A digital-twin-based shotcrete construction optimization system

Abstract

The application discloses a kind of based on digital twinning's shotcrete construction optimization system, it is related to shotcrete construction optimization technical field, the system includes attached surface information perception equipment, concrete information perception equipment, construction influence information perception equipment and control host computer;The control host computer includes attached surface perception information module, concrete information perception module, construction influence information perception module, digital twinning simulation deduction module and shotcrete PLC control module.The application is based on the full perception shotcrete material property, attached surface property and construction influence information, realizes the optimization goal of shotcrete rebound rate by digital twinning system jet flow simulation deduction, improves the economic applicability of shotcrete construction method.

Description

Technical Field

[0001] This invention relates to the field of shotcrete construction optimization technology, and in particular to a shotcrete construction optimization system based on digital twins. Background Technology

[0002] With the development of the economy and the process of urbanization, traffic pressure has been increasing year by year. Tunnel and foundation pit excavation has been widely used as an important construction solution for urban traffic problems. Because the excavation process will cause the release of ground stress, the exposed surface may sometimes be unstable. In order to prevent the surface from collapsing and being damaged, the construction workers will immediately carry out shotcrete spraying to cover the exposed surface, and carry out subsequent construction work after the surface has stabilized.

[0003] In actual production, the quality of materials arriving at the concrete mixing plant varies from batch to batch, and the quality of the shotcrete produced fluctuates greatly due to factors such as the mud content and particle size distribution of the coarse sand. After arriving at the construction site, the construction quality is also greatly affected by factors such as the varying moisture content, lithology, and inclination angle of the surface, the unstable pressure of the air compressor in the spraying machinery, and the inconsistent quality of the accelerator.

[0004] The aforementioned adverse factors significantly impact the adhesion of shotcrete to the bonding surface, causing a large amount of shotcrete to bounce back to the ground, resulting in substantial resource waste. Traditional construction methods heavily rely on the experience of construction workers to overcome these adverse factors. However, due to limitations in human cognition and learning abilities, workers cannot accurately grasp the precise conditions of materials, bonding surfaces, and construction-influencing factors. Therefore, experience-based construction methods cannot optimize shotcrete production, nor can they accurately provide feedback to the concrete mixing plant regarding concrete mixing improvements based on construction conditions.

[0005] For optimizing shotcrete construction, most existing technologies focus on the concrete material itself, attempting to add various materials and adjust proportions, thus optimizing from a single perspective. Later, some researchers noticed other influencing factors in the construction process and began to optimize construction methods and techniques. However, these technologies are limited to summarizing construction experience within certain on-site construction parameters or under certain concrete materials, restricting and specifying construction parameters, and thus exhibiting idealization and empirical reliance. Summary of the Invention

[0006] The purpose of this invention is to provide a shotcrete construction optimization system based on digital twins. This system not only covers the optimization of concrete materials in traditional techniques, but also incorporates other factors affecting concrete construction to optimize the rebound rate of shotcrete and provide dynamic feedback on the concrete mix proportion. This enables continuous dynamic optimization of shotcrete construction, making it more scientific and flexible.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A digital twin-based shotcrete construction optimization system includes: an adhesion surface information sensing device, a concrete information sensing device, a construction impact information sensing device, and a control host; the control host includes an adhesion surface sensing information module, a concrete information sensing module, a construction impact information sensing module, a digital twin simulation and deduction module, and a shotcrete construction PLC control module.

[0009] The attachment surface sensing information module is used for:

[0010] Based on the adhesion surface sensing information collected by the adhesion surface information sensing device, oblique photogrammetry 3D modeling or 3D point cloud modeling is performed to obtain a 3D model of the adhesion surface; the 3D model of the adhesion surface is used to obtain the real 3D information of the adhesion surface and determine the geometric information of the adhesion surface cross section.

[0011] The lithological and water content information of the three-dimensional model of the adhesion surface in different regions was identified based on the K-nearest neighbor classification algorithm.

[0012] The three-dimensional model of the adhesion surface is divided according to the lithological information and the water content information to obtain different model sections;

[0013] Calculate the difference between the geometric information of the model section and the geometric information of the attachment surface section, and calculate the concrete demand information required for shotcrete construction based on the difference;

[0014] The concrete information sensing module is used to acquire concrete material information collected by the concrete information sensing device;

[0015] The construction impact information sensing module is used to acquire construction impact information collected by the construction impact information sensing device;

[0016] The digital twin simulation and deduction module is used for:

[0017] A digital twin simulation model is constructed based on the information output by the adhesion surface sensing information module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module; the information output by the adhesion surface sensing information module includes lithological information, water content information, geometric information of the adhesion surface cross section, and concrete demand information.

[0018] Based on the aforementioned digital twin simulation model, with the goal of minimizing the rebound rate of shotcrete, a graded combination simulation and deduction test was conducted on the spraying path, spraying air pressure, and accelerator dosage to obtain the optimal spraying path, optimal spraying air pressure, and optimal accelerator dosage.

[0019] The PLC control module for spraying construction is used to automatically spray concrete according to the optimal spraying path, optimal spraying air pressure, and optimal accelerator dosage.

[0020] Optionally, it also includes a model database containing information on shotcrete rebound rate, concrete material, adhesion surface perception, and construction impact.

[0021] Optionally, it also includes: a concrete mix proportion information optimization module, used to predict the optimal concrete mix proportion information based on the model database and the Bi-LSTM algorithm, and to feed back the optimal concrete mix proportion information to the concrete mixing plant.

[0022] Optionally, the surface information sensing device includes a high-definition binocular camera, a 3D laser scanner, and a humidity detector;

[0023] The adhesion surface sensing information includes adhesion surface image information, adhesion surface three-dimensional laser scan information, and adhesion surface humidity information.

[0024] Optionally, the concrete information sensing equipment includes a concrete penetration strength tester, a slump cone, a density tester, a concrete mixer truck status monitoring device, a computer host for a concrete mixing plant, and a computer host for a construction site laboratory.

[0025] The concrete material information includes strength information collected by a concrete penetration tester, slump information collected by a slump cone, density information collected by a density tester, concrete transportation time information collected by concrete mixer truck condition monitoring equipment, concrete mix proportion information collected by the computer host of the concrete mixing plant, and concrete material cost information and concrete material quality information collected by the computer host of the construction site laboratory.

[0026] Optionally, the construction impact information sensing device includes a pressure sensor installed on the air compressor, a quick-setting agent flow meter, a shotcrete equipment computer host, a personnel information management system computer, and a construction site laboratory computer host;

[0027] The construction impact information includes air pressure information collected by pressure sensors, accelerator dosage information collected by accelerator flow meters, spraying machinery information collected by the computer host of the shotcrete equipment, construction personnel information collected by the computer of the personnel information management system, and accelerator quality information collected by the computer host of the site laboratory.

[0028] Optionally, in constructing a digital twin simulation model based on the information output by the adhesion surface sensing information module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module, the digital twin simulation deduction module is used for:

[0029] The information output by the adhesion surface sensing module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module are preprocessed, and a digital twin simulation model is constructed based on the preprocessed information.

[0030] Optionally, the concrete information sensing module is built around the computer host of the concrete mixing plant; the construction impact information sensing module is built around the computer host of the shotcrete equipment; and the shotcrete construction PLC control module is built around the computer host of the shotcrete equipment.

[0031] Optionally, the modules in the control host are connected to the Internet of Things (IoT) system data interface.

[0032] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0033] Currently, shotcrete optimization systems primarily rely on engineering experience, heavily depending on the knowledge and expertise of construction personnel. Related optimization experiments are often based on standard sand and standard concrete, neglecting the quality of actual materials and thus becoming detached from real-world construction. Furthermore, these systems consider only a limited range of influencing factors, focusing primarily on material optimization such as concrete mix proportions and admixtures, with secondary emphasis on construction parameter control. Consequently, their applicability to different projects, regions, and materials is poor, limiting their widespread application and promotion.

[0034] Compared with existing methods, this invention establishes a shotcrete construction optimization system with a digital twin simulation module at its core. It can perform simulations for different surface properties, concrete material information, and actual construction parameters. It features high visibility and strong data support, enabling a global grasp of shotcrete impact information. With the goal of optimizing the shotcrete rebound rate, it optimizes construction methods and provides feedback on the optimized concrete mix proportions, achieving continuous dynamic optimization in the shotcrete construction process and providing strong support for construction personnel. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 The structural flowchart of the control host in the shotcrete construction optimization system based on digital twin provided in the embodiment of the present invention. Detailed Implementation

[0037] 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.

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Terminology Explanation

[0040] Digital twins are simulation processes that integrate multiple disciplines, physical quantities, scales, and probabilities based on data such as physical models, sensor updates, and operational history. They map reality and digital models in virtual space to reflect the entire lifecycle of the corresponding entity.

[0041] Jet: refers to a fluid flow that is ejected from a pipe, orifice, or slit, or driven by a machine, and mixed with the surrounding fluid.

[0042] Shotcrete: A construction method that uses a pressure spray gun to apply fine aggregate concrete, often used for tunnel lining construction. Shotcrete is made by loading pre-mixed cement, sand, gravel, water, and a certain amount of admixtures into a shotcrete machine, using high-pressure air to deliver the mixture to the nozzle and mix it with an accelerator, then spraying it at high speed onto the surface of rock or concrete.

[0043] Bi-LSTM (Bi-directional Long Short-Term Memory): A bidirectional long short-term memory network, it is a type of recurrent neural network mainly used for modeling sequential data.

[0044] The k-Nearest Neighbor (KNN) classification algorithm is a theoretically mature method and one of the simplest machine learning algorithms. The idea behind this method is: in the feature space, if the majority of a sample's k nearest neighbors (i.e., the closest neighbors in the feature space) belong to a certain class, then that sample also belongs to that class.

[0045] Shotcrete rebound rate: The ratio of the mass of shotcrete that rebounds back to the ground after being sprayed onto a substrate without good adhesion to the surface, to the total mass of shotcrete applied. This parameter is mainly used to measure the adhesion and economy of shotcrete. A higher rebound rate means more wasted concrete, poorer adhesion, and lower economic efficiency. The formula is η = (m' / m) × 100%, where m' is the mass of concrete rebounding to the ground, and m is the total mass of shotcrete applied during construction.

[0046] This invention addresses the shortcomings of existing technologies by providing a digital twin-based shotcrete construction optimization system. This system makes shotcrete construction operations and optimization techniques more scientific, rational, rapid, and accurate, reduces the additional costs caused by shotcrete rebound, and promotes the intelligent development of shotcrete optimization and construction.

[0047] like Figure 1 As shown, this embodiment of the invention provides a digital twin-based shotcrete construction optimization system, including: an adhesion surface information sensing device, a concrete information sensing device, a construction impact information sensing device, and a control host; the control host includes an adhesion surface sensing information module, a concrete information sensing module, a construction impact information sensing module, a digital twin simulation and deduction module, and a shotcrete construction PLC control module. The modules in the control host are connected via an Internet of Things (IoT) system data interface.

[0048] The adhesion surface information sensing device consists of a high-definition binocular camera, a 3D laser scanner, a humidity detector, and other equipment. The adhesion surface information sensing device is used to acquire adhesion surface image information, adhesion surface 3D laser scan information, and adhesion surface humidity information. This information will be imported into the adhesion surface sensing information module in the control host to provide model parameter support for the construction of the digital twin model in the digital twin simulation and deduction module.

[0049] The concrete information sensing equipment consists of a concrete penetration strength tester, a slump cone, a density meter, a concrete mixer truck status monitoring device, a computer host at the concrete mixing plant, and a computer host at the construction site laboratory. These devices are connected via an Internet of Things (IoT) system data interface. The equipment acquires strength information from the concrete penetration strength tester, slump information from the slump cone, density information from the density meter, concrete transport time information from the concrete mixer truck status monitoring device, concrete mix proportion information from the concrete mixing plant computer host, and concrete material cost and quality information from the construction site laboratory computer host. This information is then imported into the concrete information sensing module in the control host.

[0050] The construction impact information sensing device consists of a pressure sensor installed on the air compressor, a quick-setting agent flow meter, a shotcrete equipment computer host, a personnel information management system computer, and a site laboratory computer host. The construction impact information sensing device is used to capture air pressure information collected by the pressure sensor, quick-setting agent dosage information collected by the quick-setting agent flow meter, shotcrete equipment computer host information, construction personnel information collected by the personnel information management system computer, and quick-setting agent quality information collected by the site laboratory computer host. This information will be imported into the construction impact information sensing module in the control host.

[0051] The attachment surface sensing information module is used for:

[0052] Based on the adhesion surface sensing information collected by the adhesion surface information sensing device, oblique photogrammetry 3D modeling or 3D point cloud modeling is performed to obtain a 3D model of the adhesion surface; the 3D model of the adhesion surface is used to obtain the real 3D information of the adhesion surface.

[0053] The lithological and water content information of the three-dimensional model of the adhesion surface in different regions is identified based on the K-nearest neighbor classification algorithm; the water content information is the water content at a certain location.

[0054] Based on the lithological information and the water content information, the three-dimensional model of the adhesion surface is divided to obtain different model sections, and surface properties are assigned to different model sections to realize the difference in the adhesion properties of shotcrete to different model sections in the simulated construction site in the digital twin simulation module.

[0055] The difference between the geometric information of the model section and the geometric information of the adhesion surface section is calculated, and the concrete demand information required for shotcrete construction is calculated based on the difference. The concrete demand information is used to control the total amount of simulated shotcreting.

[0056] The concrete information sensing module is built around the computer host of the concrete mixing plant. The concrete information sensing module acquires relevant information that can affect the sprayed concrete construction materials, and captures concrete mix proportion information, slump information that reflects the fluid properties of concrete, material quality information, concrete transportation time information (as the transportation time increases, the concrete will hydrate and harden), etc., to realize the simulation of fluid physical properties in the digital twin simulation and deduction module.

[0057] That is, the concrete information sensing module is used to acquire concrete material information collected by the concrete information sensing device.

[0058] The construction impact information perception module is built around the computer host of the shotcrete equipment. It acquires information such as air pressure of the air compressor, construction personnel information (height, age, weight, etc.), accelerator information and construction machinery information (equipment type, manufacturer, power, etc.) during construction, and uses it to realize the mapping and simulation of construction impact factors in the digital twin simulation and deduction module.

[0059] That is, the construction impact information sensing module is used to acquire the construction impact information collected by the construction impact information sensing device.

[0060] The digital twin simulation module preprocesses the information transmitted from the previous three modules, cleaning, completing, and reducing noise to ensure consistent information input. This module models and reproduces information reflecting surface perception, concrete material properties, and construction impacts, enabling jet simulation optimization. With the goal of minimizing the rebound rate of shotcrete, a graded combination simulation experiment is conducted on the spraying path, spraying pressure, and accelerator dosage. Under the aforementioned defined information, the optimal spraying path, optimal spraying pressure, and optimal accelerator dosage for minimizing the shotcrete rebound rate are determined. If the shotcrete rebound rate is optimal, achieving economic optimization, the optimal spraying path, optimal spraying pressure, and optimal accelerator dosage are imported into the shotcrete construction PLC control module. The air pressure valve control information corresponds to the optimal spraying pressure, which is achieved through valve adjustment.

[0061] That is, the digital twin simulation and deduction module is used for:

[0062] A digital twin simulation model is constructed based on the information output by the adhesion surface sensing information module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module; the information output by the adhesion surface sensing information module includes lithological information, water content information, geometric information of the adhesion surface cross section, and concrete demand information.

[0063] Based on the aforementioned digital twin simulation model, with the goal of minimizing the rebound rate of shotcrete, a graded combination simulation and deduction experiment was conducted on the spraying path, spraying air pressure, and accelerator dosage to obtain the optimal spraying path, optimal spraying air pressure, and optimal accelerator dosage.

[0064] The spraying construction PLC control module is built around the computer host of the sprayed concrete equipment. This module is used to automatically spray concrete according to the optimal spraying path, optimal spraying air pressure, and optimal accelerator dosage, thus enabling the actual spraying of concrete on the construction site. Based on the moisture content information, the mechanical nozzles of the sprayed concrete equipment are located, and compressed air is used for dehydration before spraying to improve concrete adhesion.

[0065] Furthermore, the system also includes a model database based on the on-site spraying conditions, which includes information on shotcrete rebound rate, concrete material, surface adhesion perception, and construction impact.

[0066] Furthermore, the system also includes a concrete mix proportion information optimization module, which is used to predict the optimal concrete mix proportion information based on the model database and the Bi-LSTM algorithm, and feed the optimal concrete mix proportion information back to the concrete mixing plant to optimize the mixing of concrete materials for the next construction cycle.

[0067] The key points and protection points of this invention are mainly:

[0068] 1. Considering the information on the adhesion surface, concrete, and construction impact, digital twin technology is used to simulate and optimize the sprayed concrete rebound rate.

[0069] 2. Based on the optimization results of shotcrete rebound rate, combined with the information on concrete mixing improvement from the concrete mixing plant and the information from the construction site, continuous dynamic optimization of shotcrete materials and construction is achieved.

[0070] To illustrate the innovation of this invention, it is compared with existing technologies:

[0071] 1. Existing technology discloses a shotcrete method and construction process for reducing the rebound rate of dry-sprayed concrete, belonging to the field of tunnel engineering construction technology. It can solve problems such as material waste and increased dust concentration caused by rebound during dry-sprayed concrete construction. The shotcrete mix ratio is cement:sand:crushed stone:water:accelerator = 1:1.73:1.87:0.41:0.24, mainly applicable to the dry-sprayed shotcrete construction process for tunnel initial support. Based on the existing technology, this invention also provides a shotcrete construction process based on the above mix ratio. By adopting the shotcrete mix ratio and construction process described in this invention, dry-spraying machines are used for shotcreting operations on tunnel initial support, significantly reducing the rebound rate of shotcrete. Simultaneously, the shotcrete quality is better, saving on shotcreting costs and subsequent re-spraying processes, improving construction efficiency, and resulting in better economic benefits.

[0072] 2. Existing technology discloses a shotcrete rebound suppression method based on full-process control. This method includes: determining shotcrete mix design parameters and establishing a shotcrete mix design parameter control system; determining key materials for shotcrete rebound suppression and adding these materials to the concrete mix to obtain low-rebound shotcrete; determining key process parameters for shotcreting construction and establishing a key process parameter control system for shotcreting construction; and using low-rebound shotcrete for shotcreting construction. This invention provides a full-process shotcrete rebound control method encompassing a mix design control system, key rebound suppression materials, and a key jet forming process control system. Using this method, the rebound rate of shotcrete construction can be effectively reduced, achieving a rebound rate of ≤10% for wet shotcrete construction. This method can be widely applied in the shotcrete construction process of railway and highway tunnels and is worthy of promotion.

[0073] 3. The aforementioned existing technologies, in practical application, do not consider the instability of concrete mixing materials, the specific properties of the adhesion surface, and other construction influencing factors. They cannot achieve specific analysis of specific problems, and are merely summaries of experience from existing construction methods, failing to achieve highly flexible responses to changes in construction requirements. Existing technologies cannot adapt to changes in different construction sites, adhesion surfaces, and material brands. This invention, based on digital twin technology and artificial intelligence algorithms, can overcome the shortcomings of traditional solutions, achieving a more efficient, scientific, and flexible optimization goal for concrete construction.

[0074] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0075] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A digital twin-based optimized system for shotcrete construction, characterized in that, include: Adhesion surface information sensing equipment, concrete information sensing equipment, construction impact information sensing equipment, a model database with shotcrete rebound rate, concrete material information, adhesion surface sensing information, and construction impact information, a concrete mix proportion information optimization module, and a control host; The concrete mix proportion information optimization module is used to predict the optimal concrete mix proportion information based on the model database and the Bi-LSTM algorithm, and to feed back the optimal concrete mix proportion information to the concrete mixing plant. The control host includes an adhesion surface sensing information module, a concrete information sensing module, a construction impact information sensing module, a digital twin simulation and deduction module, and a spraying construction PLC control module. The attachment surface sensing information module is used for: Based on the adhesion surface sensing information collected by the adhesion surface information sensing device, oblique photogrammetry 3D modeling or 3D point cloud modeling is performed to obtain a 3D model of the adhesion surface; the adhesion surface information sensing device includes a high-definition binocular camera, a 3D laser scanner and a humidity detector; the adhesion surface sensing information includes adhesion surface image information, adhesion surface 3D laser scan information and adhesion surface humidity information; the adhesion surface 3D model is used to obtain the true 3D information of the adhesion surface and determine the geometric information of the adhesion surface cross section; The lithological and water content information of the three-dimensional model of the adhesion surface in different regions was identified based on the K-nearest neighbor classification algorithm. The three-dimensional model of the adhesion surface is divided according to the lithological information and the water content information to obtain different model sections; Calculate the difference between the geometric information of the model section and the geometric information of the attachment surface section, and calculate the concrete demand information required for shotcrete construction based on the difference; The concrete information sensing module is used to acquire concrete material information collected by the concrete information sensing device; The construction impact information sensing module is used to acquire construction impact information collected by the construction impact information sensing device; The digital twin simulation and deduction module is used for: A digital twin simulation model is constructed based on the information output by the adhesion surface sensing information module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module; the information output by the adhesion surface sensing information module includes lithological information, water content information, geometric information of the adhesion surface cross section, and concrete demand information. Based on the aforementioned digital twin simulation model, with the goal of minimizing the rebound rate of shotcrete, a graded combination simulation and deduction test was conducted on the spraying path, spraying air pressure, and accelerator dosage to obtain the optimal spraying path, optimal spraying air pressure, and optimal accelerator dosage. The PLC control module for spraying construction is used to automatically spray concrete according to the optimal spraying path, optimal spraying air pressure and optimal accelerator dosage; it locates the mechanical nozzle of the spraying concrete equipment according to the moisture content information, and carries out the spraying operation after dehydration by compressed air.

2. The shotcrete construction optimization system based on digital twin according to claim 1, characterized in that, The concrete information sensing equipment includes a concrete penetration strength tester, a slump cone, a density tester, a concrete mixer truck condition monitoring device, a computer host for the concrete mixing plant, and a computer host for the construction site laboratory. The concrete material information includes strength information collected by a concrete penetration tester, slump information collected by a slump cone, density information collected by a density tester, concrete transportation time information collected by concrete mixer truck condition monitoring equipment, concrete mix proportion information collected by the computer host of the concrete mixing plant, and concrete material cost information and concrete material quality information collected by the computer host of the construction site laboratory.

3. The shotcrete construction optimization system based on digital twin according to claim 1, characterized in that, The construction impact information sensing equipment includes a pressure sensor installed on the air compressor, a quick-setting agent flow meter, a shotcrete equipment computer host, a personnel information management system computer, and a construction site laboratory computer host. The construction impact information includes air pressure information collected by pressure sensors, accelerator dosage information collected by accelerator flow meters, spraying machinery information collected by the computer host of the shotcrete equipment, construction personnel information collected by the computer of the personnel information management system, and accelerator quality information collected by the computer host of the site laboratory.

4. The shotcrete construction optimization system based on digital twin according to claim 1, characterized in that, In constructing a digital twin simulation model based on the information output by the adhesion surface sensing information module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module, the digital twin simulation deduction module is used for: The information output by the adhesion surface sensing module, the concrete material information output by the concrete information sensing module, and the construction impact information output by the construction impact information sensing module are preprocessed, and a digital twin simulation model is constructed based on the preprocessed information.

5. The shotcrete construction optimization system based on digital twin according to claim 1, characterized in that, The concrete information sensing module is built around the computer host of the concrete mixing plant; the construction impact information sensing module is built around the computer host of the shotcrete equipment; and the shotcrete construction PLC control module is built around the computer host of the shotcrete equipment.

6. The shotcrete construction optimization system based on digital twin according to claim 1, characterized in that, The modules in the control host are connected by an Internet of Things (IoT) system data interface.

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