Prefabricated part production auxiliary system for intelligent construction

Abstract

The invention discloses a prefabricated part production auxiliary system for intelligent construction, and belongs to the technical field of intelligent construction, and the system comprises the steps: collecting production data sets of different types of prefabricated parts in the production process, carrying out the standardization processing, and generating a production log; according to the prefabricated part production task, the type of a target part is obtained, and a corresponding target mold is called to be installed on a corresponding production station; production state data of the target mold are obtained in real time, the production state data are compared with preset standard installation data, the deviation value of the target mold is calculated, then the average position deviation and the average angle deviation of the target mold are calculated, the average position deviation and the average angle deviation are compared with corresponding preset deviation threshold values, and positioning of the target mold is calibrated; position and posture adjustment is carried out on the target mold which is positioned abnormally through a PID control algorithm; the system aims at achieving precise positioning and intelligent regulation and control of the mold in the prefabricated part production process, and the prefabricated part production efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction technology, and more specifically to a prefabricated component production auxiliary system for intelligent construction. Background Technology

[0002] With the rapid development of industrialized construction, precast component production has become an important direction for the modern construction industry. In the field of intelligent construction, the efficiency and quality of precast component production are crucial. Currently, precast component production faces numerous challenges, including but not limited to: In terms of data management, design, mold, and production status data are scattered, manual recording is prone to errors, lacks standardized integration, and is difficult to support production optimization; In the mold positioning process, the variety of mold types leads to reliance on manual installation, which can easily result in significant positional deviations, affecting component accuracy and causing time-consuming adjustments. For positioning anomalies, existing technologies mostly offer localized improvements but lack real-time monitoring and automatic control. For example, the lack of precise calibration criteria and untimely and inaccurate adjustments can easily cause production delays and quality fluctuations, failing to meet the requirements of intelligent construction for efficient and precise production of prefabricated components. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a prefabricated component production auxiliary system for intelligent construction, so as to solve the problems mentioned in the background art.

[0004] The objective of this invention can be achieved through the following technical solution: a prefabricated component production auxiliary system for intelligent construction, comprising: The data collection and processing module is used to collect design information, mold information, and production status data of different types of precast components during the production process, determine the production dataset of precast components, and generate production logs after standardizing the production dataset of precast components. The positioning module, based on the prefabricated component production task, obtains the type of the target component and calls the corresponding target mold to install it on the corresponding production station; it obtains the production status data of the target mold in real time and compares it with the preset standard installation data to calculate the deviation of the target mold; The intelligent calibration and control module, based on the deviation of the target mold, calculates the average position deviation and average angle deviation of the target mold and compares them with the corresponding preset deviation thresholds to calibrate the positioning of the target mold; and adjusts the position and attitude of the target mold with abnormal positioning through a PID control algorithm.

[0005] As a further aspect of the present invention, the process of determining the production dataset of prefabricated components includes: Based on the design drawings of prefabricated components, design information data of prefabricated components are obtained by using drawing recognition and data extraction technology, including geometric design parameters, materials used, and connection methods; The mold information includes the mold model, the corresponding precast component type, and the size parameters. Each mold is uniquely identified using radio frequency identification technology to obtain a corresponding RFID tag. Meanwhile, the production workshop of prefabricated components is scanned by LiDAR at the production station to generate three-dimensional map coordinate data. Based on the positioning sensors and angle sensors at the production station, the position coordinate data and attitude angle data of the mold are collected in real time to obtain production status data.

[0006] As a further aspect of the present invention, the process of standardizing the production dataset includes: Data cleaning: Identify and remove duplicate data in the production dataset using data deduplication algorithms, and remove outliers in the production dataset based on threshold judgment methods of mean and standard deviation; Unified data format: The numerical data in the design information, mold information, and production status data of different types of precast components are formatted in a unified manner; for character data, the character data is converted into numerical codes that are easy for computers to process according to preset encoding rules. Data normalization: The min-max normalization method is used to normalize various types of data in the production dataset, mapping them to the [0,1] interval; After standardizing the production dataset, various types of data are recorded and stored in the production log in chronological order.

[0007] As a further aspect of the present invention, the process of calculating the deviation of the target mold includes: Real-time acquisition of production status data for the target mold, including position coordinate data and attitude angle data, denoted as (x, y, z) and Where x represents the position coordinate of the target mold on the x-axis in the 3D map coordinate data; y represents the position coordinate of the target mold on the y-axis; and z represents the position coordinate of the target mold on the z-axis. The pitch angle represents the angle by which the target mold rotates around the x-axis; The roll angle represents the angle by which the target mold rotates around the y-axis. Yaw angle represents the angle by which the object rotates about the z-axis; Obtain preset standard installation data, including standard position coordinates and standard posture angles; By comparing the production status data of the target mold with the preset standard installation data, the deviation of the target mold, including positional deviation and angular deviation, is calculated. The formula for calculating the positional deviation is as follows: ; ; ; In the formula, Indicates the positional deviation of the target mold; This indicates the standard position coordinates of the target mold.

[0008] As a further aspect of the present invention, the formula for calculating the angle deviation is as follows: ; ; ; In the formula, Indicates the angular deviation of the target mold; This indicates the standard orientation angle of the target mold.

[0009] As a further aspect of the present invention, the process of calibrating the positioning of the target mold includes: Based on the deviation of the target mold, calculate the average positional deviation and average angular deviation of the target mold; the formulas for calculating the average positional deviation and average angular deviation are as follows: ; ; In the formula, D1 is the average positional deviation; D2 is the average angular deviation; The calculated average position deviation and average angle deviation are compared with the corresponding preset deviation thresholds. If the calculated average position deviation and average angle deviation are both less than or equal to the corresponding deviation thresholds, it indicates that the positioning of the target mold is correct; otherwise, it indicates that the positioning of the target mold is abnormal, and a mold adjustment signal is generated.

[0010] As a further aspect of the present invention, the process of adjusting the position and attitude of a target mold with abnormal positioning using a PID control algorithm includes: Identify mold adjustment signals, obtain the average position deviation and average angle deviation of the target mold with positioning anomalies, compare them with the corresponding preset deviation thresholds, and calculate the error e(t), which is the combined value of position error and attitude error; t is the current time of data acquisition. The PID control algorithm is used to calculate the adjustment amount of the target mold with positioning abnormality. Based on the adjustment amount, the mold adjustment command is generated to drive the actuator to adjust the position and attitude of the target mold with positioning abnormality until the recalculated average position deviation and average angle deviation are less than or equal to the corresponding deviation threshold.

[0011] As a further aspect of the present invention, the formula for calculating the adjustment amount is as follows: ; In the formula, u(t) is the adjustment amount; This is the proportionality coefficient; The integral coefficient; These are the differential coefficients; This indicates that the error is integrated. This represents the derivative of the error with respect to time, i.e., the rate of change of the error.

[0012] Compared with the existing solutions, the present invention achieves the following beneficial effects: In terms of data management, this invention generates production logs by comprehensively collecting and standardizing various types of production data, achieving accurate recording and efficient integration of production process data, and providing detailed evidence for subsequent analysis. In the positioning stage, it can quickly and accurately call the target mold according to the task and monitor its status in real time, accurately calculate the deviation, effectively avoid production problems caused by improper mold installation, and improve production efficiency and component quality. This invention can calibrate mold positioning in a timely manner by scientifically calculating deviations and comparing them with thresholds. It uses a PID control algorithm to precisely adjust abnormal molds, ensuring the accuracy of mold position and posture, greatly reducing manual intervention, improving the level of production automation, ensuring the high precision and stability of precast component production, and promoting the development of intelligent construction. Attached Figure Description

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] Figure 1 This is a modular structure diagram of the prefabricated component production auxiliary system for intelligent construction proposed in this invention. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0016] like Figure 1 As shown, the present invention is a prefabricated component production auxiliary system for intelligent construction, including a data collection and processing module, a positioning module, and an intelligent calibration and control module; The data collection and processing module is used to collect design information, mold information, and production status data of different types of precast components during the production process, determine the production dataset of precast components, and generate production logs after standardizing the production dataset of precast components. Among them, different types of prefabricated components include, but are not limited to, prefabricated exterior wall panels, prefabricated air conditioning panels, prefabricated balcony panels, and prefabricated composite panels; It should be further explained that, in the specific implementation process, the process of determining the production dataset for prefabricated components includes: Based on the design drawings of precast components, design information data of precast components are obtained by using drawing recognition and data extraction technology, including geometric design parameters, materials used, and connection methods; further, geometric design parameters include, but are not limited to, the length, width, height, and curvature of the target component; materials used include, but are not limited to, concrete, steel bars, and embedded parts; connection methods include, but are not limited to, bolted connections, keyed connections, prestressed connections, and welded connections. The mold information includes the mold model, the corresponding prefabricated component type, and the size parameters. Each mold is uniquely identified using radio frequency identification (RFID) technology to obtain a corresponding RFID tag. Meanwhile, LiDAR is used to scan the precast component production workshop at the production station to generate three-dimensional map coordinate data. Based on the positioning and angle sensors at the production station, the position coordinate data and attitude angle data of the mold are collected in real time to obtain production status data. It should be further explained that, in the specific implementation process, the standardization process for the production dataset includes: Data cleaning: Identify and remove duplicate data in the production dataset using data deduplication algorithms, and remove outliers in the production dataset based on threshold judgment methods of mean and standard deviation; Unified data format: The numerical data in the design information, mold information, and production status data of different types of precast components are standardized in a unified format; for character data, such as connection method and material type, the character data is converted into numerical codes that are easy for computers to process according to the preset encoding rules. Data normalization: The min-max normalization method is used to normalize various types of data in the production dataset, mapping them to the [0,1] interval; After standardizing the production dataset, various types of data are recorded and stored in the production log in chronological order. The positioning module, based on the prefabricated component production task, obtains the type of the target component and calls the corresponding target mold to install it on the corresponding production station; it acquires the production status data of the target mold in real time and compares it with the preset standard installation data to calculate the deviation of the target mold; where the target component refers to the prefabricated component that needs to be produced immediately. It should be further explained that, in the specific implementation process, the process of calculating the deviation of the target mold includes: Real-time acquisition of production status data for the target mold, including position coordinate data and attitude angle data, denoted as (x, y, z) and ; Where x represents the position coordinates of the target mold on the x-axis (horizontal direction) in the 3D map coordinate data; y represents the position coordinates of the target mold on the y-axis (vertical direction); and z represents the position coordinates of the target mold on the z-axis (height direction). The pitch angle represents the angle by which the target mold rotates around the x-axis; The roll angle represents the angle by which the target mold rotates around the y-axis. Yaw angle represents the angle by which the object rotates about the z-axis; Obtain preset standard installation data, including standard position coordinates and standard posture angles; the specific values ​​of the standard installation data are preset according to the standard installation requirements in the prefabricated component production task. By comparing the production status data of the target mold with the preset standard installation data, the deviation of the target mold, including positional deviation and angular deviation, is calculated. The formula for calculating the positional deviation is as follows: ; ; ; In the formula, Indicates the positional deviation of the target mold; Indicates the standard position coordinates of the target mold; The formula for calculating angular deviation is as follows: ; ; ; In the formula, Indicates the angular deviation of the target mold; This indicates the standard orientation angle of the target mold.

[0017] The intelligent calibration and control module, based on the deviation of the target mold, calculates the average position deviation and average angle deviation of the target mold and compares them with the corresponding preset deviation thresholds to calibrate the positioning of the target mold; and uses a PID control algorithm to adjust the position and attitude of the target mold with abnormal positioning. It should be further explained that, in the specific implementation process, the process of calibrating the positioning of the target mold includes: Based on the deviation of the target mold, calculate the average positional deviation and average angular deviation of the target mold; The formulas for calculating the average positional deviation and the average angular deviation are as follows: ; ; In the formula, D1 is the average positional deviation; D2 is the average angular deviation; The calculated average position deviation and average angle deviation are compared with the corresponding preset deviation thresholds. If the calculated average position deviation and average angle deviation are both less than or equal to the corresponding deviation thresholds, it indicates that the positioning of the target mold is correct; otherwise, it indicates that the positioning of the target mold is abnormal, and a mold adjustment signal is generated. It should be noted that the preset deviation thresholds include positional deviation thresholds and angular deviation thresholds, which are set by experts in the field based on the actual mold positioning accuracy requirements; It should be further explained that, in the specific implementation process, the process of adjusting the position and attitude of the target mold with abnormal positioning through the PID control algorithm includes: The system identifies mold adjustment signals and generates mold adjustment commands. It obtains the average position deviation and average angle deviation of the target mold with positioning anomalies and compares them with the corresponding preset deviation thresholds to calculate the error e(t), which is a comprehensive value of position error and attitude error; t is the current time of data acquisition. The PID control algorithm is used to calculate the adjustment amount of the target mold with positioning abnormality. Based on the adjustment amount, the actuator is driven to adjust the position and attitude of the target mold with positioning abnormality until the recalculated average position deviation and average angle deviation are less than or equal to the corresponding deviation threshold. The actuator includes, but is not limited to, motors and hydraulic servo cylinders. The formula for calculating the adjustment amount is as follows: ; In the formula, u(t) is the adjustment amount; This is a proportionality coefficient used for rapid response to changes in deviation; The integral coefficient; These are the differential coefficients, representing the trend of the prediction error; This indicates that the error is integrated. This represents the derivative of the error with respect to time, i.e., the rate of change of the error; It should be noted that the PID parameters (proportional coefficient, integral coefficient, and derivative coefficient) need to be set according to the specific control characteristics; the adjustment calculated by the PID control algorithm can be a control signal used to drive the actuator to adjust the controlled object (such as a target mold with abnormal positioning). For example, for a motor, the adjustment amount is a voltage signal used to control the motor's speed and direction; for a hydraulic servo cylinder, the adjustment amount is a current signal used to control the opening of the hydraulic valve; in this embodiment of the invention, the adjustment amount can be used to drive the motor to rotate, thereby moving or rotating the mold, and thus adjusting the position and posture of the mold. In this embodiment of the invention, the precast component production auxiliary system can achieve precise calibration and intelligent control of mold positioning through PID control algorithm, ensuring accuracy and stability in the production process.

[0018] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0019] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0020] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0021] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0022] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A prefabricated component production auxiliary system for intelligent construction, characterized in that, include: The data collection and processing module is used to collect design information data, mold information and production status data of different types of precast components during the production process, and to determine the production dataset of precast components. Production logs are generated by standardizing the production dataset of prefabricated components. The positioning module, based on the prefabricated component production task, obtains the type of the target component and calls the corresponding target mold to install it on the corresponding production station; The production status data of the target mold is acquired in real time and compared with the preset standard installation data to calculate the deviation of the target mold. The intelligent calibration and control module, based on the deviation of the target mold, calculates the average position deviation and average angle deviation of the target mold and compares them with the corresponding preset deviation thresholds to calibrate the positioning of the target mold; and adjusts the position and attitude of the target mold with abnormal positioning through a PID control algorithm.

2. The prefabricated component production auxiliary system for intelligent construction according to claim 1, characterized in that, The process of determining the production dataset for precast components includes: Based on the design drawings of prefabricated components, design information data of prefabricated components are obtained by using drawing recognition and data extraction technology, including geometric design parameters, materials used, and connection methods; The mold information includes the mold model, the corresponding precast component type, and the size parameters. Each mold is uniquely identified using radio frequency identification technology to obtain a corresponding RFID tag. Meanwhile, the production workshop of prefabricated components is scanned by LiDAR at the production station to generate three-dimensional map coordinate data. Based on the positioning sensors and angle sensors at the production station, the position coordinate data and attitude angle data of the mold are collected in real time to obtain production status data.

3. The prefabricated component production auxiliary system for intelligent construction according to claim 2, characterized in that, The process of standardizing production datasets includes: Data cleaning: Identify and remove duplicate data in the production dataset using data deduplication algorithms, and remove outliers in the production dataset based on threshold judgment methods of mean and standard deviation; Unified data format: The numerical data in the design information, mold information, and production status data of different types of precast components are formatted in a unified manner; for character data, the character data is converted into numerical codes that are easy for computers to process according to preset encoding rules. Data normalization: The min-max normalization method is used to normalize various types of data in the production dataset, mapping them to the [0,1] interval; After standardizing the production dataset, various types of data are recorded and stored in the production log in chronological order.

4. The prefabricated component production auxiliary system for intelligent construction according to claim 3, characterized in that, The process of calculating the deviation of the target mold includes: Real-time acquisition of production status data for the target mold, including position coordinate data and attitude angle data, denoted as (x, y, z) and Where x represents the position coordinate of the target mold on the x-axis in the 3D map coordinate data; y represents the position coordinate of the target mold on the y-axis; and z represents the position coordinate of the target mold on the z-axis. The pitch angle represents the angle by which the target mold rotates around the x-axis; The roll angle represents the angle by which the target mold rotates around the y-axis. Yaw angle represents the angle by which the object rotates about the z-axis; Obtain preset standard installation data, including standard position coordinates and standard posture angles; By comparing the production status data of the target mold with the preset standard installation data, the deviation of the target mold, including positional deviation and angular deviation, is calculated. The formula for calculating the positional deviation is as follows: ； ； ； In the formula, Indicates the positional deviation of the target mold; This indicates the standard position coordinates of the target mold.

5. The prefabricated component production auxiliary system for intelligent construction according to claim 4, characterized in that, The formula for calculating angular deviation is as follows: ； ； ； In the formula, Indicates the angular deviation of the target mold; This indicates the standard orientation angle of the target mold.

6. The prefabricated component production auxiliary system for intelligent construction according to claim 4, characterized in that, The process of calibrating the positioning of the target mold includes: Based on the deviation of the target mold, calculate the average positional deviation and average angular deviation of the target mold; The formulas for calculating the average positional deviation and the average angular deviation are as follows: ； ； In the formula, D1 is the average positional deviation; D2 is the average angular deviation; The calculated average position deviation and average angle deviation are compared with the corresponding preset deviation thresholds. If the calculated average position deviation and average angle deviation are both less than or equal to the corresponding deviation thresholds, it indicates that the positioning of the target mold is correct; otherwise, it indicates that the positioning of the target mold is abnormal, and a mold adjustment signal is generated.

7. The prefabricated component production auxiliary system for intelligent construction according to claim 6, characterized in that, include: The process of adjusting the position and attitude of a target mold with abnormal positioning using a PID control algorithm includes: Identify mold adjustment signals, obtain the average position deviation and average angle deviation of the target mold with positioning anomalies, compare them with the corresponding preset deviation thresholds, and calculate the error e(t), which is the combined value of position error and attitude error; t is the current time of data acquisition. The PID control algorithm is used to calculate the adjustment amount of the target mold with positioning abnormality. Based on the adjustment amount, the mold adjustment command is generated to drive the actuator to adjust the position and attitude of the target mold with positioning abnormality until the recalculated average position deviation and average angle deviation are less than or equal to the corresponding deviation threshold.

8. The prefabricated component production auxiliary system for intelligent construction according to claim 7, characterized in that, The formula for calculating the adjustment amount is: ； In the formula, u(t) is the adjustment amount; This is the proportionality coefficient; The integral coefficient; These are the differential coefficients; This indicates that the error is integrated. This represents the derivative of the error with respect to time, i.e., the rate of change of the error.

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