Construction engineering site construction monitoring management method and system

Abstract

The invention relates to the technical field of construction safety management, and discloses a construction engineering site construction monitoring management method and system, and the method comprises the steps: obtaining an original data set, carrying out the data preprocessing according to the original data set, obtaining a preprocessed feature data set, carrying out the sequential optimization of the preprocessed feature data set, and obtaining a feature data set; the method comprises the steps of optimizing a data uploading sequence to obtain an optimized data uploading sequence, performing time synchronization on the optimized data uploading sequence to obtain a unified time reference data set, performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set, and performing data clustering analysis on the spatially calibrated data set to obtain a data clustering result. And obtaining a region-level data deviation analysis result, and performing abnormal region identification according to the region-level data deviation analysis result to obtain an abnormal region report. According to the method, the abnormal region can be dynamically identified.

Description

Technical Field

[0001] The present invention relates to the technical field of construction safety management, and in particular to a construction site monitoring management method and system for a building project. Background Art

[0002] Construction project monitoring is an important technical means to ensure construction safety and improve project quality. With the expansion of the scale of modern buildings and the increase in construction complexity, traditional monitoring methods are difficult to meet the needs of real-time monitoring and intelligent management. In order to improve the monitoring accuracy and safety of construction sites, the industry has gradually introduced advanced means such as the Internet of Things, sensing technology, edge computing and intelligent analysis to improve the breadth and depth of data collection. Through multi-sensor fusion technology, core data such as support structure displacement, concrete strength, and environmental factors can be monitored in real time, and combined with big data analysis technology, construction safety can be predicted and risk assessed. The development of these technologies has greatly improved the level of intelligence in construction project monitoring, enabling construction management to more efficiently carry out safety protection and risk warning.

[0003] Current construction monitoring technology mainly relies on fixed sensor networks, which collect key parameters such as support structure displacement, concrete strength, and environmental factors (such as temperature, humidity, and wind speed) in real time through sensor equipment (such as displacement sensors, stress sensors, and environmental monitoring equipment) installed at the construction site. These systems use wireless or wired data transmission to upload monitoring data to the central processing unit for storage and analysis, and judge the safety of the construction area based on a preset threshold model. Since these thresholds are set based on experience, the system can only trigger an alarm when the collected data exceeds the preset range, and cannot adapt to the complex and changing environment of the construction site. For example, the displacement of the support structure in some construction areas fluctuates violently in a short period of time, and the traditional system cannot respond in time before the threshold is exceeded, resulting in a lag in risk identification. In addition, some advanced systems have introduced edge computing and data fusion technologies to improve data processing efficiency and reduce data transmission delays, but these systems still rely mainly on static threshold analysis and cannot dynamically adjust to the evolution trend of abnormal areas. Therefore, the existing construction monitoring technology is essentially still a static analysis mode, lacking the ability to adapt to abnormal changes in complex construction environments.

[0004] Existing construction monitoring technologies lack time-space calibration and intelligent analysis, so it is difficult to accurately identify the dynamic evolution of abnormal areas, which can easily lead to false alarms or missed alarms, thus affecting the real-time and effectiveness of construction safety monitoring. Summary of the invention

[0005] The present invention provides a construction site monitoring management method and system for a building project, so as to realize dynamic identification of abnormal areas.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a construction site monitoring and management method for a construction project, comprising: Get the original data set; Performing data preprocessing on the original data set to obtain a preprocessed feature data set; The preprocessed feature data set is sequentially optimized to obtain an optimized data upload sequence; Performing time synchronization on the optimized data upload sequence to obtain a unified time reference data set; Performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set; Performing data cluster analysis on the spatially calibrated data set to obtain regional-level data deviation analysis results; According to the regional-level data deviation analysis result, abnormal regions are identified to obtain an abnormal region report.

[0007] Preferably, the original data set includes: horizontal displacement, vertical settlement, inclination angle, compressive strength, splitting tensile strength, concrete density, temperature, humidity and wind speed.

[0008] Preferably, data preprocessing is performed according to the original data set to obtain a preprocessed feature data set, including: Performing data denoising on the original data set based on a filtering algorithm to obtain a denoised data set; Performing feature extraction according to the denoised data set to obtain a feature data set; The feature data set is compressed based on data differential coding to obtain a preprocessed feature data set.

[0009] Preferably, the preprocessed feature data set is sequentially optimized to obtain an optimized upload batch set, including: Sorting the preprocessed feature data sets into priority order to obtain a priority order list; Performing dimensionality reduction on the priority sorted list to obtain a feature data set after dimensionality reduction; Based on the dynamic scheduling strategy, the transmission order of the dimension-reduced feature data set is optimized to obtain an optimized upload batch set.

[0010] Preferably, time synchronization is performed on the optimized data upload sequence to obtain a unified time reference data set, including: Based on a distributed clock synchronization algorithm, clock deviation correction is performed on the optimized data upload sequence to obtain a clock-corrected data set; Based on a timing reconstruction algorithm, aligning the data timestamps of the clock-corrected data set to obtain a time-aligned data set; The time-aligned data set is subjected to time consistency verification to obtain a unified time reference data set.

[0011] Preferably, performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set includes: Performing device positioning error compensation on the unified time reference data set to obtain a compensated positioning data set; Performing data space coordinate alignment on the compensated positioning data set to obtain an aligned data set; The aligned data set is subjected to data consistency authentication to obtain a spatially calibrated data set.

[0012] Preferably, data analysis and calculation are performed on the spatially calibrated data set to obtain regional-level data analysis results, including: The displacement value of the regional support structure is calculated by the following formula: in, For the The displacement value of the supporting structure in each area is For the The horizontal displacement of the region, For the Vertical settlement of the area, For the The tilt angle of the area; The regional concrete strength value is calculated by the following formula: in, For the The concrete strength value of each area is For the The compressive strength of the area, For the The splitting tensile strength of the region, For the Concrete density of each area; The regional environmental detection value is calculated by the following formula: in, For the The environmental detection value of each area, For the The ambient temperature of the area, No. The humidity of the area, For the Wind speed in the area; The regional level data deviation analysis results include: support structure displacement value, concrete strength value and environmental detection value.

[0013] Preferably, according to the regional-level data deviation analysis result, abnormal region identification is performed to obtain an abnormal region report, including: If the displacement value of the regional support structure is greater than the preset support structure displacement threshold, it is determined that the displacement index of the support structure is abnormal; If the regional concrete strength value is less than the preset concrete strength threshold, it is determined that the concrete strength index is abnormal; If the regional environmental detection value is greater than the preset environmental impact threshold, it is determined that the environmental detection index is abnormal; If an area has three abnormal indicators, it will be identified as a high-risk area; if two indicators are abnormal, it will be identified as a medium-risk area; if one indicator is abnormal, it will be identified as a low-risk area; if no indicator is abnormal, it will be identified as a no-risk area; The indicators include: support structure displacement indicators, concrete strength indicators and environmental testing indicators; The abnormal area report includes: no-risk area, low-risk area, medium-risk area and high-risk area.

[0014] In a second aspect, the present invention provides a construction site monitoring and management system for a construction project, comprising: A data acquisition module, used to acquire the original data set; A preprocessing module, used to perform data preprocessing according to the original data set to obtain a preprocessed feature data set; A sequence optimization module is used to perform sequence optimization on the preprocessed feature data set to obtain an optimized data upload sequence; A time synchronization module, used to perform time synchronization on the optimized data upload sequence to obtain a unified time reference data set; A spatial calibration module, used for performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set; A cluster analysis module, used to perform data cluster analysis on the spatially calibrated data set to obtain regional-level data deviation analysis results; The abnormal area identification module is used to identify abnormal areas according to the regional data deviation analysis results and obtain abnormal area reports.

[0015] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned methods for monitoring and managing construction site construction projects.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned construction site monitoring and management methods.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention improves data transmission efficiency and real-time performance by preprocessing data and optimizing upload strategies.

[0018] The existing construction monitoring system has problems such as high transmission redundancy, large bandwidth occupancy, and long upload delay due to the huge amount of data, which affects the real-time performance and analysis efficiency of the monitoring data. The present invention uses filtering denoising, feature extraction, and data compression technology to pre-process the original monitoring data, reduce redundant data, and improve data quality. At the same time, based on data priority sorting, dimensionality reduction optimization, and dynamic scheduling strategies, the data upload order is optimized so that high-priority data can be transmitted in a timely manner, avoiding low-value data from occupying the transmission bandwidth, thereby improving the efficiency of data upload, ensuring low latency and high response of the construction monitoring system, and meeting the real-time monitoring needs of the construction site.

[0019] (2) The present invention is based on time synchronization and space calibration to improve data consistency and reliability.

[0020] Since the monitoring equipment at the construction site is widely distributed, there are time deviations and spatial errors in the data of different devices, resulting in inaccurate data analysis. The present invention adopts a distributed clock synchronization algorithm to synchronize the data of each monitoring node, correct the system clock deviation, and align the data timestamps based on the timing reconstruction algorithm, so as to obtain a unified time reference data set, ensuring that all monitoring data are analyzed under the same time reference. In addition, the present invention performs spatial calibration on the monitoring data through equipment positioning error compensation, spatial coordinate alignment and data consistency authentication, so that the data can be processed under a unified coordinate reference, improve the spatial accuracy of the monitoring data, and enhance the reliability of data analysis.

[0021] (3) The present invention uses intelligent data clustering analysis to improve the ability to identify abnormal areas.

[0022] The present invention proposes a data clustering analysis method to analyze multidimensional data such as support structure displacement, concrete strength and environmental factors, calculate the degree of data deviation in each area, and accurately identify abnormal conditions in the construction area based on dynamically adjusted abnormal judgment criteria. At the same time, the present invention adopts a four-level risk classification mechanism of no risk, low risk, medium risk and high risk, making the identification of abnormal areas more detailed and accurate, improving the intelligent level of construction monitoring, and enhancing the construction safety early warning capability.

[0023] In summary, the present invention improves the data accuracy, spatiotemporal consistency and risk identification ability of construction monitoring through technical means such as data preprocessing, time synchronization, spatial calibration, intelligent clustering analysis and dynamic anomaly recognition, and solves the problems of low data transmission efficiency, large time and space errors, and insufficient anomaly recognition ability in the prior art, making construction safety management more intelligent, real-time and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of a construction site monitoring and management method for a construction project provided by a first embodiment of the present invention; Figure 2 It is a schematic diagram of a construction site monitoring and management system for a construction project provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0026] Reference Figure 1 The first embodiment of the present invention provides a construction site monitoring and management method for a construction project, comprising the following steps: S11, obtain the original data set; S12, performing data preprocessing according to the original data set to obtain a preprocessed feature data set; S13, performing sequential optimization on the preprocessed feature data set to obtain an optimized data upload sequence; S14, performing time synchronization on the optimized data upload sequence to obtain a unified time reference data set; S15, performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set; S16, performing data cluster analysis on the spatially calibrated data set to obtain a regional level data deviation analysis result; S17, identifying abnormal regions according to the regional-level data deviation analysis results, and obtaining an abnormal region report.

[0027] In step S11, an original data set is obtained.

[0028] It should be noted that the original data set includes: horizontal displacement, vertical settlement, inclination angle, compressive strength, splitting tensile strength, concrete density, temperature, humidity and wind speed.

[0029] It is worth noting that in the construction site monitoring and management method of the construction project of the present invention, the acquisition of the original data set is the basis of the entire monitoring system, ensuring the accuracy of subsequent data analysis, abnormality identification and construction safety warning. The present invention adopts a dynamic real-time data acquisition strategy, combined with fixed monitoring equipment and mobile monitoring nodes, to achieve high-precision, multi-dimensional acquisition of key parameters of the construction site. The key parameters of data acquisition include horizontal displacement, vertical settlement, inclination angle, compressive strength, splitting tensile strength, concrete density, temperature, humidity and wind speed, each of which is based on sensor measurement, and combined with a mechanism for dynamically adjusting the acquisition frequency to adapt to real-time changes in the construction environment and improve the accuracy and reliability of data acquisition.

[0030] High-precision displacement sensors are used to obtain horizontal displacement. These sensors are installed at key support structures, reinforced concrete frames, formwork systems, and infrastructure at the construction site. The system measures the horizontal displacement of the support structure based on technologies such as photoelectric coding, laser ranging, and GPS. When material deformation, uneven foundation settlement, or structural force changes occur during construction, the sensor will record the data in real time and upload it to the monitoring system. In addition, in order to improve data accuracy, the system will dynamically adjust the acquisition frequency. When the support structure is located in a high-risk area or a significant displacement change occurs, the data acquisition frequency will automatically increase to monitor the displacement trend in more detail.

[0031] The measurement of vertical settlement depends on settlement monitors and high-precision electronic levels, which are deployed in the foundation, load-bearing walls and key structural parts of buildings. Electronic leveling technology and millimeter-level laser rangefinders are used to ensure the high accuracy of the data. During the construction process, the compression of the foundation soil layer, the settlement of backfill soil and the long-term load will lead to the occurrence of vertical settlement. The system of the present invention will fuse the data of multiple measuring points through intelligent acquisition nodes, and judge the settlement rate and abnormal conditions through time series analysis. In the early stage of construction, the vertical settlement changes rapidly, so the system will set a higher acquisition frequency, and when the settlement tends to stabilize, the acquisition frequency will be reduced, thereby improving the resource utilization of the system.

[0032] The acquisition of the tilt angle depends on the tilt sensor, which is installed in high-rise buildings, supporting structures, scaffolding and other parts to monitor the tilt state of the structure. The tilt sensor is based on MEMS accelerometer and gyroscope technology, which can detect subtle angle changes to ensure safety during construction. When the tilt angle change exceeds the warning threshold, the system will trigger the high-frequency acquisition mode to improve the time resolution of data acquisition in order to analyze the tilt change trend.

[0033] Compressive strength is an important indicator of concrete structure safety, which is measured by embedded stress sensors and non-destructive detection technology. During the concrete pouring process at the construction site, stress sensors are placed at different locations to monitor the compression inside the concrete in real time. In addition, the system also supports ultrasonic rebound detection, which measures the surface hardness and internal density of concrete through ultrasonic probes to estimate the compressive strength. As the strength of concrete increases over time, the system dynamically adjusts the acquisition frequency according to the age curve, increasing the sampling rate in the initial setting stage of concrete and reducing the sampling frequency after the strength stabilizes to improve data collection efficiency.

[0034] The splitting tensile strength is measured using wireless strain sensors and acoustic emission monitoring technology. These sensors are installed on concrete test blocks or load-bearing structures to monitor the tensile stress distribution inside the concrete. Due to vibration, load changes, and environmental temperature and humidity during the construction process, the splitting tensile strength will fluctuate. Therefore, the system will adaptively adjust the data collection frequency according to the load changes to ensure high-frequency sampling at key stages (such as concrete force testing or key construction links) to accurately determine the mechanical properties of the material.

[0035] The density of concrete directly affects the durability and impermeability of the structure, and is tested using X-ray scanning, ultrasonic transmission method and resistivity testing. The system will use embedded detection probes to measure the density inside the concrete during the construction process, and combine ultrasonic technology to analyze the defects inside the concrete. Since the change in concrete density is closely related to the pouring process and environmental conditions, the system will perform high-frequency measurements in the early construction and maintenance period, and reduce the frequency of data collection after stabilization to ensure the continuity and scientificity of monitoring.

[0036] The measurement of temperature and humidity relies on digital temperature and humidity sensors, which are installed in different areas of the construction site, including the concrete pouring point, the inside of the steel bars, the support structure, and the environmental control area. Changes in temperature and humidity have a significant impact on the development of concrete strength, material expansion and contraction, and construction progress. The system of the present invention will dynamically adjust the sampling interval of the monitoring point in combination with meteorological data, and increase the sampling frequency under weather conditions with large fluctuations in temperature and humidity to obtain more accurate environmental data.

[0037] The measurement of wind speed relies on ultrasonic wind speed sensors and mechanical anemometers, which are installed in high-altitude working areas, tower cranes, scaffolding and other locations at the construction site to monitor wind speed changes in real time. Changes in wind speed at the construction site directly affect the safety of high-altitude operations, so the system will increase the frequency of data collection when the wind speed is high based on the trend of wind speed changes, so as to provide early warnings to construction management personnel in a timely manner and reduce safety hazards.

[0038] During the entire data acquisition process, the system of the present invention adopts an intelligent node to dynamically adjust the acquisition frequency strategy, and automatically adjusts the acquisition frequency of different sensors according to the construction progress, environmental changes and risk levels. When the system detects an abnormal trend in a certain area (such as a sudden increase in displacement, a sudden change in temperature and humidity, and a sharp increase in wind speed), it will trigger a high-frequency sampling mode to improve the temporal and spatial resolution of data acquisition to ensure the accuracy of construction monitoring. At the same time, when the environment is stable and the structure changes slowly, the system will reduce the sampling frequency to reduce data redundancy, improve transmission efficiency, and ensure the efficient operation of the data acquisition system.

[0039] In summary, the original data set acquisition method of the present invention is based on multi-sensor fusion technology, combined with a dynamic adjustment of the sampling frequency strategy, to ensure that the core monitoring data of the construction site can be accurately acquired in different construction stages and different risk areas, providing solid data support for subsequent data analysis, abnormal area identification and construction safety early warning.

[0040] In step S12, data preprocessing is performed according to the original data set to obtain a preprocessed feature data set, including: Performing data denoising on the original data set based on a filtering algorithm to obtain a denoised data set; Performing feature extraction according to the denoised data set to obtain a feature data set; The feature data set is compressed based on data differential coding to obtain a preprocessed feature data set.

[0041] It is worth noting that in the data denoising stage, the original data collected by various sensors at the construction site are first obtained, including parameters such as support structure displacement, concrete strength, and ambient temperature and humidity. Due to the presence of a large amount of environmental noise at the construction site, such as equipment vibration, wind interference, and temperature fluctuations, the data collected by the sensors often have random errors or systematic errors. In order to improve the accuracy of the data, this step uses a filtering algorithm to denoise the original data. Specifically, for displacement data, since its change trend is relatively gentle, a sliding average filtering method can be used to smooth the data to reduce the impact of short-term fluctuations. For example, if the displacement sensor of a certain support structure collects data once a minute, and a single data point is abnormal due to equipment jitter, the mean of the data for the last 5 minutes is calculated by sliding a window, which can effectively reduce the impact of sudden noise. For ambient temperature and humidity data, due to the influence of instantaneous airflow or local temperature changes, this step uses the Kalman filtering method to predict the current value based on historical data and dynamically adjust the filtering weight to improve data stability. In addition, for high-frequency vibration signals (such as wind speed data), this step uses a wavelet transform denoising method to decompose the signal into multiple frequency components and remove high-frequency noise to ensure the authenticity and availability of the data. After denoising, the system obtains a denoised data set, providing more stable data input for subsequent analysis.

[0042] In the feature extraction phase, this step extracts feature parameters that have a key impact on construction safety based on the denoised data set. There are many types of construction site monitoring data. Directly using raw data for analysis leads to increased data redundancy and computational complexity. Therefore, it is necessary to extract key features to improve the representativeness and computational efficiency of the data. For example, in the support structure monitoring, in addition to directly recording the horizontal displacement and vertical settlement values, this step further calculates the displacement change rate to identify whether there is an abnormal growth trend in the displacement. Specifically, if the displacement of a certain area increases from 2 mm to 6 mm within 10 minutes, and the increase in the past hour is only 1 mm, the system can identify that the area is at risk of structural instability. In concrete strength monitoring, this step not only extracts the compressive strength value, but also combines the splitting tensile strength and density data to calculate the comprehensive strength characteristics to determine whether the overall performance of the concrete meets the design requirements. In addition, in environmental monitoring, the temperature and humidity data themselves cannot directly reflect the impact on the construction quality, so the system calculates the temperature and humidity change gradient to identify the sudden changes that affect the concrete maintenance. For example, if the temperature drops from 25 degrees Celsius to 10 degrees Celsius within 30 minutes, it will affect the concrete curing process. Through feature extraction, the system can remove redundant information and retain only the key feature data sets that are most representative for construction safety analysis.

[0043] In the data compression link, in order to reduce the overhead of data storage and transmission, this step compresses the feature data set based on data differential coding. The monitoring data of the construction site has strong temporal continuity, and the data changes at adjacent time points are small. Therefore, differential coding technology can be used to record only the data changes instead of storing complete values. For example, in the displacement monitoring of the support structure, if the displacement data of a support point is 2.1 mm, 2.2 mm, and 2.3 mm, the system can only store the increment (+0.1 mm), thereby reducing the data storage space. In addition, for environmental data such as temperature and humidity, since their changes are relatively stable, a time window sliding storage strategy can be adopted to replace a single data point with the average value within a certain time range, further compressing the data volume. For example, if the temperature change in a certain area does not exceed 1 degree Celsius within 1 hour, only the average temperature of the time period can be stored without storing all data point by point. After data compression processing, the system obtains a preprocessed feature data set, which reduces the redundancy of data storage and transmission while ensuring the integrity of key information, and improves the efficiency and response speed of subsequent data analysis.

[0044] In summary, this step ensures the accuracy, representativeness and efficiency of the construction site monitoring data through three key links: data denoising, feature extraction and data compression. Denoising reduces the impact of environmental interference on the data and improves data quality; feature extraction selects the most valuable information for construction safety analysis and reduces the computational complexity; data compression reduces the storage and transmission burden and improves the real-time performance of data processing. This data preprocessing process lays a solid data foundation for the subsequent dynamic abnormal area identification.

[0045] In step S13, the preprocessed feature data set is sequentially optimized to obtain an optimized upload batch set, including: Sorting the preprocessed feature data sets into priority order to obtain a priority order list; Performing dimensionality reduction on the priority sorted list to obtain a feature data set after dimensionality reduction; Based on the dynamic scheduling strategy, the transmission order of the dimension-reduced feature data set is optimized to obtain an optimized upload batch set.

[0046] It is worth noting that in the data priority sorting stage, the preprocessed feature data sets are first classified and prioritized according to the importance, real-time and construction risk level of the data. There are many types of data at the construction site, including support structure displacement, concrete strength, ambient temperature and humidity, etc. Different data have different impacts on construction safety, and the priority of data also varies significantly. Therefore, before uploading the data, it is necessary to determine which data should be transmitted first to ensure the real-time and accuracy of the monitoring system. For example, the horizontal displacement and vertical settlement of the support structure are key monitoring indicators. If the data is abnormal, the structure will become unstable and affect the construction safety. Therefore, it has the highest priority and should be uploaded first. Concrete compressive strength and splitting tensile strength are also core parameters of structural safety, and they are secondarily transmitted. In comparison, changes in environmental parameters such as temperature, humidity and wind speed have a relatively slow impact on construction quality, so the priority is relatively low and can be uploaded later. In addition, the system will also consider the rate of data change to adjust the priority sorting. For example, in a certain construction area, if the displacement of the support structure changes dramatically in a short period of time, the system will automatically increase the priority of the monitoring data in that area to ensure that the relevant data can be uploaded in time. After priority sorting, the system generates a priority list to ensure that critical data can enter the upload queue first so that construction managers can grasp important safety information at the first time.

[0047] In the data dimensionality reduction phase, in order to improve data processing efficiency and reduce the storage and transmission resources occupied by invalid data, the system performs dimensionality reduction processing on the priority sorting list. Construction monitoring data often contains a lot of redundant information. For example, some environmental parameters (such as temperature and humidity) change little over a long period of time. Directly uploading all data leads to a waste of storage and transmission resources. To solve this problem, the system uses the principal component analysis method to screen out the most representative data features and remove data with high correlation or redundant information. For example, in concrete strength monitoring, there is a strong correlation between compressive strength, splitting tensile strength and concrete density. Therefore, through feature extraction, only the parameters that best represent the overall quality of concrete can be uploaded, while reducing the transmission of other data. In addition, for monitoring data with time series characteristics, the system uses the time window sliding average method to calculate the mean of key data within a period of time, thereby reducing data redundancy caused by short-term fluctuations. For example, in a construction area, if the ambient temperature fluctuates within 0.5 degrees Celsius in the past hour, the system can only upload the average temperature of the time period instead of uploading data every minute, thereby greatly reducing the amount of data storage. After dimensionality reduction processing, the system obtains a reduced-dimensional feature data set, which not only retains key information, but also reduces the occupancy of invalid data and improves data transmission efficiency.

[0048] In the data transmission sequence optimization link, the system optimizes the transmission sequence of the feature data set after dimensionality reduction based on the dynamic scheduling strategy to ensure that high-priority data can be uploaded quickly and low-priority data can be uploaded delayed when network conditions permit. The network environment of the construction monitoring system is complex, and some construction areas have limited network bandwidth or data transmission delays. Therefore, it is necessary to arrange the data transmission sequence reasonably to ensure the real-time performance of important data. For example, during the peak construction period, the system will prioritize uploading key safety data such as support structure displacement and concrete strength, and delay the transmission of environmental data such as temperature and humidity to ensure the reasonable allocation of bandwidth resources. In addition, the system will also dynamically adjust according to the risk level of the monitoring area. If an abnormality occurs in a certain area, the system will immediately increase the data transmission priority of the area to ensure that the monitoring data of the area can be uploaded to the central server first. For example, at a deep foundation pit construction site, the system detected that the horizontal displacement of the support structure of a certain monitoring point increased by 3 mm in 10 minutes, exceeding the safety threshold. The system immediately adjusted the transmission sequence, prioritized uploading the data of the monitoring point, and shifted the data upload sequence of other low-risk areas to the back. After dynamic scheduling processing, the system obtains an optimized upload batch set, which ensures the real-time transmission of key data, optimizes the utilization of bandwidth resources, and improves the overall operation efficiency of the construction monitoring system.

[0049] In summary, this step achieves efficient management of monitoring data through three key links: data priority sorting, data dimension reduction, and transmission order optimization. Data priority sorting ensures that the most critical construction safety data can be uploaded first, improving the response speed of the system; data dimension reduction reduces data redundancy and reduces the consumption of storage and transmission resources; transmission order optimization combines construction risk level and network bandwidth to achieve dynamic adjustment of data upload and improve the stability and reliability of data transmission. This optimization process ensures that the construction monitoring system can achieve accurate monitoring of the construction site under limited network and computing resources, providing reliable data support for construction safety management.

[0050] In step S14, the optimized data upload sequence is time synchronized to obtain a unified time reference data set, including: Based on a distributed clock synchronization algorithm, clock deviation correction is performed on the optimized data upload sequence to obtain a clock-corrected data set; Based on a timing reconstruction algorithm, aligning the data timestamps of the clock-corrected data set to obtain a time-aligned data set; The time-aligned data set is subjected to time consistency verification to obtain a unified time reference data set.

[0051] It is worth noting that in the clock deviation correction link, in order to ensure that the data of multiple sensors at the construction site can be analyzed under the same time reference, the system uses a distributed clock synchronization algorithm to correct the clock deviation of the optimized data upload sequence. The monitoring system at the construction site consists of multiple distributed sensor nodes. There is a slight deviation in the internal clock of each node, which leads to inconsistent data timestamps, thus affecting the comparison and abnormal analysis of the data. Therefore, the system first obtains the standard time from the central server and broadcasts the standard time signal to each monitoring node using a time synchronization protocol (such as the network time protocol or the distributed clock synchronization algorithm). After receiving the time signal, each sensor node will compare it with its own local clock, calculate the clock drift, and automatically correct it. For example, in the monitoring of the foundation pit support structure, the clock of a displacement sensor is 1.2 seconds faster than the central server, while the clock of another temperature and humidity sensor is 0.8 seconds slower than the central server. In order to ensure the consistency of the data, the system will automatically adjust the time of each sensor to align it with the standard time of the central server. After the clock deviation correction, the system obtains a clock-corrected data set, which can ensure that the data of different monitoring devices are analyzed in the same time reference frame.

[0052] In the data timestamp alignment phase, the system uses a timing reconstruction algorithm to align the timestamps of the clock-corrected data sets because the sensor data at the construction site does not match the timestamps due to network delays and different signal transmission speeds. The data from different sensors are uploaded at different time points. For example, a sensor uploads data every 5 seconds, while another sensor uploads data every 10 seconds, which makes it impossible to directly compare the data in the time dimension. In order to deal with this problem, the system remaps the data with different timestamps to the standard time axis through interpolation calculation methods. For example, in the monitoring process of deep foundation pit support structures, the data obtained by the system from different sensors include the displacement data of the support structure at 12:00:05, the temperature data at 12:00:07, and the humidity data at 12:00:10. If analyzed directly, time misalignment will occur. The system will complete the time axis and interpolate the data at 12:00:07 and 12:00:10 according to adjacent time points, so that all data can be calculated in the same time reference frame, and finally a time-aligned data set is obtained.

[0053] In the time consistency verification phase, in order to ensure that all aligned data can maintain time consistency, the system will perform time consistency detection on the time-aligned data set to find time anomalies. For example, some sensors may have some timestamp anomalies due to signal interference or data loss, and the timestamps of other sensors may deviate greatly. The system will detect these anomalies and determine whether resynchronization is required. For example, in the monitoring of high-rise building construction, the timestamp of the uploaded data of the wind speed sensor suddenly changes at a certain moment, with a deviation of 2 seconds, while the time errors of other sensors are less than 0.1 seconds. This is caused by sensor failure or data transmission delay. The system will compare these data and use the time backtracking mechanism to re-acquire the data at that time point, or use historical data to correct the data at that time point, and finally ensure that the timestamps of all data are consistent and obtain a unified time reference data set.

[0054] In summary, this step ensures the time synchronization of construction monitoring data through three key links: clock deviation correction, data timestamp alignment, and time consistency verification. Clock deviation correction eliminates the impact of clock drift of different sensors on data timestamps and improves data consistency. Data timestamp alignment enables data with different sampling frequencies to be analyzed at the same time point through interpolation calculation and time axis mapping, avoiding the problem of time misalignment. Time consistency verification improves the reliability of the monitoring system by detecting timestamp anomalies to ensure that all data remains highly consistent in the time dimension. The final unified time reference data set enables the monitoring data of the construction site to be compared and calculated in the same time frame, providing a solid time foundation for subsequent data analysis and anomaly identification.

[0055] In step S15, the unified time reference data set is spatially calibrated to obtain a spatially calibrated data set, including: Performing device positioning error compensation on the unified time reference data set to obtain a compensated positioning data set; Performing data space coordinate alignment on the compensated positioning data set to obtain an aligned data set; The aligned data set is subjected to data consistency authentication to obtain a spatially calibrated data set.

[0056] It is worth noting that in the equipment positioning error compensation link, due to the wide distribution of monitoring equipment at the construction site, there is a deviation between the actual installation position of each sensor and the preset coordinates of the system. These deviations come from equipment installation errors, environmental interference or satellite signal errors. In order to ensure the spatial accuracy of the monitoring data, the system first compensates for the positioning errors of all sensors. The compensation method includes static calibration and dynamic adjustment. In the static calibration stage, when the system is installing the equipment, it uses a high-precision total station, laser rangefinder or high-precision GPS device to obtain the actual installation position of each sensor and compare it with the preset coordinates. For example, in tunnel construction monitoring, the preset coordinates of a support structure displacement sensor are X=100.5m, Y=200.8m, and the actual installation position is found to be X=100.2m, Y=200.6m through total station measurement. The system will automatically calculate the deviation and compensate for it. During the dynamic adjustment phase, the system combines historical data to analyze the stability of sensor data. If the coordinate data of a sensor drifts abnormally over time, for example, the position of a GPS device suddenly changes by 2 meters on a certain day, but there is no large-scale displacement at the construction site, the system will identify the data anomaly and automatically correct it through a data filtering algorithm, ultimately obtaining a compensated positioning data set.

[0057] In the data space coordinate alignment link, in order to ensure that all monitoring data can be analyzed in the same spatial reference system, the system coordinates the compensated positioning data set. The data sources of the construction monitoring system are diverse, and the data of different devices use different coordinate systems. For example, some sensors use geographic coordinates (latitude and longitude), while other devices use relative coordinates (local coordinates of the construction site). If coordinate alignment is not performed, the data of different devices will not be able to be directly calculated in space. For example, in the deep foundation pit construction monitoring, the surface settlement monitor uses UTM coordinates, while the stress sensor inside the foundation pit uses the local construction coordinate system. If the data is directly fused, it will cause calculation errors. Therefore, the system converts all data to the same coordinate system through the coordinate conversion algorithm. For example, for the data of the GPS device, the system uses the ellipsoid model to convert it into the relative coordinates of the construction site, and for the construction measurement data, the system uses the control points measured on site to map it to the geographic coordinate system to ensure that the spatial coordinates of all monitoring data can match, and finally obtain the aligned data set.

[0058] In the data consistency certification phase, the system performs a consistency check on the aligned data set to ensure the spatial consistency of the data of all sensors. Due to the complex construction environment, some sensors are subject to external interference, resulting in data anomalies. For example, some displacement sensors installed on the tunnel support structure cause slight deformation of the bracket due to temperature changes, which in turn affects the spatial accuracy of the data. In order to solve such problems, the system uses a spatial consistency verification algorithm to analyze whether the data of adjacent sensors have a reasonable spatial relationship. For example, in subway construction monitoring, assuming that the settlement values ​​of three adjacent settlement monitoring points in a certain area are 2.1mm, 2.3mm and 10mm respectively, the system will identify the data anomaly of the third monitoring point and further analyze the source of the error, including equipment failure, data collection error or construction disturbance. If the data is confirmed to be an error point, the system will automatically remove it or use an interpolation algorithm to correct it, ultimately ensuring the consistency of the data in space and obtaining a spatially calibrated data set.

[0059] In summary, this step ensures the spatial accuracy of construction monitoring data through three links: equipment positioning error compensation, data space coordinate alignment, and data consistency certification. Equipment positioning error compensation eliminates the impact of sensor installation errors on data position and improves positioning accuracy. Data space coordinate alignment converts data from different devices to a unified coordinate system to ensure data comparability. Data consistency certification detects and corrects abnormal points in spatial data, improving data reliability. The final spatially calibrated data set ensures accurate spatial matching of construction site monitoring data, providing an accurate spatial basis for subsequent abnormal area identification and risk assessment.

[0060] In step S16, data analysis and calculation are performed on the spatially calibrated data set to obtain regional-level data analysis results, including: The displacement value of the regional support structure is calculated by the following formula: in, For the The displacement value of the supporting structure in each area is For the The horizontal displacement of the region, For the Vertical settlement of the area, For the The tilt angle of the area; The regional concrete strength value is calculated by the following formula: in, For the The concrete strength value of each area is For the The compressive strength of the area, For the The splitting tensile strength of the region, For the Concrete density of each area; The regional environmental detection value is calculated by the following formula: in, For the The environmental detection value of each area, For the The ambient temperature of the area, No. The humidity of the area, For the Wind speed in the area; The regional level data deviation analysis results include: support structure displacement value, concrete strength value and environmental detection value.

[0061] It is worth noting that in the construction site monitoring and management method of the construction project of the present invention, the purpose of regional data analysis is to quantify the safety status of the construction area, and to obtain the displacement value of the support structure, the concrete strength value and the environmental detection value through mathematical calculation, so as to provide a reliable analysis basis for the subsequent identification of abnormal areas. The physical environment of the construction site is complex, and there is mutual influence between the various monitoring indicators. Therefore, it is necessary to integrate and calculate the data of different dimensions through mathematical models to extract representative key indicators. The present invention adopts multi-parameter fusion calculation, combined with the core monitoring data of each region, to form an accurate regional data deviation analysis result, to ensure the monitoring system's accurate assessment of construction risks.

[0062] The purpose of calculating the displacement value of the support structure is to evaluate the stability of the structure and to determine the overall deformation trend of the support structure by integrating the displacement information in different directions. The present invention uses three parameters: horizontal displacement, vertical settlement and inclination angle for calculation, wherein the horizontal displacement reflects the displacement amplitude of the structure in the horizontal direction, the vertical settlement indicates the degree of foundation subsidence, and the inclination angle is used to measure the degree of inclination of the structure. In the formula, the horizontal displacement and vertical settlement data are calculated using mean square average to ensure the representativeness of the overall displacement, while the influence of the inclination angle is increased exponentially, so that when the structural inclination exceeds a certain range, its influence shows a nonlinear growth in the calculation. This calculation method can more accurately reflect the stability of the support structure, avoid errors caused by single parameter judgment, and thus improve the reliability of construction monitoring.

[0063] The calculation of concrete strength value aims to comprehensively evaluate the bearing capacity of concrete and ensure that the quality of construction materials meets safety standards. The present invention is based on three core indicators: compressive strength, splitting tensile strength and concrete density for calculation, among which compressive strength reflects the bearing capacity of concrete under stress, splitting tensile strength is used to measure the destruction tendency of concrete under tensile stress, and concrete density is an important factor affecting the overall performance of concrete. In the calculation formula, the ratio of compressive strength to splitting tensile strength is used to measure the structural integrity of concrete, and density is used as an index term in the calculation to adjust the calculation result of concrete strength so that it can better adapt to different construction conditions. Through this calculation method, the system can more accurately evaluate the durability and bearing capacity of concrete and improve the accuracy of construction quality monitoring.

[0064] The calculation of environmental detection values ​​is used to evaluate the environmental impact factors of the construction area to ensure that the external conditions during the construction process will not have an adverse effect on the structural safety. The present invention comprehensively considers three key parameters: ambient temperature, humidity and wind speed, among which temperature has an important influence on the expansion and contraction characteristics of the material, humidity affects the maintenance quality and structural durability of concrete, and wind speed affects the construction stability of high-rise buildings. The calculation formula uses the product relationship of temperature and humidity to measure the comprehensive impact of the hot and humid environment on the construction area, and combines the square term of wind speed to emphasize the nonlinear impact of strong wind on construction safety. In addition, the exponential decay term is used to describe the dynamic changes of the impact of humidity on the environment, so that the contribution of humidity to the environmental detection value gradually decreases in a high humidity environment, thereby improving the rationality of the environmental impact assessment. This calculation method ensures that the construction monitoring system can dynamically adapt to different climatic conditions and improve the scientificity and applicability of environmental monitoring.

[0065] In summary, the present invention accurately evaluates the structural stability, material properties and environmental impact of the construction area through three calculation models: support structure displacement value, concrete strength value and environmental detection value. The calculation method of the support structure displacement value comprehensively considers the displacement information in different directions, making the structural stability analysis more comprehensive; the calculation method of the concrete strength value integrates multiple strength indicators to ensure the accuracy of the construction quality assessment; the calculation method of the environmental detection value adopts dynamic weight adjustment to adapt to changes in different climate environments. This analysis method can provide a quantitative basis for the safety management of the construction site and lay a solid data foundation for the subsequent identification of abnormal areas and the formulation of construction optimization strategies.

[0066] In step S17, according to the regional data deviation analysis result, abnormal area identification is performed to obtain an abnormal area report, including: If the displacement value of the regional support structure is greater than the preset support structure displacement threshold, it is determined that the displacement index of the support structure is abnormal; If the regional concrete strength value is less than the preset concrete strength threshold, it is determined that the concrete strength index is abnormal; If the regional environmental detection value is greater than the preset environmental impact threshold, it is determined that the environmental detection index is abnormal; If an area has three abnormal indicators, it will be identified as a high-risk area; if two indicators are abnormal, it will be identified as a medium-risk area; if one indicator is abnormal, it will be identified as a low-risk area; if no indicator is abnormal, it will be identified as a no-risk area; The indicators include: support structure displacement indicators, concrete strength indicators and environmental testing indicators; The abnormal area report includes: no-risk area, low-risk area, medium-risk area and high-risk area.

[0067] It is worth noting that in the construction site monitoring and management method of the present invention, the core of abnormal area identification lies in the calculated support structure displacement value, concrete strength value and environmental detection value, and compares them with the unique preset support structure displacement threshold, concrete strength threshold and environmental impact threshold to determine whether there is an abnormal situation in the construction area. Each threshold is a separate value used to comprehensively measure the safety status of the construction area, rather than setting a range for a specific measurement unit or a single parameter. Ensure that abnormal areas can be effectively identified under different working conditions.

[0068] The setting of the displacement threshold of the support structure is intended to evaluate the stability of the support structure during the construction process and prevent excessive deformation from affecting the safety of the structure. Through the analysis of multiple engineering cases and monitoring data, combined with the standards of the "Technical Specifications for Building Foundation Pit Support", the displacement threshold of the support structure is set to 0.6. This value represents the allowable range of the support structure under normal stress conditions during construction. When the calculated displacement value of the support structure exceeds the threshold, it means that the support system in this area is subjected to excessive loads and there is a risk of structural instability. For example, in the construction of soft soil foundations, the deformation of the support structure is large, but if the deformation exceeds 0.6, it means that the soil pressure is too large, the support stiffness is insufficient, or the foundation has excessive settlement, and reinforcement measures or adjustments to the construction process are required.

[0069] The setting of the concrete strength threshold is used to determine whether the concrete quality in the construction area meets the design requirements and ensure that the structure has sufficient bearing capacity and durability. In combination with the "Concrete Structure Design Code", the concrete strength threshold set in the present invention is 0.4. When the calculated concrete strength value is less than the threshold, it indicates that the concrete has insufficient strength, which is caused by unreasonable mix design, improper curing conditions or material quality problems. For example, in the core tube area of ​​a high-rise building, the bearing requirements of concrete are relatively high. If the concrete strength value in this area is lower than 0.4, the structural bearing capacity is insufficient, cracks appear or the durability is reduced. Therefore, in areas with abnormal strength, strength review measures should be taken, such as testing the concrete strength by rebound method or core drilling, and if necessary, increasing post-maintenance or performing surface repairs to improve the final performance of the concrete.

[0070] The setting of environmental impact threshold is mainly used to evaluate the impact of external environmental conditions on construction safety, ensure that the construction process can adapt to meteorological conditions, and reduce construction quality problems or safety hazards caused by adverse environmental factors. Based on the "Technical Specifications for Construction Safety" and statistical analysis of construction environments in many places, the environmental impact threshold is set to 0.75. This value is a comprehensive indicator calculated by parameters such as temperature, humidity and wind speed. When the calculated environmental detection value exceeds 0.75, it indicates that the environmental conditions in the area have an adverse effect on the construction process. For example, during the concrete pouring process, if the temperature is too high or the humidity is too low, the concrete hydration reaction will be accelerated, affecting the final strength, and if the wind speed is too high, it will affect the safety of high-altitude operations. Therefore, in the case of abnormal environment, the system will suggest that construction management personnel adjust the construction time or take corresponding protective measures, such as increasing watering maintenance in hot weather or suspending high-altitude operations in strong wind environments to ensure construction safety.

[0071] Through the support structure displacement threshold of 0.6, concrete strength threshold of 0.4 and environmental impact threshold of 0.75 set by the present invention, the system can accurately assess the safety of the construction site. When the calculated value of a certain area exceeds the corresponding threshold, the system will automatically mark the area as abnormal, and further divide the risk level to guide construction management personnel to take corresponding measures. The setting of the threshold is not only based on engineering standards, but also combines a large number of construction cases and data analysis to ensure that it is applicable to different construction environments and can effectively improve construction quality and safety.

[0072] During the construction of high-rise buildings, structural stability problems are prone to occur due to the high building structure, which is affected by factors such as wind load, deformation of supporting structures, and changes in concrete strength. Therefore, it is necessary to monitor the displacement of supporting structures, concrete strength, and environmental parameters at the construction site in real time to ensure construction safety. The method of the present invention combines the method flow shown in FIG1 with the system module shown in FIG2 to achieve the acquisition, processing, optimization, calibration, and abnormality identification of data during the construction process, thereby ensuring the efficiency and intelligence of construction safety management.

[0073] In step S11, the system first deploys multiple intelligent monitoring nodes, including structural health monitoring sensors (SHM), concrete strength testing equipment, environmental monitoring equipment (wind speed sensor, temperature and humidity sensor), etc., to collect key data of building structures in real time. These data include horizontal displacement, vertical settlement, tilt angle, compressive strength, splitting tensile strength, concrete density, temperature, humidity and wind speed. In the construction process of high-rise buildings, these data are crucial for evaluating structural stability, construction quality and environmental impact. For example, at a construction site, the system collects the horizontal displacement of the core tube of a certain floor as 8mm, the vertical settlement as 2.5mm, the tilt angle as 0.3°, the concrete compressive strength as 32 MPa, the splitting tensile strength as 3.8 MPa, the concrete density as 95%, the ambient temperature as 28℃, the humidity as 60%, and the wind speed as 15m / s, and transmits these data to the data acquisition module for storage and analysis.

[0074] In step S12, the raw data of the construction site is affected by environmental noise, measurement errors and equipment jitter, so the system preprocesses the data to ensure data quality. The support structure displacement data uses a sliding mean filtering algorithm to remove high-frequency noise and improve data stability. For example, the initial measurement value of a support node fluctuates between 7.8mm and 8.2mm, and the system calculates the displacement value after smoothing to be 8mm, eliminating short-term errors. The concrete strength data uses time series analysis to eliminate abnormal measurement values ​​and predict strength growth. For example, the 28-day strength of a certain area is measured to be 30 MPa. The system combines historical measurement data to predict its final strength to be 32 MPa and adjusts the data accuracy. Kalman filtering is used for data smoothing of environmental data to reduce sensor measurement errors. For example, the wind speed measurement data fluctuates between 14.5m / s and 15.5m / s. After filtering, the system determines that the stable wind speed is 15m / s. After preprocessing, the system obtains a stable feature data set, providing reliable input for subsequent calculations.

[0075] In step S13, in order to improve the efficiency of data upload, the system optimizes the order according to the data priority to ensure that key data can be processed and analyzed first. High-priority data includes support structure displacement data (affecting building stability), medium-priority data includes concrete strength data (affecting construction quality, but changing relatively slowly), and low-priority data includes environmental data (used for risk assessment, but not changing much in a short period of time). For example, when the system detects that the lateral displacement of the core tube of a certain floor is close to the warning value (10mm), the system will upload the data first and postpone the upload of wind speed and temperature and humidity data to ensure the real-time performance of key data.

[0076] In step S14, since the time bases of the monitoring devices are different, the system needs to synchronize the time to ensure that all data are analyzed under the same time reference frame. A distributed clock synchronization algorithm is used to obtain the clock deviation of each device. For example, the local time of a core tube displacement sensor is 1.2 seconds faster than the central server, while the wind speed sensor is 0.8 seconds slower. The system automatically corrects the time of all devices to align them to the standard time. Interpolation calculations are used to align monitoring data at different times. For example, if the support displacement data is recorded at 12:00:05, but the environmental data is recorded at 12:00:07, the system will perform time compensation to synchronize the data to 12:00:06 for analysis. After time synchronization processing, all data can be calculated according to a unified time series to improve the accuracy of the analysis.

[0077] In step S15, the construction monitoring equipment has spatial coordinate errors due to installation errors and geographical environment influences, so the system performs spatial calibration to ensure the consistency of the geographical location of the data. The installation error of the support structure displacement monitoring point is 10cm. The system uses the total station and GPS positioning to compensate for the position and adjust it to the correct coordinates. The installation point of the wind speed sensor is offset by 5cm relative to the design position. The system corrects the position according to the construction plan to ensure the accuracy of the monitoring data. After spatial calibration, all data can be compared in the same coordinate system to improve the spatial consistency of the analysis.

[0078] In step S16, the system analyzes the data of different regions based on the clustering algorithm to identify the risk trends in the construction process. The density clustering method is used to analyze the displacement data of the support structure and identify abnormal areas. For example, if the lateral displacement of the core tube of a certain floor continues to increase, while the displacement of the adjacent floors is stable, there is a structural stability problem in this area. The concrete strength data is clustered in time series to determine the strength growth law of different casting areas and identify the existing areas of insufficient strength. Finally, the system generates regional-level data analysis results to provide data support for subsequent anomaly detection.

[0079] In step S17, the system performs anomaly detection based on the deviation of regional-level data and divides the risk level. High-risk area: If a region has both abnormal displacement of the support structure (10mm) and excessive wind speed (18m / s), the system determines that the region is a high-risk area and recommends immediate reinforcement measures. Medium-risk area: If the displacement of the support structure in a region is normal, but the concrete strength is insufficient (30 MPa, lower than the standard 35 MPa), it is determined to be a medium-risk area and it is recommended to strengthen monitoring. Low-risk area: If there is only an environmental anomaly, such as the wind speed slightly exceeds the standard range (16m / s), but the structural data is normal, it is determined to be a low-risk area and only requires continuous monitoring. No-risk area: All monitoring data are normal and no additional adjustment is required. Finally, the system generates an abnormal area report and provides risk warnings and adjustment suggestions to construction management personnel to ensure construction safety.

[0080] This method realizes intelligent monitoring of the high-rise building construction process through data acquisition, preprocessing, optimization, synchronization, calibration, analysis, anomaly identification and other steps. Compared with traditional construction monitoring methods, the present invention can dynamically adjust the monitoring strategy, improve the accuracy and real-time performance of data analysis, and provide stronger protection for construction safety.

[0081] Reference Figure 2 The second embodiment of the present invention provides a construction site monitoring and management system for a construction project, including: A data acquisition module, used to acquire the original data set; A preprocessing module, used to perform data preprocessing according to the original data set to obtain a preprocessed feature data set; A sequence optimization module is used to perform sequence optimization on the preprocessed feature data set to obtain an optimized data upload sequence; A time synchronization module, used to perform time synchronization on the optimized data upload sequence to obtain a unified time reference data set; A spatial calibration module, used for performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set; A cluster analysis module, used to perform data cluster analysis on the spatially calibrated data set to obtain regional-level data deviation analysis results; The abnormal area identification module is used to identify abnormal areas according to the regional data deviation analysis results and obtain abnormal area reports.

[0082] It should be noted that the construction site monitoring and management system provided by the embodiment of the present invention is used to execute all the process steps of the construction site monitoring and management method of the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0083] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a construction site monitoring management program. When the processor executes the computer program, the steps in the above-mentioned construction site monitoring management method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the abnormal area identification module.

[0084] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0085] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0086] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0087] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0088] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0089] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0090] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A construction site monitoring and management method for a construction project, characterized in that: include: Get the original data set; Performing data preprocessing on the original data set to obtain a preprocessed feature data set; The preprocessed feature data set is sequentially optimized to obtain an optimized data upload sequence; Performing time synchronization on the optimized data upload sequence to obtain a unified time reference data set; Performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set; Performing data cluster analysis on the spatially calibrated data set to obtain regional-level data deviation analysis results; According to the regional-level data deviation analysis result, abnormal regions are identified to obtain an abnormal region report.

2. The construction site monitoring and management method of a construction project according to claim 1 is characterized in that: The raw data sets include: horizontal displacement, vertical settlement, inclination angle, compressive strength, splitting tensile strength, concrete density, temperature, humidity and wind speed.

3. The construction site monitoring and management method of a construction project according to claim 1, characterized in that: The step of performing data preprocessing according to the original data set to obtain a preprocessed feature data set includes: Performing data denoising on the original data set based on a filtering algorithm to obtain a denoised data set; Performing feature extraction according to the denoised data set to obtain a feature data set; The feature data set is compressed based on data differential coding to obtain a preprocessed feature data set.

4. The construction site monitoring and management method of a construction project according to claim 1, characterized in that: The step of sequentially optimizing the preprocessed feature data set to obtain an optimized upload batch set includes: Sorting the preprocessed feature data sets into priority order to obtain a priority order list; Performing dimensionality reduction on the priority sorted list to obtain a feature data set after dimensionality reduction; Based on the dynamic scheduling strategy, the transmission order of the dimension-reduced feature data set is optimized to obtain an optimized upload batch set.

5. The construction site monitoring and management method of a construction project according to claim 1, characterized in that: The step of performing time synchronization on the optimized data upload sequence to obtain a unified time reference data set includes: Based on a distributed clock synchronization algorithm, clock deviation correction is performed on the optimized data upload sequence to obtain a clock-corrected data set; Based on a timing reconstruction algorithm, aligning the data timestamps of the clock-corrected data set to obtain a time-aligned data set; The time-aligned data set is subjected to time consistency verification to obtain a unified time reference data set.

6. The construction site monitoring and management method of a construction project according to claim 1, characterized in that: The performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set includes: Performing device positioning error compensation on the unified time reference data set to obtain a compensated positioning data set; Performing data space coordinate alignment on the compensated positioning data set to obtain an aligned data set; The aligned data set is subjected to data consistency authentication to obtain a spatially calibrated data set.

7. The construction site monitoring and management method of a construction project according to claim 1, characterized in that: The data analysis and calculation are performed on the spatially calibrated data set to obtain regional data analysis results, including: The displacement value of the regional support structure is calculated by the following formula: in, For the The displacement value of the supporting structure in each area is For the The horizontal displacement of the region, For the Vertical settlement of the area, For the The tilt angle of the area; The regional concrete strength value is calculated by the following formula: in, For the The concrete strength value of each area is For the The compressive strength of the area, For the The splitting tensile strength of the region, For the Concrete density of each area; The regional environmental detection value is calculated by the following formula: in, For the The environmental detection value of each area, For the The ambient temperature of the area, No. The humidity of the area, For the Wind speed in the area; The regional level data deviation analysis results include: support structure displacement value, concrete strength value and environmental detection value.

8. The construction site monitoring and management method of a construction project according to claim 1, characterized in that: The abnormal area identification is performed according to the regional data deviation analysis result to obtain an abnormal area report, including: If the displacement value of the regional support structure is greater than the preset support structure displacement threshold, it is determined that the displacement index of the support structure is abnormal; If the regional concrete strength value is less than the preset concrete strength threshold, it is determined that the concrete strength index is abnormal; If the regional environmental detection value is greater than the preset environmental impact threshold, it is determined that the environmental detection index is abnormal; If an area has three abnormal indicators, it will be identified as a high-risk area; if two indicators are abnormal, it will be identified as a medium-risk area; if one indicator is abnormal, it will be identified as a low-risk area; if no indicator is abnormal, it will be identified as a no-risk area; The indicators include: support structure displacement indicators, concrete strength indicators and environmental testing indicators; The abnormal area report includes: no-risk area, low-risk area, medium-risk area and high-risk area.

9. A construction site monitoring and management system for a construction project, characterized in that: include: A data acquisition module, used to acquire the original data set; A preprocessing module, used to perform data preprocessing according to the original data set to obtain a preprocessed feature data set; A sequence optimization module is used to perform sequence optimization on the preprocessed feature data set to obtain an optimized data upload sequence; A time synchronization module, used to perform time synchronization on the optimized data upload sequence to obtain a unified time reference data set; A spatial calibration module, used for performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set; A cluster analysis module, used to perform data cluster analysis on the spatially calibrated data set to obtain regional-level data deviation analysis results; The abnormal area identification module is used to identify abnormal areas according to the regional data deviation analysis results and obtain abnormal area reports.

10. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the construction site construction monitoring and management method as described in any one of claims 1 to 8.

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