Building construction progress digital monitoring method and system

Abstract

The invention relates to the technical field of building engineering, and provides a building construction progress digital monitoring method and system. The method comprises the following steps: collecting three-dimensional point location data of a construction area, and comparing the three-dimensional point location data with a BIM model to extract a progress deviation; associating the construction log to a BIM component node and setting a first evaluation index; associating the material records to BIM component nodes, and setting second evaluation indexes; drawing up a resource map of construction process logic in combination with the deviation and the two indexes; and based on the atlas, according to a weight attenuation coefficient, multi-scale assessment of cumulative influence is carried out by taking efficiency as a feature, and resource compensation reminding is carried out in combination with dynamic threshold adjustment. The technical problem that progress deviation is difficult to accurately recognize due to data updating lagging and construction process logic disjunction in traditional building construction progress monitoring is solved, real-time accurate analysis of the construction progress is achieved through intelligent comparison of multi-source data fusion and the BIM model, resource allocation is dynamically optimized in combination with multi-dimensional evaluation indexes, and the construction progress monitoring efficiency is improved. And the construction management efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of construction engineering technology, and in particular to a method and system for digitally monitoring the progress of building construction. Background Art

[0002] In the construction industry, the rapid development of prefabricated buildings and intelligent construction technologies has significantly increased project complexity and the pace of construction. Traditional construction progress management relies on manual inspections and paper logs, which are subject to data lag and information fragmentation. Although BIM technology has enabled digital modeling of building components, a gap remains between the real-time dynamics of the construction site and the virtual model. Managers struggle to intuitively perceive actual progress deviations, and resource scheduling relies on empirical judgment, leading to frequent material backlogs and shortages. Especially in scenarios involving multiple processes, sudden environmental changes (such as extreme weather), process logic conflicts, or unexpected risk events often trigger cascading delays. Existing systems lack the ability to integrate and analyze construction logs, material consumption, and environmental parameters, making it impossible to establish a process-logic-driven resource early warning mechanism. For example, a delay in concrete pouring may not be promptly linked to changes in resource requirements for subsequent steel structure installation, or sudden rain or snow may not dynamically adjust the priority of door and window construction, ultimately leading to uncontrolled construction schedules. Therefore, there is an urgent need for a digital progress monitoring method that can achieve real-time awareness of progress deviations, dynamically optimize resource allocation, and proactively warn of potential risks, thereby improving project management efficiency and resource utilization. Summary of the Invention

[0003] This application provides a digital monitoring method and system for building construction progress, aiming to solve the technical problems in traditional building construction progress monitoring, such as delayed data updates and logical disconnection of construction processes, which make it difficult to accurately identify progress deviations.

[0004] The first aspect disclosed in the present application provides a digital monitoring method for building construction progress, the method comprising: collecting three-dimensional point data of a construction area, comparing it with a BIM building model, and extracting deviation information between the construction progress and the planned progress; associating a construction log with each building component node of the BIM building model, and setting a first progress evaluation index; associating a material consumption record with each building component node of the BIM building model, and setting a second progress evaluation index; formulating a building construction resource allocation map that conforms to the logical relationship of the construction process based on the deviation information, in combination with the first and second progress evaluation indicators; based on the building construction resource allocation map, performing a multi-scale evaluation of the cumulative impact range according to a weight attenuation coefficient and with resource allocation efficiency as the correlation feature, and providing a resource compensation reminder in combination with a dynamic threshold adjustment rule.

[0005] Another aspect disclosed in the present application provides a digital monitoring system for building construction progress, the system comprising: a real-time comparison module for collecting three-dimensional point data of a construction area, comparing it with a BIM building model, and extracting deviation information between the construction progress and the planned progress; a construction log association module for associating the construction log with each building component node of the BIM building model and setting a first progress evaluation index; a consumption record association module for associating material consumption records with each building component node of the BIM building model and setting a second progress evaluation index; a resource allocation map formulation module for formulating a building construction resource allocation map that conforms to the logical relationship of the construction process through the deviation information, in combination with the first and second progress evaluation indicators; a resource compensation reminder module for performing a multi-scale assessment of the cumulative impact range based on the building construction resource allocation map, according to a weight attenuation coefficient, and with resource allocation efficiency as the correlation feature, and providing a resource compensation reminder in combination with a dynamic threshold adjustment rule.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The aforementioned digital construction progress monitoring method first collects 3D point data from the construction area and compares it with the BIM building model to extract information on deviations between the construction progress and the planned progress. Subsequently, the construction log is associated with the building component nodes in the BIM model to set a first progress assessment indicator, and material consumption records are associated with each component node to set a second progress assessment indicator. Subsequently, through comprehensive analysis of these deviation information and assessment indicators, a resource allocation map that conforms to the construction process logic is developed. Finally, based on this map, a multi-scale assessment is performed, combining resource allocation efficiency and weight decay coefficients. Dynamic threshold adjustment rules are used to issue resource compensation reminders to ensure optimal resource allocation and progress during construction.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0009] Figure 1The figure is a flow chart of a method for digitally monitoring building construction progress in one embodiment.

[0010] Figure 2 The following is an architecture diagram of a digital monitoring system for building construction progress in one embodiment.

[0011] Explanation of the accompanying symbols: real-time comparison module 11, construction log association module 12, consumption record association module 13, resource allocation map formulation module 14, resource compensation reminder module 15. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a digital monitoring method and system for building construction progress, thereby solving the technical problems in traditional building construction progress monitoring, such as data update lag and logical disconnection of construction processes, which make it difficult to accurately identify progress deviations.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0015] Example 1, as Figure 1 As shown, the present application provides a method for digitally monitoring construction progress of a building, the method comprising: Collect 3D point data of the construction area, compare it with the BIM building model, and extract deviation information between the construction progress and the planned progress.

[0016] In an embodiment of the present application, during the construction process, in order to achieve accurate progress monitoring, a laser scanner or a sensor mounted on a drone is first used to periodically scan the construction area to obtain three-dimensional point cloud data containing spatial coordinates and intensity information. The obtained three-dimensional point data is then normalized. The purpose of normalization is to adjust point data from different sources and different accuracies to a unified scale so that these data are consistent in subsequent analysis. This process also applies a statistical outlier removal algorithm to remove outliers to ensure the accuracy and reliability of the data. Subsequently, the processed three-dimensional point data is aligned with the coordinate system of the BIM model using the ICP (Iterative Closest Point) algorithm to ensure consistency in the spatial reference. The aligned three-dimensional point data is then input into the three-dimensional modeling software to construct a real-time building model of the current construction area. Afterwards, the processed point cloud data is matched with the geometric information of the corresponding components in the BIM model. This involves using Euclidean distance to calculate the deviation between the actual construction coordinates of each component in the real-time building model and the planned coordinates of each component in the BIM model. The deviations are then averaged across all points to obtain the spatial positional deviation between the construction progress and the planned progress. The actual completion time is then subtracted from the planned completion time to obtain the temporal deviation between the construction progress and the planned progress. Finally, the spatial positional deviation and temporal deviation information are aggregated to form the deviation between the construction progress and the planned progress, helping construction managers identify problems and make adjustments promptly, thereby reducing the risk of project delays.

[0017] The construction log is associated with each building component node of the BIM building model, and a first progress evaluation indicator is set.

[0018] In one embodiment, an electronic log database is connected to obtain construction logs. The construction logs record the progress of construction each day, typically including construction personnel's working hours, completed processes, problems encountered during construction, and the actual progress of construction. To ensure accurate tracking of construction progress, the data in the construction logs is organized and categorized by building component, thereby obtaining work information related to each building component. Subsequently, the work information of each building component is associated with the node representing the corresponding building component in the BIM model. These nodes typically include the component's location information, size, material, and process requirements. After associating the records in the construction logs with these nodes, the system can obtain specific information about the component corresponding to the node by matching them with the nodes in the BIM model, assisting in the setting of subsequent progress assessment indicators. Next, based on the information associated with each node, primary progress assessment indicators are set. These indicators measure the completion of construction tasks and typically include project volume completion rate (the ratio of the actual installed component volume to the planned installed volume), time efficiency (the ratio of the planned component construction time to the actual construction time), and team efficiency (the ratio of the average completion time of the same construction team at different component nodes to the planned team baseline time). These assessment indicators provide a clear understanding of the actual construction status of each building component, providing a more comprehensive understanding of the overall progress and ensuring on-time project completion.

[0019] The material consumption record is associated with each building component node of the BIM building model, and a second progress evaluation indicator is set.

[0020] In one embodiment, a material management system is connected to obtain material consumption records. These records reflect actual resource consumption at each stage of the project and typically include information such as the name, specifications, consumption, and usage time of various building materials (such as cement, rebar, and concrete). To ensure accurate tracking of material consumption, the data in the material consumption records is organized and categorized by building component, thereby obtaining material information related to each building component. Subsequently, the material information for each building component is associated with the node representing the corresponding building component in the BIM model. After association, the system can obtain specific information about the component corresponding to the node by matching it with the node in the BIM model, assisting in the setting of subsequent progress assessment indicators. Secondary progress assessment indicators are then set based on the information associated with each node. These secondary progress assessment indicators are used to assess the construction progress of building components and typically include material usage deviation rate (the ratio of actual material usage to planned usage), supply timeliness rate (the ratio of planned delivery time to actual delivery time), and loss deviation rate (the ratio of the absolute difference between actual and planned material usage to the planned usage). Through these evaluation indicators, we can accurately grasp the material consumption of each component in the construction project, thereby ensuring that the project is completed efficiently and on time.

[0021] By using the deviation information and combining the first progress assessment indicator and the second progress assessment indicator, a construction resource allocation map that conforms to the logical relationship of the construction process is formulated.

[0022] In one embodiment, after obtaining deviation information, the first and second progress assessment indicators, a directed graph is constructed based on the predefined logical relationships between construction processes in the BIM model (e.g., "foundation construction → main structure → decoration and renovation"). Nodes in this directed graph represent construction processes (e.g., "rebar tying" and "concrete pouring"), and edges represent dependencies between processes (e.g., "completion of rebar tying is a prerequisite for concrete pouring"). Within the directed graph, the deviation information, the first and second progress assessment indicators are assigned to the corresponding process nodes. A sliding time window is then used to dynamically update the weights between construction processes, and the calculated weights are assigned to the corresponding edges. After configuring the edges and nodes, a construction resource allocation map based on the BIM model is generated. This map guides resource scheduling and management during the construction process, ensuring on-time completion of construction tasks, avoiding resource waste, and improving construction efficiency. By monitoring and adjusting this map in real time, managers can flexibly respond to various changes during the construction process, ensuring the efficient and smooth progress of the construction project.

[0023] Furthermore, the present application provides a method for determining the directed edges corresponding to the building construction resource configuration map through a sliding time window to connect each construction process; at the same time, marking the pre-constraint relationship, time-dependent parameters and resource association information between each construction process.

[0024] Preferably, a sliding time window is a time-based data processing method used to capture real-time progress information and dynamically update construction processes. The size of the sliding window is typically set based on the construction cycle or project requirements. For example, the window can be a daily, weekly, or monthly time period. When constructing or updating the building resource allocation map, the time window continuously advances based on the deviation information, the first progress assessment indicator, and the second progress assessment indicator. With each advance, new data enters the window and old data is excluded. The data within the time window is analyzed using a pre-trained long short-term memory (LSTM) network to calculate the connection strength between each construction process within the time window. The calculated connection strength is used as the edge weight between the corresponding construction processes. The LSTM network is trained iteratively using sample deviation information, sample progress assessment indicators, and sample influence weights through forward propagation, loss calculation (mean squared error), backpropagation, and parameter optimization (Adam optimizer). After the edge weights are set or updated, the construction processes are reconnected to form a new building resource allocation map. At each node in the building construction resource allocation map, in addition to the allocated deviation information, the first and second progress assessment indicators, the predecessor constraints, time dependency parameters, and resource association information between each construction process are also marked. Predecessor constraints are determined by the logical relationship and sequence of the construction processes. Some processes may be restricted by predecessor processes. For example, the concrete pouring process must be completed after the foundation construction is completed, and the steel structure installation must be carried out after the concrete hardens. Predecessor constraints clearly indicate the predecessor processes of each process. Only when the predecessor processes are completed and the resources are fully prepared can the process at that node be started. Time dependency parameters are determined by the time required for each construction process and the completion status of the predecessor processes. Because the execution time of some processes depends on the completion time of the predecessor processes and the complexity of the process itself, for example, the time of the concrete pouring process depends on the completion status of the foundation construction, and the time of the steel structure installation depends on the hardening time of the concrete. Time dependency parameters clearly indicate the time requirements of each process. Only when the predecessor processes are completed and the time dependency relationships are met can the subsequent processes proceed as planned. Resource association information is determined by the type and quantity of resources required for each process, because each construction process requires specific resources (such as manpower, equipment, materials, etc.). For example, a certain process may require a large amount of concrete, while another process may require specific construction equipment or labor. Resource association information can clearly display the type and quantity of resources required for each process. A process can only be started when the previous process is completed and the required resources are ready.In summary, by dynamically updating the directed edges in the construction resource allocation map and marking the prerequisite constraints, time-dependent parameters, and resource association information of each process, the construction progress can reflect the actual situation in a timely manner, thereby optimizing resource allocation, avoiding construction delays, and ensuring the smooth progress of the construction project.

[0025] Based on the building construction resource allocation map, according to the weight attenuation coefficient and with resource allocation efficiency as the correlation feature, a multi-scale assessment of the cumulative impact range is performed, and resource compensation reminders are given in combination with dynamic threshold adjustment rules.

[0026] In one embodiment, after constructing a construction resource allocation graph, an LSTM time-series-aware model is used to analyze the graph and determine its weight decay coefficient. The resource allocation efficiency of each process node in the graph is then used as a correlation feature to measure the actual resource utilization during construction. Subsequently, a multi-scale assessment is performed to measure the impact of lagging processes on subsequent processes. Specifically, during the micro-scale assessment, the weights of all edges associated with each node are summed and multiplied by the weight decay coefficient to obtain the cumulative impact value of each node. During the meso-scale assessment, the cumulative impact values ​​of all nodes in each construction zone (e.g., floor, section) are summed and divided by the number of nodes within the zone to obtain the aggregated node impact value. During the macro-scale assessment, the mean of the resource allocation efficiency of all nodes is calculated to obtain the macro-node impact value. Afterwards, the normalized results of the three impact values ​​are weighted according to the preset weights for each scale to obtain a multi-scale assessment result. This multi-scale assessment result is then compared with a dynamic threshold. This dynamic threshold is used to determine whether the current construction requires compensation. It is set dynamically based on actual business needs to adapt to the construction needs of different stages. When the multi-scale assessment result exceeds the dynamic threshold, a resource compensation reminder is triggered. These reminders help managers adjust resource allocation in a timely manner and replenish resources at key nodes and potential risk nodes, such as increasing materials, deploying labor, or adjusting equipment arrangements, thereby avoiding resource waste and maintaining a smooth construction schedule. This step can optimize resource allocation in real time, ensuring that construction projects are completed on time and with quality, and reducing delays and resource waste during construction.

[0027] Furthermore, the present application provides a method based on a weight attenuation coefficient with resource allocation efficiency as a correlation feature, the method further comprising: Dynamically assign weights to the directed edges of the building construction resource configuration map, and obtain the weight attenuation coefficient by integrating the pre-constraint relationship, time-dependent parameters and resource association information between each construction process based on LSTM time series perception.

[0028] Optionally, to ensure that construction progress and resource allocation reflect changes on the construction site in real time, directed edges in the building construction resource allocation map are dynamically weighted based on data within a sliding time window. During this process, the deviation information, the first progress assessment indicator, and the second progress assessment indicator within the sliding time window are input into the long short-term memory network to map the weight of each edge. Subsequently, based on the LSTM time-series perception model, the pre-constraint relationships, time-dependent parameters, and resource association information between the various construction processes marked in the building construction resource allocation map are integrated to calculate a weight decay coefficient. This coefficient is used to address changes during the construction process, ensure the smooth progress of each process, and promptly adjust resource allocation when necessary, thereby optimizing the efficiency and quality of the overall building construction process. Furthermore, the present application provides a method for formulating a construction resource allocation map that conforms to the logical relationship of the construction process by combining the first progress assessment indicator and the second progress assessment indicator. The method includes: A dynamic environmental parameter perception network is constructed by integrating temperature and humidity sensing units, wind speed monitoring units, and light intensity detection units to obtain construction environment data. Based on the process sub-intervals associated with the construction efficiency influence coefficient and combined with the construction environment data, an environmental progress association matrix is ​​constructed to dynamically correct the weight attenuation coefficient of the building construction resource configuration map.

[0029] To ensure that the weight decay coefficients are more consistent with current working conditions, an environmental parameter sensing network is used to collect construction environment data. This network consists of temperature and humidity sensors, wind speed monitoring equipment, and a light intensity detection unit. The temperature and humidity sensors monitor temperature and humidity changes at the construction site in real time, the wind speed monitoring unit captures wind speed data within the construction area, and the light intensity detection unit measures sunlight intensity. During the construction process, environmental factors significantly impact the efficiency of each process. For example, excessive temperature or humidity can degrade the performance of certain building materials (such as concrete and paint), impacting construction quality and efficiency. Excessive wind speed can affect the proper operation of lifting equipment, while light intensity can affect the working environment during outdoor construction. To account for these environmental factors, a construction efficiency impact coefficient is assigned to each process based on its characteristics. This coefficient is a weighted sum of the ratios of all environmental data to the ideal environmental data, reflecting the impact of the construction environment on process efficiency. These process efficiency impact coefficients are associated with process subintervals. For example, concrete pouring may require suitable temperature and humidity conditions, while steel structure installation may be limited by wind speed. Subsequently, the construction environment data and the process subintervals corresponding to the process efficiency impact coefficients are integrated to construct an environmental progress correlation matrix. This matrix intuitively reflects the mutual influence between each construction process and environmental factors. The mean of the process efficiency impact coefficients in the same row of the environmental progress correlation matrix is ​​then used as a dynamic correction factor. This factor is multiplied by the weight attenuation coefficient calculated based on the current construction resource allocation map to complete the correction of the weight attenuation coefficient. This allows the weight attenuation coefficient to reflect environmental changes in real time, avoiding resource waste or construction delays caused by environmental factors and optimizing overall construction efficiency.

[0030] Furthermore, the present application provides a construction environment progress association matrix to dynamically modify the weight attenuation coefficient of the building construction resource configuration map. The method includes: The row dimension of the environmental progress association matrix is ​​the construction environment data; the column dimension of the environmental progress association matrix includes the concrete engineering sub-interval, the steel structure installation sub-interval, and the door and window installation sub-interval; the matrix elements of the environmental progress association matrix are quantized and dynamically updated.

[0031] Optionally, first, an environmental progress correlation matrix is ​​established based on different construction environment parameters and process intervals. The row dimension of the matrix represents the construction environment data, that is, the environmental factors monitored in real time, such as temperature, humidity, wind speed, and light intensity. The column dimension represents different construction process sub-intervals, such as the concrete engineering sub-interval, the steel structure installation sub-interval, the door and window installation sub-interval, etc. Each element in the matrix represents the degree of influence of a certain environmental factor on the construction progress of a specific process sub-interval. Subsequently, the set construction efficiency impact coefficient is assigned to the corresponding position of the environmental progress correlation matrix to complete the quantification of the environmental progress correlation matrix. Then, based on the data in the environmental progress correlation matrix, the same weight attenuation coefficient correction process is performed to complete the dynamic update of the weight attenuation coefficient, thereby ensuring that the construction progress and resource utilization efficiency are maximized.

[0032] Furthermore, the present application provides that when the construction environment data does not meet the safety construction environment restrictions, the matrix element values ​​of the environment progress correlation matrix are set to 0.

[0033] Optionally, when data is detected in the construction environment data that exceeds the safe construction environment limit, an alarm mechanism will be triggered to alert project managers to environmental anomalies. At the same time, the progress impact values ​​of the construction processes related to these abnormal environmental conditions will be adjusted. That is, the relevant matrix element values ​​in the environmental progress association matrix will be set to 0. For example, if the temperature at the construction site is too high, exceeding the safe range of the concrete pouring process, the system will detect this anomaly and set the matrix element value between the temperature and the concrete engineering sub-interval to 0. This means that under such unsafe environmental conditions, the construction progress of the concrete engineering sub-interval will be deemed impossible to proceed. In this way, it can be ensured that every process in the construction process is carried out under safe and appropriate conditions, maximizing the safety of construction personnel and the quality of the project.

[0034] Furthermore, the present application provides a resource compensation reminder in combination with a dynamic threshold adjustment rule, the method comprising: The timing feature analysis is performed using a start symbol and a terminator, wherein the start symbol and the terminator are used to mark the foundation construction stage, the main structure construction stage, and the decoration and renovation stage; and the dynamic threshold adjustment rule is configured based on the timing relationship corresponding to the start symbol and the terminator.

[0035] Optionally, during the construction process, different stages of the project have different construction characteristics and requirements. Therefore, in order to effectively manage the construction progress, start and end symbols are used for timing feature analysis to mark and distinguish different construction stages. Specifically, the start and end symbols are key nodes that mark the start and end of the construction stage. Each construction stage (such as the foundation construction stage, the main structure construction stage, and the decoration and renovation stage) has a clear start and end time. These symbols mark the beginning and end of a stage, which can help the system accurately locate the stage to which the current construction progress belongs. When performing timing feature analysis on the start and end symbols, different stages are first divided according to the overall time frame of the construction project to ensure that the start and end symbols of each stage accurately correspond. Then, by monitoring the progress of each stage, the time between the start and end symbols is subtracted from the planned time, and the difference is divided by the planned time to obtain the time elasticity coefficient. If this time elasticity coefficient is greater than 0, it means that the progress is lagging, and if it is less than 0, it means that the progress is ahead of schedule. Subsequently, the time elasticity coefficient is combined with hard constraints to form a temporal relationship between different stages. A hard constraint requires that one stage must precede another. For example, the main structure construction stage can only begin after the foundation construction stage is completed, and the decoration and renovation stage must begin after the main structure construction is completed. Based on the determined temporal relationships, a risk warning knowledge graph is configured in conjunction with resource types and historical accident cases. This configured risk warning knowledge graph is then embedded in the dynamic threshold adjustment rules to ensure that each construction stage proceeds sequentially, thereby optimizing resource allocation in real time, improving construction efficiency, and avoiding delays caused by schedule delays.

[0036] Furthermore, the method comprises: Associate resource types, historical accident cases, start symbols and end symbols to set up a risk warning knowledge graph; based on the risk warning knowledge graph, identify potential risk nodes and optimize resource secondment.

[0037] Optionally, first, various project resources (such as labor, materials, and equipment) are associated with tasks in each construction phase. Each process and construction phase has specific requirements for different types of resources. By analyzing resource requirements, it is possible to assess which resources are critical at a specific phase and which resources may cause construction schedule problems due to delays or shortages. Subsequently, historical accident cases are associated with different construction phases and processes. Historical accident cases typically include common construction problems, delays, equipment failures, and material supply issues. By analyzing historical accident cases, potential risks for specific phases or resources can be identified. For example, extreme weather conditions may cause delays in certain phases, or supply chain issues may lead to shortages of certain resources (such as rebar and concrete). Based on the construction phase, the temporal relationship between start and end symbols is combined with various resources and historical accident cases. A risk warning knowledge graph is constructed using a graph convolutional network with an attention mechanism. In this graph, each node represents a different risk type or factor, and the relationships between nodes reflect their interactions and impacts during the construction process. Once the risk warning knowledge graph is constructed, potential risk nodes can be identified in real time based on this data. Specifically, the latest data from each construction phase is fed into the risk warning knowledge graph in real time. This data is then matched against various nodes in the risk warning knowledge graph to assess whether potential risks exist at each construction stage within the current construction site. This in turn identifies potential risk nodes—those construction stages where potential risks exist. Once a potential risk node is identified, a resource secondment optimization mechanism is triggered, implementing the same resource compensation described above. This allows for the proper adjustment of resource allocation to ensure that construction progress and quality are not impacted.

[0038] Furthermore, the present application provides a method for identifying potential risk nodes based on the risk warning knowledge graph, the method comprising: A graph convolutional network based on an attention mechanism is set up, and the input node features include historical accident frequency and resource availability to obtain the risk propagation weights between various construction processes; based on the risk propagation weights between the various construction processes, the key nodes and risk propagation paths of risk diffusion are determined; based on the risk warning knowledge graph and the key nodes of risk diffusion, the risk propagation path is used to simulate the chain reaction process to determine the potential risk nodes.

[0039] Optionally, to effectively identify and predict potential risks in construction, a graph convolutional network (GCNN) based on an attention mechanism is used to analyze the risk propagation weights between construction processes. A GCNN is a deep learning model for processing graph data that enables information transfer and feature updates within a graph structure. To more accurately simulate the spread of risk in a construction project, characteristic data for each construction process (such as historical accident frequency and resource availability) is input into the GCNN. Historical accident frequency reflects the potential risks a specific process may have faced due to historical reasons, while resource availability represents the sufficiency of resources, which directly affects the execution progress and quality of the process. In graph convolutional networks, each node shares information and updates features with its neighboring nodes through the propagation mechanism of the adjacency matrix. Combined with the attention mechanism, the information flow between neighboring nodes can be dynamically adjusted based on the feature value of each node. In this way, the relationship between construction processes can be modeled in a more intelligent way, thereby calculating the risk propagation weights between each process. These risk propagation weights represent the degree of risk impact that a process may have on its neighboring processes during its progress. For example, if a process has a history of many accidents and its resource availability is insufficient, the risk propagation weight of this process is large, which may have a chain reaction on multiple subsequent processes. Subsequently, based on the risk propagation weight and risk propagation weight threshold of each process, the key nodes are determined. Then, by analyzing the connection between these key nodes, risk propagation paths are mapped. Risk propagation paths show how the risk of a particular process gradually affects subsequent processes. These paths may span multiple construction phases and even different resource demand areas. Then, based on the identified risk propagation pathways, the risk warning knowledge graph is used to simulate changes in different construction phases and processes, predicting which nodes, under specific conditions, may trigger a series of subsequent risk diffusion events. If a process experiences a delay or resource allocation issue, it may not only affect the current process but also cause delays in the progress of multiple subsequent processes. For example, during the construction phase, a delay in concrete pouring could affect the subsequent progress of steel structure installation and door and window installation. By simulating this chain reaction process, potential risk nodes can be identified in advance, ensuring the implementation of early warning measures at these nodes and optimizing resource allocation based on the risk propagation pathways. These steps not only identify potential risk nodes in the construction process, but also prevent these risks by optimizing resource allocation, thereby ensuring the smooth progress of construction.

[0040] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application first collects three-dimensional point data of the construction area, compares it with the BIM building model, and extracts deviation information of the construction progress from the planned progress; then, the construction log is associated with each building component node of the BIM building model to set a first progress evaluation index; then, the material consumption record is associated with each building component node of the BIM building model to set a second progress evaluation index; then, based on the deviation information, combined with the first progress evaluation index and the second progress evaluation index, a construction resource allocation map that conforms to the logical relationship of the construction process is formulated; finally, based on the construction resource allocation map, according to the weight attenuation coefficient, with resource allocation efficiency as the correlation feature, a multi-scale assessment of the cumulative impact range is performed, and resource compensation reminders are issued in combination with dynamic threshold adjustment rules. These technical effects jointly solve the technical problems of data update lag and construction process logic disconnection in traditional construction progress monitoring, which make it difficult to accurately identify progress deviations. Through multi-source data fusion and BIM model intelligent comparison, real-time and accurate analysis of construction progress is achieved, and resource allocation is dynamically optimized in combination with multi-dimensional evaluation indicators to improve construction management efficiency.

[0041] Example 2, based on the same inventive concept as the digital monitoring method for building construction progress in the above-mentioned embodiment, Figure 2 As shown, the present application provides a digital monitoring system for building construction progress, which includes: a real-time comparison module 11: collecting three-dimensional point data of the construction area, comparing it with the BIM building model, and extracting deviation information between the construction progress and the planned progress; a construction log association module 12: associating the construction log with each building component node of the BIM building model, and setting a first progress evaluation index; a consumption record association module 13: associating the material consumption record with each building component node of the BIM building model, and setting a second progress evaluation index; a resource allocation map formulation module 14: through the deviation information, combined with the first progress evaluation index and the second progress evaluation index, formulates a building construction resource allocation map that conforms to the logical relationship of the construction process; a resource compensation reminder module 15: based on the building construction resource allocation map, according to the weight attenuation coefficient, with resource allocation efficiency as the correlation feature, a multi-scale evaluation of the cumulative impact range is performed, and a resource compensation reminder is performed in combination with the dynamic threshold adjustment rule.

[0042] Furthermore, the resource configuration map drawing module 14 is also used to execute the following method: Through the sliding time window, the directed edges corresponding to the building construction resource configuration map are determined to connect the various construction processes; at the same time, the pre-constraint relationships, time-dependent parameters and resource association information between the various construction processes are marked.

[0043] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: Dynamically assign weights to the directed edges of the building construction resource configuration map, and obtain the weight attenuation coefficient by integrating the pre-constraint relationship, time-dependent parameters and resource association information between each construction process based on LSTM time series perception.

[0044] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: A dynamic environmental parameter perception network is constructed by integrating temperature and humidity sensing units, wind speed monitoring units, and light intensity detection units to obtain construction environment data. Based on the process sub-intervals associated with the construction efficiency influence coefficient and combined with the construction environment data, an environmental progress association matrix is ​​constructed to dynamically correct the weight attenuation coefficient of the building construction resource configuration map.

[0045] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: The row dimension of the environmental progress association matrix is ​​the construction environment data; the column dimension of the environmental progress association matrix includes the concrete engineering sub-interval, the steel structure installation sub-interval, and the door and window installation sub-interval; the matrix elements of the environmental progress association matrix are quantized and dynamically updated.

[0046] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: When the construction environment data does not meet the safety construction environment restrictions, the matrix element values ​​of the environment progress correlation matrix are set to 0.

[0047] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: The timing feature analysis is performed using a start symbol and a terminator, wherein the start symbol and the terminator are used to mark the foundation construction stage, the main structure construction stage, and the decoration and renovation stage; and the dynamic threshold adjustment rule is configured based on the timing relationship corresponding to the start symbol and the terminator.

[0048] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: Associate resource types, historical accident cases, start symbols and end symbols to set up a risk warning knowledge graph; based on the risk warning knowledge graph, identify potential risk nodes and optimize resource secondment.

[0049] Furthermore, the resource compensation reminder module 15 is further configured to execute the following method: A graph convolutional network based on an attention mechanism is set up, and the input node features include historical accident frequency and resource availability to obtain the risk propagation weights between various construction processes; based on the risk propagation weights between the various construction processes, the key nodes and risk propagation paths of risk diffusion are determined; based on the risk warning knowledge graph and the key nodes of risk diffusion, the risk propagation path is used to simulate the chain reaction process to determine the potential risk nodes.

[0050] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0052] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A digital monitoring method for building construction progress, characterized in that: The method comprises: Collect 3D point data of the construction area, compare it with the BIM building model, and extract information on deviations between the construction progress and the planned progress; Associating the construction log with each building component node of the BIM building model and setting a first progress evaluation indicator; Associating the material consumption record with each building component node of the BIM building model and setting a second progress evaluation indicator; By combining the deviation information with the first and second progress assessment indicators, a construction resource allocation map that conforms to the logical relationship of the construction process is formulated; Based on the building construction resource allocation map, according to the weight attenuation coefficient and with resource allocation efficiency as the correlation feature, a multi-scale assessment of the cumulative impact range is performed, and resource compensation reminders are given in combination with dynamic threshold adjustment rules.

2. The digital monitoring method for building construction progress according to claim 1, characterized in that: Determine the directed edges corresponding to the building construction resource configuration graph through a sliding time window, and connect each construction process; At the same time, the pre-constraint relationship, time-dependent parameters and resource association information between each construction process are marked.

3. The digital monitoring method for building construction progress according to claim 2, characterized in that: According to the weight decay coefficient, with resource allocation efficiency as the correlation feature, the method further includes: Dynamically assign weights to the directed edges of the building construction resource configuration map, and obtain the weight attenuation coefficient by integrating the pre-constraint relationship, time-dependent parameters and resource association information between each construction process based on LSTM time series perception.

4. The digital monitoring method for building construction progress according to claim 1, characterized in that: The resource compensation reminder is performed in combination with the dynamic threshold adjustment rule, and the method includes: Performing time sequence feature analysis using start and end symbols, which are used to mark the foundation construction phase, main structure construction phase, and decoration and renovation phase; The dynamic threshold adjustment rule is configured according to the timing relationship between the start character and the end character.

5. The digital monitoring method for building construction progress according to claim 4, characterized in that: The method comprises: Associate resource types, historical accident cases, start and end symbols to set up a risk warning knowledge graph; Based on the risk warning knowledge graph, potential risk nodes are identified and resource secondment optimization is performed.

6. The digital monitoring method for building construction progress according to claim 5, characterized in that: Based on the risk warning knowledge graph, identifying potential risk nodes, the method includes: A graph convolutional network based on an attention mechanism is set up. The input node features include historical accident frequency and resource availability, and the risk propagation weights between each construction process are obtained. Based on the risk propagation weights between the various construction processes, determine the key nodes and risk propagation paths for risk diffusion; Based on the risk warning knowledge graph and the key nodes of risk diffusion, the risk propagation path is used to simulate the chain reaction process to determine the potential risk nodes.

7. The digital monitoring method for building construction progress according to claim 1, characterized in that: Combining the first progress assessment indicator and the second progress assessment indicator, formulating a building construction resource allocation map that conforms to the logical relationship of the construction process, the method includes: Integrate temperature and humidity sensing units, wind speed monitoring units, and light intensity detection units to build a dynamic environmental parameter perception network to obtain construction environment data; Based on the process sub-intervals associated with the construction efficiency impact coefficient and combined with the construction environment data, an environment progress association matrix is ​​constructed to dynamically correct the weight attenuation coefficient of the building construction resource configuration map.

8. The digital monitoring method for building construction progress according to claim 7, characterized in that: Constructing an environment progress correlation matrix and dynamically correcting the weight attenuation coefficient of the building construction resource configuration map, the method includes: The row dimension of the environment progress correlation matrix is ​​the construction environment data; The column dimensions of the environmental progress correlation matrix include a concrete engineering sub-interval, a steel structure installation sub-interval, and a door and window installation sub-interval; Matrix elements of the environment progress correlation matrix are quantified and dynamically updated.

9. The digital monitoring method for building construction progress according to claim 8, characterized in that: When the construction environment data does not meet the safety construction environment restrictions, the matrix element values ​​of the environment progress correlation matrix are set to 0.

10. The digital monitoring system for building construction progress is characterized by: The system is used to execute the digital monitoring method for building construction progress according to any one of claims 1 to 9, and the system comprises: Real-time comparison module: collects 3D point data of the construction area, compares it with the BIM building model, and extracts deviation information between the construction progress and the planned progress; A construction log association module is configured to associate the construction log with each building component node of the BIM building model and set a first progress evaluation indicator; A consumption record association module is configured to associate the material consumption record with each building component node of the BIM building model and set a second progress evaluation indicator; Resource allocation map formulation module: Based on the deviation information, combined with the first progress assessment indicator and the second progress assessment indicator, formulates a building construction resource allocation map that conforms to the logical relationship of the construction process; Resource compensation reminder module: Based on the building construction resource configuration map, according to the weight attenuation coefficient, with resource allocation efficiency as the correlation feature, a multi-scale assessment of the cumulative impact range is performed, and resource compensation reminders are issued in combination with dynamic threshold adjustment rules.

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