CN117391552B - Digital twinning-based building component quality control system and method - Google Patents
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Abstract
The invention discloses a digital twinning-based building component quality control system and a digital twinning-based building component quality control method, which relate to the technical field of data processing, wherein the digital twinning-based building component quality control method comprises the following steps: the target production task acquisition module is used for transmitting the target production task to the functional system; a production task instruction generation module; a real-time data set generation module; the production line model obtaining module is used for carrying out real-time simulation to obtain a virtual production line model; the simulation result generation module is used for transmitting the real-time data set, the real-time simulation data and the functional data of the virtual production line model to the twin database for simulation, so as to generate a simulation result; the quality control module performs quality control of the building components for the target production task. The invention solves the technical problems that the prior art lacks real-time perception analysis of on-site production data in the production of building components, cannot guide on-site production in real time and performs intelligent component quality control, and achieves the technical effects of improving production efficiency and efficiently scheduling production resources.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a digital twinning-based building component quality control system and method.
Background
The modularized building refers to prefabricated building parts produced by factories, and the whole production process of the building is finally completed through the combination and the splicing of units on a construction site. Compared with the traditional construction mode, the modularized building uses the same materials, but the characteristics of off-site construction enable the production of parts and on-site installation to be carried out synchronously, so that the construction time and the energy consumption in the transportation process are reduced as much as possible. Therefore, the modularized building has the characteristics of both the building industry and the manufacturing industry, and is an advanced form and future trend of building industrialization development. But not neglectable, the production process of the parts is not fine, the intelligent level of production is low, and the quality detection system is imperfect, which still restrict the large-scale popularization of the modularized building in China. Because the quality detection is highly dependent on manpower, the efficiency is low, the precision is poor, the cost is high, and the quality control of the whole production process of the parts cannot be performed, thereby bringing about the waste of the cost and the delay of the construction period. In the prior art, the technical problems that the real-time perception analysis of on-site production data is lacking in the production of building components, the on-site production cannot be guided in real time, and the intelligent component quality control is performed exist.
Disclosure of Invention
The application provides a digital twinning-based building component quality control system and a digital twinning-based building component quality control method, which are used for solving the technical problems that in the prior art, the real-time perception analysis of field production data is lacking in the production of building components, the field production cannot be guided in real time, and the intelligent component quality control is performed.
In view of the above, the present application provides a digital twinning-based building component quality control system and method.
In a first aspect of the application, there is provided a digital twinning-based building component quality control system comprising a physical production line, a virtual production line, a twinning database and a functional system, the system comprising:
the target production task acquisition module is used for acquiring a target production task and transmitting the target production task to the functional system;
the production task instruction generation module is used for analyzing the target production task, generating functional data, transmitting the functional data to a twin database and generating a production task instruction;
The real-time data set generation module is used for transmitting the production task instruction to a physical production line for component production, and utilizing the real-time data acquisition module to acquire data in the production operation process to generate a real-time data set;
The production line model obtaining module is used for transmitting the real-time data set to a virtual production line based on the real-time communication module to perform real-time simulation so as to obtain a virtual production line model;
the simulation result generation module is used for transmitting the real-time data set, the real-time simulation data and the functional data of the virtual production line model to the twin database to perform simulation, and generating a simulation result, wherein the simulation result comprises a component quality detection result;
and the quality control module is used for controlling the quality of the building components of the target production task based on the simulation result.
In a second aspect of the application, there is provided a digital twinning-based building component quality control method, the method comprising:
Acquiring a target production task and transmitting the target production task to a functional system;
Analyzing the target production task by utilizing the functional system to generate functional data, and transmitting the functional data to a twin database to generate a production task instruction;
transmitting the production task instruction to a physical production line for component production, and utilizing a real-time data acquisition module to acquire data in the production operation process to generate a real-time data set;
Transmitting the real-time data set to a virtual production line based on a real-time communication module to perform real-time simulation, so as to obtain a virtual production line model;
Transmitting the real-time data set, the real-time simulation data and the functional data of the virtual production line model to a twin database for simulation, and generating a simulation result, wherein the simulation result comprises a component quality detection result;
And performing building component quality control of the target production task based on the simulation result.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The method comprises the steps of obtaining a target production task, transmitting the target production task to a functional system, analyzing the target production task by utilizing the functional system, generating functional data, transmitting the functional data to a twin database, generating a production task instruction, transmitting the production task instruction to a physical production line for component production, utilizing a real-time data acquisition module for data acquisition in a production operation process, generating a real-time data set, transmitting the real-time data set to a virtual production line for real-time simulation based on a real-time communication module, obtaining a virtual production line model, and transmitting real-time simulation data of the real-time data set, the virtual production line model and the functional data to the twin database for simulation, generating a simulation result, wherein the simulation result comprises a component quality detection result, and then controlling the quality of a building component of the target production task based on the simulation result. The method achieves the purpose of timely finding abnormality, thereby realizing quality abnormality alarm, not only detecting the quality of the finished product, greatly reducing the reworking rate of the finished product parts, and taking the digital twin technology as a technical tool for realizing lean management concept and a data support and being applied to the production quality management of the building parts, thereby realizing the intelligent production of the building parts.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a digital twinning-based construction element quality control system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a structure for generating production task instructions in a digital twinning-based building component quality control system according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a production process dividing module in a digital twinning-based building element quality control system according to an embodiment of the present application;
Fig. 4 is a schematic flow chart of a digital twin-based building component quality control method according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a target production task acquisition module 11, a production task instruction generation module 12, a real-time data set generation module 13, a production line model acquisition module 14, a simulation result generation module 15 and a quality control module 16.
Detailed Description
The application provides a digital twinning-based building component quality control system and a digital twinning-based building component quality control method, which are used for solving the technical problems that in the prior art, the real-time perception analysis of field production data is lacking in the production of building components, the field production cannot be guided in real time, and the intelligent component quality control is performed.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a digital twinning-based building component quality control system, wherein the building component quality control system comprises a physical production line, a virtual production line, a twinning database, and a functional system, the system comprising:
The target production task acquisition module 11 is used for acquiring a target production task and transmitting the target production task to the functional system;
in one possible embodiment, the building component quality control system is constructed by building the system based on physical production lines, virtual production lines, twinning databases and functional systems for real-time dynamic simulation, emulation, monitoring of the production process of the factory site physical production lines. Wherein the physical production line is a real-world entity; the virtual production line is a mapping to a real entity and combines the factors of a rule model, a geometric model, a behavior model, a physical model, a lean management concept, a deep learning algorithm, an artificial intelligence algorithm and the like; the twin database is the core of the intelligent production lean control system of the whole building part and is responsible for providing real-time data support and guarantee for the whole system; the functional system is a set of various functions driven by the read sensing data and is responsible for providing functional services for production activities. Preferably, the target production task is a part production task requiring a new or a change. After receiving a new or changed production task of a part component, the functional system gives a production instruction to a physical production line through a twin database, generates data in the operation process of the physical production line, and perceives real-time data through a temperature and humidity sensor, a laser range finder, a high-definition camera and a thermal infrared imager.
The production task instruction generating module 12 is configured to analyze the target production task, generate functional data, and transmit the functional data to a twin database to generate a production task instruction;
Further, as shown in fig. 2, the building component quality control system further includes:
the integrated processing unit is used for carrying out integrated processing on the target production task by utilizing the functional system to generate a first processing data set;
The evaluation result generation unit is used for evaluating the first processing data set by using the description module to generate a first evaluation result;
The production state generating unit is used for diagnosing the first evaluation result by utilizing the diagnosis module to generate a real-time production state, wherein the real-time production state comprises normal, abnormal and changed;
The prediction result generation unit is used for carrying out functional analysis on the real-time production state based on the prediction module to generate an intelligent prediction result, wherein the intelligent prediction result comprises a quality risk point and a production progress optimization point;
the task instruction generation unit is used for analyzing the intelligent prediction result by utilizing the decision module, obtaining functional data, transmitting the functional data to the twin database and generating a production task instruction.
In one possible embodiment, the functional system performs an integrated process on the target production task to generate a first process data set. The first processing data is data obtained after integrating information contained in the target production task. The functional system comprises a description module, a diagnosis module, a prediction module and a decision module. The description module evaluates the first processing data, including state evaluation, capacity evaluation, resource evaluation and energy consumption evaluation. The state evaluation refers to the evaluation of the quality of the parts in the production part; the productivity assessment is to calculate the standard working hour, the total working hour, the worker configuration condition and the mechanical equipment bearing capacity of each worker according to the planned output; the resource evaluation refers to the evaluation of production resources such as raw materials; the energy consumption evaluation is to evaluate the current production state based on the energy consumption cost such as electricity charge and moisture. The diagnosis module is used for further judging the production state in real time according to the evaluation of the description module, and comprises three states: normal, abnormal, altered. The prediction module is used for further carrying out functional analysis on the data to generate an intelligent prediction result, wherein the intelligent prediction result comprises quality risk points and production progress optimization points, and is used as an intelligent guide for production of a physical production line to carry out real-time optimization guidance on the change of the physical production line. The decision module is used for analyzing the intelligent prediction result to obtain functional data, transmitting the functional data to the twin database and generating a production task instruction.
The real-time data set generating module 13 is configured to transmit the production task instruction to a physical production line for component production, and perform data acquisition on a production operation process by using a real-time data acquisition module to generate a real-time data set;
Further, the real-time data acquisition module comprises a temperature and humidity sensor, a laser range finder, a high-definition camera, an infrared thermal imager and an RFID tag, wherein the real-time data acquisition module is used for acquiring state data of a physical production line in real time.
Preferably, the physical production line performs component production after receiving the production task instruction, generates data in the running process, and performs data acquisition by using the real-time acquisition module to generate a real-time data set. The real-time data set comprises production environment temperature and humidity generated by a temperature and humidity sensor, mold positioning, steel bar positioning, embedded part positioning and finished product quality detection data generated by a laser range finder, quality detection and production line environment monitoring data are carried out on point clouds generated by a high-definition camera auxiliary laser range finder, concrete maturity judging data are obtained by an infrared thermal imager, and production information data of parts and components are collected by an RFID reader-writer.
Further, the building component quality control system further comprises a production procedure dividing module, wherein the production procedure dividing module is used for dividing a production procedure of the target production task into a production preparation unit, a production process unit and a production finished product unit, the production preparation unit comprises a cleaning die table, a die positioning unit, a mounting die and a spraying release agent, the production process unit comprises a mounting embedded part, a reinforcing steel bar blanking unit, a reinforcing steel bar binding unit, a concrete blanking unit, a concrete sweeping unit and a concrete stripping unit, and the production finished product unit comprises a finished product lifting unit, a finished product water testing unit, a finished product washing unit and a finished product checking unit.
Further, as shown in fig. 3, in order to extend quality control to the whole production process of the building part component, effective monitoring and management are realized, the production process of the target production task is divided into a production preparation unit, a production process unit and a production finished product unit by using a production process division module. The production preparation unit comprises a cleaning die table, a die positioning unit, a die mounting unit and a spraying release agent, the production process unit comprises an embedded part mounting unit, a steel bar blanking unit, a steel bar binding unit, a concrete blanking unit, a concrete sweeping unit and a concrete stripping unit, and the production finished product unit comprises a finished product lifting unit, a finished product water testing unit, a finished product washing unit and a finished product checking unit.
Preferably, after the data perceived by the physical production line and the data simulated by the virtual production line are transmitted to the functional system for integrated analysis, the production task instruction is fed back to the physical production line and the virtual production line. The method has the advantages of improving lean quality, effectively reducing productivity waste, and simultaneously providing finer and accurate data for modeling of the virtual production line.
Preferably, the whole production process is divided into three parts, including production preparation, production process and production of finished products. Wherein, the production preparation comprises the steps of cleaning a die table, positioning a die, installing the die and spraying a release agent; the production process comprises the steps of installing embedded parts, blanking steel bars, binding the steel bars, blanking concrete, sweeping concrete, and removing concrete forms; and (3) producing a finished product, wherein the finished product lifting, finished product water testing, finished product washing and finished product inspection are included.
The production line model obtaining module 14 is configured to transmit the real-time data set to a virtual production line for real-time simulation based on the real-time communication module, so as to obtain a virtual production line model;
Further, the real-time communication module comprises a main control unit, a server host and a monitoring database, wherein the main control unit is used for reading the real-time data set generated by the real-time data acquisition module, performing integrated analysis and data cleaning, and the server host is used for receiving the data cleaned by the main control unit, integrating the data and storing the data into the monitoring database.
Further, the virtual production line further comprises a virtual model module, a knowledge module and an algorithm module, wherein the virtual model module comprises a rule model, a geometric model, a behavior model and a physical model, the rule model is a reference standard model for performing state diagnosis of a functional system, the geometric model is a geometric information model of a part and mechanical equipment, the behavior model is a time sequence evolution model of mechanical properties and material states of the whole production process of the part, the physical model is a parameter model of the mechanical properties and materials of the whole production process of the part and the mechanical equipment, the knowledge module is used for driving and solving quality problems of reworking due to quality problems in production of a target production task, and the algorithm module comprises a deep learning algorithm and an artificial intelligent algorithm and is used for performing data mining according to a real-time data set to acquire quality problem knowledge and perform intelligent quality problem recognition.
In one possible embodiment, the real-time communication module is configured to provide real-time data transmission. The temperature and humidity sensor is sequentially connected to the main control unit through WiFi, the laser range finder, the Ethernet, the high-definition camera, the USB line, the infrared thermal imager and the RFID reader-writer through WiFi; the main control unit preprocesses various data and stores the data in the memory; the server host sets frequency to the main control unit remotely, and requests the main control unit to transmit data to the server host through a WiFi communication protocol; the server-side host machine performs fusion and integrated analysis on the data, simultaneously completes visualization and statistics of twin data, stores the result into the monitoring database, and then transmits the result to the next stage of data required by the monitoring database.
Preferably, the virtual production line drives the virtual model to perform real-time simulation on the physical production site according to the received real-time perception data of the physical production line, and feeds the simulation result back to the service system to generate. The invention divides modeling of a virtual production line into two parts, modeling of a part component and attaching equipment thereof and modeling of a mechanical equipment and attaching equipment thereof, thereby generating a virtual production line model.
Preferably, modeling the component parts during the production of the building component parts, including the component parts themselves, active RFID tags; is tightly adhered to the component. The active RFID tag comprises information such as unique ID of the part, used materials, production types, process completion conditions, production date and the like, and is used for identifying the part and inputting information.
The algorithm process of modeling the building part of the virtual production line is as follows: the production state of the part is determined and modeled through the data acquired by the real-time data acquisition module, and the mathematical expression is as follows: wherein/> For the f building component,/>A uniquely identifiable ID bound for the part component.
Wherein/>Is a basic attribute set of the part component, comprising the part component type/>Material used/>Geometry/>Production unitDate of delivery/>。
， />Wherein/>For feature data set in production process, the method comprises the steps of n production features/>And/>Incidence matrix/>, of dimensionsThe matrix element values 0,1, and 2 are each represented by an independent relationship between the production steps, the former preceding the latter, and the latter preceding the former.
，/>，Wherein each part production feature comprises a production whole process link/>The time T, quality standard Q, and date D of the procedure.
Wherein/>The data set of the part component, which is acquired by the real-time data acquisition module in the production process, comprises a stress data set S, a position data set L and the like.
Preferably, in a realistic production process of the part components, the malfunction of the mechanical equipment has a very large impact on the overall production planning. If the production workers and the management staff cannot find out faults of the mechanical equipment in time, the production activities are stopped, the physical safety of the production workers and the management staff can be seriously endangered, and great waste exists in the process of repairing the mechanical equipment regularly. Therefore, in order to predictively find out faults of the mechanical equipment, the invention models the mechanical equipment based on a digital twin technology, including the mechanical equipment, an RFID reader-writer and various types of sensors; the RFID reader-writer can be in radio wave communication with the active RFID tag of the part, record the production state of the building part and record the RFID tag. Each type of sensor comprises a laser range finder, a high-definition camera, a thermal infrared imager and a temperature and humidity sensor, and is used for collecting a data set of a specified positionStress dataset/>Velocity dataset/>Energy consumption dataset/>Visual dataset/>Instruction data set/>And sensing the data in the aspects of the like, and monitoring the production quality index and the position change of the building part in real time.
Furthermore, the production states of all the mechanical equipment are modeled through the data acquired by the real-time data acquisition module, and the mathematical language is expressed as follows:
wherein/> For the nth generalized production machinery in the production of building parts and components,/>For unique identification ID of mechanical equipment in production process,/>For the type of mechanical device used,/>Core parameter set of different classes of mechanical devices,/>For the real-time operating state of the mechanical device,For/>And the incidence matrix of the mechanical equipment with different dimensions.
Wherein/>The data set of the mechanical equipment acquired by the real-time data acquisition module in the operation process of the mechanical equipment comprises a position data set/>Stress dataset/>Velocity datasetEnergy consumption dataset/>Visual dataset/>Instruction data set/>。
In order to correlate the mechanical devices of all production lines to achieve efficient scheduling, a relationship between the mechanical devices is established, and the mathematical expression is as follows:
，/> Wherein, the method comprises the steps of, wherein, For the correlation between the a-th and b-th mechanical devices,/>The values 0,1,2,3 of (a) respectively represent an independent relationship, a contradictory relationship, a peer parallel relationship, and an attached relationship.
The simulation result generating module 15 is configured to transmit the real-time data set, the real-time simulation data of the virtual production line model, and the functional data to a twin database for performing simulation, and generate a simulation result, where the simulation result includes a component quality detection result;
a quality control module 16 for performing a quality control of the building component for the target production task based on the simulation results.
Further, the twin database includes specification standard data, production worker data, production machine data, production environment data, raw material supply data, and production management data:
the standard data are limits for the safety and quality control level of the modularized building part parts, which are formulated by the relevant departments of the country, and comprise the quality of raw materials, the positioning precision of the part parts, the dimensional precision, the surface quality and the load;
The production worker data comprises the number of workers, the work number of the workers, the real-time position of the workers, the working proficiency of the workers and the working time of the workers;
the production machine data comprises the running time of the mechanical equipment, the on-off state and quantity of the mechanical equipment and the time of the mechanical equipment for completing each procedure;
the production environment data comprises production workshop temperature, production workshop humidity and warehouse space capacity;
the raw material supply data comprises the consumption and the residual quantity of the reinforcing steel bars, the consumption and the residual quantity of concrete, and the consumption and the residual quantity of release agents;
The production management data includes the type of the parts to be produced, the number of the finished processes of the parts to be produced, the real-time position of the parts to be produced, the delivery time required by the order, the type and the number of the parts to be produced.
Preferably, the twin database is used as a core of the whole quality control system, classifies and gathers the sensing data collected by the physical production line, the simulation data of the virtual production line model and the functional data analyzed by the functional system, and stores the sensing data, the simulation data and the functional data in a cloud form. The standard data are limits for the safety and quality control level of the modularized building part parts, which are formulated by the relevant departments of the state, and comprise the quality of raw materials, the positioning precision of the part parts, the dimensional precision, the surface quality and the load. The production worker data comprises the number of workers, the real-time position of the workers, the working proficiency of the workers and the working time of the workers. The production machine data comprises the running time of the mechanical equipment, the on-off state and quantity of the mechanical equipment and the time of the mechanical equipment for completing each procedure. The production environment data comprise production workshop temperature, production workshop humidity and warehouse space capacity. The raw material supply data comprises the consumption and the residual quantity of the reinforcing steel bars, the consumption and the residual quantity of the concrete, and the consumption and the residual quantity of the release agent. The production management data includes the type of the parts to be produced, the number of the finished processes of the parts to be produced, the real-time position of the parts to be produced, the delivery time required by the order, the type and the number of the parts to be produced.
And furthermore, the intelligent and efficient quality control is carried out on the production process of the building component of the target production task according to the simulation result.
In summary, the embodiment of the application has at least the following technical effects:
The building component quality control system for intelligent production of the modularized building component parts, which is composed of the physical production line, the virtual production line, the twin database and the functional system, realizes real-time dynamic simulation, simulation and monitoring of the whole production process of the factory site physical production line, real-time alarm of the state of the production process, optimization and predictive analysis of production planning, saves the debugging process of a large number of physical production lines, enables production managers to efficiently and intuitively know the production state of the production lines, can comprehensively plan production of all the production lines, and achieves the technical effects of being beneficial to more efficient scheduling of production resources and shortening production project periods.
Example two
Based on the same inventive concept as the digital twinning-based building element quality control system in the foregoing embodiment, as shown in fig. 4, the present application provides a digital twinning-based building element quality control method, and the method and system embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the method comprises the following steps:
Acquiring a target production task and transmitting the target production task to a functional system;
Analyzing the target production task by utilizing the functional system to generate functional data, and transmitting the functional data to a twin database to generate a production task instruction;
transmitting the production task instruction to a physical production line for component production, and utilizing a real-time data acquisition module to acquire data in the production operation process to generate a real-time data set;
Transmitting the real-time data set to a virtual production line based on a real-time communication module to perform real-time simulation, so as to obtain a virtual production line model;
Transmitting the real-time data set, the real-time simulation data and the functional data of the virtual production line model to a twin database for simulation, and generating a simulation result, wherein the simulation result comprises a component quality detection result;
And performing building component quality control of the target production task based on the simulation result.
Further, the embodiment of the application further comprises:
The real-time data acquisition module comprises a temperature and humidity sensor, a laser range finder, a high-definition camera, an infrared thermal imager and an RFID tag, wherein the real-time data acquisition module is used for acquiring state data of a physical production line in real time.
Further, the embodiment of the application further comprises:
The real-time communication module comprises a main control unit, a server host and a monitoring database, wherein the main control unit is used for reading the real-time data set generated by the real-time data acquisition module, performing integrated analysis and data cleaning, and the server host is used for receiving the data cleaned by the main control unit, integrating the data and storing the data into the monitoring database.
Further, the embodiment of the application further comprises:
the building component quality control system further comprises a production procedure dividing module, wherein the production procedure dividing module is used for dividing a production procedure of the target production task into a production preparation unit, a production process unit and a production finished product unit, the production preparation unit comprises a cleaning die table, a die positioning, a mounting die and a spraying release agent, the production process unit comprises a mounting embedded part, a steel bar blanking, a steel bar binding, a concrete blanking, a concrete sweeping and a concrete stripping, and the production finished product unit comprises a finished product lifting, a finished product water testing, a finished product washing and a finished product checking.
Further, the embodiment of the application further comprises:
The virtual production line further comprises a virtual model module, a knowledge module and an algorithm module, wherein the virtual model module comprises a rule model, a geometric model, a behavior model and a physical model, the rule model is a reference standard model for carrying out state diagnosis on a functional system, the geometric model is a geometric information model of a part component and mechanical equipment, the behavior model is a time sequence evolution model of mechanical properties and material states of the whole production process of the part component, the physical model is a parameter model of the mechanical properties and materials of the part component and the mechanical equipment participating in the whole production process, the knowledge module is used for driving and solving quality problems of reworking due to quality problems in the production of a target production task, and the algorithm module comprises a deep learning algorithm and an artificial intelligent algorithm and is used for carrying out data mining according to a real-time data set to acquire quality problem knowledge and carry out intelligent quality problem identification.
Further, the embodiment of the application further comprises:
The twin database includes specification standard data, production worker data, production machine data, production environment data, raw material supply data, and production management data:
the standard data are limits for the safety and quality control level of the modularized building part parts, which are formulated by the relevant departments of the country, and comprise the quality of raw materials, the positioning precision of the part parts, the dimensional precision, the surface quality and the load;
The production worker data comprises the number of workers, the work number of the workers, the real-time position of the workers, the working proficiency of the workers and the working time of the workers;
the production machine data comprises the running time of the mechanical equipment, the on-off state and quantity of the mechanical equipment and the time of the mechanical equipment for completing each procedure;
the production environment data comprises production workshop temperature, production workshop humidity and warehouse space capacity;
the raw material supply data comprises the consumption and the residual quantity of the reinforcing steel bars, the consumption and the residual quantity of concrete, and the consumption and the residual quantity of release agents;
The production management data includes the type of the parts to be produced, the number of the finished processes of the parts to be produced, the real-time position of the parts to be produced, the delivery time required by the order, the type and the number of the parts to be produced.
Further, the embodiment of the application further comprises:
performing integrated processing on the target production task by utilizing a functional system to generate a first processing data set;
evaluating the first processing data set by using a description module to generate a first evaluation result;
Diagnosing the first evaluation result by using a diagnosis module to generate a real-time production state, wherein the real-time production state comprises normal, abnormal and changed states;
performing functional analysis on the real-time production state based on a prediction module to generate an intelligent prediction result, wherein the intelligent prediction result comprises quality risk points and production progress optimization points;
And analyzing the intelligent prediction result by utilizing a decision module to obtain functional data, and transmitting the functional data to the twin database to generate a production task instruction.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (6)
1. A digital twinning-based building component quality control system, the building component quality control system comprising a physical production line, a virtual production line, a twinning database, and a functional system, the system comprising:
the target production task acquisition module is used for acquiring a target production task and transmitting the target production task to the functional system;
the production task instruction generation module is used for analyzing the target production task, generating functional data, transmitting the functional data to a twin database and generating a production task instruction;
The real-time data set generation module is used for transmitting the production task instruction to a physical production line for component production, and utilizing the real-time data acquisition module to acquire data in the production operation process to generate a real-time data set;
The production line model obtaining module is used for transmitting the real-time data set to a virtual production line based on the real-time communication module to perform real-time simulation so as to obtain a virtual production line model;
the simulation result generation module is used for transmitting the real-time data set, the real-time simulation data and the functional data of the virtual production line model to the twin database to perform simulation, and generating a simulation result, wherein the simulation result comprises a component quality detection result;
The quality control module is used for controlling the quality of the building components of the target production task based on the simulation result;
The production process unit comprises a cleaning die table, a die positioning, a mounting die and a spraying release agent, the production process unit comprises a mounting embedded part, a steel bar blanking, a steel bar binding, a concrete blanking, a concrete sweeping and a concrete stripping die, and the production finished product unit comprises a finished product lifting, a finished product water testing, a finished product washing and a finished product checking;
The virtual production line further comprises a virtual model module, a knowledge module and an algorithm module, wherein the virtual model module comprises a rule model, a geometric model, a behavior model and a physical model, the rule model is a reference standard model for performing state diagnosis on a functional system, the geometric model is a geometric information model of a part and mechanical equipment, the behavior model is a time sequence evolution model of mechanical properties and material states of the whole production process of the part, the physical model is a parameter model of the mechanical properties and materials of the whole production process of the part and the mechanical equipment, the knowledge module is used for driving and solving quality problems of reworking due to quality problems in production of a target production task, and the algorithm module comprises a deep learning algorithm and an artificial intelligent algorithm and is used for performing data mining according to a real-time data set to acquire quality problem knowledge and perform intelligent quality problem recognition.
2. The system of claim 1, wherein the real-time data acquisition module comprises a temperature and humidity sensor, a laser range finder, a high-definition camera, a thermal infrared imager, and an RFID tag, and wherein the real-time data acquisition module is configured to acquire status data of a physical production line in real time.
3. The system of claim 1, wherein the real-time communication module comprises a main control unit, a server host and a monitoring database, wherein the main control unit is used for reading the real-time data set generated by the real-time data acquisition module, performing integrated analysis and data cleaning, and the server host is used for receiving the data cleaned by the main control unit, integrating the data and storing the data into the monitoring database.
4. The system of claim 1, wherein the twinning database comprises specification standard data, production worker data, production machine data, production environment data, raw material supply data, and production management data:
the standard data are limits for the safety and quality control level of the modularized building part parts, which are formulated by the relevant departments of the country, and comprise the quality of raw materials, the positioning precision of the part parts, the dimensional precision, the surface quality and the load;
The production worker data comprises the number of workers, the work number of the workers, the real-time position of the workers, the working proficiency of the workers and the working time of the workers;
the production machine data comprises the running time of the mechanical equipment, the on-off state and quantity of the mechanical equipment and the time of the mechanical equipment for completing each procedure;
the production environment data comprises production workshop temperature, production workshop humidity and warehouse space capacity;
the raw material supply data comprises the consumption and the residual quantity of the reinforcing steel bars, the consumption and the residual quantity of concrete, and the consumption and the residual quantity of release agents;
The production management data includes the type of the parts to be produced, the number of the finished processes of the parts to be produced, the real-time position of the parts to be produced, the delivery time required by the order, the type and the number of the parts to be produced.
5. The system of claim 1, wherein the building component quality control system further comprises:
the integrated processing unit is used for carrying out integrated processing on the target production task by utilizing the functional system to generate a first processing data set;
The evaluation result generation unit is used for evaluating the first processing data set by using the description module to generate a first evaluation result;
The production state generating unit is used for diagnosing the first evaluation result by utilizing the diagnosis module to generate a real-time production state, wherein the real-time production state comprises normal, abnormal and changed;
The prediction result generation unit is used for carrying out functional analysis on the real-time production state based on the prediction module to generate an intelligent prediction result, wherein the intelligent prediction result comprises a quality risk point and a production progress optimization point;
the task instruction generation unit is used for analyzing the intelligent prediction result by utilizing the decision module, obtaining functional data, transmitting the functional data to the twin database and generating a production task instruction.
6. A digital twin based building component quality control method, characterized in that the method is applied to the digital twin based building component quality control system of any of claims 1 to 5, comprising:
Acquiring a target production task and transmitting the target production task to a functional system;
Analyzing the target production task by utilizing the functional system to generate functional data, and transmitting the functional data to a twin database to generate a production task instruction;
transmitting the production task instruction to a physical production line for component production, and utilizing a real-time data acquisition module to acquire data in the production operation process to generate a real-time data set;
Transmitting the real-time data set to a virtual production line based on a real-time communication module to perform real-time simulation, so as to obtain a virtual production line model;
Transmitting the real-time data set, the real-time simulation data and the functional data of the virtual production line model to a twin database for simulation, and generating a simulation result, wherein the simulation result comprises a component quality detection result;
And performing building component quality control of the target production task based on the simulation result.
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