Smart construction management method and system

Abstract

This application relates to the field of intelligent management and provides an intelligent construction management method and system. It obtains construction process information based on a preset BIM model of a first area, then sorts multiple construction sub-plans in the construction process information according to the construction order, thereby obtaining a first construction sequence. Then, the first construction sequence is updated based on the historical climate data of the first area, thereby obtaining a second construction sequence, and finally, construction work in the first area is carried out based on the second construction sequence. This is conducive to improving the intelligence and refinement of construction management.

Description

Technical Field

[0001] The present application relates to the field of intelligent management, and more specifically, to a smart construction management method and system. Background Art

[0002] Smart construction management aims to significantly improve the sophistication and intelligence of project management through advanced technologies and methods, thereby reducing costs and ensuring construction quality and safety. Existing technologies often hinder the progress of civil engineering construction, especially outdoor construction, due to its susceptibility to weather changes. This can disrupt construction schedules, increase labor costs, and reduce construction efficiency.

[0003] Patent CN118037027B proposes an intelligent civil engineering construction management method and system. This method first determines the construction process based on a 3D construction model, generating construction process information containing multiple sub-plans. Next, a first construction sequence is generated based on the construction order. This sequence is then predicted and updated using historical climate data to create a second construction sequence that better reflects actual weather conditions. This sequence ultimately guides construction, addressing the issues of irrational construction schedules and low construction efficiency in existing systems.

[0004] However, the patent's use of historical climate data to predict and update the first construction sequence primarily relies on the similarity between historical and current climate data, ignoring long-term climate trends and cyclical changes, which can lead to inaccurate predictions. For example, the climate in some regions may exhibit significant seasonal variations or long-term warming trends, factors that are easily overlooked in prediction methods that rely solely on similarity.

[0005] Furthermore, the complexity and dynamic variations among climate parameters are also significant factors influencing forecast accuracy. While the patent utilizes a deep learning model, it fails to effectively capture the interactions and temporal dynamics between climate data. This simplified approach may not provide sufficient support for complex and changing climate conditions, compromising the scientific and rational nature of construction plans, and consequently impacting construction progress and cost control.

[0006] Therefore, an optimized smart construction management solution is needed. Summary of the Invention

[0007] This application addresses the shortcomings of the existing technology and provides a smart construction management method and system.

[0008] According to one aspect of the present application, a smart construction management method is provided, which includes:

[0009] Based on a preset BIM model of a first area, construction process information is obtained; multiple construction sub-plans in the construction process information are sorted according to a construction order to obtain a first construction sequence; based on historical climate data of the first area, the first construction sequence is updated to obtain a second construction sequence; and construction is performed on the first area based on the second construction sequence.

[0010] The updating of the first construction sequence based on the historical climate data of the first region to obtain the second construction sequence includes: predicting the climate of the first region based on the historical climate data of the first region to obtain first climate information, and updating each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence;

[0011] The step of predicting the climate of the first region based on historical climate data of the first region to obtain first climate information includes:

[0012] Acquire a time queue of historical climate data for the first region, and extract historical climate data within a construction period from the time queue of historical climate data to obtain a time queue of historical climate data for the construction period;

[0013] Performing time series regularization and historical climate time series semantic feature extraction on the time queue of the historical climate data and the time queue of the historical climate data during the construction period respectively to obtain the time series correlation feature of the historical climate multidimensional parameters and the time series correlation feature of the historical climate multidimensional parameters during the construction period;

[0014] Performing fine-grained feature cross-domain interactive coding on the historical climate multidimensional parameter time series correlation features and the historical climate multidimensional parameter time series correlation features during the construction period to obtain fine-grained semantic interactive fusion features of the historical climate multidimensional parameter time series;

[0015] Based on the time series fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameters, a prediction result is obtained as the first climate information.

[0016] According to another aspect of the present application, a smart construction management system is provided, comprising:

[0017] a construction process information data acquisition module for obtaining construction process information based on a preset BIM model of a first area; a construction sub-plan sorting module for sorting multiple construction sub-plans in the construction process information according to a construction order to obtain a first construction sequence; a construction sequence updating module for updating the first construction sequence based on historical climate data of the first area to obtain a second construction sequence; and a construction implementation module for performing construction in the first area based on the second construction sequence;

[0018] The construction sequence updating module includes: a second construction sequence generating subunit, configured to predict the climate of the first region based on historical climate data of the first region to obtain first climate information, and to update each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence;

[0019] The second construction sequence generation subunit includes:

[0020] a secondary subunit for collating historical climate data during the construction period, configured to obtain a time queue of historical climate data for the first region, and extract historical climate data within the construction period from the time queue of historical climate data to obtain a time queue of historical climate data for the construction period;

[0021] A secondary sub-unit for extracting historical climate time series features during the historical climate construction period is used to respectively perform time series regularization and historical climate time series semantic feature extraction on the time queue of the historical climate data and the time queue of the historical climate data during the construction period to obtain the time series correlation features of the historical climate multidimensional parameters and the time series correlation features of the historical climate multidimensional parameters during the construction period;

[0022] A secondary sub-unit is provided for generating fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter temporal series, which is used to perform fine-grained feature cross-domain interaction encoding on the historical climate multi-dimensional parameter temporal series correlation features and the historical climate multi-dimensional parameter temporal series correlation features during the construction period to obtain fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter temporal series;

[0023] The first climate information prediction generates a secondary subunit, which is used to obtain a prediction result as the first climate information based on the fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter time series.

[0024] This application has significant technical effects due to the adoption of the above technical solutions:

[0025] The smart construction management method and system provided by the present application extracts the historical climate data within the construction period from the time queue of the acquired historical climate data to obtain the time queue of the historical climate data during the construction period, and adopts a data analysis and processing algorithm based on deep learning to perform temporal regularization and temporal correlation of the historical climate data and the historical climate data during the construction period, so as to automatically predict the climate data of the first area based on the interactive fusion representation between the temporal correlation characteristics of the historical climate multidimensional parameters and the temporal correlation characteristics of the historical climate multidimensional parameters during the construction period. In this way, the mutual influence between different climate factors and their temporal change trends can be excavated and fully reflected, thereby significantly improving the accuracy and reliability of climate forecasts, thereby providing strong support for the scientificity and rationality of construction plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0027] Figure 1 Flowchart of a smart construction management method according to an embodiment of the present application.

[0028] Figure 2 This is a flowchart of a smart construction management method according to an embodiment of the present application, in which the climate of the first area is predicted based on historical climate data of the first area to obtain first climate information.

[0029] Figure 3 In the smart construction management method according to an embodiment of the present application, fine-grained feature cross-domain interactive encoding is performed on the historical climate multidimensional parameter time series correlation characteristics and the historical climate multidimensional parameter time series correlation characteristics of the construction period to obtain a flowchart of the fine-grained semantic interactive fusion characteristics of the historical climate multidimensional parameter time series.

[0030] Figure 4 In the smart construction management method according to an embodiment of the present application, based on the historical climate time series joint fine-grained core feature semantic clustering center vector, the set of the historical climate time series joint fine-grained core feature vectors is subjected to multidimensional feature fine-grained fusion to obtain the flowchart of the historical climate multidimensional parameter time series fine-grained semantic interactive fusion feature.

[0031] Figure 5 This is a system block diagram of the smart construction management system according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0033] Based on the problems in the above background technology, this application provides a smart construction management method. Figure 1 Flowchart of the smart construction management method according to the embodiment of the present application. Figure 1 As shown, the smart construction management method according to the embodiment of the present application includes: S110, obtaining construction process information based on a preset BIM model of the first area; S120, sorting multiple construction sub-plans in the construction process information according to the construction order to obtain a first construction sequence; S130, based on the historical climate data of the first area, updating the first construction sequence to obtain a second construction sequence; S140, constructing the first area based on the second construction sequence.

[0034] In step S110, construction process information is obtained based on the preset BIM model of the first area. Specifically, in the embodiment of the present application, the first area is the construction area to be constructed, the construction process includes multiple construction sub-plans, and the construction process information includes the construction content and ideal duration of each construction sub-plan. It should be understood that the BIM model (Building Information Model) can describe various aspects of the construction project in detail, including the overall layout, structure and detailed information of each part of the construction area. By obtaining the construction process information of the construction area to be constructed based on the BIM model, the construction content can be understood in detail, which can provide clear guidance for the construction team. Specifically, the construction process information helps to identify the dependencies between the various construction sub-plans, thereby ensuring that construction activities are carried out in the correct order and avoiding delays due to improper planning. At the same time, by understanding the ideal duration and requirements of each sub-plan, it helps to rationally allocate human, material and financial resources and improve construction efficiency.

[0035] In smart construction management, obtaining construction process information based on the pre-set BIM model of the first area is a crucial foundational task. This process involves not only the construction of the BIM model, but also the decomposition of construction tasks, the calculation of ideal duration, and the integration and verification of construction process information. The following is a detailed description of this process:

[0036] First, determine the specific location and scope of the construction project, known as the "first area." Detailed information about this area, including its geographic coordinates, topography, and surrounding environment, is required. The accuracy of this information is crucial for the subsequent BIM model construction and construction plan development. Once the construction area is defined, all relevant design data, including architectural, structural, and mechanical / electrical design drawings, must be collected. Next, using professional BIM modeling software (such as Revit and ArchiCAD), a detailed 3D Building Information Model (BIM) is created based on the design data. The BIM model not only includes the building's geometry but also various attribute information, such as material type, component dimensions, and construction techniques. Building a BIM model is a complex process, requiring specialized technicians to refine and improve it step by step based on the design drawings and actual site conditions. This process ensures not only the model's geometric accuracy but also the accuracy of the attribute information for each component within the model. Once the BIM model is completed, it provides a solid foundation for extracting information about the subsequent construction process.

[0037] Based on the BIM model, the entire construction project needs to be broken down into multiple specific construction tasks, known as construction sub-plans. Each sub-plan should include detailed information on the specific construction content, required materials, equipment, and human resources. The breakdown of construction tasks requires a comprehensive consideration of the project's scale, complexity, and construction specifications. For example, a large construction project may need to be broken down into multiple sub-plans, including foundation construction, main structure construction, interior and exterior decoration, and mechanical and electrical installation. Each sub-plan can be further broken down into smaller tasks, such as foundation construction, which can be broken down into earth excavation, pile foundation construction, and cushion layer laying.

[0038] Next, for each construction sub-plan, the ideal construction duration needs to be estimated. This requires considering factors such as construction difficulty, resource availability, and weather conditions. The ideal duration can be calculated based on historical data and experience, or through simulation. For example, based on previous experience with similar projects and actual site conditions, the average duration of foundation construction can be estimated. The ideal duration calculation also needs to account for potential uncertainties during construction, such as weather changes and equipment failures, to ensure the rationality and feasibility of the construction plan.

[0039] Next, integrate all construction sub-plans and their related information into a complete construction process profile. This profile should include the following: Construction Sub-Plan List: This lists the names, numbers, and construction content of all construction sub-plans. Ideal Duration: This lists the estimated completion time for each construction sub-plan. Resource Requirements: This lists the materials, equipment, and human resources required for each construction sub-plan. Dependencies: This identifies which construction sub-plans require the completion of other sub-plans before they can begin.

[0040] In the BIM model, each construction sub-plan must be associated with the corresponding building component or area. For example, a sub-plan might involve rebar tying for a specific floor. In this way, the BIM model should associate this sub-plan with the rebar components on that floor. This seamlessly integrates construction process information with the BIM model, supporting subsequent construction planning and resource scheduling. Furthermore, utilizing the visualization capabilities of BIM software, construction process information can be graphically displayed. The BIM model allows project managers and construction personnel to intuitively understand the location, content, and progress of each construction sub-plan, improving project management and construction efficiency. Finally, a construction process simulation is performed within the BIM model to verify the rationality and feasibility of the construction process information.

[0041] Through the above steps, based on the pre-set BIM model of the first area, we can systematically and comprehensively obtain construction process information. This information provides the basis for subsequent construction planning and provides important support for the management and coordination of the entire construction project.

[0042] In step S120, the multiple construction sub-plans in the construction process information are sorted according to the construction order to obtain a first construction sequence. It should be understood that the construction process is usually a complex system, and there are often dependencies between the various sub-plans. For example, some construction tasks must be carried out after other tasks are completed (for example, superstructure construction can only be carried out after the foundation construction is completed). In order to help identify and maintain these dependencies, avoid construction delays caused by improper sequence, reduce conflicts between different construction tasks, and ensure efficient use of resources, it is necessary to sort the multiple construction sub-plans in the construction process information according to the construction order in this application. The first construction sequence obtained by sorting is more in line with the actual construction situation and can better guide construction activities. This helps to enhance the feasibility of the construction plan and thereby reduce uncertainty and risk in the construction process.

[0043] To ensure that construction activities proceed in an orderly manner, it is necessary to sort the multiple construction sub-plans in the construction process information according to the construction sequence. This process needs to comprehensively consider multiple factors such as the logical relationship between construction tasks, resource availability, construction specifications, and project goals. The following is a detailed description of this process:

[0044] First, the logical relationships between construction tasks need to be analyzed. These relationships primarily include predecessor and parallel relationships. A predecessor relationship means that certain construction sub-plans must be completed before other sub-plans can begin. For example, foundation construction must be completed before main structure construction. A parallel relationship means that certain construction sub-plans can be carried out simultaneously within the same timeframe. For example, different floors of the main structure can be constructed simultaneously. By analyzing the logical relationships between construction tasks, the dependencies of each construction sub-plan can be determined, providing a basis for subsequent sorting. For example, if a sub-plan cannot begin until another sub-plan is completed, then there is a predecessor relationship between the two sub-plans, which needs to be reflected in the sorting process.

[0045] Next, the availability of construction resources needs to be considered. Construction resources include human resources, machinery and equipment, and materials. When determining the construction sequence, resource availability and scheduling must be considered. For example, if machinery and equipment are insufficient for a certain period, the construction sequence may need to be adjusted, prioritizing construction sub-plans that do not require a large amount of machinery and equipment. Similarly, if the supply of a certain material is unstable, the construction sequence may need to be adjusted, prioritizing construction sub-plans that are less dependent on that material. By analyzing the availability of construction resources, it is possible to ensure that each construction sub-plan is carried out with sufficient resources, avoiding construction delays caused by resource shortages. For example, project managers can use resource calendars and resource demand plans to view the available time and quantity of each resource, thereby arranging the construction sequence appropriately.

[0046] Construction specifications and project objectives are also important factors in determining the construction sequence. Construction specifications typically include local building standards, safety regulations, and environmental protection requirements. When determining the construction sequence, it is necessary to ensure that each construction sub-plan complies with relevant specifications and requirements to avoid safety accidents and quality issues caused by non-compliant operations. Project objectives include the project's schedule, cost, and quality. When determining the construction sequence, it is necessary to comprehensively consider various project objectives to ensure that construction activities can achieve cost control and quality assurance while meeting the schedule requirements. For example, if the project schedule is very tight, it may be necessary to prioritize construction sub-plans that have a greater impact on the schedule to ensure that tasks on the critical path are completed on time.

[0047] To more efficiently sequence the construction process, project management tools such as Microsoft Project and Primavera P6 can be used. These tools provide powerful project management features that can help project managers quickly generate a construction sequence. The specific steps are as follows: First, enter all construction sub-plans and their associated information into the project management tool, including sub-plan name, number, construction content, required resources, and ideal duration. Next, define the dependencies between each sub-plan in the project management tool. For example, by setting predecessor and successor tasks, clarify the predecessor and parallel relationships between each sub-plan. The project management tool automatically generates a preliminary construction sequence based on the defined dependencies. The project manager can review the generated sequence to check for omissions or errors. The generated construction sequence can be adjusted and optimized based on the project's actual situation and resource availability. For example, if resources are limited during a certain period, the construction sequence can be adjusted to prioritize construction sub-plans with lower resource requirements. After repeated adjustments and optimizations, the first construction sequence is finally generated.

[0048] After generating the first construction sequence, it needs to be verified and optimized to ensure its rationality and feasibility. The specific steps are as follows: Simulate the construction process within the project management tool to verify the rationality and feasibility of the construction sequence. This simulation can identify potential issues such as resource conflicts and incorrect construction sequences. Based on the simulation results, make necessary adjustments and optimizations to the construction sequence.

[0049] In summary, by sorting the multiple construction sub-plans in the construction process information according to the construction order, a first construction sequence can be generated.

[0050] In step S130, based on the historical climate data of the first region, the first construction sequence is updated to obtain a second construction sequence. Specifically, in an embodiment of the present application, updating the first construction sequence to obtain the second construction sequence based on the historical climate data of the first region includes: predicting the climate of the first region based on the historical climate data of the first region to obtain first climate information, and updating each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence.

[0051] It's understandable that climatic conditions are crucial to construction activities, directly impacting safety, efficiency, quality, and cost. For example, high temperatures can lead to excessive physical exertion among workers, reducing work efficiency and even causing heatstroke. High temperatures can also affect building materials. For example, concrete evaporates rapidly at high temperatures, necessitating increased water usage. When temperatures exceed 30°C, retarder may need to be added to prevent a decrease in tensile strength and cracking. Low temperatures can make certain construction processes, such as painting and concrete pouring, impossible. Low temperatures can also cause materials to become brittle, impacting construction quality. Precipitation levels directly impact construction progress, especially during open-air operations like earthmoving and welding. Rainfall can cause work to be suspended, impacting progress. Furthermore, precipitation can dampen and damage construction materials, impacting quality. Winds exceeding a certain intensity can pose a safety threat to the construction site. For example, when wind speeds reach level 3 or above, protective measures or suspension of certain operations should be implemented to ensure safety. Excessive winds can also increase the risk of working at height, necessitating windproofing of equipment. High humidity will increase the evaporation time of water, slowing down the drying of construction materials and thus extending the construction period.

[0052] A humid environment can also easily cause building materials to become damp and moldy, further extending the construction period.

[0053] Accordingly, in predicting the climate of the first region based on the historical climate data of the first region, the technical concept of the present application is to obtain a time queue of the historical climate data of the first region, and extract the historical climate data within the construction period from the time queue of the historical climate data (temperature value, humidity value, precipitation and wind speed value) to obtain a time queue of the historical climate data of the construction period, and use a data analysis and processing algorithm based on deep learning to perform temporal regularization and temporal correlation of the historical climate data and the historical climate data within the construction period, so as to automatically predict the climate data of the first region based on the interactive fusion representation between the temporal correlation characteristics of the historical climate multidimensional parameters and the temporal correlation characteristics of the historical climate multidimensional parameters of the construction period. In this way, the mutual influence between different climate factors and their temporal change trends can be excavated and comprehensively reflected, thereby significantly improving the accuracy and reliability of climate forecasts, and providing strong support for the scientificity and rationality of construction plans.

[0054] Figure 2 This is a flowchart of the smart construction management method according to an embodiment of the present application, in which the climate of the first region is predicted based on the historical climate data of the first region to obtain first climate information. Figure 2As shown, the climate of the first area is predicted based on the historical climate data of the first area to obtain the first climate information, including: S131, obtaining the time queue of the historical climate data of the first area, and extracting the historical climate data within the construction time period from the time queue of the historical climate data to obtain the time queue of the historical climate data during the construction period; S132, performing time series regularization and historical climate time series semantic feature extraction on the time queue of the historical climate data and the time queue of the historical climate data during the construction period respectively to obtain the historical climate multi-dimensional parameter time series correlation feature and the historical climate multi-dimensional parameter time series correlation feature of the construction period; S133, performing fine-grained feature cross-domain interactive encoding on the historical climate multi-dimensional parameter time series correlation feature and the historical climate multi-dimensional parameter time series correlation feature of the construction period to obtain the historical climate multi-dimensional parameter time series fine-grained semantic interactive fusion feature; S134, based on the historical climate multi-dimensional parameter time series fine-grained semantic interactive fusion feature, obtaining the prediction result as the first climate information.

[0055] In step S131, a time queue of historical climate data for the first region is obtained, and historical climate data for the construction period is extracted from the time queue of historical climate data to obtain a time queue of historical climate data for the construction period. Specifically, in the embodiment of the present application, the historical climate data includes temperature, humidity, precipitation, and wind speed. It should be understood that climate conditions are one of the important factors affecting construction activities. Climate factors such as temperature, humidity, precipitation, and wind speed can directly affect the construction process. For example, high temperatures can reduce worker efficiency, excessive humidity can affect material properties, and excessive precipitation can cause construction to be suspended. To understand basic information about climate change in the construction area and facilitate understanding and analysis of the climate conditions likely to be encountered during construction, it is necessary to obtain historical climate data for the first region and extract historical climate data for the construction period from the time queue of historical climate data to obtain a time queue of historical climate data for the construction period. The resulting time queue of historical climate data for the construction period provides sufficient data support for the climate prediction model. By analyzing historical climate data, it is possible to more accurately understand the climate characteristics of the construction area, thereby formulating scientific construction plans and measures. This helps improve construction efficiency and quality, and reduces construction delays and rework caused by climate factors.

[0056] As an important step in climate prediction and construction plan optimization, this application requires obtaining historical climate data and extracting historical climate data for the construction period. This process requires the precise collection and processing of large amounts of climate data to ensure data integrity and accuracy. The following is a detailed description of this process:

[0057] First, you need to obtain historical climate data for the first region. This data can typically be obtained from weather stations, weather service providers, and other sources. Historical climate data includes the following: temperature, humidity, precipitation, and wind speed. The collected historical climate data should cover a long period of time, typically at least the past year, though longer periods of data are acceptable to improve forecast accuracy. For example, you might select historical climate data from the past five years to better capture long-term trends and cyclical changes in the climate.

[0058] After collecting historical climate data, it needs to be processed to construct a time series. A time series stores data in chronological order, facilitating time series analysis and processing. The specific steps are as follows: First, clean the collected historical climate data to remove invalid data and outliers. For example, if the temperature value on a particular day is significantly abnormal (e.g., outside the normal range), it needs to be corrected or deleted. Data cleaning is a critical step in ensuring data quality, as any outliers can affect subsequent analysis results. Next, standardize data from different sources to ensure consistent formats. For example, standardize all temperature values ​​to degrees Celsius and all precipitation values ​​to millimeters. Data standardization eliminates discrepancies between data sources, making data easier to process and analyze. Then, align all climate parameters chronologically to form a multidimensional time series. For example, parameters such as temperature, humidity, precipitation, and wind speed should correspond to the same time point each day. Time alignment ensures that the data at each time point is complete, facilitating subsequent time series analysis. During the time series construction process, the data at each time point can be represented as a vector containing all climate parameters for that day. For example, the data for January 1, 2023 can be expressed as [20°C, 60%, 0mm, 5m / s], which respectively represent the temperature, humidity, precipitation, and wind speed of that day.

[0059] After constructing a time series of historical climate data, it is necessary to extract historical climate data for the construction period. The construction period refers to the specific time range of the project's planned construction, for example, from March 1, 2024, to December 31, 2024. The specific steps are as follows: First, define the project's construction start and end dates to form the construction period. For example, the construction period is from March 1, 2024, to December 31, 2024. Then, filter the data from the time series of historical climate data for the construction period. For example, climate data for each day from March 1, 2024, to December 31, 2024. When filtering data, ensure that the data for each time point is complete and has no missing values. If missing values ​​are found, interpolation can be used to fill them in. For example, if precipitation data for a particular day is missing, interpolation can be used to ensure data continuity and completeness.

[0060] The filtered data is arranged chronologically to form a time series of historical climate data for the construction period. The data for each time point is still represented as a vector containing all the climate parameters for that day. This method provides a complete and continuous time series of historical climate data for the construction period, providing important support for subsequent climate forecasting and construction plan optimization.

[0061] Finally, to ensure the accuracy and completeness of the historical climate data extracted during the construction period, data verification and quality control are required. The specific steps are as follows: First, check whether there is any missing data at each time point during the construction period. If there is missing data, interpolation or filling is required. For example, if the data for a certain day is missing, the data from the previous and next days can be used for interpolation. Data integrity check is a key step to ensure data continuity, and any missing values ​​may affect subsequent analysis results. Next, check whether the data during the construction period is consistent. For example, check whether the changes in parameters such as temperature, humidity, precipitation, and wind speed are logical. If abnormal data is found, it needs to be corrected or deleted. Data consistency check can ensure the reliability and accuracy of the data, and any abnormal values ​​may affect subsequent analysis results.

[0062] Through the above steps, the accuracy and completeness of the extracted historical climate data during the construction period can be ensured, providing reliable data support for subsequent climate forecasting and construction plan optimization.

[0063] In step S132, the time queue of the historical climate data and the time queue of the historical climate data during the construction period are respectively subjected to time regularization and historical climate time series semantic feature extraction to obtain historical climate multidimensional parameter time series correlation features and construction period historical climate multidimensional parameter time series correlation features. Specifically, in an embodiment of the present application, the step S132 includes: arranging the time queue of the historical climate data and the time queue of the historical climate data during the construction period according to the climate sample dimension and time to obtain a historical climate multidimensional parameter time series matrix and a construction period historical climate multidimensional parameter time series matrix; passing the historical climate multidimensional parameter time series matrix and the construction period historical climate multidimensional parameter time series matrix through a historical climate time series semantic feature extractor based on a void convolutional neural network model to obtain a historical climate multidimensional parameter time series correlation feature graph as the historical climate multidimensional parameter time series correlation feature and a construction period historical climate multidimensional parameter time series correlation feature graph as the construction period historical climate multidimensional parameter time series correlation feature.

[0064] It should be understood that, considering that the historical climate data of the first area and the historical climate data during the construction period both contain information on multiple climate parameters, and these parameters all have time series characteristics in the time dimension, and at the same time, there is a mutual correlation between these parameters in the time scale. Based on this, in the technical solution of the present application, the time queue of the historical climate data and the time queue of the historical climate data during the construction period are arranged according to the climate sample dimension and time to obtain a time series matrix of historical climate multidimensional parameters and a time series matrix of historical climate multidimensional parameters during the construction period. In particular, at least historical data within the past year is included here, and data for a longer period of time can also be selected to improve prediction accuracy.

[0065] Then, considering that the historical climate multidimensional parameter time series matrix and the construction period historical climate multidimensional parameter time series matrix both contain the deep temporal features and semantic information of their respective parameters, and considering that the void convolutional neural network model can increase the receptive field without losing spatial resolution and without introducing additional parameters, this means that the model can capture a wider range of climate data features, thereby more comprehensively understanding the laws and trends of climate change. Therefore, in the technical solution of the present application, the historical climate multidimensional parameter time series matrix and the construction period historical climate multidimensional parameter time series matrix are extracted by a historical climate time series semantic feature extractor based on a void convolutional neural network model to utilize the characteristics of void convolution to effectively capture the long-term trends in the historical climate data and the inherent structure and semantic patterns of the data, thereby obtaining a historical climate multidimensional parameter time series correlation feature map and a construction period historical climate multidimensional parameter time series correlation feature map.

[0066] In step S133, fine-grained feature cross-domain interactive coding is performed on the historical climate multi-dimensional parameter time series correlation features and the historical climate multi-dimensional parameter time series correlation features during the construction period to obtain the historical climate multi-dimensional parameter time series fine-grained semantic interactive fusion features. Specifically, Figure 3 The flowchart of the smart construction management method according to the embodiment of the present application is to perform fine-grained feature cross-domain interactive coding on the historical climate multidimensional parameter time series correlation features and the historical climate multidimensional parameter time series correlation features of the construction period to obtain the historical climate multidimensional parameter time series fine-grained semantic interactive fusion features. Figure 3 As shown, the step S133 includes: S1331, performing feature fine-grained decoupling on the historical climate multidimensional parameter time series correlation feature graph and the historical climate multidimensional parameter time series correlation feature graph during the construction period to obtain a set of historical climate multidimensional parameter time series correlation local fine-grained feature vectors and a set of historical climate multidimensional parameter time series correlation local fine-grained feature vectors during the construction period; S1332, combining the set of historical climate multidimensional parameter time series correlation local fine-grained feature vectors and the set of historical climate multidimensional parameter time series correlation local fine-grained feature vectors during the construction period with each corresponding set of historical climate multidimensional parameter time series correlation local fine-grained feature vectors. The local fine-grained feature vectors of the historical climate multi-dimensional parameter time series associated with the construction period are input into the fine-grained common feature extraction network to obtain a set of historical climate time series joint fine-grained core feature vectors; S1333, the set of historical climate time series joint fine-grained core feature vectors is input into the clustering network to obtain the historical climate time series joint fine-grained core feature semantic clustering center vector; S1334, based on the historical climate time series joint fine-grained core feature semantic clustering center vector, the set of historical climate time series joint fine-grained core feature vectors is subjected to multi-dimensional feature fine-grained fusion to obtain the historical climate multi-dimensional parameter time series fine-grained semantic interactive fusion feature.

[0067] It should be understood that although the historical climate multidimensional parameter time series correlation features and the construction period historical climate multidimensional parameter time series correlation features belong to different time domains, there is indeed a complex interaction between them. For example, if historical data shows that a certain area often experiences high temperatures in the summer, then in future summer construction, special attention should be paid to the impact of high temperatures on construction. Therefore, in order to capture the correlation and mutual influence between the two time domains and to achieve deeper feature fusion and interaction, in the technical solution of the present application, the historical climate multidimensional parameter time series correlation feature map and the construction period historical climate multidimensional parameter time series correlation feature map are subjected to fine-grained feature cross-domain interactive encoding to obtain a historical climate multidimensional parameter time series fine-grained semantic interaction fusion feature map as a historical climate multidimensional parameter time series fine-grained semantic interaction fusion feature.

[0068] Specifically, in the embodiment of the present application, step S1331 includes: expanding each characteristic matrix of the historical climate multidimensional parameter time series correlation feature map along the channel dimension into a characteristic vector to obtain a set of local fine-grained characteristic vectors of the historical climate multidimensional parameter time series correlation; expanding each characteristic matrix of the historical climate multidimensional parameter time series correlation feature map along the channel dimension into a characteristic vector to obtain a set of local fine-grained characteristic vectors of the historical climate multidimensional parameter time series correlation during the construction period. This process can be expressed as follows: ;in, and They are respectively the time series correlation characteristic diagram of the historical climate multi-dimensional parameters and the time series correlation characteristic diagram of the historical climate multi-dimensional parameters during the construction period, In order to perform fine-grained feature decoupling operations on feature maps, 、 、 and They are the first, second, and third local fine-grained feature vectors in the set of historical climate multi-dimensional parameter time series correlation. and The local fine-grained feature vectors of the historical climate multidimensional parameter time series association, 、 、 and They are the first, second, and third local fine-grained feature vectors of the time series correlation of historical climate multidimensional parameters during the construction period. and The local fine-grained feature vectors of the time series correlation of historical climate multidimensional parameters during each construction period.

[0069] That is, in order to be able to analyze local patterns in climate data in a more fine-grained manner, such as temperature fluctuations and humidity changes in a short period of time, the historical climate multidimensional parameter time series correlation feature map and the historical climate multidimensional parameter time series correlation feature map of the construction period are subjected to feature fine-grained decoupling to decompose the feature map into multiple local feature vectors. Each vector represents the climate characteristics within a small time window. The set of local fine-grained feature vectors of the historical climate multidimensional parameter time series correlation and the set of local fine-grained feature vectors of the historical climate multidimensional parameter time series correlation during the construction period are obtained, so as to capture more subtle historical climate changes and patterns.

[0070] Next, each corresponding set of the historical climate multidimensional parameter time series associated local fine-grained feature vectors and the historical climate multidimensional parameter time series associated local fine-grained feature vectors in the construction period is input into the fine-grained common feature extraction network to obtain a set of historical climate time series joint fine-grained core feature vectors. This process can be expressed as: ;in, is the number of local fine-grained feature vectors in the set of historical climate multi-dimensional parameter time series correlations. The local fine-grained feature vectors of the historical climate multidimensional parameter time series association, The number of local fine-grained feature vectors in the set of historical climate multi-dimensional parameter time series associations during the construction period is The local fine-grained feature vector of the historical climate multi-dimensional parameters time series correlation during the construction period, 、 and They are position point addition, position point multiplication and position point subtraction, and are the weight matrix and bias vector respectively, for function, It is the first in the set of joint fine-grained core feature vectors of historical climate time series. The fine-grained core feature vector of historical climate time series is combined.

[0071] That is, through the fine-grained common feature extraction network, the common features are extracted from each group of corresponding historical climate multidimensional parameter time series associated local fine-grained feature vectors and construction period historical climate multidimensional parameter time series associated local fine-grained feature vectors to reflect the similarity and consistency of historical climate data in different time periods. In this way, a more representative and generalizable set of historical climate time series joint fine-grained core feature vectors can be generated. That is, the core feature not only contains the characteristics of each historical climate and construction period climate time domain, but also contains the interactive information between them, thereby improving the accuracy of climate forecasting.

[0072] Specifically, in this embodiment of the present application, step S1333 includes calculating the positional mean vector of the set of the historical climate time series joint fine-grained core feature vectors to obtain the semantic clustering center vector of the historical climate time series joint fine-grained core feature. This process can be expressed as: ;in, It is the first in the set of joint fine-grained core feature vectors of historical climate time series. The historical climate time series are combined with fine-grained core feature vectors, is the number of vectors in the set of joint fine-grained core feature vectors of the historical climate time series, It is the central vector of semantic clustering of the fine-grained core features of historical climate time series.

[0073] That is, each core feature vector is input into the clustering network to represent the semantic commonalities between the temporal correlation characteristics of the historical climate multidimensional parameters and the temporal correlation characteristics of the historical climate multidimensional parameters during the construction period in the entire time domain, and the semantic clustering center vector of the historical climate time series joint fine-grained core feature is obtained to discover the hidden patterns and structures in the climate data.

[0074] Specifically, Figure 4 This is a flowchart of the smart construction management method according to an embodiment of the present application, based on the historical climate time series joint fine-grained core feature semantic clustering center vector, performing multidimensional feature fine-grained fusion on the set of the historical climate time series joint fine-grained core feature vectors to obtain the historical climate multidimensional parameter time series fine-grained semantic interaction fusion feature. Figure 4 As shown, the step S1334 includes: S13341, constructing a historical climate time series query vector and a historical climate time series value vector based on each historical climate time series joint fine-grained core feature vector in the set of the historical climate time series joint fine-grained core feature vectors and constructing a historical climate time series key vector based on the historical climate time series joint fine-grained core feature semantic clustering center vector, and inputting the historical climate time series query vector, the historical climate time series value vector and the historical climate time series key vector of each historical climate time series joint fine-grained core feature vector into a global semantic interaction encoder based on a converter structure to obtain a set of historical climate time series context joint fine-grained core feature vectors; S13342, performing feature reshaping on each historical climate time series context joint fine-grained core feature vector in the set of the historical climate time series context joint fine-grained core feature vectors to obtain a historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map as the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature. More specifically, in an embodiment of the present application, step S13341 includes: multiplying the query embedding matrix with the historical climate time series joint fine-grained core feature vector to obtain the historical climate time series query vector; multiplying the value embedding matrix with the historical climate time series joint fine-grained core feature vector to obtain the historical climate time series value vector; multiplying the key embedding matrix with the historical climate time series joint fine-grained core feature semantic clustering center vector to obtain the historical climate time series key vector; inputting the historical climate time series query vector, the historical climate time series value vector and the historical climate time series key vector into the global semantic interaction encoder based on the converter structure to obtain the historical climate time series context joint fine-grained core feature vector. The above process can be expressed as follows: ;in, It is the first in the set of joint fine-grained core feature vectors of historical climate time series. The historical climate time series are combined with fine-grained core feature vectors, is the query embedding matrix, yes The corresponding historical climate time series query vector, is the value embedding matrix, yes The corresponding historical climate time series value vector, is the key embedding matrix, is the historical climate time series key vector, for The transposed vector of for length, yes function, is the matrix-vector multiplication, 、 、 and They are the first, second, and third in the set of historical climate time series context joint fine-grained core feature vectors. and The historical climate time series context is combined with the fine-grained core feature vector, It is a collection of fine-grained core feature vectors of the historical climate time series context.

[0075] That is, the value vector and query vector are constructed based on the joint fine-grained core feature vectors of each historical climate time series, and the key vector is constructed based on the cluster center vector. These vectors are input into the global semantic interaction encoder based on the converter structure to utilize the self-attention mechanism of the converter to capture the global dependencies between the historical climate joint features and the correlation influence between the contexts, so as to generate a set of joint fine-grained core feature vectors that more semantically represents the historical climate time series context.

[0076] Specifically, each historical climate time series context joint fine-grained core feature vector in the set of the historical climate time series context joint fine-grained core feature vectors is feature-reshaped to obtain a historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map.

[0077] The process can be expressed as follows: ;in, is the set of fine-grained core feature vectors of the historical climate time series context, To perform feature reshape operation, It is the time series fine-grained semantic interaction fusion feature map of the historical climate multi-dimensional parameters.

[0078] In step S134, a prediction result is obtained as the first climate information based on the fine-grained semantic interaction fusion features of the historical climate multidimensional parameters in time series. Specifically, in this embodiment of the present application, obtaining the prediction result based on the fine-grained semantic interaction fusion features of the historical climate multidimensional parameters in time series includes: passing the fine-grained semantic interaction fusion feature map of the historical climate multidimensional parameters in time series through a decoder-based climate predictor to obtain the prediction result, wherein the prediction result is used to represent the decoded value of the climate prediction data for the first region. In particular, the decoded value represents the predicted climate conditions during the execution of the first construction sequence, i.e., the first climate information. That is, the fine-grained semantic interaction fusion features of the historical climate multidimensional parameters in time series, obtained by fine-grained cross-domain interaction between the historical climate multidimensional parameters in time series association feature map and the historical climate multidimensional parameters in time series association feature map during the construction period, are decoded and processed to automatically predict the climate data for the first region. In this way, the mutual influences between different climate factors and their temporal variation trends can be mined and comprehensively reflected, thereby significantly improving the accuracy and reliability of climate predictions and providing strong support for the scientific and rational nature of construction plans.

[0079] In particular, when the historical climate multidimensional parameter time series correlation feature map and the construction period historical climate multidimensional parameter time series correlation feature map represent the sample-time series dimension cross-correlation features of the historical climate data and the construction period historical climate data respectively, when performing cross-domain fine-grained feature global interaction encoding, the fine-grained interaction coding representation of the historical climate multidimensional parameter time series fine-grained semantic interaction fusion feature map in different time domain spaces will also have a selective time domain feature interaction state space representation in the distribution field due to the differences in fine-grained interaction coding in different time domains, resulting in an imbalance in the feature representation regression understanding relative to the source time domain semantics, affecting the accuracy of the decoding results.

[0080] Preferably, the process of passing the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map through a decoder-based climate predictor to obtain a prediction result includes: determining the number of zero eigenvalues ​​in the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map, and respectively calculating the reciprocal of the logarithm of the number of zero eigenvalues ​​with base two and the exponential value of the reciprocal of the number of zero eigenvalues ​​with base natural constants to obtain a first historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion representation value and a second historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion representation value; expanding the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map into a historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature vector; calculating the power function of each eigenvalue of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature vector with the reciprocal of the number of zero eigenvalues ​​as the exponent to obtain a historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion structure state vector; and converting the historical climate The multi-dimensional parameter temporal fine-grained semantic interaction fusion structure state vector is point-multiplied with the first historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion representation value to obtain the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion information representation vector; the autocorrelation matrix of the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion structure state vector is point-multiplied with the second historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion representation value and the weight hyperparameter respectively to obtain the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion regression understanding matrix; the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion information representation vector is matrix-multiplied with the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion regression understanding matrix to obtain the optimized historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion feature vector; the optimized historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion feature vector is passed through the decoder-based climate predictor to obtain the prediction result.

[0081] Here, the first historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion representation value and the second historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion representation value are expressed as: ;in, is the number of zero eigenvalues ​​in the fine-grained semantic interaction fusion feature map of the historical climate multi-dimensional parameter time series, represents the logarithmic function with base 2, represents the natural exponential function, is the first historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion representation value, The second historical climate multidimensional parameter time series fine-grained semantic interaction fusion representation value.

[0082] The optimized historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature vector is expressed as: ;in, is the fine-grained semantic interaction fusion feature vector of the historical climate multi-dimensional parameter time series, and is a row vector, is the first historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion representation value, is the number of zero eigenvalues ​​in the fine-grained semantic interaction fusion feature map of the historical climate multi-dimensional parameter time series, represents a power function that calculates each eigenvalue of the fine-grained semantic interaction fusion eigenvector of the historical climate multi-dimensional parameter time series with the inverse of the number of zero eigenvalues ​​as the exponent, Indicates point multiplication by position, is the representation vector of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion information, express The transposed vector of represents matrix multiplication, represents the weight hyperparameter, The second historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion representation value, is the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion regression understanding matrix, The optimized historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature vector is provided.

[0083] That is, in the above preferred example, by modeling the vector field zero dimension of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature vector after the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map is expanded as the global field redundant dependency, the effective long-range modeling of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map under the field linear complexity is realized, so that in the feature regression process of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map, the structured correlation self-distillation between information representation and decoding regression understanding is performed in the selective feature enhancement state space based on feature distribution, thereby maintaining the overall information complexity of the feature distribution of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map, promoting the long-range dynamic regression understanding balance of its feature convergence, so as to improve the regression understanding consistency between the feature regression and feature extraction processes, and improve the accuracy of the prediction results obtained by the decoder-based climate predictor of the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map. In this way, the mutual influence between different climate factors and their temporal changing trends can be explored and fully reflected, thereby significantly improving the accuracy and reliability of climate forecasts, and providing strong support for the scientificity and rationality of construction plans.

[0084] In an embodiment of the present application, each construction sub-plan in the construction process information is updated based on the first climate information to obtain the second construction sequence. It should be understood that in actual application, by introducing historical climate data to update the construction plan, that is, updating the first construction sequence, so that the construction plan can match the actual weather conditions, it can improve the rationality and scientific nature of the construction plan, reduce the impact of external weather conditions on the construction progress, and thus improve the construction efficiency of the construction site. In addition, considering that the weather factors associated with each construction stage in the construction process are different, and the correlation with different weather factors is different, therefore, in order to improve the rationality and scientific nature of the updated construction sequence, each construction sub-plan can be treated as an independent individual, and each construction sub-plan in the first construction sequence can be updated in sequence according to the climate information. In particular, a specific implementation method for updating each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence can be: starting from the first construction sub-plan in the first construction sequence, for each construction sub-plan, based on the first climate information, determine the weather conditions of the construction sub-plan to obtain the second climate information; based on the second climate information, judge the matching degree between the construction sub-plan and the corresponding weather conditions, and if the matching degree is lower than a preset threshold, update the construction of the construction sub-plan and subsequent construction sub-plans in the first construction sequence; based on the updated construction sub-plan, obtain the second construction sequence.

[0085] Therefore, step S130 is explained clearly, which extracts the historical climate data within the construction period from the time queue of the acquired historical climate data to obtain the time queue of the historical climate data during the construction period, and uses a data analysis and processing algorithm based on deep learning to perform temporal regularization and temporal correlation of the historical climate data and the historical climate data during the construction period, so as to automatically predict the climate data of the first area based on the interactive fusion representation between the temporal correlation characteristics of the historical climate multidimensional parameters and the temporal correlation characteristics of the historical climate multidimensional parameters during the construction period. In this way, the mutual influence between different climate factors and their temporal change trends can be excavated and fully reflected, thereby significantly improving the accuracy and reliability of climate forecasts, and providing strong support for the scientificity and rationality of construction plans.

[0086] In step S140, construction is performed in the first area based on the second construction sequence. The second construction sequence is an optimized and prioritized construction plan that fully considers the impact of climate factors on construction. Based on the second construction sequence, project managers can precisely allocate manpower and equipment, ensuring sufficient resource support at each construction stage. This avoids idle or wasted resources due to weather fluctuations, thereby improving resource utilization efficiency. This allows for more effective control of construction progress and quality, enhancing the controllability and transparency of project management.

[0087] It should be understood that constructing the first area based on the second construction sequence is a complex and delicate process. This process requires ensuring that construction activities proceed strictly according to plan while also addressing various possible emergencies. The following is a detailed description of this process:

[0088] Before construction officially begins, a series of preparatory steps are required to ensure smooth progress. First, ensure that all necessary construction permits and approvals have been completed. These approvals are the foundation for the legality and compliance of construction; any omissions could hinder construction. Second, conduct a comprehensive on-site survey of the construction area to understand the topography, geological conditions, and surrounding environment to ensure the feasibility of the construction plan. The results of the on-site survey will directly influence the development of the construction plan and the allocation of resources.

[0089] Next, prepare all necessary construction resources, including human resources, machinery and equipment, and building materials. Ensure that the quantity and quality of these resources meet construction requirements. Human resource preparation not only includes the formation of a construction team but also includes technical training and safety education for construction personnel. Machinery and equipment preparation requires ensuring that all equipment is in good working order and deployed appropriately according to the construction plan. Building material preparation requires ensuring that the quality and quantity of these materials meet construction requirements and that a stable supply chain is in place.

[0090] Technical briefings are a crucial step in the preparation process. Organizing a technical briefing for the construction team ensures that all construction personnel have a thorough understanding of the construction plan, technical requirements, and safety regulations. These briefings should cover construction methods, construction sequences, quality standards, safety measures, and other aspects, ensuring that each construction personnel clearly understands their responsibilities and tasks. Furthermore, a detailed contingency plan must be developed, including measures to address various emergencies, such as weather changes, equipment failures, and material shortages. These plans must be operational, ensuring a swift response to emergencies and minimizing the impact on the construction schedule.

[0091] Develop a detailed construction plan based on the second construction sequence. The construction plan should include the specific content, construction methods, required resources, and ideal duration of each construction sub-plan. The detailed description of each construction sub-plan should cover the construction content, construction methods, required resources, and ideal duration, ensuring that construction personnel have clear operational guidelines. The construction sequence should strictly follow the second construction sequence, clearly defining the order of each construction sub-plan to ensure that construction activities proceed as planned. Resource allocation should detail the resources required for each construction sub-plan, including human resources, machinery and equipment, and construction materials, to ensure the proper allocation of resources. Time nodes should set start and end times for each construction sub-plan to ensure that construction activities are completed on schedule. Quality control should establish quality control standards and inspection methods to ensure that construction quality meets requirements. Safety management should establish safety management measures to ensure the safety of personnel and equipment during the construction process.

[0092] During the construction process, strictly follow the secondary construction sequence and detailed construction plan. First, initiate construction activities. After confirming that all preparatory work is complete, officially start construction activities. Each sub-project will be initiated sequentially according to the construction plan. Construction supervision is a key step in ensuring that construction activities proceed as planned. A dedicated construction supervision team will be established to oversee all construction activities and ensure that they proceed strictly according to plan. Resource scheduling should be based on the construction plan, rationally allocating resources to ensure that each sub-project is adequately resourced. For example, if a sub-project requires specific machinery and equipment, the equipment's arrival time should be scheduled in advance. Quality inspections are conducted after the completion of each sub-project to ensure that construction quality meets requirements. Any issues identified are promptly rectified. Safety management strictly enforces safety management systems to ensure the safety of personnel and equipment during construction. Regular safety training and safety inspections are conducted to promptly identify and eliminate safety hazards. Progress tracking monitors construction progress in real time to ensure that each sub-project is completed on schedule. If any delays are detected, the construction plan should be adjusted promptly and measures implemented to accelerate progress.

[0093] During the construction process, you may encounter various emergencies, such as weather changes, equipment failures, and material shortages. A detailed emergency plan needs to be developed to ensure that construction activities can proceed smoothly. In response to weather changes, countermeasures should be prepared in advance based on weather forecasts. For example, if heavy rain is forecast, the construction sequence can be adjusted to prioritize construction sub-plans that are not affected by the weather. In response to equipment failures, an equipment maintenance and repair mechanism should be established to ensure that the equipment can be repaired in a timely manner when problems arise. Spare equipment should be available to deal with emergencies. In response to material shortages, a stable supply chain system should be established to ensure the timely supply of materials. If a material shortage occurs, the supplier should be contacted in a timely manner to ensure that the materials are in place as soon as possible. In response to personnel changes, a flexible human resource allocation mechanism should be established to ensure that manpower can be quickly replenished in the event of personnel changes without affecting the construction progress.

[0094] During the construction process, detailed records are required for the progress of each construction sub-plan, including construction time, content, resource usage, and quality inspection results. These records will provide a crucial basis for subsequent construction management and project summary. The construction team will record daily construction progress, including work content, resource usage, problems encountered, and solutions. Weekly and monthly construction progress reports will be compiled to summarize progress, existing problems, and improvement measures. These reports will be submitted to the project manager and relevant management personnel. Detailed records are also kept of quality inspection results for each construction sub-plan, including inspection time, content, and results. Safety aspects of the construction process will also be carefully documented, including safety inspection results, potential safety hazards, and corrective measures.

[0095] After construction is completed, conduct a project acceptance and review to ensure that construction quality meets requirements. A professional team should be organized to conduct a comprehensive inspection of the construction project, checking whether construction quality, safety, functional performance, and other aspects meet requirements. Any issues identified should be promptly rectified. A project summary report should be prepared, summarizing lessons learned during the construction process and providing suggestions for improvement. The project summary report should include the implementation of the construction plan, existing problems and solutions, achievements, and lessons learned. All documents and records generated during the construction process should be archived, including construction plans, construction records, quality inspection reports, and safety records. These documents will provide important references for subsequent project management and maintenance.

[0096] In summary, constructing the first area based on the second construction sequence is a systematic undertaking that requires comprehensive consideration of multiple factors to ensure that construction activities proceed strictly according to plan. This process includes preparation, detailed construction plan development, construction execution, emergency response, construction records and reports, and project acceptance and summary. Through scientific management and meticulous operations, we can ensure the smooth progress of the construction project, improve construction efficiency and quality, and achieve successful project delivery.

[0097] In summary, the intelligent construction management method based on the embodiment of the present application is explained, which obtains construction process information based on the preset BIM model of the first area, and then sorts the multiple construction sub-plans in the construction process information according to the construction order, thereby obtaining the first construction sequence, and then updates the first construction sequence based on the historical climate data of the first area, thereby obtaining the second construction sequence, and finally carrying out the construction work of the first area based on the second construction sequence. This is conducive to improving the intelligence and refinement level of construction management.

[0098] Figure 5 This is a system block diagram of the smart construction management system according to an embodiment of the present application. Figure 5 As shown, according to the embodiment of the present application, the smart construction management system 100 includes: a construction process information data acquisition module 110, which is used to obtain construction process information based on a preset BIM model of the first area; a construction sub-plan sorting module 120, which is used to sort multiple construction sub-plans in the construction process information according to the construction sequence to obtain a first construction sequence; a construction sequence updating module 130, which is used to update the first construction sequence based on the historical climate data of the first area to obtain a second construction sequence; a construction implementation module 140, which is used to construct the first area based on the second construction sequence; wherein, the construction sequence updating module includes: a second construction sequence generating sub-unit, which is used to predict the climate of the first area based on the historical climate data of the first area to obtain first climate information, and update each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence; wherein, the second construction sequence generating sub-unit includes: a secondary construction sequence sorting module for arranging historical climate data during the construction period; A subunit is used to obtain the time queue of historical climate data of the first area, and extract the historical climate data within the construction time period from the time queue of historical climate data to obtain the time queue of historical climate data during the construction period; a secondary subunit is used to extract historical climate time series features during the construction period, and to perform time series regularization and historical climate time series semantic feature extraction on the time queue of historical climate data and the time queue of historical climate data during the construction period respectively to obtain historical climate multi-dimensional parameter time series correlation features and construction period historical climate multi-dimensional parameter time series correlation features; a secondary subunit is used to generate historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion features, and to perform fine-grained feature cross-domain interactive encoding on the historical climate multi-dimensional parameter time series correlation features and the construction period historical climate multi-dimensional parameter time series correlation features to obtain historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion features; a secondary subunit is used to generate first climate information prediction, and to obtain a prediction result as the first climate information based on the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion features.

[0099] Here, those skilled in the art will appreciate that the specific functions and operations of the various units and modules in the above-mentioned smart construction management system 100 have been described in detail in the above-mentioned reference. Figures 1 to 4 The smart construction management method has been introduced in detail, and therefore, its repeated description will be omitted.

Claims

1. A smart construction management method, characterized in that: include: Based on a preset BIM model of a first area, construction process information is obtained; multiple construction sub-plans in the construction process information are sorted according to a construction order to obtain a first construction sequence; based on historical climate data of the first area, the first construction sequence is updated to obtain a second construction sequence; and construction is performed on the first area based on the second construction sequence. The updating of the first construction sequence based on the historical climate data of the first region to obtain the second construction sequence includes: predicting the climate of the first region based on the historical climate data of the first region to obtain first climate information, and updating each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence; The step of predicting the climate of the first region based on historical climate data of the first region to obtain first climate information includes: Acquire a time queue of historical climate data for the first region, and extract historical climate data within a construction period from the time queue of historical climate data to obtain a time queue of historical climate data for the construction period; Performing time series regularization and historical climate time series semantic feature extraction on the time queue of the historical climate data and the time queue of the historical climate data during the construction period respectively to obtain the time series correlation feature of the historical climate multidimensional parameters and the time series correlation feature of the historical climate multidimensional parameters during the construction period; The fine-grained feature cross-domain interactive encoding of the historical climate multidimensional parameter time series correlation features and the historical climate multidimensional parameter time series correlation features of the construction period is performed to obtain the fine-grained semantic interactive fusion features of the historical climate multidimensional parameter time series, including: performing feature fine-grained decoupling on the historical climate multidimensional parameter time series correlation feature graph and the historical climate multidimensional parameter time series correlation feature graph of the construction period to obtain a set of fine-grained feature vectors of the historical climate multidimensional parameter time series correlation local fine-grained feature vectors and a set of fine-grained feature vectors of the historical climate multidimensional parameter time series correlation local fine-grained feature vectors of the construction period; each group of corresponding historical climate multidimensional parameter time series correlation local fine-grained feature vectors is converted into a set of fine-grained feature vectors of the historical climate multidimensional parameter time series correlation local fine-grained feature vectors ... The local fine-grained feature vectors associated with the historical climate multi-dimensional parameter time series during the construction period are input into the fine-grained common feature extraction network to obtain a set of historical climate time series joint fine-grained core feature vectors; the set of historical climate time series joint fine-grained core feature vectors is input into the clustering network to obtain the historical climate time series joint fine-grained core feature semantic clustering center vector; based on the historical climate time series joint fine-grained core feature semantic clustering center vector, the set of historical climate time series joint fine-grained core feature vectors is subjected to multi-dimensional feature fine-grained fusion to obtain the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature; Based on the fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter time series, a prediction result is obtained as the first climate information.

2. The smart construction management method according to claim 1, characterized in that: The historical climate data includes temperature values, humidity values, precipitation values ​​and wind speed values.

3. The smart construction management method according to claim 2, characterized in that: Performing time series regularization and extracting historical climate time series semantic features on the time queue of the historical climate data and the time queue of the historical climate data during the construction period respectively to obtain the time series correlation features of the historical climate multidimensional parameters and the time series correlation features of the historical climate multidimensional parameters during the construction period, including: Arranging the time queue of the historical climate data and the time queue of the historical climate data during the construction period according to the climate sample dimension and time to obtain a historical climate multidimensional parameter time series matrix and a construction period historical climate multidimensional parameter time series matrix; The historical climate multidimensional parameter time series matrix and the historical climate multidimensional parameter time series matrix of the construction period are passed through a historical climate time series semantic feature extractor based on a void convolutional neural network model to obtain a historical climate multidimensional parameter time series correlation feature graph as the historical climate multidimensional parameter time series correlation feature and a construction period historical climate multidimensional parameter time series correlation feature graph as the construction period historical climate multidimensional parameter time series correlation feature.

4. The smart construction management method according to claim 3, characterized in that: Performing feature fine-grained decoupling on the historical climate multidimensional parameter time series correlation feature graph and the construction period historical climate multidimensional parameter time series correlation feature graph to obtain a set of local fine-grained feature vectors of historical climate multidimensional parameter time series correlation and a set of local fine-grained feature vectors of historical climate multidimensional parameter time series correlation during the construction period, including: Expanding each characteristic matrix along the channel dimension of the historical climate multi-dimensional parameter time series correlation feature map into a characteristic vector to obtain a set of local fine-grained characteristic vectors of the historical climate multi-dimensional parameter time series correlation; Each characteristic matrix along the channel dimension of the historical climate multidimensional parameter time series correlation feature graph during the construction period is expanded into a characteristic vector to obtain a set of local fine-grained characteristic vectors of the historical climate multidimensional parameter time series correlation during the construction period.

5. The smart construction management method according to claim 4, characterized in that: Based on the semantic clustering center vector of the historical climate time series joint fine-grained core feature, a multidimensional feature fine-grained fusion is performed on the set of the historical climate time series joint fine-grained core feature vectors to obtain the historical climate multidimensional parameter time series fine-grained semantic interaction fusion feature, including: Constructing a historical climate time series query vector and a historical climate time series value vector based on each historical climate time series joint fine-grained core feature vector in the set of the historical climate time series joint fine-grained core feature vectors, and constructing a historical climate time series key vector based on the historical climate time series joint fine-grained core feature semantic clustering center vector, and inputting the historical climate time series query vector, the historical climate time series value vector and the historical climate time series key vector of each historical climate time series joint fine-grained core feature vector into a global semantic interaction encoder based on a converter structure to obtain a set of historical climate time series context joint fine-grained core feature vectors; The feature shape of each historical climate time series context joint fine-grained core feature vector in the set of the historical climate time series context joint fine-grained core feature vector is reshaped to obtain a historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature map as the historical climate multi-dimensional parameter time series fine-grained semantic interaction fusion feature.

6. The smart construction management method according to claim 5, characterized in that: Based on each historical climate time series joint fine-grained core feature vector in the set of the historical climate time series joint fine-grained core feature vectors, a historical climate time series query vector and a historical climate time series value vector are constructed, and based on the historical climate time series joint fine-grained core feature semantic clustering center vector, a historical climate time series key vector is constructed. The historical climate time series query vector, the historical climate time series value vector, and the historical climate time series key vector of each historical climate time series joint fine-grained core feature vector are input into a global semantic interaction encoder based on a converter structure to obtain a set of historical climate time series context joint fine-grained core feature vectors, including: Multiplying the query embedding matrix by the historical climate time series joint fine-grained core feature vector to obtain the historical climate time series query vector; Multiplying the value embedding matrix and the historical climate time series joint fine-grained core feature vector to obtain the historical climate time series value vector; Multiplying the key embedding matrix by the historical climate time series joint fine-grained core feature semantic clustering center vector to obtain the historical climate time series key vector; The historical climate time series query vector, the historical climate time series value vector and the historical climate time series key vector are input into the global semantic interaction encoder based on the converter structure to obtain the historical climate time series context joint fine-grained core feature vector.

7. The smart construction management method according to claim 6, characterized in that: Based on the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion features, a prediction result is obtained, including: passing the historical climate multi-dimensional parameter temporal fine-grained semantic interaction fusion feature map through a decoder-based climate predictor to obtain the prediction result, and the prediction result is used to represent the decoded value of the climate prediction data of the first area.

8. A smart construction management system, used to implement the smart construction management method according to any one of claims 1 to 7, characterized in that: include: a construction process information data acquisition module for obtaining construction process information based on a preset BIM model of a first area; a construction sub-plan sorting module for sorting multiple construction sub-plans in the construction process information according to a construction order to obtain a first construction sequence; a construction sequence updating module for updating the first construction sequence based on historical climate data of the first area to obtain a second construction sequence; and a construction implementation module for performing construction in the first area based on the second construction sequence; The construction sequence updating module includes: a second construction sequence generating subunit, configured to predict the climate of the first region based on historical climate data of the first region to obtain first climate information, and to update each construction sub-plan in the construction process information based on the first climate information to obtain the second construction sequence; The second construction sequence generation subunit includes: a secondary subunit for collating historical climate data during the construction period, configured to obtain a time queue of historical climate data for the first region, and extract historical climate data within the construction period from the time queue of historical climate data to obtain a time queue of historical climate data for the construction period; A secondary sub-unit for extracting historical climate time series features during the historical climate construction period is used to respectively perform time series regularization and historical climate time series semantic feature extraction on the time queue of the historical climate data and the time queue of the historical climate data during the construction period to obtain the time series correlation features of the historical climate multidimensional parameters and the time series correlation features of the historical climate multidimensional parameters during the construction period; A secondary sub-unit is provided for generating fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter temporal series, which is used to perform fine-grained feature cross-domain interaction encoding on the historical climate multi-dimensional parameter temporal series correlation features and the historical climate multi-dimensional parameter temporal series correlation features during the construction period to obtain fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter temporal series; The first climate information prediction generates a secondary subunit, which is used to obtain a prediction result as the first climate information based on the fine-grained semantic interaction fusion features of the historical climate multi-dimensional parameter time series.

9. The intelligent construction management system according to claim 8, characterized in that: The secondary sub-unit for extracting historical climate time series features during the historical climate construction period is used to: Arranging the time queue of the historical climate data and the time queue of the historical climate data during the construction period according to the climate sample dimension and time to obtain a historical climate multidimensional parameter time series matrix and a construction period historical climate multidimensional parameter time series matrix; The historical climate multidimensional parameter time series matrix and the historical climate multidimensional parameter time series matrix of the construction period are passed through a historical climate time series semantic feature extractor based on a void convolutional neural network model to obtain a historical climate multidimensional parameter time series correlation feature graph as the historical climate multidimensional parameter time series correlation feature and a construction period historical climate multidimensional parameter time series correlation feature graph as the construction period historical climate multidimensional parameter time series correlation feature.

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