, and 24 months after planting). Acceptance personnel execute the re-measurement according to the plan, use the same intelligent acceptance terminal and multifunctional plant measuring instrument to collect data, and ensure the consistency of data sources and accuracy. All re-measurement data are automatically incorporated into the plant file.

[0126] Environmental data: Collect and correlate temperature, humidity, rainfall, soil moisture, etc. data of the area where the plants are located through IoT devices (such as weather stations, soil sensors).

[0127] Maintenance records: Manually input or system-integrated operation records such as watering, fertilizing, pruning, pest control, etc.

[0128] Then, build a growth prediction model, use the accumulated multi-project, multi-species, long-time-span plant electronic file data as the training set. Use machine learning algorithms (such as time series analysis, regression algorithm) to establish personalized growth prediction models for different tree species (even different site conditions of the same tree species). The input parameters of the model usually include: initial size, size change over the years, environmental data, maintenance records; the output is the size prediction value and confidence interval at a certain future time point. Among them, the growth prediction model uses multiple linear regression or random forest regression algorithm. The characteristic variables include plant initial size (diameter at breast height, crown width, tree height), environmental data (temperature, humidity, soil moisture), maintenance records (watering frequency, fertilizer amount) and time series data. Model training uses historical data set, selects the optimal parameters through cross-validation, and the prediction output is the size value and confidence interval at the future time point.

[0129] After each new re-measurement data is input, the system will automatically call the growth prediction model of the tree species to predict its next stage growth trend (such as "it is expected that the crown width will reach 4.5±0.2m after 6 months"). The system can analyze the growth speed and uniformity of the entire project or specific area plant population, and evaluate the stability of the overall landscape effect. Especially for the measurement data of the marked unqualified plants, it is necessary to input into the growth prediction model for evaluation and analysis, when the actual growth of a plant is continuously and significantly lower than the lower limit of the model prediction value, the system will automatically trigger a growth abnormality warning. The system will prompt the maintenance personnel to pay attention to the plant, and may give a preliminary cause analysis combined with environmental data (such as "the soil moisture has been continuously low in recent period, it is suggested to check the irrigation system"). Extend the acceptance to the maintenance stage, and strengthen the whole process control of engineering quality.

[0130] The second embodiment of the present application is:

[0131] Please refer to Figure 4 The present application provides a construction and acceptance system for the size of arbor and shrub plants in landscaping engineering, which is applied to the construction and acceptance method for the size of arbor and shrub plants in landscaping engineering provided by the first embodiment. The construction and acceptance system for the size of arbor and shrub plants in landscaping engineering comprises an acceptance task generation module 101, a standard acquisition module 102, a plant measurement module 103 and a report generation module 104.

[0132] The acceptance task generation module 101 is configured to extract plant attribute information based on a design file, and generate a corresponding acceptance task according to the plant attribute information.

[0133] The standard acquisition module 102 is configured to scan a plant label based on the acceptance task and call a corresponding standard specification threshold.

[0134] The plant measurement module 103 is configured to measure and input the diameter at breast height, crown width, tree height and branch point of the plant, and automatically perform compliance judgment and association with corresponding image data.

[0135] The report generation module 104 is configured to generate an acceptance report based on the measurement result and the judgment result, mark unqualified plants, and construct a plant growth prediction model to output plant prediction data based on current acceptance data for evaluation and early warning.

[0136] As to the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.

[0137] For the system embodiment, since it basically corresponds to the method embodiment, the related parts refer to the part of the method embodiment. The device embodiment described above is only schematic, and the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e. they can be located in one place, or distributed on multiple network units. Some or all of the modules can be selected to achieve the purpose of the scheme of the present application according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0138] Correspondingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the construction and acceptance method of arbor and shrub specifications in landscape engineering as described above. As Figure 5 shown, a hardware structure diagram of a system for construction and acceptance of arbor and shrub specifications in landscape engineering according to an embodiment of the present application is provided in any device with data processing capability. In addition to Figure 5 the processor, the memory and the network interface shown, any device with data processing capability in the embodiment of the device can also include other hardware according to the actual function of the device with data processing capability, and this will not be described again.

[0139] Correspondingly, the application further provides a computer readable storage medium, which stores computer instructions, and the instructions are executed by a processor to realize the method for construction and acceptance of the specifications of arbor and shrub in the landscaping project. The computer readable storage medium can be an internal storage unit of any device with data processing capability, such as a hard disk or a memory. The computer readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. Further, the computer readable storage medium can include both the internal storage unit of any device with data processing capability and the external storage device. The computer readable storage medium is used to store the computer program and other programs and data required by the device with data processing capability, and can also be used to temporarily store data that has been output or will be output.

[0140] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application embrace any and all variations of the present application that fall within the scope of the general inventive concept as defined by the appended claims and their equivalents.

[0141] It is to be understood that the application is not limited to the precise construction described and as shown in the attached drawings, and that various modifications and changes can be effected thereon without departing from the scope of the application.

Claims

1. A method for construction acceptance of the size of arbor and shrub in landscaping engineering, characterized in that, The method comprises the following steps: extracting plant attribute information based on the design file, and generating a corresponding acceptance task according to the plant attribute information; scanning the plant label based on the acceptance task and calling the corresponding standard specification threshold; measuring and inputting the diameter at breast height, crown width, tree height and branch point of the plant, and automatically judging the compliance and associating with the corresponding image data; generating an acceptance report based on the measurement results and judgment results, and marking the unqualified plants; extracting plant attribute information based on the design file, and generating a corresponding acceptance task according to the plant attribute information, comprising: parsing the obtained design file, identifying the symbols, blocks or specific annotations representing different arbor and shrub plants in the design file based on a landscape plant legend recognition library; by parsing the layer structure of the design file, the plant legends and text annotations placed on the corresponding layers are extracted, and the symbol shape is identified through a shape matching algorithm and verified in combination with the layer name; when extracting the attribute information, it is judged whether the design file is a parameterized file, for the parameterized design file, the attribute information embedded in the plant model object is directly extracted, including plant species, scientific name, diameter at breast height / design value, crown width design value, tree height design value; for non-parameterized files, a combination of OCR and rule matching is used to identify the text annotations near the plant legend, and the design values of various specifications are extracted by parsing the text string through a pre-defined plant specification keyword rule library; obtaining the planar coordinates of each plant legend, which is used to determine the position distribution in the acceptance task and to count the number of the same type of plants; generating a standard specification threshold range based on a landscape plant specification acceptance specification rule library and the corresponding attribute information; obtaining user acceptance information, combining the attribute information or coordinate distribution of the plant to generate an acceptance task; measuring and inputting the diameter at breast height, crown width, tree height and branch point of the plant, and automatically judging the compliance and associating with the corresponding image data, comprising: measuring the diameter at breast height, crown width, tree height and branch point of the plant; binding all the measurement results with the corresponding plant ID and uploading; comparing the received measurement results with the corresponding standard specification threshold range, and associating all the collected data with the corresponding image data; wherein the measurement of the diameter at breast height is as follows: aligning the multifunctional plant measuring instrument with the diameter at breast height of the tree trunk, the laser radar emitter built-in the instrument rotates to emit laser beams, the laser beams are reflected by the tree trunk surface and captured by the receiver, the distance and angle between each laser beam and the instrument are obtained by calculating the round-trip time of the laser, thereby obtaining the three-dimensional coordinates on the corresponding cross section of the tree trunk, forming a point cloud; filtering the point cloud, and selecting the point cloud within a certain range in the Z-axis direction; fitting a cylindrical model with the RANSAC algorithm, randomly selecting three points from the point cloud to calculate a cylindrical model determined by the selected three point cloud points; calculating the distance between all other points and the surface of the cylindrical model, and counting the number of points within a preset threshold, the points within the preset threshold are called inliers; Repeat the above process for multiple iterations, and finally select the cylinder model with the most inliers as the optimal fitting model. In the chest diameter measurement, the RANSAC algorithm is used to fit the cylinder model, and the distance threshold is set to 1-5mm, the specific value is adjusted according to the point cloud density: for point cloud with point spacing <1cm, the threshold is set to 1mm; for point cloud with point spacing ≥1cm, the threshold is set to 5mm; the number of iterations is set to 100-500 times; The diameter of the optimal fitting cylinder model is the calculated chest diameter value; Construct a plant growth prediction model, output plant prediction data based on current acceptance data for evaluation and early warning, including: Using a regression algorithm, combine the stored historical data to generate corresponding growth prediction models for different plants; Input the current acceptance data into the growth prediction model, output the corresponding plant prediction data, and compare each time's acceptance data with the corresponding plant prediction data to evaluate and warn the plant growth.

2. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, characterized in that, The method further comprises: Based on the functional division in the acceptance process, the corresponding viewing permission is obtained.

3. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, wherein, Based on the acceptance task, scan the plant label and call the corresponding standard specification threshold, including: Based on the acceptance task, use the periodic transmission of RFID to wake up the signal to obtain the plant ID and signal strength, and combine GPS and Bluetooth for auxiliary positioning; Scan the two-dimensional code signboard and compare it with the plant ID read by RFID; After successful comparison, call the corresponding plant attribute information and standard specification threshold range.

4. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, wherein, Generate an acceptance report based on the measurement results and judgment results, and mark the unqualified plants, including: Get the globally unique plant ID, all measured values of measurement items, compliance judgment results, timestamps, geographic locations, acceptance personnel IDs, and associated image evidence, and perform data cleaning and structured processing; Generate an acceptance report based on the obtained qualified rate data and deviation data, and mark the unqualified plants.

5. A construction acceptance system for the specification of arbor and shrub plants in landscape engineering, applied to the construction acceptance method for the specification of arbor and shrub plants in landscape engineering as claimed in claim 1, characterized in that, The construction acceptance system for the specification of arbor and shrub plants in landscape engineering comprises an acceptance task generation module, a standard acquisition module, a plant measurement module, and a report generation module; The acceptance task generation module is used to extract plant attribute information based on a design file, and generate corresponding acceptance tasks according to the plant attribute information; The standard acquisition module is used to scan the plant label based on the acceptance task and call the corresponding standard specification threshold; The plant measurement module is used to measure and input the chest diameter, crown width, tree height, and branch points of the plant, and automatically perform compliance judgment and association with corresponding image data; The report generation module is used to generate an acceptance report based on the measurement results and judgment results, and mark the unqualified plants, and construct a plant growth prediction model, output plant prediction data based on current acceptance data for evaluation and early warning.

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