Construction acceptance method and system for arbor and shrub plant specifications in landscaping engineering

Abstract

The invention relates to the technical field of plant specification measurement and acceptance, in particular to a construction acceptance method and system for arbor and shrub plant specifications in landscaping engineering, and the system comprises an acceptance task generation module, a standard acquisition module, a plant measurement module and a report generation module. Extracting plant attribute information based on the design file, and generating a corresponding acceptance task according to the plant attribute information; scanning a plant label based on the acceptance task and calling a corresponding standard specification threshold; the diameter at breast height, the crown breadth, the tree height and the branch point of the plant are measured and recorded, compliance judgment is automatically carried out, and correlation with corresponding image data is carried out; the acceptance report is generated based on the measurement result and the judgment result, the unqualified plants are marked, the plant growth prediction model is constructed, the plant prediction data are output based on the current acceptance data for evaluation and early warning, and the accuracy and efficiency of construction acceptance of the plants 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 and 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 acceptance of arbor is only based on diameter at breast height, 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: Extracting plant attribute information based on a design file, and generating a corresponding acceptance task according to the plant attribute information; Scanning a plant label and calling a corresponding standard specification threshold based on the acceptance task; Measuring and inputting the diameter at breast height, crown width, tree height and branch point of the plant, and automatically performing compliance judgment and association with corresponding image data; Generating an acceptance report based on the measurement results and judgment results, and marking unqualified plants.

[0005] 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: Analyzing the obtained design file and performing layer and legend recognition to extract attribute information embedded in the plant model object; 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 plant attribute information or coordinate distribution to generate an acceptance task.

[0006] The method further comprises: Obtaining the plane coordinates of each plant legend to determine the position distribution in the acceptance task and count the number of plants of the same type.

[0007] The method further comprises: Based on the division of functions in the acceptance process, the corresponding viewing permission is obtained.

[0008] The method further comprises: Based on the acceptance task, the plant ID and signal strength are obtained by periodically transmitting a wake-up signal using RFID, and GPS and Bluetooth are used for auxiliary positioning. Scan the two-dimensional code signboard and compare it with the plant ID read by RFID; After successful comparison, the corresponding plant attribute information and standard specification threshold range are retrieved.

[0009] The method further comprises: Measure the diameter at breast height, crown width, tree height, and branch point of the plant; Bind all measurement results to the corresponding plant ID and upload them; Compare the received measurement results with the corresponding standard specification threshold range, and associate all collected data with the corresponding image data.

[0010] The method further comprises: Obtain the globally unique plant ID, measured values of all measurement items, compliance determination results, time stamps, geographic locations, acceptance personnel IDs, and associated image evidence, and perform data cleaning and structured processing; Based on the obtained qualified rate data and deviation data, generate an acceptance report and mark unqualified plants.

[0011] The method further comprises: Construct a plant growth prediction model, output plant prediction data based on current acceptance data for evaluation and early warning.

[0012] The method further comprises: The regression algorithm is used to generate a corresponding growth prediction model for different plants in combination with stored historical data; The current acceptance data is input into the growth prediction model, the corresponding plant prediction data is output, and each time the acceptance data is compared with the corresponding plant prediction data to evaluate and warn the plant growth.

[0013] In a second aspect, the present application provides a construction acceptance system for the size of arbor and shrub in landscaping engineering, which is applied to the construction acceptance method for the size of arbor and shrub in landscaping engineering provided in the first aspect. The construction acceptance system for the size of arbor and shrub in landscaping 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 configured to extract plant attribute information based on a design file and generate a corresponding acceptance task according to the plant attribute information. The standard acquisition module is configured to scan a plant label and call corresponding standard size thresholds based on the acceptance task. The plant measurement module is configured to measure and input the diameter at breast height, crown width, tree height, and branch points of the plant, automatically perform compliance judgment, and associate with corresponding image data. 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, output plant prediction data based on current acceptance data, and perform evaluation and warning.

[0014] The construction acceptance method and system for the size of arbor and shrub in landscaping engineering provided by the present application comprises 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 a corresponding acceptance task is generated according to the plant attribute information. A plant label is scanned and corresponding standard size thresholds are called based on the acceptance task. The diameter at breast height, crown width, tree height, and branch points of the plant are measured and input, and automatic compliance judgment and association with corresponding image data are performed. An acceptance report is generated based on the measurement results and judgment results, unqualified plants are marked, a plant growth prediction model is constructed, plant prediction data is output based on current acceptance data, and evaluation and warning are performed. The accuracy and efficiency of plant construction acceptance are improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced as follows.

[0016] Figure 1It is a step schematic diagram of a construction acceptance method of tree and shrub specifications in a landscaping engineering according to the first embodiment of the present application.

[0017] Figure 2 It is a flow schematic diagram of a construction acceptance method of tree and shrub specifications in a landscaping engineering according to the present application.

[0018] Figure 3 It is a flow schematic diagram of step S104 according to the present application.

[0019] Figure 4 It is a structure schematic diagram of a construction acceptance system of tree and shrub specifications in a landscaping engineering according to the second embodiment of the present application.

[0020] Figure 5 It is a structure schematic diagram of an electronic device according to the present application.

[0021] In the figure: 101-acceptance task generation module, 102-standard acquisition module, 103-plant measurement module, 104-report generation module. DETAILED DESCRIPTION

[0022] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to indicate the same or similar components. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application.

[0023] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the present 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.

[0024] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used only to distinguish one from another. For example, a first information can be termed a second information, and similarly, a second information can also be termed a first information, without departing from the scope of the present application. The word "if" as used herein means "when" or "upon" or "in response to the determination" depending on the context.

[0025] The first embodiment of the present application is: Please refer to Figures 1-3 The present application provides a construction acceptance method of tree and shrub specifications in a landscaping engineering, comprising the following steps: S101, extract plant attribute information based on the design file, and generate a corresponding acceptance task according to the plant attribute information.

[0026] Specifically, first, the design file of the landscaping project is obtained, wherein the format of the design file includes, for example, 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.

[0027] The system has a built-in landscape plant legend recognition library, which can automatically identify symbols, blocks or specific annotations representing different arbor and shrub plants in the design drawing. For example, a circular symbol represents an arbor, and a triangular symbol represents a shrub, and the legend description is associated. By parsing the layer structure of the design file, such as "arbor layer", "shrub layer", "plant annotation layer", the plant legend and text annotation placed on the corresponding layer are extracted. The landscape plant legend recognition library is based on the industry standard "Landscaping Engineering Design Drawing Standard" (CJJ / T 67-2015). The arbor legend usually uses a circular symbol (diameter about 2-4 mm) to represent, and the shrub legend uses a triangular symbol (edge length about 2-3 mm) to represent, and is associated with the legend description text (such as 'arbor''shrub'). The system identifies the symbol shape by shape matching algorithm (such as Hough transform), and verifies it in combination with the layer name (such as 'arbor layer''shrub layer'). For complex legends, the system supports user-defined rule mapping.

[0028] 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), the attribute information embedded in the plant model object is directly extracted, 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), the technology of combining OCR (optical character recognition) with rule matching is adopted: identifying the text annotations near the plant legend, such as "Cinnamomum camphora, Φ18-20cm, H500-550cm, P350-400cm". The design values of each specification are extracted by parsing the text string through a pre-defined plant specification keyword rule library (such as "Φ" or "diameter" representing diameter, "H" or "tree height" representing height, "P" or "crown width" representing crown width). 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.

[0029] At the same time, the planar coordinates (X, Y) of each plant legend are extracted to determine the approximate position distribution in the acceptance plan and to count the number of plants of the same type.

[0030] The system has a built-in garden plant specification acceptance specification rule library, which integrates national, industry and local standards (such as “Garden Greening Engineering Construction and Acceptance Specification” CJJ82-2012). When the design specification value of a certain plant is extracted, the corresponding allowable deviation range, i.e. the standard specification threshold range, in the rule library is automatically matched. For example, the rule library defines that for trees with a diameter at breast height (DBH) > 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.

[0031] To increase the flexibility of the system, users (such as the owner or the supervision party) are also allowed to fine-tune the deviation range based on the rule library according to the specific project characteristics. The adjusted rules will serve as the acceptance standard unique to this project.

[0032] All extracted and calculated plant attribute information (species, design value, threshold range, planned quantity, position coordinates) is stored in the system database in a structured manner, with each record representing an acceptance standard for a plant and being assigned a unique standard plant ID.

[0033] 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 to be accepted this time 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.

[0034] To ensure data security, according to the roles of personnel in the acceptance process, the system permissions are divided into acceptance administrators, acceptance staff, construction party representatives, and supervisors, etc. Different viewing permissions and data operation permissions are divided according to different roles.

[0035] 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.

[0036] S102, scan the plant label based on the acceptance task and call the corresponding standard specification threshold.

[0037] 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".

[0038] 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 dense plant 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.

[0039] 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 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.

[0040] 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.

[0041] 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: 1. Data acquisition: 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 laser radar emitter built into the instrument rotates to emit laser beams, which are captured by the receiver after being reflected by the tree trunk surface.

[0042] 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".

[0043] 2. Data processing and calculation: Point cloud preprocessing: the algorithm first filters the original point cloud to remove obvious noise points (such as tree leaves and branches).

[0044] Cross section extraction: due to 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.

[0045] Cylinder fitting: a cylinder model is fitted using the random sample consensus algorithm (RANSAC).

[0046] Randomly sample three points from the point cloud to calculate a cylinder model determined by them.

[0047] Compute the distance of all other points to the surface of the cylinder model, count the number of points within a preset threshold (e.g. 1mm) (called "inliers").

[0048] Repeat the above process for multiple iterations, 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 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 times to ensure the fitting accuracy.

[0049] Diameter calculation: the diameter of the optimal fitting cylinder model is the calculated chest diameter value. This value is directly displayed on the screen and automatically recorded.

[0050] Ground diameter measurement: the process is exactly the same, just adjust the measurement height to 5-10cm from the ground.

[0051] Upload: the measurement results (including the local close-up images that may be taken) are automatically uploaded to the system via Bluetooth, and the App binds them with the globally unique plant ID of the current plant.

[0052] Crown width measurement: Standing posture calibration: the person stands about 2-3 meters away from the edge of the tree crown projection, and the measuring instrument lens is directed towards the tree crown.

[0053] Data acquisition: 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 work continuously, 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 the LiDAR and IMU data in real time, estimates the motion trajectory of the device itself, and gradually builds a high-precision three-dimensional point cloud model of the entire tree crown and its surrounding environment.

[0054] Data processing and calculation: Point cloud segmentation: after the scanning is completed, the algorithm first separates the point cloud belonging to the target tree from the background (such as the ground, other plants, buildings) through Euclidean clustering segmentation, in the crown width measurement, the distance threshold of Euclidean clustering segmentation is set to 0.1-0.5m. For plants with dense crown (such as shrubs), the threshold is set to 0.1m; for plants with sparse crown (such as trees), the threshold is set to 0.5m. The minimum number of clustered points is 50 points to filter noise.

[0055] Main stem location: Find the part of the canopy point cloud that connects to the ground, determine the location of the stem base (X0, Y0).

[0056] Projection and bounding box calculation: Project all 3D points of the canopy to the horizontal plane, form a 2D planar projection.

[0057] Calculate the maximum and minimum coordinate values of the projected point set in the east-west (E-W) and north-south (S-N) directions.

[0058] East-west crown width = Xmax - Xmin; North-south crown width = Ymax - Ymin; Average crown width = (East-west crown width + North-south crown width) / 2.

[0059] Projection area calculation: Calculate the convex hull (ConvexHull) of the 2D projection point set, which is the smallest convex polygon that can wrap all points.

[0060] Use the shoelace formula to calculate the area of the convex polygon, which is the projection area of the canopy.

[0061] Data association: automatically upload the results to the terminal App and bind with the plant ID.

[0062] Tree height and branch point height measurement: Aim: align the laser point of the measuring instrument with the highest point of the tree top.

[0063] Calculation process: the device fuses two types of data: Laser ranging: the straight-line distance (S) from the measuring instrument to the tree top point.

[0064] IMU inclination angle: the angle (θ) between the optical axis of the measuring instrument and the horizontal plane.

[0065] Tree height calculation: according to the trigonometric function, the tree height (H) is composed of two parts: H = H1 + H2.

[0066] H1 (height difference from the instrument to the tree base): usually, the instrument height (H_device, known) minus the operator's eye height (known) can be approximated as H1, but a more accurate method is to use both the laser measuring instrument to the tree base distance and angle to perform a review calculation.

[0067] H2 (height difference from the tree base to the tree top): H2 = S * sin(θ); Therefore, the tree height H ≈ H_device + S * sin(θ).

[0068] 2. Branch point height measurement: Data collection: In the complete tree point cloud obtained by LiDAR scanning, the information of the trunk and branches is included.

[0069] Calculation process: Point cloud clustering analysis: The algorithm clusters the point cloud to identify the trunk point cloud cluster and the branch point cloud cluster at each level.

[0070] 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.

[0071] First branch identification: Scan from bottom to top along the trunk line, detect the first position where the point cloud density suddenly increases or the direction significantly deviates from the trunk center line. This position is the first branch point.

[0072] Height calculation: Calculate the three-dimensional coordinates (X_b, Y_b, Z_b) of the branch point. The branch point height is the Z_b coordinate value (relative to the elevation reference surface set by the instrument at the time of measurement).

[0073] Data packet generation: All height data are automatically packaged and uploaded.

[0074] 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%".

[0075] During the above measurement process, the high-resolution camera of the multifunctional plant measuring instrument automatically triggers at key steps: when scanning and identifying the plant identity signboard, it automatically takes a panoramic photo containing the signboard and the overall plant environment. When the diameter / ground diameter laser scanning is performed, a local close-up photo of the measurement site is automatically taken. After the crown scanning is completed, a tree crown shape photo is automatically taken from the best angle. And the EXIF information of each photo automatically writes the following metadata: 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).

[0076] The system automatically associates all the above images with the measurement data records of this acceptance test to form an indivisible data package. When the on-site network conditions allow (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 automatically resumed when the network is restored. For all records determined to be “unqualified”, the system will automatically generate a hash value for the complete data package associated with it (including all images and measurement data) and upload it to the blockchain network for notarization, ensuring that it cannot be tampered with, serving as key evidence for later settlement or dispute resolution. This minimizes human intervention and subjective judgment, ensuring the objectivity, accuracy, and efficiency of data collection, while forming a complete and reliable chain of evidence.

[0077] S104, generating an acceptance report based on the measurement results and judgment results, and marking unqualified plants.

[0078] Specifically, the system continuously receives on-site acceptance data packages uploaded from various intelligent acceptance terminals. Each data package contains: a globally unique plant ID, measured values of all measurement items, compliance judgment results, timestamps, geographic locations, acceptance personnel IDs, and associated image evidence. First, the received raw data is automatically cleaned (such as removing obviously abnormal GPS points), then the data is sorted by acceptance task ID and globally unique plant ID, and stored in a structured project database. Then automatically calculate the overall pass rate of this acceptance task, the pass rate by plant species, and the pass rate by acceptance area. For unqualified items, automatically calculate the distribution of deviation values, such as “70% of unqualified items have a small diameter”. 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 judgment 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.

[0079] 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 via email. All versions of the report are archived in the system and can be traced and reviewed.

[0080] Unqualified plants are marked as “to be rectified”, and the construction party initiates a re-acceptance application after rectification, and the system records the entire process trace.

[0081] 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: First, the system creates an independent electronic file for each plant with a globally unique plant ID. This file not only contains initial data at the time of acceptance, but also becomes a continuously updated data log that records key data throughout the plant's life cycle: Basic data: tree species, scientific name, planting time, initial design specifications, acceptance data.

[0082] Regular monitoring data: the system automatically generates a long-term re-measurement plan (e.g., 3, 6, 12, 24 months after planting). The acceptance personnel perform 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.

[0083] Environmental data: automatically collect and associate data such as temperature, humidity, rainfall, and soil moisture in the area where the plant is located through Internet of Things devices (such as weather stations and soil sensors).

[0084] Maintenance records: manually input or system-integrated records of watering, fertilizing, pruning, and pest control operations.

[0085] Then, build a growth prediction model, using the accumulated multi-project, multi-species, and long-time-span plant electronic file data as the training set. Use machine learning algorithms (such as time series analysis and regression algorithms) to establish personalized growth prediction models for different tree species (even different site conditions for the same species). The input parameters of this model usually include: initial specifications, annual specification changes, environmental data, and maintenance records; the output is the predicted value and confidence interval of the specifications at a certain future time point. The growth prediction model uses multiple linear regression or random forest regression algorithm. The characteristic variables include plant initial specifications (diameter at breast height, crown width, tree height), environmental data (temperature, humidity, soil moisture), maintenance records (watering frequency, fertilizer amount), and time series data. The model training uses historical data sets, selects the optimal parameters through cross-validation, and the prediction output is the specification value and confidence interval at the future time point.

[0086] After each new re-measurement data entry, the system will automatically call the growth prediction model of the tree species to predict its next stage of growth trend (such as "the crown diameter will reach 4.5±0.2m in 6 months"). The system can analyze the growth speed and uniformity of the plant population in the whole project or a specific area, and evaluate the stability of the overall landscape effect. In particular, the measurement data of the marked unqualified plants must be 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 anomaly 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.

[0087] The second embodiment of the present application is: Please refer to Figure 4 The present application provides a construction and acceptance system for the specifications of arbor and shrub plants in landscaping engineering, which is applied to the construction and acceptance method for the specifications of arbor and shrub plants in landscaping engineering provided in the first embodiment. The construction and acceptance system for the specifications 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. The acceptance task generation module 101 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 102 is used to scan plant labels and call corresponding standard specification thresholds based on the acceptance tasks. The plant measurement module 103 is used to measure and input the diameter at breast height, crown diameter, tree height, and branch points of the plants, and automatically perform compliance judgment and association with corresponding image data. The report generation module 104 is used 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.

[0088] As to the system in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0089] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts are described in the part of the method embodiments. The above-described device embodiments are only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Some or all of the modules can be selected to achieve the purposes of the application according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0090] Correspondingly, the 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 acceptance method of the tree and shrub specifications in the landscaping project as described above. As Figure 5 As shown in the figure, a hardware structure diagram of an arbitrary data processing capable device of a system for construction acceptance of tree and shrub specifications in a landscaping project provided by the embodiment of the application, in addition to Figure 5 In addition to the processor, the memory and the network interface shown in the figure, the arbitrary data processing capable device where the device in the embodiment is usually according to the actual function of the arbitrary data processing capable device, can also include other hardware, which will not be described here.

[0091] Correspondingly, the application also provides a computer readable storage medium, which stores computer instructions, and the instructions are executed by a processor to implement the construction acceptance method of the tree and shrub specifications in the landscaping project as described above. The computer readable storage medium can be an internal storage unit of the arbitrary data processing capable device, 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 the arbitrary data processing capable device 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 arbitrary data processing capable device, and can also be used to temporarily store data that has been output or will be output.

[0092] 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, modifications, and adaptations of the application described herein, which are within the scope of the general inventive concept and include all such subject matter that is within the scope of the instant application and the claims hereafter presented.

[0093] It should be understood that the application is not limited to the precise construction here described and illustrated in the drawings, and that various modifications and changes can be made by those skilled in the art 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 a design file and generating a corresponding acceptance task according to the plant attribute information; scanning a plant label based on the acceptance task and calling corresponding standard specification thresholds; measuring and inputting the diameter at breast height, crown width, tree height and branch points of the plant, and automatically judging compliance and associating with corresponding image data; generating an acceptance report based on the measurement results and judgment results, and marking unqualified plants.

2. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, characterized in that, extracting plant attribute information based on a design file and generating a corresponding acceptance task, comprising: parsing the obtained design file and performing layer and legend recognition to extract attribute information embedded in the plant model object; generating standard specification threshold ranges based on the garden plant specification acceptance specification rule library and the corresponding attribute information; obtaining user acceptance information, combining plant attribute information or coordinate distribution to generate an acceptance task.

3. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 2, characterized in that, The method further comprises: obtaining the plane coordinates of each plant legend to determine the position distribution in the acceptance task and count the number of the same type of plants.

4. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, wherein, The method further comprises: based on the functional division in the acceptance process, corresponding viewing permissions.

5. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 2, characterized in that, scanning a plant label based on the acceptance task and calling corresponding standard specification thresholds, comprising: based on the acceptance task, using the periodic transmission of RFID to wake up the signal to obtain the plant ID and signal strength, and combining GPS and Bluetooth for auxiliary positioning; scanning the two-dimensional code signboard and comparing it with the plant ID read by RFID; after successful comparison, call the corresponding plant attribute information and standard specification threshold range.

6. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, wherein, measuring and inputting the diameter at breast height, crown width, tree height and branch points of the plant, and automatically judging compliance and associating with corresponding image data, comprising: measuring the diameter at breast height, crown width, tree height and branch points of the plant; uploading all measurement results and corresponding plant IDs after binding; comparing the received measurement results with the corresponding standard specification threshold range, and associating all collected data with the corresponding image data.

7. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 1, wherein, generating an acceptance report based on the measurement results and judgment results, and marking unqualified plants, comprising: obtaining the globally unique plant ID, all measured values of measurement items, compliance determination results, time stamps, geographic locations, acceptance personnel IDs and associated image evidence, and performing data cleaning and structured processing; generating an acceptance report based on the obtained qualified rate data and deviation data, and marking unqualified plants.

8. The method for construction acceptance of the size of arbor and shrub in landscaping engineering according to claim 7, wherein, The method further comprises: building a plant growth prediction model, outputting plant prediction data based on current acceptance data for evaluation and early warning.

9. The method for construction acceptance of the size of arbor and shrub in landscape engineering according to claim 8, wherein, building a plant growth prediction model, outputting plant prediction data based on current acceptance data for evaluation and early warning, comprising: using a regression algorithm to generate corresponding growth prediction models for different plants in combination with stored historical data; inputting the current acceptance data into the growth prediction model to output corresponding plant prediction data, and comparing each time's acceptance data with the corresponding plant prediction data to evaluate and warn the plant growth.

10. A construction acceptance system for tree and shrub specifications in landscaping engineering, applied to the construction acceptance method for tree and shrub specifications in landscaping engineering as claimed in claim 1, characterized in that, the construction acceptance system for tree and shrub specifications in landscaping 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 plant labels and call corresponding standard specification thresholds based on the acceptance tasks; the plant measurement module is used to measure and input the diameter at breast height, crown width, tree height and branch points of the plant, automatically judge compliance and associate with corresponding image data; the report generation module is used to generate an acceptance report based on the measurement results and judgment results, mark unqualified plants, construct a plant growth prediction model, output plant prediction data based on current acceptance data for evaluation and early warning.

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