Intelligent machine construction method and system for ground concrete base finishing

Abstract

The application discloses a kind of intelligent machine construction methods and systems for ground concrete base finishing, it is related to intelligent construction technical field, including the real-time collection of intelligent robot in construction area concrete base perception data;Construction strategy is generated by preset algorithm analysis perception data, and finishing job is automatically completed;During finishing job, real-time perception data is transmitted to cloud platform by intelligent robot, and the closed-loop monitoring system of construction quality is formed.The application realizes finishing job accurate matching base state by preset path planning, parameter adjustment and image recognition algorithm, improves finishing uniformity, reduces artificial dependence, by real-time uploading to cloud platform and carrying out comparative analysis and feedback control to the perception data in the process of robot operation, realize the instant identification and deviation correction of quality problem, whole process construction data is traceable, remote construction supervision visualization.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction technology, and in particular to an intelligent machine construction method and system for smoothing a ground concrete base. Background Art

[0002] In construction projects, finishing the concrete floor base serves as a crucial interface treatment between the decorative and structural layers. Its quality directly impacts the adhesion, surface aesthetics, and service life of subsequent floor paving materials. With the acceleration of urbanization and the increasing industrialization of the construction industry, construction companies are placing higher demands on floor construction efficiency, quality control, and intelligent capabilities. This is especially true in projects like large public buildings, industrial plants, logistics warehouses, and prefabricated housing. Floor concrete base finishing urgently needs to transition from the traditional "manual operation and manual inspection" approach to "automated operation and intelligent feedback."

[0003] While a certain foundation for intelligent development exists, existing systems for the specific concrete finishing process primarily focus on improvements to mechanical hardware, optimization of path planning algorithms, or partial manual replacement. A highly integrated system framework encompassing perception, decision-making, and closed-loop control has yet to be established. In particular, for real-time acquisition, intelligent assessment, and feedback control of finishing quality, most existing equipment still relies on manual experience and lacks environmental adaptability. These systems are unable to dynamically adjust their operating strategies based on the degree of concrete surface hardening, fluctuations in the water-cement ratio, or changes in environmental parameters in the construction area (such as temperature, humidity, and wind speed). Furthermore, traditional equipment lacks the ability to upload multi-source sensor data from the construction process to a cloud platform for real-time analysis and decision support, hindering integration with smart construction site management systems and supporting full-process visual monitoring of construction quality and multi-dimensional comparative analysis of historical data. This results in a lack of management-level sensitivity to dynamic construction changes and the ability to implement precise interventions. Due to the frequent and rapidly changing nature of construction issues such as uneven subgrades, surface bubbles, and uneven wetness, failure to identify and correct them promptly can easily lead to quality issues and require rework. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing floor concrete base finishing construction process has the problem of difficult to ensure construction quality and low efficiency, and how to realize the automated execution and dynamic quality control of the finishing operation through an intelligent robot system that integrates perception data collection, intelligent generation of construction strategies and closed-loop quality monitoring, and build an efficient, safe and traceable intelligent construction system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent machine construction method for smoothing ground concrete bases, comprising an intelligent robot collecting perception data of the concrete base in the construction area in real time; generating a construction strategy through a preset algorithm to automatically complete the smoothing operation; during the smoothing operation, the intelligent robot transmits the real-time perception data to a cloud platform to form a closed-loop monitoring system for construction quality; the construction strategy includes generating an optimal smoothing path based on three-dimensional terrain data based on a path planning algorithm, dynamically adjusting the operating pressure, rotation speed and operating rhythm of the smoothing disc through a parameter adjustment algorithm, automatically identifying defective areas through an image recognition algorithm, and generating targeted local repair strategies; the closed-loop monitoring system includes a cloud platform that receives and analyzes the construction perception data uploaded by the intelligent robot in real time, automatically compares the deviation between the actual construction status and the preset construction strategy, generates and sends correction instructions in real time, dynamically adjusts the construction parameters of the intelligent robot, and provides real-time feedback on construction quality assessment and correction measures to construction management personnel, thereby realizing dynamic closed-loop control of the construction process.

[0007] As a preferred solution of the intelligent machine construction method for smoothing the ground concrete base described in the present invention, the perception data includes the flatness, humidity, hardness and image information of the concrete base in the construction area.

[0008] As a preferred solution of the intelligent machine construction method for smoothing the ground concrete base described in the present invention, the intelligent robot collects perception data of the concrete base in the construction area in real time, including: the intelligent robot scans the construction area in real time through a lidar to obtain high-precision three-dimensional terrain data of the concrete base surface, identifies and records surface flatness information, measures the humidity and hardness data of the surface and interior of the concrete base in real time through an ultrasonic sensor, captures image information of the concrete base surface in real time through a high-definition camera, and monitors the ambient temperature and humidity data of the construction site in real time through a temperature and humidity sensor.

[0009] As a preferred solution of the intelligent machine construction method for smoothing the ground concrete base described in the present invention, the construction strategy includes: the intelligent robot inputs three-dimensional terrain data into the path planning algorithm to generate an optimal smoothing path covering the entire construction area; based on the humidity and hardness data on the surface and interior of the concrete base, the operating pressure, rotation speed, and operating rhythm of the smoothing disc are automatically determined through a parameter adjustment algorithm; based on the image information of the concrete base surface, the defect information of the concrete base surface is identified according to the image recognition algorithm, and a local repair strategy for the defective area is automatically generated.

[0010] As a preferred solution of the intelligent machine construction method for smoothing the ground concrete base described in the present invention, the automatic completion of the smoothing operation includes: the intelligent robot starts the mobile platform according to the generated construction strategy, automatically moves autonomously along the optimal smoothing path in the construction area, and dynamically adjusts the construction operation parameters of the smoothing disc based on the parameter adjustment algorithm according to the real-time perceived concrete base humidity and hardness data. When the intelligent robot recognizes that there are defects on the surface of the concrete base, it automatically performs local repeated smoothing treatment. When the ambient temperature and humidity data of the construction site exceed the preset threshold, the intelligent robot automatically suspends the operation, and resumes the operation when the ambient temperature and humidity data are lower than the preset threshold.

[0011] As a preferred solution of the intelligent machine construction method for smoothing the ground concrete base described in the present invention, the intelligent robot transmits real-time perception data to the cloud platform, including establishing a data connection channel with the cloud platform in real time through a wireless communication module, and pre-processing the three-dimensional terrain data, humidity and hardness data, surface defect image data, and environmental data of the concrete base to the cloud platform in real time.

[0012] As a preferred solution of the intelligent machine construction method for smoothing the ground concrete base described in the present invention, the closed-loop monitoring system includes: a cloud platform that receives and stores data transmitted by the intelligent robot in real time, uses a data fusion analysis method to evaluate the construction quality in real time, and automatically compares the deviation between the actual construction status and the preset construction strategy. When a deviation is detected in the actual construction status, the cloud platform automatically sends a correction instruction to the intelligent robot, and adjusts the path, pressure, speed, and angle of the intelligent robot's smoothing operation in real time, automatically generates a construction process quality assessment report and a construction deviation correction record, and displays the construction status and adjustment measures to the construction management personnel in real time through the construction management terminal.

[0013] Another object of the present invention is to provide an intelligent machine construction system for smoothing ground concrete bases, which can realize real-time perception of the concrete base status, dynamic generation and execution of construction strategies, and full-process feedback and adjustment of construction quality by integrating a perception data acquisition module, an intelligent construction strategy module, and a closed-loop quality monitoring module, thereby solving the problems in traditional construction processes where operations rely on manual experience, quality is difficult to dynamically control, and there is a lack of systematic management and data closed loops.

[0014] As a preferred solution of the intelligent machine construction system for smoothing the ground concrete base described in the present invention, it includes: a data acquisition module, an intelligent construction strategy module, and a quality closed-loop monitoring module; the data acquisition module is used to obtain the flatness, humidity, hardness, and image information of the concrete base in the construction area in real time; the intelligent construction strategy module includes a path planning unit and an automatic construction unit, the path planning unit is used to derive the construction strategy through a preset algorithm, and the automatic construction unit is used for the intelligent robot to automatically perform smoothing operations according to the construction strategy, and automatically adjust parameters through a preset algorithm during the construction process; the quality closed-loop monitoring module includes a data transmission unit and a deviation correction unit, the digital twin modeling unit is used to transmit real-time perception data to the cloud platform, and the data analysis and processing unit is used to analyze construction deviations and provide real-time feedback on correction instructions to achieve closed-loop quality control.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an intelligent machine construction method for smoothing a ground concrete base.

[0016] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an intelligent machine construction method for smoothing a ground concrete base.

[0017] The beneficial effects of the present invention are as follows: by integrating a laser radar, ultrasonic sensor, camera and temperature and humidity sensor perception system on the intelligent robot, the status information of the concrete base layer is collected in real time, the comprehensiveness and accuracy of information acquisition are improved, and through preset path planning, parameter adjustment and image recognition algorithms, the perception data is automatically analyzed to generate a construction strategy and the robot is controlled to perform the troweling operation, so that the troweling operation can accurately match the base layer status, improve the troweling uniformity, reduce manual dependence, and improve construction efficiency and quality consistency. By uploading the perception data during the robot operation process to the cloud platform in real time and conducting comparative analysis and feedback control, a closed-loop quality monitoring system is constructed, which can realize the instant identification and correction of quality problems, traceability of the entire process construction data, and visualization of remote construction supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is an overall flow chart of an intelligent machine construction method for smoothing a ground concrete base provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0021] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides an intelligent machine construction method for finishing a ground concrete base, comprising:

[0022] S1: The intelligent robot collects perception data of the concrete base in the construction area in real time.

[0023] Furthermore, the perception data includes the flatness, humidity, hardness, and image information of the concrete base in the construction area.

[0024] Furthermore, the intelligent robot collects perception data of the concrete base in the construction area in real time, including scanning the construction area in real time through lidar, obtaining high-precision three-dimensional terrain data of the concrete base surface, identifying and recording surface flatness information, measuring the humidity and hardness data on the surface and inside of the concrete base in real time through ultrasonic sensors, capturing image information of the concrete base surface in real time through high-definition cameras, and monitoring the ambient temperature and humidity data of the construction site in real time through temperature and humidity sensors.

[0025] It should be noted that for flatness perception, the intelligent robot's LiDAR can scan terrain point clouds with millimeter-level accuracy, at a scanning frequency of at least 10Hz. By spatially fitting the point cloud data and calculating flatness residuals, the system can efficiently identify high-convex and low-concave areas, providing key information for trowel path planning and trowel wheel posture adjustment. To collect information on moisture and hardness, the ultrasonic sensor uses a phased array ranging method to detect changes in wave velocity at different depths on the concrete base surface. By comparing echo delay and attenuation, the porosity and surface hardening degree of the concrete structure can be inferred. For image acquisition, the high-definition camera module integrates illumination compensation and image stabilization, enabling high-frame-rate imaging of concrete base surfaces under complex lighting conditions. The system's built-in image recognition model classifies and labels defects such as cracks, honeycombing, and laitance, and synchronizes data using spatial coordinates and timestamps to facilitate subsequent repair strategy development and quality traceability. A temperature and humidity sensor, deployed on the robot's top, dynamically senses changes in ambient air temperature and humidity.

[0026] S2: Analyze perception data through preset algorithms to generate construction strategies and automatically complete the finishing work.

[0027] Furthermore, the construction strategy includes the intelligent robot inputting three-dimensional terrain data into the path planning algorithm to generate the optimal finishing path covering the entire construction area; based on the humidity and hardness data of the surface and interior of the concrete base, the parameter adjustment algorithm automatically determines the operating pressure, rotation speed, and operating rhythm of the trowel disc; based on the image information of the concrete base surface, the image recognition algorithm is used to identify the defect information of the concrete base surface and automatically generate a local repair strategy for the defective area.

[0028] It should be noted that the path planning algorithm is expressed as:

[0029] ;

[0030] in, Represents the final generated smoothing path, Indicates the fitting based on the control points Spline path generating function, Represents three-dimensional terrain data Based on the constraints Perform heuristic pathfinding. The three-dimensional terrain data obtained by LiDAR, For path constraint parameters such as operation boundary, obstacle location, navigation channel width, rotation radius, etc., the intelligent robot inputs the three-dimensional terrain data obtained by the lidar into the path planning algorithm and uses Algorithm combination Spline curve ( ) to perform path fitting and generate an optimal troweling path with good continuity and smooth steering, which can ensure the maximum coverage of the robot while avoiding unnecessary sharp turns and overlaps, which is conducive to improving construction efficiency and stability.

[0031] The parameter adjustment algorithm is expressed as:

[0032] ;

[0033] in, For the pressure of wiping disc, is the rotation speed, To run the beat, is the concrete humidity, is the surface hardness of concrete, 、 、 、 、 Represents the adjustment weight coefficient. Through the parameter adjustment algorithm, the operating parameters are adaptively adjusted according to the real-time physical state of the concrete to prevent quality defects such as overpressure, flying slurry, and hard defects, and improve construction adaptability.

[0034] The local repair strategy includes identifying the defect information on the surface of the concrete base through image recognition algorithm, outputting the defect location and defect area, and When , it is marked as a local repeated smearing area and a local repair strategy is executed, where is the actual area of ​​the defect region, is the defect area threshold, and the preferred defect area threshold of the present invention is It can effectively avoid the system from frequently responding to minor defects that have no actual impact, reduce ineffective operations, and improve the work efficiency of robots. The threshold matches the robot's minimum controllable troweling unit to ensure that each repeated operation has physical meaning and repair effect. The optimal trigger standard formed by combining actual construction data helps to improve appearance consistency, reduce rework rate, and enhance customer satisfaction. The defect area threshold is written into the model configuration item as an adjustable parameter, which can be expanded and adjusted according to different engineering scenarios and construction requirements, and has good scene adaptability and portability.

[0035] The local repair strategy is expressed as:

[0036] ;

[0037] in, Number of repetitions of the defective area, is the empirical repair coefficient, which is set based on the average of multiple batch tests and represents the coverage margin. Standard reference area (corresponding to the typical defect unit that can be handled by one trowel).

[0038] Furthermore, the automatic completion of the smoothing operation includes the intelligent robot starting the mobile platform according to the generated construction strategy, automatically moving along the optimal smoothing path in the construction area, and dynamically adjusting the construction parameters of the smoothing disc based on the parameter adjustment algorithm according to the real-time perceived concrete base humidity and hardness data. When the intelligent robot recognizes defects on the surface of the concrete base, it automatically performs local repeated smoothing treatment. When the ambient temperature and humidity data of the construction site exceed the preset threshold, the intelligent robot automatically suspends the operation, and resumes the operation when the ambient temperature and humidity data are lower than the preset threshold.

[0039] It should also be noted that once the optimal troweling path covering the construction area is generated, the intelligent robot controls the mobile platform below it to autonomously move along this path. This mobile platform is controlled by a path control module, incorporating inertial navigation, visual assistance, and terrain matching to achieve precise movement within the construction area, ensuring efficient coverage of all areas to be troweled without omission or overlap. During movement, the intelligent robot continuously collects moisture and hardness data from the concrete base, and dynamically adjusts the trowel disc's operating pressure, rotation speed, and operating rhythm through a parameter adjustment algorithm. Local repair strategies are then used to repeatedly trowel defects on the concrete base surface.

[0040] The ambient temperature and humidity data thresholds are expressed as:

[0041] ;

[0042] ;

[0043] in, is the ambient temperature, For ambient humidity, when the ambient temperature and humidity data exceed the threshold, the robot suspends operation. When the continuous detection results return to the threshold range and the duration is greater than 5 minutes, the intelligent robot resumes operation.

[0044] When the temperature is lower than When the temperature is higher than 0.05, the cement hydration reaction rate is significantly reduced, and the hardness and softness of the concrete surface are uncontrollable, which can easily lead to "unable to be applied" or "shelling". or humidity is lower than When the evaporation rate is too fast, it is easy to produce "dry wipe" and cause powder floating and peeling. ) can lead to water accumulation in the base layer, preventing compaction of the slurry and affecting the finish quality. The optimal temperature and humidity thresholds cover most common construction seasons and offer excellent scalability and adjustability. In actual deployments, threshold parameters can be written into configuration items and dynamically adjusted via the cloud platform based on region, season, and concrete grade, ensuring both accuracy and versatility, and ensuring excellent adaptability to various scenarios.

[0045] S3: During the smoothing operation, the intelligent robot transmits real-time perception data to the cloud platform, forming a closed-loop monitoring system for construction quality.

[0046] Furthermore, the intelligent robot transmits real-time perception data to the cloud platform, including establishing a data connection channel with the cloud platform in real time through a wireless communication module, and transmitting the concrete base three-dimensional terrain data, humidity and hardness data, surface defect image data, and environmental data to the cloud platform in real time after data pre-processing.

[0047] It should also be noted that during operation, the intelligent robot establishes a real-time data connection channel with the cloud platform via a built-in wireless communication module (such as Wi-Fi, 4G / 5G, LoRa, etc.). This communication link supports two-way interaction, allowing not only data upload but also the receipt of adjustment commands from the platform. Once the communication link is established, the intelligent robot locally structures and organizes the following multi-source perception data and uploads it to the cloud platform in batches: 3D terrain data: Point cloud data collected by lidar, including grid coordinates of the construction area and surface height differences, supports surface flatness assessment.

[0048] Moisture and hardness data: obtained by ultrasonic sensors, used to assess the moisture status of concrete and the surface strength maturity respectively.

[0049] Defect image data: collected by high-definition cameras, and the defect type, area, and location coordinates are marked based on image recognition results.

[0050] Environmental parameter data: including temperature, humidity, air pressure and other external operating environment information obtained by the temperature and humidity module.

[0051] Job log data: includes the robot's current path segment number, parameter execution record, repeated repair flag, and other dynamic operating status.

[0052] Furthermore, the closed-loop monitoring system includes a cloud platform that receives and stores data transmitted by the intelligent robot in real time, uses data fusion analysis methods to evaluate construction quality in real time, and automatically compares the deviation between the actual construction status and the preset construction strategy. When a deviation is detected in the actual construction status, the cloud platform automatically sends correction instructions to the intelligent robot, and adjusts the path, pressure, speed, and angle of the intelligent robot's troweling operation in real time, automatically generates a construction process quality assessment report and construction deviation correction record, and displays the construction status and adjustment measures to construction management personnel in real time through the construction management terminal.

[0053] It should also be noted that when the cloud platform identifies a significant deviation between the actual construction status of any of the above categories and the preset strategy, the system will automatically generate a correction instruction and send it to the robot end via wireless communication. The correction instruction may include adjusting the trowel path, optimizing the working angle, increasing or decreasing the pressure, changing the rotation speed or increasing local repeated operations and other parameters, so as to correct the deviation in time and improve the construction consistency and finished product quality. While executing the correction instruction, the cloud platform will also record the category, location, correction method and corresponding time point of this deviation, and automatically generate a construction quality assessment report. The report content includes the trowel completeness of the current working area, defect repair records, environmental adaptability analysis and system recommendation scores. The present invention preferably pushes the report and the current construction status to the construction management terminal interface simultaneously, such as the tablet computer equipped on the construction site, the background scheduling system or the large screen of the smart construction site management platform, so that construction management personnel can grasp the robot operation status in real time, understand abnormal areas, review construction feedback results, and perform manual intervention or auxiliary scheduling when necessary, thereby improving the visualization and controllability of the entire construction project.

[0054] Example 2 is an embodiment of the present invention, which provides an intelligent machine construction method for smoothing the ground concrete base. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0055] The experimental site was selected at the ground construction section of a residential building project. Six standard concrete base experimental areas with the same area (approximately 20m²) were selected on site. The concrete pouring completion time was consistent, the material ratio was the same, and the construction environment temperature was stable at 23-26°C and the relative humidity was within the range of 65%-75%.

[0056] Before the experiment, the initial flatness and moisture content of each experimental area were manually recorded using a laser ruler and a surface scanning system. An intelligent robotic device equipped with the functions of the present invention was then deployed, activating a real-time sensing system to collect information including flatness, elevation point clouds, substrate moisture content, and initial hardness, and caching the data locally. Once the data was collected, the robot automatically generated path planning and parameter configuration using a pre-set algorithm module, then began the troweling operation.

[0057] During the troweling process, the robot uploads data to the cloud platform as it performs its work. This data includes terrain changes within each cycle, images of identified surface defects, construction status parameters (such as pressure and rotational speed), and environmental data. The cloud platform performs real-time quality evaluation and comparison on the uploaded data. When deviations are identified, the platform automatically adjusts parameters and controls the robot to perform closed-loop repair operations. Defect recognition uses an improved YOLOv5 algorithm, with a defect area threshold of 12.5 cm². Localized re-troweling is performed when the defect area meets the recognition threshold. After the experiment, the final flatness is collected again, and the number of closed-loop repairs and defect resolution are counted. The experimental data is shown in Table 1.

[0058] Table 1. Data of construction quality experiment of intelligent trowel robot

[0059] Experiment number Initial flatness (mm) Flatness after troweling (mm) Initial humidity of concrete base (%) Working pressure (N) Number of defects identified Number of closed-loop repairs RBT-01 6.5 2.1 75 210 4 2 RBT-02 5.8 1.8 80 220 3 1 RBT-03 7.2 2.4 78 215 6 3 RBT-04 6 1.9 77 218 2 1 RBT-05 5.5 1.6 74 212 2 1 RBT-06 7 2.2 79 217 5 2

[0060] The experimental data above demonstrates that, after introducing the proposed intelligent robotic construction method, the post-trowelling surface smoothness of all experimental areas was significantly better than the initial state. The average post-trowelling smoothness was controlled within 2.0 mm, with a maximum value of no more than 2.4 mm, significantly lower than the 3 mm upper limit recommended by the "Concrete Structure Engineering Construction Quality Acceptance Code." This demonstrates that the construction strategy generation algorithm and operation parameter adjustment mechanism constructed in this invention can effectively adapt to the varying initial conditions of the concrete base, achieving consistent and excellent construction results.

[0061] Judging by the two parameters of "number of defects identified" and "number of closed-loop repairs," the intelligent robot is able to accurately identify local flaws that occur during construction and complete repair operations through dynamic control via the cloud platform. Taking experiment number RBT-03 as an example, the system identified six visible defect areas with poor initial flatness and numerous defects. These were ultimately successfully eliminated through three closed-loop control commands. After smoothing, the flatness decreased from 7.2mm to 2.4mm, demonstrating stable results and verifying the accuracy and reliability of the closed-loop feedback mechanism.

[0062] Traditional troweling techniques rely on manual experience to determine the concrete's wetness and timing, without the ability to dynamically adjust pressure and path. This can easily lead to insufficient compaction in some areas, surface dehydration, or repeated treatments, resulting in significant quality fluctuations and delayed defect resolution. This invention, however, builds an automated response system through a process of real-time perception, strategy calculation, job execution, cloud-based evaluation, and closed-loop control, enabling real-time adjustment and data traceability.

[0063] The preferred control parameters set in this invention (such as a humidity range of 70%-80% and an operating pressure of 210N-220N) have been proven to be reasonable in practice, enabling stable operation under varying initial material conditions and possessing excellent engineering applicability and promotional value. The entire system implements digitized, algorithmic, and intelligent management of the construction process, significantly different from traditional semi-automatic mechanical processes and demonstrating significant technical innovation in structural design, control logic, and data processing.

[0064] Example 3 is an embodiment of the present invention, which provides an intelligent machine construction system for finishing the ground concrete base, including a data acquisition module, an intelligent construction strategy module, and a quality closed-loop monitoring module.

[0065] The data acquisition module is used to obtain the flatness, humidity, hardness and image information of the concrete base in the construction area in real time.

[0066] It should also be noted that the data acquisition module is used to obtain the flatness, humidity, hardness, and image information of the concrete base in real time, and provide the perception data to the intelligent construction strategy module and the quality closed-loop monitoring module.

[0067] The intelligent construction strategy module includes a path planning unit and an automatic construction unit. The path planning unit is used to derive the construction strategy through a preset algorithm. The automatic construction unit is used for the intelligent robot to automatically perform smoothing operations according to the construction strategy and automatically adjust parameters through a preset algorithm during the construction process.

[0068] It should also be noted that the intelligent construction strategy module receives the perception data provided by the data acquisition module, wherein the path planning unit generates the construction path based on the terrain data, and the automatic construction unit generates the operation parameters based on the humidity, hardness and other data and controls the robot to perform the smoothing operation.

[0069] The quality closed-loop monitoring module includes a data transmission unit and a deviation correction unit. The digital twin modeling unit is used to transmit real-time perception data to the cloud platform. The data analysis and processing unit is used to analyze construction deviations and provide real-time feedback on correction instructions to achieve closed-loop quality control.

[0070] It should also be noted that the quality closed-loop monitoring module receives real-time perception data provided by the data acquisition module, which is uploaded to the cloud platform by the data transmission unit. The deviation correction unit analyzes the deviation between the actual construction status and the preset strategy, generates correction instructions, and feeds back to the intelligent construction strategy module to adjust the operation path and parameters.

Claims

1. An intelligent machine construction method for finishing a concrete base, characterized in that: include: Intelligent robots collect real-time perception data of the concrete base within the construction area; Analyze perception data through preset algorithms to generate construction strategies and automatically complete the finishing work; During the troweling process, the intelligent robot transmits real-time perception data to the cloud platform, forming a closed-loop monitoring system for construction quality; The construction strategy includes generating the optimal trowel path using 3D terrain data based on a path planning algorithm, dynamically adjusting the trowel's operating pressure, rotation speed, and operating rhythm through a parameter adjustment algorithm, and automatically identifying defective areas through an image recognition algorithm to generate targeted local repair strategies. The parameter adjustment algorithm is expressed as: Among them, P is the operating pressure of the screed, ω is the rotation speed, T is the operating cycle, H is the concrete humidity, K is the concrete surface hardness, α1, β1, α2, β2, γ represent the adjustment weight coefficients; The local repair strategy is expressed as: Among them, N i The number of repeated operations on the defect area, λ is the empirical repair coefficient, which is set based on the average of multiple batch tests and represents the coverage margin, A std Standard reference area; The closed-loop monitoring system includes a cloud platform that receives and analyzes construction perception data uploaded by intelligent robots in real time, automatically compares deviations between the actual construction status and the preset construction strategy, generates and sends corrective instructions in real time, dynamically adjusts the construction parameters of the intelligent robots, and provides real-time feedback on construction quality assessments and corrective measures to construction management personnel, achieving dynamic closed-loop control of the construction process. Automatic completion of the troweling operation includes the intelligent robot starting the mobile platform according to the generated construction strategy, automatically moving along the optimal troweling path in the construction area, and dynamically adjusting the construction parameters of the troweling disc based on the parameter adjustment algorithm according to the real-time perceived concrete base humidity and hardness data. When the intelligent robot identifies defects on the surface of the concrete base, it automatically performs local repeated troweling treatment. When the ambient temperature and humidity data of the construction site exceed the preset threshold, the intelligent robot automatically suspends the operation, and resumes the operation when the ambient temperature and humidity data are lower than the preset threshold.

2. The intelligent machine construction method for finishing a ground concrete base as claimed in claim 1, characterized in that: The perception data includes the flatness, humidity, hardness, and image information of the concrete base in the construction area.

3. The intelligent machine construction method for finishing a ground concrete base as claimed in claim 2, characterized in that: The intelligent robot collects perception data of the concrete base in the construction area in real time, including: the intelligent robot scans the construction area in real time through a lidar to obtain high-precision three-dimensional terrain data of the concrete base surface, identifies and records surface flatness information, measures the humidity and hardness data of the surface and interior of the concrete base in real time through an ultrasonic sensor, captures image information of the concrete base surface in real time through a high-definition camera, and monitors the ambient temperature and humidity data of the construction site in real time through a temperature and humidity sensor.

4. The intelligent machine construction method for finishing a ground concrete base as claimed in claim 3, characterized in that: The construction strategy includes: the intelligent robot inputs three-dimensional terrain data into a path planning algorithm to generate an optimal finishing path covering the entire construction area; based on the humidity and hardness data on the surface and interior of the concrete base, the parameter adjustment algorithm automatically determines the operating pressure, rotation speed, and operating rhythm of the trowel; based on the image information of the concrete base surface, the image recognition algorithm identifies the defect information of the concrete base surface and automatically generates a local repair strategy for the defective area.

5. The intelligent machine construction method for finishing a ground concrete base as claimed in claim 4, characterized in that: The intelligent robot transmits real-time perception data to the cloud platform, including: the intelligent robot establishes a data connection channel with the cloud platform in real time through a wireless communication module, and transmits the concrete base three-dimensional terrain data, humidity and hardness data, surface defect image data, and environmental data to the cloud platform in real time after data pre-processing.

6. The intelligent machine construction method for finishing a ground concrete base as claimed in claim 5, characterized in that: The closed-loop monitoring system includes a cloud platform that receives and stores data transmitted by the intelligent robot in real time, uses data fusion analysis methods to evaluate construction quality in real time, and automatically compares the deviation between the actual construction status and the preset construction strategy. When a deviation is detected in the actual construction status, the cloud platform automatically sends a correction instruction to the intelligent robot, and adjusts the path, pressure, speed, and angle of the intelligent robot's troweling operation in real time, automatically generates a construction process quality assessment report and a construction deviation correction record, and displays the construction status and adjustment measures to construction management personnel in real time through the construction management terminal.

7. A system using the intelligent machine construction method for troweling a ground concrete base according to any one of claims 1 to 6, characterized in that: Including data acquisition module, intelligent construction strategy module, and quality closed-loop monitoring module; The data acquisition module is used to obtain the flatness, humidity, hardness and image information of the concrete base in the construction area in real time; The intelligent construction strategy module includes a path planning unit and an automatic construction unit. The path planning unit is used to derive a construction strategy through a preset algorithm. The automatic construction unit is used for the intelligent robot to automatically perform troweling operations according to the construction strategy and automatically adjust parameters through the preset algorithm during the construction process. The quality closed-loop monitoring module includes a data transmission unit and a deviation correction unit. The digital twin modeling unit is used to transmit real-time perception data to the cloud platform. The data analysis and processing unit is used to analyze construction deviations and provide real-time feedback on correction instructions to achieve closed-loop quality control.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent machine construction method for smoothing a ground concrete base layer according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent machine construction method for troweling a ground concrete base layer according to any one of claims 1 to 6 are implemented.

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