A method, system, device, and medium for construction process progress management

Abstract

The application discloses a kind of construction process progress management method, system, equipment and medium, it is related to building information model and construction management technical field, including: determining the order of construction task;Through AI system processing camera data, automatically identify construction activity, and the matching of identification result and the construction task corresponding to BIM model;Construction activity is monitored through sensor environmental condition and equipment state;Through AI system, the progress trend of construction task is predicted, construction plan is adjusted, and is updated to BIM model simultaneously.The application is applied to construction process progress management by integrating artificial intelligence and building information model technology, improves resource allocation efficiency and the reliability of construction plan.Using machine learning to analyze historical data, the system can accurately predict process time consumption, resource demand and potential delay, real-time monitor construction site and automatically trigger early warning, quickly respond to progress deviation and potential problems.

Description

Technical Field

[0001] This invention relates to the fields of building information modeling and construction management technology, specifically to a method and system for construction process progress management. Background Technology

[0002] In modern building project management, Building Information Modeling (BIM) technology has become a key tool for optimizing design, construction, and operation processes. BIM technology provides three-dimensional digital models that not only simulate the physical and functional characteristics of a building but also manage the application of data throughout its entire lifecycle. Furthermore, the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies enables real-time monitoring and intelligent decision support during the construction process. For example, by deploying sensors on the construction site, environmental data, material status, and construction progress can be collected in real time. AI algorithms can process this data to predict potential project risks and schedule deviations, achieving optimal resource allocation. The integrated application of these technologies significantly improves project management efficiency, reduces wasted costs and time, and enhances construction safety.

[0003] Despite significant advancements in existing technologies, several shortcomings remain. First, current BIM systems and AI integration applications often lack sufficient flexibility to respond to unforeseen events, such as the impact of extreme weather conditions on construction progress, limiting their ability to dynamically adjust in a timely manner. Furthermore, existing technologies often rely on predetermined rules for resource allocation, lacking in-depth analysis of real-time data and the ability to respond instantly, making resource allocation inefficient and imprecise when facing complex and changing construction conditions. This invention proposes a novel BIM system integrating advanced data analysis technology, a real-time monitoring system, and dynamic resource management strategies. This system not only monitors the environment and construction status in real time but also uses innovative AI algorithms to predict and adjust construction progress in real time. Particularly in the face of unforeseen weather changes and other external disturbances, it can quickly reallocate resources to ensure the smooth progress of construction projects. Moreover, through an improved data feedback mechanism, the system can more accurately grasp resource usage, optimize project costs and time efficiency, thus offering significant advantages over existing technologies in improving the scientific rigor and accuracy of construction project management. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing construction progress management technologies based on AI and BIM have significant shortcomings in terms of data integration, AI algorithm accuracy, dynamic management, and visualization interaction. In particular, they fail to effectively combine AI and BIM, which limits their application effect in actual construction management.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for construction process progress management, comprising the following steps: determining the sequence of construction tasks; processing camera data through an AI system to automatically identify construction activities and matching the identification results with the construction tasks corresponding to the BIM model; monitoring environmental conditions and equipment status through sensors during construction activities; predicting the progress trend of construction tasks through the AI ​​system, adjusting the construction plan, and synchronously updating it to the BIM model.

[0007] As a preferred embodiment of the construction process progress management method of the present invention, the step of determining the order of construction tasks includes: constructing a dependency matrix of construction tasks, representing the dependency relationship between construction tasks through the dependency matrix of construction tasks, determining a list of construction tasks through a topological sorting algorithm, inputting the list of construction tasks into a BIM model, the BIM model converting the list of construction tasks into construction tasks with a construction order, and carrying out construction activities according to the order of construction tasks.

[0008] If the construction tasks include construction tasks that do not have any dependencies, the urgency and resource availability of the construction tasks that do not have any dependencies are assessed to obtain priority scores, and the construction tasks that do not have any dependencies are then ranked a second time based on the priority scores.

[0009] As a preferred embodiment of the construction process progress management method described in this invention, the urgency is assessed based on the deadline of the construction task, the scope of impact, or the impact on the critical path of the construction project.

[0010] Resource availability is assessed through real-time monitoring of resource status, combined with AI predictive analysis of demand and supply, and a comprehensive evaluation using a scoring system.

[0011] As a preferred embodiment of the construction process progress management method described in this invention, the identification of construction activities includes: an AI system monitoring the construction site in real time, identifying camera data through AI algorithms to determine whether there are safety hazards, and taking specific measures based on the existence of safety hazards.

[0012] As a preferred embodiment of the construction process progress management method described in this invention, the monitoring of environmental conditions and equipment status includes, for ongoing construction tasks, sensors collecting humidity, equipment temperature, and noise in real time and transmitting them to an AI system. The AI ​​system first makes a preliminary judgment based on the noise. If the current noise is less than or equal to the maximum acceptable noise level, it calculates the feasibility of the construction task based on humidity and equipment temperature, and synchronizes the real-time calculated feasibility data to the BIM model. The BIM optimizes the scheduling of construction tasks based on the feasibility and urgency of the construction tasks. If the current noise is greater than the maximum acceptable noise level, it indicates that the current construction task is not feasible.

[0013] The AI ​​system integrates a meteorological data API, which provides weather conditions for future time periods, including rainfall and temperature. Based on these weather conditions, the system adjusts the construction tasks in the BIM model. Specifically, the system marks each construction task in the BIM model according to its sensitivity to weather conditions. If the rainfall or temperature exceeds a set threshold for a construction task in the future time period, the AI ​​system automatically adjusts the corresponding construction task in the BIM model and marks it as unworkable for the future time period.

[0014] As a preferred embodiment of the construction process progress management method described in this invention, the method of predicting the progress trend of construction tasks includes: when the construction task is affected by weather conditions, predicting the completion time of the construction task through an AI system, and adjusting the construction plan based on the prediction results of the AI ​​system.

[0015] like If the deadline for construction task i is set, it indicates that construction task i cannot be completed on time. When construction task i cannot be completed on time, resources will be allocated from other construction tasks to construction task i according to the scheduling rules. This indicates the completion time of construction task i.

[0016] The scheduling rules include: calculating the minimum amount of resources still required for construction task i to complete the construction task; traversing other construction tasks; calculating the remaining amount of resources when the construction task is completed; if there is a construction task j with remaining resources that meets the minimum amount of resources still required for construction task i to complete the construction task, then scheduling from construction task j to construction task i; if there is no single construction task that meets the minimum amount of resources still required for construction task i, then scheduling the remaining resources to construction task i according to the priority of each construction task with remaining resources, until the minimum amount of resources still required for construction task i is met.

[0017] As a preferred embodiment of the construction process progress management method described in this invention, the step of synchronously updating to the BIM model includes updating the corresponding construction task nodes in the BIM model based on the collected resource scheduling and progress information, and displaying the updated construction progress and resource status through visualization tools.

[0018] Another objective of this invention is to provide a system for managing the progress of construction procedures, which can solve the problems of slow response speed and low resource utilization in existing methods when dealing with complex construction environments and sudden changes by analyzing and adaptively adjusting the construction plan in real time.

[0019] To address the aforementioned technical problems, this invention provides the following technical solution: a construction process progress management system based on AI and BIM models, comprising a data acquisition module, a data processing module, a BIM model integration module, and a risk management module.

[0020] The data acquisition module is responsible for monitoring on-site construction activities and collecting data on humidity, equipment temperature, and noise.

[0021] The data processing module is responsible for cost and efficiency optimization analysis.

[0022] The BIM model integration module is responsible for synchronously updating the processed data and analysis results to the BIM model.

[0023] The risk management module is responsible for identifying and reporting potential security risks.

[0024] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of a construction process schedule management method as described above.

[0025] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a construction process schedule management method as described above.

[0026] The beneficial effects of this invention are as follows: By integrating artificial intelligence (AI) and building information modeling (BIM) technologies into construction process schedule management, this invention significantly improves resource allocation efficiency and the reliability of construction plans. Utilizing machine learning to analyze historical data, the system can accurately predict process duration, resource requirements, and potential delays, while simultaneously monitoring the construction site in real time and automatically triggering early warnings to quickly respond to schedule deviations and potential problems. Furthermore, the introduction of AI optimizes cost and risk control, providing scientific risk assessment and cost management solutions. The AI ​​system also promotes cross-disciplinary team collaboration, ensuring timely information transmission and seamless workflow through automatic updates of design changes and construction task adjustments. Ultimately, through data-driven decision support and continuous quality monitoring, this invention not only improves decision-making efficiency but also ensures construction quality and project sustainability, greatly enhancing the overall effectiveness of construction project management. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 The first embodiment of the present invention provides an overall flowchart of a method for managing construction process progress.

[0029] Figure 2 This is an overall framework diagram of a construction process progress management system provided for the second embodiment of the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0031] Example 1

[0032] Reference Figure 1 As an embodiment of the present invention, a method for construction process progress management is provided, characterized in that:

[0033] S1: Deploy a network of cameras and sensors.

[0034] In step S1, the network architecture for sensors and cameras is designed based on the scale and layout of the construction site. Most devices should be connected to a local area network (LAN) or wireless local area network (Wi-Fi) to ensure real-time data transmission.

[0035] Specifically, cameras and sensors should be distributed reasonably according to the characteristics of the construction area to ensure coverage of key monitoring points (such as construction site, equipment storage area, construction machinery, personnel entry and exit areas, etc.).

[0036] The camera should be installed at a high or suitable location to ensure a wide field of view without blind spots.

[0037] Sensors should be installed in locations most relevant to the monitored target, such as material storage areas or around equipment.

[0038] Specifically, it's crucial to ensure that sensor and camera data are compatible with the BIM system. Typically, sensor data is transmitted via a gateway to the cloud or local server to interface with the BIM model for subsequent data analysis and progress management.

[0039] Each sensor and camera transmits data to the data center in real time. Sensor data typically includes temperature, humidity, pressure, vibration, etc., while cameras provide real-time images of the construction site.

[0040] Specifically, ensure that the timestamps of all devices are synchronized so that data from different sensors and cameras can be accurately correlated. Synchronization can be achieved through the Network Time Protocol (NTP) to ensure accurate alignment of data with the progress in the BIM model.

[0041] Specifically, the collected data undergoes preliminary processing and cleaning, such as removing outliers and filling in missing data, to ensure data quality and reliability.

[0042] S2: Determine the sequence of construction tasks.

[0043] In step S2, the first step is to list all the major construction tasks in the project. These tasks include, but are not limited to, foundation construction, structural construction, electrical installation, and decoration.

[0044] Based on the project plan, identify which tasks can only begin after other tasks are completed. For example, concrete pouring (task A) can only be carried out after rebar tying (task B), therefore task A depends on task B.

[0045] Determining the order of construction tasks includes: constructing a dependency matrix of construction tasks to represent the dependencies between construction tasks; determining a list of construction tasks using a topological sorting algorithm; inputting the list of construction tasks into the BIM model; the BIM model transforming the list of construction tasks into construction tasks with a construction order; and carrying out construction activities according to the order of construction tasks.

[0046] The dependency matrix is ​​filled based on the dependencies between construction tasks. If construction task a only needs to start after construction task b is completed, it means that construction task a has only a one-way dependency on construction task b, and the in-degree of construction task a is 1.

[0047] Define the list of construction tasks, including calculating the in-degree of each construction task, and creating a queue to add all construction tasks with an in-degree of 0 to the queue, indicating that there are no dependencies.

[0048] Randomly remove construction task z from the queue and add it to the topologically sorted list of construction tasks. Check all construction tasks pointed to by construction task z and integrate them into a set of construction tasks. Decrement the in-degree of all construction tasks in the set by 1. If the in-degree of construction task k in the set is 0, add construction task k to the queue. Repeat the removal process until the queue is empty. After the topological sorting is completed, a list of construction tasks correctly sorted by dependency is obtained.

[0049] If the construction tasks include construction tasks that do not have any dependencies, the urgency and resource availability of the construction tasks that do not have any dependencies are assessed to obtain a priority score, and the construction tasks that do not have any dependencies are then ranked again based on the priority score.

[0050] Specifically, if there are construction tasks m and n in the set of construction tasks that do not have any dependency relationship, the urgency and resource availability of construction tasks m and n are evaluated to obtain priority scores, and construction tasks m and n are then sorted a second time based on the priority scores.

[0051] Priority scores are represented as follows:

[0052] ,

[0053] ,

[0054] in, , Let m and n represent the priority scores for construction tasks, respectively. Indicates the weighting factor. This indicates the urgency and resource availability of the construction task m. This indicates the urgency of the construction task n and the availability of resources.

[0055] like > If so, then construction task m is executed before construction task n.

[0056] Here, m and n are examples.

[0057] Urgency is assessed based on the deadline of the construction task, the scope of impact, or the impact on the critical path of the construction project.

[0058] Resource availability is assessed through real-time monitoring of resource status, combined with AI predictive analysis of demand and supply, and a comprehensive evaluation using a scoring system.

[0059] Furthermore, by constructing a construction task dependency matrix, using a topological sorting algorithm, and employing a secondary sorting based on priority scores, the execution order of construction tasks was rationally arranged. This ensured that the dependencies between tasks were strictly adhered to and also optimized resource allocation and the handling of task urgency.

[0060] Dependency matrices and topological sorting can scientifically determine the order of construction tasks, reducing conflicts and resource waste between tasks. Priority scores further improve the flexibility of task scheduling, ensuring that critical tasks can be completed first when resources are scarce or tasks conflict.

[0061] This optimized task sorting and scheduling enabled the rational arrangement of construction tasks, avoided unnecessary delays and waste of resources, and greatly improved construction efficiency and overall project progress control.

[0062] S3: The AI ​​system processes camera data, automatically identifies construction activities, and matches the identification results with the construction tasks corresponding to the BIM model.

[0063] In step S3, high-definition cameras are installed at key locations on the construction site (such as around buildings, work areas for construction workers, and material storage areas) to ensure comprehensive coverage of the construction site. These cameras transmit video stream data to the AI ​​system in real time.

[0064] Specifically, the AI ​​system receives real-time video data streams from the camera and begins processing them. The data is then sent to the cloud or a local AI computing platform for rapid analysis and processing.

[0065] Identifying construction activities includes using an AI system to monitor the construction site in real time, identifying camera data through AI algorithms to determine if there are any safety hazards, and then taking specific measures based on the existence of these hazards.

[0066] Identification includes object recognition, behavior recognition, and security hazard recognition.

[0067] Object recognition is used to check whether construction workers are wearing safety helmets; behavior recognition is used to determine whether construction workers have fallen; and safety hazard recognition is used to check whether there are unstable stacked materials.

[0068] The AI ​​system monitors in real time whether construction workers are wearing necessary safety equipment such as helmets, safety belts, and work clothes. Using object recognition algorithms, it detects the head area of ​​each worker and determines whether they are wearing a helmet.

[0069] Using deep learning algorithms (such as convolutional neural networks and pose estimation algorithms), AI systems can identify whether construction workers are in a fall. This technology determines whether construction workers have fallen or exhibited abnormal movements by monitoring human posture in real time.

[0070] The formula for determining unstable stacking is expressed as follows:

[0071] ,

[0072] Where H represents the height of the highest point of the material, L represents the height of the lowest point of the material, and W represents the horizontal distance between the highest and lowest points of the material. This represents the arctangent function, used to calculate the angle corresponding to a given slope. Indicates the angle of material tilt.

[0073] If the material is tilted at an angle If the value exceeds the safety threshold, the material is determined to be unstable.

[0074] Based on the existence of potential safety hazards, specific measures will be taken, including:

[0075] If any of the following situations occur, such as not wearing a safety helmet, workers falling down, or materials being stacked unstably, it indicates a safety hazard.

[0076] When a safety hazard is detected, the AI ​​system immediately sends an early warning to the management personnel and marks the location of the problem on the BIM model.

[0077] When no safety hazards exist, the AI ​​system identifies different construction activities through camera data and matches the identified activities with the sequence of construction tasks in the BIM model. If the sequence of construction activities in the BIM model is reasonable, the corresponding construction task is marked as in progress. If the dependent construction tasks of the construction activity in the BIM model are not completed, the construction activity is marked as not to be carried out, and the management team is notified for analysis.

[0078] Furthermore, as described above, in order to ensure a human-centered approach, regardless of whether the construction activities conform to the sequence of construction tasks, a preliminary assessment must be conducted to determine whether there are any safety hazards.

[0079] The AI ​​system automatically identifies construction activities and detects safety hazards by processing data collected from cameras. This intelligent identification process includes object recognition, behavior analysis, and safety risk assessment, enabling timely detection and handling of issues such as not wearing safety helmets, falls, or unstable material stacking, thus improving safety management at the construction site. Furthermore, matching the identification results with construction tasks in the BIM model ensures more timely and accurate project progress updates, guaranteeing real-time information synchronization and efficient management.

[0080] S4: During construction activities, environmental conditions and equipment status are monitored using sensors.

[0081] In step S4, sensor deployment involves deploying various sensors at the construction site to monitor environmental conditions and equipment status in real time, such as humidity, equipment temperature, and noise. Common sensors include humidity sensors, temperature sensors, and noise sensors. All sensors will transmit data to the AI ​​system in real time via a wireless network.

[0082] Specifically, the sensors periodically collect environmental data from the site and send it to the AI ​​system. This data includes:

[0083] Humidity, used to indicate the air humidity at the construction site.

[0084] Equipment temperature is used to indicate the real-time temperature of equipment (such as machinery, generators, etc.).

[0085] Noise is used to indicate the noise level at a construction site to ensure that it meets health and safety standards.

[0086] The system monitors environmental conditions and equipment status. For ongoing construction tasks, sensors collect humidity, equipment temperature, and noise data in real time and transmit them to the AI ​​system. The AI ​​system first makes a preliminary judgment based on the noise level. If the current noise level is less than or equal to the maximum acceptable noise level, it calculates the feasibility of the construction task based on humidity and equipment temperature. The real-time calculated feasibility data is synchronized to the BIM model. The BIM model optimizes the scheduling of construction tasks based on their feasibility and urgency. If the current noise level is greater than the maximum acceptable noise level, it indicates that the current construction task is not feasible.

[0087] The degree of procedural capability is represented as follows:

[0088] Z= ,

[0089] Where Z represents the feasibility level and B represents the current humidity. Indicates the optimal humidity. , These represent the upper and lower limits of humidity, respectively, and T represents the current temperature of the device. Indicates the ideal operating temperature of the equipment. , Indicates the highest and lowest operating temperatures of the equipment. , This represents the adjustment factor.

[0090] Optimize the scheduling of construction tasks, including: if the feasibility Z is greater than or equal to the first-level threshold, it means that the current construction task is proceeding normally; if the feasibility Z is greater than or equal to the second-level threshold but less than the first-level threshold, it is judged in conjunction with the urgency of the current construction task; if the feasibility Z is less than the second-level threshold, it is judged as infeasible without considering urgency, and the construction task is scheduled.

[0091] A comprehensive assessment is made, including evaluating the urgency of the current construction task. If the urgency exceeds the urgency threshold, the task can proceed normally.

[0092] Furthermore, the real-time acquisition of sensor data and the calculation of feasibility can provide timely feedback on the current feasibility of construction tasks. Combined with the scheduling optimization of the AI ​​system, dynamic adjustments to construction tasks are achieved, avoiding unnecessary work stoppages and project delays.

[0093] The AI ​​system integrates a meteorological data API, which provides weather conditions for future time periods, including rainfall and temperature. Based on these weather conditions, the system adjusts construction tasks in the BIM model. Specifically, it marks each construction task in the BIM model according to its sensitivity to weather conditions. If the rainfall or temperature exceeds a set threshold for a construction task in the future, the AI ​​system automatically adjusts the corresponding construction task in the BIM model and marks it as unworkable for that period.

[0094] In addition, after determining the sequence of construction tasks, relevant technical personnel will set the sensitivity of each construction task in the BIM model.

[0095] S5: Predict the progress trend of construction tasks through the AI ​​system, adjust the construction plan, and update it to the BIM model in a synchronous manner.

[0096] Predicting construction progress trends includes using an AI system to predict the completion time of construction tasks when they are affected by weather, and adjusting the construction plan based on the predictions from the AI ​​system.

[0097] The predicted completion time for the construction task is expressed as follows:

[0098] ,

[0099] ,

[0100] in, This indicates the estimated completion time of construction task i. This represents the estimated completion time of construction task i under ideal conditions. Indicates the delay time due to weather conditions. Indicates the current temperature. Indicates the temperature threshold. This represents the total rainfall. Indicates the rainfall threshold. Indicates the temperature effect coefficient. Indicates the impact coefficient of rainfall. This represents the max function, which takes the maximum number of days affected by both temperature and rainfall when both factors are present.

[0101] To further explain, the present invention addresses... The definition in this invention (days / degrees Celsius) indicates how much the expected completion time will increase for every degree Celsius above a certain temperature threshold.

[0102] Similarly, This indicates how much the estimated completion time will increase for each percentage point exceeding the rainfall threshold.

[0103] like When the time limit for construction task i is set, it means that construction task i cannot be completed on time. When construction task i cannot be completed on time, resources will be allocated from other construction tasks to construction task i according to the scheduling rules.

[0104] The scheduling rules include: calculating the minimum amount of resources still required for construction task i to complete the construction task; traversing other construction tasks; calculating the remaining amount of resources when the construction task is completed; if there is a construction task j with remaining resources that meets the minimum amount of resources still required for construction task i to complete the construction task, then scheduling from construction task j to construction task i; if there is no single construction task that meets the minimum amount of resources still required for construction task i, then scheduling the remaining resources to construction task i according to the priority of each construction task with remaining resources, until the minimum amount of resources still required for construction task i is met.

[0105] To further explain, taking i and j in the scheduling rules as an example, the position of construction task j in the construction list must be after construction task i. According to the priority of each construction task with remaining quantity, it means that the position of each scheduled construction task in the construction list is after construction task i.

[0106] Resources include labor, materials, equipment and machinery, and capital.

[0107] Finally, the AI ​​system's schedule prediction function allows project managers to see potential schedule deviations in advance and adjust the construction plan accordingly. This intelligent prediction is not only based on historical data models but also takes into account real-time environment and construction status, making adjustments to the construction plan more scientific and timely. This greatly enhances the project's ability to adapt to uncertainty and ensures the smooth progress and on-time completion of the project.

[0108] Synchronous updates to the BIM model include updating the corresponding construction task nodes in the BIM model based on the collected resource scheduling and progress information, and displaying the updated construction progress and resource status through visualization tools.

[0109] Example 2

[0110] Reference Figure 2 As an embodiment of the present invention, a system for managing construction process progress is provided, characterized in that it includes a data acquisition module, a data processing module, a BIM model integration module, and a risk management module.

[0111] The data acquisition module is responsible for monitoring on-site construction activities and collecting data on humidity, equipment temperature, and noise.

[0112] Specifically, the data acquisition module monitors construction activities in real time through high-definition cameras installed at the construction site. The cameras capture various activities at the construction site (such as personnel operations, object handling, etc.).

[0113] By using AI algorithms to analyze surveillance video streams in real time, it can identify potential safety hazards such as whether construction workers are wearing safety helmets, whether there are dangerous behaviors such as falls, and whether there are unstable materials piled up.

[0114] The monitored image data is correlated with other environmental data on site (humidity, temperature, noise, etc.) to support subsequent decision-making.

[0115] The data acquisition module is equipped with multiple sensors for real-time monitoring of environmental parameters:

[0116] Specifically, it is used to monitor the relative humidity of the air on site. The humidity sensor provides real-time feedback on changes in humidity in the environment, especially during the rainy season, when changes in humidity directly affect the feasibility of construction tasks.

[0117] Humidity sensors typically use capacitive or resistive sensors to accurately measure air humidity and convert it into electronic signals.

[0118] Specifically, the sensor collects humidity data at regular intervals (e.g., every minute) and transmits it to the data acquisition module.

[0119] Temperature sensors are used to monitor the temperature of equipment at construction sites, especially construction equipment (such as excavators and cranes) and the ambient temperature. Excessively high temperatures can affect the normal operation of equipment and even cause malfunctions. Temperature sensors typically use thermocouples or RTD (resistance temperature detector) technology to monitor temperature changes in real time.

[0120] The temperature sensor collects data at set time intervals, such as once every 15 seconds or once per minute, and the collected device and ambient temperature data are sent to the data acquisition module in real time.

[0121] Noise sensors are used to monitor noise levels on site to ensure that construction noise complies with environmental standards. Excessive noise can affect the health of construction workers and may also violate local noise pollution regulations.

[0122] Noise sensors are typically acoustic sensors or microphones that assess noise levels by measuring the intensity of sound waves (decibels).

[0123] Specifically, the noise sensor continuously monitors the noise level at the construction site by sampling at regular intervals (e.g., once per minute) and transmits the data to the data acquisition module.

[0124] The data processing module is responsible for cost and efficiency optimization analysis.

[0125] Specifically, the data processing module is a key component of the construction management system. It is responsible for in-depth analysis of the collected raw data to optimize resource allocation and utilization during construction, reduce costs, and improve efficiency. This module analyzes various data related to construction tasks (such as time, resource consumption, and environmental factors) through algorithms and models, provides optimization solutions, and ultimately adjusts the construction plan and scheduling through a feedback mechanism.

[0126] The implementation process of the data processing module can be divided into the following steps:

[0127] 1. Data input and preprocessing.

[0128] Data Sources: The data processing module receives various types of data from the data acquisition module, BIM model, sensors, and AI system. This data includes:

[0129] Environmental data, including environmental monitoring data such as humidity, equipment temperature, and noise.

[0130] Construction progress data includes the current progress and estimated completion time of each construction task.

[0131] Resource data, including data on human resources, equipment, materials, and funds.

[0132] Weather data includes future weather forecasts such as rainfall and temperature.

[0133] Data preprocessing:

[0134] Specifically, this involves removing outliers, duplicate data, or erroneous data to ensure the quality of input data; using interpolation or prediction algorithms to fill in missing parts of the data to ensure data integrity during the analysis process; and converting data from different sources into a unified standard format to ensure comparability between different datasets.

[0135] 2. Construction of cost and efficiency analysis model.

[0136] After data preprocessing, the data processing module optimizes cost and efficiency by establishing various analytical models.

[0137] Specifically, this includes cost analysis models and efficiency analysis models.

[0138] The cost analysis model is used to calculate the total cost of the construction task, and the main factors considered include labor costs, material costs, equipment costs, resource allocation costs, and environmental impact costs.

[0139] Among them, labor cost is used to calculate the labor cost of construction tasks based on the working hours required for each task and the wage standards of the labor force.

[0140] Material cost is used to calculate the cost of materials based on the type, quantity, and purchase price of the materials required.

[0141] Equipment cost is used to calculate equipment costs based on the rental or maintenance costs of the equipment required for construction.

[0142] Resource scheduling cost is used to calculate the scheduling cost based on the time and priority of resource scheduling if a task needs to schedule resources from other tasks.

[0143] Environmental impact costs are the indirect costs of construction delays or equipment damage caused by weather changes (such as rainfall and temperature).

[0144] The efficiency analysis model is used to evaluate the execution efficiency of construction tasks.

[0145] Specifically, the work efficiency of each task is evaluated based on the ratio of the progress of the construction task to the actual completion time.

[0146] The utilization efficiency of various resources is evaluated based on the ratio of actual resources consumed to planned resources.

[0147] Analyze the schedule delays caused by factors such as environmental changes, equipment failures, and resource shortages in the construction task, and calculate the impact of the delay time on the overall schedule.

[0148] Based on the urgency and feasibility of the construction tasks, resource allocation is optimized through scheduling algorithms to reduce conflicts between tasks and resource waste.

[0149] 3. Optimize analysis and decision support.

[0150] The results of the optimization analysis will be provided to project managers and decision-makers. They will be displayed in real-time through a visual dashboard.

[0151] Specifically, this includes determining whether the current construction task is over budget, whether there is wasted resources, whether there are task delays, which tasks require resource allocation to ensure timely completion, and proposing scheduling suggestions or resource reallocation plans based on resource utilization efficiency.

[0152] 4. Integration and synchronization with BIM models.

[0153] Based on the optimization analysis results, the data processing module adjusts the priority, resource allocation, and schedule of construction tasks, and updates this information synchronously in the BIM model. Through the BIM model, construction managers can view the optimized construction task scheduling, resource allocation, and cost consumption in real time.

[0154] Real-time feedback and adjustment: The data processing module dynamically adjusts and optimizes the plan based on real-time data from the construction site (such as progress, cost, and environmental data), and updates it synchronously to the BIM model and AI system. This ensures that construction progress and resource allocation are always at their best, avoiding plan failures due to unforeseen circumstances (such as weather changes or equipment malfunctions).

[0155] The BIM model integration module is responsible for synchronizing and updating the processed data and analysis results to the BIM model.

[0156] Specifically, the BIM model integration module first receives data processed by the data processing module through an interface with the data processing module, including: construction progress data, resource allocation data, cost analysis data, efficiency optimization data, and environmental condition data.

[0157] Specifically, the construction progress data includes the current progress of each construction task, the estimated completion time, and any delays in the construction tasks.

[0158] Resource allocation data includes the distribution of resources such as labor, equipment, and materials.

[0159] Cost analysis data includes the budget, actual cost, and cost control status of the construction task.

[0160] Efficiency optimization data includes the work efficiency and resource utilization rate of each construction task.

[0161] Environmental condition data, such as real-time environmental data like humidity, temperature, and rainfall, as well as adjusted data based on weather forecasts.

[0162] Based on the received processing results, the BIM model integration module will update the various construction tasks, resource configurations, and schedules in the BIM model, using the following update strategy:

[0163] Construction task milestone update:

[0164] Based on the construction task progress calculated by the data processing module, the BIM model integration module updates the actual progress of each task node to the BIM model. By updating visual elements such as node colors and progress bars, construction managers can intuitively see the actual execution status of each task.

[0165] Update the estimated completion time for each task based on the current progress of the construction tasks and environmental factors (such as weather, equipment status, etc.).

[0166] If a construction task is delayed or the risk exceeds expectations, the relevant task node in the BIM model will be marked as "delayed" or "risk" and the reason for the delay will be displayed (such as insufficient resources, unfavorable environment, etc.).

[0167] Resource configuration and scheduling updates:

[0168] According to the resource scheduling optimization plan, the BIM model integration module will adjust the resource allocation required for each construction task to ensure efficient resource utilization. For example, if a construction task requires more equipment or labor due to delays, the BIM model will update the resource requirements for that task and adjust the resource allocation for other tasks.

[0169] The BIM model will update the status of resources (such as equipment, materials, and labor) in real time, showing which resources are in use, which resources are idle, and the remaining amount of resources. This can help project managers quickly identify resource bottlenecks and scheduling problems.

[0170] Cost and Budget Updates:

[0171] Specifically, the BIM model will display a comparison between the budget and actual costs of construction tasks, including labor, equipment, materials, and other expenses. If a task exceeds the budget, the BIM model will alert managers through indicators or alarms, facilitating timely adjustments.

[0172] When the actual cost of a construction task exceeds a certain threshold of the budget, the BIM model will automatically generate an early warning to remind project managers to adjust the budget.

[0173] The BIM model integration module adjusts construction tasks in real time based on weather data (such as rainfall, temperature, wind speed, etc.) and updates the data synchronously in the BIM model.

[0174] When predicted weather conditions (such as rainfall, temperature, and wind) exceed a set threshold, the BIM model will automatically mark the corresponding construction task as "unworkable" or "postponed" and recalculate the task's progress and completion time based on the adjusted weather data.

[0175] Based on the specific impact of weather data, the BIM model integration module automatically adjusts the schedule and priority of construction tasks. If some tasks cannot be completed on time due to severe weather, the system will reschedule these tasks, prioritizing tasks that are less sensitive to weather conditions.

[0176] The BIM model integration module will collaborate with the AI ​​system in real time to obtain construction task scheduling optimization schemes proposed by the AI ​​system based on the analysis results of the data processing module, and synchronize these optimization schemes into the BIM model:

[0177] Based on the urgency, feasibility, and resource requirements of the construction tasks, the optimized task scheduling scheme provided by the AI ​​system will be reflected in the BIM model, updating the priority of construction tasks, the order of resource scheduling, and the construction plan.

[0178] When the priority of construction tasks changes, the BIM model integration module will dynamically adjust the task plan and optimize resource allocation to ensure that the entire construction process is not affected by resource conflicts and delays.

[0179] The BIM model integration module presents all data update results to project managers through a visual interface.

[0180] Specifically, progress bars, Gantt charts, and bar charts are used to visually display the completion progress, delays, and estimated completion time of each construction task. Construction managers can view the actual progress of each task in real time and quickly determine whether resources or construction plans need to be adjusted.

[0181] By displaying the current allocation of resources through charts or 3D views, managers can intuitively understand the usage status and remaining quantity of equipment, materials, and labor, and optimize resource scheduling.

[0182] The cost comparison chart between budget and actual costs displays the cost of each task during construction and reflects overspending in real time, helping managers control construction costs.

[0183] By using weather data charts and weather impact markers for construction tasks, the potential impact of future weather on construction tasks can be shown, helping managers to make adjustments in advance.

[0184] The risk management module is responsible for identifying and reporting potential security risks.

[0185] Specifically, the risk management module is a key component of the entire construction management system. It aims to monitor the safety status of the construction site in real time, identify potential safety risks, and provide detailed risk reports to project managers. By integrating sensor data, environmental monitoring, personnel behavior analysis, and equipment status information, the risk management module can promptly detect anomalies, issue early warnings, and help the construction management team take effective preventative and responsive measures, thereby reducing the probability of construction accidents and ensuring the safety of the construction process.

[0186] Example 3

[0187] One embodiment of the present invention differs from the previous two embodiments in that:

[0188] Specifically, if the construction process progress management method is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0189] The computer program includes several instructions that enable a computer device (such as a server, personal computer, or other network device) to perform the following steps:

[0190] Data collection at the construction site: The instructions enable the computer equipment to collect various types of data from the construction site, including environmental data (such as temperature, humidity, rainfall, wind speed, and noise), equipment data (such as equipment temperature, vibration, and load), personnel behavior data (such as worker location and safety equipment wearing status), and data from other monitoring sensors.

[0191] Real-time data transmission: The command enables the collected data to be transmitted to the processing module or server in real time, ensuring the timeliness and accuracy of the data.

[0192] Data preprocessing: Instructions enable computer equipment to clean, denoise, and standardize the collected data for subsequent analysis.

[0193] Schedule prediction analysis: The instructions enable computer equipment to analyze the progress trend of construction tasks through AI algorithms (such as regression analysis, machine learning models, etc.) and predict the construction progress based on historical data, weather conditions, equipment and personnel status, etc.

[0194] Cost and efficiency analysis: The instructions enable computer equipment to analyze the cost and efficiency of construction tasks, assess factors such as resource consumption and project delays, and propose optimization suggestions.

[0195] Progress information updated to BIM model: The instruction enables the computer equipment to update the corresponding task nodes in the BIM model based on AI-predicted construction task completion time, resource scheduling and other data, and to display real-time construction progress, resource status and other information.

[0196] Dynamic adjustment of construction plan: Based on the schedule deviation predicted by the AI ​​system, the computer equipment automatically adjusts the construction plan in the BIM model, including task priority, resource scheduling, etc., and updates it synchronously in the BIM model.

[0197] Security risk identification and assessment: The instructions enable computer equipment to identify potential security risks (such as abnormal temperature, equipment failure, etc.) and assess the severity of the risks by using risk assessment models based on collected environmental data, equipment status data, etc.

[0198] Risk warning and response: The command enables the computer equipment to generate a security risk warning, issue a warning to relevant personnel, and dynamically adjust the construction plan and resource allocation based on the warning information.

[0199] Resource demand forecasting: The instructions enable computer equipment to predict the resources (such as labor, materials, equipment, funds, etc.) required for construction tasks and adjust resource demands in conjunction with the construction schedule.

[0200] Resource scheduling optimization: Based on the progress and resource requirements of construction tasks, instructions enable computer equipment to automatically or according to scheduling rules allocate resources from other construction tasks, optimizing resource allocation to ensure timely project completion.

[0201] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0202] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0203] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0204] Example 4

[0205] In this embodiment, to verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. In construction project management, traditional methods typically rely on human experience and static planning and management strategies, such as Gantt charts and the Critical Path Method (CPM). While these methods provide an organizational structure in the initial stages of project planning, they often lack flexibility and real-time responsiveness when dealing with changes and uncertainties in the project. When encountering unforeseen weather changes, supply chain problems, or fluctuations in human resources, traditional methods often require significant time and manual intervention to re-plan resources and adjust schedules, which can lead to project delays and cost overruns.

[0206] This invention proposes an advanced construction process schedule management method integrating AI and BIM technologies. This method automatically collects on-site data, updates project status in real time, and predicts potential risks and delays through machine learning algorithms, thereby achieving dynamic resource scheduling and optimization. This approach significantly improves the project's adaptability to change, reduces human intervention, and accelerates the decision-making process, thus ensuring smooth construction progress and optimal costs.

[0207] To verify the effectiveness and superiority of the present invention, the following theoretical experiments were designed to compare the performance of the conventional method and the present invention in several key aspects, as shown in Table 1.

[0208] Table 1 Comparison of Experimental Results

[0209] Experimental indicators Traditional methods Method of the present invention Schedule Deviation (days) 15 2 Resource waste rate (%) 20 4 Quality rework rate (%) 10 2

[0210] In summary, this invention significantly improves the accuracy of project scheduling and reduces deviations through real-time monitoring and prediction algorithms. Optimized resource scheduling algorithms and accurate demand forecasting reduce resource waste and improve utilization efficiency. Enhanced quality control processes and real-time monitoring reduce rework and defects, thereby improving construction quality.

[0211] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for managing the progress of construction procedures, characterized in that, include: Determine the sequence of construction tasks; The AI ​​system processes camera data, automatically identifies construction activities, and matches the identification results with the construction tasks corresponding to the BIM model. Sensors are used to monitor environmental conditions and equipment status during construction activities; The AI ​​system predicts the progress trend of construction tasks, adjusts the construction plan, and updates it to the BIM model simultaneously. The determination of the order of construction tasks includes, A dependency matrix for construction tasks is constructed to represent the dependencies between construction tasks. A list of construction tasks is determined using a topological sorting algorithm and then input into the BIM model. The BIM model transforms the list of construction tasks into construction tasks with a construction sequence, and construction activities are carried out according to the sequence of construction tasks. If the construction tasks include construction tasks that do not have any dependencies, the urgency and resource availability of the construction tasks that do not have any dependencies are assessed to obtain priority scores, and the construction tasks that do not have any dependencies are then ranked a second time based on the priority scores. The monitoring environmental conditions and equipment status include, For ongoing construction tasks, sensors collect humidity, equipment temperature, and noise data in real time and transmit them to the AI ​​system. The AI ​​system first makes a preliminary judgment based on the noise level. If the current noise level is less than or equal to the maximum acceptable noise level, it calculates the feasibility of the construction task based on humidity and equipment temperature. The real-time calculated feasibility data is then synchronized to the BIM model. The BIM model optimizes the scheduling of construction tasks based on their feasibility and urgency. If the current noise level is greater than the maximum acceptable noise level, it indicates that the current construction task is not feasible. The AI ​​system integrates a meteorological data API, which provides weather conditions for future time periods, including rainfall and temperature. Based on these weather conditions, the system adjusts construction tasks in the BIM model. Specifically, these adjustments include... In the BIM model, each construction task is marked according to its sensitivity to weather conditions. If the rainfall or temperature exceeds the set threshold for the construction task in the future time period, the AI ​​system will automatically adjust the corresponding construction task in the BIM model and mark it as unworkable in the future time period. The predicted construction task progress trend includes... When construction tasks are affected by weather conditions, the completion time of the construction tasks is predicted by the AI ​​system, and the construction plan is adjusted based on the prediction results of the AI ​​system. like When the deadline for construction task i is set, it indicates that construction task i cannot be completed on time. When construction task i cannot be completed on time, resources will be allocated from other construction tasks to construction task i according to the scheduling rules. Indicates the completion time of construction task i; The scheduling rules include, Calculate the minimum amount of resources still required for construction task i to complete the construction task. Iterate through other construction tasks and calculate the remaining resources when the construction task is completed. If there is a construction task j with remaining resources that meets the minimum amount of resources still required for construction task i to complete the construction task, then schedule construction task j to construction task i. If there is no single construction task that meets the minimum amount of resources still required for construction task i, then schedule the remaining resources to construction task i according to the priority of each construction task with remaining resources, until the minimum amount of resources still required for construction task i is met.

2. The method for construction process progress management as described in claim 1, characterized in that: The urgency is assessed based on the deadline of the construction task, the scope of its impact, or its impact on the critical path of the construction project. Resource availability is assessed through real-time monitoring of resource status, combined with AI predictive analysis of demand and supply, and a comprehensive evaluation using a scoring system.

3. The method for construction process progress management as described in claim 2, characterized in that: The identification of construction activities includes, The AI ​​system monitors the construction site in real time, uses AI algorithms to identify camera data, determines whether there are any safety hazards, and then takes specific measures based on the existence of these hazards.

4. A method for construction process progress management as described in any one of claims 1, 2, or 3, characterized in that: The synchronous update to the BIM model includes, Based on the collected resource scheduling and progress information, the corresponding construction task nodes in the BIM model are updated, and the updated construction progress and resource status are displayed through visualization tools.

5. A system for managing construction process progress, employing the method for managing construction process progress as described in any one of claims 1 to 4, characterized in that, include: The data acquisition module is responsible for monitoring on-site construction activities and collecting data on humidity, equipment temperature, and noise. The data processing module is responsible for cost and efficiency optimization analysis; The BIM model integration module is responsible for synchronizing and updating the processed data and analysis results to the BIM model. The risk management module is responsible for identifying and reporting potential security risks.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the construction process progress management method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the construction process progress management method according to any one of claims 1 to 4.

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