Construction process management and control system based on digital twinning

Abstract

The invention discloses a construction process management and control system based on digital twinning, and relates to the technical field of building construction management, and the system is a three-dimensional digital twinning system constructed based on a BIM model and multi-source sensing data, and can achieve the precise monitoring and management of the construction site temperature, the heat island effect and the hydration heat risk. Firstly, the system provides detailed basic data for thermal risk assessment through accurate marking and data acquisition of a material stacking area on a construction site; by calculating the stacking density coefficient and heat source distribution of each sub-region, the system can identify and analyze the potential heat island effect region in time, and the hysteresis and deficiency of the temperature change of the construction site in the traditional method are avoided. Secondly, the heat island risk identification model can monitor the local heat island effect of the construction site in real time, and provides a specific early warning strategy according to the heat risk level, thereby effectively reducing the negative effects of high temperature on the health of workers, the operation of equipment and the construction quality.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction management, and specifically to a construction process management and control system based on digital twins. Background Art

[0002] In modern construction, with the continuous expansion of building scale and the increasing sophistication of construction technology, safety risks on construction sites are also increasing. This is particularly true during processes such as concrete pouring, where the heat island effect and hydration heat pose a particular safety risk. The heat island effect in traditional construction is primarily caused by the dense stacking of materials on the construction site, the heat accumulation of construction equipment and machinery, and the heat accumulation of the building itself. This leads to abnormally high temperatures in localized areas, which in turn poses safety hazards on the construction site. For example, high temperatures can easily cause heatstroke among workers, equipment failure, or material performance degradation, affecting the quality and progress of construction and even threatening the lives of construction workers.

[0003] During the concrete pouring process, the hydration reaction of cement generates a large amount of heat, a process that is particularly noticeable during large-volume concrete construction. Due to the accumulation of hydration heat, the temperature gradient within the concrete varies significantly, which can easily lead to cracks in the concrete and, in severe cases, may affect the structural safety of the building. Furthermore, during large-volume concrete construction, without scientific maintenance and temperature control measures, the temperature differential stress caused by hydration heat can lead to uneven distribution of concrete strength, thus affecting construction quality and the long-term stability of the project. Currently, there are still significant deficiencies in the monitoring and early warning mechanisms for the heat island effect and hydration heat at construction sites. Many construction units rely on traditional empirical judgment and manual inspections, unable to accurately and timely grasp the temperature changes at the construction site and the release of concrete hydration heat. This results in insufficient and ineffective thermal risk control and temperature management measures at the construction site. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a construction process management and control system based on digital twins to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A construction process control system based on digital twins, comprising:

[0006] The construction site modeling module is used to build a three-dimensional digital twin model of the target construction area based on the BIM model, lidar point cloud data and multi-source sensor information, and spatially mark the high-density material stacking area in the target construction area to obtain the dense distribution data of materials, including steel concentration area, template stacking area and concrete pouring area, and calculate the stacking density coefficient R of the i-th sub-area. i ;

[0007] Thermal risk identification module, used to extract the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i , build a heat island risk identification model, and calculate the local heat island formation coefficient Rs of the i-th sub-region i , and first classify the heat island risk level in the sub-region and output the corresponding heat accumulation warning strategy;

[0008] The hydration heat simulation module is used to simulate the hydration heat release process of large-volume concrete at different ambient temperatures, collect cement heat release rate I and temperature gradient data of concrete components, and predict the hydration heat risk coefficient Sr of the f-th concrete component in the i-th sub-area of the f-th poured concrete component. i,f , and conduct a second classification of the hydration heat risk levels of different components to generate corresponding maintenance and control strategies.

[0009] Preferably, the construction site modeling module includes a model building unit and an area division unit;

[0010] The model building unit is used to access and analyze the BIM model of the construction project, extract the structural hierarchy, construction sequence and component attributes, and use LiDAR to collect point cloud data of the target construction area. After spatial registration with the BIM model, a three-dimensional digital twin model of the target construction area is established;

[0011] The area division unit is used in the three-dimensional digital twin model to divide the target construction area into several sub-areas, which are marked as {Q1, Q2, Q3, ..., Q n}; n represents the number of sub-regions.

[0012] Preferably, the construction site modeling module also includes a multi-source sensor data fusion unit, which is used to set detection points in each sub-area and collect information including temperature and humidity sensors, RFID tags and video recognition camera data sources to establish a sub-area status data set for supplementing material distribution, on-site heat sources and equipment status data, and uniformly map the data timestamps and spatial coordinates of the sub-area status data set to the three-dimensional digital twin model.

[0013] Preferably, the construction site modeling module further includes a material category identification unit and a material density identification unit;

[0014] The material classification identification unit is used to automatically obtain the material label and type by using the RFID reader on the electronic tags of the construction materials in each sub-area; the classification results include: steel bars, wooden formwork, cement bags and assembly components;

[0015] The material density identification unit is used to use the voxel space partitioning method and density clustering algorithm to output: cluster density η i , the specific steps are:

[0016] S11. Use the voxel method to divide the three-dimensional space of the construction site into cubic units. Specifically, each voxel unit is defined as V x,y,z , the side length is set to l v , set to 0.5m-1m;

[0017] S12. After registering the point cloud data, map it into a voxel grid to form a dense lattice voxel spatial structure dataset. Perform cluster analysis on the dense lattice voxel spatial structure dataset using a density clustering algorithm, wherein the clustering parameters include the domain radius and the minimum number of points MinPts; output the cluster boundary of each sub-region and the cluster density η of the j-th type of material. j ; Based on the material label and the clustering density η of the j-th type of material j , clustering the density accumulation areas of the same type of materials to form steel concentration areas, formwork stacking areas and concrete pouring areas;

[0018] S13, combined with cluster density η i And the classification results are used to calculate the packing density coefficient R of the i-th sub-area i :

[0019]

[0020] Where m represents the total number of material types identified in the sub-area, D j It represents the theoretical density per unit volume of the jth type of material in kg / m3, V j represents the voxel volume of the jth type of material, η j Represents the clustering density η of the j-th type of material j , obtained by step S12; V zone,i represents the total voxel volume of the i-th sub-region of the target.

[0021] Preferably, the heat risk identification module includes a weather collection unit, a construction equipment heat source collection unit, a heat island effect analysis unit and a first classification unit;

[0022] The weather collection unit is used to use a temperature sensor to collect and obtain the ambient temperature T of the i-th sub-area. i, and collect the ambient temperature of all sub-areas of the target construction area, and obtain the sub-area temperature mean T by summing and averaging. env , the ambient temperature T of the i-th sub-area i and the mean temperature of the sub-region T env Subtract and obtain the ambient temperature difference T of the i-th sub-area w,i ;

[0023] The construction equipment heat source acquisition unit is used to use a drone equipped with a thermal imaging camera to scan the equipment heat source distribution in each sub-area, obtain a heat distribution image, and identify the heat distribution image to obtain the total heat source power P of the i-th sub-area. total,i ;

[0024] The heat island effect analysis unit is used to use a convolutional neural network to construct an initial convolutional neural network model and use the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i The initial convolutional neural network model is trained and tested, and the trained initial convolutional neural network model is used as the heat island risk identification model. At the same time, the intermediate layer output of the equipment operation status model is used as the feature vector to identify the feature information. The heat island risk identification model is trained and tested based on the obtained feature information. The trained heat island risk identification model is used as data for running prediction to calculate the local heat island formation coefficient Rs of the i-th sub-region. i :

[0025] Extract the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i Normalize it to the range of 0 to 1 and calculate the local heat island formation coefficient Rs of the i-th sub-region using the following formula: i :

[0026] Rs i =a1×R i '+a2×T w,i '+a3×P total,i ';

[0027] Where a1, a2 and a3 represent the normalized packing density coefficient R of the ith sub-region, respectively. i ', the ambient temperature difference T of the i-th sub-area w,i ', the total heat source power P of the i-th sub-region total,i ' weight coefficient, and the weight sum is 1;

[0028] The normalization process is as follows:

[0029]

[0030] Where R max and R min are the maximum and minimum values of the packing density coefficient of all sub-regions, respectively;

[0031] T w,max and T w,min are the maximum and minimum values of the ambient temperature differences of all sub-areas, respectively;

[0032] P max and P min are the maximum and minimum values of the total power of heat sources in all sub-regions, respectively.

[0033] Preferably, the first classification unit is used to classify the local heat island formation coefficient Rs of the i-th sub-region according to the local heat island formation coefficient Rs of the i-th sub-region. i The first classification includes:

[0034] When the local heat island formation coefficient Rs of the i-th sub-region i <0.3, indicating that there is temperature variation in the sub-area, but no personnel risk during construction. It is marked as the first low-risk area, and the first heat accumulation warning strategy is output, including: continuing construction and continuous monitoring;

[0035] When 0.3≤Rs i If the value is ≤0.7, the sub-area is subject to a heat island effect, and the accumulated building materials are subject to expansion and deformation during construction. The area is marked as a second-moderate-risk area, and a second-level heat accumulation warning strategy is implemented, including adjusting construction hours to avoid 12:00 AM to 3:00 PM, laying thermal insulation curtains in steel material collection areas, formwork stacking areas, and concrete pouring areas, and sprinkling water every 3-4 hours to reduce temperatures.

[0036] When Rs i >0.7, indicating that the construction environment in this sub-area has a heat island effect, which is higher than the risk in the second medium-risk area. There are risks of high-temperature operation, heat source efficiency failure, material performance deformation, and plasticity loss. It is marked as the third highest-risk area and outputs the third heat concentration warning strategy, including: adjusting the construction time to avoid 11:00 am-16:00 pm, laying insulation curtains in the steel concentration area, formwork stacking area, and concrete pouring area, and sprinkling water every 1-2 hours to cool down; and adjusting the settings to limit the operation of 20-30% of the regional heat source equipment, and configuring first aid points, water and electricity supply stations, and summer rest stations.

[0037] Preferably, the hydration heat simulation module includes a concrete simulation acquisition unit, a pouring temperature gradient acquisition unit and a hydration heat identification unit;

[0038] The concrete simulation collection unit is used to perform a simulation test on the f-th concrete pouring component in the i-th sub-area and collect the cement heat release rate I before the concrete pouring process;

[0039] Cement heat release rate I includes: when the P.O4 of Portland cement is 2.5, the cement heat release rate I = 2.5-3.5 J / g·h; when the P.O4 of Portland cement is medium-heat, the cement heat release rate I = 1.5-2.5 J / g·h; when the P.O4 of Portland cement is low-heat, the cement heat release rate I = 0.8-1.4 J / g·h; when the P.O4 of Portland cement concrete is mixed with 30% fly ash, the cement heat release rate I = 1.0-2.0 J / g·h; when the P.O4 of Portland cement concrete is mixed with 20% slag, the cement heat release rate I = 1.2-2.2 J / g·h; when the P.O4 of high-performance concrete is mixed with 10% silica fume, the cement heat release rate I = 3.5-4.0 / g·h;

[0040] The pouring temperature gradient acquisition unit is used to bury a temperature monitoring line in the concrete pouring area of each sub-area, record the temperature data inside the concrete changing with time, and obtain the temperature gradient data of the f-th poured concrete component in the i-th sub-area.

[0041] Preferably, the hydration heat identification unit is used to perform in-depth analysis based on the cement heat release rate I and the temperature gradient data of the f-th cast concrete component in the i-th sub-region acquired in the concrete simulation acquisition unit to obtain the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-region. i,f ;

[0042] Hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area i,f The specific way to obtain it is:

[0043] S21. Calculate the total amount of hydration heat released per unit volume Qtotal within a fixed period of time t t , the expression is:

[0044]

[0045] S22. Calculate the core temperature rise ΔTcore using the relationship between specific heat capacity and density. t , the expression is:

[0046]

[0047] Where C is the specific heat capacity of concrete, which is set to 900-1000 J / kg·K; ρ represents the density of concrete; and the core temperature rise ΔTcore is obtained from the t The maximum temperature value is extracted from the sequence as the core peak temperature Tpeak of the f-th poured concrete component in the i-th sub-area i :

[0048] S23. Use the exponential cooling model to calculate the surface temperature Tsurface t :

[0049] Tsurface t =T i +θ×e -γt

[0050] Where, T i The ambient temperature of the i-th sub-region, θ represents the initial temperature difference, and γ represents the surface cooling coefficient, which are obtained in the experiment;

[0051] S24, based on surface temperature Tsurface t and core temperature rise ΔTcore t , calculate the temperature gradient ΔTgrad t :

[0052] ΔTgrad t =ΔTcore t -Tsurface t

[0053] And record the maximum temperature gradient ΔTgrad of the f-th poured concrete component in the i-th sub-area i,k , ΔTgrad i,k =maxΔTgrad t ;

[0054] S25, the total amount of hydration heat released per unit volume Qtotal within a fixed time period t obtained by calculation in S21 to S24 t , the core peak temperature Tpeak of the f-th poured concrete component in the i-th sub-area i,k and the maximum temperature difference gradient ΔTgrad of the f-th cast concrete component in the i-th sub-area i,k After dimensionless processing, the normalized model is used to calculate the hydration heat risk coefficient Sr of the f-th concrete component in the i-th sub-area i,f :

[0055]

[0056] Where, Indicates the safety threshold of the total amount of hydration heat released per unit volume, Indicates the core peak temperature safety threshold, It represents the temperature gradient safety threshold, a4, a5 and a6 are weight coefficients, and the sum of the weights is 1.

[0057] Preferably, the hydration heat simulation module further includes a second classification unit;

[0058] The second classification unit is used to classify the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area according to the hydration heat risk coefficient Sr i,f The second classification includes:

[0059] When the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area i,f <0.3, indicating that there is no hydration heat risk during the pouring process of the concrete component, and it is marked as a first-level qualified component;

[0060] When 0.3≤Sr i,f If the value is ≤0.7, the concrete component is at risk of hydration heat during the pouring process and is marked as a second-level unqualified component. The first maintenance control strategy is output, including: shielding the component surface with a sunshade net or covering, including straw curtains or plastic film, controlling the cooling rate to no more than 3°C per hour, increasing the cooling time by 10-20%, and spraying water every 4-5 hours for cooling for 24 hours after pouring.

[0061] When Sr i,f >0.7, indicating that the concrete component has a hydration heat risk during the pouring process, and is marked as a third-level unqualified component, which has a higher risk than the second-level unqualified component. The second maintenance control strategy is output, including: shielding the component surface with a sunshade net or cover, including straw curtains or plastic film, controlling the cooling rate to no more than 2°C per hour, increasing the cooling time by 21-30%, and sprinkling water every 2-3 hours for cooling for 24 hours after pouring.

[0062] The present invention provides a construction process management and control system based on digital twins. It has the following beneficial effects:

[0063] (1) By spatially marking and collecting intensive data on the material stacking areas at the construction site, the system can calculate the stacking density of each sub-area, thereby providing basic data for the assessment of the heat island effect. This detailed sub-area division helps understand the heat risks in different areas and make more targeted early warnings and controls.

[0064] (2) By extracting the packing density coefficient, ambient temperature difference and total heat source power of each sub-area, the system constructs a heat island risk identification model. This model can evaluate the heat island effect of each area in real time during the construction process and calculate the local heat island formation coefficient of each sub-area to determine whether there is a heat risk. Based on the local heat island formation coefficient, the system can divide the heat risk of each sub-area into different levels and provide corresponding heat accumulation warning strategies. Through this warning system, when the heat risk reaches a critical value, control measures can be taken in advance to avoid the impact of high temperature on worker health, equipment operation and material quality.

[0065] (3) The system uses a hydration heat simulation module to simulate the hydration heat release process of large-volume concrete at different ambient temperatures. By simulating the heat release rate of cement and the temperature gradient of concrete components, the system can predict the hydration heat risk coefficient of different sub-regions and concrete components. Hydration heat risk level classification and maintenance control strategy: The system can classify the hydration heat risks of different concrete components and generate corresponding maintenance control strategies in a timely manner. These strategies include temperature control measures, adjustment of the curing cycle, etc., to ensure the uniform distribution of concrete strength and avoid cracks and structural safety problems caused by hydration heat. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a construction process control system based on digital twins according to the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Example 1

[0069] See also Figure 1 The present invention provides a construction process management and control system based on digital twins, comprising:

[0070] The construction site modeling module is used to build a three-dimensional digital twin model of the target construction area based on the BIM model, lidar point cloud data and multi-source sensor information, and spatially mark the high-density material stacking area in the target construction area to obtain the dense distribution data of materials, including steel concentration area, template stacking area and concrete pouring area, and calculate the stacking density coefficient R of the i-th sub-area. i ;

[0071] Thermal risk identification module, used to extract the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i , build a heat island risk identification model, and calculate the local heat island formation coefficient Rs of the i-th sub-region i , and first classify the heat island risk level in the sub-region and output the corresponding heat accumulation warning strategy;

[0072] The hydration heat simulation module is used to simulate the hydration heat release process of large-volume concrete at different ambient temperatures, collect cement heat release rate I and temperature gradient data of concrete components, and predict the hydration heat risk coefficient Sr of the f-th concrete component in the i-th sub-area of the f-th poured concrete component. i,f , and conduct a second classification of the hydration heat risk levels of different components to generate corresponding maintenance and control strategies.

[0073] In this embodiment, a three-dimensional digital twin model is constructed based on the BIM model and multi-source sensor data: by combining the BIM (Building Information Modeling) model, lidar point cloud data and multiple sensor data, the system can accurately construct a three-dimensional digital twin model of the target construction area. This model provides accurate data support for temperature monitoring, heat source distribution and hydration heat effect analysis during the construction process. By spatially marking and intensively collecting data on the material stacking area at the construction site, the system can calculate the stacking density of each sub-area, thereby providing basic data for the assessment of the heat island effect. This detailed sub-area division helps to understand the heat risks in different areas and make more targeted early warnings and controls.

[0074] By extracting the bulk density coefficient, ambient temperature difference, and total heat source power for each sub-area, the system constructs a heat island risk identification model. This model can assess the heat island effect of each area in real time during construction and calculate the local heat island formation coefficient to determine whether there is a heat risk.

[0075] Based on the local heat island formation coefficient, the system categorizes the heat risk of each sub-area into different levels and provides corresponding heat accumulation warning strategies. This early warning system enables proactive control measures when heat risk reaches critical levels, minimizing the impact of high temperatures on worker health, equipment operation, and material quality.

[0076] The system utilizes a hydration heat simulation module to simulate the hydration heat release process of large-volume concrete under different ambient temperatures. By simulating the heat release rate of cement and the temperature gradient of concrete components, the system can predict the hydration heat risk coefficient for different sub-regions and concrete components. Hydration heat risk classification and maintenance control strategies: The system can classify the hydration heat risk of different concrete components and promptly generate corresponding maintenance control strategies. These strategies include temperature control measures and adjustments to the curing cycle to ensure uniform distribution of concrete strength and avoid cracks and structural safety issues caused by hydration heat.

[0077] Example 2

[0078] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically, the construction site modeling module includes a model building unit and an ,area division unit;

[0079] The model building unit is used to access and analyze the BIM model of the construction project, extract the structural hierarchy, construction sequence and component attributes, and use LiDAR to collect point cloud data of the target construction area. After spatial registration with the BIM model, a three-dimensional digital twin model of the target construction area is established;

[0080] The area division unit is used in the three-dimensional digital twin model to divide the target construction area into several sub-areas, which are marked as {Q1, Q2, Q3, ..., Q n}; n represents the number of sub-regions.

[0081] The construction site modeling module also includes a multi-source sensor data fusion unit, which is used to set detection points in each sub-area and collect information including temperature and humidity sensors, RFID tags and video recognition camera data sources to establish a sub-area status data set for supplementing material distribution, on-site heat sources and equipment status data, and uniformly map the data timestamps and spatial coordinates of the sub-area status data set to the three-dimensional digital twin model.

[0082] In this embodiment, by accessing and parsing the BIM model, extracting the structural hierarchy, construction sequence, and component attributes of the construction project, and combining LiDAR technology to collect point cloud data of the target construction area, the system can generate an accurate three-dimensional digital twin model. This model can accurately reflect the actual conditions of the building structure and construction site, providing basic data support for subsequent construction process management. Spatial registration of the LiDAR point cloud data with the BIM model ensures consistency between the three-dimensional model of the actual construction area and the design model during construction, improving spatial accuracy during construction and thus avoiding construction errors or safety hazards caused by model errors. The target construction area is divided into several sub-areas, allowing independent monitoring and management of different areas of the construction site. This detailed regional division enables more precise management and allows the formulation of different construction strategies and safety warning measures based on the different characteristics of each sub-area. By dynamically dividing the number of sub-areas n, the system can adjust according to the actual needs of the construction site, providing flexible space management. This facilitates personalized management and optimization of material stacking, equipment deployment, construction progress, etc. in different areas.

[0083] Monitoring points are set up in each sub-area to collect information from multiple data sources, including temperature and humidity sensors, RFID tags, and video recognition cameras, ensuring comprehensive and real-time status monitoring. The collection of these data sources not only helps obtain dynamic information about the construction environment, but also provides real-time insights into key factors such as material distribution, on-site heat sources, and equipment status, thus supporting subsequent risk assessment, schedule management, and resource scheduling.

[0084] Example 3

[0085] This embodiment is explained in Example 2, please refer to Figure 1 ,Specifically, the construction site modeling module also includes a material category ,identification unit and a material density identification unit;

[0086] The material classification identification unit is used to automatically obtain the material label and type by using the RFID reader on the electronic tags of the construction materials in each sub-area; the classification results include: steel bars, wooden formwork, cement bags and assembly components;

[0087] The material density identification unit is used to use the voxel space partitioning method and density clustering algorithm to output: cluster density η i , the specific steps are:

[0088] S11. Use the voxel method to divide the three-dimensional space of the construction site into cubic units. Specifically, each voxel unit is defined as V x,y,z , the side length is set to l v , set to 0.5m-1m;

[0089] S12. After registering the point cloud data, map it into a voxel grid to form a dense lattice voxel spatial structure dataset. Perform cluster analysis on the dense lattice voxel spatial structure dataset using a density clustering algorithm, wherein the clustering parameters include the domain radius and the minimum number of points MinPts; output the cluster boundary of each sub-region and the cluster density η of the j-th type of material. j ; Based on the material label and the clustering density η of the j-th type of material j , clustering the density accumulation areas of the same type of materials to form steel concentration areas, formwork stacking areas and concrete pouring areas;

[0090] S13, combined with cluster density η i And the classification results are used to calculate the packing density coefficient R of the i-th sub-area i :

[0091]

[0092] Where m represents the total number of material types identified in the sub-area, D jIt represents the theoretical density per unit volume of the jth type of material in kg / m3, V j represents the voxel volume of the jth type of material, η j Represents the clustering density η of the j-th type of material j , obtained by step S12; V zone,i represents the total voxel volume of the i-th sub-region of the target.

[0093] In this embodiment, RFID readers automatically identify the electronic tags attached to construction materials in each sub-area, allowing the system to obtain real-time information on the material types, including rebar, wooden formwork, cement bags, and assembly components. This process, which does not rely on manual operation and is highly automated, can significantly improve the accuracy and efficiency of material identification. The material classification unit can dynamically update the material types at the construction site and automatically classify different types of materials. This facilitates precise material management on the construction site, ensuring that different materials are stored and utilized appropriately during processes such as concrete pouring and rebar installation.

[0094] The voxelization method is used to discretely divide the three-dimensional space of the construction site to form an accurate spatial data structure. By reasonably setting the side length of the voxel unit (0.5m-1m), the system can simulate and analyze the spatial structure of the construction site with high precision and further optimize the stacking layout of materials. Based on voxelization, the system uses a density clustering algorithm to perform cluster analysis on the dense lattice voxel space, which can identify the stacking density and clustering areas of different materials. Through the clustering results, the system can accurately calibrate key areas such as steel concentration areas, formwork stacking areas, and concrete pouring areas, which helps to improve material stacking management on the construction site and reduce the risk of heat island effect. The domain radius and minimum number of points (MinPts) parameters in the clustering algorithm can be adjusted according to the specific conditions of the construction site, providing flexible spatial density analysis functions. This enables accurate stacking density analysis and classification in construction projects of different scales and complexities.

[0095] Based on the cluster density and material category, the system can calculate the stacking density coefficient of the i-th sub-area. This coefficient reflects the density of material stacking in each sub-area, which helps to predict the heat island effect and the accumulation risk of hydration heat. The calculation formula of the stacking density coefficient takes into account the density and volume distribution of various materials, thus providing an accurate basis for temperature control management, risk identification and early warning mechanisms during the construction process. Through the calculation of this density coefficient, the system can accurately identify the stacking density of different areas during the construction process and provide real-time data support to construction personnel. This helps to adjust the material stacking position in a timely manner to avoid safety hazards caused by over-crowding in local areas, such as equipment failure or heat stroke caused by the heat island effect.

[0096] Example 4

[0097] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically, the heat risk identification module includes a weather collection unit, a ,construction equipment heat source collection unit, a heat island effect ,analysis unit and a first classification unit;

[0098] The weather collection unit is used to use a temperature sensor to collect and obtain the ambient temperature T of the i-th sub-area. i , and collect the ambient temperature of all sub-areas of the target construction area, and obtain the sub-area temperature mean T by summing and averaging. env , the ambient temperature T of the i-th sub-area i and the mean temperature of the sub-region T env Subtract and obtain the ambient temperature difference T of the i-th sub-area w,i ;

[0099] The construction equipment heat source acquisition unit is used to use a drone equipped with a thermal imaging camera to scan the equipment heat source distribution in each sub-area, obtain a heat distribution image, and identify the heat distribution image to obtain the total heat source power P of the i-th sub-area. total,i ;

[0100] The heat island effect analysis unit is used to use a convolutional neural network to construct an initial convolutional neural network model and use the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i The initial convolutional neural network model is trained and tested, and the trained initial convolutional neural network model is used as the heat island risk identification model. At the same time, the intermediate layer output of the equipment operation status model is used as the feature vector to identify the feature information. The heat island risk identification model is trained and tested based on the obtained feature information. The trained heat island risk identification model is used as data for running prediction to calculate the local heat island formation coefficient Rs of the i-th sub-region. i :

[0101] Extract the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i Normalize it to the range of 0 to 1 and calculate the local heat island formation coefficient Rs of the i-th sub-region using the following formula: i :

[0102] Rs i =a i ×R i '+a2×T w,i '+a3×Ptotal,i ';

[0103] Where a1, a2 and a3 represent the normalized packing density coefficient R of the ith sub-region, respectively. i ', the ambient temperature difference T of the i-th sub-area w,i ', the total heat source power P of the i-th sub-region total,i ' weight coefficient, and the weight sum is 1;

[0104] The normalization process is as follows:

[0105]

[0106] Where R max and R min are the maximum and minimum values of the packing density coefficient of all sub-regions, respectively;

[0107] T w,max and T w,min are the maximum and minimum values of the ambient temperature differences of all sub-areas, respectively;

[0108] P max and P min are the maximum and minimum values of the total power of heat sources in all sub-regions, respectively.

[0109] The first classification unit is used to classify the local heat island formation coefficient Rs of the i-th sub-region according to the local heat island formation coefficient Rs of the i-th sub-region. i The first classification includes:

[0110] When the local heat island formation coefficient Rs of the i-th sub-region i <0.3, indicating that there is temperature variation in the sub-area, but no personnel risk during construction. It is marked as the first low-risk area, and the first heat accumulation warning strategy is output, including: continuing construction and continuous monitoring;

[0111] When 0.3≤Rs i If the value is ≤0.7, the sub-area is subject to a heat island effect, and the accumulated building materials are subject to expansion and deformation during construction. The area is marked as a second-moderate-risk area, and a second-level heat accumulation warning strategy is implemented, including adjusting construction hours to avoid 12:00 AM to 3:00 PM, laying thermal insulation curtains in steel material collection areas, formwork stacking areas, and concrete pouring areas, and sprinkling water every 3-4 hours to reduce temperatures.

[0112] When Rs i>0.7, indicating that the construction environment in this sub-area has a heat island effect, which is higher than the risk in the second medium-risk area. There are risks of high-temperature operation, heat source efficiency failure, material performance deformation, and plasticity loss. It is marked as the third highest-risk area and outputs the third heat concentration warning strategy, including: adjusting the construction time to avoid 11:00 am-16:00 pm, laying insulation curtains in the steel concentration area, formwork stacking area, and concrete pouring area, and sprinkling water every 1-2 hours to cool down; and adjusting the settings to limit the operation of 20-30% of the regional heat source equipment, and configuring first aid points, water and electricity supply stations, and summer rest stations.

[0113] Here is an example chart of the data for a sample of subregions:

[0114]

[0115] In this embodiment, by adjusting the weight coefficients of various factors (packing density coefficient, ambient temperature difference, and total heat source power), the system can prioritize the impact of certain factors based on actual conditions, further improving the accuracy of the heat island risk assessment model. For example, in specific construction environments, the impact of packing density may be more significant than the temperature difference. When the local heat island formation coefficient is low, indicating that there are temperature fluctuations in the area but no significant threat to the safety of construction workers, the system marks the sub-area as a low-risk area and outputs a warning strategy of "continue construction and continue monitoring." This strategy helps avoid unnecessary intervention while ensuring the normal progress of construction. When the local heat island formation coefficient is high, it indicates that the area has a significant heat island effect, which may cause expansion or deformation of building materials. The system marks the area as a medium-risk area and recommends adjusting the construction time to avoid high-temperature periods (such as 12:00-15:00 noon). At the same time, it implements insulation curtains and regular watering to cool high-temperature areas such as steel, formwork, and concrete. These measures effectively control the temperature of the local area and reduce the impact of high temperatures on materials and equipment. For areas with extremely high local heat island formation coefficients, the system identifies them as high-risk areas, indicating potential risks such as heat source efficiency failure, high-temperature operation risks, and material deformation. The system then recommends adjusting construction times to avoid the high-temperature period between 11:00 AM and 4:00 PM and strengthening cooling measures, including frequent watering and the installation of thermal curtains. It also sets operating restrictions for regional heat source equipment and provides first aid points, water and electricity recharge stations, and summer rest areas at the construction site to ensure the safety and health of construction workers. Through a detailed thermal risk classification system, the system provides tailored solutions for varying degrees of heat island effects. Whether low-risk, medium-risk, or high-risk areas, targeted early warning and intervention strategies are provided to comprehensively ensure construction site safety. Based on the heat island risk distribution, construction managers can more effectively optimize construction plans, adjust work schedules, and effectively schedule construction equipment and personnel. For example, they can avoid intensive construction during high-temperature periods or move heat-sensitive materials and equipment to cooler areas to avoid interference from heat sources.

[0116] Example 5

[0117] Cement is primarily composed of silicate minerals (such as C3S and C2S). When these react with water, they form hydrated silicate gel and calcium hydroxide, releasing heat. This reaction is called hydration, and the heat released is called hydration heat. Hydration is exothermic, so when cement and water are mixed, concrete generates heat. During the initial pouring phase, the hydration reaction releases a significant amount of heat, causing the concrete's internal temperature to rise sharply. In large concrete structures (such as dams and foundations), internal temperatures can reach 50°C or even higher due to the accumulation of hydration heat. This high internal temperature and relatively low surface temperature create a temperature difference between the concrete's interior and surface. This large temperature difference creates a temperature gradient, which can induce thermal stress. The hydration reaction slows down over time. Therefore, during the initial pouring phase, concrete strength increases rapidly due to the high temperatures, especially in warm environments. However, during the hardening phase at high temperatures, incomplete cement hydration can occur, affecting the concrete's ultimate strength.

[0118] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically, the hydration heat simulation module includes a concrete simulation acquisition unit, a pouring temperature gradient acquisition unit and a hydration heat identification unit;

[0119] The concrete simulation collection unit is used to perform a simulation test on the f-th concrete pouring component in the i-th sub-area and collect the cement heat release rate I before the concrete pouring process;

[0120] Cement heat release rate I includes: when the P.O4 of Portland cement is 2.5, the cement heat release rate I = 2.5-3.5 J / g·h; when the P.O4 of Portland cement is medium-heat, the cement heat release rate I = 1.5-2.5 J / g·h; when the P.O4 of Portland cement is low-heat, the cement heat release rate I = 0.8-1.4 J / g·h; when the P.O4 of Portland cement concrete is mixed with 30% fly ash, the cement heat release rate I = 1.0-2.0 J / g·h; when the P.O4 of Portland cement concrete is mixed with 20% slag, the cement heat release rate I = 1.2-2.2 J / g·h; when the P.O4 of high-performance concrete is mixed with 10% silica fume, the cement heat release rate I = 3.5-4.0 / g·h;

[0121] The pouring temperature gradient acquisition unit is used to bury a temperature monitoring line in the concrete pouring area of each sub-area, record the temperature data inside the concrete changing with time, and obtain the temperature gradient data of the f-th poured concrete component in the i-th sub-area.

[0122] The hydration heat identification unit is used to perform in-depth analysis based on the cement heat release rate I and the temperature gradient data of the f-th cast concrete component in the i-th sub-region acquired in the concrete simulation acquisition unit to obtain the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-region.i,f ;

[0123] Hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area i,f The specific way to obtain it is:

[0124] S21. Calculate the total amount of hydration heat released per unit volume Qtotal within a fixed period of time t t , the expression is:

[0125]

[0126] Where I(t) represents the heat release rate of cement in a fixed period of time t.

[0127] By calculating the total hydration heat release per unit volume, we can understand the heat release of concrete over a fixed period of time. This step helps us estimate the heat generated during the hydration reaction and, in turn, predict potential heat buildup, thereby avoiding cracks or damage caused by excessive heat buildup.

[0128] S22. Calculate the core temperature rise ΔTcore using the relationship between specific heat capacity and density. t , the expression is:

[0129]

[0130] Where C is the specific heat capacity of concrete, which is set to 900-1000 J / kg·K; ρ represents the density of concrete; and the core temperature rise ΔTcore is obtained from the t The maximum temperature value is extracted from the sequence as the core peak temperature Tpeak of the f-th poured concrete component in the i-th sub-area i : The maximum temperature value is extracted as the core peak temperature to identify the highest temperature that may occur during the hydration process, helping to assess whether the concrete is prone to cracks or other structural damage due to large temperature differences.

[0131] S23. Use the exponential cooling model to calculate the surface temperature Tsurface t :

[0132] Tsurface t =T i +θ×e -γt

[0133] Where, T iThe ambient temperature of the i-th subregion, θ represents the initial temperature difference, and γ represents the surface cooling coefficient, which are obtained experimentally. Calculating the surface temperature using an exponential cooling model helps understand the temperature difference between the concrete surface and the interior. Accurately predicting the surface temperature helps avoid excessive temperature differences between the inside and outside of the concrete caused by rapid surface cooling, which can lead to cracks or thermal stresses.

[0134] S24, based on surface temperature Tsurface t and core temperature rise ΔTcore t , calculate the temperature gradient ΔTgrad t :

[0135] ΔTgrad t =ΔTcore t -Tsurface t

[0136] And record the maximum temperature gradient ΔTgrad of the f-th poured concrete component in the i-th sub-area i,k , ΔTgrad i,k =maxΔTgrad t Calculating temperature gradients helps assess thermal stress differences between the concrete interior and surface, identifying areas of elevated temperature that could lead to cracks. Temperature gradients are a key indicator of hydration heat risk. Recording the maximum temperature gradient provides crucial data for subsequent risk assessments, ensuring identification and early warning of any potential issues, and improving the safety and stability of concrete structures.

[0137] S25, the total amount of hydration heat released per unit volume Qtotal within a fixed time period t obtained by calculation in S21 to S24 t , the core peak temperature Tpeak of the f-th poured concrete component in the i-th sub-area i,k and the maximum temperature difference gradient ΔTgrad of the f-th cast concrete component in the i-th sub-area i,k After dimensionless processing, the normalized model is used to calculate the hydration heat risk coefficient Sr of the f-th concrete component in the i-th sub-area i,f :

[0138]

[0139] Where, Indicates the safety threshold of the total amount of hydration heat released per unit volume, Indicates the core peak temperature safety threshold, represents the temperature gradient safety threshold, while a4, a5, and a6 are weighting coefficients, summing to 1. By processing the calculated results using a normalized model, we can comprehensively consider multiple factors, including the total amount of hydration heat release, the peak core temperature, and the temperature gradient, to derive a dimensionless hydration heat risk coefficient. The use of weighting coefficients makes risk assessment more scientific and accurate, allowing the contribution of each factor to the overall risk to be adjusted based on actual conditions, thereby providing a quantitative analysis of potential risks during the construction process.

[0140] In this embodiment, through the above steps, the hydration heat simulation module can accurately assess the potential risks associated with hydration heat during concrete construction. The calculations and assessments at each step provide important support for ensuring the quality and safety of concrete components and temperature control measures during construction. This module can monitor and identify hydration heat risks in real time, helping construction personnel optimize construction methods and equipment configuration, and avoiding project quality issues caused by excessive temperature differences or excessively rapid hydration heat release.

[0141] Example 6

[0142] This embodiment is explained in Example 5, please refer to Figure 1 ,Specifically, the hydration heat simulation module also includes a second classification unit;

[0143] The second classification unit is used to classify the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area according to the hydration heat risk coefficient Sr i,f The second classification includes:

[0144] When the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area i,f <0.3, indicating that there is no hydration heat risk during the pouring process of the concrete component, and it is marked as a first-level qualified component;

[0145] When 0.3≤Sr i,f If the value is ≤0.7, the concrete component is at risk of hydration heat during the pouring process and is marked as a second-level unqualified component. The first maintenance control strategy is output, including: shielding the component surface with a sunshade net or covering, including straw curtains or plastic film, controlling the cooling rate to no more than 3°C per hour, increasing the cooling time by 10-20%, and spraying water every 4-5 hours for cooling for 24 hours after pouring.

[0146] When Sr i,f>0.7, indicating that the concrete component has a hydration heat risk during the pouring process, and is marked as a third-level unqualified component, which has a higher risk than the second-level unqualified component. The second maintenance control strategy is output, including: shielding the component surface with a sunshade net or cover, including straw curtains or plastic film, controlling the cooling rate to no more than 2°C per hour, increasing the cooling time by 21-30%, and sprinkling water every 2-3 hours for cooling for 24 hours after pouring.

[0147] In this embodiment, by classifying the risks of concrete hydration heat and adopting corresponding maintenance and control strategies according to different risk levels, problems such as cracks and uneven strength caused by hydration heat can be effectively prevented. This measure not only ensures the quality of concrete and the stability of the structure, but also reduces the maintenance costs and later repair risks in the project through scientific maintenance strategies, and improves the long-term durability of the concrete structure. The effective management of hydration heat reduces the occurrence of concrete cracking, bursting, etc., and also reduces the risk of other safety accidents caused by excessive concrete temperature (such as equipment overload, sudden discomfort of operators, etc.). In addition, appropriate maintenance strategies can maintain the stability of concrete and avoid instability caused by excessive cooling, thereby reducing the probability of accidents during construction.

[0148] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0149] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by those skilled in the art according to actual conditions. The above is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A construction process management and control system based on digital twins, characterized by: include: The construction site modeling module is used to build a three-dimensional digital twin model of the target construction area based on the BIM model, lidar point cloud data and multi-source sensor information, and spatially mark the high-density material stacking area in the target construction area to obtain the dense distribution data of materials, including steel concentration area, template stacking area and concrete pouring area, and calculate the stacking density coefficient R of the i-th sub-area. i ; Thermal risk identification module, used to extract the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i , build a heat island risk identification model, and calculate the local heat island formation coefficient Rs of the i-th sub-region i , and first classify the heat island risk level in the sub-region and output the corresponding heat accumulation warning strategy; The hydration heat simulation module is used to simulate the hydration heat release process of large-volume concrete at different ambient temperatures, collect cement heat release rate I and temperature gradient data of concrete components, and predict the hydration heat risk coefficient Sr of the f-th concrete component in the i-th sub-area of the f-th poured concrete component. i,f , and conduct a second classification of the hydration heat risk levels of different components to generate corresponding maintenance and control strategies.

2. A construction process management and control system based on digital twins according to claim 1, characterized in that: The construction site modeling module includes a model building unit and an area division unit; The model building unit is used to access and analyze the BIM model of the construction project, extract the structural hierarchy, construction sequence and component attributes, and use LiDAR to collect point cloud data of the target construction area. After spatial registration with the BIM model, a three-dimensional digital twin model of the target construction area is established; The area division unit is used in the three-dimensional digital twin model to divide the target construction area into several sub-areas, which are marked as {Q1, Q2, Q3, ..., Q n }; n represents the number of sub-regions.

3. A construction process management and control system based on digital twins according to claim 2, characterized in that: The construction site modeling module also includes a multi-source sensor data fusion unit, which is used to set detection points in each sub-area and collect information including temperature and humidity sensors, RFID tags and video recognition camera data sources to establish a sub-area status data set for supplementing material distribution, on-site heat sources and equipment status data, and uniformly map the data timestamps and spatial coordinates of the sub-area status data set to the three-dimensional digital twin model.

4. A construction process management and control system based on digital twins according to claim 3, characterized in that: The construction site modeling module also includes a material category identification unit and a material density identification unit; The material category identification unit is used to automatically obtain the material label and category by using an RFID reader on the electronic tags of the construction materials in each sub-area; The classification results include: steel bars, wooden formwork, cement bags and assembled components; The material density identification unit is used to use the voxel space partitioning method and density clustering algorithm to output: cluster density η i , the specific steps are: S11. Use the voxel method to divide the three-dimensional space of the construction site into cubic units. Specifically, each voxel unit is defined as V x,y,z , the side length is set to l v , set to 0.5m-1m; S12. After registering the point cloud data, map it into a voxel grid to form a dense lattice voxel spatial structure dataset. Perform cluster analysis on the dense lattice voxel spatial structure dataset using a density clustering algorithm, wherein the clustering parameters include the domain radius and the minimum number of points MinPts; output the cluster boundary of each sub-region and the cluster density η of the j-th type of material. j ; Based on the material label and the clustering density η of the j-th type of material j , clustering the density accumulation areas of the same type of materials to form steel concentration areas, formwork stacking areas and concrete pouring areas; S13, combined with cluster density η i And the classification results are used to calculate the packing density coefficient R of the i-th sub-area i : Where m represents the total number of material types identified in the sub-area, D j It represents the theoretical density per unit volume of the jth type of material in kg / m3, V j represents the voxel volume of the jth type of material, η j Represents the clustering density η of the j-th type of material j , obtained by step S12; V zone,i represents the total voxel volume of the i-th sub-region of the target.

5. A construction process management and control system based on digital twins according to claim 4, characterized in that: The heat risk identification module includes a weather collection unit, a construction equipment heat source collection unit, a heat island effect analysis unit and a first classification unit; The weather collection unit is used to use a temperature sensor to collect and obtain the ambient temperature T of the i-th sub-area. i , and collect the ambient temperature of all sub-areas of the target construction area, and obtain the sub-area temperature mean T by summing and averaging. env , the ambient temperature T of the i-th sub-area i and the mean temperature of the sub-region T env Subtract and obtain the ambient temperature difference T of the i-th sub-area w,i ; The construction equipment heat source acquisition unit is used to use a drone equipped with a thermal imaging camera to scan the equipment heat source distribution in each sub-area, obtain a heat distribution image, and identify the heat distribution image to obtain the total heat source power P of the i-th sub-area. total,i ; The heat island effect analysis unit is used to use a convolutional neural network to construct an initial convolutional neural network model and use the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i The initial convolutional neural network model is trained and tested, and the trained initial convolutional neural network model is used as the heat island risk identification model. At the same time, the intermediate layer output of the equipment operation status model is used as the feature vector to identify the feature information. The heat island risk identification model is trained and tested based on the obtained feature information. The trained heat island risk identification model is used as data for running prediction to calculate the local heat island formation coefficient Rs of the i-th sub-region. i : Extract the packing density coefficient R of the i-th sub-area i , the ambient temperature difference T of the i-th sub-area w,i , the total heat source power P of the i-th sub-region total,i Normalize it to the range of 0 to 1 and calculate the local heat island formation coefficient Rs of the i-th sub-region using the following formula: i : Rs i =a1×R i ′+a2×T w,i ′+a3×P total,i ′; Where a1, a2 and a3 represent the normalized packing density coefficient R of the ith sub-region, respectively. i ', the ambient temperature difference T of the i-th sub-area w,i ', the total heat source power P of the i-th sub-region total,i ' weight coefficient, and the sum of the weights is 1.

6. A construction process management and control system based on digital twins according to claim 5, characterized in that: The first classification unit is used to classify the local heat island formation coefficient Rs of the i-th sub-region according to the local heat island formation coefficient Rs of the i-th sub-region. i The first classification includes: When the local heat island formation coefficient Rs of the i-th sub-region i <0.3, indicating that there is temperature variation in the sub-area, but no personnel risk during construction. It is marked as the first low-risk area, and the first heat accumulation warning strategy is output, including: continuing construction and continuous monitoring; When 0.3≤Rs i If the value is ≤0.7, the sub-area is subject to a heat island effect, and the accumulated building materials are subject to expansion and deformation during construction. The area is marked as a second-moderate-risk area, and a second-level heat accumulation warning strategy is implemented, including adjusting construction hours to avoid 12:00 AM to 3:00 PM, laying thermal insulation curtains in steel material collection areas, formwork stacking areas, and concrete pouring areas, and sprinkling water every 3-4 hours to reduce temperatures. When Rs i >0.7, indicating that the construction environment in this sub-area has a heat island effect, which is higher than the risk in the second medium-risk area. There are risks of high-temperature operation, heat source efficiency failure, material performance deformation, and plasticity loss. It is marked as the third highest-risk area and outputs the third heat concentration warning strategy, including: adjusting the construction time to avoid 11:00 am-16:00 pm, laying insulation curtains in the steel concentration area, formwork stacking area, and concrete pouring area, and sprinkling water every 1-2 hours to cool down; and adjusting the settings to limit the operation of 20-30% of the regional heat source equipment, and configuring first aid points, water and electricity supply stations, and summer rest stations.

7. A construction process management and control system based on digital twins according to claim 1, characterized in that: The hydration heat simulation module includes a concrete simulation acquisition unit, a pouring temperature gradient acquisition unit and a hydration heat identification unit; The concrete simulation collection unit is used to perform a simulation test on the f-th concrete pouring component in the i-th sub-area and collect the cement heat release rate I before the concrete pouring process; Cement heat release rate I includes: when the P.O4 of Portland cement is 2.5, the cement heat release rate I = 2.5-3.5 J / g·h; when the P.O4 of Portland cement is medium-heat, the cement heat release rate I = 1.5-2.5 J / g·h; when the P.O4 of Portland cement is low-heat, the cement heat release rate I = 0.8-1.4 J / g·h; when the P.O4 of Portland cement concrete is mixed with 30% fly ash, the cement heat release rate I = 1.0-2.0 J / g·h; when the P.O4 of Portland cement concrete is mixed with 20% slag, the cement heat release rate I = 1.2-2.2 J / g·h; when the P.O4 of high-performance concrete is mixed with 10% silica fume, the cement heat release rate I = 3.5-4.0 / g·h; The pouring temperature gradient acquisition unit is used to bury a temperature monitoring line in the concrete pouring area of each sub-area, record the temperature data inside the concrete changing with time, and obtain the temperature gradient data of the f-th poured concrete component in the i-th sub-area.

8. A construction process management and control system based on digital twins according to claim 7, characterized in that: The hydration heat identification unit is used to perform in-depth analysis based on the cement heat release rate I and the temperature gradient data of the f-th cast concrete component in the i-th sub-region acquired in the concrete simulation acquisition unit to obtain the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-region. i,f ; Hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area i,f The specific way to obtain it is: S21. Calculate the total amount of hydration heat released per unit volume Qtotal within a fixed period of time t t , the expression is: S22. Calculate the core temperature rise ΔTpeak using the relationship between specific heat capacity and density. i , the expression is: Where C is the specific heat capacity of concrete, which is set to 900-1000 J / kg·K; ρ represents the density of concrete; And from the core temperature rise ΔTpeak i The maximum temperature value is extracted from the sequence as the core peak temperature Tpeak of the f-th poured concrete component in the i-th sub-area i : S23. Use the exponential cooling model to calculate the surface temperature Tsurface t : Tsurface t =T i +θ×e -γt Where, T i The ambient temperature of the i-th sub-region, θ represents the initial temperature difference, and γ represents the surface cooling coefficient, which are obtained in the experiment; S24, based on surface temperature Tsurface t and core temperature rise ΔTpeak t , calculate the temperature gradient ΔTgrad t : ΔTgrad t =ΔTcore t -Tsurface t And record the maximum temperature gradient ΔTgrad of the f-th poured concrete component in the i-th sub-area i,k , ΔTgrad i,k =maxΔTgrad t ; S25, the total amount of hydration heat released per unit volume Qtotal within a fixed time period t obtained by calculation in S21 to S24 t , the core peak temperature Tpeak of the f-th poured concrete component in the i-th sub-area i,k and the maximum temperature difference gradient ΔTgrad of the f-th cast concrete component in the i-th sub-area i,k After dimensionless processing, the normalized model is used to calculate the hydration heat risk coefficient Sr of the f-th concrete component in the i-th sub-area i,f : Where, Indicates the safety threshold of the total amount of hydration heat released per unit volume, Indicates the core peak temperature safety threshold, It represents the temperature gradient safety threshold, a4, a5 and a6 are weight coefficients, and the sum of the weights is 1.

9. A construction process management and control system based on digital twins according to claim 8, characterized in that: The hydration heat simulation module further includes a second classification unit; The second classification unit is used to classify the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area according to the hydration heat risk coefficient Sr i,f The second classification includes: When the hydration heat risk coefficient Sr of the f-th cast concrete component in the i-th sub-area i,f <0.3, indicating that there is no hydration heat risk during the pouring process of the concrete component, and it is marked as a first-level qualified component; When 0.3≤Sr i,f If the value is ≤0.7, the concrete component is at risk of hydration heat during the pouring process and is marked as a second-level unqualified component. The first maintenance control strategy is output, including: shielding the component surface with a sunshade net or covering, including straw curtains or plastic film, controlling the cooling rate to no more than 3°C per hour, increasing the cooling time by 10-20%, and spraying water every 4-5 hours for cooling for 24 hours after pouring. When Sr i,f >0.7, indicating that the concrete component has a hydration heat risk during the pouring process, and is marked as a third-level unqualified component, which has a higher risk than the second-level unqualified component. The second maintenance control strategy is output, including: shielding the component surface with a sunshade net or cover, including straw curtains or plastic film, controlling the cooling rate to no more than 2°C per hour, increasing the cooling time by 21-30%, and sprinkling water every 2-3 hours for cooling for 24 hours after pouring.

Images