Building engineering quality monitoring system

Abstract

The invention discloses a building engineering quality monitoring system, and relates to the technical field of building engineering monitoring, the building engineering quality monitoring system comprises a collection module, an analysis module, a monitoring module and an early warning module, the collection module collects first quality monitoring data of building engineering and obtains historical monitoring data, and transmits the data to the analysis module; performing feature extraction and data analysis on the first quality monitoring data to obtain second quality monitoring data, storing historical monitoring data and presetting quality standard data, transmitting the second quality monitoring data and the quality standard data to a monitoring module, performing dynamic comparison on the real-time second quality monitoring data and the preset quality standard data, and outputting the result. The method comprises the steps of generating quality anomaly feature parameters, transmitting the quality anomaly feature parameters to an early warning module, matching a preset early warning strategy according to the quality anomaly feature parameters, sending out graded early warning signals, and carrying out multi-dimensional early warning by integrating multi-dimensional data acquisition and analysis, dynamic feature index extraction and dynamic standard construction, so that the engineering quality monitoring accuracy and the management and control timeliness are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction engineering monitoring, and in particular to a construction engineering quality monitoring system. BACKGROUND

[0002] In recent years, with the development of construction engineering towards complexity and large-scale, engineering quality and safety problems have been increasingly concerned, and the construction engineering monitoring technology as the core means to ensure engineering quality and safety has become increasingly important. Traditional monitoring methods rely on manual inspection and single-point data collection, which have defects such as insufficient real-time performance, single data dimension and weak dynamic analysis capability, and are difficult to systematically monitor the coupling effect of structure physical parameters and environmental parameters. Moreover, there is a lack of dynamic early warning mechanism based on historical data and real-time state, which leads to insufficient early identification ability of quality abnormalities and cannot meet the needs of modern engineering fine management.

[0003] The deficiencies of the prior art are reflected in three aspects. Firstly, at the data collection level, there is a lack of synchronous collection and time series calibration capability for multi-source heterogeneous data such as structure physical parameters and environmental parameters, making it difficult to build a complete engineering quality monitoring system. Secondly, at the data analysis level, traditional methods only stay at single parameter threshold comparison without introducing feature extraction, correlation analysis and time series dynamic processing, which cannot mine deep correlation features and periodic fluctuation rules of data. Thirdly, at the early warning mechanism level, the preset threshold lacks dynamic correction capability and is not combined with real-time state and historical data for adaptive adjustment, and the early warning level division is extensive, making it difficult to accurately identify abnormal types, spatial influence range and severity. SUMMARY

[0004] The technical problem solved by the present application is that the deficiencies of the prior art are reflected in three aspects. Firstly, at the data collection level, there is a lack of synchronous collection and time series calibration capability for multi-source heterogeneous data such as structure physical parameters and environmental parameters, making it difficult to build a complete engineering quality monitoring system. Secondly, at the data analysis level, traditional methods only stay at single parameter threshold comparison without introducing feature extraction, correlation analysis and time series dynamic processing, which cannot mine deep correlation features and periodic fluctuation rules of data. Thirdly, at the early warning mechanism level, the preset threshold lacks dynamic correction capability and is not combined with real-time state and historical data for adaptive adjustment, and the early warning level division is extensive, making it difficult to accurately identify abnormal types, spatial influence range and severity.

[0005] To solve the above technical problems, the present application provides the following technical scheme: a construction engineering quality monitoring system comprising a collection module, an analysis module, a monitoring module and an early warning module; The collection module is used for collecting first quality monitoring data of the construction engineering and obtaining historical monitoring data, and transmitting to the analysis module. The analysis module is configured to perform feature extraction and data analysis on the first quality monitoring data, obtain second quality monitoring data, store the historical monitoring data, and preset quality standard data, and transmit the second quality monitoring data and the quality standard data to the monitoring module. The monitoring module is configured to dynamically compare the real-time second quality monitoring data with the preset quality standard data, generate quality abnormality characteristic parameters, and transmit the quality abnormality characteristic parameters to the early warning module. The early warning module matches a preset early warning strategy according to the quality abnormality characteristic parameters, and sends a hierarchical early warning signal.

[0006] As a preferred scheme of the building engineering quality monitoring system, the first quality monitoring data includes structural physical parameters and environmental parameters. The structural physical parameters include displacement monitoring values, settlement amounts, crack widths, stress values, strain values, and vibration frequencies. The environmental parameters include temperatures, humidities, wind speeds, wind directions, and rainfall amounts. The historical monitoring data includes historical real-time monitoring data, historical processing data, and quality evaluation historical records.

[0007] As a preferred scheme of the building engineering quality monitoring system, the analysis module includes a data processing unit and a data storage unit. The second quality monitoring data is obtained by performing feature extraction and data analysis on the first quality monitoring data by the data processing unit, and specifically includes the following steps. The first quality monitoring data is labeled with a time stamp in chronological order, to generate first quality monitoring data with a time stamp. The first quality monitoring data with a time stamp is subjected to feature extraction, to obtain characteristic parameters. The characteristic parameters include structural physical characteristic parameters and environmental characteristic parameters. The structural physical characteristic parameters include displacement change rates, settlement acceleration, crack expansion rates, stress gradients, and strain periodic fluctuation values. The environmental characteristic parameters include temperature change rates, humidity fluctuation amplitudes, wind speed gradients, wind direction change frequencies, and rainfall accumulation rates. The first quality monitoring data with a time stamp is subjected to time series analysis algorithm to generate a historical change trend curve and periodic fluctuation characteristics. The characteristic parameters, the historical change trend curve, and the periodic fluctuation characteristics constitute second quality monitoring parameters.

[0008] As a preferred scheme of the building engineering quality monitoring system, the historical monitoring data is stored by the data storage unit and preset quality standard data, specifically including: The historical monitoring data is stored in a structured manner according to data types, monitoring times and building engineering parts to form a historical database; According to long-term monitoring data in the historical database, combining with building engineering design specifications, industry standards and engineering acceptance requirements, preset quality standard data is set, including a baseline threshold range of structure physical parameters and environmental parameters and a baseline interval of correlation coefficients of structure-environment parameter pairs; The baseline threshold range of structure physical parameters and environmental parameters includes an upper limit of displacement change rate, a stress safety threshold value and an environmental parameter influence coefficient baseline value; The correlation coefficients of structure-environment parameter pairs in the historical monitoring data are calculated and stored to form correlation coefficient data of structure-environment parameter pairs; According to the latest historical monitoring data, the preset quality standard data is periodically corrected to generate dynamic quality standards suitable for real-time engineering states.

[0009] As a preferred scheme of the building engineering quality monitoring system, the real-time second quality monitoring data is dynamically compared with the preset quality standard data to generate quality abnormal characteristic parameters, specifically including: Based on the first quality monitoring data with timestamps, structure physical characteristic parameters and environmental characteristic parameters in the second quality monitoring data are time-synchronized and calibrated, and data points with missing or abnormal timestamps are removed; The data processing unit is used to perform dimension unification on the synchronized structure physical characteristic parameters and environmental characteristic parameters to generate dimensionless standardized parameter sequences; Pearson correlation coefficient algorithm or Spearman rank correlation algorithm is used to calculate real-time correlation coefficient matrices of structure physical characteristic parameter sequences and environmental characteristic parameter sequences to obtain real-time correlation coefficients between any structure physical characteristic parameter sequence and environmental characteristic parameters; The real-time correlation coefficients are compared with the mean values of the correlation coefficient data of structure-environment parameter pairs stored in the historical database, and if the real-time correlation coefficients exceed the mean values ± X times of the standard deviation, it is determined that there is an abnormal parameter coupling combination.

[0010] As a preferred scheme of the building engineering quality monitoring system, wherein: based on the reference threshold range of the structure physical parameters and the environmental parameters of the preset quality standard data, a floating threshold interval dynamically adjusted over time is generated by combining a time series analysis algorithm with the periodic fluctuation characteristics of the second quality monitoring data, and a threshold deviation of the structure physical characteristic parameters and the environmental characteristic parameters from the floating threshold is calculated by using a deviation calculation algorithm. The threshold deviation, the real-time correlation coefficient, and the periodic fluctuation characteristics jointly generate the quality abnormal characteristic parameters.

[0011] As a preferred scheme of the building engineering quality monitoring system, wherein: the quality abnormal characteristic parameters are obtained by quantitatively combining the real-time correlation coefficient, the threshold deviation, and the periodic fluctuation characteristics according to a preset abnormality determination logic, specifically including: When the threshold deviation of the structure physical parameters or the environmental parameters exceeds the reference threshold range of the structure physical parameters or the environmental parameters a certain number of times, the abnormal type of the structure physical parameters or the environmental parameters is determined; When the real-time correlation coefficient is determined to be an abnormal parameter coupling combination, the continuous duration of the abnormal state of the abnormal parameter coupling combination is counted, and the abnormal type and the spatial influence range of the structure-environment parameter pair associated abnormality are determined in combination with the monitoring site distribution of the abnormal structure-environment parameter pair in the building engineering; The sum of the threshold deviations of the structure physical parameters and the environmental parameters is calculated and compared with the periodic fluctuation characteristics to generate a comprehensive abnormal severity index, specifically including: If the sum of the threshold deviations is within the range of the historical periodic fluctuation characteristics, it is determined to be a slight abnormality; If the sum of the threshold deviations exceeds the range of the historical periodic fluctuation characteristics but does not reach a critical value, it is determined to be a moderate abnormality in combination with the threshold deviation degree; If the sum of the threshold deviations is far beyond the historical fluctuation range or the historical periodic fluctuation characteristics are completely abnormal, it is determined to be a serious abnormality.

[0012] As a preferred scheme of the building engineering quality monitoring system, wherein: the quality abnormal characteristic parameters include the abnormal type of the structure physical parameters or the environmental parameters, the abnormal type and the spatial influence range of the structure-environment parameter pair associated abnormality, and the comprehensive abnormal severity index; The abnormal type of the structure physical parameters or the environmental parameters includes structure physical parameter abnormality and environmental parameter abnormality; The abnormal type of the structure-environment parameter pair associated abnormality includes parameter coupling strength abnormality and time sequence association abnormality; The spatial influence range includes local influence, regional influence, and overall influence. The comprehensive abnormality severity index includes slight abnormality, moderate abnormality and severe abnormality.

[0013] As a preferred scheme of the building engineering quality monitoring system, wherein: the preset early warning strategy is matched and obtained according to the quality abnormality characteristic parameter; The quality abnormality characteristic parameter matching includes basic early warning level division, early warning coverage range adjustment and early warning content; The basic early warning level is triggered according to the comprehensive abnormality severity, and specifically includes: When the comprehensive abnormality severity index is slight abnormality, a first-level early warning is triggered; When the comprehensive abnormality severity index is moderate abnormality, a second-level early warning is triggered; When the comprehensive abnormality severity index is severe abnormality, a third-level early warning is triggered; The early warning coverage range is adjusted according to the space influence range, and specifically includes: If the space influence range is local influence, the early warning signal is only pushed to the management terminal of the corresponding monitoring part; If the space influence range is regional influence, it is pushed to the regional person in charge and the engineering management hub; If the space influence range is overall influence, it is synchronously pushed to the engineering whole system management terminal and the supervision department.

[0014] As a preferred scheme of the building engineering quality monitoring system, wherein: the early warning content is configured according to the abnormality type, and specifically includes: The abnormality type includes structure physical parameter abnormality, environment parameter abnormality and structure-environment parameter pair correlation abnormality; If the abnormality type is structure physical parameter abnormality, the early warning content includes the specific position of the corresponding structure member and the inspection suggestion; If the abnormality type is environment parameter abnormality, the real-time monitoring curve and trend prediction of the environmental factor are attached; If the abnormality type is structure-environment parameter pair correlation abnormality, the structure-environment parameter pair and the coupling influence analysis are marked; The hierarchical early warning signal includes sound and light alarm, SMS notification, system pop-up window and paper report generation, and when the third-level early warning is triggered, the emergency response process is synchronously triggered, and the preset emergency plan is associated.

[0015] The beneficial effects of the present application: by integrating multi-dimensional data acquisition and deep analysis technology, the comprehensiveness and accuracy of building engineering quality monitoring are significantly improved. The scheme supports synchronous acquisition of structural physical parameters such as displacement, settlement, stress and environmental parameters such as temperature, humidity, wind and rain load, breaking through the limitations of traditional single parameter monitoring, thereby capturing the dynamic correlation of engineering structure response and environmental action in real time, converting the original data into dynamic characteristic indexes such as change rate and fluctuation amplitude through feature parameter extraction technology, combining historical data to build periodic fluctuation rules and dynamic quality standards, accurately identifying structural risks such as displacement change rate overrun, stress gradient anomaly and environmental influences such as excessive temperature and humidity fluctuations and wind and rain load coupling anomalies, the early warning mechanism is based on multi-dimensional matching of abnormal types, spatial influence range and severity, and outputs early warning information including component position, trend prediction and coupling analysis in a targeted manner, synchronously triggering sound and light alarms, hierarchical push and emergency response processes, effectively shortening the quality anomaly identification cycle, improving the timeliness and intelligence level of risk control, and providing full-cycle multi-dimensional technical support for engineering quality and safety under complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A basic flowchart of a building engineering quality monitoring system is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] To make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments.

[0018] Embodiment, refer to Figure 1 For an embodiment of the present application, a building engineering quality monitoring system is provided, which includes an acquisition module, an analysis module, a monitoring module and an early warning module. The acquisition module is used to acquire first quality monitoring data of building engineering and obtain historical monitoring data, and transmit to the analysis module. The analysis module is used to perform feature extraction and data analysis on the first quality monitoring data, obtain second quality monitoring data, store the historical monitoring data and preset quality standard data, and transmit the second quality monitoring data and the quality standard data to the monitoring module. The monitoring module is used to dynamically compare the real-time second quality monitoring data with the preset quality standard data, generate quality abnormal feature parameters, and transmit to the early warning module. The early warning module matches the preset early warning strategy according to the quality abnormal feature parameters, and sends a hierarchical early warning signal.

[0019] In one of the embodiments, the system comprises a collection module, an analysis module, a monitoring module and a warning module, which work together to realize the whole-process quality monitoring. The collection module collects the structural physical parameters, environmental parameters and historical monitoring data of the construction project in real time and transmits them to the analysis module. The analysis module extracts features and analyzes data of the first quality monitoring data, generates the second quality monitoring data, stores the historical monitoring data as a historical database, combines the standard data with the historical monitoring data to preset the quality standard data, and transmits the second quality monitoring data and the standard data to the monitoring module. The monitoring module dynamically compares the real-time second quality monitoring data with the preset standard, generates quality abnormal feature parameters and transmits them to the warning module. The warning module matches the preset strategy according to the feature parameters, triggers the corresponding warning according to the abnormal type, spatial influence range and severity, sends signals through sound and light alarm and hierarchical push mode, and synchronously triggers the emergency response process through three-level warning. Through the module linkage, the system realizes multi-source data collection, in-depth analysis and accurate warning, and improves the timeliness and comprehensiveness of the engineering quality risk control.

[0020] The first quality monitoring data comprises structural physical parameters and environmental parameters. The structural physical parameters comprise displacement monitoring value, settlement amount, crack width, stress value, strain value and vibration frequency. The environmental parameters comprise temperature, humidity, wind speed, wind direction and rainfall. The historical monitoring data comprises historical real-time monitoring data, historical processing data and quality evaluation history record.

[0021] The analysis module comprises a data processing unit and a data storage unit. The second quality monitoring data is obtained by the data processing unit extracting features and analyzing data of the first quality monitoring data, specifically including: annotating the first quality monitoring data with time stamps in the order of collection time to generate the first quality monitoring data with time stamps; extracting features from the first quality monitoring data with time stamps to obtain feature parameters; The feature parameters comprise structural physical feature parameters and environmental feature parameters. The structural physical feature parameters comprise displacement change rate, settlement acceleration, crack expansion rate, stress gradient and strain periodic fluctuation value. The environmental feature parameters comprise temperature change rate, humidity fluctuation amplitude, wind speed gradient, wind direction change frequency and rainfall accumulation rate. generating historical change trend curves and periodic fluctuation features of the first quality monitoring data with time stamps through time series analysis algorithm; The feature parameters, historical change trend curves and periodic fluctuation features constitute the second quality monitoring parameters.

[0022] In one of the embodiments, the first quality monitoring data includes structural physical parameters and environmental parameters, wherein the structural physical parameters include displacement monitoring values, settlement amounts, crack widths, stress values, strain values, and vibration frequencies, and the environmental parameters include temperatures, humidities, wind speeds, wind directions, and rainfall amounts, and historical monitoring data is also acquired, the historical monitoring data including historical real-time monitoring data, historical processing data, and quality evaluation history records, the analysis module is composed of a data processing unit and a data storage unit, the second quality monitoring data is obtained by the data processing unit performing feature extraction and data analysis on the first quality monitoring data, and the first quality monitoring data is first labeled with a time stamp in chronological order of collection time to generate the first quality monitoring data with the time stamp; then feature extraction is performed on the first quality monitoring data with the time stamp to acquire structural physical feature parameters (including displacement change rates, settlement acceleration rates, crack expansion rates, stress gradient values, and strain periodic fluctuation values) and environmental feature parameters (including temperature change rates, humidity fluctuation amplitudes, wind speed gradient values, wind direction change frequencies, and rainfall accumulation rates); and historical change trend curves and periodic fluctuation characteristics are generated by a time series analysis algorithm, and the structural physical feature parameters, the environmental feature parameters, the historical change trend curves, and the periodic fluctuation characteristics jointly constitute the second quality monitoring parameters.

[0023] The historical monitoring data is stored by the data storage unit and preset quality standard data is preset, and the preset quality standard data specifically includes: The historical monitoring data is stored in a structured manner according to data types, monitoring times, and building engineering positions to form a historical database; According to long-term monitoring data in the historical database, combining with building engineering design specifications, industry standards, and engineering acceptance requirements, the quality standard data is preset, and the preset quality standard data includes a reference threshold range of the structural physical parameters and the environmental parameters and a reference interval of a correlation coefficient of a structure-environment parameter pair; The reference threshold range of the structural physical parameters and the environmental parameters includes an upper limit of a displacement change rate, a stress safety threshold value, and an environmental parameter influence coefficient reference value; The correlation coefficient of the structure-environment parameter pair in the historical monitoring data is calculated and stored to form a correlation coefficient data of the structure-environment parameter pair; The preset quality standard data is periodically corrected according to the latest historical monitoring data to generate dynamic quality standards adapted to real-time states of projects.

[0024] In one of the embodiments, the historical monitoring data (including historical real-time monitoring data, historical processing data and quality evaluation history) is stored in the data storage unit of the analysis module and preset quality standard data, and the historical monitoring data is stored in a structured manner according to data types, monitoring time and building engineering parts, a historical database is constructed; according to the long-term monitoring data in the historical database, combined with the building engineering design specification, industry standard and engineering acceptance requirements, the quality standard data is preset, including the benchmark threshold range of the structure physical parameters and the environmental parameters (including the upper limit of the displacement change rate, the stress safety threshold and the environmental parameter influence coefficient benchmark value), and the correlation coefficient benchmark interval of the structure-environment parameter pair; at the same time, the correlation coefficient of the structure physical parameters and the environmental parameters in the historical monitoring data (such as the displacement monitoring value and the temperature parameter pair, the stress value and the wind speed parameter pair) is calculated, the structured correlation coefficient data is formed and stored in the historical database; finally, according to the latest collected historical monitoring data, the preset quality standard data is periodically corrected, the benchmark threshold range and the correlation coefficient benchmark interval are dynamically adjusted, the dynamic quality standard suitable for the real-time state of the project is generated, and the quality evaluation standard is ensured to be updated synchronously with the actual working condition of the project.

[0025] The real-time second quality monitoring data is dynamically compared with the preset quality standard data to generate quality abnormal characteristic parameters, which specifically include: Based on the first quality monitoring data with time stamp, the structure physical characteristic parameters and the environmental characteristic parameters in the second quality monitoring data are time-synchronized and calibrated, and the data points with missing or abnormal time stamp are eliminated; The structure physical characteristic parameters and the environmental characteristic parameters after synchronization are dimensionally unified by the data processing unit to generate a sequence of dimensionless standardized parameters; The real-time correlation coefficient matrix of the structure physical characteristic parameter sequence and the environmental characteristic parameter sequence is calculated by using the Pearson correlation coefficient algorithm or the Spearman rank correlation algorithm, and the real-time correlation coefficient between any structure physical characteristic parameter sequence and environmental characteristic parameter is obtained; The real-time correlation coefficient is compared with the mean value of the correlation coefficient data of the structure-environment parameter pair stored in the historical database, if the real-time correlation coefficient exceeds the mean value ± X times the standard deviation, it is determined that the abnormal parameter coupling combination is formed.

[0026] In one of the embodiments, the structural physical characteristic parameters and environmental characteristic parameters in the second quality monitoring data are calibrated by time synchronization to eliminate abnormal data points, ensuring that the structural physical characteristic parameters and environmental characteristic parameters are strictly aligned in the time dimension, solving the problem of time and space asynchronization of multi-source data, and providing a reliable data basis for subsequent analysis; the dimension unified processing eliminates the influence of unit difference of parameters, making different types of displacement change rate and temperature change rate parameters comparable, and improving the scientificity of data fusion analysis; the real-time correlation coefficient is calculated by using the Pearson algorithm, which can quantitatively mine the dynamic coupling relationship between the structural physical parameters and the environmental parameters, breaking through the limitations of traditional single parameter monitoring; the abnormal parameter coupling combination is determined by comparing with the historical correlation data mean value and standard deviation, and the parameter correlation abnormality (such as abnormal coupling of sudden increase of stress value and over-limit of wind speed) beyond the normal fluctuation range is accurately identified; X is set according to the specific engineering scene, which effectively captures the quality risk mode and provides key basis for graded early warning, significantly improves the timeliness and accuracy of engineering quality abnormality identification, reduces the risk of missed judgment and misjudgment, and provides more reliable technical support for building engineering safety management.

[0027] Based on the reference threshold range of the structural physical parameters and the environmental parameters of the preset quality standard data, the floating threshold interval dynamically adjusted over time is generated by combining the time series analysis algorithm with the periodic fluctuation characteristics of the second quality monitoring data, and the threshold deviation of the structural physical characteristic parameters and the environmental characteristic parameters from the floating threshold is calculated by using the deviation calculation algorithm; The threshold deviation, the real-time correlation coefficient and the periodic fluctuation characteristics jointly generate the quality abnormality characteristic parameters.

[0028] The quality abnormality characteristic parameters are quantitatively synthesized from the real-time correlation coefficient, the threshold deviation and the periodic fluctuation characteristics according to the preset abnormality determination logic, specifically including: When the threshold deviation of the structural physical parameters or the environmental parameters exceeds the number of times of the reference threshold range of the structural physical parameters or the environmental parameters, the abnormal type of the structural physical parameters or the environmental parameters is determined; When the real-time correlation coefficient determines the abnormal parameter coupling combination, the continuous duration of the abnormal state of the abnormal parameter coupling combination is counted, and the abnormal type and spatial influence range of the structural-environmental parameter pair correlation abnormality are determined in combination with the monitoring site distribution of the abnormal structural-environmental parameter pair in the building engineering; The sum of the threshold deviations of the structural physical parameters and the environmental parameters is calculated, and compared with the periodic fluctuation characteristics to generate a comprehensive abnormality severity index, specifically including: If the sum of the threshold deviations is within the range of the historical periodic fluctuation characteristics, it is determined as a slight abnormality; If the sum of the threshold deviations exceeds the range of the historical periodic fluctuation characteristics but does not reach the critical value, it is determined as a moderate abnormality in combination with the threshold deviation degree; If the threshold offset sum is far beyond the historical fluctuation range or the historical periodic fluctuation characteristics are completely abnormal, it is determined as a serious abnormality.

[0029] The quality abnormality characteristic parameter includes an abnormal type of a structure physical parameter or an environment parameter, an abnormal type of a structure-environment parameter pair associated abnormality, a spatial influence range, and a comprehensive abnormality severity index. The abnormal type of the structure physical parameter or the environment parameter includes a structure physical parameter abnormality and an environment parameter abnormality. The abnormal type of the structure-environment parameter pair associated abnormality includes a parameter coupling strength abnormality and a time sequence associated abnormality. The spatial influence range includes a local influence, a regional influence, and a whole influence. The comprehensive abnormality severity index includes a slight abnormality, a moderate abnormality, and a serious abnormality.

[0030] In one of the embodiments, the generation of the dynamic floating threshold interval, combined with the time sequence analysis and the periodic fluctuation characteristics, upgrades the threshold determination from a static standard to a dynamic adaptation mode that changes with the engineering time sequence, effectively avoiding the misjudgment of the static threshold caused by the environmental periodic fluctuation (such as the diurnal temperature and humidity change and the seasonal load difference), and improving the accuracy of the basic data comparison. The joint analysis of the threshold offset, the real-time correlation coefficient, and the periodic fluctuation characteristics not only accurately captures the independent abnormality of the single structure physical parameter and the environment parameter (such as the stress value exceeding the limit and the rainfall accumulation rate abnormality), but also deeply identifies the coupling abnormality of the structure-environment parameter pair (such as the coupling strength abnormality of the structure-environment parameter pair and the time sequence associated abnormality of the structure-environment parameter pair), breaking through the limitations of traditional single-dimensional monitoring and achieving all-around coverage of abnormal risk. Based on the quantitative determination logic of the abnormal type subdivision (including the structure physical parameter abnormality, the environment parameter abnormality, the coupling strength abnormality, and the time sequence associated abnormality), the spatial influence range division (including the local influence, the regional influence, and the whole influence), and the severity classification (including the slight abnormality, the moderate abnormality, and the serious abnormality), the quality abnormality characteristic parameter is more directional - identifying the risk source through the abnormal type, locking the influence area through the spatial range, matching the response level through the severity, for example, strengthening the monitoring for the local slight abnormality, starting the special investigation for the regional moderate abnormality, and directly triggering the emergency mechanism for the whole serious abnormality, significantly improving the accuracy of risk control and the efficiency of resource allocation, and providing data-driven intelligent decision support for the whole cycle quality and safety of the construction engineering.

[0031] The preset early warning strategy is obtained according to the quality abnormality characteristic parameter matching. The quality abnormality characteristic parameter matching includes basic early warning level division, early warning coverage range adjustment, and early warning content. The basic early warning level is triggered according to the comprehensive abnormality severity, specifically including: When the comprehensive abnormality severity index is slight abnormality, a first-level early warning is triggered; When the comprehensive abnormality severity index is moderate abnormality, a second-level early warning is triggered; When the comprehensive abnormality severity index is severe abnormality, a third-level early warning is triggered; The early warning coverage is adjusted according to the spatial influence range, specifically including: If the spatial influence range is local influence, the early warning signal is only pushed to the management terminal of the corresponding monitoring part; If the spatial influence range is regional influence, it is pushed to the regional person in charge and the engineering management hub; If the spatial influence range is overall influence, it is synchronously pushed to the engineering whole system management terminal and the supervision department.

[0032] The early warning content is configured according to the abnormality type, specifically including: The abnormality type includes structure physical parameter abnormality, environment parameter abnormality and structure-environment parameter pair correlation abnormality; If the abnormality type is structure physical parameter abnormality, the early warning content includes the specific position of the corresponding structure member and the inspection suggestion; If the abnormality type is environment parameter abnormality, the real-time monitoring curve and trend prediction of the environmental factor are attached; If the abnormality type is structure-environment parameter pair correlation abnormality, the structure-environment parameter pair and the coupling influence analysis are marked; The hierarchical early warning signal includes sound and light alarm, SMS notification, system pop-up window and paper report generation, when the third-level early warning is triggered, the emergency response process is synchronously triggered, and the preset emergency plan is associated.

[0033] In one of the embodiments, based on a three-level early warning mechanism of comprehensive abnormal severity (level one warning corresponds to slight abnormality, level two warning corresponds to moderate abnormality, and level three warning corresponds to serious abnormality), the warning level is accurately matched with the risk hazard degree, avoiding the response redundancy or deficiency caused by one-size-fits-all alarm, for example, the level three warning synchronously triggers the emergency response process, ensuring the immediate control of serious risks; the warning coverage range is dynamically adjusted according to spatial influence (including local influence, regional influence and overall influence), realizing the directional push of warning information - local abnormality only informs the corresponding management terminal, avoiding the interference of whole system alarm; the overall abnormality synchronously reaches the supervision department, ensuring the cross-level collaborative disposal of major risks, significantly improving the efficiency and pertinence of warning information transmission; the warning content is differentially configured according to abnormal types (explicit component position for structural parameter abnormality, trend prediction for environmental parameter abnormality and parameter correlation analysis for coupling abnormality), providing key information that can directly guide the troubleshooting work for engineering personnel, reducing the risk positioning time; the linkage mechanism of hierarchical warning signals (including sound and light alarm, SMS notification, system pop-up window and paper report) and emergency plan, builds a full closed-loop management from risk identification to disposal, for example, the level three warning automatically associates the emergency plan, shortens the manual decision-making time, improves the intelligent level and emergency response efficiency of engineering risk control, and provides a multi-dimensional and multi-level early warning protection system for building engineering safety.

[0034] The present application significantly improves the comprehensiveness and accuracy of building engineering quality monitoring by integrating multi-dimensional data acquisition and deep analysis technology. The scheme supports synchronous acquisition of structural physical parameters such as displacement, settlement, stress and environmental parameters such as temperature, humidity, wind and rain load, breaking through the limitation of traditional single parameter monitoring, thereby capturing the dynamic correlation of engineering structure response and environmental action in real time, converting the original data into dynamic characteristic indexes such as change rate and fluctuation amplitude through feature parameter extraction technology, combining historical data to build periodic fluctuation rules and dynamic quality standards, accurately identifying structural risks such as displacement change rate overrun and stress gradient abnormality, and environmental influences such as excessive temperature and humidity fluctuation and wind and rain load coupling abnormality, the early warning mechanism is based on multi-dimensional matching of abnormal type, spatial influence range and severity, and outputs early warning information including component position, trend prediction and coupling analysis, synchronously triggers sound and light alarm, hierarchical push and emergency response process, effectively shortens the quality abnormality identification period, improves the timeliness and intelligent level of risk control, and provides full-cycle multi-dimensional technical support for engineering quality and safety under complex working conditions.

[0035] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (or computer- readable storage media) having computer-usable program code embodied in the medium. The medium can be any available medium or combination thereof that is accessible by a general purpose or special purpose computer. By way of example, such computer-usable storage media can include a volatile memory, a non-volatile memory, a hard disk drive, a solid state drive, a magnetic diskette, an optical disk (e.g., a compact disk (CD) or a digital versatile disk (DVD)), or a floppy disk, all of which are tangible of computer- readable media. The computer-usable program code, when executed, can enable a general purpose computer, special purpose computer, or other programmable data processing apparatus to perform a method in accordance with the article of manufacture containing the computer program code. The software is able to operate in Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0036] It should be noted that the above-mentioned embodiments are only used to illustrate but not to limit the technical solutions of the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A construction engineering quality monitoring system, characterized in that... It includes a data acquisition module, an analysis module, a monitoring module, and an early warning module; The acquisition module is used to acquire the first quality monitoring data and historical monitoring data of the construction project and transmit them to the analysis module; The analysis module is used to extract features and analyze data from the first quality monitoring data, obtain the second quality monitoring data, store the historical monitoring data and preset quality standard data, and transmit the second quality monitoring data and the quality standard data to the monitoring module. The monitoring module is used to dynamically compare the real-time second quality monitoring data with the preset quality standard data, generate quality anomaly characteristic parameters, and transmit them to the early warning module. The early warning module matches a preset early warning strategy based on the quality anomaly characteristic parameters and issues a graded early warning signal.

2. The construction engineering quality monitoring system as described in claim 1, characterized in that: The first quality monitoring data includes structural physical parameters and environmental parameters; The structural physical parameters include displacement monitoring values, settlement, crack width, stress value, strain value, and vibration frequency; The environmental parameters include temperature, humidity, wind speed, wind direction, and rainfall. The historical monitoring data includes historical real-time monitoring data, historical processed data, and historical quality assessment records.

3. The construction engineering quality monitoring system as described in claim 2, characterized in that: The analysis module includes a data processing unit and a data storage unit; The second quality monitoring data is obtained by the data processing unit through feature extraction and data analysis of the first quality monitoring data, specifically including: The first quality monitoring data is timestamped according to the collection time sequence to generate first quality monitoring data with timestamps; Feature extraction is performed on the first quality monitoring data with timestamps to obtain feature parameters; The characteristic parameters include structural physical characteristic parameters and environmental characteristic parameters; The structural physical characteristic parameters include displacement change rate, settlement acceleration, crack propagation rate, stress gradient, and strain period fluctuation value. The environmental characteristic parameters include temperature change rate, humidity fluctuation range, wind speed gradient, wind direction change frequency, and rainfall accumulation rate. The time-stamped first quality monitoring data is used to generate historical trend curves and periodic fluctuation characteristics through time series analysis algorithms; The characteristic parameters, historical trend curves, and periodic fluctuation characteristics constitute the second quality monitoring parameters.

4. The construction engineering quality monitoring system as described in claim 3, characterized in that: The historical monitoring data is stored in a data storage unit and pre-set with quality standard data, specifically including: The historical monitoring data is stored in a structured manner according to data type, monitoring time, and building location to form a historical database; Based on long-term monitoring data in the historical database, combined with building engineering design specifications, industry standards and engineering acceptance requirements, preset quality standard data is generated. The preset quality standard data includes the benchmark threshold range of structural physical parameters and environmental parameters and the benchmark interval of the correlation coefficient of structural-environmental parameter pairs. The reference threshold ranges for the structural physical parameters and environmental parameters include the upper limit of displacement change rate, stress safety threshold, and reference value of environmental parameter influence coefficient; Calculate and store the correlation coefficients of structure-environment parameter pairs in historical monitoring data to form correlation coefficient data of structure-environment parameter pairs; The preset quality standard data is periodically revised based on the latest historical monitoring data to generate dynamic quality standards that adapt to the real-time status of the project.

5. The construction engineering quality monitoring system as described in claim 4, characterized in that: The real-time second quality monitoring data is dynamically compared with the preset quality standard data to generate quality anomaly characteristic parameters, specifically including: Based on the first quality monitoring data with timestamps, the structural physical characteristic parameters and environmental characteristic parameters in the second quality monitoring data are time-synchronized and calibrated to remove data points with missing or abnormal timestamps. The data processing unit unifies the dimensions of the synchronized structural physical characteristic parameters and environmental characteristic parameters to generate a dimensionless standardized parameter sequence. The real-time correlation coefficient matrix between the structural physical characteristic parameter sequence and the environmental characteristic parameter sequence is calculated using the Pearson correlation coefficient algorithm or the Spearman rank correlation algorithm, and the real-time correlation coefficient between any structural physical characteristic parameter sequence and the environmental characteristic parameter is obtained. The real-time correlation coefficient is compared with the mean of the correlation coefficient data of structure-environment parameter pairs stored in the historical database. If the real-time correlation coefficient exceeds the mean ± X times the standard deviation, it is determined to be an abnormal parameter coupling combination.

6. The construction engineering quality monitoring system as described in claim 5, characterized in that: Based on the baseline threshold range of the structural physical parameters and environmental parameters of the preset quality standard data, a floating threshold range that is dynamically adjusted over time is generated by combining the periodic fluctuation characteristics of the second quality monitoring data with the time series analysis algorithm. The threshold offset of the structural physical characteristic parameters and environmental characteristic parameters to the floating threshold is calculated by the deviation calculation algorithm. The threshold offset, real-time correlation coefficient, and periodic fluctuation characteristics are used together to generate quality anomaly characteristic parameters.

7. The construction engineering quality monitoring system as described in claim 6, characterized in that: The quality anomaly characteristic parameters are obtained by quantifying and synthesizing the real-time correlation coefficient, threshold offset, and periodic fluctuation characteristics according to a preset anomaly judgment logic, specifically including: The abnormality type of the structural physical parameter or environmental parameter is determined when the threshold offset of the structural physical parameter or environmental parameter exceeds the reference threshold range of the structural physical parameter or environmental parameter a certain number of times. When the real-time correlation coefficient is determined to be an abnormal parameter coupling combination, the continuous duration of the abnormal state of the abnormal parameter coupling combination is statistically analyzed. Combined with the distribution of abnormal structural-environmental parameters in the monitoring locations of the building project, the abnormal type and spatial impact range of the structural-environmental parameters on the associated abnormality are determined. The sum of the threshold offsets between the structural physical parameters and environmental parameters is calculated and compared with the periodic fluctuation characteristics to generate a comprehensive anomaly severity index, specifically including: If the sum of the threshold offsets is within the range of historical periodic fluctuation characteristics, it is determined to be a slight anomaly; If the sum of the threshold offsets exceeds the range of historical periodic fluctuations but does not reach the critical value, it is judged as a moderate anomaly based on the degree of threshold deviation. If the sum of the threshold offsets far exceeds the historical fluctuation range or the historical periodic fluctuation characteristics are completely abnormal, it is judged as a serious anomaly.

8. The construction engineering quality monitoring system as described in claim 7, characterized in that: The quality anomaly characteristic parameters include the anomaly type of the structural physical parameters or environmental parameters, the anomaly type and spatial impact range of the anomalies associated with the structural-environmental parameter pairs, and the comprehensive anomaly severity index. The abnormal types of the structural physical parameters or environmental parameters include structural physical parameter abnormalities and environmental parameter abnormalities; The types of anomalies in the structure-environment parameter correlation include parameter coupling strength anomalies and temporal correlation anomalies; The spatial impact range includes local impact, regional impact, and overall impact; The comprehensive anomaly severity index includes minor anomalies, moderate anomalies, and severe anomalies.

9. The construction engineering quality monitoring system as described in claim 8, characterized in that: The preset early warning strategy is obtained by matching the quality anomaly characteristic parameters; The quality anomaly characteristic parameter matching includes basic early warning level classification, early warning coverage adjustment, and early warning content; The basic early warning level is triggered based on the severity of the overall anomaly, specifically including: When the comprehensive anomaly severity index is slightly abnormal, a Level 1 warning is triggered; When the comprehensive anomaly severity index is moderately abnormal, a level-two warning is triggered; When the comprehensive anomaly severity index is severely abnormal, a level three warning is triggered; The warning coverage area is adjusted according to the spatial impact range, specifically including: If the spatial impact range is localized, the warning signal will only be pushed to the management terminal of the corresponding monitoring location; If the spatial impact range is a regional impact, then the request will be sent to the regional manager and the project management center; If the spatial impact range is an overall impact, it will be simultaneously pushed to the project's entire management system terminal and regulatory departments.

10. The construction engineering quality monitoring system as described in claim 9, characterized in that: The warning content is configured according to the anomaly type, specifically including: The anomaly types include structural physical parameter anomalies, environmental parameter anomalies, and structural-environment parameter alignment anomalies. If the anomaly type is an abnormality in structural physical parameters, the warning content includes the specific location of the corresponding structural component and inspection suggestions; If the anomaly type is an environmental parameter anomaly, a real-time monitoring curve of environmental factors and trend prediction are attached. If the anomaly type is a structure-environment parameter pair correlation anomaly, label the structure-environment parameter pair and the coupling effect analysis; The tiered early warning signals include audible and visual alarms, SMS notifications, system pop-ups, and paper report generation. When a Level 3 early warning is triggered, the emergency response process is triggered simultaneously, and the preset emergency plan is linked.

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