Concrete curing optimization method and system based on combination of BIM and meteorological data

Abstract

The application provides a concrete maintenance optimization method and system based on BIM and meteorological data combination. First, BIM model data and real-time meteorological data of a region where a concrete structure is located are acquired to obtain a set of structure information including geometric parameters and material properties of the concrete member and meteorological data such as temperature, humidity and wind force. Then, a correlation mapping relationship between concrete structure maintenance requirements and meteorological factors is established. According to the mapping result and the pouring completion time, an initial maintenance scheme is generated. The initial scheme is updated and adjusted in combination with subsequent real-time meteorological data to obtain an optimized maintenance scheme. Finally, the optimized maintenance scheme is output to a concrete maintenance execution system to perform maintenance operations, so that the scientific optimization of concrete maintenance is realized.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for optimizing concrete curing based on the combination of BIM and meteorological data. Background Technology

[0002] In the field of concrete structure construction and curing, ensuring the quality and performance of concrete is of paramount importance. Concrete curing, as a crucial step in guaranteeing key properties such as concrete strength development and durability, directly impacts the quality and service life of concrete structures through the formulation and implementation of its curing plan.

[0003] Traditionally, concrete curing plans have relied heavily on empirical formulas and fixed curing specifications. Construction workers determine curing operation types, durations, and curing medium supply parameters based on past experience and general standards. However, this approach has significant shortcomings. Firstly, the environments of concrete structures vary greatly across different regions. Meteorological conditions such as temperature, humidity, and wind significantly impact the hydration reaction and drying shrinkage of concrete. Traditional methods struggle to fully account for these dynamically changing meteorological factors, leading to curing plans that are not accurately adapted to the actual environment, potentially resulting in under- or over-curing. Secondly, traditional methods lack detailed analysis of the inherent characteristics of concrete structures, such as the geometric parameters, material properties, and pouring area distribution of concrete components. These factors also affect the hydration process and curing requirements. Therefore, traditional concrete curing methods fail to achieve scientific, precise, and efficient curing, and cannot meet the high quality requirements of modern construction projects. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for optimizing concrete curing based on a combination of BIM and meteorological data, the method comprising:

[0005] Obtain BIM model data of the area where the concrete structure is located and real-time meteorological data of the area to obtain concrete structure information and real-time meteorological data set. The concrete structure information includes the geometric parameters, material properties and pouring area distribution of the concrete components. The real-time meteorological data set includes temperature change data, humidity change data and wind force change data.

[0006] Based on the concrete structure information and real-time meteorological data set, a correlation mapping relationship between the curing requirements of concrete structures and meteorological factors is established to obtain the curing correlation mapping result. The curing correlation mapping result is used to characterize the type and duration of curing operations required for concrete components under different meteorological conditions.

[0007] Based on the curing association mapping results and combined with the concrete component pouring completion time information, an initial concrete curing plan is generated. The initial concrete curing plan includes curing operation nodes, curing medium supply parameters, and curing monitoring frequency.

[0008] Based on the curing operation nodes and curing medium supply parameters in the initial curing scheme of the concrete, and combined with the updated data of the subsequent real-time meteorological data set, the curing medium supply parameters and curing monitoring frequency are adjusted to obtain the optimized curing scheme of the concrete.

[0009] The optimized concrete curing plan is output to the concrete curing execution system, so that the concrete curing execution system performs curing operations according to the optimized concrete curing plan.

[0010] In another aspect, embodiments of the present invention also provide a concrete curing optimization system based on the combination of BIM and meteorological data, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions or code. The processor is used to execute the programs, instructions or code in the machine-readable storage medium to implement the above-mentioned method.

[0011] Based on the above, this embodiment of the invention acquires BIM model data and real-time meteorological data of the area where the concrete structure is located. Based on this data, it establishes a correlation mapping relationship between the curing requirements of the concrete structure and meteorological factors. This allows for in-depth analysis of the curing requirements of concrete components under different meteorological conditions. An initial curing plan is generated based on the curing correlation mapping results. Combined with the concrete component's pouring completion time information, the initial curing plan is subsequently adjusted based on updated real-time meteorological data. This achieves dynamic optimization of the curing plan, enabling timely adaptation to changes in meteorological conditions and avoiding improper curing due to sudden weather changes. Finally, the optimized curing plan is output to the concrete curing execution system, ensuring precise execution of curing operations. This effectively improves the quality and efficiency of concrete curing, reduces the risk of quality problems in the concrete structure, and extends the service life of the concrete structure. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the execution flow of the concrete curing optimization method based on the combination of BIM and meteorological data provided in the embodiments of the present invention.

[0013] Figure 2 This is a schematic diagram of exemplary hardware and software components of a concrete curing optimization system based on the combination of BIM and meteorological data provided in an embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a concrete curing optimization method based on the combination of BIM and meteorological data, provided in one embodiment of the present invention. The following is a detailed description of this concrete curing optimization method based on the combination of BIM and meteorological data.

[0015] Step S110: Obtain BIM model data of the area where the concrete structure is located and real-time meteorological data of the area to obtain concrete structure information and real-time meteorological data set. The concrete structure information includes the geometric parameters, material properties and pouring area distribution of the concrete components. The real-time meteorological data set includes temperature change data, humidity change data and wind force change data.

[0016] This embodiment uses the curing of concrete floor slabs in a high-rise building as an application scenario. In this scenario, it is necessary to first collect the BIM model data of the high-rise building project. This BIM model data, stored in the project management database, contains the 3D modeling information of all concrete floor slabs. Simultaneously, multiple meteorological monitoring points are set up at and around the construction site. These monitoring points are equipped with temperature sensors, humidity sensors, and anemometers to collect real-time meteorological data for the area. The BIM model data and real-time meteorological data are aggregated to a data processing center via a data transmission protocol. After preliminary data verification and removal of invalid data, a set of concrete structure information and real-time meteorological data is formed.

[0017] Step S111: Call the BIM model parsing tool to extract structural parameters from the BIM model data of the area where the concrete structure is located, and obtain the geometric parameters, material properties and pouring area distribution of the concrete components. The geometric parameters include component size, component connection method and spatial location of the component. The material properties include concrete strength grade, aggregate type and admixture type.

[0018] In the data processing center, a BIM model parsing tool is run, accessing BIM model data from the project management database via its API interface. This tool features component identification and parameter extraction capabilities, distinguishing concrete floor slabs from other building components such as steel beams and walls. For the identified concrete floor slabs, geometric parameters are extracted, including dimensional data such as length, width, and thickness; the connection method between the slab and beams (e.g., cast-in-place or precast); and the slab's specific spatial location within the building, such as its location in the northeast area of ​​the third floor. Simultaneously, material properties are extracted, including the concrete strength grade (e.g., the design-required strength grade), the type of aggregate used (e.g., basalt aggregate), and the types of admixtures added (e.g., retarders). Furthermore, based on the floor slab's construction zones, the distribution of its pouring areas is determined, such as the pouring area belonging to the first construction section.

[0019] Step S1111: Start the BIM model parsing tool, import the BIM model data of the area where the concrete structure is located, and establish a data interaction channel between the BIM model data and the BIM model parsing tool.

[0020] Open the client program of the BIM model parsing tool. In the tool's interface, click the "Data Import" button. In the pop-up file selection window, locate the file path in the project management database where the BIM model data is stored, and select the corresponding BIM model file to import. During the import process, the tool will automatically verify file format compatibility. If a format mismatch occurs, it will prompt you to convert the format. After the file import is complete, the tool establishes a continuous data interaction channel with the BIM model data through its internal data interface, ensuring that the data information in the model can be read and parsed in real time during parameter extraction.

[0021] Step S1112: Using the component identification module of the BIM model parsing tool, automatically identify the concrete components in the BIM model, distinguish the concrete components from other types of components, and generate a preliminary identification list of concrete components.

[0022] Once the component identification module of the BIM model parsing tool is activated, it can traverse the imported BIM model layer by layer. This module incorporates a component type identification algorithm that analyzes the material parameters, structural features, and attribute labels of components in the BIM model to distinguish concrete slabs from other types of components such as steel structures and masonry. For example, based on the value of the "Material Type" field in the component attribute label, components with a value of "Concrete" are marked as concrete components. After identification, the BIM model parsing tool generates a preliminary identification list of concrete components, which includes a unique identifier for each concrete slab and its location coordinates in the BIM model.

[0023] Step S1113: Extract parameters for each component in the preliminary identification list of concrete components. The extracted parameters include geometric parameters and material properties. When extracting geometric parameters, the length, width, height and cross-sectional shape data of the component are obtained through the dimension measurement function of the BIM model analysis tool. The connection method and spatial location data of the component are obtained through the connection relationship analysis function of the BIM model analysis tool.

[0024] For each concrete slab in the preliminary identification list, the dimensional measurement function of the BIM model analysis tool is invoked. This function calculates the slab's length, width, and thickness by reading the 3D coordinate information of the components in the BIM model. For example, the length and width data are obtained by measuring the coordinate difference between two opposite edges of the slab; the thickness data is obtained by measuring the coordinate difference between the upper and lower surfaces of the slab. Simultaneously, the tool's cross-sectional shape analysis function scans the slab's cross-section to determine its shape, such as a rectangular cross-section. When acquiring connection information, the connection relationship analysis function examines the connection node attributes between the slab and adjacent components, determining the connection method based on the node's structural characteristics, such as checking for the presence of post-cast concrete areas to determine if it's a cast-in-place connection. For the component's spatial location data, the tool extracts the center point coordinates and floor number of the slab to determine its specific location within the building.

[0025] Step S1114: When extracting material properties, use the property reading function of the BIM model parsing tool to obtain the strength grade, aggregate type and admixture type data of the concrete component, mark the component identifier with missing attributes, and supplement the missing data from the associated documents of the BIM model.

[0026] Using the attribute reading function of the BIM model parsing tool, the attribute database of each concrete floor slab is accessed. This function retrieves material-related information from attribute fields, such as the value corresponding to the "concrete strength grade" field, the material name corresponding to the "aggregate type" field, and the additive list corresponding to the "admixture type" field. During the extraction process, if a material attribute data for a concrete floor slab is found to be missing, such as the aggregate type not being recorded, a unique identifier for that floor slab is marked in the identification list, and the missing attribute item is noted. Subsequently, the associated documents of the BIM model are opened, including construction design specifications, material arrival inspection reports, etc., and the corresponding material information for that floor slab is retrieved from these documents to complete the missing data.

[0027] Step S1115: Using the area division module of the BIM model analysis tool, based on the spatial location data of the concrete components, group the concrete components in the BIM model according to the pouring area, determine the identifier of the concrete components contained in each pouring area, and generate a pouring area distribution table.

[0028] The BIM model parsing tool's area division module is activated. This module first acquires the spatial location data of all concrete floor slabs, including floor numbers and planar coordinates. Based on the pouring zoning principles determined in the construction plan, such as division by floor or by planar area, the concrete floor slabs are grouped. For example, concrete floor slabs on the same floor and located within the same construction section are divided into one pouring area. Each pouring area is assigned a unique area number. The module then compiles a list of unique identifiers for all concrete floor slabs belonging to that area, generating a pouring area distribution table. The distribution table contains information such as area number, area name, and component identifiers.

[0029] Step S1116: Associate the extracted geometric parameters and material properties of the concrete components with the pouring area distribution table, establish the correspondence between parameters and areas according to the component identifier, and generate a structured data table containing component identifier, geometric parameters, material properties and the pouring area to which it belongs.

[0030] The geometric parameters and material properties of each previously extracted concrete slab are matched against a unique component identifier and a distribution table of pouring areas. For example, if the unique identifier of a concrete slab belongs to area A in the distribution table, then the slab's length, width, strength grade, and other parameters are associated with the information of area A. Through a data association algorithm, a correspondence is established between component identifiers and geometric parameters, material properties, and the pouring area to which they belong, ultimately generating a structured data table. This structured data table is in tabular form, with each row corresponding to a concrete slab, and columns for component identifier, length, width, thickness, connection method, spatial location, strength grade, aggregate type, admixture type, and pouring area.

[0031] Step S1117: Perform parameter extraction again to ensure that the dimensional data extracted by the BIM model parsing tool is consistent with the dimensional data displayed in the BIM model, and that the extracted material attribute data is consistent with the material requirements in the design document. Finally, obtain the geometric parameters, material attributes, and pouring area distribution of the concrete component.

[0032] The data in the generated structured data tables were reviewed. The BIM model parsing tool was used to re-extract the dimensional data of some concrete slabs and compare it with the dimensional data displayed in the BIM model. If discrepancies were found, the parameter settings of the extraction algorithm were checked, adjusted, and extracted again until they matched. For material attribute data, the extracted data was checked against the material requirements in the construction design documents, such as whether the concrete strength grade met the design standards and whether the aggregate type was consistent with the design specifications. If inconsistencies were found, the cause was investigated, and data was re-extracted or supplemented. After multiple reviews and adjustments to ensure data accuracy, the final geometric parameters, material properties, and pouring area distribution of the concrete components were determined.

[0033] Step S112: Collect real-time meteorological data of the area where the concrete structure is located using meteorological data acquisition equipment. The collected real-time meteorological data includes temperature change data, humidity change data, and wind force change data. The temperature change data includes ambient temperature values ​​at different times, the humidity change data includes relative humidity values ​​at different times, and the wind force change data includes wind speed values ​​and wind direction information at different times.

[0034] At the construction site of the high-rise building and surrounding meteorological monitoring points, temperature sensors collect ambient temperature data hourly at the top of the hour, such as 8:00 AM and 9:00 AM, to obtain temperature variation data for different time periods. Humidity sensors similarly collect relative humidity data hourly, recording humidity levels at different times. Anemometers continuously monitor wind speed and direction, recording wind speed and direction information (e.g., easterly, northwesterly) every half hour, generating wind force variation data. This collected real-time meteorological data is transmitted wirelessly to a data processing center and stored in a meteorological database.

[0035] Step S113: The extracted geometric parameters, material properties, and pouring area distribution of the concrete component are correlated with the collected temperature change data, humidity change data, and wind force change data in a time dimension, so that the structural information of each concrete component corresponds to the meteorological data of a specific time period.

[0036] Structural information for each concrete slab is extracted from a structured data table, including geometric parameters, material properties, and the area to which it was poured. Simultaneously, meteorological data for each time period is extracted from a meteorological database. A temporal correlation is established based on the pouring time of the concrete slab and the collection time of the meteorological data. For example, for a concrete slab completed at 9:00 AM on a certain day, its structural information is correlated with meteorological data for that time period and thereafter, ensuring that the structural information of each concrete slab corresponds to specific time-specific temperature, humidity, and wind speed data. This correlation process is achieved through timestamp matching; a pouring completion timestamp is added to the structural information of each concrete slab, and a collection timestamp is added to the meteorological data. Structural information and meteorological data with similar timestamps are then correlated.

[0037] Step S114: Based on the information associated with the time dimension, generate a structured data table containing concrete component identifiers, structural parameter items, and meteorological data items for the corresponding time period, as the initial form of the concrete structure information and real-time meteorological data set.

[0038] Based on the information correlated along the time dimension, a structured data table is generated. Each row of the table corresponds to the associated information of a concrete slab during a specific time period. Columns include concrete component identification, length, width, thickness, strength grade, aggregate type, admixture type, pouring area, associated time period, ambient temperature, relative humidity, wind speed, and wind direction during that time period. For example, if a concrete slab is identified as L001, the table will have a row recording its structural parameters such as length and width, as well as meteorological data for the period from 10:00 AM to 11:00 AM on a certain day. This table represents the initial form of the set of concrete structural information and real-time meteorological data.

[0039] Step S115: Collect missing structural parameters of concrete components or meteorological data for the corresponding time period in the structured data table to make all data items in the structured data table complete, and finally obtain a set of concrete structure information and real-time meteorological data.

[0040] The generated structured data tables are checked for missing data. If a structural parameter for a concrete slab is missing, such as the type of admixture not being recorded, the construction records or related documents in the BIM model are reviewed to supplement the parameter. If meteorological data for a certain period is missing, such as humidity data not being collected at 2 PM on a certain day, the humidity data for that period is estimated using interpolation, or data from backup meteorological monitoring points is used to supplement it. After supplementation and improvement, ensuring that all data items in the structured data tables are complete and accurate, the table at this point represents the final set of concrete structure information and real-time meteorological data.

[0041] Step S120: Based on the concrete structure information and real-time meteorological data set, establish a correlation mapping relationship between the curing requirements of concrete structures and meteorological factors, and obtain the curing correlation mapping result. The curing correlation mapping result is used to characterize the type and duration of curing operations required for concrete components under different meteorological conditions.

[0042] By combining final concrete structure information with real-time meteorological data, the relationship between the curing requirements of concrete slabs and meteorological factors is analyzed. Data mining techniques are used to identify the types of curing operations required for concrete slabs under different temperature, humidity, and wind conditions, and their corresponding operation durations. For example, the analysis examines whether concrete slabs require more frequent water spraying under high temperature and low humidity conditions, and how long these operations should last. By establishing these correlation mapping relationships, a curing correlation mapping result is formed, which clarifies the corresponding curing measures for different meteorological conditions.

[0043] Step S121: Extract the material properties of the concrete components and the corresponding temperature and humidity change data from the concrete structure information and real-time meteorological data set, and determine the key meteorological factors affecting the concrete curing effect. The key meteorological factors include the temperature change range, the duration of humidity change, and the degree of wind influence.

[0044] From the concrete structure information and real-time meteorological data set, the material properties of each concrete slab, such as strength grade and aggregate type, as well as corresponding temperature and humidity variation data, were selected. This data was analyzed to calculate the temperature variation range (the difference between the highest and lowest temperatures over a period of time); to calculate the duration of humidity variation (the duration of relative humidity below a certain threshold); and to assess the impact of wind, combining wind speed and direction to determine the magnitude of wind's influence on moisture evaporation from the concrete surface. By analyzing the correlation between these data and concrete curing effectiveness, the temperature variation range, humidity variation duration, and wind influence were identified as the key meteorological factors affecting concrete curing results.

[0045] Step S122: Based on the concrete curing process requirements, determine the required curing operation types for concrete components with different material properties under different key meteorological conditions. The curing operation types include water spraying curing, covering curing, and heat preservation curing, and each curing operation type corresponds to a specific range of meteorological conditions.

[0046] Referring to concrete curing process standards and specifications, the types of curing operations that should be adopted for concrete floor slabs with different material properties under different key meteorological conditions are clarified. For example, for high-strength concrete floor slabs, thermal insulation curing is required to prevent temperature cracks when the temperature variation range is large; water spraying curing is required to keep the surface moist when the humidity variation is prolonged and the air is dry; and covering curing is required to reduce moisture loss when strong winds cause rapid evaporation of surface moisture. At the same time, specific meteorological condition ranges are defined for each type of curing operation; for example, thermal insulation curing is initiated when the temperature variation range exceeds a certain value.

[0047] Step S123: Construct a maintenance demand-meteorological factor correlation model, taking the material properties and geometric parameters in the concrete structure information and the key meteorological factors in the real-time meteorological data set as input parameters, and the maintenance operation type and operation duration as output parameters.

[0048] Step S1231: Determine the core input dimensions of the maintenance requirements-meteorological factor correlation model. The core input dimensions include material property dimensions, geometric parameter dimensions, and key meteorological factor dimensions. Each core input dimension contains specific input parameter items. The input parameter items of the material property dimension include concrete strength grade, aggregate type, and admixture type. The input parameter items of the geometric parameter dimension include component size and component cross-sectional shape. The input parameter items of the key meteorological factor dimension include temperature fluctuation range, humidity duration, and wind influence range.

[0049] The core input dimensions of the clear maintenance needs-meteorological factor correlation model are material properties, geometric parameters, and key meteorological factors. Under the material properties dimension, specific input parameters include concrete strength grade, aggregate type, and admixture type; these parameters affect the hydration reaction rate and strength development of concrete. Input parameters for the geometric parameters dimension include component dimensions and cross-sectional shape; for example, the thickness and cross-sectional shape of a floor slab affect its heat dissipation and moisture retention capabilities. Input parameters for the key meteorological factors dimension include temperature fluctuation range, humidity duration, and wind influence range; these parameters directly affect the curing conditions of concrete.

[0050] Step S1232: Determine the output dimension of the maintenance demand-meteorological factor association model. The output dimension includes the maintenance operation type dimension and the operation duration dimension. The output parameter items of the maintenance operation type dimension include the identifiers of water spraying maintenance, covering maintenance and heat preservation maintenance. The output parameter items of the operation duration dimension include the duration corresponding to each maintenance operation type.

[0051] The output dimensions of the maintenance demand-meteorological factor correlation model are defined as maintenance operation type and operation duration. The output parameters for the maintenance operation type dimension are identifiers for water spraying, covering, and insulation maintenance, which can be represented by specific codes. The output parameters for the operation duration dimension are the duration corresponding to each maintenance operation type, i.e., the length of time the maintenance operation needs to be performed.

[0052] Step S1233: Select the association modeling method and construct a training sample set based on historical concrete curing data. The training sample set includes concrete structure information, historical meteorological data, and corresponding curing operation types and operation duration data from historical projects.

[0053] Random forest algorithm from machine learning was chosen as the association modeling method. Data from multiple historical high-rise building concrete slab curing projects were collected. This data included structural information of the concrete slabs in the historical projects, such as strength grade and dimensions; historical meteorological data, such as temperature and humidity changes; and the corresponding curing operation types and durations. This historical data was cleaned and preprocessed to remove outliers and duplicate data. Then, a training sample set was constructed according to the correspondence between input and output parameters.

[0054] Step S1234: Perform feature encoding processing on the input parameter items in the training sample set, converting classification parameters such as concrete strength grade, aggregate type, admixture type, component cross-sectional shape and curing operation type into numerical encoding form, and normalizing continuous parameters such as component size, temperature fluctuation range, humidity maintenance duration, wind influence range and operation duration.

[0055] For classification parameters in the training sample set, such as concrete strength grade, different strength grades are represented by different integers, such as C30 being represented by 1, C40 by 2, etc.; aggregate type, such as basalt aggregate being represented by 1, limestone aggregate by 2, etc. For continuous parameters, such as the length in component dimensions, the min-max normalization method is used to transform its value to the range of 0-1. Through the above feature encoding processing, the data in the training sample set can be adapted to the input requirements of the random forest algorithm.

[0056] Step S1235: Input the encoded training samples into the preset association modeling framework, calculate the association weights between different input parameter items and output parameter items through the feature association layer in the association modeling framework, and establish the correspondence between input parameter combinations and output parameters through the output mapping layer in the association modeling framework.

[0057] The encoded training samples are input into an association modeling framework built based on the random forest algorithm. The feature association layer in the framework analyzes the correlation between each input parameter and output parameter, calculating the association weight; for example, temperature fluctuation has a higher association weight with the maintenance operation type. The output mapping layer establishes a correspondence between the input parameters and the output parameters based on the combination of input parameters; that is, when the input parameters are a certain combination, the corresponding maintenance operation type and operation duration are output.

[0058] Step S1236: Train the maintenance demand-meteorological factor association model based on the training sample set, and adjust the association weight parameters in the maintenance demand-meteorological factor association model so that the matching degree between the maintenance operation type and operation duration output by the maintenance demand-meteorological factor association model and the actual data in the training sample set reaches the preset requirements.

[0059] A maintenance demand-meteorological factor correlation model was trained using a training sample set. During training, the correlation weight parameters in the model were continuously adjusted through iterative calculations. After each iteration, the maintenance operation type and duration output by the model were compared with the actual data in the training sample set to calculate the degree of matching. If the degree of matching did not meet the preset requirement (e.g., a preset matching degree of 90%), the correlation weight parameters were adjusted based on the comparison results, increasing the weight of input parameter items with a high degree of matching with the output parameters and decreasing the weight of input parameter items with a low degree of matching. This iterative calculation and parameter adjustment was repeated until the degree of matching between the maintenance operation type and duration output by the model and the actual data in the training sample set reached the preset requirement.

[0060] Step S1237: Use a test sample set to verify the output accuracy of the maintenance demand-meteorological factor association model. The test sample set includes concrete structure information, meteorological data and corresponding maintenance data that were not used in the training. Supplement the training sample set and retrain the maintenance demand-meteorological factor association model so that the matching degree between the maintenance operation type and operation duration output by the maintenance demand-meteorological factor association model and the actual data in the test sample set reaches the preset requirements, and finally obtain the maintenance demand-meteorological factor association model.

[0061] A test sample set is formed by selecting unused concrete structure information, meteorological data, and corresponding curing data from historical data. The input parameters in the test sample set are feature-encoded using the same processing method as the training sample set and then input into the trained curing demand-meteorological factor association model to obtain the model's output curing operation type and duration. These output results are compared with the actual curing data in the test sample set to calculate the matching degree. If the matching degree does not meet the preset requirements, the reasons for the mismatch are analyzed, and relevant sample data is added to the training sample set from historical data for retraining and adjustment. After multiple rounds of sample addition and retraining, until the model's output curing operation type and duration match the actual data in the test sample set to the preset requirements, the model at this point is the final curing demand-meteorological factor association model.

[0062] Step S124: Input the concrete structure information and the corresponding data in the real-time meteorological data set into the maintenance demand-meteorological factor association model, and calculate the matching degree of maintenance operation type and operation time adaptation value of different concrete components under the current meteorological conditions.

[0063] From the concrete structure information and real-time meteorological data set, the material properties, geometric parameters, and corresponding key meteorological factors of each concrete slab are extracted. After processing according to the previous feature encoding method, the data is input into the maintenance demand-meteorological factor association model. Based on the input data, the model calculates the matching degree between the concrete slab and each type of maintenance operation under the current meteorological conditions, such as the matching degree with water spraying curing and the matching degree with covering curing. At the same time, it calculates the appropriate operation time value for each type of maintenance operation, that is, the suitable operation time for the concrete slab.

[0064] Step S125: Based on the matching degree of the maintenance operation type and the adaptation value of the operation time, generate a correspondence table between the concrete component identifier and the maintenance operation type and operation time, which serves as the core content of the maintenance association mapping result.

[0065] Based on the calculated matching degree of the curing operation type, the curing operation type with the highest matching degree is selected for each concrete slab. Then, the operation time adaptation value corresponding to this curing operation type is determined as the curing operation time for that concrete slab. The unique identifier of the concrete slab, the selected curing operation type, and the corresponding operation time are compiled into a correspondence table. For example, the concrete slab identified as L001 corresponds to water spraying curing with an operation time of several hours; the concrete slab identified as L002 corresponds to covering curing with an operation time of several hours, and so on. This correspondence table is the core content of the curing association mapping result.

[0066] Step S126: Adjust the input parameter weights of the maintenance demand-meteorological factor association model, recalculate the maintenance operation type and operation time of concrete components with the same material properties and similar geometric parameters under similar meteorological conditions, update the corresponding relationship table, and finally obtain the maintenance association mapping result.

[0067] For concrete slabs with the same material properties, similar geometric parameters, and under similar meteorological conditions, if there are significant differences in the curing operation types or durations assigned to them in the correspondence table, the weights of the relevant input parameters in the curing demand-meteorological factor association model are adjusted. For example, if concrete slabs of the same strength grade and similar dimensions under similar temperature and humidity conditions calculate different curing operation types, the weights of material properties and meteorological factor parameters are increased. After adjusting the weights, the curing operation types and durations for these concrete slabs are recalculated, and the correspondence table is updated based on the new calculation results. After multiple adjustments and updates to ensure the data in the correspondence table is reasonable and consistent, the final curing association mapping result is obtained.

[0068] Step S130: Based on the curing association mapping results and combined with the concrete component pouring completion time information, generate an initial concrete curing plan. The initial concrete curing plan includes curing operation nodes, curing medium supply parameters, and curing monitoring frequency.

[0069] Based on the curing operation type and duration corresponding to each concrete slab in the curing correlation mapping results, and combined with the pouring completion time information of each concrete slab, the specific arrangement of curing operations is planned. The start, monitoring, and end points of the curing operations are determined, and the supply parameters of the curing medium, such as the supply volume per unit time, are calculated, as well as the frequency of curing monitoring, such as how often to monitor. This information is then integrated to form the initial curing plan for the concrete.

[0070] Step S131: Extract the curing operation type and operation duration corresponding to each concrete component from the curing association mapping result, and determine the curing operation start conditions for each concrete component. The curing operation start conditions include the time interval requirement after pouring and the ambient temperature threshold requirement.

[0071] The curing operation type and duration for each concrete slab are obtained from the curing association mapping results. Then, based on general specifications and experience in concrete curing, the starting conditions for curing operations for each concrete slab are determined. For example, for concrete slabs requiring water spraying curing, curing may need to begin within several hours of pouring, and the ambient temperature may not be lower than a certain threshold; for concrete slabs requiring thermal insulation curing, curing may need to begin immediately after pouring, and insulation may need to be strengthened if the ambient temperature is lower than a certain threshold. These starting conditions will serve as the basis for determining when to begin curing operations.

[0072] Step S132: Obtain the pouring completion time information of the concrete component, establish the time correspondence between the pouring completion time and the curing operation start conditions, and determine the curing operation start time point for each concrete component.

[0073] By reviewing construction records or BIM model data on construction progress, the completion time of each concrete slab's pouring is obtained, such as a specific date and time. Based on the time interval requirements in the curing operation start conditions, the start time for curing operations on each concrete slab is calculated. For example, if a concrete slab's pouring completion time is 8:00 AM, and the start conditions require a two-hour interval before curing begins, then the start time for curing operations is 10:00 AM. Simultaneously, considering the ambient temperature threshold requirements, if the ambient temperature does not meet the requirements at the calculated start time, the operation is postponed until the temperature meets the requirements, thus ultimately determining the start time for curing operations on each concrete slab.

[0074] Step S133: Based on the start time of the curing operation and the corresponding operation duration, plan the curing operation nodes for each concrete component. The curing operation nodes include the curing start node, the curing process monitoring node, and the curing end node.

[0075] Based on the start time of the curing operation for each concrete slab, and considering its operation duration, the curing start point is determined as the initial curing time. Then, according to the operation duration and the needs of curing monitoring, multiple monitoring points are set during the curing process, such as setting a monitoring point at regular intervals after the start of curing. The curing end point is the start time plus the operation duration. For example, if the curing start time for a concrete slab is 10:00 AM, and the operation duration is three days, monitoring points can be set at the same time each day, and the end point is 10:00 AM three days later.

[0076] Step S134: Based on the curing operation type, determine the type of curing medium required for each curing operation. The type of curing medium includes curing water, heat insulation film and moisture-retaining cloth. Calculate the supply amount of each curing medium per unit time based on the geometric parameters of the concrete component, and use it as the curing medium supply parameter.

[0077] Step S1341: Establish a correspondence table between maintenance operation types and maintenance medium types. Define the maintenance medium type corresponding to water spraying maintenance as maintenance water, the maintenance medium type corresponding to covering maintenance as moisturizing cloth or plastic film, and the maintenance medium type corresponding to heat preservation maintenance as heat preservation cotton or heat preservation blanket.

[0078] Create a mapping table between maintenance operation types and maintenance medium types, clearly specifying the maintenance medium used for different maintenance operation types. For example, the maintenance medium for spray curing is curing water; the maintenance medium for covering curing can be a moisturizing cloth or a plastic film, depending on the actual situation; and the maintenance medium for thermal insulation curing is thermal insulation cotton or a thermal insulation blanket. This mapping table allows for quick determination of the media type required for each maintenance operation.

[0079] Step S1342: Extract the geometric parameters of each concrete component from the concrete structure information, and extract the surface area, volume and number of exposed surfaces of the concrete component, wherein the number of exposed surfaces is the number of surfaces of the component that are in direct contact with the air.

[0080] The geometric parameters of each concrete slab are obtained from the concrete structure information. Its surface area is calculated as the sum of the areas of all surfaces of the slab; its volume is calculated as the product of its length, width, and thickness; and the number of exposed surfaces is counted, i.e., the number of surfaces in direct contact with air. For example, the upper and side surfaces of the slab are usually exposed, while the lower surface, if in contact with the formwork, is not considered exposed. These geometric parameters will be used to calculate the supply of curing medium.

[0081] Step S1343: For water spraying curing, determine the curing water coverage requirement based on the surface area and number of exposed surfaces of the concrete component, determine the curing water demand rate per unit surface area per unit time by combining the concrete strength grade in the component material properties, and calculate the curing water supply per unit time by multiplying the surface area by the demand rate per unit surface area.

[0082] For concrete slabs cured by spraying, the required coverage area and curing water volume are determined based on the surface area and the number of exposed surfaces. Then, referring to the curing requirements corresponding to the concrete strength grade, the curing water demand rate per unit surface area per unit time is determined. Different strength grades may have different demand rates; higher strength grades may require higher demand rates. Multiplying the surface area by the demand rate per unit surface area yields the curing water supply per unit time.

[0083] Step S1344: For the covered curing type, determine the coverage area requirement of the curing medium based on the surface area and exposed surface shape of the concrete component, determine the required quantity of the curing medium based on the specifications of the curing medium, and determine the replenishment amount of the curing medium per unit time based on the usage requirements of the curing medium, which is used as the supply amount of the covered curing medium per unit time.

[0084] For concrete slabs requiring curing, the area to be covered by the curing medium is determined based on its surface area and the shape of the exposed surface, such as rectangular or irregular shapes. Then, the required quantity of curing medium is calculated by considering its specifications, such as the area of ​​a single sheet of the moisture-retaining cloth. Simultaneously, the replenishment amount per unit time is determined based on the usage requirements of the curing medium, such as the possibility of the moisture-retaining cloth being damaged by wind during use and requiring replenishment. This replenishment amount is the supply quantity of the curing medium per unit time.

[0085] Step S1345: For thermal insulation curing type, determine the required covering thickness of the thermal insulation medium based on the volume of the concrete component and the number of exposed surfaces, determine the required volume of the thermal insulation medium based on the thermal conductivity of the thermal insulation medium, and determine the amount of thermal insulation medium lost and replenished per unit time based on the required service time of the thermal insulation medium, which is used as the supply amount of the thermal insulation curing medium per unit time.

[0086] For concrete slabs undergoing thermal insulation curing, the required thickness of the insulation medium is calculated based on its volume and the number of exposed surfaces. Larger slabs with more exposed surfaces may require a thicker insulation layer. The required volume of the insulation medium is determined by considering its thermal conductivity; lower conductivity results in better insulation. Then, the required service life of the insulation medium is taken into account, taking into account losses during use. For example, insulation cotton may reduce its insulation effect due to compression and require replenishment. This replenishment amount per unit time is the supply of the thermal insulation curing medium per unit time.

[0087] Step S1346: Organize the calculated curing medium supply per unit time corresponding to different curing operation types according to the concrete component identification, and mark the curing medium type and supply quantity corresponding to each concrete component at different curing stages.

[0088] The calculated curing medium supply per unit time for each type of curing operation is categorized and organized according to the unique identifier of the concrete slab. For concrete slabs that require a change in curing medium type during the curing process, such as first performing water spraying curing and then covering curing, the curing medium type and supply volume for each stage are labeled separately. For example, for a concrete slab identified as L003, water spraying curing is used for the first few hours with a certain supply volume per unit time, followed by covering curing with a different supply volume per unit time.

[0089] Step S1347: Recheck the geometric parameter extraction results and demand rate calculation logic, adjust the supply quantity value, so that the curing medium supply quantity of concrete components of the same type and similar geometric parameters is within a reasonable range, and finally obtain the curing medium supply parameters.

[0090] For concrete slabs with the same curing operation type and similar geometric parameters, check the accuracy of their extracted geometric parameters and the consistency of the demand rate calculation logic. If errors are found in the extracted surface area or volume, or if differences in the demand rate calculation logic lead to unreasonable supply values, re-extract the geometric parameters and correct the calculation logic. Adjust the supply values ​​based on the corrected results to ensure that the curing medium supply for concrete slabs of the same type and similar geometric parameters is within a reasonable and similar range, and finally determine the curing medium supply parameters.

[0091] Step S135: Based on the time interval of the curing operation nodes, determine the curing monitoring frequency. The curing monitoring frequency is the time interval between two adjacent curing process monitoring nodes. The monitoring content includes concrete surface temperature, concrete surface humidity and remaining amount of curing medium.

[0092] The maintenance monitoring frequency is determined based on the time interval between two adjacent maintenance process monitoring nodes. For example, if there is a two-hour interval between two monitoring nodes, the maintenance monitoring frequency is once every two hours. The monitoring content for each session includes the temperature of the concrete surface, measured by a temperature sensor; the humidity of the concrete surface, measured by a humidity sensor; and the remaining amount of curing media, such as the remaining amount of curing water and the integrity of the moisture-retaining cloth.

[0093] Step S136: Classify and integrate the curing operation nodes, curing medium supply parameters and curing monitoring frequency according to the concrete component identification, and generate a document containing component identification, curing operation details and monitoring requirements as the initial curing plan for concrete.

[0094] The curing operation nodes (start, monitoring, and end times), curing medium supply parameters (medium type, supply rate per unit time), and curing monitoring frequency (monitoring interval, monitoring content) of each concrete floor slab are categorized and organized according to their unique identifiers. A curing record is created for each concrete floor slab, containing all the above information, forming an initial concrete curing plan that lists the curing operation details and monitoring requirements for each concrete floor slab.

[0095] Step S137: Adjust the start time of curing operations for some concrete components to obtain a time-coordinated initial curing plan for concrete.

[0096] If the start times of curing operations for multiple concrete slabs are too concentrated, it may lead to insufficient curing equipment and personnel. In such cases, the start times of curing operations for some concrete slabs should be adjusted. For example, the start time for some concrete slabs can be appropriately delayed or advanced to avoid peak curing periods. Adjustments must ensure that the starting conditions for curing operations are not violated, such as remaining within the specified time intervals and meeting ambient temperature requirements. After adjustment, the initial concrete curing plan should be updated to obtain a time-coordinated version.

[0097] Step S140: Based on the curing operation nodes and curing medium supply parameters in the initial curing scheme of the concrete, and combined with the updated data of the subsequent real-time meteorological data set, adjust the curing medium supply parameters and curing monitoring frequency to obtain the optimized curing scheme of the concrete.

[0098] During the implementation of the initial concrete curing plan, real-time meteorological data is continuously collected. The newly collected updated data is compared with the meteorological data used when the initial plan was formulated to analyze the changes in meteorological factors. Based on the changes, the supply parameters of the curing medium and the frequency of curing monitoring are adjusted, such as increasing the curing water supply and shortening the monitoring interval when the weather is dry, thereby forming an optimized concrete curing plan.

[0099] Step S141: Collect real-time meteorological data for subsequent periods according to the maintenance monitoring frequency in the initial concrete curing plan, and obtain updated data of the real-time meteorological data set. The updated data includes newly added content such as temperature change data, humidity change data and wind force change data for subsequent periods.

[0100] Based on the monitoring frequency set in the initial concrete curing plan, real-time meteorological data for subsequent periods are collected simultaneously at each monitoring time point. This data includes temperature changes (e.g., hourly temperature), humidity changes (e.g., hourly relative humidity), and wind changes (e.g., wind speed and direction every half hour). These newly added data are then added to the real-time meteorological dataset to form updated data.

[0101] Step S142: Compare the updated data of the real-time meteorological data set with the real-time meteorological data used when the initial curing scheme for concrete was generated, analyze the changing trends of meteorological factors, and determine the temperature change range, humidity change range, and wind force change range.

[0102] The updated real-time meteorological data is compared one by one with the meteorological data used when formulating the initial curing plan for concrete. The magnitude of temperature change (the maximum difference between the updated and initial temperatures), humidity change (the maximum difference between the updated and initial relative humidity), and wind change (the maximum difference between the updated and initial wind speeds) are calculated. These magnitudes are then used to analyze whether the meteorological factors are trending upwards, downwards, or fluctuating.

[0103] Step S143: Based on the changing trends of the meteorological factors and in conjunction with the maintenance correlation mapping results, assess whether the supply of maintenance water per unit time in the maintenance medium supply parameters needs to be increased, and assess whether the coverage of moisturizing maintenance media needs to be expanded.

[0104] Based on the changing trends of meteorological factors, such as an upward trend in temperature and a downward trend in humidity, it indicates that the environment may become drier. In this case, combined with the curing correlation mapping results regarding the requirements for curing in dry environments, it is necessary to assess whether the supply of curing water per unit time needs to be increased to keep the concrete surface moist. Simultaneously, it is assessed whether the coverage area of ​​moisture-retaining curing media, such as moisture-retaining cloths or plastic films, needs to be expanded to reduce moisture evaporation. For example, when wind speeds increase, it may be necessary to expand the coverage area to block direct wind from blowing onto the concrete surface.

[0105] Step S144: Based on the evaluation results, adjust the maintenance medium supply parameters, increase the maintenance water supply per unit time and determine the increased supply value, expand the coverage of moisturizing maintenance medium and determine the expanded coverage area boundary.

[0106] If the assessment indicates a need to increase the supply of curing water per unit time, the increase ratio or specific value should be calculated based on the degree of weather dryness and the surface area of ​​the concrete slab to determine the increased supply. For example, if the humidity drop is significant, the supply can be increased by a certain percentage. For situations requiring an expansion of the coverage area for moisture-retaining curing media, the expansion range should be determined based on wind speed and the dimensions of the concrete slab, such as extending a certain distance beyond the edge of the slab, and the boundaries of the expanded coverage area should be clearly defined to ensure effective moisture retention.

[0107] Step S145: Simultaneously, based on the variation range of the meteorological factors, adjust the maintenance monitoring frequency, shorten the time interval between two adjacent maintenance process monitoring nodes to increase the maintenance monitoring frequency, maintain the original maintenance monitoring frequency, or extend the monitoring interval.

[0108] Depending on the magnitude of changes in meteorological factors, if the changes in temperature, humidity, or wind force are significant, they may have a substantial impact on the curing effect of concrete. In this case, the time interval between monitoring points of two adjacent curing processes should be shortened, and the monitoring frequency should be increased to promptly grasp the state of the concrete. If the changes are minor and have little impact on the curing effect, the original monitoring frequency should be maintained. If the meteorological conditions tend to be stable and conducive to curing, and the changes are minimal, the monitoring interval can be appropriately extended.

[0109] Step S146: Integrate the adjusted curing medium supply parameters and curing monitoring frequency with other contents in the initial curing plan of the concrete, update the curing process monitoring node time in the curing operation node, and generate the initial draft of the adjusted curing plan.

[0110] The adjusted curing medium supply parameters (such as increased curing water supply and expanded coverage) and curing monitoring frequency (such as shortened monitoring intervals) are integrated with other elements in the initial concrete curing plan, including the start and end points of curing operations and the type of curing medium. Based on the new curing monitoring frequency, the monitoring times for each curing process are updated. For example, monitoring nodes that were originally every two hours are adjusted to once per hour, and the specific time points for each monitoring node are recalculated and marked. For instance, the original curing process monitoring nodes for a concrete slab were 10:00 AM, 12:00 PM, and 2:00 PM; after adjustment, they become 10:00 AM, 11:00 AM, and 12:00 PM. This updated information is then integrated with other elements in the initial plan, such as the start and end times of curing operations and the type of curing medium, to form a draft of the adjusted curing plan, ensuring that all elements in the plan are mutually compatible and logically consistent.

[0111] Step S147: Readjust the curing medium supply parameters or curing monitoring frequency in the initial draft of the curing plan so that the curing medium supply parameters are within the supply capacity of existing curing resources, and finally obtain the optimized curing plan for concrete.

[0112] The revised draft of the curing plan is reviewed, with a focus on whether the curing medium supply parameters are within the supply capacity of existing curing resources. If it is found that the unit time supply of curing water exceeds the maximum supply capacity of the on-site water supply equipment, or the required amount of moisture-retaining cloth exceeds the existing inventory, the curing medium supply parameters need to be readjusted. For example, the unit time supply of curing water can be appropriately reduced, or the coverage area of ​​the moisture-retaining cloth can be narrowed, and the curing monitoring frequency can be adjusted accordingly to compensate for any potential impact. If adjusting the curing medium supply parameters still cannot meet the requirements, the curing monitoring frequency can be adjusted, such as extending the monitoring interval to reduce the operating pressure on the monitoring equipment, thereby indirectly coordinating curing resources. After multiple adjustments and balancing, until the curing medium supply parameters are completely within the supply capacity of existing curing resources, the plan at this point is the optimized concrete curing plan.

[0113] Step S1471: Obtain the existing curing resource information for the current concrete curing project. The existing curing resource information includes the current inventory of curing media, the maximum supply capacity of curing media per unit time, the number of curing monitoring devices, and the number of curing monitoring personnel.

[0114] The project's material management system was used to query the existing curing resources for the current concrete curing project. This included: the current inventory of curing media (such as the amount of curing water, the quantity of moisture-retaining cloth, and the number of rolls of insulation cotton); the maximum supply capacity of curing media per unit time (such as the maximum hourly water supply of the water supply equipment and the maximum hourly coverage of the moisture-retaining cloth laying equipment); the number of curing monitoring devices (such as the number of temperature and humidity sensors); and the number of curing monitoring personnel (i.e., the number of personnel involved in curing monitoring work). This information was compiled into a list to serve as the basis for evaluating the feasibility of the initial draft curing plan.

[0115] Step S1472: Extract the adjusted curing medium supply parameters from the initial draft of the curing plan, calculate the total unit time supply of different curing media in each time period, and calculate the total demand of curing media in each time period. The total demand is the sum of the unit time supply of curing media for all concrete components in that time period.

[0116] Extract the curing medium supply parameters for each concrete slab at different times from the initial draft of the curing plan, such as the unit time supply of curing water and the unit time replenishment of moisture-retaining cloth. Summarize the supply parameters of different curing media according to time periods, and calculate the total unit time supply of each curing medium in each time period. For example, in the period from 8:00 to 9:00 AM, calculate the sum of the unit time supply of curing water for all concrete slabs to obtain the total demand for curing water during that period; similarly, calculate the total demand for other curing media such as moisture-retaining cloth and insulation cotton.

[0117] Step S1473: Coordinate to increase the supply channels of curing media or adjust the curing operation type of some concrete components to reduce the total demand for curing media or increase the supply capacity, so that the total demand for curing media in each period is within the maximum supply capacity of curing media per unit time, and the existing inventory of curing media can meet the total demand throughout the entire curing cycle.

[0118] The total demand for curing media in each time period is compared with the corresponding maximum supply capacity per unit time. If the total demand exceeds the maximum supply capacity, measures are taken to coordinate. On the one hand, the supply channels for curing media can be increased, such as contacting additional water trucks to replenish curing water or urgently transporting moisture-retaining cloth to increase inventory. On the other hand, the curing operation type of some concrete components can be adjusted, such as changing some spray curing to covering curing, to reduce the total demand for curing water. Through these measures, the total demand for curing media in each time period is ensured not to exceed its maximum supply capacity per unit time, while ensuring that the existing inventory can meet the total demand throughout the entire curing cycle, avoiding supply interruptions.

[0119] Step S1474: Extract the adjusted maintenance monitoring frequency from the initial draft of the maintenance plan, and calculate the monitoring workload required for each monitoring node in the maintenance process. The monitoring workload includes the number and duration of maintenance monitoring equipment used, and the working hours of maintenance monitoring personnel.

[0120] Based on the revised maintenance monitoring frequency in the initial draft of the maintenance plan, determine the time and number of concrete slabs involved for each monitoring node in the maintenance process. Calculate the monitoring workload required for each monitoring node: the number of maintenance monitoring devices used, i.e., the number of temperature and humidity sensors required to be used simultaneously at that node; the usage time, i.e., the time for sensors to be installed on the concrete slabs for data collection; and the working time for maintenance monitoring personnel, i.e., the time spent by personnel to arrive at the monitoring site, install equipment, and record data. Summarize these workloads according to the monitoring nodes and assess whether existing monitoring resources can meet the requirements.

[0121] Step S1475: Coordinate the increase of maintenance monitoring equipment and personnel or extend the time interval between monitoring nodes so that the monitoring workload of each maintenance process monitoring node is within the capacity of the number of maintenance monitoring equipment and personnel.

[0122] If the number of monitoring devices required for a certain maintenance process monitoring node exceeds the existing number of devices, or if the working hours of monitoring personnel exceed a reasonable range, coordination is necessary. This can involve contacting the equipment management department to increase the number of maintenance monitoring devices or temporarily assigning additional maintenance monitoring personnel to share the workload. If it is not possible to increase equipment and personnel in a timely manner, the time interval between monitoring nodes should be appropriately extended, such as changing monitoring from once every hour to once every two hours, thereby reducing the monitoring workload per unit time and ensuring that the workload of each monitoring node remains within the capacity of existing equipment and personnel.

[0123] Step S1476: Repeatedly coordinate and adjust the measures related to the supply of curing media and the measures related to monitoring resources to ensure that the curing media supply parameters in the initial draft of the curing plan are within the supply capacity of existing curing resources, and finally determine a feasible optimized curing plan for concrete.

[0124] After implementing the aforementioned coordination measures, the curing medium supply parameters and monitoring workload in the initial draft of the curing plan should be re-examined to ensure they meet the requirements. If there are still instances where the total demand exceeds the supply capacity or the monitoring workload is excessive during certain periods, the curing medium supply channels, curing operation types, monitoring equipment and personnel configurations, or monitoring intervals should be adjusted again. This process should be repeated until all curing medium supply parameters in the initial draft of the curing plan are within the supply capacity of existing curing resources, and the monitoring workload can be handled by existing monitoring resources. At this point, the plan is determined to be a feasible optimized concrete curing plan.

[0125] Step S150: Output the optimized concrete curing plan to the concrete curing execution system, so that the concrete curing execution system performs the curing operation according to the optimized concrete curing plan.

[0126] The finalized optimized concrete curing plan is transmitted to the concrete curing execution system in the form of a data file. After receiving the optimized concrete curing plan, the concrete curing execution system controls the curing equipment to operate according to the requirements of the optimized plan, arranges monitoring work, and makes dynamic adjustments during the curing process to ensure that the curing operation is strictly carried out in accordance with the optimized plan.

[0127] For example, step S151: The optimized concrete curing scheme is converted into a data format that can be recognized by the concrete curing execution system.

[0128] The concrete curing optimization plan is processed using a data format conversion tool. This involves converting the time information of curing operation nodes into a system-recognizable timestamp format; converting the numerical information in the curing medium supply parameters into the system-defined numerical type; and converting the curing monitoring frequency into an interval format that the system can understand. For example, "8:00 AM" is converted into a corresponding Unix timestamp, and "supplying 50 liters of water per hour" is converted into a numerical representation internal to the system. It is ensured that all converted content can be correctly read and parsed by the concrete curing execution system.

[0129] Step S152: Establish a data transmission channel with the concrete curing execution system, and transmit the format-converted optimized concrete curing plan to the concrete curing execution system so that the concrete curing execution system can extract the curing operation nodes, corresponding curing medium supply parameters and curing monitoring requirements of each concrete component after receiving the optimized concrete curing plan, and establish the correspondence between component identification and curing task.

[0130] A connection is established with the concrete curing execution system via a network interface or data transmission line to ensure stable data transmission. The optimized concrete curing plan, after format conversion, is transmitted to the execution system through this channel. Upon receiving the plan, the execution system parses the content, extracting the curing operation nodes (start, monitoring, and end times), corresponding curing medium supply parameters (medium type, supply rate per unit time), and curing monitoring requirements (monitoring content, frequency) for each concrete slab. This information is then associated with the unique identifier of the concrete slab, establishing a correspondence between component identification and curing tasks. For example, a concrete slab identified as L005 corresponds to a curing task starting at 9:00 AM on a certain day, covered with a moisture-retaining cloth, and monitored every two hours.

[0131] Step S153: Control the concrete curing execution system to generate a curing task execution schedule according to the time requirements of the curing operation nodes, and send control commands to the curing equipment according to the time nodes in the curing task execution schedule, so as to control the curing equipment to supply curing medium according to the curing medium supply parameters, control the water spraying equipment to spray curing water according to the supply amount per unit time, and control the covering equipment to lay moisturizing cloth or heat insulation material.

[0132] The concrete curing execution system integrates all curing tasks based on the time requirements of each concrete slab's curing operation nodes, generating a detailed curing task execution schedule. This schedule clearly defines the curing operations to be performed at each time node and the corresponding concrete slab. At the scheduled time node, the execution system sends control commands to the corresponding curing equipment. For example, at the start time of water spraying curing, a command is sent to the water spraying equipment to spray curing water onto the designated concrete slab according to the set unit time supply; at the start time of covering curing, a command is sent to the covering equipment to control it to lay moisture-retaining cloth or insulation material on the designated concrete slab.

[0133] Step S154: During the curing process, the concrete curing execution system is controlled to trigger the curing monitoring equipment to collect curing status data of the concrete component according to the curing monitoring frequency requirements. The curing status data includes concrete surface temperature, concrete surface humidity and remaining curing medium. The collected curing status data is compared with the preset requirements in the optimized concrete curing scheme.

[0134] During the curing process, the concrete curing execution system triggers the curing monitoring equipment at set intervals according to the curing monitoring frequency. Temperature and humidity sensors collect temperature and humidity data from the concrete surface, while the remaining curing medium monitoring device collects data such as the remaining amount of curing water and the integrity of the moisture-retaining cloth. The execution system compares this collected curing status data with the preset requirements in the optimized concrete curing plan, such as comparing the actual surface temperature with the temperature range specified in the plan and comparing the actual surface humidity with the preset humidity threshold.

[0135] Step S155: If the curing status data meets the preset requirements, control the concrete curing execution system to continue to perform the curing operation according to the original plan; if the curing status data does not meet the preset requirements, control the concrete curing execution system to generate early warning information, feed the early warning information back to the curing management terminal, and suspend the current curing operation so that after the management personnel adjust the concrete optimized curing plan according to the early warning information, the concrete curing execution system will re-execute the curing operation according to the adjusted plan.

[0136] When the curing status data matches the preset requirements, the concrete curing execution system continues to operate the curing equipment according to the original optimized concrete curing plan, maintaining the current curing medium supply parameters and monitoring frequency. If the curing status data does not meet the preset requirements, such as the concrete surface temperature exceeding the specified range or the humidity falling below the threshold, the execution system immediately generates an early warning message. The message includes the abnormal concrete component identifier, specific abnormal data, and the corresponding preset requirements. The early warning message is transmitted to the curing management terminal via the network, and the execution system simultaneously suspends the current curing operation for that concrete component. After reviewing the early warning message, the management personnel analyze the cause of the anomaly and adjust the optimized concrete curing plan, such as adding insulation measures or increasing the water spraying frequency. After the adjusted plan is transmitted to the execution system, the system restarts the curing operation according to the new plan.

[0137] Step S156: When the curing operation reaches the curing end node in the optimized concrete curing plan, control the concrete curing execution system to stop the operation of the curing equipment, collect the concrete state data at the end of curing, generate a curing execution report, the curing execution report includes the actual execution time of the curing operation, the actual consumption of curing medium and the final state data of the concrete, and store the curing execution report in the system database and output it to the curing management terminal.

[0138] When the curing operation reaches the end point in the optimized concrete curing plan, the concrete curing execution system sends a stop command to the curing equipment, shutting down the water spraying equipment, covering equipment, etc., and stopping all curing operations. Subsequently, the execution system controls the monitoring equipment to collect concrete state data at the end of curing, including the final surface temperature and humidity. Based on the records during the curing process, the actual execution time of the curing operation (the actual time from start to finish) and the actual consumption of curing media (such as the total amount of curing water used and the actual number of moisture-retaining cloths used) are calculated. These data are integrated with the final concrete state data to generate a curing execution report. The execution system stores this report in the system database for subsequent data analysis and project summary, and also outputs the report to the curing management terminal for management personnel to view and archive.

[0139] Figure 2 The illustration shows exemplary hardware and software components of a concrete curing optimization system 100 based on BIM and meteorological data, which can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the concrete curing optimization system 100 based on BIM and meteorological data and to perform the functions in this application.

[0140] The concrete curing optimization system 100 based on BIM and meteorological data can be a general-purpose server or a special-purpose server; both can be used to implement the concrete curing optimization method based on BIM and meteorological data of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0141] For example, a concrete curing optimization system 100 based on BIM and meteorological data may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the concrete curing optimization system 100 based on BIM and meteorological data may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The concrete curing optimization system 100 based on BIM and meteorological data also includes an I / O interface 150 between the computer and other input / output devices.

[0142] For ease of explanation, only one processor is described in the concrete curing optimization system 100 based on BIM and meteorological data. However, it should be noted that the concrete curing optimization system 100 based on BIM and meteorological data in this application may also include multiple processors. Therefore, the steps performed by one processor as described in this application may also be performed jointly by multiple processors or individually. For example, if the processor of the concrete curing optimization system 100 based on BIM and meteorological data performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually by one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0143] Furthermore, this embodiment of the invention also provides a readable storage medium, which has computer-executable instructions pre-set in it. When the processor executes the computer-executable instructions, the above-mentioned concrete curing optimization method based on the combination of BIM and meteorological data is implemented.

[0144] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for concrete curing optimization based on BIM and meteorological data combination, characterized in that, The method comprises: Obtain BIM model data of the area where the concrete structure is located and real-time meteorological data of the area to obtain a set of concrete structure information and real-time meteorological data, wherein the concrete structure information comprises geometric parameters, material properties and pouring area distribution of the concrete member, and the set of real-time meteorological data comprises temperature change data, humidity change data and wind change data; Based on the set of concrete structure information and real-time meteorological data, an associated mapping relationship between concrete structure maintenance requirements and meteorological factors is established to obtain a maintenance associated mapping result, which is used to represent the required maintenance operation type and operation time length of the concrete member under different meteorological conditions; According to the maintenance associated mapping result, combined with the pouring completion time information of the concrete member, a concrete initial maintenance scheme is generated, which comprises a maintenance operation node, a maintenance medium supply parameter and a maintenance monitoring frequency; Based on the maintenance operation node and the maintenance medium supply parameter in the concrete initial maintenance scheme, combined with the update data of the subsequently collected set of real-time meteorological data, the maintenance medium supply parameter and the maintenance monitoring frequency are adjusted to obtain a concrete optimized maintenance scheme; The concrete optimized maintenance scheme is output to a concrete maintenance execution system, so that the concrete maintenance execution system performs maintenance operation according to the concrete optimized maintenance scheme; The concrete initial maintenance scheme is generated according to the maintenance associated mapping result combined with the pouring completion time information of the concrete member, which comprises: From the maintenance associated mapping result, the maintenance operation type and operation time length corresponding to each concrete member are extracted to determine the maintenance operation starting condition of each concrete member, which comprises time interval requirement and environmental temperature threshold requirement after pouring completion; The pouring completion time information of the concrete member is obtained to establish a time correspondence between the pouring completion time and the maintenance operation starting condition, and the maintenance operation starting time point of each concrete member is determined; Based on the maintenance operation starting time point and the corresponding operation time length, the maintenance operation node of each concrete member is planned, which comprises a maintenance start node, a maintenance process monitoring node and a maintenance end node; According to the maintenance operation type, the required maintenance medium type of each maintenance operation is determined, which comprises maintenance water, heat preservation film and moisture retention cloth, and the supply amount of each maintenance medium per unit time is calculated as the maintenance medium supply parameter combined with the geometric parameters of the concrete member; Based on the time interval of the maintenance operation node, the maintenance monitoring frequency is determined, which is the time interval between adjacent two maintenance process monitoring nodes, and the monitoring content comprises concrete surface temperature, concrete surface humidity and maintenance medium remaining amount; The maintenance operation node, the maintenance medium supply parameter and the maintenance monitoring frequency are classified and integrated according to the concrete member identification to generate a document comprising member identification, maintenance operation details and monitoring requirements as the concrete initial maintenance scheme; The maintenance operation starting time point of part of the concrete members is adjusted to obtain a time-coordinated concrete initial maintenance scheme.

2. The method of claim 1, wherein the method is based on a combination of BIM and weather data. The BIM model data of the area where the concrete structure is located and real-time meteorological data of the area are acquired to obtain a concrete structure information and real-time meteorological data set, including: A BIM model analysis tool is called to perform structure parameter extraction processing on the BIM model data of the area where the concrete structure is located to obtain geometric parameters, material properties and pouring area distribution of the concrete member, wherein the geometric parameters include member size, member connection mode and spatial position of the member, and the material properties include concrete strength grade, aggregate type and admixture type; Real-time meteorological data of the area where the concrete structure is located is collected by a meteorological data collection device, and the collected real-time meteorological data includes temperature change data, humidity change data and wind force change data, wherein the temperature change data includes environmental temperature values at different time periods, the humidity change data includes air relative humidity values at different time periods, and the wind force change data includes wind speed values and wind direction information at different time periods; The extracted geometric parameters, material properties and pouring area distribution of the concrete member are subjected to time-dimension association processing with the collected temperature change data, humidity change data and wind force change data, so that the structure information of each concrete member corresponds to meteorological data of a specific time period; Based on the information associated in the time dimension, a structured data table containing concrete member identification, structure parameter items and corresponding time period meteorological data items is generated as an initial form of the concrete structure information and real-time meteorological data set; The missing concrete member structure parameters or corresponding time period meteorological data in the structured data table are supplemented to make all data items in the structured data table complete, and finally a concrete structure information and real-time meteorological data set is obtained.

3. The method of claim 1, wherein the method further comprises: Based on the concrete structure information and real-time meteorological data set, an associated mapping relationship between concrete structure maintenance requirements and meteorological factors is established to obtain a maintenance associated mapping result, including: The material properties of the concrete member and the corresponding temperature change data, humidity change data are extracted from the concrete structure information and real-time meteorological data set to determine key meteorological factors affecting the concrete maintenance effect, wherein the key meteorological factors include temperature change range, humidity change duration and wind force influence degree; According to the concrete maintenance process requirements, the required maintenance operation types of concrete members with different material properties under different key meteorological factors are determined, wherein the maintenance operation types include water spraying maintenance, covering maintenance and heat preservation maintenance, and each maintenance operation type corresponds to a specific meteorological condition range; A maintenance requirement-meteorological factor association model is constructed, and the material properties, geometric parameters in the concrete structure information and the key meteorological factors in the real-time meteorological data set are taken as input parameters, and the maintenance operation type and operation time length are taken as output parameters; The corresponding data in the concrete structure information and real-time meteorological data set are input into the maintenance requirement-meteorological factor association model to calculate the maintenance operation type matching degree and operation time length adaptation value of different concrete members under the current meteorological conditions; Generate a corresponding relationship table of concrete member identification, curing operation type and operation time length based on the curing operation type matching degree and operation time length adaptation value, as the core content of the curing association mapping result; Adjust the input parameter weight of the curing demand-weather factor association model, recalculate the curing operation type and operation time length of concrete members with the same material properties, similar geometric parameters and similar weather conditions, update the corresponding relationship table, and finally obtain the curing association mapping result.

4. The method of claim 1, wherein, Based on the curing operation nodes and curing medium supply parameters in the initial concrete curing scheme, combined with the update data of the real-time weather data set collected subsequently, adjust the curing medium supply parameters and curing monitoring frequency to obtain an optimized concrete curing scheme, including: Collect real-time weather data in the subsequent period according to the curing monitoring frequency in the initial concrete curing scheme to obtain update data of the real-time weather data set, which contains the added content of temperature change data, humidity change data and wind change data in the subsequent period; Compare the update data of the real-time weather data set with the real-time weather data used when generating the initial concrete curing scheme, analyze the change trend of weather factors, and determine the temperature change amplitude, humidity change amplitude and wind change amplitude; Based on the change trend of weather factors, combined with the curing association mapping result, evaluate whether the curing water supply per unit time needs to be increased and whether the coverage of the moisture-retaining curing medium needs to be expanded; According to the evaluation result, adjust the curing medium supply parameters, increase the curing water supply per unit time and determine the numerical value of the increased supply, expand the coverage of the moisture-retaining curing medium and determine the boundary of the expanded coverage area; At the same time, based on the change amplitude of the weather factors, adjust the curing monitoring frequency, shorten the time interval between adjacent two curing process monitoring nodes to increase the curing monitoring frequency, or maintain the original curing monitoring frequency, or prolong the monitoring interval; Integrate the adjusted curing medium supply parameters and curing monitoring frequency with other contents in the initial concrete curing scheme, update the curing process monitoring node time in the curing operation node, and generate an initial draft of the adjusted curing scheme; Re-adjust the curing medium supply parameters or the curing monitoring frequency in the initial draft of the curing scheme to make the curing medium supply parameters within the supply capacity of the existing curing resources, and finally obtain the optimized concrete curing scheme.

5. The method of claim 2, wherein the method further comprises: The BIM model analysis tool is called to perform structural parameter extraction processing on the BIM model data of the area where the concrete structure is located, to obtain the geometric parameters, material properties and pouring area distribution of the concrete members, including: Start the BIM model analysis tool, import the BIM model data of the area where the concrete structure is located, and establish a data interaction channel between the BIM model data and the BIM model analysis tool; Through the component recognition module of the BIM model analysis tool, automatically recognize the concrete members in the BIM model, distinguish the concrete members from other types of members, and generate a preliminary identification list of the concrete members; The parameter extraction is performed on each component in the preliminary identification list of the concrete component, and the extracted parameters include geometric parameters and material properties. When the geometric parameters are extracted, the length, width, height and cross-sectional shape data of the component are obtained through the size measurement function of the BIM model analysis tool, and the connection mode and spatial position data of the component are obtained through the connection relationship analysis function of the BIM model analysis tool; When the material properties are extracted, the strength grade, aggregate type and admixture type data of the concrete component are obtained through the attribute reading function of the BIM model analysis tool, and the component identification of the missing attribute is marked, and the missing data is supplemented and obtained from the associated documents of the BIM model; The BIM model analysis tool is used to divide the concrete components in the BIM model into groups according to the pouring area based on the spatial position data of the concrete components, determine the component identification contained in each pouring area, and generate a pouring area distribution table; The extracted geometric parameters and material property data of the concrete component are associated with the pouring area distribution table, and the corresponding relationship between the parameters and the area is established according to the component identification, and a structured data table containing the component identification, geometric parameters, material properties and belonging pouring area is generated; The parameter extraction operation is performed again, so that the size data extracted by the BIM model analysis tool is consistent with the size data displayed in the BIM model, and the material property data extracted is consistent with the material requirement in the design document, and finally the geometric parameters, material properties and pouring area distribution of the concrete component are obtained.

6. The method of claim 3, wherein the method further comprises: The construction maintenance requirement-weather factor correlation model uses the material properties, geometric parameters in the concrete structure information and the key weather factors in the real-time weather data set as input parameters, and uses the maintenance operation type and operation time as output parameters, and includes: Determine the core input dimension of the maintenance requirement-weather factor correlation model, which includes the material property dimension, the geometric parameter dimension and the key weather factor dimension, and each core input dimension includes specific input parameter items. The input parameter items of the material property dimension include concrete strength grade, aggregate type and admixture type, the input parameter items of the geometric parameter dimension include component size and component cross-sectional shape, and the input parameter items of the key weather factor dimension include temperature fluctuation amplitude, humidity maintenance time and wind influence range; Determine the output dimension of the maintenance requirement-weather factor correlation model, which includes the maintenance operation type dimension and the operation time dimension. The output parameter items of the maintenance operation type dimension include the identification of water spraying maintenance, covering maintenance and heat preservation maintenance, and the output parameter items of the operation time dimension include the corresponding duration of each maintenance operation type; Select an association modeling method, construct a training sample set based on historical concrete maintenance data, and the training sample set includes concrete structure information, historical weather data and corresponding maintenance operation type and operation time data in historical projects; The input parameter items in the training sample set are subjected to feature coding processing, and the concrete strength grade, aggregate type, admixture type, component cross-section shape and curing operation type classification parameters are converted into numerical coding form; the component size, temperature fluctuation amplitude, humidity maintenance time length, wind influence range and operation time length continuous parameters are subjected to normalization processing; The coded training samples are input into a preset correlation modeling framework, the correlation weights between different input parameter items and output parameter items are calculated through a feature correlation layer in the correlation modeling framework, and the corresponding relationship between input parameter combinations and output parameters is established through an output mapping layer in the correlation modeling framework; The curing demand-weather factor correlation model is trained based on the training sample set, the correlation weight parameters in the curing demand-weather factor correlation model are adjusted, the matching degree of the curing operation type and operation time length output by the curing demand-weather factor correlation model and the actual data in the training sample set reaches a preset requirement; The output accuracy of the curing demand-weather factor correlation model is verified using a test sample set, the test sample set contains concrete structure information, weather data and corresponding curing data that are not involved in the training, the training sample set is supplemented and the curing demand-weather factor correlation model is retrained, so that the matching degree of the curing operation type and operation time length output by the curing demand-weather factor correlation model and the actual data in the test sample set reaches a preset requirement, and finally the curing demand-weather factor correlation model is obtained.

7. The method of claim 1, wherein the method further comprises: According to the curing operation type, the required curing medium type of each curing operation is determined, and the unit time supply amount of each curing medium is calculated based on the geometric parameters of the concrete component as the curing medium supply parameter, including: A corresponding relationship table of curing operation type and curing medium type is established, the curing medium type corresponding to water spraying curing is defined as curing water, the curing medium type corresponding to covering curing is defined as moisture-proof cloth or plastic film, and the curing medium type corresponding to heat preservation curing is defined as heat preservation cotton or heat preservation quilt; The geometric parameters of each concrete component are extracted from the concrete structure information, the surface area, volume and exposed surface number of the concrete component are extracted, and the exposed surface number is the number of surfaces directly contacted with air; For water spraying curing type, the covering demand of curing water is determined according to the surface area and exposed surface number of the concrete component, the curing water demand rate per unit surface area per unit time is determined combined with the concrete strength grade in the component material attribute, and the unit time supply amount of curing water is calculated through the product of the surface area and the unit surface area demand rate; For covering curing type, the covering area demand of the curing medium is determined according to the surface area and exposed surface shape of the concrete component, the required number of curing media is determined combined with the specification parameters of the curing media, and the replenishment amount of the curing media per unit time is determined according to the use requirements of the curing media as the unit time supply amount of the covering curing medium; For the type of curing and maintenance, the covering thickness requirement of the thermal insulation medium is determined according to the volume and the number of exposed surfaces of the concrete member, the volume of the required thermal insulation medium is determined in combination with the thermal conductivity of the thermal insulation medium, the loss and replenishment amount of the thermal insulation medium per unit time is determined according to the use time length requirement of the thermal insulation medium, and the unit time supply amount of the thermal insulation and maintenance medium is taken as the unit time supply amount of the thermal insulation and maintenance medium; The calculated unit time supply amount of the curing medium corresponding to different curing operation types is sorted according to the concrete member identifier, and the curing medium type and supply amount corresponding to each concrete member in different curing stages are marked; The geometry parameter extraction result and the demand rate calculation logic are rechecked, and the supply amount value is adjusted so that the curing medium supply amount of the concrete members of the same type and similar geometry parameters is within a reasonable range, and finally the curing medium supply parameters are obtained.

8. The method of claim 4, wherein the method further comprises: The curing medium supply parameters or the curing monitoring frequency in the preliminary draft of the curing scheme are re-adjusted so that the curing medium supply parameters are within the supply capacity range of the existing curing resources, including: Obtaining existing curing resource information of the current concrete curing project, the existing curing resource information including existing inventory amount of the curing medium, maximum unit time supply capacity of the curing medium, number of curing monitoring equipment and configuration number of curing monitoring personnel; Extracting the adjusted curing medium supply parameters from the preliminary draft of the curing scheme, counting the total unit time supply amount of different curing media in each period, and calculating the total demand amount of the curing medium in each period, the total demand amount being the sum of the unit time supply amounts of the curing media corresponding to all concrete members in the period; Coordinating to increase the supply channel of the curing medium or adjust the curing operation type of part of the concrete members, reducing the total demand amount of the curing medium or increasing the supply capacity, so that the total demand amount of the curing medium in each period is within the maximum unit time supply capacity range of the curing medium, and the existing inventory amount of the curing medium can meet the total demand amount in the entire curing period; Extracting the adjusted curing monitoring frequency in the preliminary draft of the curing scheme, calculating the required monitoring workload of each curing process monitoring node, and the monitoring workload including the number and use time length of the curing monitoring equipment and the working time length of the curing monitoring personnel; Coordinating to increase the curing monitoring equipment and the curing monitoring personnel or prolong the monitoring node time interval, so that the monitoring workload of each curing process monitoring node is within the bearing range of the number of curing monitoring equipment and the configuration number of curing monitoring personnel; The measures related to the supply of the curing medium and the measures related to the monitoring resources are repeatedly coordinated and adjusted so that the curing medium supply parameters in the preliminary draft of the curing scheme are within the supply capacity range of the existing curing resources, and finally a feasible concrete optimized curing scheme is determined. 9.A concrete curing optimization system based on BIM and meteorological data combination, characterized in that, The concrete curing optimization system based on BIM and meteorological data combination includes a processor and a memory, the memory and the processor are connected, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to realize the concrete curing optimization method based on BIM and meteorological data combination in any one of claims 1-8.

Images