Assembly type cavity component intelligent production system based on SPCS system and whole-process quality control method

Abstract

The invention relates to a fabricated cavity component intelligent production system based on an SPCS system and a whole-process quality control method. The system comprises a modular flexible production line, an intelligent turnover machine and a concrete pouring intelligent cooperative control system, and efficient and intelligent production of components is achieved. According to the full-process quality control method, high quality and stability of production are ensured through multi-dimensional quality defect AI pre-judgment, process parameter dynamic optimization, full-life-cycle quality tracing and a self-adaptive process correction mechanism. The production efficiency is improved, the labor dependence is reduced, the environmental pollution is reduced, and a design-production-construction-operation and maintenance whole industrial chain solution is provided for the prefabricated building industry.

Description

Technical Field

[0001] This invention relates to the field of prefabricated building component production, and in particular to an intelligent production system for prefabricated cavity components based on the SPCS system and a method for full-process quality control. Background Technology

[0002] With the rapid development of prefabricated buildings, the SPCS system, as a highly efficient prefabricated building structural system, is experiencing an increasing demand for its hollow components (such as walls and columns). However, existing production methods largely rely on manual operation, resulting in low production efficiency, unstable quality, high costs, and environmental pollution. In the field of prefabricated building component production, existing technologies face numerous challenges, including low production efficiency, unstable quality control, and insufficient construction technology and management. Traditional production models rely on manual operation with low levels of mechanization and automation, leading to long production cycles, high costs, and difficulty in guaranteeing component quality. Simultaneously, issues such as joint treatment and waterproofing / insulation frequently occur during construction, affecting structural safety and durability. Furthermore, existing technological systems have significant shortcomings in intelligent production and full-process quality control, lacking effective dynamic optimization and quality traceability mechanisms.

[0003] Therefore, developing an intelligent, efficient, and environmentally friendly prefabricated cavity component production system and a comprehensive quality control method is of great significance. Addressing these technical bottlenecks, this invention proposes an intelligent prefabricated cavity component production system and a comprehensive quality control method based on the SPCS (Special Process Control System). Through intelligent production technologies, such as modular flexible production lines, intelligent rotation, and intelligent collaborative control of concrete pouring, production efficiency and quality stability are significantly improved. Simultaneously, combined with comprehensive quality control measures, including multi-dimensional AI-based prediction of quality defects, dynamic optimization of process parameters, and full lifecycle quality traceability, comprehensive quality assurance is achieved from raw materials to construction. This invention aims to fill the gaps in existing technologies regarding intelligent production and comprehensive quality control, providing the prefabricated building industry with an efficient, environmentally friendly, and low-cost end-to-end solution.

[0004] Therefore, it is evident that the existing intelligent production system and full-process quality control method for prefabricated cavity components based on the SPCS system still have inconveniences and shortcomings in terms of methodology, manufacturing methods, and application, and urgently need further improvement. To address the problems of the intelligent production system and full-process quality control method for prefabricated cavity components based on the SPCS system, relevant manufacturers have spared no effort in seeking solutions. However, for a long time, no suitable design has been developed, and neither general methods, manufacturing methods, processing methods, nor the SPCS-based intelligent production system and full-process quality control method for prefabricated cavity components have been found to adequately solve the aforementioned problems. This is clearly a problem that relevant industries urgently need to address.

[0005] In view of the shortcomings of existing intelligent production systems and full-process quality control methods for prefabricated cavity components based on the SPCS system, the inventor, based on years of rich practical experience and professional knowledge in the design and manufacture of such products, and in conjunction with the application of theoretical principles, actively researched and innovated to create a new intelligent production system and full-process quality control method for prefabricated cavity components based on the SPCS system. This new system can improve upon existing intelligent production systems and full-process quality control methods for prefabricated cavity components based on the SPCS system, making them more practical. After continuous research, design, and repeated trials and improvements, this invention, which has proven to be of practical value, has finally been created. Summary of the Invention

[0006] The main objective of this invention is to overcome the shortcomings of existing intelligent production systems and full-process quality control methods for prefabricated cavity components based on the SPCS system, and to provide a new intelligent production system and full-process quality control method for prefabricated cavity components based on the SPCS system. The technical problem to be solved is to enable intelligent production of prefabricated cavity components, thereby making them more practical and having industrial application value.

[0007] Another objective of this invention is to provide an intelligent production system for prefabricated cavity components based on the SPCS system and a method for full-process quality control. The technical problem to be solved is to ensure high quality and high precision in component production, thereby making it more suitable for practical use.

[0008] Another objective of this invention is to provide an intelligent production system for prefabricated cavity components based on the SPCS system and a method for full-process quality control. The technical problem to be solved is to build a full life-cycle quality traceability system, thereby making it more suitable for practical use.

[0009] Another objective of this invention is to provide an intelligent production system for prefabricated cavity components based on the SPCS system and a method for full-process quality control. The technical problem to be solved is to promote the green, environmentally friendly and sustainable development of the prefabricated building industry, thereby making it more suitable for practical use.

[0010] Prefabricated buildings are gradually becoming a mainstream trend in the construction industry due to their advantages such as high efficiency and environmental friendliness. However, traditional prefabricated component production methods suffer from problems such as low production efficiency, unstable quality, and high reliance on manual labor. To address these issues, this invention proposes an intelligent production system and full-process quality control method for prefabricated hollow components based on SPCS (Structural Precast Concrete System). The aim is to achieve efficient and high-quality prefabricated hollow component production through intelligent technology and automated equipment.

[0011] The objective of this invention and the technical problem it solves are achieved through the following technical solution. Based on the SPCS (Special Process Control System) framework, this invention proposes an intelligent production system and full-process quality control method for prefabricated hollow components. Through a modular flexible production line, a turning machine, an intelligent collaborative control system for concrete pouring, and a full-process quality control method, it solves the problems of low production efficiency, unstable quality, and high reliance on manual labor in traditional prefabricated component production, achieving efficient, intelligent production and high-quality control of prefabricated hollow components.

[0012] The objectives of this invention and the solutions to its technical problems can be further achieved by the following technical measures. In the aforementioned intelligent production system and full-process quality control method for prefabricated cavity components based on the SPCS system, the modular flexible production line uses a high-precision servo motor to drive mold assembly, significantly improving the efficiency of mold assembly; the flipping machine dynamically optimizes the threading path through AI algorithms, avoiding wrinkles or offset problems caused by traditional manual operation; the intelligent collaborative control system for concrete pouring includes an intelligent start-stop device for the vibration table, which automatically adjusts the vibration parameters based on the concrete density feedback to avoid over-vibration / under-vibration; the full-process quality control method ensures high quality and stability of component production through multi-dimensional AI prediction of quality defects, dynamic optimization of process parameters, full life-cycle quality traceability, and an adaptive process correction mechanism.

[0013] Compared with the prior art, the present invention has significant advantages and beneficial effects. As can be seen from the above technical solution, in order to achieve the aforementioned objectives, the main technical contents of the present invention are as follows:

[0014] A modular flexible production line is provided, which is used to quickly switch between production of components of different sizes and shapes according to the structural characteristics of SPCS cavity components through an intelligent robotic arm and an adaptive mold system. The intelligent robotic arm can automatically adjust its motion path according to the preset component size and shape parameters, and the adaptive mold system can automatically adjust the mold shape and size according to the component size.

[0015] The intelligent tilting machine is used to monitor the tilting status and position deviation in real time through an automated tilting system that integrates position sensors and visual positioning algorithms, ensuring that the thickness error of the cavity concrete is ≤2mm.

[0016] The intelligent collaborative control system for concrete pouring, based on multi-sensor fusion, constructs a closed-loop control system for concrete pouring quality, enabling intelligent start / stop of the vibrating table and real-time slump compensation. The multi-sensor fusion includes a concrete slump sensor, a vibrating table position sensor, and a concrete density sensor. The system can automatically adjust the start / stop time and vibration parameters of the vibrating table based on sensor feedback. Preferably, the modular flexible production line uses a high-precision servo motor to drive mold assembly, improving mold assembly efficiency by more than 50%.

[0017] Preferably, the intelligent flipping machine dynamically optimizes the flipping path through AI algorithms to avoid wrinkles or displacement problems caused by traditional manual operation.

[0018] Preferably, the intelligent collaborative control system for concrete pouring includes an intelligent start-stop device for the vibration table, which automatically adjusts the vibration parameters based on the concrete density feedback to avoid over-vibration / under-vibration.

[0019] This invention also provides a method for full-process quality control of prefabricated cavity components based on the SPCS system, characterized by the following steps:

[0020] A multi-dimensional AI-based quality defect prediction system was built, based on machine vision and deep learning, to achieve real-time detection of appearance quality defects with an accuracy of 99.5%. High-definition industrial cameras were deployed at key workstations to automatically compare the defects with preset appearance quality standards of ≤0.5mm surface flatness error and ≤5% color uniformity deviation, achieving millisecond-level defect classification and early warning.

[0021] A dynamic optimization engine for process parameters was established to adjust production parameters in real time through big data analysis; based on the correlation data between "turning time" and the initial setting strength of concrete, the timing of turning was dynamically optimized to reduce the component breakage rate by 30%.

[0022] A full lifecycle quality traceability platform is built, based on blockchain technology, to achieve full-chain data archive management from raw materials to finished components; the platform automatically links supplier test reports, records the process parameters of each component in real time, and links with the construction preparation stage to push component quality data to the construction site in advance; an adaptive process correction mechanism is developed, and for the "cavity + post-pouring" node unique to the SPCS system, the steel mesh machine ensures that the steel tie bands achieve three-level closed-loop feedback control.

[0023] Preferably, the adaptive process correction mechanism is designed for the unique "cavity + post-cast" nodes of the SPCS system, and uses a steel mesh machine to ensure that the steel tie bands achieve three-level closed-loop feedback control.

[0024] Preferred, primary correction: when a single point of insufficient binding is detected, the equipment automatically reverts to rebind (maximum 3 retries); intermediate correction: if 5 consecutive nodes fail to meet the requirements, the equipment self-diagnoses and calibrates the gripper posture and torque output; advanced correction: when the cumulative failure rate exceeds 5%, the binding path parameters are optimized through the digital twin platform, and a maintenance work order is pushed.

[0025] Preferably, the multi-dimensional quality defect AI prediction system deploys high-definition industrial cameras at key workstations, automatically comparing them according to preset appearance quality standards to achieve millisecond-level defect classification and early warning.

[0026] Preferably, the preset appearance quality standard is a surface flatness error ≤ 0.5 mm and a color uniformity deviation ≤ 5%.

[0027] Preferably, the process parameter dynamic optimization engine dynamically optimizes the flipping timing based on the correlation data between "flipping time" and the initial setting strength of concrete, reducing the component breakage rate by 30%.

[0028] Preferably, the full lifecycle quality traceability platform is based on blockchain technology, automatically links supplier inspection reports, records the process parameters of each component in real time, and links with the construction preparation stage to push component quality data to the construction site in advance.

[0029] Preferably, the adaptive process correction mechanism uses a rebar mesh machine to control the binding force of the ring reinforcement through force feedback, and ensures that the rebar tie bands achieve three-level closed-loop feedback control through the rebar mesh machine.

[0030] The working principle of the modular flexible production line is as follows: the intelligent robotic arm automatically adjusts its motion path according to the preset production plan and component parameters to complete the gripping, handling and assembly of components; the adaptive mold system automatically adjusts the shape and size of the mold according to the input component size parameters to ensure the precise matching of the mold and the component; the high-precision servo motor drives the mold assembly, and achieves efficient and accurate mold assembly by precisely controlling the speed and position of the motor.

[0031] The modular flexible production line consists of the following systems:

[0032] Intelligent robotic arm: Equipped with high-precision sensors and end effectors, it can automatically adjust the motion path according to preset component size and shape parameters, enabling rapid switching production of components of different sizes and shapes.

[0033] Adaptive mold system: It adopts a modular design and can automatically adjust the shape and size of the mold according to the size of the component to ensure a precise match between the mold and the component.

[0034] High-precision servo motors drive mold assembly, improving mold assembly efficiency by more than 50% and ensuring the accuracy and speed of mold assembly.

[0035] The objectives of this invention and the solutions to its technical problems are also achieved through the following technical solutions. Based on the modular flexible production line proposed in this invention, the combination of an intelligent robotic arm and an adaptive mold system enables rapid switching between production of components of different sizes and shapes, significantly improving production efficiency and flexibility.

[0036] The objectives of this invention and the solutions to its technical problems can be further achieved by the following technical measures. In the aforementioned intelligent production system for prefabricated cavity components based on the SPCS system, the modular flexible production line uses a high-precision servo motor to drive mold assembly, further improving the accuracy and efficiency of mold assembly and ensuring the stability and efficiency of the production process.

[0037] The working principle of the tilting machine in this invention is to use a geared motor or hydraulic unit to drive the tilting machine, which in turn rotates the workpiece at a uniform speed. The equipment includes a base and a tilting body connected by a tilting hinge. The tilting machine is connected to the base via the tilting hinge. A rubber roller is located in the middle of the tilting machine, and the roller frame is connected to the base, allowing for convenient and quick tilting of goods at any angle. It can also be attached to a conveyor line, employing multi-speed control and limit switches to move goods to the next production line after tilting them at any angle. This achieves a three-dimensional process for goods transportation, eliminating the need for manual labor and overhead cranes, and features high conveying rates. The tilting machine has a simple structure, low cost, high efficiency, and is safe and reliable. In the SPCS system, the tilting machine may be used in the production process of assembled cavity components to achieve the tilting operation of the cavity components, thereby facilitating subsequent processing, assembly, or packaging. Specific application details may involve coordination with automated production lines and integration with other automated equipment, such as intelligent robotic arms and adaptive mold systems, to achieve automation and intelligence of the entire production process.

[0038] The objectives of this invention and the solutions to its technical problems are also achieved through the following technical solutions. The flipping machine proposed in this invention replaces manual labor with mechanized operation, significantly improving production efficiency and operational accuracy, while reducing the labor intensity of workers and improving the working environment. It can accurately flip workpieces to a specified angle, adapting to workpieces of different sizes and weights, optimizing logistics and storage processes, and is easily integrated into automated production lines, thereby reducing enterprise operating costs and improving product quality.

[0039] The working principle of the intelligent collaborative control system for concrete pouring is as follows: the multi-sensor fusion system monitors the slump of the concrete, the position of the vibrating table, and the density of the concrete in real time, and generates a comprehensive feedback signal through data fusion algorithm; the intelligent start-stop device of the vibrating table automatically adjusts the vibration parameters according to the concrete density feedback; the real-time slump compensation system adjusts the slump of the concrete in real time according to the feedback of the concrete slump sensor to ensure the uniformity and fluidity of the concrete, thereby ensuring the quality of concrete pouring.

[0040] Composition of the intelligent collaborative control system for concrete pouring:

[0041] Multi-sensor fusion system: including concrete slump sensor, vibration table position sensor and concrete density sensor, to monitor the concrete pouring status in real time.

[0042] Intelligent start / stop device for vibration table: Automatically adjusts vibration parameters based on concrete density feedback to avoid over-vibration or under-vibration.

[0043] Real-time slump compensation system: Based on feedback from concrete slump sensors, the slump of the concrete is adjusted in real time to ensure the uniformity and fluidity of the concrete.

[0044] The objectives of this invention and the solutions to its technical problems are also achieved through the following technical solutions. The intelligent collaborative control system for concrete pouring proposed in this invention, through multi-sensor fusion and intelligent start-stop devices, realizes intelligent control of the concrete pouring process, effectively avoiding over-vibration or under-vibration problems, and ensuring the uniformity and stability of concrete pouring quality.

[0045] The objectives of this invention and the technical problems it addresses can be further achieved through the following technical measures. In the aforementioned intelligent production system for prefabricated hollow components based on the SPCS system, the intelligent collaborative control system for concrete pouring further optimizes the fluidity of concrete through a real-time slump compensation system, ensuring the efficiency and stability of the concrete pouring process.

[0046] The whole-process quality control method consists of a multi-dimensional quality defect AI prediction system, a process parameter dynamic optimization engine, a full life cycle quality traceability platform, and an adaptive process correction mechanism.

[0047] The multi-dimensional quality defect AI prediction system, based on machine vision and deep learning, is equipped with high-definition industrial cameras and AI algorithms to achieve real-time detection of appearance quality defects. Its working principle involves deploying high-definition industrial cameras at key workstations and automatically comparing them against preset appearance quality standards (surface flatness error ≤0.5mm, color uniformity deviation ≤5%) to achieve millisecond-level defect classification and early warning, with a detection accuracy of 99.5%.

[0048] The dynamic optimization engine for process parameters adjusts production parameters in real time through big data analysis, equipped with a data acquisition system and optimization algorithms. Its working principle is based on the correlation data between "turning time" and the initial setting strength of concrete, dynamically determining the timing of turning to reduce component breakage by 30%. By monitoring various parameters in the production process in real time and combining them with big data analysis, the engine dynamically adjusts production parameters to ensure the stability and efficiency of the production process.

[0049] The full lifecycle quality traceability platform, based on blockchain technology and equipped with a data storage system and blockchain nodes, enables end-to-end data archive management from raw materials to finished components. Its working principle involves the platform automatically linking supplier inspection reports, recording the process parameters of each component in real time, and coordinating with the construction preparation phase to push component quality data to the construction site in advance. Blockchain technology ensures the authenticity and immutability of the data, achieving transparency and efficiency in quality traceability.

[0050] The adaptive process correction mechanism, specifically designed for the "cavity + post-cast" nodes unique to the SPCS system, employs a rebar mesh machine and a force feedback control system. The rebar mesh machine utilizes a force feedback control loop to ensure the binding strength of the rebar mesh, achieving a three-level closed-loop feedback control for the rebar tie bands. During the binding process, the rebar mesh machine monitors the force feedback signals in real time and dynamically adjusts the binding strength based on these signals to ensure the stability and consistency of the binding operation, thereby guaranteeing the overall quality of the component. Through the above technical solution, the intelligent production system and full-process quality control method for prefabricated cavity components based on the SPCS system of this invention have at least the following advantages:

[0051] The technical solution of the present invention has the following significant advantages:

[0052] 1. Significantly improved production efficiency: Through modular flexible production line design and unmanned hoisting and yard management, the production of components of different sizes and shapes can be quickly switched, shortening the production cycle by 40%, meeting the construction target of "one floor every four days", and significantly improving production efficiency.

[0053] 2. Significantly improved product quality: The adoption of intelligent core mold insertion and positioning technology, intelligent collaborative control of concrete pouring, and a multi-dimensional AI-based quality defect prediction system ensures that the first-pass yield of cavity components reaches over 98%, far exceeding industry standards, effectively improving product quality and stability.

[0054] 3. Cost optimization and resource conservation: Reduce reliance on manual labor by 70% and lower labor costs; at the same time, through intelligent maintenance and waste recycling systems, reduce dust and wastewater pollution indicators by more than 50%, reduce resource waste, and achieve green and environmentally friendly production.

[0055] 4. Full lifecycle quality traceability and data management: A full lifecycle quality traceability platform is built based on blockchain technology to realize full-process data recording and traceability from raw materials to construction, providing strong support for quality control and reducing correction costs in the construction process.

[0056] 5. Process optimization and intelligent correction: Through a dynamic optimization engine for process parameters and an adaptive process correction mechanism, production parameters are adjusted in real time to optimize the process flow, ensuring that the production process meets the high standards of the SPCS system and reducing component breakage rate and quality defects.

[0057] 6. Technology Integration and Industry Breakthrough: Deeply integrates intelligent manufacturing technology with the process characteristics of the SPCS system, filling the gap in the intelligent production of prefabricated cavity components throughout the entire process, and providing the prefabricated building industry with a complete "design-production-construction-operation and maintenance" full-industry chain solution, thus promoting technological progress in the industry.

[0058] As described above, this invention relates to an intelligent production system for prefabricated hollow components based on the SPCS (Specialized Production Control System) framework and a comprehensive quality control method. Through modular flexible production lines, tilting machine technology, intelligent collaborative control of concrete pouring, and unmanned hoisting and storage management, it achieves efficient and intelligent production of prefabricated hollow components. The comprehensive quality control method ensures high quality and stability in component production through multi-dimensional AI-based defect prediction, dynamic optimization of process parameters, full lifecycle quality traceability, and an adaptive process correction mechanism. This invention significantly improves production efficiency, reduces reliance on manual labor, and minimizes environmental pollution. Simultaneously, it provides the prefabricated building industry with a complete "design-production-construction-operation and maintenance" full-industry chain solution.

[0059] In summary, this invention presents a unique intelligent production system and full-process quality control method for prefabricated hollow components based on the SPCS system. Through intelligent production technology and full-process quality control, it achieves efficient, high-quality, and low-cost production of prefabricated hollow components, filling the industry gap in intelligent full-process production and providing the prefabricated building industry with a complete "design-production-construction-operation and maintenance" full-industry chain solution. It possesses numerous advantages and practical value, and is truly innovative as no similar designs have been publicly disclosed or used in similar methods and manufacturing processes. It represents a significant improvement in methodology, manufacturing methods, and functionality, demonstrating substantial technological advancement and producing user-friendly and practical results. Compared to existing intelligent production systems and full-process quality control methods based on the SPCS system, it offers several enhanced functions, making it more suitable for practical application and possessing broad industrial value. It is indeed a novel, progressive, and practical new design.

[0060] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0061] The specific methods, manufacturing methods, and structures of the present invention are given in detail in the following embodiments and accompanying drawings. Attached Figure Description

[0062] Figure 1 Flowchart of a whole-process quality control method for prefabricated cavity components based on SPCS system. Detailed Implementation

[0063] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, manufacturing methods, steps, structures, features, and effects of the prefabricated cavity component intelligent production system and full-process quality control method based on the SPCS system proposed by the present invention.

[0064] Please see Figure 1 As shown, the preferred embodiment of the present invention, a prefabricated cavity component intelligent production system and a full-process quality control method based on the SPCS system, mainly includes the following steps:

[0065] Example 1:

[0066] (I) Implementation of Modular Flexible Production Line

[0067] I. Configuration and Operation of Intelligent Robotic Arm

[0068] Configuration: 6-axis industrial robotic arm with a maximum load capacity of 500kg and an end effector accuracy of ±0.1mm.

[0069] Operating steps:

[0070] 1. Path Planning: Based on the component's dimensions (e.g., length 2.5m, width 1.2m, height 0.3m) and shape, the robotic arm automatically plans the motion path for grasping, transporting, and assembling by inputting preset parameters through the control system. The path planning algorithm uses the A* algorithm to ensure the shortest path while avoiding obstacles.

[0071] 2. Grasping and Transporting: The robotic arm precisely grasps prefabricated components according to the planned path and smoothly transports them to the designated location. The transport speed is set to 1.5m / s to ensure efficiency and stability.

[0072] 3. Assembly Operation: After reaching the designated position, the robotic arm precisely places the components and completes the assembly according to the preset assembly program. The assembly accuracy is controlled within ±0.5mm.

[0073] II. Application of Adaptive Mold Systems

[0074] Configuration: The mold system consists of multiple modules, each of which can be adjusted independently with an adjustment accuracy of ±0.2mm.

[0075] Operating steps:

[0076] 1. Dimension Input: Input the component's dimensional parameters (such as length 2.5m, width 1.2m, height 0.3m) through the control system.

[0077] 2. Mold Adjustment: The adaptive mold system automatically adjusts the shape and size of the mold based on input parameters. The adjustment time is no more than 3 minutes, ensuring a precise match between the mold and the component.

[0078] 3. Mold locking: After adjustment, the mold is locked, ready for subsequent pouring operations.

[0079] III. Driving High-Precision Servo Motors

[0080] Configuration: High-precision servo motor with a speed accuracy of ±0.01rpm and a position feedback accuracy of ±0.05mm.

[0081] Operating steps:

[0082] 1. Motor Start-up: Based on the control system instructions, the servo motor starts and drives the mold to move. The motor speed is set to 50 rpm to ensure smooth operation.

[0083] 2. Position Feedback: The motor provides real-time position information via an encoder to ensure precise mold positioning. The position feedback frequency is 100Hz.

[0084] 3. Assembly complete: After the mold is moved into place, the assembly is complete and ready to proceed to the next process.

[0085] (II) Implementation of Intelligent Tilting Machine

[0086] I. Use of the Tilting Machine

[0087] Configuration: Automated flipping machine is selected, with a maximum load capacity of 1000kg and a flipping accuracy of ±0.05mm.

[0088] Operating steps:

[0089] Tilting Path Planning: Based on the cavity shape and size of the component, the tilting path of the tilting machine is planned through the control system. The path planning algorithm uses B-spline curve fitting to ensure a smooth path.

[0090] Automatic flipping: The automated flipping machine starts and precisely flips the component to the designated position. The flipping time is no more than 2 minutes.

[0091] Flipping complete: After flipping, the component is ready for subsequent pouring operations.

[0092] II. Application of Rebar Mesh Machine

[0093] Configuration: The steel mesh machine is equipped with a high-precision force feedback sensor.

[0094] Operating steps:

[0095] Rebar mesh fabrication: The rebar mesh machine performs ring tying according to the preset tying path and force requirements (e.g., tying force of 10N). The tying speed is 0.1m / s.

[0096] Force feedback adjustment: The force feedback control system monitors the binding force in real time and dynamically adjusts it. The rebar mesh machine ensures three-level closed-loop feedback control of the rebar ties. The adjustment time does not exceed 0.1 seconds. II. Operation of the automated threading and pulling system

[0097] Configuration: Equipped with an automatic threading and pulling mechanism, with a threading and pulling accuracy of ±0.2mm and a threading and pulling speed of 0.5m / s.

[0098] Operating steps:

[0099] 1. Path Planning: Based on the cavity shape and size of the component, the control system plans the insertion and extraction paths of the mandrel. The path planning algorithm uses B-spline curve fitting to ensure smooth path movement.

[0100] 2. Automatic insertion: The automated insertion and extraction system is activated, precisely inserting the mandrel into the cavity. Insertion time does not exceed 2 minutes.

[0101] 3. Automatic extraction: After casting is completed, the automated extraction system will extract the core mold, completing one production cycle. The extraction time will not exceed 1.5 minutes.

[0102] III. Application of Visual Positioning Algorithms

[0103] Configuration: Deploy high-definition industrial cameras at key workstations with a resolution of 1920×1080, and use the SIFT algorithm for image processing.

[0104] Operating steps:

[0105] 1. Image Acquisition: A high-definition industrial camera acquires real-time position images of the core mold at a frequency of 30fps.

[0106] 2. Image Processing: The deviation between the actual position and the preset position of the core mold is calculated using the SIFT algorithm. The algorithm processing time is no more than 0.1 seconds.

[0107] 3. Position Correction: Based on the deviation value, the automated system adjusts the position of the mandrel to ensure precise positioning. The correction accuracy is ±0.1mm.

[0108] (III) Implementation of the Intelligent Collaborative Control System for Concrete Pouring

[0109] I. Application of Multi-Sensor Fusion Systems

[0110] Configuration: Install concrete slump sensors (measurement range 0-200mm, accuracy ±1mm), vibration table position sensors (measurement range 0-3m, accuracy ±0.01m), and concrete density sensors (measurement range 0-100%, accuracy ±0.5%) in the pouring area.

[0111] Operating steps:

[0112] 1. Sensor installation: Install the sensors at key locations in the pouring area.

[0113] 2. Data Acquisition: Sensors collect real-time data on concrete slump (set to 180mm), the position of the vibrating table, and concrete density. The data acquisition frequency is 1Hz.

[0114] 3. Data transmission: The collected data is transmitted to the control system wirelessly.

[0115] II. Operation of the Intelligent Start-Stop Device for the Vibration Table

[0116] Configuration: The vibration table is equipped with an intelligent start / stop device, the vibration frequency is 50Hz, and the vibration force is adjustable from 0-100N.

[0117] Operating steps:

[0118] 1. Parameter setting: Based on the size and shape of the component, the vibration parameters (vibration frequency 50Hz, vibration force 50N) are set through the control system.

[0119] 2. Automatic start / stop: The vibrating table automatically starts or stops vibration based on feedback from the density sensor. When the density reaches 95%, the vibrating table stops vibrating.

[0120] 3. Parameter adjustment: Based on the real-time feedback of compaction data, the control system dynamically adjusts the vibration parameters to ensure uniform vibration effect.

[0121] III. Operation of the Real-time Slump Compensation System

[0122] Configuration: Install a real-time slump compensation system in the concrete mixing area with a compensation accuracy of ±1mm.

[0123] Operating steps:

[0124] 1. Slump monitoring: Slump sensors monitor the slump of concrete in real time.

[0125] 2. Data Feedback: Monitoring data is fed back to the control system in real time. The feedback frequency is 1Hz.

[0126] 3. Real-time adjustment: The control system adjusts the concrete mix ratio or mixing time in real time based on feedback data to ensure that the slump meets the requirements (180±10mm).

[0127] (iv) Implementation of the whole-process quality control method

[0128] I. Operation of the Multi-Dimensional Quality Defect AI Prediction System

[0129] Configuration: Deploy high-definition industrial cameras at key workstations with a resolution of 1920×1080. The AI ​​algorithm adopts convolutional neural networks (CNN) in deep learning. The preset appearance quality standards are surface flatness error ≤0.5mm and color uniformity deviation ≤5%.

[0130] Operating steps:

[0131] 1. Camera installation: Install high-definition industrial cameras at various key workstations in component production.

[0132] 2. Image Acquisition: The camera acquires images of the component's appearance in real time at a frequency of 30fps.

[0133] 3. AI Analysis: AI algorithms analyze the acquired images and automatically compare them against preset appearance quality standards, achieving millisecond-level defect classification and early warning. The algorithm processing time is less than 0.05 seconds, with a detection accuracy of 99.5%.

[0134] II. Application of Dynamic Parameter Optimization Engine

[0135] Configuration: Production parameters are adjusted in real time through a big data analytics system. The data acquisition frequency is 1Hz, and the data analysis algorithms employ linear regression and decision tree algorithms.

[0136] Operating steps:

[0137] 1. Data Acquisition: Real-time acquisition of various parameters during the production process, including core mold pulling time (set value is 30 seconds) and initial concrete setting strength (set value is 10MPa), etc.

[0138] 2. Data Analysis: Analyze the correlation between parameters using linear regression and decision tree algorithms. Analysis time should not exceed 1 minute.

[0139] 3. Parameter optimization: Based on the analysis results, production parameters are dynamically adjusted, such as optimizing the flipping timing to reduce the component breakage rate by 30%. The optimized flipping time is adjusted to 28 seconds.

[0140] III. Operation of the Full Lifecycle Quality Traceability Platform

[0141] Configuration: A full lifecycle quality traceability platform is established based on blockchain technology. Data storage uses a distributed ledger, and data encryption employs the SHA-256 algorithm.

[0142] Operating steps:

[0143] 1. Data entry: Record raw material information (such as cement brand, sand and gravel origin), production process parameters (such as pouring time, vibration parameters), and quality inspection data (such as appearance defect detection results, density detection results) for each component in real time.

[0144] 2. Data Linking: Automatically links supplier test reports to ensure data integrity and consistency. Data linking time is no more than 5 seconds.

[0145] 3. Data Traceability: Through blockchain technology, full-chain data traceability is achieved from raw materials to finished components, ensuring the authenticity and immutability of the data. Traceability query time is no more than 10 seconds.

[0146] IV. Implementation of Adaptive Process Correction Mechanism

[0147] Configuration: For the "cavity + post-cast" nodes unique to the SPCS system, an intelligent binding robot is provided with a binding accuracy of ±0.1mm and a force feedback control system accuracy of ±0.05N.

[0148] Operating steps:

[0149] 1. Rebar mesh machine configuration: The rebar mesh machine is equipped with a high-precision force feedback sensor.

[0150] 2. Binding Operation: The rebar mesh machine is configured to bind the ring reinforcement bars according to the preset binding path and force requirements (e.g., binding force of 10N). The binding speed is 0.1m / s.

[0151] 3. Force Feedback Adjustment: The force feedback control system monitors the binding force in real time and dynamically adjusts it. The rebar mesh machine ensures three-level closed-loop feedback control of the rebar tie bands. The adjustment time does not exceed 0.1 seconds.

[0152] The detailed descriptions of the above specific embodiments demonstrate the specific operational procedures and implementation details of the intelligent production system for prefabricated cavity components based on the SPCS system and its full-process quality control method. These embodiments include specific data, algorithm calculation methods, big data analysis methods, and specific content on full lifecycle quality traceability, ensuring the efficiency, intelligence, and high quality of the production process, and providing strong technical support for the prefabricated building industry.

[0153] Example 2:

[0154] (I) Implementation of Modular Flexible Production Line

[0155] I. Configuration and Operation of Intelligent Robotic Arm

[0156] Configuration: 7-axis industrial robotic arm with a maximum load capacity of 800kg and an end effector accuracy of ±0.05mm, suitable for the production of large-sized cavity floor slabs.

[0157] Operating steps:

[0158] Path planning: For hollow floor slabs (3m×1.5m×0.2m in size), the robotic arm uses the Dijkstra algorithm to plan the motion path for grasping, transporting and assembling by inputting preset parameters through the control system, ensuring that the path is the shortest and avoids obstacles.

[0159] Grasping and transporting: The robotic arm precisely grasps the prefabricated floor slabs according to the planned path and smoothly transports them to the designated location. The transport speed is set to 2m / s to ensure efficiency and stability.

[0160] Assembly Operation: Upon reaching the designated position, the robotic arm precisely places and assembles the floor slab according to the preset assembly program. The assembly accuracy is controlled within ±0.3mm.

[0161] II. Application of Adaptive Mold Systems

[0162] Configuration: The mold system consists of multiple modules, each of which can be adjusted independently with an adjustment accuracy of ±0.1mm, making it suitable for the production of floor slabs of different thicknesses.

[0163] Operating steps:

[0164] Size input: Input the size parameters of the floor slab (e.g., length 3m, width 1.5m, height 0.2m) through the control system.

[0165] Mold Adjustment: The adaptive mold system automatically adjusts the shape and size of the mold based on input parameters. The adjustment time is no more than 2 minutes, ensuring a precise match between the mold and the floor slab.

[0166] Mold locking: After adjustment, the mold is locked, ready for subsequent pouring operations.

[0167] III. Driving High-Precision Servo Motors

[0168] Configuration: It adopts a high-precision servo motor with a speed accuracy of ±0.005rpm and a position feedback accuracy of ±0.02mm, which is suitable for high-precision mold assembly.

[0169] Operating steps:

[0170] Motor Start-up: The servo motor starts according to the control system commands, driving the mold to move. The motor speed is set to 30 rpm to ensure smooth operation.

[0171] Position feedback: The motor provides real-time position information via an encoder to ensure precise mold positioning. The position feedback frequency is 200Hz.

[0172] Assembly complete: After the mold is moved into place, the assembly is complete and ready to proceed to the next process.

[0173] (II) Implementation of Intelligent Tilting Machine

[0174] I. Use of the Tilting Machine

[0175] Configuration: Automated flipping machine is selected, with a maximum load capacity of 1000kg and a flipping accuracy of ±0.05mm.

[0176] Operating steps:

[0177] Tilting Path Planning: Based on the cavity shape and size of the component, the tilting path of the tilting machine is planned through the control system. The path planning algorithm uses B-spline curve fitting to ensure a smooth path.

[0178] Automatic flipping: The automated flipping machine starts and precisely flips the component to the designated position. The flipping time is no more than 2 minutes.

[0179] Flipping complete: After flipping, the component is ready for subsequent pouring operations.

[0180] II. Application of Rebar Mesh Machine

[0181] Configuration: The steel mesh machine is equipped with a high-precision force feedback sensor.

[0182] Operating steps:

[0183] Rebar mesh fabrication: The rebar mesh machine performs ring tying according to the preset tying path and force requirements (e.g., tying force of 10N). The tying speed is 0.1m / s.

[0184] Force feedback adjustment: The force feedback control system monitors the binding force in real time and dynamically adjusts it. The rebar mesh machine ensures that the rebar tie bands achieve three-level closed-loop feedback control. The adjustment time does not exceed 0.1 seconds.

[0185] III. Application of Visual Positioning Algorithms

[0186] Configuration: Deploy high-definition industrial cameras at key workstations with a resolution of 2560×1920. The image processing algorithm adopts the ORB algorithm, which is suitable for high-precision positioning.

[0187] Operating steps:

[0188] Image acquisition: A high-definition industrial camera acquires real-time position images of the core mold at a frequency of 60fps.

[0189] Image processing: The deviation between the actual position and the preset position of the core mold is calculated using the ORB algorithm. The algorithm processing time is no more than 0.08 seconds.

[0190] Position correction: Based on the deviation value, the automated system adjusts the position of the mandrel to ensure precise positioning. The correction accuracy is ±0.05mm.

[0191] (III) Implementation of the Intelligent Collaborative Control System for Concrete Pouring

[0192] I. Application of Multi-Sensor Fusion Systems

[0193] Configuration: Install concrete slump sensor (measuring range 0-250mm, accuracy ±0.5mm), vibration table position sensor (measuring range 0-4m, accuracy ±0.005m) and concrete density sensor (measuring range 0-100%, accuracy ±0.2%) in the pouring area.

[0194] Operating steps:

[0195] Sensor installation: Install the sensors at key locations in the pouring area.

[0196] Data Acquisition: Sensors collect real-time data on concrete slump (set to 200mm), the position of the vibrating table, and concrete density. The data acquisition frequency is 2Hz.

[0197] Data transmission: The collected data is transmitted to the control system wirelessly.

[0198] II. Operation of the Intelligent Start-Stop Device for the Vibration Table

[0199] Configuration: The vibration table is equipped with an intelligent start / stop device, the vibration frequency is 60Hz, and the vibration force is adjustable from 0-150N.

[0200] Operating steps:

[0201] Parameter settings: Based on the size and shape of the floor slab, the vibration parameters (vibration frequency 60Hz, vibration force 70N) are set through the control system.

[0202] Automatic start / stop: The vibrating table automatically starts or stops vibration based on feedback from the density sensor. When the density reaches 97%, the vibrating table stops vibrating.

[0203] Parameter adjustment: Based on the real-time feedback of compaction data, the control system dynamically adjusts the vibration parameters to ensure uniform vibration effect.

[0204] III. Operation of the Real-time Slump Compensation System

[0205] Configuration: Install a real-time slump compensation system in the concrete mixing area with a compensation accuracy of ±0.5mm.

[0206] Operating steps:

[0207] Slump monitoring: Slump sensors monitor the slump of concrete in real time.

[0208] Data feedback: Monitoring data is fed back to the control system in real time. The feedback frequency is 2Hz.

[0209] Real-time adjustment: The control system adjusts the concrete mix ratio or mixing time in real time based on feedback data to ensure that the slump meets the requirements (200±10mm).

[0210] (iv) Implementation of the whole-process quality control method

[0211] I. Operation of the Multi-Dimensional Quality Defect AI Prediction System

[0212] Configuration: Deploy high-definition industrial cameras at key workstations with a resolution of 2560×1920. The AI ​​algorithm adopts convolutional neural networks (CNN) in deep learning. The preset appearance quality standards are surface flatness error ≤0.3mm and color uniformity deviation ≤3%.

[0213] Operating steps:

[0214] Camera installation: Install high-definition industrial cameras at various key workstations in the floor slab production process.

[0215] Image acquisition: The camera acquires real-time images of the floor slab's exterior at a frequency of 60fps.

[0216] AI Analysis: AI algorithms analyze the acquired images and automatically compare them against preset appearance quality standards, achieving millisecond-level defect classification and early warning. The algorithm's processing time is less than 0.03 seconds, with a detection accuracy of 99.8%.

[0217] II. Application of Dynamic Parameter Optimization Engine

[0218] Configuration: Production parameters are adjusted in real time through a big data analytics system. The data acquisition frequency is 2Hz, and the data analysis algorithms employ random forest and neural network algorithms.

[0219] Operating steps:

[0220] Data Acquisition: Real-time acquisition of various parameters during the production process, including core mold pulling time (set value is 35 seconds) and initial concrete setting strength (set value is 12MPa).

[0221] Data Analysis: Analyze the correlations between parameters using random forest and neural network algorithms. Analysis time should not exceed 0.5 minutes.

[0222] Parameter optimization: Based on the analysis results, production parameters are dynamically adjusted, such as optimizing the flipping timing to reduce the component breakage rate by 35%. The optimized flipping time is adjusted to 32 seconds.

[0223] III. Operation of the Full Lifecycle Quality Traceability Platform

[0224] Configuration: A full lifecycle quality traceability platform is established based on blockchain technology. Data storage uses a distributed ledger, and data encryption employs the SHA-256 algorithm.

[0225] Operating steps:

[0226] Data entry: Real-time recording of raw material information (such as cement brand, sand and gravel origin), production process parameters (such as pouring time, vibration parameters), and quality inspection data (such as appearance defect detection results, density detection results) for each floor slab.

[0227] Data Linking: Automatically links supplier testing reports to ensure data integrity and consistency. Data linking time is no more than 3 seconds.

[0228] Data traceability: Through blockchain technology, full-chain data traceability is achieved from raw materials to finished floor slabs, ensuring the authenticity and immutability of the data. Traceability query time is no more than 5 seconds.

[0229] IV. Implementation of Adaptive Process Correction Mechanism

[0230] Configuration: For the "cavity + post-cast" nodes unique to the SPCS system, an intelligent binding robot is provided with a binding accuracy of ±0.05mm and a force feedback control system accuracy of ±0.02N.

[0231] Operating steps:

[0232] Robot configuration: The intelligent tying robot is equipped with a high-precision force feedback sensor.

[0233] Binding operation: The robot performs ring binding according to the preset binding path and force requirements (e.g., binding force of 12N). The binding speed is 0.15m / s.

[0234] Force feedback adjustment: The force feedback control system monitors the binding force in real time and dynamically adjusts it. The rebar mesh machine ensures that the rebar tie bands achieve three-level closed-loop feedback control. The adjustment time does not exceed 0.05 seconds.

[0235] The detailed description of the above specific embodiments further demonstrates the adaptability and advantages of the SPCS-based intelligent production system for prefabricated hollow components and its full-process quality control method in the production of different component types, thus addressing the diversified production needs of the prefabricated building industry.

[0236] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the methods and techniques disclosed above without departing from the scope of the present invention to create equivalent embodiments. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A prefabricated intelligent production system for cavity components based on the SPCS (Specialized Production Control System) architecture, characterized in that, include: A modular flexible production line is used to quickly switch between production of components of different sizes and shapes based on the structural characteristics of SPCS cavity components through an intelligent robotic arm and an adaptive mold system. The intelligent robotic arm can automatically adjust its motion path according to preset component size and shape parameters, and the adaptive mold system can automatically adjust the mold shape and size according to the component size. The intelligent tilting machine is used to monitor the tilting status and position deviation in real time through an automated tilting system that integrates position sensors and visual positioning algorithms, ensuring that the thickness error of the cavity concrete is ≤2mm. The intelligent collaborative control system for concrete pouring is based on multi-sensor fusion to construct a closed-loop control system for concrete pouring quality, realizing intelligent start and stop of the vibrating table and real-time slump compensation. The multi-sensor fusion includes a concrete slump sensor, a vibrating table position sensor, and a concrete density sensor. The system can automatically adjust the start and stop time of the vibrating table and the vibration parameters according to the sensor feedback.

2. The intelligent production system for prefabricated cavity components based on the SPCS system according to claim 1, characterized in that, The modular flexible production line uses a high-precision servo motor to drive mold assembly, improving mold assembly efficiency by more than 50%.

3. The intelligent production system for prefabricated cavity components based on the SPCS system according to claim 1, characterized in that, The intelligent flipping machine dynamically optimizes the flipping path through AI algorithms, avoiding wrinkles or misalignment problems caused by traditional manual operation.

4. The intelligent production system for prefabricated cavity components based on the SPCS system according to claim 1, characterized in that, The intelligent collaborative control system for concrete pouring includes an intelligent start-stop device for the vibration table, which automatically adjusts the vibration parameters based on feedback of concrete density.

5. A method for full-process quality control of prefabricated cavity components based on the SPCS system, characterized in that, Includes the following steps: A multi-dimensional AI-based quality defect prediction system was built, based on machine vision and deep learning, to achieve real-time detection of appearance quality defects with an accuracy of 99.5%. High-definition industrial cameras were deployed at key workstations to automatically compare the defects with preset appearance quality standards of ≤0.5mm surface flatness error and ≤5% color uniformity deviation, achieving millisecond-level defect classification and early warning. A dynamic optimization engine for process parameters was established to adjust production parameters in real time through big data analysis; based on the correlation data between "turning time" and the initial setting strength of concrete, the timing of turning was dynamically optimized to reduce the component breakage rate by 30%. Build a full lifecycle quality traceability platform based on blockchain technology to achieve full-chain data archive management from raw materials to finished components; The platform automatically links supplier testing reports, records the process parameters of each component in real time, and links with the construction preparation stage to push component quality data to the construction site in advance; it develops an adaptive process correction mechanism, and for the "cavity + post-pouring" node unique to the SPCS system, it uses a steel mesh machine to ensure that the steel tie bands achieve three-level closed-loop feedback control.

6. The method for full-process quality control of prefabricated cavity components based on the SPCS system according to claim 5, characterized in that, The multi-dimensional quality defect AI prediction system deploys high-definition industrial cameras at key workstations and automatically compares them according to preset appearance quality standards to achieve millisecond-level defect classification and early warning.

7. The method for full-process quality control of prefabricated cavity components based on the SPCS system according to claim 6, characterized in that, The preset appearance quality standards are: surface flatness error ≤ 0.5mm and color uniformity deviation ≤ 5%.

8. The method for full-process quality control of prefabricated cavity components based on the SPCS system according to claim 5, characterized in that, The process parameter dynamic optimization engine dynamically optimizes the timing of flipping based on the correlation data between "flipping time" and the initial setting strength of concrete, reducing the component breakage rate by 30%.

9. The method for full-process quality control of prefabricated cavity components based on the SPCS system according to claim 5, characterized in that, The full lifecycle quality traceability platform is based on blockchain technology, automatically links supplier inspection reports, records the process parameters of each component in real time, and links with the construction preparation stage to push component quality data to the construction site in advance.

10. The method for full-process quality control of prefabricated cavity components based on the SPCS system according to claim 5, characterized in that, The adaptive process correction mechanism uses a rebar mesh machine to control the binding force of the ring reinforcement through force feedback, and ensures that the rebar tie bands achieve three-level closed-loop feedback control through the rebar mesh machine.

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