Intelligent construction progress real-time monitoring system based on BIM

Abstract

The invention discloses an intelligent construction progress real-time monitoring system based on BIM, relates to the technical field of project progress management, solves the technical problems that the construction progress condition cannot be comprehensively and accurately analyzed, and meanwhile, the resource allocation process lacks intelligent analysis, and adopts dynamic threshold adjustment and a scientific anomaly grading standard to realize real-time monitoring of the construction progress. The progress abnormality is divided into three levels of slight, serious and emergency, compared with traditional single threshold value judgment, the progress abnormality condition can be more accurately recognized, a more clear basis is provided for subsequent processing, the progress normality analysis unit analyzes construction progress risk factors and sets weights and threshold values through historical data and a trend analysis method, and the progress normality analysis unit is used for analyzing the construction progress risk factors. The current risk factor occurrence frequency is evaluated in combination with historical data, potential risks are recognized in advance, influence evaluation information is generated, managers can take prevention measures in advance, and the risk occurrence probability and the influence degree are reduced.

Description

Technical Field

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

[0002] With the rapid development of social economy, the scale and complexity of construction projects have gradually increased, and construction management has become increasingly complex. In order to ensure the effective management of construction projects and timely respond to problems that arise during the construction process, construction progress management systems have emerged.

[0003] According to the patent application with publication number CN117236684A, a construction progress management system and operation method are disclosed. The construction progress management system and operation method include multiple mobile terminals and a construction progress management platform, and the mobile terminals and the construction progress management platform are connected via the Internet; the mobile terminals include a real-time monitoring module for obtaining the construction progress of the project and monitoring the progress information parameters of each group and the information parameters of each abnormal progress group; the construction progress management platform includes a data processing module for monitoring and analyzing individual progress based on the construction progress information obtained by the data processing module, and evaluating the actual completion progress risk coefficient, adjusting the individual progress, the progress risk coefficient of each abnormal progress group and the progress risk management coefficient.

[0004] Traditional construction progress monitoring relies primarily on manual inspection records and paper-based statistical reports, resulting in issues such as delayed data collection, low accuracy, and a single analytical dimension. With the development of BIM (Building Information Modeling) technology, some projects have begun applying BIM to construction progress management. However, most projects only achieve a simple comparison between planned and actual progress, lacking integrated analysis of multi-source data during the construction process. This inability to dynamically assess risks makes it difficult to quickly develop scientific and rational resource allocation plans in the face of construction anomalies, making it difficult to meet the demand for real-time progress monitoring and precise management in the complex construction environments of modern construction projects. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a BIM-based intelligent construction progress real-time monitoring system, which solves the problems of being unable to comprehensively and accurately analyze the construction progress status and lacking intelligent analysis in the resource allocation process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a BIM-based intelligent construction progress real-time monitoring system, comprising: The construction progress analysis unit is used to calculate the difference between the actual construction progress and the planned progress, compare it with the preset progress value, generate a normal or abnormal construction signal, and transmit both signals simultaneously; The progress normality analysis unit is used to analyze the acquired construction normality signal, analyze and obtain the construction progress risk factors in the historical data through trend analysis method, calculate the corresponding occurrence frequency, and screen the risk factors greater than the threshold as early warning factors. Combined with the corresponding construction period progress impact, it generates impact assessment information and transmits it to the real-time monitoring information output unit; The progress anomaly analysis unit is used to analyze the acquired construction anomaly signals, determine the same abnormal areas with the same abnormal conditions, and perform separate deployment analysis based on personnel anomalies, material anomalies, and equipment anomalies, generate deployment information, and transmit it to the real-time monitoring information output unit.

[0007] As a further solution of the present invention, a real-time monitoring information output unit is also included, which is used to display the acquired impact assessment information and deployment information to corresponding management personnel.

[0008] As a further solution of the present invention, the specific method in which the construction progress analysis unit generates a construction normal or abnormal signal is: Collect real-time construction data and actual construction progress, calculate the progress difference with the planned progress corresponding to the BIM model, and compare it with the preset progress value; If the progress difference is greater than the preset progress value, a normal construction signal is generated. Conversely, if the progress difference is less than the preset progress value, an abnormal construction signal is generated.

[0009] As a further solution of the present invention, the specific method in which the progress normality analysis unit analyzes the construction normality signal is: Obtain historical construction data and perform data preprocessing. At the same time, trend analysis is used to analyze and obtain risk factors for the construction progress. Appropriate weights and thresholds are set for each risk factor. The frequency of occurrence of each risk factor at the current construction site is analyzed based on historical data, and the obtained frequency is compared with the set threshold. If the frequency is greater than the threshold, it means that the corresponding risk factor will appear and it will be marked as a warning factor. Conversely, if the frequency is less than the threshold, it will not be processed.

[0010] As a further solution of the present invention, the specific method in which the progress normality analysis unit generates the impact assessment information in combination with the corresponding construction period progress impact is: Obtain all warning factors and classify their risk severity based on the progress duration value. Progress duration values ​​between [0, 2] are classified as low risk, progress duration values ​​between (2, 5] are classified as medium risk, and progress duration values ​​greater than 5 are classified as high risk. The impact of early warning factors on the progress and duration is obtained based on historical data, and the corresponding evaluation level is generated in combination with the progress and duration numerical range to generate the corresponding impact assessment information.

[0011] As a further solution of the present invention, the specific method in which the abnormal progress analysis unit analyzes the abnormal construction signal is as follows: Obtain different construction areas and label them as i, where i=1, 2, …, j, where j represents the number of construction areas. Determine the abnormal situation corresponding to construction area i based on the construction abnormality signal, and screen the construction areas with the same abnormal situation and record them as the same abnormal area. Then obtain the abnormal situation corresponding to the same abnormal area and perform deployment analysis from the perspectives of personnel abnormality, material abnormality, and equipment abnormality.

[0012] As a further solution of the present invention, the specific method for deploying and analyzing personnel abnormalities is as follows: Obtain and evaluate the construction personnel information in the same abnormal area to determine whether the number of personnel meets the construction conditions. If so, generate deployment information based on the personnel information and positions; if not, first deploy existing personnel, and then select qualified idle personnel from the normal construction area to supplement and generate deployment information.

[0013] As a further solution of the present invention, the specific method for analyzing material abnormalities is as follows: First, obtain the material usage and remaining situation in the same abnormal area, analyze the average material usage by cycle, calculate the available time of the remaining materials and formulate a procurement plan, and then allocate the shortfall materials from the normal construction area to generate allocation information.

[0014] As a further solution of the present invention, the specific method of deploying and analyzing equipment abnormalities is as follows: Obtain the number of equipment corresponding to the same abnormal area, and determine whether the number of equipment in the same abnormal area meets the construction needs. If it is insufficient, obtain idle equipment in the normal construction area, match its idle time with the usage time of the equipment in the abnormal area, and generate deployment information after screening suitable equipment.

[0015] The present invention provides a BIM-based intelligent construction progress real-time monitoring system. Compared with the existing technology, it has the following advantages: The present invention divides progress anomalies into three levels: minor, severe, and urgent, by adopting dynamic threshold adjustment and scientific anomaly classification standards. Compared with traditional single threshold judgment, it can more accurately identify progress anomalies and provide a clearer basis for subsequent processing. The normal progress analysis unit uses historical data and trend analysis methods to analyze construction progress risk factors, set weights and thresholds, and evaluate the frequency of occurrence of current risk factors in combination with historical data, identify potential risks in advance, and generate impact assessment information, so that managers can take preventive measures in advance to reduce the probability of risk occurrence and the degree of impact.

[0016] When faced with construction anomalies, the progress anomaly analysis unit conducts intelligent resource allocation analysis across three dimensions: personnel, materials, and equipment. Personnel allocation is based on a multi-dimensional skill evaluation system and personnel capability profiling for precise allocation. Material allocation combines mean value calculation and demand forecasting to optimize procurement and allocation strategies. Equipment allocation improves equipment utilization efficiency by matching idle time. Compared to traditional methods, this intelligent resource allocation approach allows for the rapid development of more scientific and rational solutions, effectively resolving construction anomalies and ensuring smooth progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] Example 1 See also Figure 1 This application provides a BIM-based intelligent construction progress real-time monitoring system, including: a construction progress analysis unit, a normal progress analysis unit, an abnormal progress analysis unit and a real-time monitoring information output unit, and combines Figure 1 It can be known that the functional units are electrically connected in a unidirectional manner.

[0020] The construction progress analysis unit is used to collect real-time construction data, obtain the corresponding actual construction progress, and compare it with the planned progress corresponding to the BIM model, calculate the progress difference between the actual construction progress and the planned progress, and compare the progress difference with the progress preset value. The specific value of the progress preset value is set by the operator. If the progress difference is greater than the progress preset value, it means that the actual construction progress is normal, and a construction normal signal is generated. Specifically, it means that the actual construction progress exceeds the planned progress. Conversely, if the progress difference is less than the progress preset value, it means that the actual construction progress is abnormal, and a construction abnormality signal is generated. Specifically, it means that the actual construction progress does not exceed the planned progress. Then the generated normal construction signal is transmitted to the normal progress analysis unit, and the abnormal construction signal is transmitted to the abnormal progress analysis unit.

[0021] In addition to basic construction progress data, IoT sensor data (such as crane operation data and concrete curing temperature), video surveillance data, personnel attendance data, and weather warning data are integrated to establish a unified data format and encoding rules, clean and reduce noise in the collected data, and ensure data quality. The construction progress is broken down by sub-projects and process nodes. Not only is the overall progress compared, but the progress of key nodes is also analyzed separately, and the corresponding progress differences are calculated. At the same time, progress anomalies are graded according to the progress differences, resulting in: Slight anomaly: The progress difference is between 50% and 100% of the threshold; Serious exception: the progress difference exceeds the threshold; Emergency exception: The progress variance exceeds the threshold of 150%, or work on the critical path is delayed; For example, during the main structure construction phase of an office building project, the planned progress was obtained through the BIM model: the main structure construction of floors 3-5 should be completed in the 8th week; IoT sensors collected actual data: only three floors of construction had been completed, and the average daily operating time of the concrete pouring equipment was two hours less than planned; Personnel attendance data showed that the actual number of people present in the carpentry team was 80% of the planned number; The planned completion rate was 66.7% (3 / 4.5 floors), the actual completion rate was 42.9% (3 / 7 floors), and the difference was -23.8%. Based on the calculated difference, a corresponding construction abnormality signal was generated.

[0022] The progress normalization unit is used to analyze the acquired construction normalization signals and obtain historical construction data, including construction progress data, resource input, weather conditions, equipment failure records, quality issues, and processing time from similar projects in the past. This unit also performs data preprocessing, including standardizing data formats and coding rules. For example, for construction progress data, specific dates or construction phases are clearly defined as time nodes. Resource input data is classified and statistically analyzed according to different resource categories. Trend analysis is used to analyze and obtain risk factors for the construction progress, such as severe weather, material supply delays, and equipment failures. A corresponding weight and threshold are set for each risk factor. The unit then analyzes the frequency of each risk factor at the current construction site based on historical data and compares the obtained frequency with the set threshold. For example, risk factor 1 is heavy rain (weight 0.25, threshold: continuous rainfall for more than 3 days), risk factor 2 is cradle equipment failure (weight 0.20, threshold: daily failure time for more than 4 hours), and risk factor 3 is concrete supply delay (weight 0.15, threshold: delay for more than 2 hours). If the frequency is greater than the threshold, it means that the corresponding risk factor will appear and it will be marked as a warning factor. Conversely, if the frequency is less than the threshold, it means that the corresponding risk factor will not appear. Then all warning factors are obtained, and the severity of the warning factors is evaluated. Impact assessment information is generated and transmitted to the real-time monitoring information output unit. The specific method of evaluating the severity is as follows: Using a combination of qualitative and quantitative methods, we categorize risk severity into different levels, such as high, medium, and low. This categorization is based on the project duration. For example, a delay of more than 5 days is considered high risk, 2-5 days is considered medium risk, and less than 2 days is considered low risk. We analyze the impact of early warning factors on project duration based on historical data, and generate corresponding assessment levels and impact assessment information based on the impact. For example, a delay of more than 10 days is considered high risk, 5-10 days is considered medium risk, and less than 5 days is considered low risk. For cost increases, exceeding the budget by 10% is considered high risk, 5%-10% is considered medium risk, and less than 5% is considered low risk. Detailed grading standards are developed for each assessment indicator based on the specific project circumstances and management requirements.

[0023] A real-time monitoring information output unit is used to display the acquired impact assessment information to corresponding management personnel.

[0024] Example 2 As the second embodiment of the present invention, it is implemented on the basis of the first embodiment, and differs from the first embodiment in the following aspects: The progress anomaly analysis unit is used to analyze the acquired construction anomaly signals, determine the specific anomaly by analyzing the actual construction progress, and perform intelligent deployment analysis based on the specific anomaly; Obtain different construction areas and label them as i, where i = 1, 2, ..., j, where j represents the number of construction areas. Determine the abnormal situation corresponding to construction area i based on the construction abnormality signal, and select construction areas with the same abnormal situation, which are recorded as the same abnormality areas. Then obtain the abnormal situation corresponding to the same abnormality areas and perform deployment analysis based on personnel abnormality, material abnormality, and equipment abnormality respectively. The specific method of deploying and analyzing personnel anomalies is to obtain the information of construction personnel in the same abnormal area and evaluate and process it. The evaluation here specifically means classifying the construction personnel according to their skills and specialties, and judging whether the number of construction personnel meets the current construction conditions. If so, the corresponding positions are deployed according to the information of different construction personnel at the same time, and deployment information is generated. Otherwise, the existing construction personnel are deployed, and the remaining required personnel are obtained. The construction personnel information corresponding to the normal construction area is obtained, and the idle personnel who meet the conditions are selected for deployment to generate deployment information. Establish a multi-dimensional skills evaluation system, including job skills, tool usage, safety level, etc., and update personnel ability scores in real time based on actual work performance; Example of personnel capability profile: Worker A: Concrete worker (skill score 92), pump truck operator (score 78), safety level S; Worker B: Rebar worker (score 85), quality inspector qualification (score 80), with rapid training record; Worker C: a versatile worker (concrete / rebar score of 75), who has worked on similar projects and rushed them; For example, during the main construction phase of a residential project, Area 3 (East Unit) and Area 4 (West Unit) simultaneously reported a shortage of concrete workers; Area 3 was short of 4 workers, and Area 4 was short of 3 workers; Abnormal type: Insufficient number of workers Area 3 currently has 6 concrete workers (average skill score 85) and needs 10 more; Region 4 currently has 5 concrete workers (average skill score 82) and needs 8 more; Available resources: There are 2 concrete workers (skill score 90 / 92) in area 1 (foundation area). There are 3 concrete workers (skill points 88 / 86 / 87) in area 2 (the main center area); There is 1 All-rounder (Concrete skill score 78) in Area 5 (Decoration Area) who is immediately available.

[0025] Area 2 → Area 3: 2 players (skill points 88 / 86); Area 1 → Area 4: 2 players (skill points 90 / 92); Area 5 → Area 4: 1 person (skill score 78).

[0026] The specific method of allocating and analyzing material anomalies is to obtain the material usage corresponding to the same abnormal area and the remaining material situation, and to conduct material procurement analysis based on the stage usage. Specifically, with time t as the cycle, the usage of different materials in different time periods is analyzed, and the corresponding usage average is calculated. At the same time, the usage time corresponding to the remaining material situation is calculated based on the average, and a plan is set in advance based on the usage time. Then, the same material corresponding to the normal construction area is obtained, and the shortage quantity corresponding to the same abnormal area is allocated to generate allocation information. During the curtain wall construction phase of a commercial complex project, both Area A and Area B experienced an abnormal shortage of aluminum profiles. The RFID system revealed that Area A had only enough aluminum profiles for two days of use, while Area B had less than one day's supply. Based on the normal schedule, the average daily demand for these two areas is 5 tons and 3 tons, respectively. The system retrieved historical data and found that recent actual aluminum profile usage was 15% higher than the planned average, primarily due to increased installation losses. Using the LSTM model, it was predicted that over the next seven days, total demand for aluminum profiles in Area A would be 40 tons, and in Area B would be 25 tons, far exceeding the existing inventory. After checking the inventory of other construction areas, it was found that there were 10 tons of aluminum profiles of the same specifications idle in Area C, and they were allocated to Area B first. The system automatically matched high-quality suppliers, selected the manufacturer closest to the site and able to supply within 48 hours, and urgently purchased 30 tons of aluminum profiles. At the same time, an expedited transportation plan was initiated.

[0027] The specific method of deploying and analyzing equipment anomalies is to obtain the number of equipment corresponding to the same abnormal area and determine whether it meets the current construction needs. If so, no processing will be done. Otherwise, if not, the equipment in the normal construction area will be deployed and processed. All idle equipment corresponding to the normal construction area will be obtained, and the idle time corresponding to the idle equipment will be determined in combination with the construction needs corresponding to the normal construction area. The obtained idle time will be matched with the usage time corresponding to the same abnormal area, and the idle equipment that matches the two will be screened to generate deployment information. During the main pier construction phase of a bridge project, both Area 3 (Main Pier A) and Area 4 (Main Pier B) reported insufficient concrete mixing equipment. Area 3 had two mixers (one faulty and one with a 30% drop in efficiency), and Area 4 had one mixer (expected to fail in two hours). The anomaly type was: insufficient equipment and degraded performance. Faulty mixer in area 3: Estimated repair time: 4 hours. Zone 3 low-efficiency blender: Health index 65 points, recommended for use only in secondary areas Mixer in zone 4: bearing temperature 92°C (threshold 85°C), bearing seizure expected in 2 hours; Area 1 (Prefabrication): 2 idle mixers (Health Rating 90 / 88), estimated release time 30 minutes Area 2 (approach bridge): 1 idle mixer (health index 75), immediately available but requires 30 minutes to transport; Generate the corresponding deployment plan based on the above analysis: Zone 1 → Zone 3: 1 blender (Health Index 90) Zone 2 → Zone 4: 1 blender (Health Index 75).

[0028] The generated deployment information is transmitted to the real-time monitoring information output unit.

[0029] A real-time monitoring information output unit is used to display the acquired deployment information to the corresponding management personnel.

[0030] Example 3 As the third embodiment of the present invention, the focus is on combining the implementation processes of the first and second embodiments.

[0031] Some of the data in the above formulas are calculated based on their numerical values ​​and are not substituted into parameter units for calculation. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0032] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. BIM-based intelligent construction progress real-time monitoring system, characterized by: include: The construction progress analysis unit is used to calculate the difference between the actual construction progress and the planned progress, compare it with the preset progress value, generate a normal or abnormal construction signal, and transmit both signals simultaneously; The progress normality analysis unit is used to analyze the acquired construction normality signal, analyze and obtain the construction progress risk factors in the historical data through trend analysis method, calculate the corresponding occurrence frequency, and screen the risk factors greater than the threshold as early warning factors. Combined with the corresponding construction period progress impact, it generates impact assessment information and transmits it to the real-time monitoring information output unit; The progress anomaly analysis unit is used to analyze the acquired construction anomaly signals, determine the same abnormal areas with the same abnormal conditions, and perform separate deployment analysis based on personnel anomalies, material anomalies, and equipment anomalies, generate deployment information, and transmit it to the real-time monitoring information output unit.

2. The BIM-based intelligent construction progress real-time monitoring system according to claim 1 is characterized in that: It also includes a real-time monitoring information output unit for displaying the acquired impact assessment information and deployment information to corresponding management personnel.

3. The BIM-based intelligent construction progress real-time monitoring system according to claim 1 is characterized in that: The specific method for the construction progress analysis unit to generate a construction normal or abnormal signal is: Collect real-time construction data and actual construction progress, calculate the progress difference with the planned progress corresponding to the BIM model, and compare it with the preset progress value; If the progress difference is greater than the preset progress value, a normal construction signal is generated. Conversely, if the progress difference is less than the preset progress value, an abnormal construction signal is generated.

4. The BIM-based intelligent construction progress real-time monitoring system according to claim 1 is characterized in that: The specific method for the progress normality analysis unit to analyze the construction normality signal is as follows: Obtain historical construction data and perform data preprocessing. At the same time, trend analysis is used to analyze and obtain risk factors for the construction progress. Appropriate weights and thresholds are set for each risk factor. The frequency of occurrence of each risk factor at the current construction site is analyzed based on historical data, and the obtained frequency is compared with the set threshold. If the frequency is greater than the threshold, it means that the corresponding risk factor will appear and it will be marked as a warning factor. Conversely, if the frequency is less than the threshold, it will not be processed.

5. The BIM-based intelligent construction progress real-time monitoring system according to claim 1 is characterized in that: The specific method for the progress normality analysis unit to generate the impact assessment information in combination with the corresponding construction period progress impact is: Obtain all warning factors and classify their risk severity based on the progress duration value. Progress duration values ​​between [0, 2] are classified as low risk, progress duration values ​​between (2, 5] are classified as medium risk, and progress duration values ​​greater than 5 are classified as high risk. The impact of early warning factors on the progress and duration is obtained based on historical data, and the corresponding evaluation level is generated in combination with the progress and duration numerical range to generate the corresponding impact assessment information.

6. The BIM-based intelligent construction progress real-time monitoring system according to claim 1 is characterized in that: The specific method for the abnormal progress analysis unit to analyze the abnormal construction signal is as follows: Obtain different construction areas and label them as i, where i=1, 2, …, j, where j represents the number of construction areas. Determine the abnormal situation corresponding to construction area i based on the construction abnormality signal, and screen the construction areas with the same abnormal situation and record them as the same abnormal area. Then obtain the abnormal situation corresponding to the same abnormal area and perform deployment analysis from the perspectives of personnel abnormality, material abnormality, and equipment abnormality.

7. The BIM-based intelligent construction progress real-time monitoring system according to claim 6 is characterized in that: The specific methods for deploying and analyzing personnel anomalies are as follows: Obtain and evaluate the construction personnel information in the same abnormal area to determine whether the number of personnel meets the construction conditions. If so, generate deployment information based on the personnel information and positions; if not, first deploy existing personnel, and then select qualified idle personnel from the normal construction area to supplement and generate deployment information.

8. The BIM-based intelligent construction progress real-time monitoring system according to claim 6 is characterized in that: The specific methods for analyzing material anomalies are as follows: First, obtain the material usage and remaining situation in the same abnormal area, analyze the average material usage by cycle, calculate the available time of the remaining materials and formulate a procurement plan, and then allocate the shortfall materials from the normal construction area to generate allocation information.

9. The BIM-based intelligent construction progress real-time monitoring system according to claim 6 is characterized in that: The specific methods for deploying and analyzing equipment anomalies are as follows: Obtain the number of equipment corresponding to the same abnormal area, and determine whether the number of equipment in the same abnormal area meets the construction needs. If it is insufficient, obtain idle equipment in the normal construction area, match its idle time with the usage time of the equipment in the abnormal area, and generate deployment information after screening suitable equipment.

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