Tunnel cavern slag number intelligent manufacturing system and tunnel cavern slag number intelligent manufacturing method

Abstract

The invention discloses a tunnel cavern slag number intelligent manufacturing system and a tunnel cavern slag number intelligent manufacturing method. The system comprises a mobile main frame, a raw material intelligent control unit, an edge AI controller, a stirring main machine, a micro-fog cleaning and wastewater recycling system, a cloud chain traceability platform and a mobile interaction terminal. The raw material intelligent control unit senses aggregate components and water content in real time; and the edge AI controller dynamically optimizes the proportion according to the sensing data and the production target, and controls the step-by-step stirring process: firstly, putting part of the aggregate, all the cementing materials, part of the water and the REV cryogel polymer to carry out nano material pre-dispersion, and then adding the rest of the aggregate and CO2 to carry out mineralization reaction. The system realizes on-site recycling of hole slag, zero discharge of production wastewater and whole-process data chain tracing. The method solves the problems of quality fluctuation and high carbon emission in waste hole slag utilization, and is suitable for low-carbon intelligent construction of linear projects such as tunnels.

Description

Technical Field

[0001] This invention relates to the field of civil engineering building material preparation technology, specifically to a digital intelligent manufacturing system and method for tunnel slag. Background Technology

[0002] During the construction of linear or point-based projects such as tunnels, mines, and underground spaces, a large amount of rock debris, known as "caving debris," is generated. Among these, soft rock caving debris, such as slate, phyllite, gneiss, and argillaceous shale, has poor physical and mechanical properties, complex and variable composition, and is prone to softening and turning into mud when exposed to water. Traditionally, it is difficult to use directly as aggregate in concrete preparation and is often treated as solid waste for dumping or landfilling. This practice not only occupies a large amount of land resources and poses safety hazards such as slope instability, but also causes continuous damage to the surrounding soil, water bodies, and other ecological environments, violating the principles of green and sustainable engineering construction.

[0003] At the same time, traditional concrete mixing plants have many inherent shortcomings in addressing the high-value utilization of such waste resources:

[0004] 1. Its fixed layout contradicts the mobile construction requirements of linear projects such as tunnels and railways, resulting in the need for long-distance transportation of finished concrete products or raw materials, which leads to high costs and carbon emissions.

[0005] 2. The level of intelligence is low, making it impossible to optimize the proportions in real time and dynamically based on waste aggregates with drastic fluctuations in composition, resulting in unstable product quality;

[0006] 3. The lack of active activation and enhancement technologies for low-quality aggregates makes it difficult to overcome the bottlenecks in the strength and durability of concrete;

[0007] 4. The production process itself has high carbon emissions and lacks proactive carbon capture and fixation methods;

[0008] 5. The resource recycling rate is low, and wastewater from the mixing plant cleaning process has not been treated and reused in a closed loop.

[0009] Therefore, there is an urgent need for an on-site intelligent manufacturing equipment system that integrates rapid mobile deployment, deep raw material perception and intelligent decision-making, performance improvement of low-quality aggregates, low-carbon production process, and full-process recycling of resources, so as to realize on-site material sourcing, on-site conversion, and on-site use, and fundamentally solve the contradiction between resource waste, environmental pressure, and engineering quality control in the resource utilization of engineering waste. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the prior art and provide a system and method for intelligent manufacturing of tunnel slag.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A smart manufacturing system for tunnel slag includes: a mobile main frame, a raw material intelligent control unit, an edge AI controller, a mixing host, a micro-mist cleaning and wastewater reuse system, a cloud chain traceability platform, and a mobile interactive terminal;

[0013] The raw material intelligent control unit, edge AI controller, mixing host, and micro-mist cleaning and wastewater reuse system are integrated into the mobile host frame;

[0014] The raw material intelligent control unit is communicatively connected to the edge AI controller;

[0015] The edge AI controller is communicatively connected to the mixing host, the micro-mist cleaning and wastewater reuse system, the cloud chain traceability platform, and the mobile interactive terminal.

[0016] The intelligent manufacturing system is used to prepare low-carbon concrete on-site from tunnel rock debris.

[0017] Preferably, the raw material intelligent control unit includes: a magnetized water treatment device, a carbon dioxide mineralization utilization system, a REV crystal polymer storage and metering system, and an online sensing subsystem;

[0018] The online sensing subsystem is communicatively connected to the edge AI controller and is used to collect data on the mineral composition and moisture content of the aggregate in real time.

[0019] The magnetized water treatment device, the carbon dioxide mineralization utilization system, and the REV crystal polymer storage and metering system are respectively connected to the edge AI controller and are controlled to supply corresponding materials to the stirring host.

[0020] Preferably, the online sensing subsystem includes a near-infrared spectroscopy analyzer installed on the aggregate conveying device;

[0021] The near-infrared spectroscopy analyzer is used to perform real-time scanning analysis on the aggregate during transportation to obtain its mineral composition and moisture content data.

[0022] Preferably, the carbon dioxide mineralization and utilization system includes a liquid CO2 storage tank, a gasification device, and a precision injection device;

[0023] The precision injection device is installed in the stirring chamber of the stirring host and is used to inject CO2 in the form of microbubbles into the stirring process.

[0024] Preferably, the REV crystal polymer stored in the REV crystal polymer storage and metering system contains graphene dispersion and rare earth nano-modified components.

[0025] The REV crystal polymer is added to the system at a rate of 1% to 1.5% of the total mass of the cementitious material.

[0026] Preferably, the edge AI controller has a built-in multi-objective optimization AI model;

[0027] The AI ​​model is trained based on historical production data, material test data, and numerical simulation data, and is used to dynamically optimize the concrete mix proportion based on real-time perceived aggregate characteristic data and production targets.

[0028] Preferably, the edge AI controller is configured to control the mixing host to perform a step-by-step feeding and mixing process, which includes:

[0029] The material feeding system is controlled to feed in the first part of aggregate, all cementitious materials, the first part of mixing water and all REV crystal polymer, and the mixing host is controlled to stir for a first set time.

[0030] The material feeding system is controlled to feed in the remaining aggregate and CO2-containing components, and the mixing host is controlled to continue mixing for a second set time.

[0031] Preferably, the first set time is 50-70 seconds, and the second set time is 60-90 seconds.

[0032] Secondly, this application provides a method for producing low-carbon concrete using the above-described system, comprising the following steps:

[0033] S1. Transport the system to the construction site for deployment and connect it to external supplies;

[0034] S2. Transport the tunnel slag aggregate to the system, and obtain the mineral composition and moisture content characteristics data of the aggregate in real time through the online sensing subsystem;

[0035] S3. The edge AI controller runs an optimization model based on real-time sensing data and input production targets, and dynamically generates the optimal blending ratio including the amount of REV polymer and CO2.

[0036] S4. Control the mixing host to perform a step-by-step feeding and mixing process, including first feeding in the first part of aggregate, all cementitious materials, the first part of mixing water and all REV polymer for the first stage of mixing, and then feeding in the remaining aggregate and CO2-containing components for the second stage of mixing.

[0037] S5. After the concrete mixing is completed, unload the concrete and start the micro-mist cleaning and wastewater reuse system;

[0038] S6. Upload all key data throughout the process to the cloud chain traceability platform to generate a blockchain carbon footprint traceability file for this batch of concrete.

[0039] Preferably, the stepwise feeding and mixing process is as follows:

[0040] First, add 50% of the aggregate, all the cementitious materials, 50% of the mixing water and all the REV polymer to the mixing host and stir for 50-70 seconds. Then add the remaining 50% of the aggregate and the CO2-containing components and continue stirring for 60-90 seconds.

[0041] Compared with the prior art, the present invention has the following significant advantages:

[0042] 1. Significant performance improvement: Through the dual synergistic mechanism of nanoscale composite reinforcement of REV crystal polymer (i.e., graphene toughening and rare earth catalysis to optimize microstructure) and pore filling by CO2 mineralization, the performance of cement matrix and the interfacial transition zone between matrix and low-quality cavitary aggregate are fundamentally improved, making it possible to stably prepare high-toughness and high-durability concrete with C30-C50 or even higher strength grades using soft rock cavitary aggregate.

[0043] 2. Intelligent and Stable Quality: Pioneering a "deep online perception + edge AI dynamic optimization" model, this upgrades concrete production from a traditional model relying on experience and fixed formulas to a data-driven, real-time adaptive intelligent model that adapts to raw material fluctuations. The AI ​​model, based on multi-source historical and real-time data, ensures that product quality maintains extremely high stability and consistency even when faced with complex and ever-changing waste aggregates.

[0044] 3. Outstanding green and low-carbon benefits: It achieves 100% resource utilization and high-value utilization of engineering waste slag; by integrating CO2 mineralization technology, it turns waste into treasure, converting greenhouse gases into effective components of concrete and achieving active carbon sequestration; the closed-loop treatment of production wastewater achieves zero discharge; mobile on-site production greatly reduces carbon emissions from long-distance transportation of finished concrete products, and the overall carbon footprint is far lower than that of the traditional model.

[0045] 4. Flexible deployment and cost-effectiveness: The modular and vehicle-mounted design allows the system to be flexibly moved around like a "mobile factory" to follow the progress of the project. It is particularly suitable for linear projects such as tunnels, railways, and water conservancy projects, which significantly reduces material transportation costs and site construction costs, and improves overall economic benefits.

[0046] 5. Transparent and Trustworthy Process Traceability: Based on digital twin and blockchain technologies, the entire production process is digitized, visualized, and made trustworthy. The tamper-proof carbon footprint traceability record provides solid data support for green building evaluation, low-carbon product certification, and future participation in carbon market trading, enhancing the product's market competitiveness and environmental credibility. Attached Figure Description

[0047] Figure 1 This is a modular composition diagram of a tunnel slag intelligent manufacturing system according to the present invention.

[0048] Figure 2This is a flowchart of the intelligent manufacturing method for tunnel slag data in this invention.

[0049] Figure 3 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present disclosure.

[0050] Figure 4 This schematic diagram illustrates the composition of a storage medium in an exemplary embodiment of the present disclosure. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Furthermore, in this invention, an element referred to as fixed to or disposed on another element may be directly disposed on the other element, or there may be an intermediate element. When an element is considered to be connected to another element, it may be directly connected to the other element, or there may be an intermediate element present simultaneously. The terms vertical, horizontal, left, right, and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0053] Example 1

[0054] Combination Figures 1-4 This invention aims to overcome the aforementioned deficiencies of existing technologies and provide a highly integrated, modular, intelligent, and green mobile intelligent manufacturing center system and corresponding production method. The core objective of this invention is to systematically solve key problems encountered in the preparation of concrete using soft rock slag, such as weak aggregate-slurry interface bonding, unstable product quality, low production efficiency, and high carbon emissions, by integrating advanced materials science nano-modification, CO2 mineralization, intelligent sensing and control technologies (AI algorithms), edge computing, and modular equipment design.

[0055] To achieve the above-mentioned objectives, such as Figure 1 As shown: This invention adopts the following technical solution: a tunnel slag intelligent manufacturing system and method; the system is a complex mechatronics system integrating physical equipment, intelligent control, materials technology and digital platform, specifically including the following seven core functional units:

[0056] Mobile Main Unit 110: This unit serves as the physical carrier and integration foundation of the entire system. It adopts a standard container modular design or a heavy-duty special trailer chassis integrated design, with all other functional subsystems integrated into it in modular form. This design enables the system to have complete road transport capabilities, allowing for rapid hoisting, assembly, deployment, and relocation on the construction site. It perfectly adapts to the advancing construction needs of linear projects such as tunnels, railways, and water conservancy, forming a mobile intelligent concrete factory.

[0057] Raw Material Intelligent Control Unit 120: This unit is responsible for preprocessing and real-time status sensing of various incoming raw materials, serving as the data source for ensuring product quality and enabling intelligent decision-making. It integrates the following key subsystems:

[0058] Magnetized water treatment device: This device applies a pulsed magnetic field with specific parameters to the mixing water to reduce the surface tension and viscosity of the water, thereby improving its permeability and chemical reactivity, which helps to promote the full hydration of cement particles.

[0059] Carbon dioxide mineralization system: This system includes a liquid carbon dioxide storage tank, a gasification device, and a precision flow control and injection device. Its core function is to directly and uniformly inject carbon dioxide into the concrete mixture in the form of micron-sized bubbles, or to prepare carbonated water for addition. The injected carbon dioxide can react with alkaline substances such as calcium hydroxide in the cement hydration products to generate stable calcium carbonate. This process not only achieves active fixation of greenhouse gases, but the generated calcium carbonate can also effectively fill the micropores inside the concrete, enhancing its density and early strength.

[0060] REV Crystalline Polymer Storage and Metering System: This system is used for storing and precisely metering the core nano-reinforcing material of this invention: REV crystalline polymer. REV crystalline polymer is a composite nano-modifier, which may include, for example, highly stable graphene dispersions and nano-oxide or hydroxide modifiers of rare earth elements such as lanthanum, cerium, and yttrium. Graphene can form a two-dimensional network structure in the cement matrix, effectively bridging microcracks and significantly improving the tensile strength, toughness, and impermeability of concrete; the rare earth nano-components can catalyze the hydration reaction of silicate minerals, optimize the morphology and distribution of hydration products, and refine the pore structure. The typical dosage of this material is 1.0% to 1.5% of the total mass of the cementitious materials.

[0061] Online Sensing Subsystem: This is the data input front-end for realizing AI algorithm sensing. Its core equipment includes a near-infrared spectrometer mounted on the aggregate conveyor belt. This instrument can perform real-time, non-destructive, and rapid scanning of the conveying aggregate flow, analyzing its key mineral components, especially clay mineral content and carbonate content that affect performance, as well as moisture content. In addition, this subsystem also integrates a laser particle shape analyzer, a microwave moisture sensor, a level gauge, and a high-precision electronic belt scale, enabling comprehensive and synchronous acquisition of the physical and chemical properties of the aggregate, as well as real-time inventory data of each material bin.

[0062] Edge AI Controller 130: This unit is the core control component of the system, typically deployed in an industrial-grade edge computing server on-site to achieve millisecond-level rapid response. At its core is a trained multi-objective optimization artificial intelligence model. The model's training data comes from a large-scale historical concrete production database, a material performance test database covering different lithological cavitation muck, and numerical simulation data based on microscopic reaction mechanisms. Through deep learning, the model establishes a complex nonlinear mapping relationship from raw material characteristics, production proportions, and process parameters to various performance indicators of the final product, production costs, and carbon emissions.

[0063] The objective function is to maximize the overall benefit under multivariate constraints, which can be mathematically simplified to: maximizing "the weighted value of performance indicators + carbon sequestration - cost - environmental impact index". Here, the performance indicators are a weighted combination of key indicators such as compressive strength and workability; constraints include upper and lower limits for the amount of each material, the water-cement ratio range, and slump requirements. The weighting coefficients can be flexibly configured according to the priorities of specific engineering projects.

[0064] During operation, the controller receives production targets from operators via mobile interactive terminals and simultaneously acquires real-time aggregate data streams uploaded by the online sensing subsystem. Subsequently, the controller instantly invokes the AI ​​optimization model to solve the problem, outputting not only the current optimal static mix proportion but also dynamically adjusting key parameters such as water consumption and admixture dosage in response to real-time fluctuations in aggregate characteristics, forming a highly robust "sensing-decision-adjustment" closed-loop control. Finally, based on the optimization results, the controller issues sequential and time-controlled precise control commands to each actuator, driving the entire production process.

[0065] Mixer 140: A high-performance mixer employing the differential planetary mixing principle. Its mixing blades revolve around the axis of the mixing tank while simultaneously rotating at high speed, creating a strong three-dimensional composite flow field that generates efficient shearing, convection, and diffusion effects. This powerful mixing effect is crucial for ensuring the uniform dispersion of nano-sized REV crystal polymers in the slurry, preventing the agglomeration of graphene and other nanomaterials, and ensuring the uniform distribution of carbon dioxide microbubbles throughout the mixture, providing sufficient contact opportunities and kinetic conditions for their mineralization reaction with cement minerals.

[0066] Micro-mist Cleaning and Wastewater Reuse System 150: This system is dedicated to achieving cleaner production processes and resource recycling.

[0067] High-pressure micro-mist self-cleaning function: An array of high-pressure micro-mist nozzles is arranged on the inner wall of the mixing host, the unloading hopper, the conveyor belt, and other areas where materials are prone to sticking. Utilizing an ultra-high-pressure pump to generate micron-sized water mist particles, it impacts the adhered materials with extremely low water consumption, achieving highly efficient cleaning. The water consumption is only 10% to 20% of that of traditional water flushing.

[0068] Zero-discharge wastewater treatment function: All equipment cleaning wastewater and site flushing water are collected in the sedimentation tank of the system. The wastewater undergoes a combination of processes including multi-stage physical sedimentation, chemical flocculation, and precision filtration to remove suspended solids, colloidal substances, and some dissolved ions. The purified water meets the standards for reuse in production and is pumped back to the magnetized water treatment device or clean water tank, thereby achieving "zero external discharge" of production wastewater and internal resource recycling within the plant.

[0069] Cloud Chain Traceability Platform 160: This platform is built on cloud servers to achieve digital management and reliable traceability of the entire production process.

[0070] Digital twin: A 3D virtual model built in the cloud, corresponding one-to-one with the physical manufacturing center, is synchronized in real time with the equipment operating status, production data, energy consumption information, etc., through an IoT interface. It supports remote 3D visualization monitoring, equipment fault prediction and health management, energy efficiency analysis and optimization, and automatic generation of production reports for managers.

[0071] Blockchain carbon traceability: Leveraging the distributed storage and immutability of blockchain technology, a unique "digital product ID" is generated for every cubic meter of concrete. From aggregate arrival detection to precise measurement data of all materials, key parameters in the production process, complete records of AI decision-making, laboratory test reports of the final product, and calculated carbon dioxide sequestration, all data is encrypted and stored on the blockchain. This complete traceability file can be verified at any time by authorized parties such as owners, supervisors, and third-party carbon verification agencies, providing a real, transparent, and reliable data foundation for green building material product certification, carbon footprint accounting, and future participation in carbon market trading.

[0072] Mobile Interactive Terminal 170: Through a custom-developed industrial-grade mobile application or web interface, it provides a user-friendly and convenient human-machine interaction window for different roles such as on-site operators and remote project managers. Main functions include: remote equipment start / stop, production task assignment and queuing management, real-time data monitoring throughout the entire process, historical data query and export, real-time push and processing of alarm information, and quick access to product blockchain traceability information by scanning a QR code.

[0073] Example 2: Detailed Description of Production Method and Flow

[0074] like Figure 2 As shown: The method for producing low-carbon concrete using the above-mentioned intelligent manufacturing system is characterized by performing the following steps in sequence:

[0075] S1. Rapid On-Site Deployment. Transport the entire intelligent manufacturing system to the tunnel entrance or other designated area on the construction site, and complete the rapid assembly and mechanical fixing of each module. Connect external power, water, and communication networks, and connect to a liquid carbon dioxide supply source to complete system debugging and put it into standby mode.

[0076] S2. Aggregate Feeding and AI Algorithm Sensing. Pre-crushed and screened tunnel slag aggregate, within the acceptable particle size range, is conveyed into the system's aggregate bins via loading equipment or belt conveyors. During aggregate conveying, the online sensing subsystem automatically activates, and sensors such as near-infrared spectrometers continuously scan the aggregate flow, acquiring key data such as mineral composition, moisture content, and surface characteristics in real time. This high-dimensional sensing data stream is then uploaded to the edge AI controller in real time.

[0077] S3.AI Dynamic Mix Design Generation and Adjustment. Operators input or select production targets for this batch of concrete via a mobile interactive terminal, such as design strength grade, slump requirements, specific durability indicators, and minimum carbon sequestration targets. The edge AI controller immediately activates the optimization algorithm, integrating real-time aggregate characteristic data with the input production targets, and running a multi-objective optimization AI model while considering the current actual inventory levels in each material silo. The model calculates the optimal production mix design adapted to the current aggregate condition within seconds. This mix design is dynamic and includes the type and precise dosage of cementitious materials, the proportion of each aggregate grade, the amount of magnetized water, the precise dosage of REV crystal polymer, the amount of carbon dioxide injected, and adjustments to various admixtures.

[0078] S4. Stepwise mixing and synergistic mineralization enhancement. Based on instructions from the AI ​​controller, the system automatically executes the following stepwise mixing process:

[0079] The first step, pre-dispersion and matrix nano-reinforcement: Approximately 50% of the slag aggregate, all the cementitious materials, approximately 50% of the magnetized water, and all of the REV crystalline polymer are added to a planetary high-intensity mixer. The mixer is started and stirred at a high speed for 50 to 70 seconds, preferably 60 seconds. The purpose of this stage is to utilize the strong shear force generated by planetary mixing in an environment with relatively low solids content to achieve sufficient dispersion and pre-mixing of the nano-components in the REV crystalline polymer at the molecular or nanoscale level within the cementitious slurry, laying the foundation for the subsequent formation of a uniform reinforcing network throughout the concrete.

[0080] The second step, uniform mixing and carbon dioxide mineralization and consolidation: While the mixing host continues to run, the remaining approximately 50% of the slag aggregate is added. Simultaneously, a precise amount of carbon dioxide calculated by the AI ​​model is uniformly injected into the mixing chamber in the form of microbubbles through a precision injection device. Mixing continues for 60 to 90 seconds. This stage completes the final uniform mixing of all solid and liquid materials. At the same time, under the strong stirring action, the carbon dioxide microbubbles fully contact the alkaline slurry, undergoing a mineralization reaction to generate calcium carbonate, achieving simultaneous carbon fixation and microstructure filling and reinforcement.

[0081] S5. Unloading, Construction, and Intelligent Cleaning. After the mixing process is completed, the concrete is unloaded through the unloading device and can be directly used for on-site pouring, pumping, or spraying. During production breaks or at the end of each production task, a high-pressure micro-mist self-cleaning system is activated with a single button to automatically clean key components such as the mixing host and conveyor belt. All wastewater generated is introduced into a recycling system for purification, awaiting reuse.

[0082] S6. Data On-Chain and Full Lifecycle Traceability. From step S2 (aggregate sensing) to step S5 (cleaning), all key data, including sensing data, the final mix design determined by AI, actual measurement data of each material, process parameters such as mixing time and energy consumption, and final output, are automatically synchronized to a cloud-based digital twin and blockchain platform via an encrypted communication channel. This platform generates an immutable blockchain carbon footprint traceability file for each batch or batch of concrete, linked to a unique QR code. This QR code can be affixed to concrete transport vehicles or construction records, allowing owners, supervisors, or quality inspection departments to scan and verify it at any time.

[0083] Example 3

[0084] Implementation scenario: A long tunnel project on a mountain highway. The surrounding rock of the tunnel is mainly silty shale with low strength and easy to turn into mud when exposed to water. A huge amount of caving debris is generated during the construction process, and traditional treatment methods are difficult and costly.

[0085] Implementation steps:

[0086] Center Deployment and Commissioning: The intelligent manufacturing center was transported to a level site near the tunnel entrance in the form of multiple standard container modules. Within one week, the hoisting, assembly, and piping and wiring connections of all functional modules were completed. Mains power was connected, a construction water source was established, a dedicated network was erected, and liquid carbon dioxide storage tanks were installed, with gas supply pipelines connected.

[0087] Task assignment: The tunnel site technician creates a production task order using a dedicated application on an industrial tablet: selects the product type as "C30 shotcrete", sets the performance requirement as slump of 180±20 mm, and specifies that at least 7.5 kg of carbon dioxide is fixed per cubic meter of concrete.

[0088] Aggregate Supply and AI Algorithm Sensing: After the slag from the tunnel is crushed and screened by a jaw crusher and an impact crusher, it yields crushed stone of 5-10 mm and manufactured sand with a fineness modulus of approximately 2.8. The mixed aggregate is conveyed to the silo in the intelligent manufacturing center via a belt conveyor. The online sensing subsystem integrated on the belt conveyor starts working: a near-infrared spectrometer scans the aggregate flow in real time, and the analysis results show that the average moisture content is 4.2% and the clay mineral content is approximately 8%; a laser particle shape analyzer provides synchronous feedback on the particle shape index. These real-time data packets are continuously sent to the edge AI controller.

[0089] AI-powered dynamic blending decision-making and execution:

[0090] Decision-making: After receiving the production target and real-time sensing data, the edge AI controller calls the optimization model to complete the calculation within 0.5 seconds. The model comprehensively considers the current high clay content and moisture content of the aggregate, as well as the set carbon sequestration target, and generates a dynamic optimization ratio instruction, such as: 390 kg of P.O42.5 cement, 40 kg of Class II fly ash, 1700 kg of soft rock mechanical mixed aggregate, 170 kg of total water (all magnetized), 4.8 kg of REV crystal polymer, and 7.5 kg of carbon dioxide injection.

[0091] Execution: The system automatically executes a step-by-step mixing process. Step 1: Precisely measure and add 850 kg of aggregate, all 430 kg of cementitious material, 80 kg of magnetized water, and all 4.8 kg of REV crystal polymer. The planetary mixer operates at high speed for 60 seconds. Step 2: While mixing continues, add the remaining 850 kg of aggregate, simultaneously injecting 7.5 kg of carbon dioxide gas in microbubbles into the mixing vortex through an annular microporous nozzle. Continue mixing for 75 seconds.

[0092] Material Discharge and Construction Application: After mixing, the concrete is discharged into the wet shotcrete machine hopper. The measured concrete slump is 200 mm, with good workability and moderate cohesion, fully meeting the requirements of the wet shotcrete process. After wet shotcrete application for initial tunnel support, observations show that the rebound rate is reduced by approximately 15% compared to the traditional mix proportion.

[0093] Cleaning and Data Archiving & Traceability: After a cumulative production of 120 cubic meters per day, the high-pressure micro-mist self-cleaning system is activated with a single click, completing the main unit cleaning within 15 minutes, and all wastewater is recycled and treated. The cloud platform automatically aggregates all production data for the day, calculates the total carbon sequestration to be 900 kg, and encrypts and writes the complete information package, including original sensing data, mixing execution records for each batch, energy consumption data, and carbon sequestration calculation results, into the blockchain. The platform generates a unique traceability QR code for each truckload of concrete, which is then provided to the construction party for archiving.

[0094] This example demonstrates that the system and method described in this invention successfully transforms waste, low-performance shale silt into qualified concrete that meets engineering requirements, and effectively stabilizes product quality through real-time AI sensing and dynamic adjustment. Simultaneously, it achieves large-scale carbon dioxide fixation that can be accurately measured and authoritatively reported, fully demonstrating its comprehensive advantages of being "intelligent, green, efficient, and flexible" in treating engineering waste resources and promoting the transformation of civil engineering towards low-carbon intelligent manufacturing.

[0095] Example 4

[0096] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0097] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0098] The following reference Figure 3 To describe an electronic device 300 according to this embodiment of the present invention. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0099] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, and a bus 330 connecting different system components (including storage unit 320 and processing unit 310).

[0100] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 310 can perform actions such as... Figure 2 The process is as follows: S1. Transport the system to the construction site for deployment and connect it to external supplies; S2. Transport the tunnel rock slag aggregate to the system and acquire the mineral composition and moisture content characteristics of the aggregate in real time through the online sensing subsystem; S3. The edge AI controller runs an optimization model based on the real-time sensing data and the input production target to dynamically generate the optimal mix proportion including REV polymer and CO2 dosage; S4. Control the mixing host to execute a step-by-step feeding and mixing process, including first adding the first part of aggregate, all cementitious materials, the first part of mixing water and all REV polymer for the first stage of mixing, and then adding the remaining aggregate and CO2-containing components for the second stage of mixing; S5. After the concrete mixing is completed, unload the concrete and start the micro-mist cleaning and wastewater reuse system; S6. Upload the key data of the entire process to the cloud chain traceability platform to generate a blockchain carbon footprint traceability file for this batch of concrete.

[0101] Storage unit 320 may include readable media in the form of volatile storage units, such as random access memory (RAM) 3201 and / or cache memory 3202, and may further include read-only memory (ROM) 3203.

[0102] Storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0103] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0104] Electronic device 300 can also communicate with one or more external devices 200 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 300, and / or with any device that enables electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. As shown, network adapter 360 communicates with other modules of electronic device 300 via bus 330. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0105] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0106] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0107] refer to Figure 4 As shown, a program product 400 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0108] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0109] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0110] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0111] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0112] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0113] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A digital intelligent manufacturing system for tunnel slag, characterized in that, include: Mobile main unit, raw material intelligent control unit, edge AI controller, mixing host, micro-mist cleaning and wastewater reuse system, cloud chain traceability platform and mobile interactive terminal; The raw material intelligent control unit, edge AI controller, mixing host, and micro-mist cleaning and wastewater reuse system are integrated into the mobile host frame; The raw material intelligent control unit is communicatively connected to the edge AI controller; The edge AI controller is communicatively connected to the mixing host, the micro-mist cleaning and wastewater reuse system, the cloud chain traceability platform, and the mobile interactive terminal. The intelligent manufacturing system is used to prepare low-carbon concrete on-site from tunnel rock debris.

2. The system according to claim 1, characterized in that, The raw material intelligent control unit includes: a magnetized water treatment device, a carbon dioxide mineralization utilization system, a REV crystal polymer storage and metering system, and an online sensing subsystem; The online sensing subsystem is communicatively connected to the edge AI controller and is used to collect data on the mineral composition and moisture content of the aggregate in real time. The magnetized water treatment device, the carbon dioxide mineralization utilization system, and the REV crystal polymer storage and metering system are respectively connected to the edge AI controller and are controlled to supply corresponding materials to the stirring host.

3. The system according to claim 2, characterized in that, The online sensing subsystem includes a near-infrared spectroscopy analyzer installed on the aggregate conveying device; The near-infrared spectroscopy analyzer is used to perform real-time scanning analysis on the aggregate during transportation to obtain its mineral composition and moisture content data.

4. The system according to claim 2, characterized in that, The carbon dioxide mineralization and utilization system includes a liquid CO2 storage tank, a gasification device, and a precision injection device; The precision injection device is installed in the stirring chamber of the stirring host and is used to inject CO2 in the form of microbubbles into the stirring process.

5. The system according to claim 2, characterized in that, The REV crystal polymer stored in the REV crystal polymer storage and metering system contains graphene dispersion and rare earth nano-modified components. The REV crystal polymer is added to the system at a rate of 1% to 1.5% of the total mass of the cementitious material.

6. The system according to claim 1, characterized in that, The edge AI controller has a built-in multi-objective optimization AI model; The AI ​​model is trained based on historical production data, material test data, and numerical simulation data, and is used to dynamically optimize the concrete mix proportion based on real-time perceived aggregate characteristic data and production targets.

7. The system according to claim 6, characterized in that, The edge AI controller is configured to control the mixing host to perform a step-by-step feeding and mixing process, which includes: The material feeding system is controlled to feed in the first part of aggregate, all cementitious materials, the first part of mixing water and all REV crystal polymer, and the mixing host is controlled to stir for a first set time. The material feeding system is controlled to feed in the remaining aggregate and CO2-containing components, and the mixing host is controlled to continue mixing for a second set time.

8. The system according to claim 7, characterized in that, The first set time is 50-70 seconds, and the second set time is 60-90 seconds.

9. A method for producing low-carbon concrete using the system according to any one of claims 1-8, characterized in that, Includes the following steps: S1. Transport the system to the construction site for deployment and connect it to external supplies; S2. Transport the tunnel slag aggregate to the system, and obtain the mineral composition and moisture content characteristics data of the aggregate in real time through the online sensing subsystem; S3. The edge AI controller runs an optimization model based on real-time sensing data and input production targets, and dynamically generates the optimal blending ratio including the amount of REV polymer and CO2. S4. Control the mixing host to perform a step-by-step feeding and mixing process, including first feeding in the first part of aggregate, all cementitious materials, the first part of mixing water and all REV polymer for the first stage of mixing, and then feeding in the remaining aggregate and CO2-containing components for the second stage of mixing. S5. After the concrete mixing is completed, unload the concrete and start the micro-mist cleaning and wastewater reuse system; S6. Upload all key data throughout the process to the cloud chain traceability platform to generate a blockchain carbon footprint traceability file for this batch of concrete.

10. The method according to claim 9, characterized in that, The step-by-step feeding and mixing process is specifically as follows: First, add 50% of the aggregate, all the cementitious materials, 50% of the mixing water and all the REV polymer to the mixing host and stir for 50-70 seconds. Then add the remaining 50% of the aggregate and the CO2-containing components and continue stirring for 60-90 seconds.

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