Construction progress real-time monitoring system based on BIM and Internet of Things

Abstract

The invention relates to the technical field of building construction monitoring, and discloses a construction progress real-time monitoring system based on BIM and Internet of Things, and the system comprises an Internet of Things data collection module which obtains real-time construction data through a sensor network and divides the real-time construction data into a spatial position flow and an equipment state flow; the BIM data integration module processes the two types of data streams to generate a first progress index and a second progress index; the progress deviation analysis module is combined with a preset progress reference model, and the progress indexes are fused and then mapped into a progress deviation value; the response decision module calls a preset scheme based on the deviation value to generate a target response strategy; the central processing unit is responsible for data transmission and logic verification. In addition, the system is further provided with a construction linkage module, an instruction output module and a progress storage module which are respectively used for optimizing resource scheduling, sending control commands and archiving data. According to the system, real-time monitoring, deviation analysis and dynamic adjustment of the construction progress are realized, and the intelligence and accuracy of construction management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction monitoring, and in particular to a real-time construction progress monitoring system based on BIM and the Internet of Things. Background Art

[0002] In the construction industry, construction progress management is a core component of ensuring on-time project delivery, controlling costs, and ensuring quality. Traditional construction progress monitoring relies primarily on manual inspections, paper records, and regular reporting. This approach suffers from significant drawbacks, including delayed data collection, untimely information transmission, and insufficient dynamic tracking capabilities. With the continuous expansion of construction projects and the increasing complexity of construction processes, traditional methods are no longer able to meet the real-time, precise, and intelligent demands of modern construction management.

[0003] Under manual monitoring, managers need to expend considerable time and effort visiting construction sites to manually record the progress of each process. This results in inefficient data collection and is prone to human error. For example, in large-scale complex projects, manually tallying the progress of sub-projects such as rebar binding and formwork support on each floor can lead to data distortion due to limited viewing angles or omissions in records, which in turn affects the adjustment of schedules and the rational allocation of resources. Furthermore, paper-based records and manual summarization methods result in significant delays in information transmission, making it difficult for project management to keep abreast of the actual progress on site and to quickly respond to sudden changes during construction, such as equipment failures, material shortages, or design changes. This can easily lead to construction delays and increased costs.

[0004] Traditional monitoring methods lack the ability to dynamically track and conduct multi-dimensional analysis of the construction process. Construction progress not only involves spatially determined progress (such as the completion of structural construction in each area), 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 only a few elements in isolation, failing to integrate and analyze multi-source data, making it difficult to comprehensively grasp the factors influencing construction progress and its development trends. For example, the operating status of equipment (such as the operating frequency of tower cranes and the energy consumption of construction machinery) can directly reflect construction efficiency, but traditional methods are unable to obtain and analyze this data in real time, resulting in a delay in identifying issues such as idle or overused equipment, which in turn hinders the optimization of the construction progress.

[0005] With the gradual application of BIM and IoT technologies in the construction sector, although some research has attempted to combine the two for construction management, most existing systems suffer from problems such as insufficient data integration, a single progress analysis model, and insufficiently intelligent response and decision-making mechanisms. For example, some systems only implement simple data connection between BIM models and IoT devices, failing to fully utilize BIM's three-dimensional spatial modeling advantages and the IoT's real-time data collection capabilities. Construction progress monitoring remains at a relatively rudimentary stage. In terms of progress 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 progress deviations and provide scientific adjustment strategies. In the response and decision-making phase, the pre-set solution database is insufficiently rich and well-matched, resulting in the generated adjustment parameters lacking pertinence and effectiveness. Summary of the Invention

[0006] The purpose of the present invention is to provide a real-time monitoring system for construction progress based on BIM and the Internet of Things to solve the problems raised in the above background technology.

[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: An IoT data acquisition module, configured to acquire real-time construction data through a sensor network at the construction site and to divide the real-time construction data into a spatial location stream and an equipment status stream based on preset data classification rules; A BIM data integration module is configured to perform three-dimensional point cloud alignment on the spatial position stream to generate a first progress index, and perform state code parsing on the equipment status stream to generate a second progress index; a progress deviation analysis module, configured to, based on a preset progress benchmark model, perform index fusion on the first progress index and the second progress index, and map them to corresponding progress deviation values, and use the progress deviation values ​​as evaluation parameters for the current construction stage; a response decision module, configured to call a target response strategy in a preset solution database based on the progress deviation value, and use the target response strategy as an adjustment parameter for the current construction phase; A 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 perform logical verification on the evaluation parameters and the adjustment parameters to generate a final response instruction.

[0008] Preferably, the BIM data integration module performs status code parsing on the equipment status stream, including: Dividing the 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 the energy consumption monitoring data in the device status stream, extracting energy consumption fluctuation characteristics of each distribution area and constructing an energy consumption distribution map; The state feature set is spatially correlated and fused with the energy consumption distribution map to generate the second progress index.

[0009] Preferably, the preset data classification rules include a basic collection set and an auxiliary collection set; the basic collection set includes a location coordinate identifier, an equipment number identifier and a material identifier; the auxiliary collection set includes an environmental parameter identifier and a personnel identifier, and each identifier corresponds to an independent data processing channel.

[0010] Preferably, the system further comprises a data interface unit, which is used to realize communication docking between the IoT data acquisition module, the BIM data integration module, the progress deviation analysis module and the response decision module and the construction IoT network respectively; The Internet of Things data acquisition module divides the real-time construction data based on the preset data classification rules, including: Receiving a comprehensive data packet from the construction Internet of Things network in real time through the data interface unit, and matching the core tag of the comprehensive data packet according to the identifier in the basic collection set to separate the basic data segment; traversing the additional tags of the integrated data packet according to the identifier in the auxiliary acquisition set to extract the auxiliary data segment; The basic data segment and the auxiliary data segment are aligned according to the timestamps and then written into the spatial position storage area and the device status buffer area respectively.

[0011] Preferably, when the preset progress benchmark model adopts a segmented quantization model, the progress deviation value is a segmented mapping result of a composite fusion value of the first progress index and the second progress index; When the preset progress benchmark model adopts an adaptive adjustment model, the progress deviation value is a continuous variable set obtained by dynamically calibrating a joint analysis result of the first progress index and the second progress index through an iterative algorithm.

[0012] Preferably, the system further comprises a construction linkage module connected to the central processing unit, and the construction linkage module is connected to the construction resource database via the data interface unit; The construction linkage module is used to screen the available resource list 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.

[0013] Preferably, the construction linkage module generates a resource scheduling sequence including: Loading a three-dimensional construction grid model, and locating a real-time location node of each resource in the available resource list in the grid model; Calculate the optimal scheduling path from the deployment location of each resource to the target construction area based on the path planning algorithm, and prioritize the list of available resources according to execution time; The optimal scheduling path and the priority ranking are integrated into the grid model to generate a visual resource scheduling sequence.

[0014] Preferably, when the central processing unit performs logical verification on the evaluation parameters and the adjustment parameters, a dual audit mode of integrity verification mechanism and conflict detection mechanism is adopted, wherein the integrity verification mechanism is used to confirm the integrity of the data field, and the conflict detection mechanism is used to resolve logical conflicts between parameters.

[0015] Preferably, the system further comprises an instruction output module connected to the central processing unit, the instruction output module being used to convert the final response instruction into a control command code, and to send the control command code to the designated construction equipment through the data interface unit to start the adjustment procedure.

[0016] Preferably, the system also includes a progress storage module connected to the central processing unit, and 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 generate a construction progress record chain in time series.

[0017] Compared with the prior art, the present invention has the following beneficial effects: At the data collection 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. Combining a multi-dimensional identification system consisting of basic collection sets (location coordinates, equipment numbers, material identification) and auxiliary collection sets (environmental parameters, personnel identification), this enables refined classification and efficient separation of construction data. The introduction of a data interface unit ensures reliable communication between each module and the construction IoT network. Using timestamp alignment technology, basic and auxiliary data are stored in a spatial location storage area and an equipment status buffer, respectively. This provides a structured, time-series, high-quality data source for subsequent BIM data integration, resolving the issues of delayed data collection and ambiguous classification encountered in traditional methods.

[0018] The BIM data integration module processes the spatial position stream using 3D point cloud alignment technology to generate a first progress index, achieving precise spatial mapping of the construction progress. Simultaneously, it parses the equipment status stream through state encoding, divides equipment into operating and idle groups, calculates operation frequencies, constructs energy consumption distribution maps, and performs spatial correlation fusion to generate a second progress index, deeply exploring the inherent connection 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 focuses solely on spatial progress or a single equipment status, and achieving a multi-dimensional dynamic representation of the construction progress.

[0019] The schedule deviation analysis module fuses and maps dual schedule indices based on a preset schedule benchmark model (either a segmented quantitative model or an adaptive adjustment model), allowing for flexible selection of analysis modes based on project characteristics. The segmented quantitative model quantitatively assesses deviations through segmented mapping of composite fusion values, making it suitable for construction phases with a high degree of standardization. The adaptive adjustment model dynamically calibrates deviation values ​​through an iterative algorithm, adapting to dynamic changes during the construction process. The combination of these two modes ensures both accuracy and flexibility in schedule deviation analysis, providing a scientific basis for subsequent response decisions.

[0020] The linkage mechanism between the response decision module and the preset solution database enables rapid matching of target response strategies based on progress deviation values. Combined with the central processing unit's logical verification (dual mechanisms of completeness verification and conflict detection), this ensures the rationality and reliability of adjustment parameters. The construction linkage module further generates a resource scheduling sequence based on the final response instruction. By loading a three-dimensional grid model, calculating the optimal scheduling path, and prioritizing it, it enables visual dynamic allocation of construction resources and optimizes the efficiency of the adjustment process. The configuration of the instruction output module and the progress storage module, respectively, ensures the precise execution of adjustment instructions and the full-process traceability of construction data, forming a complete closed loop from data collection and analysis to decision-making and execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a working principle diagram of the real-time construction progress monitoring system based on BIM and the Internet of Things according to the present invention; Figure 2 Design drawings for BIM data integration module status coding and parsing; Figure 3 Design diagram for the coordinated work of the data interface unit and the data acquisition module; Figure 4 This is the design drawing for the three-dimensional grid scheduling of the construction linkage module. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] See also Figures 1-4 The present invention relates to a real-time construction progress monitoring system based on BIM and the Internet of Things. The system comprises: an Internet of Things data acquisition module, a BIM data integration module, a progress deviation analysis module, a response decision module, and a central processing unit. Specifically, the system comprises the following steps: The IoT data acquisition module uses a network of sensors deployed at the construction site (such as RFID tags, GPS positioning devices, and environmental sensors) to acquire real-time construction data, including personnel location, equipment operating status, material delivery information, and environmental parameters. After acquiring this data, the module then divides the real-time construction data into a spatial location stream and an equipment status stream based on pre-set data classification rules. The spatial location stream primarily contains data related to spatial location, such as the coordinates of personnel and equipment, and the location of material stacks. The equipment status stream primarily contains equipment operating status data, such as equipment start / stop status, energy consumption data, and operating frequency.

[0024] The BIM data integration module receives the spatial position stream and equipment status stream data transmitted by the IoT data acquisition module. For the spatial position stream, 3D point cloud alignment technology is used to match the real-time acquired spatial position data with the 3D coordinates in the BIM model to generate a first progress index, which represents the progress of the construction object in the spatial dimension. For the equipment status stream, status codes are parsed to generate a second progress index, which represents the operating efficiency and status changes of the construction equipment in the temporal dimension.

[0025] The progress deviation analysis module fuses the first progress index and the second progress index based on a preset progress benchmark model (such as a construction schedule, a resource allocation plan, etc.), and maps the fused index value to the corresponding progress deviation value through algorithm calculation. The deviation value is used as an evaluation parameter for the current construction stage to determine whether the construction progress meets the planned requirements.

[0026] The response decision module calls the corresponding target response strategy from the preset solution database based on the progress deviation value, such as adjusting the construction sequence, increasing construction resources, optimizing equipment configuration, etc. This strategy serves as the adjustment parameter of the current construction stage and provides guidance for construction adjustments.

[0027] The central processing unit, the system's core control unit, is responsible for data transmission and logical verification. Specifically, it transmits real-time construction data acquired by the IoT data acquisition module to the BIM data integration module; transmits the first and second progress indexes generated by the BIM data integration module to the progress deviation analysis module; and performs logical verification on the evaluation parameters output by the progress deviation analysis module and the adjustment parameters output by the response decision module. Once verification passes, it generates the final response instruction, ensuring its accuracy and feasibility.

[0028] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1

[0029] This example specifically illustrates how the BIM data integration module processes equipment status streams within a real-time construction progress monitoring system based on BIM and the Internet of Things. During system operation, the IoT data acquisition module collects real-time equipment status stream data from a network of sensors deployed at the construction site (such as current sensors, voltage sensors, and displacement sensors). This data is transmitted to the BIM data integration module in the form of a continuous time series. This data includes information such as equipment status changes, energy consumption parameters, and operation duration during operation.

[0030] After receiving the equipment status stream, the BIM data integration module performs grouping of continuous state sequences. Specifically, the continuous state sequence is identified using preset state thresholds. States with valid operational actions (such as equipment startup, processing, and transportation) during equipment operation are grouped into the equipment operation group, while states without valid operational actions (such as shutdown, standby, and fault alarms) are grouped into the equipment idle group. For example, for tower crane equipment, states corresponding to actions such as lifting, slewing, and boom adjustment are grouped into the equipment operation group, while states such as shutdown awaiting instructions or maintenance are grouped into the equipment idle group. During the grouping process, the system timestamps the start and end times of each state, forming a discrete set of state segments.

[0031] After completing the grouping, the module calculates the operation frequency of the equipment operation group based on the preset state transition model. The preset state transition model is a finite state automaton model built based on historical construction data, which defines the legal transition rules between equipment operation states (such as the state transition path of "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 "mixing state duration ratio" and "mixing and feeding state transition frequency" to characterize the actual operating efficiency and operation mode of the equipment.

[0032] The module performs energy consumption distribution segmentation processing on the energy consumption monitoring data in the equipment status stream. The energy consumption monitoring data includes the instantaneous power, cumulative energy consumption, voltage and current waveforms of the equipment, etc. The module segments the continuous energy consumption data through the sliding window algorithm, and combines the density clustering algorithm (such as DBSCAN) to divide the energy consumption data into different distribution areas. Each area corresponds to the energy consumption characteristics of the equipment under specific working conditions (such as no-load, light-load, and full-load conditions). For example, the energy consumption data of the excavator during excavation operations and the energy consumption data during no-load driving will be divided into different areas. Within each distribution area, the module extracts energy consumption fluctuation characteristics, including peak energy consumption, average energy consumption, energy consumption standard deviation, energy consumption trend slope, etc., and constructs an energy consumption distribution graph with the time axis as the horizontal axis and the energy consumption value as the vertical axis to intuitively display the distribution of equipment energy consumption under different time periods and working conditions.

[0033] The module performs spatial correlation fusion operations on the state feature set and the energy consumption distribution map. The specific method is: based on the timestamp, each operation state segment in the state feature set is aligned with the corresponding time period in the energy consumption distribution map, and a mapping relationship between the state feature and the energy consumption feature is established. For example, in the time period corresponding to the "full load operation" state in the equipment operation group, a high energy consumption fluctuation area appears in the energy consumption distribution map. The module determines the average energy consumption value, energy consumption fluctuation range and other parameters in this state through correlation analysis to form a "state-energy consumption" correlation matrix. Through this matrix, the system can identify the energy consumption efficiency of the equipment in different operating states, such as whether the high-frequency operation state is accompanied by abnormal energy consumption fluctuations, so as to determine whether the equipment is operating normally or whether there is energy waste.

[0034] To generate the second progress index, the module normalizes the associated and fused state and energy consumption characteristics, converting parameters of different dimensions (such as time, energy consumption, and frequency) into normalized numerical ranges (e.g., [0, 1]) to eliminate the impact of dimensional differences on progress assessment. Principal component analysis (PCA) is used to reduce the dimensionality of the standardized data and extract principal component factors that characterize the core characteristics of the equipment state flow, such as the "operation efficiency factor" and the "energy consumption stability factor." These principal component factors are linearly combined according to preset weights to generate the second progress index value. This index numerically quantifies the overall performance of the equipment during the construction process. Higher values ​​indicate more efficient and stable equipment operation, while lower values ​​indicate inefficiency or abnormal equipment operation.

[0035] Throughout the entire processing process, the BIM data integration module achieves the conversion from raw data to structured features through multi-level analysis of equipment status streams, providing refined equipment status data support for construction progress monitoring. The system can track equipment operation in real time through the second progress index, promptly identifying problems such as equipment failure and inefficient operation, and providing key evidence for progress deviation analysis and response decisions. For example, if the second progress index values ​​of multiple devices in a certain area continuously fall below the 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 progress proceeds as planned.

[0036] The above processing is performed dynamically based on real-time data collection. The module uses a cache mechanism to store recent equipment status stream data for historical data comparison and trend analysis. Furthermore, the system supports manual configuration or automatic optimization of preset state transition models and energy consumption segmentation parameters to meet the monitoring needs of different types of construction equipment. For example, for newly-added construction equipment, operating specifications and energy consumption parameters can be imported from the equipment manual to quickly build a unique state transition model and energy consumption analysis rules, ensuring the system's versatility and flexibility.

[0037] Example 2

[0038] This embodiment specifically describes the preset data classification rules and workflow of the data interface unit in a real-time construction progress monitoring system based on BIM and the Internet of Things. During system operation, the IoT data acquisition module acquires multi-source heterogeneous data in real time from the construction site's IoT network. This data is collected through sensors, RFID tags, smart terminals, and other devices, and includes various types of information related to the personnel, equipment, materials, and 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 collection set and an auxiliary collection set. These two sets correspond to different data processing channels, ensuring the independent transmission and processing of core data and auxiliary data.

[0039] The basic collection set includes location coordinate identifiers, equipment number identifiers, and material identifiers, which are core data elements for construction progress monitoring. Location coordinate identifiers are used to 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 system. Equipment number identifiers assign a unique identifier to each piece of construction equipment (such as an RFID tag number) to distinguish different pieces of equipment and track their operating status. Material identifiers correspond to information such as material type, specification, and batch, such as the property data of materials like rebar and concrete marked with barcodes or QR codes. The auxiliary collection set includes environmental parameter identifiers and personnel identifiers. Environmental parameter identifiers are used to collect construction environment data such as temperature, humidity, light intensity, and wind speed, which are acquired in real time through environmental sensors. Personnel identifiers record the identity information, job type, and work permissions of construction personnel, and can be obtained through access control systems or badge punching devices. Each identifier corresponds to an independent data processing channel to prevent confusion or conflict between different types of data during transmission and processing.

[0040] The system's data interface unit serves as a bridge connecting each functional module with the construction IoT network, utilizing standardized communication protocols (such as MQTT and HTTP) for bidirectional data transmission. The IoT data acquisition module, while classifying real-time construction data based on pre-set data classification rules, receives comprehensive data packets from the construction IoT network in real time via the data interface unit. This data packet is a collection of raw data, encompassing all information from the basic and auxiliary collection sets, encapsulated in JSON or XML format and containing metadata such as timestamps and device identifiers. For example, a comprehensive data packet might include the location coordinates of a tower crane, the equipment number, the batch of concrete material currently being used, the ambient temperature during that period, and the operator's employee ID.

[0041] The module performs the separation operation of the basic data segment. According to the identifiers in the basic collection set, the core tags of the comprehensive data package are matched 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; at the same time, the spatial position data corresponding to the position coordinate identifier is extracted through the "coordinate X / Y / Z" field, and the material information corresponding to the material identifier is extracted 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 the core information directly related to the 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".

[0042] After completing the separation of the basic data segments, the module extracts the auxiliary data segments. Based on the identifiers in the auxiliary collection set, the module traverses the additional tags of the comprehensive data packet. For example, the temperature and humidity data corresponding to the environmental parameter identifiers are extracted through the "ambient temperature" and "ambient humidity" fields, and the personnel information corresponding to the personnel identifier is extracted through the "personnel number" and "personnel type" fields. The extraction process of the auxiliary data segments must ensure that the identifiers of all auxiliary collection sets are covered. For example, when the comprehensive data packet contains other environmental parameters such as wind speed and light intensity, the module needs to synchronously extract the relevant field data to form a complete auxiliary data record, such as "ambient temperature: 28°C, ambient humidity: 65%, operator number: P007, job type: crane operator".

[0043] The module aligns the timestamps of the basic and auxiliary data segments. Because different data types may be collected at slightly different times (e.g., due to varying sensor sampling frequencies), the two types of data must be synchronized using the timestamp field in the data packet (accurate to the millisecond level). This process works as follows: Using the timestamp of the basic data segment as a reference, the module searches for the record with the closest timestamp in the auxiliary data segment. Time differences are filled using linear interpolation or the nearest neighbor method to ensure a one-to-one correspondence between the basic and 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," the auxiliary data record is linked to the basic data record to form a complete data pair at the same point in time.

[0044] After the timestamps are aligned, the module writes the basic data segment and the auxiliary data segment into the spatial location storage area and the equipment status buffer, respectively. The spatial location storage area is a specific table structure in the database that is used to store basic data related to spatial location. It supports 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 period of time. The equipment status buffer is a cache space in memory that is 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 equipment's energy consumption data and operator information from the buffer to generate a second progress index.

[0045] 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, the cache retransmission mechanism is triggered, temporarily storing unsuccessfully transmitted data packets in a local cache and resending them after the network is restored, ensuring the integrity and continuity of data collection. Simultaneously, the system monitors the traffic flow of each data processing channel. When the data transmission volume of a channel exceeds a preset threshold, the system automatically adjusts bandwidth allocation to prioritize the data transmission efficiency of the basic collection set and avoid core data loss or delays.

[0046] Preset data classification rules support dynamic configuration, allowing construction managers to add or remove identifier types and adjust the priority of data processing channels based on actual needs. For example, when a new material is introduced to a construction site, a unique identifier for that material can be added to the basic collection set and corresponding tag matching rules configured, enabling the system to automatically identify and process data for the new material. This flexibility ensures the system can adapt to the individual needs of different construction projects and enhances the versatility of data collection and processing.

[0047] Example 3

[0048] This example specifically illustrates how two different preset progress benchmark models are handled by the progress deviation analysis module in a real-time construction progress monitoring system based on BIM and the Internet of Things. During system operation, the progress deviation analysis module analyzes the first progress index (spatial dimension progress data) and the second progress index (equipment status dimension progress data) generated by the BIM data integration module based on the preset progress benchmark model to quantify the difference between the construction progress and the planned progress. The preset progress benchmark models include a segmented quantification model and an adaptive adjustment model, which differ significantly in data processing logic and deviation calculation methods.

[0049] When the preset progress benchmark model adopts a segmented quantitative model, the calculation of the progress deviation value is based on a predefined discrete threshold interval and is achieved by segmentally mapping the composite fusion result of the first progress index and the second progress index. The specific process is as follows: The module indexes the first progress (denoted as ) and the second progress index (denoted as ) for composite fusion.

[0050] The composite fusion process adopts the weighted summation method, and the formula is:

[0051] in, is the composite fusion value, and The weight coefficients of the space progress index and the device status progress index respectively The weight coefficient is pre-set by the construction management requirements. For example, when the spatial progress (such as the structural construction progress) is the core indicator of the project, it can be set , to highlight the importance of spatial dimension data.

[0052] Generate composite fusion value After that, the module will be based on the threshold interval preset by the segmented quantization model. Mapped to the corresponding progress deviation level. The threshold interval is usually divided into three levels: progress ahead, progress on track, and progress behind schedule. For example, the preset threshold interval is: Time is the progress ahead of schedule ( is the baseline composite value of the planned schedule), when The progress is normal when Each level corresponds to a specific progress deviation value range, such as the progress ahead of schedule corresponds to a deviation value of , the progress corresponds to normal , progress lag corresponds to .

[0053] During the mapping process, the system matches the composite fusion value with the threshold range through the table lookup method. For example, if the calculated value at a certain moment is , then it is determined that the progress is ahead of schedule, and the corresponding progress deviation value is (Take the middle value of the interval.) This method converts continuous progress data into intuitive deviation levels through discretization, making it easier for construction managers to quickly determine progress status and formulate appropriate adjustment strategies.

[0054] The advantages of the segmented quantitative model lie in its simple logic and ease of implementation. It is suitable for projects with relatively fixed construction processes and few external factors. For example, in standardized factory construction projects, due to the high repetitiveness of construction processes, stable threshold intervals and weighting coefficients can be pre-determined using historical data, enabling rapid progress assessment.

[0055] When the preset progress benchmark model adopts an adaptive adjustment model, the calculation of the progress deviation value is based on a dynamic learning mechanism. The first progress index and the second progress index are jointly analyzed through an iterative algorithm to generate a deviation set in the form of a continuous variable. The specific process is as follows: First, the module constructs the state space of the adaptive adjustment model and sets the first progress index and the second progress index As an input variable, the dynamic baseline value of the planned progress As output variable, Represents construction time (unit: day or hour). The model is trained with historical construction data and uses iterative algorithms such as recursive least squares (RLS) or Kalman filtering to continuously optimize model parameters. It can reflect the reasonable progress benchmark under current construction conditions in real time.

[0056] In the real-time analysis phase, the module first calculates the joint analysis value at the current moment , the formula is:

[0057] in, is the baseline value of the spatial dimension in the planned schedule, It is the baseline value of the equipment status dimension in the planning schedule. The adjustment coefficient of the impact of equipment status deviation on the overall progress .

[0058] The formula quantifies the dual deviation of the spatial dimension and the equipment status dimension by comparing the actual progress index with the planned baseline value, where Used to adjust the weight of the impact of equipment status on the overall progress. For example, when equipment operating efficiency is the key influencing factor, the value to highlight the effect of equipment status deviation.

[0059] The module uses an iterative algorithm to Perform dynamic calibration to generate a set of progress deviation values ​​in the form of continuous variables .

[0060] The core idea of ​​the iterative algorithm is to use the deviation value at the current moment to update the model parameters so that the subsequent benchmark values Able to adjust adaptively. For example, when multiple moments are detected (actual progress ahead), the model will automatically improve to reflect the improvement of construction efficiency; on the contrary, when When the model is running, it will lower the baseline value to avoid misjudgment of progress due to unforeseen factors.

[0061] The advantage of the adaptive adjustment model lies in its ability to dynamically adapt to environmental changes, process adjustments, and other factors during the construction process. For example, during bridge construction under complex geological conditions, when continuous rainfall causes equipment efficiency to decline, the model can automatically adjust its baseline values ​​through real-time learning, avoiding misinterpretation of weather-related schedule delays as construction management issues. The continuous variable deviation set generated by this model contains rich detailed information, such as the contribution of each dimension's deviation and the trend of deviation changes, providing data support for accurately pinpointing the root causes of schedule issues.

[0062] In actual applications, the segmented quantitative model and the adaptive adjustment model can be switched or used in conjunction according to the different needs of the construction phase. For example, in the early stages of a project (such as the foundation construction phase), due to unstable construction conditions, the adaptive adjustment model can be used to track progress changes in real time. After entering the main construction phase, if the construction process tends to stabilize, the segmented quantitative model is switched to improve assessment efficiency. In addition, the system supports running both models simultaneously, and by comparing their output results, the reliability of the progress deviation assessment is verified. For example, if the segmented quantitative model determines that the progress is lagging behind and 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.

[0063] During the data processing, the progress deviation analysis module analyzes the input data of the two models ( and ) to perform consistency checks to ensure that the data timestamp, unit, value range, etc. meet the model requirements.

[0064] For example, if If the spatial coordinate unit is meter and the model default unit is foot, the module will automatically convert the units to avoid deviation in analysis results due to data format problems.

[0065] Example 4

[0066] This embodiment specifically describes the functions of the construction linkage module and the process of generating a resource scheduling sequence within a real-time construction progress monitoring system based on BIM and the Internet of Things. During system operation, if the final response instruction generated by the central processing unit includes a resource allocation request (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 via 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 the tower crane, the operating status of the concrete mixer truck, the inventory quantity and storage location of steel reinforcement materials, the types of work performed by construction personnel, and the work areas they perform.

[0067] For example, consider a high-rise building main structure construction scenario. Suppose the central processing unit (CPU), based on progress deviation analysis, determines that the fifth-floor slab concrete pour is lagging behind schedule. Two concrete pumps and 30 cubic meters of C30 concrete must be deployed from other construction areas. Upon receiving the final response command, which includes "deploy concrete pumps and concrete materials," the construction linkage module first filters available resources from the construction resource database based on the resource type and quantity requirements specified in the command. For concrete pumps, the filtering criteria include: the device status is "Idle" or "Movable," the device 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 fifth-floor construction area). This filtering yields a list of available equipment. For example, two pumps, numbered 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 (needing to complete maintenance before being moved). Therefore, the actual available equipment is P-003. For C30 concrete, the screening conditions 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 stored in the mixing plant on the north side of the site were obtained, which met the quantity requirements.

[0068] 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, including the construction site's topography, building structures, roads, material storage areas, and equipment parking areas. Built on a BIM model with millimeter-level accuracy, the model uses real-time equipment and material location data (such as the GPS coordinates of P-003 and the BIM coordinates of the concrete storage tank) to locate the real-time location node of each resource in the available resource list. For example, the location node for P-003 is marked as the east side of the construction site (X=150, Y=200, Z=0), the location node for the concrete storage tank is marked as the north mixing plant (X=50, Y=100, Z=0), and the location node for the ground material storage area corresponding to the target construction area is (X=120, Y=180, Z=0). The vertical transportation channel (crane operating range) covers the area from X=100-160, Y=150-220, and Z=0-100.

[0069] Next, the module uses a path planning algorithm to calculate the optimal dispatch path from each resource's deployment location to the target construction area. For the concrete pump P-003, its deployment location is the east equipment parking area, and its target area is the ground material storage area on the 5th floor. It must first be transported via the construction site road to the crane's operating area, and then hoisted to the 5th floor by the crane. Path planning must avoid obstacles (such as erected scaffolding and temporarily stacked formwork) and select the shortest route with good traffic conditions. The algorithm calculated the optimal path: east parking area → east main road → south intersection → crane operating area, a total distance of approximately 450 meters, with no obstacles. For C30 concrete, it must be transported from the north mixing plant to the crane operating area by a mixer truck, and then hoisted to the 5th floor by the crane. The optimal path is: north mixing plant → north auxiliary road → east main road → crane operating area, a total distance of approximately 380 meters, requiring passage through a passage with a 3-meter height restriction (the mixer truck's height is 2.8 meters, which meets the requirements).

[0070] The module also prioritizes the list of 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, has a startup time of 5 minutes, and a 450-meter transport path is expected to take 10 minutes. Concrete materials need to be mixed and prepared 15 minutes in advance, and a 380-meter transport path is expected to take 8 minutes. Because concrete pouring requires equipment to be in place beforehand, resource priority is set: P-003 (total time of 15 minutes) takes precedence over concrete materials (total time of 23 minutes).

[0071] After completing path calculation and priority sorting, the module integrates the optimal scheduling path and priority ranking into the 3D grid model, generating a visual resource scheduling sequence. This visualization includes: Equipment and material transportation paths are marked with colored lines within the 3D model (e.g., red lines represent the movement path of P-003, blue lines represent the path of the concrete mixer truck), dynamic icons display the real-time location of resources (e.g., equipment icons move along the path over time), and a priority list is displayed on the right side of the model interface (e.g., Sequence No. 1: P-003, ETA 10:15; Sequence No. 2: C30 Concrete, ETA 10:23). This visual sequence allows construction managers to intuitively view the time nodes and spatial paths of resource scheduling, allowing them to coordinate with on-site personnel in advance to clear roads and prepare for the receipt of equipment and materials.

[0072] During resource scheduling, the construction linkage module monitors resource movement in real time through the data interface unit. For example, when P-003 arrives at the south intersection along its route, the module updates its position in the 3D model using the device's built-in GPS positioning data. If it detects a temporary obstacle blocking the path, it automatically triggers a re-routing algorithm, generating an alternative route (e.g., west auxiliary road → north main road → crane operation area, a total of 500 meters, requiring an additional 3 minutes). The visual sequence and priority list are also updated simultaneously.

[0073] Furthermore, the construction linkage module supports data exchange with construction workers' terminal devices (such as smart helmets and handheld PDAs). For example, once a resource scheduling sequence is generated, the module sends scheduling instructions (including equipment number, target location, and estimated arrival time) to the personnel responsible for equipment operation, and sends material preparation notifications (including material type, quantity, and transportation route) to material management personnel. After receiving instructions through terminal devices, personnel can provide feedback on the execution status (such as "Equipment started" and "Materials loaded"). The module updates the resource scheduling progress in real time to ensure coordinated operations across all links.

[0074] For complex construction scenarios (such as simultaneous construction of multiple buildings or cross-functional operations), the Construction Interaction Module simulates the resource scheduling process using a 3D grid model to proactively identify potential conflicts. For example, if a crane is scheduled to lift rebar to an adjacent area during the same timeframe as P-003, the module uses collision detection to identify the intersection of the two cranes' paths and automatically adjust the scheduling time of one crane to avoid a conflict in the overhead operations.

[0075] Example 5

[0076] This example specifically illustrates the logic verification mechanism of the central processing unit (CPU) and the functions of the command output module and progress storage module in a real-time construction progress monitoring system based on BIM and the Internet of Things. Taking the concrete pouring construction of a commercial complex as an example, during system operation, the CPU performs logic verification on the evaluation parameters output by the progress deviation analysis module (e.g., a progress deviation of -15% indicates a delay) and the adjustment parameters output by the response decision module (e.g., invoking the response strategy of "adding two concrete vibrators") to ensure parameter accuracy and feasibility.

[0077] The central processing unit utilizes a dual audit model, combining completeness verification and conflict detection. The completeness verification mechanism checks the integrity of the data fields for both the evaluation and adjustment parameters. For example, evaluation parameters must include fields such as the progress deviation value, the deviation occurrence period, and the affected area, while adjustment parameters must include fields such as the response strategy number, resource type, quantity, and scheduling time. If the "resource scheduling time" field in the adjustment parameters is missing, the system automatically triggers the data completion process, sending a request for re-entry to the response decision module. The system will not proceed to the next step of verification until all fields are complete.

[0078] Execute a conflict detection mechanism to troubleshoot logical conflicts between parameters. For example, if the adjustment parameter requires "adding two concrete vibrators," but the construction resource database shows only one idle vibrator on site, the system will identify a "resource quantity conflict," automatically flag the adjustment parameter, and trigger a conflict resolution process. The conflict resolution process includes: ① Re-querying the construction resource database to confirm whether other resources are available (such as equipment inventory in nearby sections); ② If no resources are available, return to the response decision module, prompting the adjustment strategy to be modified to "deploy one device and extend the operation time"; ③ If the response decision module cannot provide an alternative strategy, the central processing unit generates an exception report and notifies construction management personnel to intervene.

[0079] After double verification, the central processing unit generates a final response instruction, such as "Move one concrete vibrating device (No. V-005) to construction area 3 at 2:00 PM on June 10, 2025, and extend operating hours to 10:00 PM." The instruction output module receives this instruction and converts it into a control command code. This control command code uses a standardized communication protocol (such as MODBUS RTU) and contains information such as the device address, operation type (such as "move" or "start"), and parameter values ​​(such as target coordinates and operation duration). For the V-005 vibrating device as an example, the converted control command code is: "010600010064789A," where "01" is the device address, "06" is the write single register operation code, "0001" is the target coordinate register address, "0064" is the target coordinate value (decimal 100), and "789A" is the checksum. The command output module sends the code to the controller of the designated construction equipment (V-005) through the data interface unit. After receiving it, the equipment parses and executes the command to move to the target area and start the extension operation.

[0080] The progress storage module synchronously archives key information, including real-time construction data, the first progress index, the second progress index, the progress deviation value, and the final response instruction. Taking concrete pouring construction as an example, real-time construction data includes: the location coordinates of the vibrating equipment V-005 (X=200, Y=150, Z=5), its operating status (continuous operation time of 45 minutes), and its energy consumption data (accumulated power consumption of 20 kWh); the first progress index is the concrete pouring volume progress generated by 3D point cloud alignment (current completion of 80 cubic meters, planned completion of 100 cubic meters); the second progress index is the correlation between the equipment status feature set and the energy consumption distribution map (operation efficiency factor 0.7, energy consumption stability factor 0.8); the progress deviation value is -15% (actual progress lags behind the planned progress by 15%); and the final response instruction is the aforementioned equipment scheduling instruction.

[0081] The progress storage module generates a construction progress record chain in time series. Each record contains fields such as timestamp, data type, and data details. For example, the record at 13:00 on June 10, 2025 is: Timestamp: 2025-06-10 13:00:00 Data type: Real-time construction data Data details: Device V-005 coordinates (200,150,5), operating status "operating", energy consumption 20kWh Data type: First progress index Data details: Concrete pouring volume progress 80 / 100 cubic meters Data type: Schedule deviation value Data details: -15%, the reason for the lag is "insufficient equipment efficiency" Construction managers can query historical records in the progress storage module to trace the evolution of progress issues. For example, by comparing records from 12:00 PM and 1:00 PM, they discovered 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 has led to a decrease in efficiency, providing a basis for subsequent adjustments to equipment rotation strategies.

[0082] In multi-disciplinary collaborative construction scenarios, the central processing unit's logic verification mechanism prevents cross-disciplinary command conflicts. For example, if the structural construction team requests a tower crane to lift rebar, and the decoration construction team simultaneously requests the same crane to lift materials, the central processing unit identifies the "crane usage conflict" through its conflict detection mechanism. Based on the preset priority rules (structural construction takes precedence over decoration construction), it automatically adjusts the decoration team's dispatch instructions to ensure that the critical process is executed first.

[0083] The command output module supports batch command transmission to multiple devices. For example, before nighttime construction, a control command code can be sent to all lighting equipment and transport vehicles that need to be activated, enabling batch start and stop of equipment and improving construction preparation efficiency. The system also monitors the status of command transmission in real time. If a device fails to provide a confirmation signal within a preset time (e.g., 5 minutes), the system automatically resends the command or triggers a manual inspection process to ensure reliable execution.

[0084] The progress storage module uses blockchain technology to ensure tamper-proof data storage, adding a hash value check to each record to ensure data integrity and traceability. For example, when a subsequent audit needs to verify the construction progress of a certain period, the blockchain browser can query the record chain with the corresponding timestamp to verify the consistency and authenticity of the data.

[0085] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0086] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring system for construction progress based on BIM and the Internet of Things, characterized by: include: An IoT data acquisition module, configured to acquire real-time construction data through a sensor network at the construction site and to divide the real-time construction data into a spatial location stream and an equipment status stream based on preset data classification rules; A BIM data integration module is configured to perform three-dimensional point cloud alignment on the spatial position stream to generate a first progress index, and perform state code parsing on the equipment status stream to generate a second progress index; a progress deviation analysis module, configured to, based on a preset progress benchmark model, perform index fusion on the first progress index and the second progress index, and map them to corresponding progress deviation values, and use the progress deviation values ​​as evaluation parameters for the current construction stage; a response decision module, configured to call a target response strategy in a preset solution database based on the progress deviation value, and use the target response strategy as an adjustment parameter for the current construction phase; A 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 perform logical verification on the evaluation parameters and the adjustment parameters to generate a final response instruction.

2. The real-time monitoring system for construction progress based on BIM and the Internet of Things according to claim 1 is characterized in that: The BIM data integration module performs status code parsing on the equipment status stream, including: Dividing the 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 the energy consumption monitoring data in the device status stream, extracting energy consumption fluctuation characteristics of each distribution area and constructing an energy consumption distribution map; The state feature set is spatially correlated and fused with the energy consumption distribution map to generate the second progress index.

3. The real-time monitoring system for construction progress based on BIM and the Internet of Things according to claim 1 is characterized in that: The preset data classification rules include a basic collection set and an auxiliary collection set; the basic collection set includes a location coordinate identifier, an equipment number identifier, and a material identifier; the auxiliary collection set includes an environmental parameter identifier and a personnel identifier, and each identifier corresponds to an independent data processing channel.

4. The real-time construction progress monitoring system based on BIM and the Internet of Things according to claim 3 is characterized in that: The system further includes a data interface unit, which is used to realize communication docking between the IoT data acquisition module, the BIM data integration module, the progress deviation analysis module, and the response decision module and the construction IoT network respectively; The Internet of Things data acquisition module divides the real-time construction data based on the preset data classification rules, including: Receiving a comprehensive data packet from the construction Internet of Things network in real time through the data interface unit, and matching the core tag of the comprehensive data packet according to the identifier in the basic collection set to separate the basic data segment; traversing the additional tags of the integrated data packet according to the identifier in the auxiliary acquisition set to extract the auxiliary data segment; The basic data segment and the auxiliary data segment are aligned according to the timestamps and then written into the spatial position storage area and the device status buffer area respectively.

5. The real-time monitoring system for construction progress based on BIM and Internet of Things according to claim 1 is characterized in that: When the preset progress benchmark model adopts a segmented quantization model, the progress deviation value is a segmented mapping result of a composite fusion value of the first progress index and the second progress index; When the preset progress benchmark model adopts an adaptive adjustment model, the progress deviation value is a continuous variable set obtained by dynamically calibrating a joint analysis result of the first progress index and the second progress index through an iterative algorithm.

6. The real-time construction progress monitoring system based on BIM and the Internet of Things according to claim 1 is characterized in that: It also 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; The construction linkage module is used to screen the available resource list 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.

7. The real-time construction progress monitoring system based on BIM and Internet of Things according to claim 6 is characterized in that: The construction linkage module generates a resource scheduling sequence including: Loading a three-dimensional construction grid model, and locating a real-time location node of each resource in the available resource list in the grid model; Calculate the optimal scheduling path from the deployment location of each resource to the target construction area based on the path planning algorithm, and prioritize the list of available resources according to execution time; The optimal scheduling path and the priority ranking are integrated into the grid model to generate a visual resource scheduling sequence.

8. The real-time construction progress monitoring system based on BIM and Internet of Things according to claim 1 is characterized in that: When the central processing unit performs logical verification on the evaluation parameters and the adjustment parameters, a dual audit mode of integrity verification mechanism and conflict detection mechanism is adopted. The integrity verification mechanism is used to confirm the integrity of the data field, and the conflict detection mechanism is used to resolve logical conflicts between parameters.

9. The real-time construction progress monitoring system based on BIM and Internet of Things according to claim 1 is characterized in that: It also includes an instruction output module connected to the central processing unit, which 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.

10. The real-time construction progress monitoring system based on BIM and Internet of Things according to claim 1 is characterized in that: It also includes a progress storage module connected to the central processing unit, which 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 generate a construction progress record chain in time series.

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