A coke oven refractory brick masonry full-process intelligent management system based on digital twinning

Abstract

This invention belongs to the field of coke oven refractory bricklaying construction management technology, and specifically discloses an intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins. The system includes: a physical and digital model layer, a data acquisition and input layer, a data engine, a process control and dynamic collaboration layer, and a visualization and early warning feedback layer. The system divides coke oven design data into virtual bricklaying units according to the minimum construction logic, binds physical brick IDs to construct a digital twin model, and assigns a state machine. The acquisition layer uses IoT terminals to acquire raw data of the entire lifecycle behavior. The data engine collects data into unit data packets according to mapping relationships and drives state machine transitions. The process control layer uses task packages to perform dual verification of brick identification for both ownership and sequence and outputs control commands. The visualization layer listens to status signals and performs color-coded highlighting and multi-level early warning. This invention effectively prevents the use of incorrect bricks and incorrect laying order, realizing proactive error prevention and closed-loop intelligent management of the entire coke oven bricklaying process.

Description

Technical Field

[0001] This invention belongs to the field of coke oven refractory brick construction management technology, and more specifically, relates to an intelligent management system for the entire process of coke oven refractory brick construction based on digital twins. Background Technology

[0002] In the field of coking engineering, the coke oven, as a core production equipment, is constructed from tens of thousands of refractory bricks of different specifications. The accuracy of the bricklaying directly determines the service life, production efficiency, and operational safety of the coke oven. By ensuring that each refractory brick is accurately positioned according to the design model and sequence, systematic deviations caused by incorrect brick placement are fundamentally eliminated, providing a reliable prerequisite for subsequent bricklaying operations to achieve millimeter-level precision.

[0003] Traditional management of coke oven refractory bricklaying relies entirely on manual record keeping and on-site inspections. When dealing with complex masonry structures composed of tens of thousands of irregularly shaped refractory bricks, this approach suffers from numerous inherent and insurmountable flaws: misuse of bricks and incorrect laying order are frequent problems. Despite the availability of brick layout diagrams, the disorganized brick storage in the on-site warehouse forces workers to rely on memory and experience to find the correct brick, which is not only time-consuming and labor-intensive but also highly prone to errors in order or type, leading to extensive rework. Furthermore, information is fragmented, relying on manual post-construction updates. Outbound slips, layout diagrams, laying records, and inspection reports are scattered across different positions and paper forms. Information on the entire process from brick leaving the warehouse to its placement requires workers to recall and manually link information afterward, which is highly error-prone and cannot be synchronized in real time, creating numerous "information silos." Finally, problem localization and tracing are extremely difficult. When an inspection reveals that a brick in a certain location is substandard, it is necessary to manually review discrete records from multiple stages, which is time-consuming and lengthy in order to vaguely trace its origin, the person in charge, and the information of the matching bricks, delaying the opportunity for rectification and making it difficult to define responsibilities. Cross-process collaboration is severely lagging behind. Problems caused by the previous process (such as incorrect slab matching or wrong bricks being sent from the brick warehouse) are often not discovered until the next process (masonry) or even later, resulting in large-scale rework and project delays. Data utilization is low, and manually recorded data is messy, making it impossible to optimize the construction process through analysis and making it difficult to achieve refined management.

[0004] In actual coke oven masonry construction, traditional management methods require frequent on-site communication and manual verification among managers, construction workers, and inspectors. This not only increases labor costs but also makes management loopholes prone to human error. Furthermore, construction workers must work for extended periods in complex environments with high temperatures and dust, raising concerns about safety. Therefore, with the widespread adoption of digital twins and IoT technologies in industry, digitalization and intelligentization have become key directions for addressing the pain points of traditional management. However, current technologies primarily focus on macro-level progress simulation or general material tracking, and no technical solution has yet been found that can provide an intelligent control system capable of achieving automatic and strong data correlation, proactive error prevention during construction, and real-time problem closure in the unique and complex scenario of coke oven masonry construction, characterized by "numerous brick types, strict logical sequences, millimeter-level precision requirements, and multiple overlapping stages." Therefore, an innovative management paradigm and technological approach are urgently needed to fundamentally solve the numerous problems in traditional coke oven masonry management. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a digital twin-based intelligent management system for the entire process of coke oven refractory bricklaying. It focuses on the long-standing industry pain point of using the wrong bricks and laying them out of order due to the "multiple brick types, strong logical connections, and strict sequence requirements" in coke oven bricklaying. By constructing a digital twin model of the coke oven with "laying units" as the core data structure, a group of refractory bricks belonging to the same structural part (such as a single-layer ring wall of a combustion chamber) is predefined as a logical whole in the digital space and bound to a complete laying task flow and state machine. The system integrates multiple data acquisition methods such as RFID, QR codes, and IoT positioning devices, automatically aggregating discrete refractory brick entry / exit, slab preparation, laying, and inspection data to the corresponding laying units, forming structured data packages. Based on the real-time status of the laying units (such as "laying in progress" or "inspection alarm"), the system dynamically drives the visual updates of the digital twin model and triggers high-priority warnings and collaborative instructions, achieving a rapid response closed loop from data acquisition to problem identification and on-site intervention. The system can proactively prevent the use of incorrect bricks and incorrect laying order during the warehousing and masonry processes, ensuring that each refractory brick is accurately placed according to the design drawings. In the event of quality problems, it can trace the entire chain of information and the responsible party with one click, significantly improving the positional accuracy and first-time success rate of coke oven masonry, construction efficiency, and cross-positional collaborative effectiveness.

[0006] To achieve the above objectives, this invention proposes an intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins, comprising: The entity and digital model layer is used to acquire coke oven design data. Based on the coke oven design data, virtual masonry units are divided according to the minimum independent construction logic. Each virtual masonry unit is mapped and bound to a set of physical brick IDs to construct a digital twin model. Each virtual masonry unit is assigned a multi-state machine. The data acquisition input layer includes multiple types of IoT acquisition terminals, which are used to acquire raw data of physical behavior at each stage in the entire life cycle of refractory bricks and transmit the raw data of physical behavior to the data engine in real time. The data engine is used to receive the original data of the entity behavior, and automatically collect the original data of the entity behavior as sub-data into the corresponding virtual masonry unit structured data packet according to the preset mapping relationship between bricks and virtual masonry units; at the same time, according to the built-in state transition rules and the completeness of the sub-data, it automatically drives the multi-state machine of the corresponding virtual masonry unit to perform state transition. The process control and dynamic collaboration layer is used to acquire production plans, decompose construction tasks into masonry task packages based on virtual masonry units, and dynamically distribute them. During outbound and masonry operations, it acquires real-time scanned brick identification information, performs dual verification of the brick identification information's attribution and order based on the preset attributes and current priority logic of the masonry task packages, and outputs release or interception commands based on the verification results. The visualization and early warning feedback layer is used to monitor the state machine transition signals output by the data engine, and to perform state color-coded highlight rendering of the corresponding virtual masonry unit in the digital twin model according to the state machine transition signals; and when an abnormal state signal or a process inspection failure signal is obtained, multi-level early warning is triggered according to the preset alarm threshold, and the structured data packet of the virtual masonry unit is pushed to the associated collaborative responsibility terminal.

[0007] As a further preferred embodiment, the entity and digital model layer specifically includes: The parametric geometric model module is used to acquire the input coke oven design drawings and refractory brick process parameters, and to perform spatial geometric analysis based on the coke oven design drawings and refractory brick process parameters to obtain a three-dimensional solid geometric base that accurately maps the solid coke oven to a 1:1 scale and a parametric refractory brick family library. The virtual unit logic partitioning module and the parameterized geometric model module are used to acquire the spatial geometric data of the three-dimensional solid geometric base. According to the preset construction organization design and the minimum independently constructable structural logic, the spatial geometric data is divided into groups to obtain multiple virtual masonry units with independent spatial boundaries and unique logical identification codes. The data link mapping module is connected to the parameterized geometric model module and the virtual unit logical division module respectively. It is used to obtain the spatial coordinate range of each virtual masonry unit, automatically traverse and extract the three-dimensional design coordinates and identity attributes of each physical refractory brick contained in the spatial coordinate range, statically construct a one-to-one correspondence mapping table between "virtual masonry unit ID" and "list of all brick IDs contained", and store the corresponding mapping table in the static mapping relationship library to realize the static binding between virtual space and physical entity; The state attribute injection module, which is connected to the virtual unit logic partitioning module, is used to obtain initial configuration parameters and dynamically attach a finite state machine with multi-state transition logic to the attribute list of each virtual masonry unit according to the initial configuration parameters.

[0008] As a further preferred embodiment, the structural logic based on the preset construction organization design and minimum independently constructable structure includes: By analyzing the spatial three-dimensional geometric data of the coke oven BIM model, the spatial logical dividing boundary of each virtual masonry unit is automatically calculated. The specific calculation formula is as follows: In the formula, Let k be the current virtual masonry unit according to the overall construction sequence, and satisfy the following conditions: K represents the total number of virtual masonry units divided into the entire furnace. For the virtual masonry unit The set of three-dimensional coordinate points in the digital twin model, (x, y, z) represents the three-dimensional axial coordinate values ​​in the absolute coordinate system of the coke oven BIM. and These are the minimum and maximum boundary coordinates of the element along the furnace length direction, respectively. and These are the minimum and maximum boundary coordinates of the unit in the furnace width direction, respectively. and These are the minimum and maximum boundary coordinates of the unit in the furnace height direction, respectively.

[0009] As a further preferred embodiment, the static mapping relationship library opens a matching interface to the external data engine, which is used by the data engine to determine the behavior attribution according to the corresponding mapping table and send a state change signal to the state attribute injection component when the data engine obtains the original data of the on-site entity behavior. The state attribute injection component opens a rendering interface to the external visualization layer, which is used by the visualization layer to perform state color-coded highlight rendering of the corresponding virtual masonry unit in the digital twin 3D interface according to the current running state when the visualization layer obtains the state change signal.

[0010] As a further preferred embodiment, the data acquisition input layer includes a pre-processing quality acceptance module, a multi-source IoT identification module, an image data acquisition module, and a mobile multi-terminal interaction module, wherein... The pre-quality acceptance module is used to acquire the self-quality inspection data input during the refractory brick warehousing process, and to make compliance judgment on the self-quality inspection data according to the preset quality acceptance standards. It obtains the brick quality grade identifier that is classified as Class A working surface usable, Class B non-working surface usable, or Class C prohibited, and binds and encapsulates the brick quality grade identifier with the corresponding brick body identity identifier to generate the initial entity behavior raw data. The multi-source IoT identification module includes QR code scanning terminals, RFID readers, and IoT positioning devices deployed in physical warehouses and construction sites. The QR code scanning terminals are used to acquire brick identification identifiers by scanning the QR codes on the surface of the refractory bricks during refractory brick outbound processing, batching, and construction. They also acquire physical storage location identifiers by scanning the QR codes on the storage location during outbound processing. The RFID readers are used to automatically acquire identifiers bound to the transfer carrier via electromagnetic induction during refractory brick batching and transfer processes. The RFID tag is used for identification; the IoT positioning device is used to acquire the transient spatial positioning coordinates of the positioning tag in real time when the refractory brick enters the masonry process; the multi-source IoT identification module is used to acquire the above-mentioned identification, physical storage location identification and transient spatial positioning coordinates, and according to the order in which each identification and coordinate is acquired, the brick identification is dynamically associated and matched with the corresponding physical storage location identification and transient spatial positioning coordinates through preset time sequence logic to obtain the original data of the flow entity behavior covering the location and time sequence information of the entire process; The image data acquisition module includes a high-definition camera, the output of which is electrically connected to the multi-source IoT identification module. It is used to acquire on-site images in the panel preparation process and the masonry process. Based on the flow trigger signal output by the QR code scanning terminal or RFID reader in the multi-source IoT identification module, the on-site images are subjected to time-domain cropping and matching to obtain the original data of image entity behavior covering the on-site process form. The mobile multi-terminal interaction module includes a handheld mobile terminal with distributed permissions, which is wirelessly connected to the pre-quality acceptance module, the multi-source IoT identification module, and the image data acquisition module, respectively. It is used to acquire process parameters, process confirmation signals, and inspection results manually input by construction personnel, warehouse personnel, and inspection personnel. According to the job permissions of the logged-in user, the process parameters, process confirmation signals, and inspection results are mapped with the brick identity identifier obtained through the QR code scanning terminal to obtain the original text entity behavior data covering the personnel's job performance status.

[0011] As a further preferred embodiment, the signal output terminals of the pre-quality acceptance module, the multi-source IoT identification module, the image data acquisition module, and the mobile multi-terminal interaction module are all asynchronously connected to the input terminal of the external data engine, for real-time aggregation and transmission of the initial entity behavior raw data, the flowing entity behavior raw data, the image entity behavior raw data, and the text entity behavior raw data to the data engine.

[0012] As a further preferred embodiment, the data engine includes a data receiving submodule, a mapping and aggregation submodule, a structured storage submodule, and a state machine driving submodule; The data receiving submodule is used to receive the original data of the entity behavior transmitted by the external data acquisition input layer, and to parse and cache it to obtain multi-source stream data; The mapping and aggregation submodule is connected to the data receiving submodule and is used to acquire the multi-source stream data, determine the virtual masonry unit to which the current data belongs based on the preset mapping relationship between bricks and virtual masonry units, and obtain unit sub-data. The structured storage submodule is connected to the mapping and collection submodule and the external data storage module, respectively. It is used to acquire the unit subdata and append it to the corresponding virtual masonry unit structured data packet. The structured storage submodule is configured with a panel design risk assessment mechanism, which is used to calculate the panel design defect risk index of the virtual masonry unit based on the physical property similarity of adjacent brick types and the probability of historical masonry defects before the masonry task package is issued, and to trigger the issuance of an interception signal when the risk index exceeds the safety threshold. The state machine driving submodule is connected to the structured storage submodule and the external entity and digital model layer, respectively. It is used to obtain the data update signal of the structured data packet, and automatically drive the multi-state state machine to perform state transitions based on the completeness of the sub-data in the current virtual masonry unit. The state machine driving submodule is configured with a construction energy efficiency dynamic scheduling mechanism, which is used to obtain the entity behavior time sequence of physical bricks in the state transition process, calculate the process sequence disorder degree and construction efficiency profile score of the work team, and dynamically output production organization optimization instructions based on the construction efficiency profile score.

[0013] As a further preferred embodiment, the panel design risk assessment mechanism includes: By extracting the external dimensions and material grades of adjacent refractory bricks to calculate geometric and material similarity, and combining this with data on rectification of non-compliance issues during historical construction and renovation processes, the panel design defect risk index is calculated using the following formula. : , In the formula, For the current virtual masonry unit, This represents the total number of physically refractory bricks contained within the unit. and These are the i-th and i-th adjacent blocks in the task package according to the construction priority logic. A block of refractory bricks, The geometric and material similarity is given. The weighting of the historical mis-laying frequency of this brick type combination. This refers to the number of times this type of virtual masonry unit has historically failed to meet standards and required rectification. The system's preset standard masonry work hours, and The preset weight adjustment coefficient, and satisfies .

[0014] As a further preferred embodiment, the construction energy efficiency dynamic scheduling mechanism obtains the construction time interval between two adjacent bricks by capturing the real-time timestamp sequence of the scanned brick identification identifiers. Dynamically calculate the process sequence entropy, which reflects the disorder of the process sequence. The calculation formula is as follows: , In the formula, For the current virtual masonry unit, For the virtual masonry unit The total number of physically refractory bricks contained herein, where i is the number of bricks ordered from 1 to 1 according to the construction sequence logic. Increasing construction step sequence loop variable, Let be the construction time interval between two adjacent refractory bricks during the i-th step of construction, and j be the traversal variable used to sum the total working hours within the unit. Let be the time interval between the construction of two adjacent refractory bricks during step j. Let be the probability distribution projection of the masonry time for step i in the total actual construction time.

[0015] As a further preferred embodiment, before the multi-state machine jumps to the process inspection state, the process timing entropy is used as a reference. Average actual masonry time per brick Compared with the standard single-brick masonry work hour quota Energy efficiency mapping was performed to calculate the comprehensive construction efficiency profile score of the work team. The calculation formula is as follows: In the formula, The calculated process timing entropy, To create a comprehensive performance profile and score for the current work team. The standard temporal entropy benchmark value for the optimal skilled worker. This refers to the average actual bricklaying time per brick in this virtual masonry unit, as recorded by the multi-source IoT identification module. This is a pre-set standard single-brick masonry work hour quota. and This is the preset sensitivity penalty coefficient.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention mainly possess the following technical advantages: 1. This invention proposes a new paradigm of "masonry unit" management, fundamentally eliminating the problem of "information silos" caused by fragmented multi-source data and achieving efficient and accurate traceability across the entire chain. This invention overcomes the inherent defects of traditional management, which relies on manual ledgers, scattered paper forms, and broken information connections. By pre-defining discrete bricks belonging to the same minimum independent construction logic as "virtual masonry units" in the digital space, a strongly correlated mapping table of "unit ID" and "list of all contained brick IDs" is statically constructed, and a multi-state finite state machine is dynamically attached. This mechanism achieves a four-dimensional deep binding of "brick—unit—process—responsibility," enabling the automatic and real-time aggregation of discrete data from warehousing, slab preparation, masonry, and inspection into the structured data package of the corresponding unit. When process defects or anomalies occur, managers can instantly retrieve the entire lifecycle structured data with a single click, reducing the traditional hours of investigation and coordination work to minutes, significantly improving the real-time detection rate of quality problems, accurate location capabilities, and clarity of responsibility definition.

[0017] 2. This invention establishes a collaborative control mechanism of "pre-warehousing grading" and "dual error prevention verification of task packages," achieving a substantial leap from passive rework to proactive location prevention. Addressing the industry pain point of incorrect brick usage and incorrect laying order caused by the "numerous types of bricks and strict logical sequence" in coke oven refractory bricklaying, this invention implements pre-warehousing quality grading control. It forcibly isolates Class C prohibited bricks through system-preset rules and automatically locks Class B bricks with appearance defects to non-working units, thus safeguarding the quality baseline of key parts and avoiding material waste caused by simplistic scrapping. Simultaneously, the system transforms the laying plan into laying task packages based on units. During material requisition, it provides guidance and dual confirmation of physical warehouse stack layers. During laying, it provides dynamic guidance through handheld terminals and performs dual barcode scanning verification of "unit affiliation" and "process sequence." This control chain proactively intercepts erroneous operations during the outbound and laying stages, ensuring that every refractory brick is accurately placed in the correct position and sequence according to the design drawings, eliminating rework losses caused by human error at the source.

[0018] 3. This invention introduces an intelligent algorithm based on "brick arrangement risk index" and "process temporal entropy," driving the digital twin system to upgrade from a "passive recording and display" paradigm to "proactive intelligent empowerment." This invention is not a simple superposition of IoT hardware and construction management, but rather a deep integration of advanced digital evaluation and scheduling mechanisms at the data engine layer. Before task packages are issued, the system uses a dynamic evaluation algorithm for brick arrangement process configured in the structured storage submodule to quantitatively calculate the similarity of physical properties of adjacent brick types and historical error rates, obtaining a brick arrangement design defect risk index. This dynamically identifies error-prone brick type combinations and implements forced interception, guiding improvements in brick arrangement design from the outset. Simultaneously, the state machine-driven submodule is configured with a transient temporal entropy scheduling algorithm. By dynamically capturing the flow timestamp sequence of brick masonry, it quantitatively evaluates the process temporal disorder and construction efficiency of work teams. Even in the transient stage of "masonry in progress," it can identify on-site organizational disorder or hidden conflicts in advance, outputting production organization optimization instructions early, achieving closed-loop optimization of intelligent empowerment and dynamic collaborative scheduling.

[0019] 4. This invention constructs a highly adaptive human-computer interaction and security protection architecture, ensuring long-term, high-security data retrieval while adapting to complex and ever-changing industrial scenarios at zero cost. The invention balances versatility, security, and environmental reliability in its system physical and architectural layout. The system employs a dual storage architecture of cloud servers and local servers, preventing data loss due to network interruptions while meeting the requirements for long-term secure data retention and efficient retrieval, at least equivalent to the lifespan of a coke oven. The user interaction module features a multi-position, multi-dimensional access control weighted matrix, clearly defining the operational responsibilities of each position and standardizing the operating procedures. Furthermore, the built-in ambient light intensity adaptive dynamic gain mechanism and anti-mistouch pixel expansion control algorithm in the interactive interface can adaptively adjust the backlight power and touch hotspot area based on external transient light intensity and mechanical jitter rate, ensuring the accuracy of worker operations in harsh working environments such as strong light, high dust levels, and when wearing heavy gloves. The entire system adopts a modular design, and each module can be independently debugged and flexibly adapted. It does not require large-scale modification of existing construction equipment and can be easily promoted to the construction management scenarios of other high-temperature industrial furnaces such as blast furnaces and rotary kilns. Attached Figure Description

[0020] Figure 1 This is a structural block diagram of the intelligent management system for the entire process of coke oven refractory brick laying based on digital twins, which is involved in the embodiments of the present invention. Figure 2 This is a flowchart illustrating the intelligent management of the entire process of coke oven refractory bricklaying based on digital twins, as described in this embodiment of the invention. Figure 3 This is a schematic diagram of the construction and control of the H40 layer of the coke oven according to an embodiment of the present invention; Figure 4This is a schematic diagram of the attribute information tracing function interface involved in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] like Figures 1-4 As shown, this invention provides an intelligent management system for the entire process of coke oven refractory brick masonry based on digital twins. By introducing a new paradigm of digital management for "masonry units," integrating digital twin technology with IoT data acquisition technology, and optimizing the control process and structural design, it solves problems such as information fragmentation, inefficient control, difficulty in traceability, lagging collaboration, and error-prone construction in traditional management methods. This significantly improves the intelligence level, construction efficiency, and quality stability of coke oven refractory brick masonry. The core innovation of this invention's intelligent management system for the entire process of coke oven refractory brick masonry based on digital twins lies in introducing a data engine for the masonry unit as the basic carrier for all data aggregation, processing, and status management, rather than managing individual discrete bricks. Through the collaborative work of various modules, it achieves digital, intelligent, and collaborative control of the entire coke oven refractory brick masonry process. Specifically, the system includes: The entity and digital model layer is used to acquire coke oven design data. Based on the coke oven design data, virtual masonry units are divided according to the minimum independent construction logic. Each virtual masonry unit is mapped and bound to a set of physical brick IDs to construct a digital twin model. Each virtual masonry unit is assigned a multi-state machine. The data acquisition input layer includes multiple types of IoT acquisition terminals, which are used to acquire raw data of physical behavior at each stage in the entire life cycle of refractory bricks and transmit the raw data of physical behavior to the data engine in real time. The data engine is used to receive the original data of the entity behavior, and automatically collect the original data of the entity behavior as sub-data into the corresponding virtual masonry unit structured data packet according to the preset mapping relationship between bricks and virtual masonry units; at the same time, according to the built-in state transition rules and the completeness of the sub-data, it automatically drives the multi-state machine of the corresponding virtual masonry unit to perform state transition. The process control and dynamic collaboration layer is used to acquire production plans, decompose construction tasks into masonry task packages based on virtual masonry units, and dynamically distribute them. During outbound and masonry operations, it acquires real-time scanned brick identification information, performs dual verification of the brick identification information's attribution and order based on the preset attributes and current priority logic of the masonry task packages, and outputs release or interception commands based on the verification results. The visualization and early warning feedback layer is used to monitor the state machine transition signals output by the data engine, and to perform state color-coded highlight rendering of the corresponding virtual masonry unit in the digital twin model according to the state machine transition signals; and when an abnormal state signal or a process inspection failure signal is obtained, multi-level early warning is triggered according to the preset alarm threshold, and the structured data packet of the virtual masonry unit is pushed to the associated collaborative responsibility terminal.

[0023] In one embodiment of the invention, the physical and digital model layers are based on the coke oven BIM design model (which can import coke oven design drawings in CAD / BIM format). "Masonry units" are created and divided according to the smallest independently constructible structural logic (e.g., the north wall of the H40th floor of a combustion chamber). Each masonry unit is an independent object in the digital twin model, with attributes including: unit ID, a list of all included brick IDs, design coordinates, planned masonry time, and a multi-state machine (e.g., pending delivery, in progress, pending construction, in progress, pending inspection, qualified, unqualified). Simultaneously, this module constructs a parametric refractory brick family library, inputting parameters for all refractory brick specifications (including dimensions, material grade, and refractory rating), generating a digital twin model that is precisely mapped 1:1 to the physical coke oven. This supports real-time updates of the model based on the on-site construction progress, achieving dynamic synchronization between the physical construction and the virtual model.

[0024] More specifically, the entity and digital model layer includes: The parametric geometric model module is used to acquire the input coke oven design drawings and refractory brick process parameters, and to perform spatial geometric analysis based on the coke oven design drawings and refractory brick process parameters to obtain a three-dimensional solid geometric base that accurately maps the solid coke oven to a 1:1 scale and a parametric refractory brick family library. The virtual unit logic partitioning module and the parameterized geometric model module are used to acquire the spatial geometric data of the three-dimensional solid geometric base. According to the preset construction organization design and the minimum independently constructable structural logic, the spatial geometric data is divided into groups to obtain multiple virtual masonry units with independent spatial boundaries and unique logical identification codes. The data link mapping module is connected to the parameterized geometric model module and the virtual unit logical division module respectively. It is used to obtain the spatial coordinate range of each virtual masonry unit, automatically traverse and extract the three-dimensional design coordinates and identity attributes of each physical refractory brick contained in the spatial coordinate range, statically construct a one-to-one correspondence mapping table between "virtual masonry unit ID" and "list of all brick IDs contained", and store the corresponding mapping table in the static mapping relationship library to realize the static binding between virtual space and physical entity; The state attribute injection module, which is connected to the virtual unit logic partitioning module, is used to obtain initial configuration parameters and dynamically attach a finite state machine with multi-state transition logic to the attribute list of each virtual masonry unit according to the initial configuration parameters.

[0025] In a specific embodiment of the present invention, the step of following the preset construction organization design and minimum independently constructable structural logic includes: By analyzing the spatial three-dimensional geometric data of the coke oven BIM model, the spatial logical dividing boundary of each virtual masonry unit is automatically calculated. The specific calculation formula is as follows: In the formula, Let k be the current virtual masonry unit according to the overall construction sequence, and satisfy the following conditions: K represents the total number of virtual masonry units divided into the entire furnace. For the virtual masonry unit The set of three-dimensional coordinate points in the digital twin model, (x, y, z) represents the three-dimensional axial coordinate values ​​in the absolute coordinate system of the coke oven BIM. and These are the minimum and maximum boundary coordinates of the element along the furnace length direction, respectively. and These are the minimum and maximum boundary coordinates of the unit in the furnace width direction, respectively. and These are the minimum and maximum boundary coordinates of the unit in the furnace height direction, respectively.

[0026] More specifically, the virtual masonry unit The dynamic extraction and calculation formula for the three-dimensional spatial geometric boundary coordinates is as follows: In the formula, , , These are the BIM absolute coordinate reference values ​​of the preset starting point of the coke oven masonry entity; This refers to the standard center distance between adjacent combustion chambers as determined by the design drawings; The standard masonry thickness of a single combustion chamber wall, as determined by the design drawings; The standard length of a single independent construction flow section is determined according to the pre-set construction organization design and divided along the width of the furnace. M is the standard height of a single-layer wall construction determined according to the preset single-layer coke oven refractory brick masonry process; M is the total number of independent construction flow sections divided along the width of a single combustion chamber as determined by the construction organization design; H is the total number of independent construction process layers divided along the furnace height as determined by the minimum independently constructable structural logic. The floor operator is 'round down'; mod is the modulo operator. After the virtual unit logical partitioning module determines the spatial logical partitioning boundary of each virtual masonry unit through the formula, it packages the three-dimensional design coordinates and identity attributes of a set of physical refractory bricks contained within each boundary and outputs them to the data link mapping module to complete the static mapping binding.

[0027] In this embodiment, the static mapping relationship library opens a matching interface to the external data engine. When the data engine obtains the original data of the on-site entity behavior, it determines the behavior ownership according to the corresponding mapping table and sends a state change signal to the state attribute injection component. The state attribute injection component opens a rendering interface to the external visualization layer. When the visualization layer obtains the state change signal, it performs state color-coded highlight rendering of the corresponding virtual masonry unit in the digital twin 3D interface according to the current running state.

[0028] In another embodiment of the present invention, the data acquisition input layer is equipped with various types of acquisition devices, including RFID readers, QR code scanning terminals, IoT positioning devices (such as UWB positioning base stations with a positioning accuracy of ±3cm, suitable for construction site environments), high-definition cameras, and handheld mobile terminals. Continuing with existing mature acquisition technologies, this enables the automatic acquisition and real-time transmission of key data across the entire process of refractory brick issuance, batching, construction, and inspection. The acquired data includes, but is not limited to: brick ID, brick type number, issuance time, receiving team, handler, batching data, batching images, construction location, construction time, construction personnel, process parameters, inspection results, inspection personnel, and inspection time, ensuring that data at each stage is complete and traceable. More specifically, the data acquisition input layer includes a pre-quality acceptance module, a multi-source IoT identification module, an image data acquisition module, and a mobile multi-terminal interaction module, wherein: The pre-construction quality acceptance module is used to acquire the self-quality inspection data input during the refractory brick warehousing process. Based on preset quality acceptance standards, it performs compliance judgment on the self-construction quality inspection data, obtaining a brick quality grade identifier that classifies it as usable on a Class A working surface, usable on a Class B non-working surface, or prohibited in a Class C. The brick quality grade identifier is then bound and encapsulated with the corresponding brick identity identifier to generate initial entity behavior raw data. In this embodiment, a mandatory quality acceptance node is set up during the refractory brick warehousing process, bringing the brick's own quality inspection forward to prevent unqualified bricks from entering the masonry process. Quality inspectors scan the brick's QR code using a handheld terminal, and according to the "Coke Oven Masonry Quality Acceptance Standard," inspect the brick's appearance dimensions, edge damage, cracks, etc., and input the data into the system. The system automatically determines the brick grade based on preset rules: Class A (usable on working surfaces): No defects or defects located within the permissible range of non-working surfaces, usable in any part of the furnace; Class B (usable on non-working surfaces): Exhibits appearance defects but is structurally intact and has qualified strength. The system automatically locks it to preset non-working surface masonry units such as resistance walls, furnace roof, and flues. If a worker attempts to use it on working surfaces such as the carbonization chamber, the terminal immediately alarms and prevents it from leaving the warehouse / being used; Class C (prohibited): Exhibits serious defects such as structural damage, dimensional deviations, and through cracks. The system automatically locks and freezes the inventory, prohibits its release, and notifies the purchasing and quality departments for return or exchange. Through this graded control, Class C bricks are completely isolated, and Class B bricks are limited to use in permitted areas, thus maintaining the quality baseline of key areas and avoiding material waste caused by simplistic scrapping. All graded data is linked to the brick ID and its corresponding masonry unit, forming a complete brick quality file.

[0029] The multi-source IoT identification module includes QR code scanning terminals, RFID readers, and IoT positioning devices deployed in physical warehouses and construction sites. The QR code scanning terminals are used to acquire brick identification identifiers by scanning the QR codes on the surface of the refractory bricks during refractory brick outbound processing, batching, and construction. They also acquire physical storage location identifiers by scanning the QR codes on the storage location during outbound processing. The RFID readers are used to automatically acquire identifiers bound to the transfer carrier via electromagnetic induction during refractory brick batching and transfer processes. The RFID tag is used for identification; the IoT positioning device is used to acquire the transient spatial positioning coordinates of the positioning tag in real time when the refractory brick enters the masonry process; the multi-source IoT identification module is used to acquire the above-mentioned identification, physical storage location identification and transient spatial positioning coordinates, and according to the order in which each identification and coordinate is acquired, the brick identification is dynamically associated and matched with the corresponding physical storage location identification and transient spatial positioning coordinates through preset time sequence logic to obtain the original data of the flow entity behavior covering the location and time sequence information of the entire process; The image data acquisition module includes a high-definition camera, the output of which is electrically connected to the multi-source IoT identification module. It is used to acquire on-site images in the panel preparation process and the masonry process. Based on the flow trigger signal output by the QR code scanning terminal or RFID reader in the multi-source IoT identification module, the on-site images are subjected to time-domain cropping and matching to obtain the original data of image entity behavior covering the on-site process form. The mobile multi-terminal interaction module includes a handheld mobile terminal with distributed permissions, which is wirelessly connected to the pre-quality acceptance module, the multi-source IoT identification module, and the image data acquisition module, respectively. It is used to acquire process parameters, process confirmation signals, and inspection results manually input by construction personnel, warehouse personnel, and inspection personnel. According to the job permissions of the logged-in user, the process parameters, process confirmation signals, and inspection results are mapped with the brick identity identifier obtained through the QR code scanning terminal to obtain the original text entity behavior data covering the personnel's job performance status.

[0030] The signal output terminals of the pre-quality acceptance module, the multi-source IoT identification module, the image data acquisition module, and the mobile multi-terminal interaction module are all asynchronously connected to the input terminal of the external data engine, and are used to aggregate and transmit the initial entity behavior raw data, the flowing entity behavior raw data, the image entity behavior raw data, and the text entity behavior raw data to the data engine in real time.

[0031] In one embodiment of the present invention, the masonry unit data engine is the core of the system's background data processing, undertaking the key functions of data collection, status judgment, and logic triggering. It receives raw data transmitted from the full-process data acquisition module in real time. When it receives a data entry (e.g., "Brick A has been scanned out of the warehouse"), the engine immediately locates the masonry unit to which the brick belongs based on the preset "brick ID - masonry unit ID" mapping relationship, and stores the raw data entry as a sub-data entry in the structured data packet of that masonry unit. Simultaneously, the engine automatically drives the status update of the masonry unit according to built-in status transition rules (e.g., "All bricks in this unit have been out of the warehouse" triggers the unit status to transition from "pending out" to "out of the warehouse," and "All bricks in this unit have been completed" triggers the unit status to transition from "under construction" to "completed construction, awaiting inspection"). The status update delay does not exceed 5 seconds, providing data support for subsequent visual management and early warning. More specifically, the data engine includes a data receiving submodule, a mapping and collection submodule, a structured storage submodule, and a state machine driving submodule. The data receiving submodule is used to receive the original data of the entity behavior transmitted by the external data acquisition input layer, and to parse and cache it to obtain multi-source stream data; The mapping and aggregation submodule is connected to the data receiving submodule and is used to acquire the multi-source stream data, determine the virtual masonry unit to which the current data belongs based on the preset mapping relationship between bricks and virtual masonry units, and obtain unit sub-data. The structured storage submodule is connected to the mapping and aggregation submodule and the external data storage module, respectively. It is used to acquire the unit sub-data and append it to the corresponding virtual masonry unit structured data package. The structured storage submodule is configured with a slab design risk assessment mechanism. Before the masonry task package is issued, it calculates the slab design defect risk index of the virtual masonry unit based on the physical property similarity of adjacent brick types and the probability of historical masonry defects in the structured data package. When the risk index exceeds a safety threshold, it triggers the issuance of an interception signal. More specifically, in one embodiment of the invention, the structured storage submodule adopts a dual storage architecture of cloud server and local server to ensure secure data retention and efficient retrieval. The storage period is not less than the coke oven's design service life (typically 25-30 years). This module mainly stores three types of data: first, digital twin model data (including masonry unit models, parametric refractory brick family libraries, coke oven design drawing data, etc.); second, raw data collected throughout the entire process and masonry unit structured data packages; and third, system configuration data (such as user permissions, early warning thresholds, construction plans, report data, etc.). The dual storage architecture avoids the security risks of a single storage mode (local storage prevents data loss during network interruptions, while cloud storage enables simultaneous access from multiple terminals) and enables efficient retrieval based on data access needs, with a retrieval response time of no more than 10 seconds, providing data assurance for quality traceability, data statistical analysis, and management optimization.

[0032] The state machine driving submodule is connected to the structured storage submodule and the external entity and digital model layer, respectively. It is used to acquire data update signals from the structured data packets and automatically drive the multi-state state machine to perform state transitions based on the completeness of the sub-data within the current virtual masonry unit. The state machine driving submodule is configured with a dynamic scheduling mechanism for construction energy efficiency, used to acquire the time sequence of physical bricks during state transitions, calculate the process sequence disorder and construction efficiency profile score of the work team, and dynamically output production organization optimization instructions based on the construction efficiency profile score. Furthermore, in actual implementation, the task package driving and guiding module deeply collaborates with the state machine driving submodule and the external entity and digital model layer in the data engine to transform the top-level production plan into a refined on-site control chain at the bottom level. After receiving the daily masonry plan from the upper-level management system, the task package driving and guiding module decomposes the construction task into "masonry task packages" based on the spatial location and process logic in the daily masonry plan. These packages are then automatically associated and bound to the corresponding virtual masonry units in the digital twin space using a unique spatial logical identification code. During the material requisition guidance phase, warehouse personnel receive specific masonry task packages issued by the task package driving and guiding module through their decentralized warehouse interaction terminal. Based on the attributes of the virtual masonry units associated with the task package, the task package driving and guiding module dynamically renders and displays a spatial guidance diagram on the visualization interface of the decentralized warehouse interaction terminal, showing the specific stacking location and layer number of the required refractory bricks in the physical warehouse. When warehouse personnel perform a physical brick retrieval operation, the decentralized warehouse interaction terminal prompts and compels them to sequentially scan the stacking code on the physical storage location and the QR code on the surface of the target refractory brick. The task package driving and guiding module dynamically acquires the scanned identification information and performs dual matching and verification: first, it verifies whether the brick identification matches the preset brick type and quantity in the current masonry task package; second, it verifies whether the physical storage location identification matches the indicated storage location. After successful verification, the terminal outputs a release prompt, and the external data acquisition input layer transmits the corresponding outbound behavior raw data to the data engine, thereby proactively preventing material source errors such as issuing the wrong bricks from the warehouse. During the masonry guidance implementation phase, on-site masonry workers receive the current masonry task package through a handheld mobile terminal. The task package driving and guiding module, based on the predefined masonry task flow in the digital twin model for the corresponding virtual masonry unit, dynamically displays or lists the standard masonry sequence animation of each physical brick in the current task package in three dimensions on the handheld mobile terminal.Before laying each refractory brick onto the wall, the bricklayer must scan the QR code on the brick's surface using the handheld mobile terminal. The task package driver and guidance module automatically executes a dual error-proofing verification logic based on the acquired transient scan information: the first verification is a unit affiliation verification, which checks whether the currently scanned brick's identifier belongs to the virtual bricklaying unit space corresponding to the current bricklaying task package; the second verification is a process sequence verification, which checks whether the currently scanned brick's identifier is the standard brick in the current preset bricklaying sequence. When both error-proofing verifications pass, the handheld mobile terminal prompts a "Correct, please lay" release instruction. After the worker completes the physical laying of the current refractory brick and clicks confirmation, the real-time timestamp and brick status update signal generated by this action are instantly transmitted to the data engine as the original data of the entity action. After receiving the update signal, the data engine automatically updates the virtual state of the specific brick in the digital twin model to "built" and triggers the visualization and early warning feedback layer to render the three-dimensional geometric entity of the brick as a preset transition color (such as yellow) during the building process. If any verification fails, the handheld mobile terminal immediately pops up an interception alarm prompt and outputs error information (such as "the brick does not belong to the current building unit" or "the building order is incorrect"), while keeping the handheld mobile terminal locked to prevent the next incorrect building operation, fundamentally eliminating human errors and rework losses caused by using the wrong brick or building in the wrong order from the control layer. During this process, the state machine driving submodule continuously acquires and parses the entity behavior time sequence generated by the handheld mobile terminal during the above-mentioned scanning and building process through the data receiving submodule and the mapping and collection submodule. The state machine driving submodule uses the built-in transient temporal entropy scheduling algorithm to dynamically calculate the process temporal entropy (i.e., temporal disorder) and comprehensive construction efficiency profile score of the current work team in the current virtual building unit construction based on the timestamp interval between two adjacent refractory bricks when they are physically placed on the wall. When a work group experiences excessively high time interval dispersion during continuous masonry work, leading to an abnormal increase in the process sequence entropy, or when the actual average masonry time per brick significantly exceeds the benchmark quota, resulting in the construction efficiency profile score falling below the preset safety warning threshold, it indicates a disordered on-site construction rhythm or the presence of hidden conflicts. In this case, without waiting for the final quality acceptance stage, the state machine-driven submodule outputs production organization optimization instructions early, during the transition phase when the current virtual masonry unit is in the "masonry in progress" state. These instructions are then pushed to the on-site foreman or team leader's terminal via the collaborative feedback interface, enabling dynamic fine-tuning of the construction process sequence and efficient collaborative closed-loop management across different positions. The panel design risk assessment mechanism includes: By extracting the external dimensions and material grades of adjacent refractory bricks to calculate geometric and material similarity, and combining this with data on rectification of non-compliance issues during historical construction and renovation processes, the panel design defect risk index is calculated using the following formula. : , In the formula, For the current virtual masonry unit, This represents the total number of physically refractory bricks contained within the unit. and These are the i-th and i-th adjacent blocks in the task package according to the construction priority logic. A block of refractory bricks, The geometric and material similarity is given. The weighting of the historical mis-laying frequency of this brick type combination. This refers to the number of times this type of virtual masonry unit has historically failed to meet standards and required rectification. The system's preset standard masonry work hours, and The preset weight adjustment coefficient, and satisfies .

[0033] The construction energy efficiency dynamic scheduling mechanism obtains the construction time interval between two adjacent bricks by capturing the real-time timestamp sequence of the scanned brick identification identifiers. Dynamically calculate the process sequence entropy, which reflects the disorder of the process sequence. The calculation formula is as follows: , In the formula, For the current virtual masonry unit, For the virtual masonry unit The total number of physically refractory bricks contained herein, where i is the number of bricks ordered from 1 to 1 according to the construction sequence logic. Increasing construction step sequence loop variable, Let be the construction time interval between two adjacent refractory bricks during the i-th step of construction, and j be the traversal variable used to sum the total working hours within the unit. Let be the time interval between the construction of two adjacent refractory bricks during step j. Let be the probability distribution projection of the masonry time for step i in the total actual construction time.

[0034] In one embodiment of the present invention, before the multi-state machine jumps to the process inspection state, the process timing entropy is used as a reference. Average actual masonry time per brick Compared with the standard single-brick masonry work hour quota Energy efficiency mapping was performed to calculate the comprehensive construction efficiency profile score of the work team. The calculation formula is as follows: In the formula, The calculated process timing entropy, To create a comprehensive performance profile and score for the current work team. The standard temporal entropy benchmark value for the optimal skilled worker. This refers to the average actual bricklaying time per brick in this virtual masonry unit, as recorded by the multi-source IoT identification module. This is a pre-set standard single-brick masonry work hour quota. and This is the preset sensitivity penalty coefficient.

[0035] The visualization and early warning feedback layer of this invention includes a dynamic visualization and early warning module, which establishes a real-time monitoring connection with the state machine driving submodule in the data engine. When the state machine driving submodule outputs a state transition signal for a specific virtual masonry unit, the dynamic visualization and early warning module dynamically acquires the state transition signal and, relying on the three-dimensional geometry engine of the digital twin model, performs real-time coloring and color-separated highlight rendering of the surface material of the virtual masonry unit in the graphics rendering interface. In specific implementation, the system presets a state-color mapping matrix, which strongly correlates the static or transitional state of the finite state machine with the RGB color space: when the acquired state is "out of stock," it is rendered as purple; when the state is "under construction," it is rendered as yellow; when the state is "completed," it is rendered as light blue; when the state is "qualified," it is rendered as green; and when the state is "unqualified," it is rendered as red. Through this color-separated dynamic rendering mechanism, the real-time construction progress and process quality status of each area of ​​the entire furnace are intuitively presented in the three-dimensional visualization interface.

[0036] In this embodiment, to achieve refined control over on-site construction progress and early intervention in quality risks, the dynamic visualization and early warning module not only passively receives inspection results but also incorporates a dynamic deterioration assessment algorithm for progress lag. The system digitally and quantitatively monitors the real-time turnover efficiency of each virtual masonry unit based on the standard construction period input from the external production plan. When a specific virtual masonry unit... When a time delay occurs during the process, the dynamic visualization and early warning module dynamically calculates the severe lag index of the unit's progress using the following formula. : In the formula, For the current virtual masonry unit, The calculated severe delay index is used to determine the progress. The current IoT network standard timestamp for the system. This virtual masonry unit is pre-set for the production plan. The absolute deadline timestamp for when the work should be completed and the status should be changed to "qualified". m is the loop variable of the process stages that the unit has gone through so far, with a value range from 1 to P, where P is the total number of processes that have been completed (such as outbound, masonry, pending inspection, etc.). This refers to the actual time consumed by this unit in the m-th process, as recorded by the full-process data acquisition module. This refers to the standard time limit for the m-th process preset for this type of coke oven. and These are preset time scale adjustment coefficients and process delay penalty factors.

[0037] When the dynamic visualization and early warning module calculates the severe delay index When the system's preset dynamic time threshold is exceeded (corresponding to a physical time lag of more than 24 hours), or when a process inspection failure signal or quality parameter exceeding the standard is obtained from the inspection personnel, the module immediately renders a red flashing alarm for the virtual masonry unit on the digital twin 3D interactive interface. Simultaneously, the early warning mechanism triggers multi-level alarm prompts, including audible and visual alerts, pop-ups, and SMS messages, on the terminals of managers at all levels based on the dynamic risk level. At the same time as the alarm is triggered, the dynamic visualization and early warning module sends a data extraction request to the external structured storage submodule to instantly acquire the virtual masonry unit. The system generates a full lifecycle structured data package, which includes warehousing and acceptance records, brick quality grades, outbound records, panel matching data, process images, construction personnel information, and reasons for non-compliance. This data package is then pushed to the corresponding site foremen, technical departments, or procurement management-related responsible terminals in a targeted and rapid manner, ensuring that on-site construction deviations and process defects can be accurately located and collaboratively addressed within minutes.

[0038] In another embodiment of the present invention, the visualization and early warning feedback layer further includes a user interaction module, which implements a weighted matrix and environmental adaptive control. In actual implementation, the user interaction module serves as the business logic shell for human-computer interaction and is configured with a multi-position, multi-dimensional access control weighted matrix. The system supports login from multiple devices, including computers, mobile handheld terminals, and on-site large screens, and establishes a high-concurrency data retrieval channel through a dual cloud and local storage architecture. Based on the logged-in user's identity, the user interaction module automatically classifies and assigns them to at least one specific position among management personnel, construction personnel, inspection personnel, and warehouse personnel, and dynamically configures the corresponding operation permissions and interface function views based on the weighted matrix. The specific implementation arrangement is as follows: When a manager logs into the system, the user interaction module activates the advanced global control view. The manager interface generates a macro-level progress report for the entire project, supplier quality profiles, and multi-dimensional statistical charts of process defects by calling the data collection interface of the structured storage submodule. Simultaneously, it opens alarm information unlocking interfaces and construction plan optimization and adjustment interfaces, allowing managers to unlock frozen team tasks or reissue revised production plans after completing collaborative closed-loop rectification. When a warehouse worker logs into the system, the user interaction module dynamically switches to a warehousing and logistics guidance view. The warehouse worker interface only opens the material requisition task package receiving interface and material data entry function. In this view, the system displays a spatial stacking layer diagram of the refractory bricks required for the current task package in the physical brick warehouse. When warehouse workers use handheld terminals to scan the stacking code and brick QR code, the system presents the matching verification results in real time, guiding warehouse workers to complete the brick outbound scanning and inventory freeze entry in a standardized manner. When a construction worker logs into the system, the user interaction module switches to a site masonry error prevention guidance view. The construction worker interface opens the masonry task package receiving and single brick status confirmation interfaces. The interface dynamically plays an animation of the bricklaying sequence within the current virtual masonry unit. After workers complete each step of the scanning guidance and double verification, a quick interactive control is provided to update the construction status, assisting in standardized operations. When an inspector logs into the system, the user interaction module activates the quality closed-loop data entry view. The inspector interface provides digital forms for process quality acceptance indicators (including brick joint thickness, wall verticality, flatness, and mortar fullness), and sets a deterministic single-selection trigger button for "qualified / unqualified". When "unqualified" is selected, the interface forcibly pops up a drop-down box for reasons for non-compliance (including masonry process issues, panel design issues, operational compliance issues, and issues where the quality of the bricks themselves was missed). Inspectors can submit the data entry results with one click, triggering the entire system's quality collaborative closed-loop process in real time.

[0039] To ensure the high reliability of the aforementioned weighted view in the extremely harsh environment of the coke oven masonry construction site, the handheld terminal interface of the user interaction module incorporates an ambient light intensity adaptive dynamic gain mechanism and an anti-accidental touch pixel expansion control algorithm. When the photosensitive sensor in the multi-source IoT identification module detects that the external ambient light intensity exceeds a preset strong light threshold, the user interaction module dynamically adjusts the screen backlight brightness, chromaticity gain, and touch hotspot area of ​​the interface using the following formula: In the formula, The system calculates and outputs the adaptive backlight output power of the visual screen of the handheld terminal at the site. The preset base backlight power of the terminal, The data acquisition input layer obtains the transient ambient light intensity of the construction site in real time. The reference illumination constant triggered by a strong light environment. This is the backlight brightness gain adjustment coefficient. This refers to the adaptive control area radius mapped onto the capacitive touchscreen for any touchable graphical control (such as "Outbound Confirmation", "Construction Completed", or "Submit Quality Inspection Results") on the current hierarchical interface. The physical pixel radius of controls in a standard software interface design. This is a binary configuration parameter. The value is 1 when the user selects "Wear Heavy Gloves Mode" in the system settings after logging in, and 0 when it is not selected. This refers to the absolute value of the mechanical vibration rate of the terminal's physical behavior, captured in real time by the three-axis gyroscope inside the handheld mobile terminal, caused by vibrations from high dust levels and complex high-altitude operations. The maximum permissible construction vibration threshold is preset for the system. To prevent accidental touches, the pixel thermal expansion coefficient is set.

[0040] This system adopts a modular design, with each module capable of independent debugging and flexible adaptation. It can meet the construction management needs of different types of coke ovens and can also be extended to construction management scenarios of other high-temperature industrial furnaces such as blast furnaces and rotary kilns, thus expanding the application scope. The system supports integration with existing ERP and MES systems of coking enterprises to achieve data interconnection and avoid duplicate data entry.

[0041] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A full-process intelligent management system for coke oven refractory brick masonry based on digital twinning, characterized in that, include: The entity and digital model layer is used to acquire coke oven design data. Based on the coke oven design data, virtual masonry units are divided according to the minimum independent construction logic. Each virtual masonry unit is mapped and bound to a set of physical brick IDs to construct a digital twin model. Each virtual masonry unit is assigned a multi-state machine. The data acquisition input layer includes multiple types of IoT acquisition terminals, which are used to acquire raw data of physical behavior at each stage in the entire life cycle of refractory bricks and transmit the raw data of physical behavior to the data engine in real time. The data engine is used to receive the original data of the entity behavior, and automatically collect the original data of the entity behavior as sub-data into the corresponding virtual masonry unit structured data packet according to the preset mapping relationship between bricks and virtual masonry units; at the same time, according to the built-in state transition rules and the completeness of the sub-data, it automatically drives the multi-state machine of the corresponding virtual masonry unit to perform state transition. The process control and dynamic collaboration layer is used to acquire production plans, decompose construction tasks into masonry task packages based on virtual masonry units, and dynamically distribute them. During outbound and masonry operations, it acquires real-time scanned brick identification information, performs dual verification of the brick identification information's attribution and order based on the preset attributes and current priority logic of the masonry task packages, and outputs release or interception commands based on the verification results. The visualization and early warning feedback layer is used to monitor the state machine transition signals output by the data engine, and to perform state color-coded highlight rendering of the corresponding virtual masonry unit in the digital twin model according to the state machine transition signals; and when an abnormal state signal or a process inspection failure signal is obtained, multi-level early warning is triggered according to the preset alarm threshold, and the structured data packet of the virtual masonry unit is pushed to the associated collaborative responsibility terminal.

2. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 1, characterized in that, The entity and digital model layer specifically includes: The parametric geometric model module is used to acquire the input coke oven design drawings and refractory brick process parameters, and to perform spatial geometric analysis based on the coke oven design drawings and refractory brick process parameters to obtain a three-dimensional solid geometric base that accurately maps the solid coke oven to a 1:1 scale and a parametric refractory brick family library. The virtual unit logic partitioning module and the parameterized geometric model module are used to acquire the spatial geometric data of the three-dimensional solid geometric base. According to the preset construction organization design and the minimum independently constructable structural logic, the spatial geometric data is divided into groups to obtain multiple virtual masonry units with independent spatial boundaries and unique logical identification codes. The data link mapping module is connected to the parameterized geometric model module and the virtual unit logical division module respectively. It is used to obtain the spatial coordinate range of each virtual masonry unit, automatically traverse and extract the three-dimensional design coordinates and identity attributes of each physical refractory brick contained in the spatial coordinate range, statically construct a one-to-one correspondence mapping table between "virtual masonry unit ID" and "list of all brick IDs contained", and store the corresponding mapping table in the static mapping relationship library to realize the static binding between virtual space and physical entity; The state attribute injection module, which is connected to the virtual unit logic partitioning module, is used to obtain initial configuration parameters and dynamically attach a finite state machine with multi-state transition logic to the attribute list of each virtual masonry unit according to the initial configuration parameters.

3. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 2, characterized in that, The structural logic based on the preset construction organization design and minimum independently constructable structure includes: By analyzing the spatial three-dimensional geometric data of the coke oven BIM model, the spatial logical dividing boundary of each virtual masonry unit is automatically calculated. The specific calculation formula is as follows: ， In the formula, Let k be the current virtual masonry unit according to the overall construction sequence, and satisfy the following conditions: K represents the total number of virtual masonry units divided into the entire furnace. For the virtual masonry unit The set of three-dimensional coordinate points in the digital twin model, (x, y, z) represents the three-dimensional axial coordinate values ​​in the absolute coordinate system of the coke oven BIM. and These are the minimum and maximum boundary coordinates of the element along the furnace length direction, respectively. and These are the minimum and maximum boundary coordinates of the unit in the furnace width direction, respectively. and These are the minimum and maximum boundary coordinates of the unit in the furnace height direction, respectively.

4. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 2, characterized in that, The static mapping relationship library opens a matching interface to the external data engine. When the data engine obtains the original data of the behavior of the on-site entity, it determines the behavior ownership according to the corresponding mapping table and sends a status change signal to the status attribute injection component. The status attribute injection component opens a rendering interface to the external visualization layer. When the visualization layer obtains the status change signal, it performs status color-coded highlight rendering of the corresponding virtual masonry unit in the digital twin 3D interface according to the current running status.

5. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 1, characterized in that, The data acquisition and input layer includes a pre-processing quality acceptance module, a multi-source IoT identification module, an image data acquisition module, and a mobile multi-terminal interaction module, wherein... The pre-quality acceptance module is used to acquire the self-quality inspection data input during the refractory brick warehousing process, and to make compliance judgment on the self-quality inspection data according to the preset quality acceptance standards. It obtains the brick quality grade identifier that is classified as Class A working surface usable, Class B non-working surface usable, or Class C prohibited, and binds and encapsulates the brick quality grade identifier with the corresponding brick body identity identifier to generate the initial entity behavior raw data. The multi-source IoT identification module includes QR code scanning terminals, RFID readers, and IoT positioning devices deployed in physical warehouses and construction sites. The QR code scanning terminals are used to acquire brick identification identifiers by scanning the QR codes on the surface of the refractory bricks during refractory brick outbound processing, batching, and construction. They also acquire physical storage location identifiers by scanning the QR codes on the storage location during outbound processing. The RFID readers are used to automatically acquire identifiers bound to the transfer carrier via electromagnetic induction during refractory brick batching and transfer processes. The RFID tag is used for identification; the IoT positioning device is used to acquire the transient spatial positioning coordinates of the positioning tag in real time when the refractory brick enters the masonry process; the multi-source IoT identification module is used to acquire the above-mentioned identification, physical storage location identification and transient spatial positioning coordinates, and according to the order in which each identification and coordinate is acquired, the brick identification is dynamically associated and matched with the corresponding physical storage location identification and transient spatial positioning coordinates through preset time sequence logic to obtain the original data of the flow entity behavior covering the location and time sequence information of the entire process; The image data acquisition module includes a high-definition camera, the output of which is electrically connected to the multi-source IoT identification module. It is used to acquire on-site images in the panel preparation process and the masonry process. Based on the flow trigger signal output by the QR code scanning terminal or RFID reader in the multi-source IoT identification module, the on-site images are subjected to time-domain cropping and matching to obtain the original data of image entity behavior covering the on-site process form. The mobile multi-terminal interaction module includes a handheld mobile terminal with distributed permissions, which is wirelessly connected to the pre-quality acceptance module, the multi-source IoT identification module, and the image data acquisition module, respectively. It is used to acquire process parameters, process confirmation signals, and inspection results manually input by construction personnel, warehouse personnel, and inspection personnel. According to the job permissions of the logged-in user, the process parameters, process confirmation signals, and inspection results are mapped with the brick identity identifier obtained through the QR code scanning terminal to obtain the original text entity behavior data covering the personnel's job performance status.

6. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 5, characterized in that, The signal output terminals of the pre-quality acceptance module, the multi-source IoT identification module, the image data acquisition module, and the mobile multi-terminal interaction module are all asynchronously connected to the input terminal of the external data engine, and are used to aggregate and transmit the initial entity behavior raw data, the flowing entity behavior raw data, the image entity behavior raw data, and the text entity behavior raw data to the data engine in real time.

7. A fully intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in any one of claims 1-6, characterized in that, The data engine includes a data receiving submodule, a mapping and aggregation submodule, a structured storage submodule, and a state machine driving submodule; The data receiving submodule is used to receive the original data of the entity behavior transmitted by the external data acquisition input layer, and to parse and cache it to obtain multi-source stream data; The mapping and aggregation submodule is connected to the data receiving submodule and is used to acquire the multi-source stream data, determine the virtual masonry unit to which the current data belongs based on the preset mapping relationship between bricks and virtual masonry units, and obtain unit sub-data. The structured storage submodule is connected to the mapping and collection submodule and the external data storage module, respectively. It is used to acquire the unit subdata and append it to the corresponding virtual masonry unit structured data packet. The structured storage submodule is configured with a panel design risk assessment mechanism, which is used to calculate the panel design defect risk index of the virtual masonry unit based on the physical property similarity of adjacent brick types and the probability of historical masonry defects before the masonry task package is issued, and to trigger the issuance of an interception signal when the risk index exceeds the safety threshold. The state machine driving submodule is connected to the structured storage submodule and the external entity and digital model layer, respectively. It is used to obtain the data update signal of the structured data packet, and automatically drive the multi-state state machine to perform state transitions based on the completeness of the sub-data in the current virtual masonry unit. The state machine driving submodule is configured with a construction energy efficiency dynamic scheduling mechanism, which is used to obtain the entity behavior time sequence of physical bricks in the state transition process, calculate the process sequence disorder degree and construction efficiency profile score of the work team, and dynamically output production organization optimization instructions based on the construction efficiency profile score.

8. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 7, characterized in that, The panel design risk assessment mechanism includes: By extracting the external dimensions and material grades of adjacent refractory bricks to calculate geometric and material similarity, and combining this with data on rectification of non-compliance issues during historical construction and renovation processes, the panel design defect risk index is calculated using the following formula. : ， In the formula, For the current virtual masonry unit, This represents the total number of physically refractory bricks contained within the unit. and These are the i-th and i-th adjacent blocks in the task package according to the construction priority logic. A block of refractory bricks, The geometric and material similarity is given. The weighting of the historical mis-laying frequency of this brick type combination. This refers to the number of times this type of virtual masonry unit has historically failed to meet standards and required rectification. The system's preset standard masonry work hours, and The preset weight adjustment coefficient, and satisfies .

9. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 8, characterized in that, The construction energy efficiency dynamic scheduling mechanism obtains the construction time interval between two adjacent bricks by capturing the real-time timestamp sequence of the scanned brick identification identifiers. Dynamically calculate the process sequence entropy, which reflects the disorder of the process sequence. The calculation formula is as follows: , ， In the formula, For the current virtual masonry unit, For the virtual masonry unit The total number of physically refractory bricks contained herein, where i is the number of bricks ordered from 1 to 1 according to the construction sequence logic. An incremental construction step sequence loop variable. Let be the construction time interval between two adjacent refractory bricks during the i-th step of construction, and j be the traversal variable used to sum the total working hours within the unit. Let be the time interval between the construction of two adjacent refractory bricks during step j. Let be the probability distribution projection of the masonry time for step i in the total actual construction time.

10. The intelligent management system for the entire process of coke oven refractory bricklaying based on digital twins as described in claim 9, characterized in that, Before the multi-state machine transitions to the process inspection state, the process timing entropy is used as a reference. Average actual masonry time per brick Compared with the standard single-brick masonry work hour quota Energy efficiency mapping was performed to calculate the comprehensive construction efficiency profile score of the work team. The calculation formula is as follows: ， In the formula, The calculated process timing entropy, To create a comprehensive performance profile and score for the current work team. The standard temporal entropy benchmark value for the optimal skilled worker. This refers to the average actual bricklaying time per brick in this virtual masonry unit, as recorded by the multi-source IoT identification module. This is a pre-set standard single-brick masonry work hour quota. and This is the preset sensitivity penalty coefficient.

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