Precast concrete component production stage carbon emission management method and system based on digital twinning

Abstract

The application discloses a prefabricated concrete component production stage carbon emission control method and system based on digital twinning. The method comprises the following steps: collecting material and mechanical energy consumption dynamic heterogeneous data by relying on a sensing network at a physical production entity; constructing a virtual twin entity embedded with a thermodynamic mechanism model, and establishing virtual and real dynamic mapping based on a space-time coding protocol; combining manufacturing process classification to account for carbon emissions, and generating a component-level space-time carbon flow evolution model; importing the model into a simulation optimization engine, and deducing optimal low-carbon process control parameters and flow transfer beats under production constraints; accordingly, generating instructions to reversely drive the physical production entity to complete adaptive adjustment, and feeding back the running state of the physical production entity to the simulation optimization engine to complete closed-loop feedback control. The application overcomes the technical limitations of rough distortion and lack of feedback adjustment, realizes fine dynamic carbon flow accounting, and realizes closed-loop feedback and low-carbon adaptive adjustment of production equipment.

Description

Technical Field

[0001] This invention belongs to the field of digital twin and green manufacturing technology, and particularly relates to a method and system for carbon emission control during the production stage of precast concrete components based on digital twins. Background Technology

[0002] With the deepening development of industrialized construction, precast concrete components have been widely used in prefabricated construction, making carbon emission control during their production stage crucial. However, the manufacturing process of precast concrete components exhibits high discreteness and complexity, with the entire production chain deeply coupled with the spatiotemporal flow of heterogeneous materials and the dynamic coordination of energy-intensive machinery. Currently, carbon emission management during the component production stage is largely limited to traditional passive monitoring, and a data-driven dynamic control system has not yet been established. In practical engineering implementation, it mainly faces the following two technical bottlenecks:

[0003] On the one hand, carbon emission control suffers from limitations such as crude calculations and data distortion. Existing technologies do not classify and calculate the main building materials, auxiliary connecting materials, and turnover support materials of components in a detailed manner based on their physical mechanisms. They also fail to effectively utilize existing component detailed design data, mold geometric parameters, and material metering data, resulting in a disconnect between data sources and on-site working conditions, and a serious lack of calculation accuracy.

[0004] On the other hand, carbon emission control suffers from a lack of feedback control and adaptive adjustment. Existing technologies are mostly limited to the presentation of carbon emission accounting data, lacking a feedback control link that can reverse the low-carbon simulation strategy in virtual space to the physical site, and thus cannot tap the carbon reduction potential of process links through adaptive adjustment of production equipment parameters and global workshop scheduling.

[0005] In summary, how to leverage digital twin technology to construct a deeply integrated on-site multi-dimensional sensor network to achieve refined carbon flow accounting, and to complete low-carbon simulation optimization and equipment closed-loop control while meeting order delivery deadlines, is a technical challenge that urgently needs to be overcome in the field of green manufacturing of precast concrete components. Summary of the Invention

[0006] To address the problems of crude calculations and data distortion, as well as the lack of feedback control and adaptive adjustment in the existing carbon emission control of precast concrete component production, the present invention aims to provide a method and system for carbon emission control of precast concrete component production based on digital twins, so as to open up the technical link from multi-dimensional data perception of physical production, dynamic spatiotemporal mapping of virtual twins, accurate calculation of carbon flow of precast components, optimization of low-carbon scheduling through virtual simulation, to closed-loop feedback control of physical equipment.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for carbon emission control during the production stage of precast concrete components based on digital twins, comprising the following steps:

[0009] S1. Multi-dimensional data perception of physical production: Relying on the sensor network and metering unit deployed at the physical production entity, dynamic heterogeneous data of precast concrete components in continuous manufacturing process are collected in real time; the dynamic heterogeneous data includes material consumption data for calculating implicit carbon emissions, and time series data of production machinery energy consumption for calculating direct carbon emissions.

[0010] S2. Virtual Twin Spatiotemporal Dynamic Mapping: The dynamic heterogeneous data stream is injected into the virtual twin entity in real time via an industrial communication gateway. A dynamic mapping relationship between the physical production entity and the virtual twin entity is established using a unified spatiotemporal coding protocol. The spatial geometric evolution trajectory and physical state evolution process of the physical components in the continuous manufacturing process are presented in high-fidelity synchronously in the virtual space.

[0011] S3. Precise carbon flow calculation of precast components: Based on the dynamic mapping and association of the virtual twin entity, the spatiotemporal carbon flow of a single precast concrete component is digitally tracked; combined with the characteristics of continuous manufacturing process, the absolute carbon emissions of the main building materials, the additional carbon emissions of auxiliary connecting materials, and the amortized carbon emissions of turnover support materials are dynamically calculated respectively, and the carbon emissions of energy consumption of production machinery are superimposed to generate a high-resolution component-level spatiotemporal carbon flow evolution model.

[0012] S4. Low-carbon scheduling virtual simulation optimization: The component-level spatiotemporal carbon flow evolution model is imported into the simulation optimization engine. Production scheduling boundary constraints are established inside the virtual twin entity. The distributed discrete event simulation program is run to perform multiple batch trial and error simulations. The optimal set of low-carbon process control parameters and the on-site logistics flow rhythm are calculated under the premise of meeting the order delivery deadline.

[0013] S5. Physical Equipment Closed-Loop Feedback Control: Based on the optimal set of low-carbon process control parameters and the flow rate of materials in the field, a target control command sequence is generated, which acts in reverse on the industrial control nodes and execution terminals of the corresponding physical production entities, dynamically adjusting the operating parameters of the physical production equipment, and driving the physical production line to complete low-carbon adaptive execution; the physical production equipment provides real-time feedback of its operating status to the simulation optimization engine, providing rolling updated operating status data.

[0014] Furthermore, the physical production entity mentioned in step S1 includes the underlying hardware facilities corresponding to the continuous manufacturing process of precast concrete components, specifically including: a production platform system for frame installation and formwork assembly operations, hoisting and on-site logistics equipment for material handling and component relocation, pouring and vibration equipment for concrete pouring and forming, a steam curing kiln for accelerating concrete setting and hardening, and prestressed tensioning jacks and duct grouting equipment for post-processing operations.

[0015] Furthermore, the sensor network and metering unit mentioned in step S1 include: an electronic weighing sensor for the batching machine, a flow meter for water and admixture pipelines, and a material barcode scanning terminal for collecting material consumption data; a smart meter for the main circuit of the equipment and a steam pipeline flow meter for collecting time-series data of energy consumption of production machinery; and a thermocouple temperature sensor inside the steam curing kiln for collecting workshop environmental data.

[0016] Furthermore, the method for constructing the virtual twin entity and the data synchronization method described in step S2 are as follows:

[0017] Solid construction: Extract detailed design data of precast concrete components and geometric three-dimensional parameters of molds, initialize the spatial topological boundary of the component body and mold in virtual space, and embed the thermodynamic mechanism model of concrete setting and hardening.

[0018] Data synchronization: The industrial communication gateway periodically captures the dynamic heterogeneous data collected in step S1 through the industrial automation standard communication protocol; after receiving the dynamic heterogeneous data, the unified spatiotemporal coding protocol automatically parses the identity and location attributes of the data source node, generates and assigns a unique identification code of the prefabricated component, mold number and workstation timestamp for each data stream, and then binds the dynamic heterogeneous data with the above-mentioned multi-dimensional identifiers in real time to realize the high-frequency synchronous update of the event-driven virtual twin state.

[0019] Furthermore, the specific calculation logic for the spatiotemporal carbon flow described in step S3 is as follows:

[0020] Absolute carbon emission calculation of main building materials: The main building materials include steel bars, concrete, steel strands and reserved corrugated pipes; by comparing the theoretical building material usage in the detailed design data with the actual consumption of the main building materials collected by the electronic weighing sensor of the batching machine, the absolute carbon emission of the main building materials is calculated.

[0021] Additional carbon emissions calculation for auxiliary connecting materials: The auxiliary connecting materials include reinforcing sleeves, bolts and nuts, anchor clamps and grouting materials; the single consumption of the auxiliary connecting materials is extracted based on the production work order records, and the additional carbon emissions of the auxiliary connecting materials are calculated.

[0022] Carbon emission accounting for depreciation of turnover support materials: The turnover support materials include molds and support steel pipes; based on the total carbon emissions of the turnover support materials over their life cycle and the current actual turnover times of the turnover support materials, the dynamic depreciation amortization carbon emission calculation of the turnover support materials is performed.

[0023] Carbon emission accounting for energy consumption of production machinery: Based on the instantaneous power and start / stop timestamps of lifting equipment, mixer trucks, vibrators, tension jacks and grouting pumps captured in real time by smart meters and steam flow meters, the carbon emission of energy consumption of each piece of production machinery is calculated by integration.

[0024] Furthermore, the low-carbon scheduling virtual simulation optimization described in step S4 specifically includes the following execution mechanism:

[0025] Construct a fitness function with minimizing total carbon emissions in the workshop as the single evaluation index;

[0026] The real-time extraction of the mold occupancy status, production equipment idle time, raw material inventory balance, and absolute order delivery deadline mapped within the virtual twin entity serves as the production scheduling boundary constraints.

[0027] An optimization algorithm is used to perform multi-generation iterative simulations on the process queue in a virtual space. Without violating the absolute deadline constraint of the order delivery, the set of low-carbon process control parameters and the cycle time of the internal logistics are output through virtual trial and error. These parameters include the optimal steam curing heating step curve, the optimal start-up and shutdown sequence of the tensioning equipment, and the optimal flow path of the internal logistics.

[0028] Furthermore, the physical equipment closed-loop feedback control described in step S5 includes two modes: equipment-level direct control and workshop-level scheduling control.

[0029] Equipment-level direct control: The set of low-carbon process control parameters is compiled into industrial control protocol instructions and sent to the field programmable logic controller (PLC) to perform adaptive adjustments of multi-dimensional parameters on the production equipment. Specifically, these adjustments include: for thermodynamic equipment, automatically adjusting the opening of the steam solenoid valve of the steam curing kiln to approximate the optimal temperature rise curve; for power and molding equipment, automatically adjusting the variable frequency speed of the concrete mixer and the excitation frequency of the vibrating table to reduce redundant power consumption; and for post-processing equipment, automatically setting the hydraulic servo holding parameters of the prestressed tensioning jack and the grouting pressure of the grouting pump.

[0030] Workshop-level scheduling and control: Synchronizes the on-site logistics flow rhythm to the manufacturing execution system, generating a global digital dashboard and mobile task assignment, and coordinating spatiotemporal intervention for materials and resources. This intervention specifically includes spatial logistics coordination and resource timing coordination: In spatial logistics coordination, it guides crane shifts to execute the optimal spatial hoisting trajectory of the wing plate steel reinforcement skeleton and coordinates the command of concrete transport vehicles to complete precise on-site fixed-point pouring; in resource timing coordination, it dynamically instructs the frequency of dismantling and cleaning of turnover support materials and adaptively adjusts the work connection sequence between continuous manufacturing stations to eliminate carbon emissions from equipment standby.

[0031] In a second aspect, the present invention provides a carbon emission control system for the production stage of precast concrete components based on digital twins, used to implement the method described in any one of the first aspects, the system comprising:

[0032] Multi-dimensional sensing module: used to collect material consumption data and mechanical energy consumption data of physical production entities in each manufacturing process in real time;

[0033] Dynamic mapping module: used to drive virtual twin entities and establish spatiotemporal mapping relationships between physical production entities in continuous processes;

[0034] Carbon Flow Calculation Module: Used for classifying and calculating the carbon emissions of main building materials, auxiliary connecting materials, turnover support materials, and production machinery;

[0035] Simulation optimization module: used to perform multi-objective iterative simulation and output a set of low-carbon process control parameters and material flow cycle time;

[0036] Closed-loop control module: used to provide closed-loop feedback on the operating parameters and scheduling behavior of on-site machinery via programmable logic controller and manufacturing execution system terminal.

[0037] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the steps of the method described in any of the first aspects.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, it implements the steps of the method described in any one of the first aspects.

[0039] Overall, compared with the prior art, the present invention has the following significant advantages:

[0040] 1. This invention overcomes the limitations of existing technologies in terms of coarse calculations and data distortion, achieving refined dynamic carbon flow calculation. Based on the detailed design data of concrete components and the geometric parameters of the molds, the invention constructs physical boundaries and classifies consumed materials into main building materials, auxiliary connecting materials, and turnover support materials. Through metering data acquired via a multi-dimensional sensor network on-site, this invention can accurately track the spatiotemporal carbon flow of components in continuous processes, effectively solving the technical problem of the disconnect between traditional data monitoring and actual on-site conditions.

[0041] 2. This invention overcomes the limitations of existing technologies in lacking feedback control and adaptive adjustment, achieving closed-loop feedback control of on-site production equipment. Within a virtual space, the invention uses order delivery deadlines as boundary constraints to perform iterative deductions, thereby generating optimal low-carbon control parameters. Furthermore, this invention transforms the low-carbon simulation strategy into execution instructions and sends them to the manufacturing execution system, directly driving the adaptive adjustment of parameters in the underlying production equipment, effectively tapping into the carbon reduction potential of real manufacturing processes. Attached Figure Description

[0042] Figure 1 A flowchart of the method provided in an embodiment of the present invention;

[0043] Figure 2 This is a logical architecture diagram of virtual twin spatiotemporal dynamic mapping and data synchronization provided in an embodiment of the present invention;

[0044] Figure 3 A diagram illustrating the refined spatiotemporal carbon flow calculation mechanism for prefabricated components provided in this embodiment of the invention;

[0045] Figure 4 The flow diagram of low-carbon scheduling virtual simulation optimization and physical equipment closed-loop feedback control provided in the embodiments of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0047] In this embodiment, a detailed description is provided using a real production line of a precast component factory for highway bridges as an example. This production line produces C50 post-tensioned prestressed 30m simply supported T-beams (middle beam, beam height 2.0m, top slab width 2.4m). The manufacture of these 30m simply supported T-beams involves continuous high-carbon emission processes such as long-span frame binding, large-volume concrete pouring, long-term steam curing, and large-tonnage prestressing tensioning.

[0048] like Figure 1 As shown in the figure, this embodiment provides a carbon emission control method for the production stage of precast concrete components based on digital twins, which fully covers the closed-loop link of sensing, mapping, accounting, optimization and control, and specifically includes the following steps:

[0049] Step S1, Multi-dimensional data perception of physical production: Relying on the sensor network and metering unit deployed at the physical production site, real-time dynamic heterogeneous data of material consumption and mechanical energy consumption of precast concrete components in continuous manufacturing process are collected.

[0050] In this embodiment, the physical production entity includes underlying hardware facilities corresponding to continuous manufacturing processes, specifically including: a production platform system for frame installation and template assembly operations; hoisting and on-site logistics equipment for material handling and component relocation; pouring and vibration equipment for concrete pouring and molding; a steam curing kiln for accelerating concrete setting and hardening; and prestressed tensioning jacks and duct grouting equipment for post-processing operations.

[0051] Meanwhile, the sensor network and metering units deployed on the physical site include: electronic weighing sensors for batching machines, flow meters for water and admixture pipelines, and material barcode scanning terminals for collecting material consumption data; smart meters for the main circuit of equipment and flow meters for steam pipelines for collecting time-series data of energy consumption of production machinery; and thermocouple temperature sensors inside the steam curing kiln for collecting workshop environmental data.

[0052] Step S2, Virtual Twin Spatiotemporal Dynamic Mapping: The dynamic heterogeneous data stream is injected into the virtual twin entity in real time via an industrial communication gateway. A dynamic mapping relationship between the physical production entity and the virtual twin entity is established using a unified spatiotemporal coding protocol. The spatial geometric evolution trajectory and physical state evolution process of the physical components in the continuous manufacturing process are presented in high-fidelity synchronously in the virtual space.

[0053] like Figure 2 As shown, the logical architecture of this step is divided into a physical perception layer, a data transmission and protocol layer, and a virtual twin layer from bottom to top. In the top-level virtual twin layer, the system first extracts the detailed design data of the precast concrete component and the geometric three-dimensional parameters of the mold. It then initializes and constructs the spatial topological boundaries of the component body and the mold in virtual space, thus completing the initial construction of the virtual twin entity. Based on this, a thermodynamic mechanism model for concrete setting and hardening is embedded. The aforementioned injected dynamic heterogeneous dataset is provided with spatial physical constraints by this virtual twin entity model and serves as an input variable to drive the thermodynamic mechanism model. The two are deeply integrated and merged, ultimately generating a dynamic mapping model describing the state of the physical entity. Its virtual twin spatiotemporal state set equation can be expressed as:

[0054]

[0055] In the formula, Indicates time The virtual twin T-beam composite state tuple at that time; This indicates the unique identification code of the 30m precast T-beam in the section; This indicates the number of the currently occupied 30m steel formwork; This indicates the real-time three-dimensional spatial coordinates of the T-beam during the continuous work of binding, pouring, curing, and tensioning. This represents a dynamic heterogeneous dataset that integrates weighing, flow meter, and electricity meter data from the mixing plant.

[0056] In the underlying physical sensing layer, data originates from the sensor network and metering units at the physical production entity. The industrial communication gateway, located in the middle data transmission and protocol layer, periodically captures the dynamic heterogeneous data collected in step S1 using industrial automation standard communication protocols. Upon receiving the dynamic heterogeneous data, the unified spatiotemporal coding protocol unit within the gateway automatically parses the identity and location attributes of the data source nodes, generating and assigning a unique identifier for each prefabricated component, a mold number, and a workstation timestamp for each data stream. This allows for real-time binding of the dynamic heterogeneous data with these multi-dimensional identifiers, enabling high-frequency synchronous updates of the event-driven virtual twin state. During this binding and alignment process, the protocol unit aggregates and encapsulates the dynamic heterogeneous data collected in step S1 at the software level, generating a dynamic heterogeneous dataset, which is then injected as a structured data packet into the top-level virtual twin layer.

[0057] Step S3: Precise calculation of carbon flow of precast components: Based on the dynamic mapping and association of the virtual twin entity, the spatiotemporal carbon flow of a single precast concrete component is digitally tracked; combined with the characteristics of continuous manufacturing process, the absolute carbon emissions of the main building materials, the additional carbon emissions of auxiliary connecting materials, and the amortized carbon emissions of turnover support materials are dynamically calculated respectively, and the carbon emissions of energy consumption of production machinery are superimposed to generate a high-resolution component-level spatiotemporal carbon flow evolution model.

[0058] like Figure 3 As shown, this accounting mechanism presents a three-tiered topology from bottom to top: a data input module, a classification and accounting module, and an aggregation and output module. In the aggregation and output module, the core formula for the total carbon emission model of a single precast T-beam is as follows:

[0059]

[0060] In the formula, This represents the total spatiotemporal carbon emissions at the component level for a single precast T-beam; This indicates the absolute carbon emissions of the main building materials; Indicates the additional carbon emissions from auxiliary connecting materials; This indicates the amortized carbon emissions of the turnover support materials; This indicates the carbon emissions from energy consumption in production machinery.

[0061] Combination Figure 3 From the input parameters of the data input module and the calculation logic of the classification calculation module, we can see that:

[0062] Regarding the absolute carbon emission calculation of the main building materials, these materials include reinforcing steel, concrete, steel strands, and pre-installed corrugated pipes. The calculation is performed by comparing the theoretical material usage in the detailed design data with the actual consumption collected by the electronic weighing sensor of the batching machine. The calculation formula is as follows:

[0063]

[0064] In the formula, This indicates the absolute carbon emissions of the main building materials; This indicates the total number of types of main building materials; Indicates the first The actual consumption of various building materials; Indicates the first Carbon emission factors of various building materials; This represents the correction factor for material loss on site.

[0065] Regarding the additional carbon emission accounting for auxiliary connecting materials, these materials include reinforcing sleeves, bolts and nuts, anchor clamps, and grouting materials. The calculation is based on the single-use consumption of these auxiliary connecting materials extracted from production work order records, using the following formula:

[0066]

[0067] In the formula, Indicates the additional carbon emissions from auxiliary connecting materials; This indicates the total number of types of auxiliary connecting materials; This indicates the first one extracted from the production work order record. The actual consumption of a single material; Indicates the first Carbon emission factors of auxiliary materials.

[0068] Regarding the carbon emission accounting for the amortization of turnover support materials, the turnover support materials include molds and support steel pipes; this embodiment performs dynamic depreciation amortization carbon emission calculation based on the total life cycle carbon emissions of the turnover support materials and their current actual turnover times, and the calculation formula is as follows:

[0069]

[0070] In the formula, This indicates the total amortized carbon emissions of turnover support materials such as T-beam steel molds; This indicates the total number of types of turnover support materials; Indicates the first Total carbon emissions over the lifecycle of various turnover support materials; Indicates the first The design limit of the number of turnovers for this type of turnover support material; Indicates based on the current actual turnover rate The fatigue loss and deformation depreciation penalty coefficient.

[0071] In the aforementioned carbon emission accounting for the amortization of turnover support materials, regarding the first... Total carbon emissions over the life cycle of various turnover support materials As a constant for system initialization, it can be directly obtained from the third-party product carbon footprint certification declaration document of the turnover support material; if there is no certification document, it is calculated by comprehensively considering the physical nameplate weight of the turnover support material, the carbon emission factor of the material at the time of manufacture, and the end-of-life recycling offset factor. The calculation formula is as follows:

[0072]

[0073] In the formula, Indicates the first Total carbon emissions over the lifecycle of various turnover support materials; This indicates the physical weight of the material on the nameplate. This indicates the standard manufacturing carbon emission factor of the material; This indicates the expected scrap recovery rate of the turnover support material at the end of the period; This indicates the emission reduction offset factor for recycling waste materials.

[0074] Regarding the carbon emission accounting for energy consumption of production machinery, the system performs integral calculations based on the instantaneous power and start / stop timestamps of lifting equipment, mixer trucks, vibrators, tension jacks, and grouting pumps captured in real time by smart meters and steam flow meters. The calculation formula is as follows:

[0075]

[0076] In the formula, This represents the total carbon emissions from energy consumption in production machinery. This represents the total number of mechanical devices that consume electrical energy. Indicates the first The equipment is in Instantaneous electrical power at any given moment; This indicates that during the steam curing stage of C50 concrete, the kiln... Instantaneous steam consumption flow rate at any given moment; , These represent the energy carbon emission factors of the regional power grid and steam, respectively. , They represent the first The start and end timestamps of the machine's operation; , These represent the start and end timestamps of the steam curing process, respectively.

[0077] Step S4, Low-carbon scheduling virtual simulation optimization: such as Figure 4 As shown, the low-carbon scheduling virtual simulation optimization and physical equipment closed-loop feedback control present a three-level topology structure from bottom to top: a physical execution module, a dual-track distribution module, and a decision optimization module. In the decision optimization module, the component-level spatiotemporal carbon flow evolution model is imported into the simulation optimization engine. Production scheduling boundary constraints are established within the virtual twin entity, and a distributed discrete event simulation program is run to perform multiple batch trial and error simulations. The optimal set of low-carbon process control parameters and the on-site material flow rhythm are calculated under the premise of meeting the order delivery deadline.

[0078] like Figure 4 As shown, when faced with workshop production scheduling tasks, the system constructs a fitness function with minimizing the total carbon emissions of the workshop as the single evaluation index. The objective function formula is as follows:

[0079]

[0080] In the formula, This represents the fitness function with the minimization of total carbon emissions in the workshop as the evaluation index; This represents a matrix of control variables, including the T-beam production schedule, the C50 concrete curing temperature rise curve, and the tensioning schedule. This indicates the total number of T-beams to be produced in the current batch; Indicates the first Each prefabricated component under control parameters The total amount of carbon emissions.

[0081] During the optimization simulation, the system extracts in real time the mold occupancy status, production equipment idle time, raw material inventory balance, and order delivery deadline mapped within the virtual twin entity as production scheduling boundary constraints. The formula for the production scheduling boundary constraints is as follows:

[0082]

[0083] In the formula, Indicate boundary constraints; The simulation results show that the first... The completion time of each component; Indicates the highway section for the first The absolute deadline for the delivery of each component; Indicates the first Each component The current status of mold occupancy must be such that the total occupancy does not exceed the existing four sets of 30m T-beam steel molds in the workshop. express Time of the first Inventory balance of various materials; Indicates the production of the current prefabricated component for the first The consumption demand for this type of material.

[0084] An optimization algorithm is used to perform multi-generation iterative simulations on the process queue in a virtual space. Without violating the absolute deadline constraint of the order delivery, the set of low-carbon process control parameters and the cycle time of the internal logistics are output through virtual trial and error. These parameters include the optimal steam curing heating step curve, the optimal start-up and shutdown sequence of the tensioning equipment, and the optimal flow path of the internal logistics.

[0085] Step S5, Physical Equipment Closed-Loop Feedback Control: Based on the optimal low-carbon process control parameter set and the flow rate of materials in the production area, a target control command sequence is generated and applied in reverse to the industrial control nodes and execution terminals of the corresponding physical production entities. The operating parameters of the physical production equipment are dynamically adjusted to drive the physical production line to complete low-carbon adaptive execution. The physical production equipment provides real-time feedback of its operating status to the simulation optimization engine, providing continuously updated operating status data.

[0086] like Figure 4 As shown, this embodiment includes two modes: direct equipment-level control and workshop-level scheduling control.

[0087] In terms of direct equipment-level control, the set of low-carbon process control parameters is compiled into industrial control protocol instructions and sent to the field programmable logic controller (PLC) to perform adaptive adjustment of multi-dimensional parameters on the production equipment. For thermodynamic equipment, an incremental proportional-integral-derivative (PID) control algorithm is used to automatically adjust the opening of the steam solenoid valve of the steam curing kiln to approximate the optimal temperature rise step curve. The control formula is as follows:

[0088]

[0089]

[0090] In the formula, This indicates the opening control signal output to the steam solenoid valve, ranging from 0% to 100%. Indicates the temperature deviation value; This indicates the optimal target temperature for low-carbon maintenance as output by the simulation. This indicates the actual temperature collected in real time by thermocouples inside the kiln. , , These represent the proportional, integral, and derivative servo control gain parameters, respectively. Indicates the current control moment; This represents the integral variable.

[0091] Meanwhile, for power and molding equipment, the variable frequency speed of the concrete mixer host and the excitation frequency of the vibrating table are automatically adjusted to reduce redundant power consumption; for post-processing equipment, the hydraulic servo holding parameters of the prestressed tensioning jack and the grouting pressure of the grouting pump are automatically set.

[0092] In terms of workshop-level scheduling and control, the on-site logistics flow rhythm is synchronized to the manufacturing execution system, generating a global digital dashboard and mobile task assignment, and enabling spatiotemporal collaborative intervention for materials and resources. This intervention specifically includes spatial logistics coordination and resource timing coordination: in spatial logistics coordination, it guides crane shifts to execute the optimal spatial hoisting trajectory of the wing plate steel reinforcement skeleton and coordinates the command of concrete transport vehicles to complete precise on-site fixed-point pouring; in resource timing coordination, it dynamically instructs the frequency of dismantling and cleaning of turnover support materials and adaptively adjusts the work connection sequence between various continuous manufacturing stations to eliminate carbon emissions from equipment standby.

[0093] As a second aspect of the present invention, embodiments of the present invention also provide a carbon emission control system for the production stage of precast concrete components based on digital twins for implementing the above-described method embodiments. The system as a whole corresponds to the method described in any one of claims 1 to 7, specifically comprising a multi-dimensional sensing module, a dynamic mapping module, a carbon flow calculation module, a simulation optimization module, and a closed-loop control module. The multi-dimensional sensing module is used to collect material consumption data and mechanical energy consumption data of the physical production entity in each manufacturing process in real time; the dynamic mapping module is used to drive the virtual twin entity and establish a spatiotemporal mapping association of the physical production entity in continuous processes; the carbon flow calculation module is used to classify and calculate the carbon emissions of main building materials, auxiliary connecting materials, turnover support materials, and production machinery; the simulation optimization module is used to perform multi-generation iterative simulations under fitness functions and red-line delivery constraints, outputting a set of low-carbon process control parameters and material flow rhythm; the closed-loop control module is used to perform closed-loop feedback on on-site machinery operating parameters and scheduling behavior via a programmable logic controller and a manufacturing execution system terminal.

[0094] As a third aspect of the present invention, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for carbon emission control during the production stage of precast concrete components based on digital twins. Specifically, the computer device can be an industrial server or a workshop control host computer. The processor can be a central processing unit or an industrial-grade microprocessor. The memory may include high-speed random access memory or non-volatile memory, and a communication bus is used to realize communication between the processor, the memory, and the underlying control interface.

[0095] As a fourth aspect of the present invention, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in any of the above method embodiments. The computer-readable storage medium can be a non-transitory medium capable of persistently storing program code, such as a USB flash drive, portable hard drive, read-only memory, random access memory, magnetic disk, or optical disk.

Claims

1. A method for carbon emission control during the production stage of precast concrete components based on digital twins, characterized in that, Includes the following steps: S1. Relying on the sensor network and metering unit deployed at the physical production entity, dynamic heterogeneous data of precast concrete components are collected in real time during the continuous manufacturing process. The dynamic heterogeneous data includes material consumption data for calculating implicit carbon emissions and time-series data of production machinery energy consumption for calculating direct carbon emissions. S2. The dynamic heterogeneous data stream is injected into the virtual twin entity in real time via the industrial communication gateway. A dynamic mapping relationship between the physical production entity and the virtual twin entity is established using a unified spatiotemporal coding protocol. The spatial geometric evolution trajectory and physical state evolution process of the physical components in the continuous manufacturing process are presented in high-fidelity synchronously in the virtual space. S3. Based on the dynamic mapping and association of the virtual twin entity, digitally track the spatiotemporal carbon flow of a single precast concrete component; Combining the characteristics of continuous manufacturing processes, the absolute carbon emissions of main building materials, the additional carbon emissions of auxiliary connecting materials, and the amortized carbon emissions of turnover support materials are dynamically calculated separately, and the carbon emissions of energy consumption of production machinery are superimposed to generate a high-resolution component-level spatiotemporal carbon flow evolution model. S4. Import the component-level spatiotemporal carbon flow evolution model into the simulation optimization engine, establish production scheduling boundary constraints inside the virtual twin entity, run the distributed discrete event simulation program to perform multiple batch trial and error simulations, and calculate the optimal set of low-carbon process control parameters and the on-site logistics flow rhythm under the premise of meeting the order delivery deadline. S5. Based on the optimal set of low-carbon process control parameters and the flow rate of materials in the field, generate a target control command sequence, which acts in reverse on the industrial control nodes and execution terminals of the corresponding physical production entities, dynamically adjusts the operating parameters of the physical production equipment, and drives the physical production line to complete low-carbon adaptive execution; the physical production equipment provides real-time feedback of its operating status to the simulation optimization engine, providing rolling updated operating status data.

2. The method for carbon emission control during the production stage of precast concrete components based on digital twins according to claim 1, characterized in that, The physical production entity mentioned in step S1 includes the underlying hardware facilities corresponding to the continuous manufacturing process of precast concrete components, specifically including: Production platform system for skeleton installation and template assembly operations; Lifting and hoisting equipment and on-site logistics equipment used for material handling and component relocation; Pouring and vibrating equipment used for concrete pouring and forming; Steam curing kiln used to accelerate the setting and hardening of concrete; Prestressed tensioning jacks and grouting equipment for post-processing operations.

3. The method for carbon emission control during the production stage of precast concrete components based on digital twins according to claim 1, characterized in that, The sensing network and metering unit mentioned in step S1 include: Electronic weighing sensors for batching machines, flow meters for water and admixture pipelines, and material barcode scanning terminals for collecting material consumption data; Smart meters for the main circuit of equipment and steam pipeline flow meters used to collect time-series data on energy consumption of production machinery; Temperature sensors for internal thermocouples in steam curing kilns used to collect environmental data in the workshop.

4. The method for carbon emission control during the production stage of precast concrete components based on digital twins according to claim 1, characterized in that, The method for constructing the virtual twin entity and the data synchronization method described in step S2 are as follows: Solid construction: Extract detailed design data of precast concrete components and geometric three-dimensional parameters of molds, initialize the spatial topological boundary of the component body and mold in virtual space, and embed the thermodynamic mechanism model of concrete setting and hardening. Data synchronization: The industrial communication gateway periodically captures the dynamic heterogeneous data collected in step S1 through the industrial automation standard communication protocol; after receiving the dynamic heterogeneous data, the unified spatiotemporal coding protocol automatically parses the identity and location attributes of the data source node, generates and assigns a unique identification code of the prefabricated component, mold number and workstation timestamp for each data stream, and then binds the dynamic heterogeneous data with the above-mentioned multi-dimensional identifiers in real time to realize the high-frequency synchronous update of the event-driven virtual twin state.

5. The method for carbon emission control during the production stage of precast concrete components based on digital twins according to claim 1, characterized in that, The specific calculation logic for the spatiotemporal carbon flow mentioned in step S3 is as follows: Absolute carbon emission calculation of main building materials: The main building materials include steel bars, concrete, steel strands and reserved corrugated pipes; by comparing the theoretical building material usage in the detailed design data with the actual consumption of the main building materials collected by the electronic weighing sensor of the batching machine, the absolute carbon emission of the main building materials is calculated. Additional carbon emissions calculation for auxiliary connecting materials: The auxiliary connecting materials include reinforcing sleeves, bolts and nuts, anchor clamps and grouting materials; the single consumption of the auxiliary connecting materials is extracted based on the production work order records, and the additional carbon emissions of the auxiliary connecting materials are calculated. Carbon emission accounting for depreciation of turnover support materials: The turnover support materials include molds and support steel pipes; based on the total carbon emissions of the turnover support materials over their life cycle and the current actual turnover times of the turnover support materials, the dynamic depreciation amortization carbon emission calculation of the turnover support materials is performed. Carbon emission accounting for energy consumption of production machinery: Based on the instantaneous power and start / stop timestamps of lifting equipment, mixer trucks, vibrators, tension jacks and grouting pumps captured in real time by smart meters and steam flow meters, the carbon emission of energy consumption of each piece of production machinery is calculated by integration.

6. The method for carbon emission control during the production stage of precast concrete components based on digital twins according to claim 1, characterized in that, The low-carbon scheduling virtual simulation optimization described in step S4 specifically includes the following steps: S4.1 Construct a fitness function with minimizing total carbon emissions in the workshop as the single evaluation index; S4.

2. Extract the mold occupancy status, production equipment idle time, raw material inventory balance, and absolute order delivery deadline mapped in the virtual twin entity in real time as production scheduling boundary constraints. S4.

3. An optimization algorithm is used to perform multi-generation iterative simulation of the process queue in the virtual space. Without violating the absolute deadline constraint of the order delivery, the set of low-carbon process control parameters and the cycle time of the internal logistics are output through virtual trial and error, including the optimal steam curing heating step curve, the optimal start-up and shutdown sequence of the tensioning equipment, and the optimal flow path of the internal logistics.

7. The method according to claim 1, characterized in that, The physical equipment closed-loop feedback control described in step S5 includes two modes: equipment-level direct control and workshop-level scheduling control. Equipment-level direct control: The set of low-carbon process control parameters is compiled into industrial control protocol instructions and sent to the field programmable logic controller (PLC) to perform adaptive adjustment of multi-dimensional parameters on the production equipment. Specifically, this adjustment includes: for thermodynamic equipment, automatically adjusting the opening of the steam solenoid valve of the steam curing kiln to approximate the optimal temperature rise curve; for power and molding equipment, automatically adjusting the variable frequency speed of the concrete mixer and the excitation frequency of the vibrating table to reduce redundant power consumption; and for post-processing equipment, automatically setting the hydraulic servo holding parameters of the prestressed tensioning jack and the grouting pressure of the grouting pump. Workshop-level scheduling and control: Synchronizes the on-site logistics flow rhythm to the manufacturing execution system, generating a global digital dashboard and mobile task assignment, and coordinating spatiotemporal intervention for materials and resources. This intervention specifically includes spatial logistics coordination and resource timing coordination: In spatial logistics coordination, it guides crane shifts to execute the optimal spatial hoisting trajectory of the wing plate steel reinforcement skeleton and coordinates the command of concrete transport vehicles to complete precise on-site fixed-point pouring; in resource timing coordination, it dynamically instructs the frequency of dismantling and cleaning of turnover support materials and adaptively adjusts the work connection sequence between continuous manufacturing stations to eliminate carbon emissions from equipment standby.

8. A carbon emission control system for the production stage of precast concrete components based on digital twins, used to implement the carbon emission control method for the production stage of precast concrete components based on digital twins as described in any one of claims 1-7, characterized in that, The system includes: Multi-dimensional sensing module: used to collect material consumption data and mechanical energy consumption data of physical production entities in each manufacturing process in real time; Dynamic mapping module: used to drive virtual twin entities and establish spatiotemporal mapping relationships between physical production entities in continuous processes; Carbon Flow Calculation Module: Used for classifying and calculating the carbon emissions of main building materials, auxiliary connecting materials, turnover support materials, and production machinery; Simulation optimization module: used to perform multi-objective iterative simulation and output a set of low-carbon process control parameters and material flow cycle time; Closed-loop control module: used to provide closed-loop feedback on the operating parameters and scheduling behavior of on-site machinery via programmable logic controller and manufacturing execution system terminal.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 7.

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