Real-time construction progress monitoring system based on BIM and IoT

Abstract

This invention relates to the field of construction monitoring technology and discloses a real-time construction progress monitoring system based on BIM and the Internet of Things (IoT). The system includes: an IoT data acquisition module, which acquires real-time construction data through a sensor network and divides it into spatial location streams and equipment status streams; a BIM data integration module, which processes the two data streams to generate a first progress index and a second progress index; a progress deviation analysis module, which combines a preset progress benchmark model to map the merged progress indices to progress deviation values; a response decision module, which generates a target response strategy based on the deviation values ​​by calling preset schemes; and a central processing unit responsible for data transmission and logical verification. In addition, the system also includes a construction linkage module, an instruction output module, and a progress storage module, used for optimizing resource scheduling, sending control commands, and archiving data, respectively. This system enables real-time monitoring, deviation analysis, and dynamic adjustment of construction progress, improving the intelligence and accuracy of construction management.

Description

Technical Field

[0001] This invention relates to the field of construction monitoring technology, specifically a real-time construction progress monitoring system based on BIM and the Internet of Things. Background Technology

[0002] In the field of construction engineering, construction schedule management is a core element in ensuring on-time project delivery, cost control, and quality assurance. Traditional construction schedule monitoring mainly relies on manual inspections, paper records, and periodic reports, which have significant drawbacks such as delayed data collection, untimely information transmission, and insufficient dynamic tracking capabilities. As the scale of construction projects continues to expand and construction processes become increasingly complex, traditional methods are no longer sufficient to meet the demands of modern construction management for real-time performance, accuracy, and intelligence.

[0003] In a manual monitoring model, managers must spend considerable time and effort visiting the construction site to manually record the progress of each process. This data collection is inefficient and prone to human error. For example, in large-scale mixed-use projects, manually tracking the progress of sub-projects such as rebar tying and formwork support on each floor can lead to data distortion due to limited perspective or oversights, affecting schedule adjustments and resource allocation. Furthermore, paper-based records and manual summarization result in significant delays in information transmission, making it difficult for project management to promptly grasp the actual progress on-site and respond quickly to unexpected changes during construction, such as equipment failures, material shortages, or design changes, potentially leading to project delays and increased costs.

[0004] Traditional monitoring methods lack the ability to dynamically track and analyze the construction process in multiple dimensions. Construction progress involves not only spatial progress (such as the completion status of structural construction in various areas), but is also closely related to the operating status of construction equipment, material consumption and supply, personnel efficiency, and environmental parameters. Traditional methods often focus on isolated elements, failing to integrate and analyze multi-source data, and making it difficult to grasp the influencing factors and development trends of construction progress as a whole. For example, the operating status of equipment (such as the operating frequency of tower cranes and the energy consumption of construction machinery) directly reflects construction efficiency, but traditional methods cannot acquire and analyze this data in real time, leading to the inability to promptly detect problems such as equipment idleness or overuse, thus affecting the optimization of construction progress.

[0005] With the gradual application of BIM and IoT technologies in the construction field, although some research has attempted to combine the two for construction management, existing systems mostly suffer from problems such as insufficient data integration, simplistic schedule analysis models, and inadequately intelligent response decision-making mechanisms. For example, some systems only achieve simple data docking between BIM models and IoT devices, failing to fully utilize the advantages of BIM's three-dimensional spatial modeling and the real-time data acquisition capabilities of IoT, leaving the monitoring of construction progress at a relatively rudimentary stage. In terms of schedule deviation analysis, there is a lack of benchmark models and dynamic adjustment mechanisms that can flexibly adapt to the characteristics of different projects, making it difficult to accurately quantify schedule deviations and provide scientific adjustment strategies. In the response decision-making stage, the richness and matching degree of the pre-set solution database are insufficient, resulting in a lack of targetedness and effectiveness in the generated adjustment parameters. Summary of the Invention

[0006] The purpose of this invention is to provide a real-time construction progress monitoring system based on BIM and the Internet of Things to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a real-time construction progress monitoring system based on BIM and the Internet of Things, the system comprising:

[0008] The Internet of Things (IoT) data acquisition module is used to acquire real-time construction data through the sensor network at the construction site, and to divide the real-time construction data into spatial location stream and equipment status stream based on preset data classification rules.

[0009] The BIM data integration module is used to perform three-dimensional point cloud alignment on the spatial location stream to generate a first progress index, and to perform state code parsing on the equipment status stream to generate a second progress index.

[0010] The schedule deviation analysis module is used to map the first schedule index and the second schedule index to the corresponding schedule deviation value after index fusion according to the preset schedule benchmark model, and use the schedule deviation value as the evaluation parameter of the current construction stage.

[0011] The response decision module is used to call the target response strategy in the preset scheme database based on the schedule deviation value, and use the target response strategy as the adjustment parameter for the current construction stage;

[0012] The central processing unit is used to send the real-time construction data to the BIM data integration module, send the first progress index and the second progress index to the progress deviation analysis module, and also to perform logical verification on the evaluation parameters and the adjustment parameters to generate the final response instruction.

[0013] Preferably, the BIM data integration module performs state coding parsing on the equipment state stream, including:

[0014] The continuous state sequence in the device state stream is divided into a device operation group and a device idle group. The operation frequency of the device operation group is calculated based on a preset state transition model to generate a state feature set.

[0015] The energy consumption monitoring data in the device status stream is processed by energy consumption distribution segmentation, and the energy consumption fluctuation characteristics of each distribution area are extracted and an energy consumption distribution map is constructed.

[0016] The state feature set and the energy consumption distribution map are spatially correlated and fused to generate the second progress index.

[0017] Preferably, the preset data classification rules include a basic data collection set and an auxiliary data collection set; the basic data collection set includes location coordinate identifiers, equipment number identifiers, and material identifiers; the auxiliary data collection set includes environmental parameter identifiers and personnel identifiers, and each identifier corresponds to an independent data processing channel.

[0018] Preferably, the system further includes a data interface unit, which is used to enable the IoT data acquisition module, the BIM data integration module, the schedule deviation analysis module and the response decision module to communicate with the construction IoT network respectively;

[0019] The IoT data acquisition module classifies real-time construction data according to the preset data classification rules, including:

[0020] The data interface unit receives integrated data packets in real time from the construction IoT network, and matches the core markers of the integrated data packets with the identifiers in the basic data acquisition set to separate the basic data segments.

[0021] The auxiliary data segments are extracted by traversing the additional markers of the comprehensive data packet according to the identifiers in the auxiliary acquisition set.

[0022] The basic data segment and auxiliary data segment are aligned according to the timestamp and then written into the spatial location storage area and the device status buffer, respectively.

[0023] Preferably, when the preset progress benchmark model adopts a segmented quantization model, the progress deviation value is the segmented mapping result of the composite fusion value of the first progress index and the second progress index;

[0024] When the preset progress benchmark model adopts an adaptive adjustment model, the progress deviation value is a set of continuous variables that are dynamically calibrated by using an iterative algorithm to analyze the joint results of the first progress index and the second progress index.

[0025] Preferably, the system further includes a construction linkage module connected to the central processing unit, and the construction linkage module is connected to the construction resource database through the data interface unit;

[0026] The construction coordination module is used to filter the list of available resources from the construction resource database according to the resource allocation requirements in the final response instruction, and generate a resource scheduling sequence to optimize the construction adjustment process.

[0027] Preferably, the resource scheduling sequence generated by the construction linkage module includes:

[0028] Load the construction 3D mesh model and locate the real-time location node of each resource in the list of available resources in the mesh model;

[0029] The optimal scheduling path from the deployment location of each resource to the target construction area is calculated based on the path planning algorithm, and the list of available resources is prioritized according to the execution time.

[0030] The optimal scheduling path and the priority ranking are integrated into the grid model to generate a visual resource scheduling sequence.

[0031] Preferably, when the central processing unit performs logical verification on the evaluation parameters and adjustment parameters, it adopts a dual audit mode of completeness verification mechanism and conflict detection mechanism. The completeness verification mechanism is used to confirm the integrity of data fields, and the conflict detection mechanism is used to resolve logical conflicts between parameters.

[0032] Preferably, the system further includes an instruction output module connected to the central processing unit. The instruction output module is used to convert the final response instruction into a control command code and send the control command code to the designated construction equipment through the data interface unit to start the adjustment program.

[0033] Preferably, the system further includes a progress storage module connected to the central processing unit. The progress storage module is used to archive the real-time construction data, the first progress index, the second progress index, the progress deviation value, and the final response instruction, and to generate a construction progress record chain in time sequence.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] At the data acquisition and processing level, the IoT data acquisition module acquires construction data in real time through a sensor network and divides the data into spatial location streams and equipment status streams based on preset classification rules. Combined with a multi-dimensional identification system of basic acquisition sets (location coordinates, equipment numbers, material identifiers) and auxiliary acquisition sets (environmental parameters, personnel identifiers), it achieves refined classification and efficient separation of construction data. The introduction of the data interface unit ensures reliable communication between each module and the construction IoT network. Through timestamp alignment technology, basic data and auxiliary data are stored separately in the spatial location storage area and the equipment status buffer, providing a structured, time-series, high-quality data source for subsequent BIM data integration, solving the problems of data acquisition lag and ambiguous classification in traditional methods.

[0036] The BIM data integration module processes the spatial location flow using 3D point cloud alignment technology to generate a first progress index, achieving precise spatial mapping of construction progress. Simultaneously, it performs status coding and parsing of the equipment status flow, dividing equipment into operating and idle groups, calculating operation frequency, constructing energy consumption distribution maps, and performing spatial correlation and fusion to generate a second progress index. This deeply explores the intrinsic relationship between equipment operating status and construction efficiency. This dual-index mechanism organically combines BIM's 3D spatial modeling capabilities with the IoT's equipment status monitoring capabilities, breaking through the limitations of traditional monitoring that only focuses on spatial progress or a single equipment status, and achieving a multi-dimensional dynamic representation of construction progress.

[0037] The schedule deviation analysis module, based on a preset schedule baseline model (segmented quantification model or adaptive adjustment model), fuses and maps dual schedule indices, allowing for flexible selection of the analysis mode according to project characteristics. The segmented quantification model achieves quantitative assessment of deviations through segmented mapping of composite fusion values, suitable for construction phases with a high degree of standardization; the adaptive adjustment model dynamically calibrates deviation values ​​through iterative algorithms, adapting to dynamic changes during construction. The combination of these two modes ensures the accuracy and flexibility of schedule deviation analysis, providing a scientific basis for subsequent response decisions.

[0038] The linkage mechanism between the response decision module and the preset scheme database enables rapid matching of target response strategies based on schedule deviation values. Combined with the logical verification of the central processing unit (a dual mechanism of completeness verification and conflict detection), the rationality and reliability of adjustment parameters are ensured. The construction linkage module further generates a resource scheduling sequence based on the final response command. By loading a 3D mesh model, calculating the optimal scheduling path, and prioritizing, it achieves visualized dynamic allocation of construction resources, optimizing the execution efficiency of the adjustment process. The setting of the command output module and the schedule storage module respectively ensures the accurate execution of adjustment commands and the full-process traceability of construction data, forming a complete closed loop from data collection and analysis to decision-making and execution. Attached Figure Description

[0039] Figure 1 This is a schematic diagram illustrating the working principle of the real-time construction progress monitoring system based on BIM and IoT as described in this invention.

[0040] Figure 2 Design drawings for BIM data integration module status code parsing;

[0041] Figure 3 Design diagram for collaborative operation of the data interface unit and data acquisition module;

[0042] Figure 4 Design drawing for the three-dimensional grid scheduling of the construction linkage module. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figures 1-4 This invention relates to a real-time construction progress monitoring system based on BIM and the Internet of Things (IoT). The system includes: an IoT data acquisition module, a BIM data integration module, a progress deviation analysis module, a response decision module, and a central processing unit. Specifically, it includes the following steps:

[0045] The IoT data acquisition module acquires real-time construction data through a sensor network deployed at the construction site (such as RFID tags, GPS positioning devices, and environmental sensors), including personnel locations, equipment operating status, material arrival information, and environmental parameters. After acquiring the data, based on preset data classification rules, the real-time construction data is divided into spatial location streams and equipment status streams. The spatial location stream mainly contains data related to spatial location, such as the coordinates of personnel and equipment, and the location of material stacking; the equipment status stream mainly contains equipment operating status data, such as equipment start / stop status, energy consumption data, and operating frequency.

[0046] The BIM data integration module receives spatial location and equipment status data transmitted from the IoT data acquisition module. For the spatial location stream, it matches the real-time acquired spatial location data with the 3D coordinates in the BIM model using 3D point cloud alignment technology to generate a first progress index, which characterizes the progress of the construction object in the spatial dimension. For the equipment status stream, it performs status code parsing to generate a second progress index, which characterizes the operating efficiency and status changes of the construction equipment in the time dimension.

[0047] The schedule deviation analysis module is based on a preset schedule benchmark model (such as a construction schedule plan, resource allocation plan, etc.). It merges the first schedule index and the second schedule index, and uses an algorithm to map the merged index value to the corresponding schedule deviation value. This deviation value is used as an evaluation parameter for the current construction stage to determine whether the construction progress meets the plan requirements.

[0048] Based on the schedule deviation value, the response decision module calls the corresponding target response strategy from the preset scheme database, such as adjusting the construction sequence, increasing construction resources, and optimizing equipment configuration. This strategy serves as the adjustment parameter for the current construction stage, providing guidance for construction adjustments.

[0049] The central processing unit, as the core control unit of the system, is responsible for data transmission and logical verification. Specifically, it sends real-time construction data acquired by the IoT data acquisition module to the BIM data integration module; it sends the first and second progress indices generated by the BIM data integration module to the progress deviation analysis module; and it performs logical verification on the evaluation parameters output by the progress deviation analysis module and the adjustment parameters output by the response decision module. After the verification is successful, it generates the final response command to ensure the accuracy and feasibility of the command.

[0050] The present invention will be further described below with reference to Examples 1 to 5:

[0051] Example 1

[0052] This embodiment specifically describes the process of the BIM data integration module processing equipment status stream in a BIM and IoT-based real-time construction progress monitoring system. During system operation, the IoT data acquisition module collects equipment status stream data in real time through a sensor network deployed at the construction site (such as current sensors, voltage sensors, displacement sensors, etc.). This data is transmitted to the BIM data integration module in continuous time series format, including information such as equipment status changes, energy consumption parameters, and operation duration during equipment operation.

[0053] After receiving the equipment status stream, the BIM data integration module performs grouping processing of continuous status sequences. Specifically, the continuous status sequences are identified using preset status thresholds. Statuses with valid operational actions (such as equipment startup, processing, and transportation) are classified into the equipment operation group, while statuses without valid operational actions (such as shutdown, standby, and fault alarm) are classified into the equipment idle group. For example, for tower cranes, the statuses corresponding to lifting, slewing, and luffing actions are assigned to the equipment operation group, while the shutdown waiting for instructions or maintenance status is assigned to the equipment idle group. During the grouping process, the system timestamps the start and end times of each status, forming a discrete set of status segments.

[0054] After grouping, the module calculates the operation frequency of the equipment operation groups based on a preset state transition model. The preset state transition model is a finite state automaton model built based on historical construction data, defining the legal transition rules between equipment operation states (such as the state transition path "start → run → pause → stop"). The module traverses each state segment in the equipment operation group, counts the number of transitions and time intervals between adjacent states, calculates the state transition frequency per unit time, and generates a state feature set containing parameters such as operation state duration, number of transitions, and frequency distribution. For example, for a concrete mixer, the state feature set may include parameters such as "percentage of mixing state duration" and "frequency of mixing and feeding state transitions," used to characterize the actual operating efficiency and operation mode of the equipment.

[0055] The module performs energy consumption distribution segmentation processing on the energy consumption monitoring data in the equipment state stream. Energy consumption monitoring data includes the equipment's instantaneous power, cumulative energy consumption, voltage and current waveforms, etc. The module segments the continuous energy consumption data using a sliding window algorithm and combines this with a density clustering algorithm (such as DBSCAN) to divide the energy consumption data into different distribution regions. Each region corresponds to the equipment's energy consumption characteristics under specific operating conditions (such as no-load, light-load, and full-load conditions). For example, the energy consumption data of an excavator during digging operations and its energy consumption data during no-load driving will be divided into different regions. Within each distribution region, the module extracts energy consumption fluctuation characteristics, including peak energy consumption, average energy consumption, energy consumption standard deviation, and the slope of the energy consumption change trend. An energy consumption distribution map is constructed with the time axis as the horizontal axis and energy consumption value as the vertical axis, visually displaying the distribution of equipment energy consumption under different time periods and operating conditions.

[0056] The module performs a spatial correlation and fusion operation between the state feature set and the energy consumption distribution map. Specifically, using timestamps as a reference, each operational state segment in the state feature set is aligned with the corresponding time period in the energy consumption distribution map, establishing a mapping relationship between state features and energy consumption features. For example, within the time period corresponding to the "full load operation" state in the equipment operation group, the energy consumption distribution map shows a high energy consumption fluctuation area. The module determines parameters such as the average energy consumption value and energy consumption fluctuation range under this state through correlation analysis, forming a "state-energy consumption" correlation matrix. Through this matrix, the system can identify the energy efficiency of the equipment under different operating states, such as whether high-frequency operating states are accompanied by abnormal energy consumption fluctuations, thereby determining whether the equipment is operating normally or whether there is energy waste.

[0057] During the generation of the second progress index, the module standardizes the associated and fused state features and energy consumption features, converting parameters of different dimensions (such as time, energy consumption, and frequency) into normalized numerical ranges (such as [0,1]) to eliminate the impact of dimensional differences on progress assessment. The standardized data is then subjected to dimensionality reduction through principal component analysis (PCA) to extract principal component factors that characterize the core features of the equipment's state flow, such as "operational efficiency factor" and "energy consumption stability factor." These principal component factors are linearly combined according to preset weights to ultimately generate the second progress index value. This index value quantifies the overall performance of the equipment during construction in numerical form; a higher value indicates higher equipment operating efficiency and a more stable state, while a lower value indicates low equipment operating efficiency or an abnormal state.

[0058] Throughout the process, the BIM data integration module, through multi-level analysis of equipment status flows, transforms raw data into structured features, providing refined equipment status data support for construction progress monitoring. The system can track equipment operation in real time via the second progress index, promptly identifying equipment failures and inefficient operation, providing crucial evidence for progress deviation analysis and response decisions. For example, when the second progress index values ​​of multiple devices in a certain area continuously fall below a threshold, the system can determine that the construction efficiency in that area is insufficient, triggering the response decision module to invoke equipment maintenance or resource allocation strategies to ensure that construction progresses as planned.

[0059] The above processing steps are all dynamically executed based on real-time data acquisition. The module stores recent equipment status stream data through a caching mechanism for historical data comparison and trend analysis. Simultaneously, the system supports manual configuration or automatic optimization of preset state transition models and energy consumption segmentation parameters to adapt to the monitoring needs of different types of construction equipment. For example, for newly arrived construction equipment, custom state transition models and energy consumption analysis rules can be quickly built by importing operating specifications and energy consumption parameters from the equipment manual, ensuring the system's versatility and flexibility.

[0060] Example 2

[0061] This embodiment details the preset data classification rules and the workflow of the data interface unit in a BIM and IoT-based real-time construction progress monitoring system. During system operation, the IoT data acquisition module needs to acquire multi-source heterogeneous data in real time from the IoT network at the construction site. This data is collected through devices such as sensors, RFID tags, and smart terminals, and includes various information related to personnel, equipment, materials, and the environment involved in the construction process. To achieve efficient data processing and classification, the system presets data classification rules, dividing the data into a basic acquisition set and an auxiliary acquisition set. These two sets correspond to different data processing channels, ensuring independent transmission and processing of core and auxiliary data.

[0062] The basic data collection set includes location coordinate identifiers, equipment number identifiers, and material identifiers, forming the core data elements for construction progress monitoring. Location coordinate identifiers record the spatial location information of construction personnel, equipment, and materials, such as real-time coordinate data obtained through GPS positioning devices or the BeiDou Navigation Satellite System. Equipment number identifiers assign a unique identity (such as an RFID tag number) to each piece of construction equipment, distinguishing different devices and tracking their operational status. Material identifiers correspond to information such as material type, specifications, and batch, such as attribute data for materials like steel bars and concrete marked with barcodes or QR codes. The auxiliary data collection set includes environmental parameter identifiers and personnel identifiers. Environmental parameter identifiers collect construction environment data, such as temperature, humidity, light intensity, and wind speed, obtained in real-time through environmental sensors. Personnel identifiers record the identity information, job type, and work permissions of construction personnel, obtainable through access control systems or work badge attendance devices. Each identifier corresponds to an independent data processing channel to avoid confusion or conflict between different types of data during transmission and processing.

[0063] The system's data interface unit acts as a bridge connecting various functional modules with the construction IoT network, employing standardized communication protocols (such as MQTT and HTTP) to achieve bidirectional data transmission. When the IoT data acquisition module categorizes real-time construction data based on preset data classification rules, it receives comprehensive data packets from the construction IoT network in real-time through the data interface unit. This data packet is a collection of raw data, containing all information from the basic and auxiliary acquisition sets, encapsulated in JSON or XML format, and includes metadata such as timestamps and device identifiers. For example, a single comprehensive data packet might contain the tower crane's location coordinates, equipment number, the current batch of concrete material being processed, and information such as the ambient temperature and operator employee numbers for that period.

[0064] The module performs the separation operation of the basic data segment. Based on the identifiers in the basic acquisition set, it matches the core markers of the comprehensive data package one by one. Taking the equipment number identifier as an example, the module scans the "Equipment ID" field in the data package, extracts the value that matches the preset equipment number list, and determines the corresponding equipment identity. Simultaneously, it extracts the spatial location data corresponding to the location coordinate identifier through the "Coordinate X / Y / Z" field, and extracts the material information corresponding to the material identifier through the "Material Type" and "Material Batch" fields. Through the above matching process, the module separates the basic data segment from the comprehensive data package. This data segment only contains core information directly related to location, equipment, and material, forming a structured basic data record, such as "Equipment Number: T001, Location Coordinates: (100, 200, 50), Material Type: C30 Concrete, Material Batch: 2025-06-01-001".

[0065] After separating the basic data segments, the module extracts auxiliary data segments. Based on the identifiers in the auxiliary acquisition sets, it iterates through the additional markers in the comprehensive data package. For example, it extracts the temperature and humidity data corresponding to the environmental parameter identifiers using the "Ambient Temperature" and "Ambient Humidity" fields, and extracts the personnel information corresponding to the personnel identifiers using the "Employee ID" and "Employee Type" fields. The extraction process for auxiliary data segments must ensure coverage of all identifiers in the auxiliary acquisition sets. For instance, when the comprehensive data package contains other environmental parameters such as wind speed and light intensity, the module must simultaneously extract the relevant field data to form a complete auxiliary data record, such as "Ambient Temperature: 28℃, Ambient Humidity: 65%, Operator Employee ID: P007, Job Type: Crane Operator".

[0066] The module performs timestamp alignment on the basic data segment and the auxiliary data segment. Since there may be slight differences in the acquisition times of different types of data (such as different sensor sampling frequencies), it is necessary to synchronize the two types of data using the timestamp field (accurate to the millisecond level) in the data packet. The specific operation is as follows: Using the timestamp of the basic data segment as a reference, find the record with the closest timestamp in the auxiliary data segment, and fill the time difference using linear interpolation or the nearest neighbor method to ensure a one-to-one correspondence between the basic data and the auxiliary data at the same time. For example, if the timestamp of the basic data segment is "2025-06-10 10:00:00.000", and the closest timestamp in the auxiliary data segment is "2025-06-10 10:00:00.003", then the auxiliary data record is associated with the basic data record to form a complete data pair at the same time point.

[0067] After timestamp alignment, the module writes the basic data segment and auxiliary data segment to the spatial location storage area and the equipment status buffer, respectively. The spatial location storage area is a specific table structure in the database used to store basic data related to spatial location, supporting fast queries based on coordinate ranges, time intervals, and other conditions, such as querying the distribution of personnel and equipment in a specified construction area within a certain time period. The equipment status buffer is a cache space in memory used to temporarily store equipment status streams and auxiliary environmental data for real-time access by the subsequent BIM data integration module. For example, the BIM data integration module can read the energy consumption data and operator information of the equipment from the buffer to generate a second progress index.

[0068] During data processing, the data interface unit continuously monitors the connection status of the construction IoT network. When a network interruption or data transmission anomaly is detected, a buffer retransmission mechanism is triggered, temporarily storing unsuccessfully transmitted data packets in a local cache. These packets are then retransmitted once the network is restored, ensuring the integrity and continuity of data collection. Simultaneously, the system monitors the traffic of each data processing channel. When the data transmission volume of a certain channel exceeds a preset threshold, bandwidth allocation is automatically adjusted to prioritize the data transmission efficiency of the basic data collection set, preventing the loss or delay of core data.

[0069] The preset data classification rules support dynamic configuration, allowing construction managers to add or delete identifier types and adjust the priority of data processing channels according to actual needs. For example, when a new material is introduced to the construction site, a unique identifier for that material can be added to the basic data collection set, and corresponding tag matching rules can be configured, enabling the system to automatically identify and process the data for the new material. This flexibility ensures that the system can adapt to the personalized needs of different construction projects, improving the versatility of data collection and processing.

[0070] Example 3

[0071] This embodiment details the processing methods of two different preset schedule benchmark models in the schedule deviation analysis module of a BIM and IoT-based real-time construction progress monitoring system. During system operation, the schedule deviation analysis module analyzes the first schedule index (spatial dimension schedule data) and the second schedule index (equipment status dimension schedule data) generated by the BIM data integration module based on the preset schedule benchmark models to quantify the difference between the construction progress and the planned progress. The preset schedule benchmark models include a segmented quantification model and an adaptive adjustment model, which differ significantly in their data processing logic and deviation calculation methods.

[0072] When the preset schedule baseline model adopts a segmented quantization model, the schedule deviation value is calculated based on a predefined discrete threshold interval, which is achieved by segmenting and mapping the composite fusion result of the first schedule index and the second schedule index. The specific process is as follows:

[0073] The module uses the first progress index (denoted as) ) and second progress index (denoted as ( ) to carry out composite fusion.

[0074] The composite fusion process uses a weighted summation method, and the formula is as follows:

[0075]

[0076] in, For composite fusion values, and Weighting coefficients for the spatial schedule index and the equipment status schedule index, respectively. The weighting coefficients are preset according to construction management requirements. For example, when spatial progress (such as structural construction progress) is a core indicator of the project, a certain weighting coefficient can be set. This is to highlight the importance of spatial dimension data.

[0077] Generate composite fusion value Then, the module, based on the threshold range preset by the segmented quantization model, will... Mapped to the corresponding schedule deviation level. Threshold ranges are typically divided into three levels: ahead of schedule, on schedule, and behind schedule. For example, a preset threshold range is: when... The schedule was ahead of schedule ( (as the baseline composite value of the planned schedule), when When the progress is normal, This is considered a schedule lag. Each level corresponds to a specific range of schedule deviation values; for example, if the schedule is ahead of schedule, the corresponding deviation value is... The progress is normal. The progress lag corresponds to .

[0078] During the mapping process, the system matches the composite fusion value with the threshold range using a lookup table method. For example, if the calculated value at a certain time... If so, it is determined to be ahead of schedule, and the corresponding schedule deviation value is (Take the midpoint of the interval). This method uses discretization to convert continuous progress data into intuitive deviation levels, making it easier for construction managers to quickly assess the progress status and formulate corresponding adjustment strategies.

[0079] The advantage of segmented quantification models lies in their simple logic and ease of implementation, making them suitable for projects with relatively fixed construction processes and few external interference factors. For example, in standardized factory construction projects, due to the high repetition of construction processes, stable threshold ranges and weight coefficients can be pre-determined using historical data to achieve rapid progress assessment.

[0080] When the preset schedule baseline model adopts an adaptive adjustment model, the calculation of schedule deviation values ​​is based on a dynamic learning mechanism. An iterative algorithm is used to jointly analyze the first and second schedule indices to generate a set of deviations in the form of continuous variables. The specific process is as follows:

[0081] First, the module constructs an adaptive adjustment model state space, indexing the first progress step. Second Progress Index As an input variable, the dynamic baseline value of the planned schedule As output variables, where This represents construction time (in days or hours). The model is trained using historical construction data and employs iterative algorithms such as Recursive Least Squares (RLS) or Kalman filtering to continuously optimize model parameters. It can reflect the reasonable progress benchmark under the current construction conditions in real time.

[0082] During the real-time analysis phase, the module first calculates the joint analysis value at the current moment. The formula is:

[0083] in, This serves as the baseline value for the spatial dimension in the planned schedule. This serves as the baseline value for the equipment status dimension within the planned schedule. Adjustment coefficient for the impact of equipment condition deviation on overall schedule .

[0084] This formula quantifies the dual deviations in both spatial and equipment status dimensions by comparing the actual progress index with the planned baseline value. This is used to adjust the weight of the impact of equipment status on the overall schedule. For example, when equipment operating efficiency is a key influencing factor, it can be increased. The value is used to highlight the role of equipment condition deviation.

[0085] The module uses an iterative algorithm to... Perform dynamic calibration to generate a set of schedule deviation values ​​in the form of continuous variables. .

[0086] The core idea of ​​the iterative algorithm is to update the model parameters using the deviation value at the current moment, so that the subsequent baseline values... It can adaptively adjust. For example, when multiple consecutive time points are detected... When the actual progress is ahead of schedule, the model will automatically improve. The benchmark value is used to reflect the improvement in construction efficiency; conversely, when... At times, the model will lower the baseline value to avoid misjudging progress due to unforeseen factors.

[0087] The advantage of adaptive adjustment models lies in their ability to dynamically adapt to environmental changes and process adjustments during construction. For example, in bridge construction under complex geological conditions, when continuous rainfall leads to a decrease in equipment operating efficiency, the model can automatically adjust baseline values ​​through real-time learning, avoiding misjudging weather-related delays as construction management issues. The continuous variable deviation set generated by this model contains rich details, such as the contribution of deviations in each dimension and the trend of deviation changes, providing data support for accurately locating the root cause of schedule problems.

[0088] In practical applications, the segmented quantification model and the adaptive adjustment model can be switched or used in combination according to different needs at different construction stages. For example, in the early stages of a project (such as the foundation construction stage), due to unstable construction conditions, the adaptive adjustment model can be used to track progress changes in real time. Once the main construction stage begins, if the construction process becomes more stable, the segmented quantification model can be switched to improve evaluation efficiency. Furthermore, the system supports running both models simultaneously, verifying the reliability of the progress deviation assessment by comparing their outputs. For example, if the segmented quantification model determines that the progress is lagging while the adaptive adjustment model determines that the progress is normal, the system will trigger a manual review process to check for data anomalies or model parameter setting issues.

[0089] During data processing, the schedule deviation analysis module analyzes the input data of the two models ( and Perform consistency checks to ensure that data timestamps, units, and value ranges meet the model requirements.

[0090] For example, if If the spatial coordinate unit is meters, while the model's default unit is feet, the module will automatically convert the units to avoid deviations in the analysis results due to data format issues.

[0091] Example 4

[0092] This embodiment details the functions of the construction linkage module and the generation process of resource scheduling sequences in a BIM and IoT-based real-time construction progress monitoring system. During system operation, if the final response command generated by the central processing unit includes resource allocation requirements (such as adding construction equipment to a certain area or adjusting material transportation routes), the construction linkage module extracts relevant resource information from the construction resource database through the data interface unit. The construction resource database stores the real-time status of resources such as construction equipment, materials, and personnel, for example: the current location of tower cranes, the operating status of concrete mixer trucks, the inventory quantity and storage location of steel reinforcement materials, and the job types and work areas of construction personnel.

[0093] Taking the construction scenario of the main structure of a high-rise building as an example, assuming the central processing unit determines, based on the progress deviation analysis, that the concrete pouring progress of the 5th floor slab is lagging behind, requiring the allocation of two concrete pumps from other construction areas to this area, along with an additional 30 cubic meters of C30 concrete. After receiving the final response instruction containing "allocate concrete pumps and concrete materials," the construction linkage module first filters the list of available resources from the construction resource database based on the resource type and quantity requirements in the instruction. For concrete pumps, the filtering criteria include: equipment status is "idle" or "mobile," equipment model matches the pouring requirements, and the current location is within 500 meters of the target area (the ground material storage area corresponding to the 5th floor construction area). After filtering, a list of available equipment is obtained. For example, two pumps with equipment numbers P-003 and P-007 are currently located in the equipment parking area on the east side of the construction site (400 meters from the target area) and the maintenance area on the west side (requiring maintenance before movement), respectively. Therefore, the actual available equipment is P-003. For C30 concrete, the screening criteria are: the material status is "unused" and the storage location is close to the vertical transportation channel (such as the coverage area of ​​the tower crane). After screening, 35 cubic meters of C30 concrete were obtained and stored at the mixing plant on the north side of the site, which meets the quantity requirements.

[0094] When generating a resource scheduling sequence, the construction linkage module first loads the project's 3D construction mesh model. This model contains 3D spatial information of the construction site, including topography, building structure, roads, material storage areas, and equipment parking areas. It is built based on a BIM model with millimeter-level accuracy. In the 3D mesh model, the module locates the real-time location node of each resource in the available resource list using real-time location data of equipment and materials (such as the GPS coordinates of P-003 and the BIM coordinates of the concrete storage tank). For example, the location node of P-003 is marked as the east side of the construction site (X=150, Y=200, Z=0), the location node of the concrete storage tank is marked as the north mixing plant (X=50, Y=100, Z=0), the location node of the ground material storage area corresponding to the target construction area is (X=120, Y=180, Z=0), and the coverage area of ​​the vertical transportation channel (tower crane operating range) is X=100-160, Y=150-220, Z=0-100.

[0095] Next, the module calculates the optimal scheduling path from the deployment location of each resource to the target construction area based on a path planning algorithm. For concrete pump P-003, its deployment location is the east equipment parking area, and the target area is the 5th floor ground material storage area. It needs to be transported to the tower crane's operating area via the construction site road, and then hoisted to the 5th floor by the tower crane. Path planning must avoid obstacles (such as existing scaffolding or temporarily stacked formwork) and select the shortest route with good traffic conditions. The optimal path calculated by the algorithm is: east parking area → east main road → south intersection → tower crane operating area, approximately 450 meters long, with no obstacles. For C30 concrete, it needs to be transported from the north mixing plant to the tower crane operating area via a mixer truck, and then hoisted to the 5th floor by the tower crane. The optimal path is: north mixing plant → north auxiliary road → east main road → tower crane operating area, approximately 380 meters long, requiring passage through a 3-meter height restriction passage (the mixer truck is 2.8 meters high, meeting the passage requirements).

[0096] Meanwhile, the module prioritizes available resources based on execution time. Execution time is calculated based on factors such as resource scheduling path length, equipment startup time, and material preparation time. For example, P-003 is idle with a startup time of 5 minutes, and its 450-meter transport path is expected to take 10 minutes; concrete materials need to be mixed and prepared 15 minutes in advance, and their 380-meter transport path is expected to take 8 minutes. Since concrete pouring requires equipment to be in place first, the resource priority is: P-003 (total time 15 minutes) takes precedence over concrete materials (total time 23 minutes).

[0097] After completing path calculation and priority ranking, the module integrates the optimal scheduling path and priority ranking into a 3D mesh model, generating a visualized resource scheduling sequence. The visualization includes: marking the transportation paths of equipment and materials in the 3D model with lines of different colors (e.g., red lines represent the movement path of P-003, and blue lines represent the path of the concrete mixer truck); displaying the real-time location of resources with dynamic icons (e.g., equipment icons move along the path over time); and listing the priority list on the right side of the model interface (Sequence 1: P-003, estimated arrival time 10:15; Sequence 2: C30 concrete, estimated arrival time 10:23). Construction managers can intuitively view the time nodes and spatial paths of resource scheduling through this visualized sequence, allowing them to coordinate on-site personnel in advance to clear roads and prepare for receiving equipment and materials.

[0098] During resource scheduling, the construction linkage module monitors the movement status of resources in real time through the data interface unit. For example, when P-003 travels to the south intersection along the route, the module updates its position in the 3D model using the device's built-in GPS positioning data. If a temporary obstacle is detected that obstructs the path, the module automatically triggers the path replanning algorithm to generate alternative routes (such as the west auxiliary road → the north main road → the tower crane operation area, a total distance of 500 meters, which will take 3 minutes longer), and simultaneously updates the visual sequence and priority list.

[0099] Furthermore, the construction coordination module supports data interaction with construction workers' terminal devices (such as smart safety helmets and handheld PDAs). For example, after a resource scheduling sequence is generated, the module sends scheduling instructions (including equipment number, target location, and estimated arrival time) to personnel responsible for equipment operation and material preparation notifications (including material type, quantity, and transportation route) to material management personnel. After receiving the instructions through their terminal devices, personnel can provide feedback on the execution status (such as "equipment started" or "materials loaded"). The module updates the resource scheduling progress in real time to ensure coordinated operation across all stages.

[0100] For complex construction scenarios (such as simultaneous construction of multiple buildings or overlapping operations at different levels), the construction coordination module can simulate the resource scheduling process through a 3D mesh model to identify potential conflicts in advance. For example, while scheduling P-003, if another tower crane plans to lift steel bars to an adjacent area at the same time, the module can identify the intersection of their paths through collision detection and automatically adjust the scheduling time of one of them to avoid conflicts in aerial operations.

[0101] Example 5

[0102] This embodiment specifically describes the logic verification mechanism and the functions of the instruction output module and progress storage module of the central processing unit in a BIM and IoT-based real-time construction progress monitoring system. Taking the concrete pouring construction of a commercial complex project as an example, during system operation, the central processing unit needs to logically verify the evaluation parameters output by the progress deviation analysis module (such as a progress deviation value of -15%, which is judged as a progress delay) and the adjustment parameters output by the response decision module (such as calling the response strategy of "adding 2 concrete vibrating devices") to ensure the accuracy and feasibility of the parameters.

[0103] The central processing unit employs a dual-audit model combining a completeness verification mechanism and a conflict detection mechanism. The completeness verification mechanism checks the integrity of data fields in the evaluation and adjustment parameters. For example, evaluation parameters must include fields such as schedule deviation value, deviation occurrence time, and affected area; adjustment parameters must include fields such as response strategy number, resource type, quantity, and scheduling time. If the "resource scheduling time" field is found to be missing in the adjustment parameters, the system automatically triggers a data completion process, sending a request to the response decision module to supplement the data until the field is complete before proceeding to the next verification step.

[0104] A conflict detection mechanism is implemented to identify logical conflicts between parameters. For example, if the parameter adjustment proposes "adding 2 concrete vibrators," but the construction resource database shows that there is only 1 idle vibrator on site, the system will identify a "resource quantity conflict," automatically mark the adjustment parameter, and trigger the conflict resolution process. The conflict resolution process includes: ① re-querying the construction resource database to confirm whether there are other available resources (such as equipment inventory in adjacent sections); ② if no available resources are found, returning to the response decision module, prompting that the adjustment strategy needs to be changed to "use 1 piece of equipment and extend the operation time"; ③ if the response decision module cannot provide an alternative strategy, the central processing unit generates an anomaly report and notifies construction management personnel to intervene.

[0105] After double verification, the central processing unit generates a final response instruction, such as "At 14:00 on June 10, 2025, move one concrete vibrator (number V-005) to the 3rd construction area and extend the operation time to 22:00." The instruction output module receives this instruction and converts it into a control command code. The control command code uses a standardized communication protocol (such as MODBUSRTU) and includes information such as the device address, operation type (e.g., "move" or "start"), and parameter values ​​(e.g., target coordinates, operation duration). Taking the vibrator V-005 as an example, the converted control command code is: "010600010064789A", where "01" is the device address, "06" is the write single register opcode, "0001" is the target coordinate register address, "0064" is the target coordinate value (decimal 100), and "789A" is the checksum. The instruction output module sends the code to the controller of the designated construction equipment (V-005) through the data interface unit. After receiving the code, the equipment parses it and executes the instruction to move to the target area and start extended operations.

[0106] The progress storage module synchronously archives key information such as real-time construction data, the first progress index, the second progress index, progress deviation values, and final response instructions. Taking concrete pouring construction as an example, the real-time construction data includes: the location coordinates of the vibratory compactor V-005 (X=200, Y=150, Z=5), its operating status (continuous operation time 45 minutes), and energy consumption data (cumulative power consumption 20kWh); the first progress index is the concrete pouring volume progress generated based on 3D point cloud alignment (currently 80 cubic meters completed, planned to complete 100 cubic meters); the second progress index is the correlation result between the equipment status feature set and the energy consumption distribution map (operational efficiency factor 0.7, energy consumption stability factor 0.8); the progress deviation value is -15% (actual progress lags behind planned progress by 15%); and the final response instruction is the aforementioned equipment scheduling instruction.

[0107] The progress storage module generates a chain of construction progress records based on time sequence. Each record includes fields such as timestamp, data type, and data details. For example, the record for 13:00 on June 10, 2025 is:

[0108] Timestamp: 2025-06-10 13:00:00

[0109] Data type: Real-time construction data

[0110] Data details: Equipment V-005 coordinates (200, 150, 5), operating status "In Operation", energy consumption 20kWh

[0111] Data type: First progress index

[0112] Data details: Concrete pouring volume progress 80 / 100 cubic meters

[0113] Data type: Schedule deviation value

[0114] Data details: -15%, lagging due to "insufficient equipment efficiency"

[0115] Construction managers can query historical records through the progress storage module to trace the evolution of progress issues. For example, by comparing the records at 12:00 and 13:00, it was found that the continuous operation time of equipment V-005 increased from 30 minutes to 45 minutes, while the pouring volume of the first progress index only increased by 10 cubic meters. This indicates that the prolonged operation of the equipment led to a decrease in efficiency, providing a basis for adjusting the equipment rotation strategy in the future.

[0116] In multi-disciplinary collaborative construction scenarios, the logic verification mechanism of the central processing unit can avoid cross-disciplinary command conflicts. For example, when the structural construction team requests to use a tower crane to lift steel bars, and the decoration construction team simultaneously requests to use the same tower crane to lift materials, the central processing unit identifies the "tower crane usage conflict" through the conflict detection mechanism. Based on the preset priority rules (structural construction takes precedence over decoration construction), it automatically adjusts the scheduling instructions of the decoration team to ensure that key processes are executed first.

[0117] The command output module supports batch command sending to multiple devices. For example, before nighttime construction, control command codes can be sent to all lighting equipment and transport vehicles that need to be started, enabling batch start-up and shutdown of equipment and improving construction preparation efficiency. Simultaneously, the system monitors the command sending status in real time. If a device fails to provide a confirmation signal within a preset time (e.g., 5 minutes), the command is automatically resent or a manual inspection process is triggered to ensure the reliability of command execution.

[0118] The progress storage module uses blockchain technology to ensure immutable data storage. Each record is verified with a hash value to ensure data integrity and traceability. For example, when a subsequent audit needs to verify the construction progress for a certain period, the blockchain explorer can query the record chain with the corresponding timestamp to verify the consistency and authenticity of the data.

[0119] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0120] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A BIM and Internet of Things based construction progress real-time monitoring system, characterized in that, The system comprises: an Internet of Things data acquisition module, configured to acquire real-time construction data through a sensor network of a construction site, and divide the real-time construction data into a spatial position stream and a device state stream based on a preset data classification rule; a BIM data integration module, configured to perform three-dimensional point cloud alignment on the spatial position stream to generate a first progress index, and perform state code analysis on the device state stream to generate a second progress index; a progress deviation analysis module, configured to map the first progress index and the second progress index to a corresponding progress deviation value after index fusion according to a preset progress benchmark model, and use the progress deviation value as an evaluation parameter of a current construction stage; a response decision module, configured to call a target response strategy in a preset scheme database based on the progress deviation value, and use the target response strategy as an adjustment parameter of the current construction stage; a central processing unit, configured to send the real-time construction data to the BIM data integration module, send the first progress index and the second progress index to the progress deviation analysis module, and perform logical verification on the evaluation parameter and the adjustment parameter to generate a final response instruction. The BIM data integration module performing state code analysis on the device state stream comprises: dividing a continuous state sequence in the device state stream into a device operation group and a device idle group, and performing operation frequency calculation on the device operation group based on a preset state transition model to generate a state feature set; performing energy consumption distribution segmentation processing on energy consumption monitoring data in the device state stream, extracting energy consumption fluctuation features of each distribution region, and constructing an energy consumption distribution graph; spatially associating and fusing the state feature set and the energy consumption distribution graph to generate the second progress index. 2.The BIM and Internet of Things based construction progress real-time monitoring system according to claim 1, wherein, The preset data classification rule comprises a basic acquisition set and an auxiliary acquisition set; the basic acquisition set comprises position coordinate identifiers, device number identifiers, and material identifiers; the auxiliary acquisition set comprises environment parameter identifiers and personnel identifiers, and each identifier corresponds to an independent data processing channel. 3.The BIM and Internet of Things based construction progress real-time monitoring system according to claim 2, characterized in that, The system further comprises a data interface unit, which is configured to realize communication docking of the Internet of Things data acquisition module, the BIM data integration module, the progress deviation analysis module, and the response decision module with a construction Internet of Things network respectively; The Internet of Things data acquisition module divides real-time construction data based on the preset data classification rule, comprising: real-time receiving a comprehensive data packet from the construction Internet of Things network through the data interface unit, and matching core markers of the comprehensive data packet according to identifiers in the basic acquisition set to separate a basic data segment; traversing additional markers of the comprehensive data packet according to identifiers in the auxiliary acquisition set to extract an auxiliary data segment; aligning the basic data segment and the auxiliary data segment according to timestamps, and then writing them into a spatial position storage area and a device state buffer area respectively.

4. The BIM and IoT based construction progress real-time monitoring system as claimed in claim 1, wherein, When the preset progress benchmark model adopts a segmented quantization model, the progress deviation value is a segmented mapping result of a complex fusion value of the first progress index and the second progress index. When the preset progress reference model adopts an adaptive adjustment model, the progress deviation value is a set of continuous variables dynamically calibrated by iterative algorithm on the joint analysis results of the first progress index and the second progress index. 5.The BIM and Internet of Things based construction progress real-time monitoring system according to claim 3, characterized in that, The construction linkage module is connected with the central processing unit, and the construction linkage module is connected with the construction resource database through the data interface unit. The construction linkage module is configured to filter an available resource list from the construction resource database according to a resource allocation requirement in the final response instruction, and generate a resource scheduling sequence to optimize a construction adjustment process. 6.The BIM and Internet of Things based construction progress real-time monitoring system according to claim 5, wherein, The construction linkage module generates the resource scheduling sequence by: loading a three-dimensional grid model of the construction, locating a real-time position node of each resource in the available resource list in the grid model; calculating an optimal scheduling path from a deployment position of each resource to a target construction area based on a path planning algorithm, and prioritizing the available resource list according to execution timeliness; integrating the optimal scheduling path and the priority ranking into the grid model to generate a visual resource scheduling sequence. 7.The BIM and IoT-based construction progress real-time monitoring system of claim 1, wherein, When the central processing unit logically verifies the evaluation parameters and the adjustment parameters, a dual review mode of a completeness verification mechanism and a conflict detection mechanism is adopted, the completeness verification mechanism is used to confirm data field integrity, and the conflict detection mechanism is used to solve logical conflicts between parameters. 8.The BIM and Internet of Things based construction progress real-time monitoring system according to claim 3, wherein, The instruction output module is connected with the central processing unit, and the instruction output module is configured to convert the final response instruction into a control command code and send the control command code to a designated construction equipment through the data interface unit to start an adjustment program. 9.The BIM and Internet of Things based construction progress real-time monitoring system according to claim 1, wherein, The progress storage module is connected with the central processing unit, and the progress storage module is configured to archive the real-time construction data, the first progress index, the second progress index, the progress deviation value and the final response instruction, and generate a construction progress record chain in time sequence.

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