Efficient cooperation method for laying of waterfall-overlapped stone

Abstract

The invention relates to the technical field of landscape engineering construction, and discloses a high-efficiency cooperation method for waterfall stone laying, and the method comprises the following steps: BIM cooperative modeling and scheme optimization, stone precise processing and traceability management, transportation and field scheduling, and multi-device cooperative laying. Comprising the steps of base layer treatment, positioning calibration, automatic paving and dynamic acceptance and feedback adjustment. According to the efficient cooperation method for laying the stacked stones, by means of BIM parameterized splitting and factory precise machining, by combining RFID full-process tracing, misuse, waste and the loss rate of special-shaped stones are avoided, and stone loss and material cost can be reduced by means of a stacked project; and by designing the mode of real-time data transmission and no information lag, automatic paving is achieved, the construction period of the overlapping area is shortened, and the overall construction efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of landscape engineering construction technology, specifically to an efficient collaborative method for laying cascading stone paving. Background Technology

[0002] As a core landscape element in garden engineering, cascading waterfalls combine the diversity of water forms with water purification and microclimate regulation. In recent years, their application in urban parks and cultural tourism projects has been increasing year by year. Traditional design methods using two-dimensional drawings cannot accurately represent the spatial relationships of irregularly shaped stones. Furthermore, the geological parameters of artificially constructed hillsides (such as soil bearing capacity and settlement) are not considered in the design, leading to discrepancies in stone dimensions and slope deviations during construction, resulting in a rework rate as high as 20% (referencing statistics from the "Code for Acceptance of Construction Quality of Landscape Engineering" CJJ82-2012). There is also poor coordination between different stages: data transmission between the design, processing, and construction ends relies on paper documents or simple spreadsheets, with information delays of 2-3 days. This easily leads to contradictions such as "processed stones have arrived, but the construction plan has been changed," resulting in a stone backlog rate exceeding 15%. In existing technologies, patent number 202221928652.8 discloses a brick-laying robot, which only automates a single process and does not integrate with the design and processing stages, failing to form a full-chain collaborative mechanism and thus unable to meet the high precision, high efficiency, and low-loss requirements of cascading stone construction. Therefore, we propose an efficient collaborative method for cascading stone paving. Summary of the Invention

[0003] The purpose of this invention is to provide an efficient collaborative method for laying cascading stone paving to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an efficient collaborative method for cascading stone paving, comprising the following steps: S1. BIM Collaborative Modeling and Scheme Optimization: Based on design drawings and on-site geological survey data, a cascading waterfall BIM model is established, integrating a hydrodynamic simulation module. The stone splicing gap and paving slope are adjusted parametrically. A stone processing list, construction coordinates, and a water flow effect prediction report are output. The accuracy of the construction coordinates is ±0.5mm, and the data is synchronized to the cloud platforms at both the processing and construction ends. The on-site geological survey data includes soil compaction, moisture content, and bearing capacity. The stone splicing gap is 3-8mm, and the paving slope is 1:2.5-1:5. S2. Precision stone processing and traceability management: The processing plant receives BIM model data and uses waterjet cutting or infrared cutting technology to control the processing accuracy to ±1mm; IoT RFID tags are affixed to each piece of stone, and specifications, texture direction, processing accuracy test data and material parameters are recorded. The processing accuracy test data shows flatness ≤0.3mm / m and angle tolerance ≤0.1°; the installation coordinates of the BIM model are linked through the cloud platform. S3. Transportation and on-site scheduling: The transport vehicles are equipped with GPS positioning modules and temperature and humidity sensors. The positioning accuracy of the GPS positioning module is ≤10m, and the measurement range of the temperature and humidity sensors is 0-60℃ and 20%-90%RH. The system uploads location information and data on the stone storage environment inside the vehicle to the material management system in real time. The system automatically generates the unloading sequence according to the construction schedule, guides the stone to be stacked according to the paving area, and sets up a moisture-proof mat in the stacking area. The stone is paved according to the bottom of the pool, the waterfall facade, and the riverbed side wall. The thickness of the moisture-proof mat is ≥50mm. S4. Multi-device collaborative installation, including substrate preparation, positioning calibration, and automated installation; The base course treatment involves layering the base course according to the BIM model elevation, with each layer ≤50cm thick. Each layer is compacted using a vibratory roller, achieving a compaction coefficient ≥0.94. This is followed by a 10-15cm thick medium-coarse sand cushion layer with a unit area mass ≥4000g / m³. 2 The bentonite waterproof blanket and the plain concrete cushion layer with a strength grade of C20 and a thickness of 8-12cm are used. The edge of the waterproof blanket is buried in the planting soil trench for fixation, with a burial depth of ≥30cm. The positioning calibration uses a positioning device with a planar accuracy of ±1cm + 1ppmRTK to verify the benchmark coordinates of the construction area, compares them with the BIM model in real time, and corrects the points in areas where the coordinate deviation exceeds 1cm. The automated paving process involves a paving robot identifying the stone's RFID tag using a high-definition camera component with a resolution ≥20 million pixels, retrieving corresponding positioning data from the BIM model. After the destacking component picks up the stone, the fabric application component automatically applies epoxy resin-based adhesive with a thickness of 3-5mm and a bonding strength ≥2.5MPa. The robotic arm presses the stone into the installation position according to RTK positioning data, controlling the pressure during the pressing process at 0.3-0.5MPa. The vibration component is activated to ensure a tight bond, and installation time, coordinate deviation, and other data are simultaneously uploaded to the cloud platform. The vibration component operates at a frequency of 50-60Hz. S5. Dynamic Acceptance and Feedback Adjustment: A 3D laser scanner is used to scan the paved area, generating a point cloud model for comparison with the BIM design model. Points with deviations exceeding 2mm trigger rework prompts. A water flow test is initiated, using a high-speed camera to capture the waterfall shape and comparing it with the simulated water flow effect. If abnormal water flow breaks or splashing occurs, the height of the corresponding stone or the splicing gap is fine-tuned through the BIM model to form a closed loop in the acceptance report. The adjustment amount for stone height is ≤3mm, and the adjustment amount for splicing gap is ≤1mm. Among them, the point cloud density of the 3D laser scanner is ≥100 points / m. 2 The water flow test is conducted in three levels: 80%, 100%, and 120% of the design flow rate. A high-speed camera with a frame rate of ≥240fps is used, and the adjustment range of the height of the stone in the area is ≤3mm. The adjustment range of the splicing gap is ≤1mm.

[0005] Optionally, the BIM model in S1 includes the layered structure of the artificially constructed mountain, the nodes of the reinforced concrete retaining wall, and the coverage area of ​​the waterproof blanket. The automatic optimization of the stone layout is achieved by writing parametric scripts through the Dynamo plugin. The hierarchical structure includes: Base layer: a mixture of construction waste and raw soil, 1.5-3m thick; Planting soil layer: ordinary planting soil, humus soil, thickness 0.8-1.2m; The cross-sectional dimensions of the reinforced concrete retaining wall joints are ≥300mm×500mm, and the reinforcement ratio is ≥0.8%; The waterproof blanket covers the top surface of the base layer and the inside of the retaining wall. Optionally, the paving robot in S4 is equipped with a two-dimensional displacement sensing component and an adjustable frequency vibration component. The X / Y axis positioning accuracy of the two-dimensional displacement sensing component is ±0.2mm; the frequency of the adjustable frequency vibration component is 50-80Hz; the machine body adopts a tracked walking mechanism, which can be compatible with cascading stone materials with a thickness of 50-200mm and a single piece weight ≤300kg, and the paving speed reaches 20-25m / s. 2 / hour, continuous operation time ≥ 8 hours.

[0006] Optionally, the material management system is linked to the supplier database and the construction progress management module; the supplier database includes data on stone inventory, production cycle, and transportation costs, and sets automatic early warning thresholds: when the stone inventory is lower than 120% of the current work area demand, a replenishment warning is triggered; when the transport vehicle deviates from the planned route by more than 5km or is delayed by more than 2 hours, a dispatch warning is triggered.

[0007] Optionally, the hydrodynamic simulation module in S1 uses Fluent software. The input parameters include design flow rate, water temperature, and stone surface roughness. The simulation duration is ≥120s. The output data include waterfall water film thickness, water flow velocity distribution, and water splash range, which serve as the basis for optimizing the stone splicing gap.

[0008] Optionally, the RFID tag in S2 adopts the UHF band, has an IP68 protection level, a reading distance of 0.5-3m, a storage capacity of ≥2KB, and can withstand ambient temperatures of -30-80℃ and vibration and shock during stone processing and transportation, ensuring a data reading success rate of ≥98% throughout the entire process.

[0009] Optionally, the S5 water flow test is divided into three stages: the first stage is 30 minutes to check for leakage in the base layer; the second stage is 60 minutes to compare the water flow pattern with the simulation effect; and the third stage is 30 minutes to verify the structural stability. The test data is uploaded to the cloud platform in real time to form a traceable record.

[0010] Optionally, the geotextile used for the base treatment in S4 is a short-fiber needle-punched nonwoven geotextile with a unit area mass ≥200g / m². 2 The tensile strength is ≥8kN / m, the puncture resistance of CBR is ≥1.8kN, the overlap width with the bentonite waterproof blanket is ≥15cm, and the connection is made by hot air welding process to prevent the stone splicing cracks caused by the settlement of the base layer.

[0011] Compared with existing technologies, this invention provides a highly efficient collaborative method for cascading stone paving, which has the following beneficial effects: 1. The efficient collaborative method for cascading stone paving utilizes BIM parametric decomposition and precise factory processing, combined with RFID full-process traceability to avoid misuse and waste, reduce the loss rate of irregularly shaped stones, and decrease the material cost of cascading projects by reducing stone waste; through real-time transmission of design data without information lag, automated paving is achieved, shortening the construction cycle of the cascading area and improving overall construction efficiency.

[0012] 2. The efficient collaborative method for cascading stone paving avoids rework and reduces project delays through dynamic acceptance; at the same time, RTK positioning and 3D scanning ensure paving accuracy; and graded water flow testing ensures structural waterproofing and stability, thus extending the service life of the cascading landscape.

[0013] 3. This efficient collaborative method for cascading stone paving reduces transportation delays and inventory backlogs through a practical material management system, thereby lowering labor costs. At the same time, the digital management of as-built archives reduces subsequent operation and maintenance costs. It is also compatible with stones of different materials and thicknesses, and can be adapted to various scenarios such as artificially constructed mountains, natural canyons, and cascading pools. It is especially suitable for complex cascading projects with irregularly shaped stones, solving the problem of insufficient adaptability of traditional methods in complex scenarios. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, the present invention provides a technical solution: an efficient collaborative method for cascading stone paving, comprising the following steps: S1. BIM Collaborative Modeling and Scheme Optimization: Based on design drawings and on-site geological survey data, a cascading BIM model is established, integrating a hydrodynamic simulation module. The stone splicing gap and paving slope are adjusted parametrically. The output includes a stone processing list, construction coordinates, and a water flow effect prediction report, which are synchronized to the cloud platforms of the processing and construction ends. On-site geological survey data includes soil compaction, moisture content, and bearing capacity. The stone splicing gap is 3-8mm, and the paving slope is 1:2.5-1:5. The BIM model includes the layered structure of the artificially constructed mountain, the nodes of the reinforced concrete retaining wall, and the coverage area of ​​the waterproof blanket. Parametric scripts are written using the Dynamo plugin to achieve automatic optimization of the stone layout.

[0017] The hydrodynamic simulation module uses Fluent software. Input parameters include design flow rate, water temperature, and stone surface roughness. The simulation duration is ≥120s. The output data include waterfall water film thickness, water flow velocity distribution, and water splash range, which serve as the basis for optimizing the gaps between stone joints.

[0018] S2. Precision stone processing and traceability management: The processing plant receives BIM model data and uses waterjet cutting or infrared cutting technology to cut irregularly shaped stones with waterjet cutting and regular stones with infrared cutting, controlling the processing accuracy to ±1mm; IoT RFID tags are affixed to each piece of stone, and specifications, texture direction, processing accuracy detection data and material parameters are recorded; the installation coordinates of the corresponding BIM model are linked through the cloud platform; The RFID tags use the UHF band, have an IP68 protection rating, a reading distance of 0.5-3m, a storage capacity of ≥2KB, and can withstand ambient temperatures of -30-80℃ and vibrations and shocks during stone processing and transportation, ensuring a data reading success rate of ≥98% throughout the entire process. S3. Transportation and on-site scheduling: The transport vehicles are equipped with GPS positioning modules and temperature and humidity sensors to upload location information and data on the stone storage environment inside the vehicle to the material management system in real time; The system automatically generates the unloading sequence according to the construction schedule and guides the stone to be stacked according to the paving area, and a moisture-proof mat is set in the stacking area. The material management system is linked with the supplier database and construction progress management module, and sets automatic early warning thresholds: when the stone inventory is lower than 120% of the current work area demand, a replenishment warning is triggered; when the transport vehicle deviates from the planned route by more than 5km or is delayed by more than 2 hours, a dispatch warning is triggered.

[0019] S4. Multi-device collaborative installation, including substrate preparation, positioning calibration, and automated installation; The base course is constructed in layers according to the BIM model elevation, with each layer ≤50cm thick. Each layer is compacted using a vibratory roller, achieving a compaction coefficient ≥0.94. This is followed by a 10-15cm thick medium-coarse sand cushion layer with a unit area mass ≥4000g / m³. 2 The bentonite waterproof blanket and the plain concrete cushion layer with a strength grade of C20 and a thickness of 8-12cm are used. The edge of the waterproof blanket is buried in the planting soil trench for fixation, with a burial depth of ≥30cm. The positioning calibration uses a positioning device with a planar accuracy of ±1cm +1ppmRTK to verify the benchmark coordinates of the construction area, compares them with the BIM model in real time, and corrects the points in areas where the coordinate deviation exceeds 1cm. The automated paving system uses a paving robot to identify the stone's RFID tag using a high-definition camera component at the end, and retrieves the corresponding positioning data from the BIM model; after the destacking component picks up the stone, the cloth-laying component automatically applies epoxy resin-based adhesive; the robotic arm presses the stone into the installation position according to the RTK positioning data, the vibration component is activated to ensure tight adhesion, and the installation time, coordinate deviation and other data are uploaded to the cloud platform simultaneously. The paving robot is equipped with a dual-dimensional displacement sensing component and an adjustable frequency vibration unit. Its tracked walking mechanism allows it to accommodate cascading stone materials with a thickness of 50-200mm and a single piece weight of ≤300kg, achieving a paving speed of 20-25m / s. 2 / hour, continuous operation time ≥ 8 hours.

[0020] The geotextile used for the base treatment is a short-fiber needle-punched nonwoven geotextile with a unit area mass ≥200g / m². 2 The tensile strength is ≥8kN / m, the puncture resistance of CBR is ≥1.8kN, the overlap width with the bentonite waterproof blanket is ≥15cm, and the connection is made by hot air welding process to prevent the stone splicing cracks caused by the settlement of the base layer.

[0021] S5. Dynamic acceptance and feedback adjustment: A 3D laser scanner is used to scan the paved area to generate a point cloud model, which is then compared with the BIM design model. Points with deviations exceeding 2mm are triggered to prompt rework. A water flow test is initiated, using a high-speed camera to capture the waterfall shape and compare it with the simulated water flow effect. If water flow breaks or water splashing occurs abnormally, the height of the stone in the corresponding area or the splicing gap is finely adjusted through the BIM model to form a closed loop of the acceptance report. The water flow test is divided into three stages: the first stage is 30 minutes to check for leakage in the base layer; the second stage is 60 minutes to compare the water flow pattern with the simulation effect; and the third stage is 30 minutes to verify the structural stability. The test data is uploaded to the cloud platform in real time to form a traceable record.

[0022] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A highly efficient collaborative method for cascading stone paving, characterized in that, Includes the following steps: S1. BIM Collaborative Modeling and Scheme Optimization: Based on design drawings and on-site geological survey data, a cascading BIM model is established, integrating a hydrodynamic simulation module. The stone splicing gap and paving slope are adjusted parametrically. A stone processing list, construction coordinates, and a water flow effect prediction report are output and synchronized to the cloud platforms at both the processing and construction ends. The on-site geological survey data includes soil compaction, moisture content, and bearing capacity. The stone splicing gap is 3-8mm; the paving slope is 1:2.5-1:

5. S2. Precision stone processing and traceability management: The processing plant receives BIM model data and uses waterjet cutting or infrared cutting technology to control the processing accuracy to ±1mm; IoT RFID tags are affixed to each piece of stone, and specifications, texture direction, processing accuracy detection data and material parameters are recorded; the BIM model is linked to the corresponding installation coordinates through the cloud platform. S3. Transportation and on-site scheduling: The transport vehicles are equipped with GPS positioning modules and temperature and humidity sensors to upload location information and data on the stone storage environment inside the vehicle to the material management system in real time; The system automatically generates the unloading sequence according to the construction schedule and guides the stone to be stacked according to the paving area, and a moisture-proof mat is set in the stacking area. S4. Multi-device collaborative installation, including substrate preparation, positioning calibration, and automated installation; The base course treatment involves layering the base course according to the BIM model elevation, with each layer ≤50cm thick. Each layer is compacted using a vibratory roller, achieving a compaction coefficient ≥0.

94. This is followed by a 10-15cm thick medium-coarse sand cushion layer with a unit area mass ≥4000g / m³. 2 The bentonite waterproof blanket and the plain concrete cushion layer with a strength grade of C20 and a thickness of 8-12cm are used. The edge of the waterproof blanket is buried in the planting soil trench for fixation, with a burial depth of ≥30cm. The positioning calibration uses a positioning device with a planar accuracy of ±1cm + 1ppmRTK to verify the benchmark coordinates of the construction area, compares them with the BIM model in real time, and corrects the points in areas where the coordinate deviation exceeds 1cm. The automated paving process involves a paving robot identifying the stone's RFID tag using a high-definition camera component at the end and retrieving the corresponding positioning data from the BIM model; after the destacking component picks up the stone, the material spreading component automatically applies epoxy resin-based adhesive; the robotic arm presses the stone into the installation position according to the RTK positioning data, the vibration component is activated to ensure tight adhesion, and the installation time, coordinate deviation, and other data are simultaneously uploaded to the cloud platform. S5. Dynamic acceptance and feedback adjustment: A 3D laser scanner is used to scan the paved area, generate a point cloud model and compare it with the BIM design model. Points with deviations exceeding 2mm are triggered to prompt rework. Initiate a trial water flow test, using a high-speed camera to capture the waterfall's shape and compare it with the simulated water flow effect; if abnormalities such as water flow breakage or splashing occur, fine-tune the height of the corresponding stone or the splicing gap through the BIM model to form a closed loop in the acceptance report.

2. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The BIM model in S1 includes the layered structure of the artificially constructed mountain, the nodes of the reinforced concrete retaining wall, and the coverage area of ​​the waterproof blanket. The automatic optimization of the stone layout is achieved by writing parametric scripts through the Dynamo plugin.

3. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The S4 paving robot is equipped with a dual-dimensional displacement sensing component and an adjustable frequency vibration component. The body adopts a tracked walking mechanism, which can be compatible with waterfall stone with a thickness of 50-200mm and a single piece weight of ≤300kg. The paving speed reaches 20-25㎡ / hour, and the continuous operation endurance is ≥8 hours.

4. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The material management system is linked with the supplier database and the construction progress management module, and sets automatic early warning thresholds: when the stone inventory is lower than 120% of the current work area demand, a replenishment warning is triggered; when the transport vehicle deviates from the planned route by more than 5km or is delayed by more than 2 hours, a dispatch warning is triggered.

5. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The hydrodynamic simulation module in S1 uses Fluent software. Input parameters include design flow rate, water temperature, and stone surface roughness. The simulation duration is ≥120s. The output data include waterfall water film thickness, water flow velocity distribution, and water splash range, which serve as the basis for optimizing the gap between stone joints.

6. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The RFID tag in S2 uses the UHF band, has an IP68 protection rating, a reading distance of 0.5-3m, a storage capacity of ≥2KB, and can withstand ambient temperatures of -30-80℃ and vibration and shock during stone processing and transportation, ensuring a data reading success rate of ≥98% throughout the entire process.

7. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The S5 test flow test is divided into three stages: the first stage is 30 minutes to check for leakage in the base layer; the second stage is 60 minutes to compare the water flow pattern with the simulation effect. The third phase of testing lasted 30 minutes, verifying the structural stability. The test data was uploaded to the cloud platform in real time to form a traceable record.

8. The efficient collaborative method for cascading stone paving according to claim 1, characterized in that: The geotextile used in the base treatment of S4 is a short-fiber needle-punched nonwoven geotextile with a unit area mass ≥200g / m². 2 The tensile strength is ≥8kN / m, the puncture resistance of CBR is ≥1.8kN, the overlap width with the bentonite waterproof blanket is ≥15cm, and the connection is made by hot air welding process to prevent the stone splicing cracks caused by the settlement of the base layer.

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