Big Data-Based Engineering Supervision Quality Assessment Methods and Systems

Abstract

This invention relates to the field of engineering quality monitoring technology, specifically disclosing a method and system for engineering supervision quality assessment based on big data. By collecting real-time data on construction layout points and excavation depth, the system calculates the abnormal coefficient of layout deviation and the cumulative value of excavation depth deviation, and combines this with the overall correlation coefficient to construct a dynamic monitoring model. This model comprehensively evaluates the actual impact of layout accuracy on excavation depth. Through comprehensive and time series analysis, it identifies abnormal points in the construction process in real time, dynamically monitors deviation trends, and triggers an early warning mechanism when the comprehensive abnormal coefficient exceeds a threshold. This marks abnormal areas and guides the construction team to adjust layout coordinates and excavation depth, reducing the impact of cumulative deviation.

Description

Technical Field

[0001] This invention relates to the field of engineering quality monitoring technology, specifically to a method and system for engineering supervision quality assessment based on big data. Background Technology

[0002] In the construction process of engineering supervision, setting out and excavation are crucial steps to ensure construction quality. The accuracy of setting out directly affects the accuracy of subsequent construction operations, while deviations in excavation depth can lead to safety and stability issues in the construction structure. Therefore, real-time monitoring and deviation control of construction setting out points and excavation depth are core means to ensure project quality. With the widespread application of big data technology and intelligent monitoring equipment, achieving precise control of the construction process through efficient data acquisition and analysis techniques has become a major direction for industry development. However, how to effectively identify anomalies in construction using multi-source data and dynamically adjust construction operations to improve quality control remains a problem that urgently needs to be solved.

[0003] The existing technology has the following shortcomings:

[0004] Current construction monitoring methods typically rely on manual inspection and data collection from single devices, resulting in low monitoring accuracy, slow response speed, and insufficient comprehensive data analysis capabilities. They lack quantitative assessment methods for the correlation between layout accuracy and excavation depth, making it impossible to accurately identify the actual impact of layout errors on construction quality, leading to a lack of clear basis for construction adjustments. Secondly, the lack of a real-time dynamic monitoring mechanism makes it difficult to promptly detect anomalies during construction and trigger effective early warnings, often relying on manual judgment or post-event adjustments, which delays optimization opportunities and reduces the efficiency of construction quality management. Therefore, existing technologies still have shortcomings in the comprehensiveness, dynamism, and guidance of data analysis. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for evaluating the quality of engineering supervision based on big data, so as to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] Big data-based engineering supervision quality assessment methods include:

[0008] S1: During construction, real-time data on construction layout points and excavation depth are collected.

[0009] S2: Analyze the construction layout point data, determine the error between the layout point and the design value through deviation calculation, and identify abnormal points in layout accuracy in combination with the characteristics of the construction area;

[0010] S3: Analyze the excavation depth data, calculate the deviation of the excavation depth data, calculate the cumulative deviation value based on the numerical difference between the excavation depth and the design depth, comprehensively evaluate the accuracy of the construction layout data and the deviation data of the excavation depth, and analyze the impact of the layout accuracy on the excavation depth.

[0011] S4: Based on the results of the comprehensive impact analysis, identify the abnormal points in the construction process, make construction adjustments to address the impact of deviations in the construction process, dynamically monitor the deviation trend in the construction process based on time series analysis, trigger the early warning mechanism through the comprehensive quality evaluation coefficient, and optimize the construction quality.

[0012] As a further aspect of the present invention: the analysis of construction layout point data, the determination of the error between the layout points and the design values ​​through deviation calculation, and the identification of abnormal points in layout accuracy in combination with the characteristics of the construction area, specifically includes:

[0013] Real-time data collection of construction layout points is performed to obtain the actual coordinate values ​​of each layout point.

[0014] Calculate the deviation value of each layout point and compare it with the design value to obtain the deviation error;

[0015] The environmental impact factor is calculated to correct deviations, adjust the accuracy of the layout data, and analyze the impact of the correction on the deviations.

[0016] Based on the analysis results, calculate the abnormal coefficient of the laying-out deviation, and determine whether the abnormal coefficient of the laying-out deviation is greater than or equal to the preset threshold. If it is, it is recorded as an abnormal point of laying-out accuracy; otherwise, it is recorded as a non-abnormal point of laying-out accuracy.

[0017] As a further aspect of the present invention: the process for obtaining the environmental impact coefficient is as follows:

[0018] Obtain real-time environmental data, including: ambient humidity data. air pressure data Land type and ambient temperature data ;

[0019] Establish a Bayesian network model, in which environmental humidity data is used. air pressure data Land type and ambient temperature data As an input node, construction deviation As an output node, the environmental impact coefficient As the target variable;

[0020] Based on environmental data, define the conditional probability distribution among nodes, specifically including:

[0021] Conditional probability of ambient humidity at a given temperature Conditional probability of air pressure at a given temperature and humidity Conditional probability of soil type under given humidity, air pressure and temperature The conditional probability of construction deviation under given humidity, air pressure, temperature and soil type. ;

[0022] The joint probability of all environmental factors and construction deviations is calculated using the following expression:

[0023] ;

[0024] Where, Represents the joint probability;

[0025] The environmental impact coefficient is calculated using joint probability, and the expression is as follows:

[0026] ;

[0027] in, Indicates the environmental impact coefficient. This represents the weighting function.

[0028] As a further aspect of the present invention: the process for obtaining the abnormal coefficient of the wire laying deviation is as follows:

[0029] During construction, the actual coordinate values ​​of each layout point are collected in real time:

[0030] ;

[0031] in Number the layout points. It is a positive integer greater than 0. The dimension of the coordinates. It is a positive integer greater than 0. Indicates the first The actual coordinates of each laying point;

[0032] Obtain the design coordinates of each layout point:

[0033] ;

[0034] in, Indicates the first Design coordinates of each layout point;

[0035] Calculate the deviation between each layout point and its corresponding design coordinate. The calculation expression is as follows:

[0036] ;

[0037] in, Indicates the first Deviation value of each laying point;

[0038] An isolated forest model is constructed by building multiple trees and randomly partitioning them to calculate the isolation depth of each layout point. The isolation depth of each tree The isolated path length of the line-laying point on the corresponding tree is given by the average isolation depth calculated from multiple trees. The calculation expression is as follows:

[0039] ;

[0040] In the formula, This indicates the number of trees in an isolated forest. Represents a tree in an isolated forest. Indicates the first The first tree The isolation depth of each point Indicates the average isolation depth;

[0041] Based on the average isolation depth, the anomaly score for each survey point is calculated using the following expression:

[0042] ;

[0043] in, It is a constant. Indicates the first Anomaly score for each laying point;

[0044] ;

[0045] In the formula, Indicates the first Abnormal coefficient of layout deviation at each layout point This represents the maximum value of the deviation values ​​for all layout points.

[0046] As a further aspect of the present invention: the analysis of excavation depth data, the calculation of deviations in the excavation depth data, and the calculation of the cumulative deviation value based on the numerical difference between the excavation depth and the design depth specifically include:

[0047] During construction, excavation depth data and design depth data for each construction point are collected in real time.

[0048] For each construction point, calculate the deviation between the actual excavation depth and the design depth. The calculation expression is as follows:

[0049] ;

[0050] In the formula, Indicates the first Deviation value at each point Indicates the first The actual excavation depth at each point Indicates the first The design excavation depth at each point, Indicates the excavation point;

[0051] Construct a training dataset, using the design depth data of each construction point as input features. Deviation value As the target variable, a training dataset is formed;

[0052] The model is trained using a random forest regression algorithm. Based on the designed deep data features, the bias value is predicted using multiple decision trees, where the output bias prediction value of each tree is: ;

[0053] Where, Indicates the first tree to the first Prediction of deviation at a single point Indicates the first Input features of points, Indicates the first A decision tree, Indicates the number of the decision tree;

[0054] The final bias prediction value is obtained by averaging the prediction results of all decision trees. The calculation expression is as follows:

[0055] ;

[0056] Where, Indicates the first The final predicted deviation value for each point. Indicates the total number of decision trees;

[0057] Calculate the sum of the predicted deviations for all construction points to obtain the cumulative deviation value.

[0058] As a further aspect of the present invention: the comprehensive evaluation of the accuracy of the construction layout data and the deviation data of the excavation depth, and the analysis of the impact of the layout accuracy on the excavation depth, specifically includes:

[0059] Obtain the abnormal coefficient of the layout deviation at each layout point and the corresponding cumulative deviation value at each excavation point;

[0060] Build the input dataset and target output dataset ;

[0061] in, This represents the cumulative deviation of the excavation depth. This indicates the total number of data collection points. This represents the deviation coefficient of the laying-out line. This represents the actual preset influence coefficient of the layout accuracy on the excavation depth;

[0062] The collection points refer to the line surveying points and the corresponding excavation collection points;

[0063] Construct a support vector machine regression model, and calculate the expression as follows:

[0064] ;

[0065] In the formula, Represents the support vector coefficients. Represents the kernel function. Indicates the bias term. Let represent the data collection point, where the kernel function is calculated as follows:

[0066] ;

[0067] In the formula, These represent the adjustment parameters of the kernel function;

[0068] The influence of the layout accuracy and excavation depth is predicted using the trained support vector machine regression model. The calculation expression is as follows:

[0069] ;

[0070] In the formula, Indicates the first Predicted impact value of each collection point;

[0071] Based on the predicted impact value, the overall correlation coefficient is calculated using the following expression:

[0072] ;

[0073] In the formula, Indicates the overall correlation coefficient;

[0074] The overall correlation coefficient is used to evaluate the impact of layout accuracy on excavation depth.

[0075] As a further aspect of the present invention: the step of identifying abnormal points in the construction process based on the comprehensive impact analysis results and adjusting the construction process to address deviations specifically includes:

[0076] A dynamic monitoring data matrix is ​​established by real-time collection of the abnormal coefficient of layout deviation, the overall correlation influence coefficient, and the cumulative deviation value at each excavation point. Based on the numerical fluctuation characteristics during construction, the weights of the abnormal coefficient of layout deviation and the cumulative deviation value are set as follows: and The comprehensive anomaly coefficient is obtained by combining the overall correlation coefficient and the overall correlation coefficient. The calculation expression is as follows:

[0077] ;

[0078] Where, Represents the comprehensive anomaly coefficient. This indicates the total number of data collection points. Indicates the collection point. Indicates the first The cumulative deviation of the excavation depth corresponding to each collection point. Indicates the first Abnormal coefficient of layout deviation at each collection point This represents the overall correlation coefficient. and Indicates the preset weighting factor;

[0079] Determine whether the comprehensive anomaly coefficient is greater than or equal to the preset threshold. If it is, record it as a construction anomaly point; otherwise, record it as a construction normal point.

[0080] For identified anomalies, the impact of cumulative deviations is reduced by adjusting the coordinate values ​​of the layout and re-excavating.

[0081] As a further aspect of the present invention: the dynamic monitoring of deviation trends during construction based on time series analysis, and the triggering of an early warning mechanism through a comprehensive quality evaluation coefficient to optimize construction quality, specifically includes:

[0082] During construction, the abnormal coefficient of construction layout deviation, the cumulative value of excavation depth deviation, and the overall correlation influence coefficient are collected in real time, and the dynamic changes of the data are recorded in the form of time series.

[0083] By continuously calculating the comprehensive anomaly coefficient of each construction point, the deviation trend during the construction process is analyzed.

[0084] When the overall anomaly coefficient exceeds the preset threshold, the system triggers an early warning mechanism, marks the abnormal area, and identifies potential quality problems during construction.

[0085] Based on the results of the early warning mechanism, the construction team immediately took optimization measures, including: calibrating the construction layout points and adjusting the excavation depth.

[0086] A big data-based engineering supervision quality assessment system includes:

[0087] The data acquisition module is used to collect construction layout point data and excavation depth data in real time during the construction process.

[0088] The outlining anomaly identification module is used to analyze the construction outlining point data, determine the error between the outlining point and the design value through deviation calculation, and identify outlining accuracy anomalies in combination with the characteristics of the construction area.

[0089] The construction deviation and impact assessment module is used to analyze excavation depth data, calculate deviations from the excavation depth data, calculate the cumulative deviation value based on the numerical difference between the excavation depth and the design depth, comprehensively evaluate the accuracy of the construction layout data and the deviation data of the excavation depth, and analyze the impact of layout accuracy on the excavation depth.

[0090] The comprehensive analysis and dynamic monitoring and evaluation module identifies anomalies in the construction process based on the comprehensive impact analysis results, adjusts the construction process to address deviations, dynamically monitors deviation trends based on time series analysis, and triggers an early warning mechanism through a comprehensive quality evaluation coefficient to optimize construction quality.

[0091] The beneficial effects of this invention are:

[0092] (1) This invention constructs an evaluation system centered on the abnormal coefficient of layout deviation, the cumulative value of excavation depth deviation, and the overall correlation influence coefficient, thereby achieving a comprehensive quantitative analysis of the mutual influence between layout accuracy and excavation depth during construction. This invention combines real-time data collection at the construction site with precise calculation and comprehensive analysis techniques to dynamically capture layout deviations caused by environmental changes or operational errors during construction and their gradual cumulative impact on excavation depth. By establishing a multi-dimensional correlation model, it can not only intuitively assess the error range of layout accuracy but also clarify its potential impact on subsequent construction procedures, providing a scientific basis for construction adjustments. Especially in complex construction scenarios, this method significantly improves the controllability of construction quality through precise location of anomalies and a trend early warning mechanism, ensuring the overall stability and high-quality delivery of the project.

[0093] (2) This invention constructs a dynamic monitoring model with a comprehensive anomaly coefficient, which integrates the anomaly coefficient of the layout deviation, the cumulative value of the excavation depth deviation, and the overall correlation influence coefficient according to a set weight, and combines time series analysis to comprehensively monitor the dynamic change trend of deviations during construction. Through multi-dimensional analysis of real-time collected data, this model can accurately capture construction anomalies, identify deviation fluctuation patterns, and dynamically trigger an early warning mechanism based on changes in the comprehensive anomaly coefficient, providing an efficient anomaly detection and response solution for construction quality management. When the system detects that the deviation exceeds the threshold, it can promptly mark the abnormal area and provide optimization suggestions to the construction party, including correcting the layout points and adjusting the excavation operation, thereby quickly repairing the problem area. This invention significantly improves the automation and scientific nature of construction quality management through real-time monitoring and accurate deviation assessment, ensuring high-quality execution of the construction process and the reliability of the final project results. Attached Figure Description

[0094] The invention will now be further described with reference to the accompanying drawings.

[0095] Figure 1 This is a flowchart illustrating the specific steps of the big data-based engineering supervision quality assessment method of the present invention.

[0096] Figure 2 This is a flowchart of the engineering supervision quality assessment system based on big data in this invention. Detailed Implementation

[0097] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0098] Please see Figure 1 As shown, this invention is a big data-based method for evaluating the quality of engineering supervision, comprising the following steps:

[0099] S1: During construction, real-time data on construction layout points and excavation depth are collected.

[0100] S2: Analyze the construction layout point data, determine the error between the layout point and the design value through deviation calculation, and identify abnormal points in layout accuracy in combination with the characteristics of the construction area;

[0101] S3: Analyze the excavation depth data, calculate the deviation of the excavation depth data, calculate the cumulative deviation value based on the numerical difference between the excavation depth and the design depth, comprehensively evaluate the accuracy of the construction layout data and the deviation data of the excavation depth, and analyze the impact of the layout accuracy on the excavation depth.

[0102] S4: Based on the results of the comprehensive impact analysis, identify the abnormal points in the construction process, make construction adjustments to address the impact of deviations in the construction process, dynamically monitor the deviation trend in the construction process based on time series analysis, trigger the early warning mechanism through the comprehensive quality evaluation coefficient, and optimize the construction quality.

[0103] In S1, during construction, real-time data on construction layout points and excavation depth are collected, specifically including:

[0104] During construction, the acquisition of construction layout point data was accomplished using high-precision surveying equipment, including total stations, GNSS (Global Navigation Satellite System) receivers, and electronic distance measuring instruments. These devices can acquire the actual coordinate values ​​of the layout points in real time and transmit the collected data to the construction monitoring system wirelessly. Total stations provide high-precision coordinate measurements in complex construction environments, while GNSS receivers are suitable for large construction sites, supporting real-time positioning and navigation. Combined with electronic distance measuring instruments, the length of the construction layout lines is accurately measured, ensuring the accuracy and reliability of the layout data.

[0105] Excavation depth data is collected in real time using a laser rangefinder. Suitable for open-air construction sites, the laser rangefinder measures excavation depth through beam reflection, providing high-precision depth data. All data collected by the equipment can be transmitted wirelessly or via wired network and synchronized in real time to a central monitoring system for subsequent analysis and quality assessment.

[0106] In S2, the construction layout point data is analyzed. Deviation calculations determine the error between the layout points and the design values. Furthermore, considering the characteristics of the construction area, anomalies in layout accuracy are identified, including:

[0107] The analysis of construction layout point data, the determination of the error between the layout points and the design values ​​through deviation calculation, and the identification of abnormal layout accuracy points in combination with the characteristics of the construction area, specifically includes:

[0108] Real-time data collection of construction layout points is performed to obtain the actual coordinate values ​​of each layout point.

[0109] Calculate the deviation value of each layout point and compare it with the design value to obtain the deviation error;

[0110] The environmental impact factor is calculated to correct deviations, adjust the accuracy of the layout data, and analyze the impact of the correction on the deviations.

[0111] Based on the analysis results, calculate the abnormal coefficient of the laying-out deviation, and determine whether the abnormal coefficient of the laying-out deviation is greater than or equal to the preset threshold. If it is, it is recorded as an abnormal point of laying-out accuracy; otherwise, it is recorded as a non-abnormal laying-out accuracy.

[0112] The process for obtaining the environmental impact coefficient is as follows:

[0113] Obtain real-time environmental data, including: ambient humidity data. air pressure data Land type and ambient temperature data ;

[0114] Establish a Bayesian network model, in which environmental humidity data is used. air pressure data Land type and ambient temperature data As an input node, construction deviation As an output node, the environmental impact coefficient As the target variable;

[0115] Based on environmental data, define the conditional probability distribution among nodes, specifically including:

[0116] Conditional probability of ambient humidity at a given temperature Conditional probability of air pressure at a given temperature and humidity Conditional probability of soil type under given humidity, air pressure and temperature The conditional probability of construction deviation under given humidity, air pressure, temperature and soil type. ;

[0117] The joint probability of all environmental factors and construction deviations is calculated using the following expression:

[0118] ;

[0119] In the formula, Represents the joint probability;

[0120] The environmental impact coefficient is calculated using joint probability, and the expression is as follows:

[0121] ;

[0122] in, Indicates the environmental impact coefficient. Represents the weighting function;

[0123] It should be noted that the environmental impact coefficient is used to correct the accuracy of certain data when calculating the deviation of the survey line.

[0124] The process for obtaining the abnormal coefficient of the laying deviation is as follows:

[0125] During construction, the actual coordinate values ​​of each layout point are collected in real time:

[0126] ;

[0127] in Number the layout points. It is a positive integer greater than 0. The dimension of the coordinates. It is a positive integer greater than 0. Indicates the first The actual coordinates of each laying point;

[0128] Obtain the design coordinates of each layout point:

[0129] ;

[0130] in, Indicates the first Design coordinates of each layout point;

[0131] Calculate the deviation between each layout point and its corresponding design coordinate. The calculation expression is as follows:

[0132] ;

[0133] in, Indicates the first Deviation value of each laying point;

[0134] An isolated forest model is constructed by building multiple trees and randomly partitioning them to calculate the isolation depth of each layout point. The isolation depth of each tree The isolated path length of the line-laying point on the corresponding tree is given by the average isolation depth calculated from multiple trees. The calculation expression is as follows:

[0135] ;

[0136] In the formula, This indicates the number of trees in an isolated forest. Represents a tree in an isolated forest. Indicates the first The first tree The isolation depth of each point Indicates the average isolation depth;

[0137] Based on the average isolation depth, the anomaly score for each survey point is calculated using the following expression:

[0138] ;

[0139] in, It is a constant. Indicates the first Anomaly score for each laying point;

[0140] ;

[0141] Where, Indicates the first Abnormal coefficient of layout deviation at each layout point This represents the maximum value of the deviation values ​​for all layout points.

[0142] It should be noted that the abnormal coefficient of the setting-out deviation reflects the degree of setting-out deviation during the construction process, and the larger the value of the abnormal coefficient of the setting-out deviation, the higher the degree of setting-out deviation during the construction process.

[0143] In S3, the excavation depth data is analyzed, and deviations are calculated. The cumulative deviation is calculated based on the numerical difference between the excavation depth and the design depth. The accuracy of the construction layout data and the deviation data of the excavation depth are comprehensively evaluated to analyze the impact of layout accuracy on the excavation depth, specifically including:

[0144] The analysis of excavation depth data, the calculation of deviations from the excavation depth data, and the calculation of the cumulative deviation value based on the numerical difference between the excavation depth and the design depth specifically include:

[0145] During construction, excavation depth data and design depth data for each construction point are collected in real time.

[0146] For each construction point, calculate the deviation between the actual excavation depth and the design depth. The calculation expression is as follows:

[0147] ;

[0148] Where, Indicates the first Deviation value at each point Indicates the first The actual excavation depth at each point Indicates the first The design excavation depth at each point, Indicates the excavation point;

[0149] Construct a training dataset, using the design depth data of each construction point as input features. Deviation value As the target variable, a training dataset is formed;

[0150] The model is trained using a random forest regression algorithm. Based on the designed deep data features, the bias value is predicted using multiple decision trees, where the output bias prediction value of each tree is: ;

[0151] Where, Indicates the first tree to the first Prediction of deviation at a single point Indicates the first Input features of points, Indicates the first A decision tree, Indicates the number of the decision tree;

[0152] The final bias prediction value is obtained by averaging the prediction results of all decision trees. The calculation expression is as follows:

[0153] ;

[0154] Where, Indicates the first The final predicted deviation value for each point. Indicates the total number of decision trees;

[0155] Calculate the sum of the predicted deviations for all construction points to obtain the cumulative deviation value.

[0156] The process of comprehensively evaluating the accuracy of construction layout data and the deviation data of excavation depth, and analyzing the impact of layout accuracy on excavation depth, specifically includes:

[0157] Obtain the abnormal coefficient of the layout deviation at each layout point and the corresponding cumulative deviation value at each excavation point;

[0158] Build the input dataset and target output dataset ;

[0159] in, This represents the cumulative deviation of the excavation depth. This indicates the total number of data collection points. This represents the deviation anomaly coefficient in the layout. This represents the actual preset influence coefficient of the layout accuracy on the excavation depth;

[0160] The collection points refer to the line surveying points and the corresponding excavation collection points;

[0161] Construct a support vector machine regression model, and calculate the expression as follows:

[0162] ;

[0163] Where, Represents the support vector coefficients. Represents the kernel function. Indicates the bias term. Let represent the data collection point, where the kernel function is calculated as follows:

[0164] ;

[0165] In the formula, These represent the adjustment parameters of the kernel function;

[0166] The influence of the layout accuracy and excavation depth is predicted using the trained support vector machine regression model. The calculation expression is as follows:

[0167] ;

[0168] In the formula, Indicates the first Predicted impact value of each collection point;

[0169] Based on the predicted impact value, the overall correlation coefficient is calculated using the following expression:

[0170] ;

[0171] In the formula, Indicates the overall correlation coefficient;

[0172] The overall correlation coefficient is used to evaluate the impact of layout accuracy on excavation depth.

[0173] It should be noted that the cumulative deviation value reflects the deviation of the excavation depth. The larger the cumulative deviation value, the higher the degree of abnormality of the corresponding excavation depth. Furthermore, the larger the value of the overall correlation coefficient, the higher the degree of influence of inaccurate layout on the excavation depth.

[0174] In S4, based on the results of the comprehensive impact analysis, anomalies in the construction process are identified, construction adjustments are made to address deviations, and deviation trends are dynamically monitored based on time series analysis. An early warning mechanism is triggered through the comprehensive quality evaluation coefficient to optimize construction quality. Specifically, this includes:

[0175] The process of identifying anomalies in the construction process based on the comprehensive impact analysis results and adjusting the construction to address deviations includes:

[0176] A dynamic monitoring data matrix is ​​established by real-time collection of the abnormal coefficient of layout deviation, the overall correlation influence coefficient, and the cumulative deviation value at each excavation point. Based on the numerical fluctuation characteristics during construction, the weights of the abnormal coefficient of layout deviation and the cumulative deviation value are set as follows: and The comprehensive anomaly coefficient is obtained by combining the overall correlation coefficient and the overall correlation coefficient. The calculation expression is as follows:

[0177] ;

[0178] In the formula, Represents the comprehensive anomaly coefficient. This indicates the total number of data collection points. Indicates the collection point. Indicates the first The cumulative deviation of the excavation depth corresponding to each collection point. Indicates the first Abnormal coefficient of layout deviation at each collection point This represents the overall correlation coefficient. and Indicates the preset weighting factor;

[0179] Determine whether the comprehensive anomaly coefficient is greater than or equal to the preset threshold. If it is, record it as a construction anomaly point; otherwise, record it as a construction normal point.

[0180] For identified anomalies, the impact of cumulative deviations is reduced by adjusting the layout coordinates and re-excavating to correct them.

[0181] The method of dynamically monitoring deviation trends during construction based on time series analysis and triggering an early warning mechanism through a comprehensive quality evaluation coefficient to optimize construction quality specifically includes:

[0182] During construction, the abnormal coefficient of construction layout deviation, the cumulative value of excavation depth deviation, and the overall correlation influence coefficient are collected in real time, and the dynamic changes of the data are recorded in the form of time series.

[0183] By continuously calculating the comprehensive anomaly coefficient of each construction point, the deviation trend during the construction process is analyzed.

[0184] When the overall anomaly coefficient exceeds the preset threshold, the system triggers an early warning mechanism, marks the abnormal area, and identifies potential quality problems during construction.

[0185] Based on the results of the early warning mechanism, the construction team immediately took optimization measures, including: calibrating the construction layout points and adjusting the excavation depth.

[0186] It should be noted that the comprehensive anomaly coefficient obtained through calculation can be used to determine whether each construction point in the construction process is abnormal, and can also be used for dynamic monitoring and early warning of the construction project.

[0187] Please see Figure 2 As shown, the engineering supervision quality assessment system based on big data includes:

[0188] The data acquisition module is used to collect construction layout point data and excavation depth data in real time during the construction process.

[0189] The outlining anomaly identification module is used to analyze the construction outlining point data, determine the error between the outlining point and the design value through deviation calculation, and identify outlining accuracy anomalies in combination with the characteristics of the construction area.

[0190] The construction deviation and impact assessment module is used to analyze excavation depth data, calculate deviations from the excavation depth data, calculate the cumulative deviation value based on the numerical difference between the excavation depth and the design depth, comprehensively evaluate the accuracy of the construction layout data and the deviation data of the excavation depth, and analyze the impact of layout accuracy on the excavation depth.

[0191] The comprehensive analysis and dynamic monitoring and evaluation module identifies anomalies in the construction process based on the comprehensive impact analysis results, adjusts the construction process to address deviations, dynamically monitors deviation trends based on time series analysis, and triggers an early warning mechanism through a comprehensive quality evaluation coefficient to optimize construction quality.

[0192] The working principle of this invention is as follows: Real-time acquisition of construction layout point data and excavation depth data, combined with the use of a total station, GNSS receiver, and laser rangefinder to obtain actual coordinate values ​​and depth information, ensures the accuracy of data acquisition. In the data analysis phase, the error between the layout points and design values ​​is first obtained through deviation calculation. Considering influencing factors such as environmental humidity, air pressure, and soil type, an environmental impact coefficient is calculated to correct the layout data. Furthermore, an anomaly coefficient for layout deviation is calculated to identify anomalies. Simultaneously, cumulative value analysis of excavation depth deviation is performed, and a support vector machine regression model is used to predict the overall correlation between layout accuracy and excavation depth. By dynamically monitoring changes in the comprehensive anomaly coefficient, deviation trends are analyzed based on time series data, and an early warning mechanism is triggered in conjunction with the comprehensive quality evaluation coefficient to guide adjustments and optimizations in the construction process. This invention, through an innovative calculation method for the comprehensive anomaly coefficient, establishes a precise anomaly identification and dynamic monitoring mechanism for construction quality, improving the intelligence level and quality control capabilities of engineering supervision.

[0193] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0194] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0195] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0196] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0197] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A big data-based method for evaluating the quality of engineering supervision, characterized in that, Includes the following steps: S1: During construction, real-time data on construction layout points and excavation depth are collected. S2: Analyze the construction layout point data, determine the error between the layout point and the design value through deviation calculation, and identify abnormal points in layout accuracy in combination with the characteristics of the construction area; S3: Analyze the excavation depth data, calculate the deviation of the excavation depth data, calculate the cumulative deviation value based on the numerical difference between the excavation depth and the design depth, comprehensively evaluate the accuracy of the construction layout data and the deviation data of the excavation depth, and analyze the impact of the layout accuracy on the excavation depth. S4: Based on the results of the comprehensive impact analysis, identify the abnormal points in the construction process, make construction adjustments to address the impact of deviations in the construction process, dynamically monitor the deviation trend in the construction process based on time series analysis, trigger the early warning mechanism through the comprehensive quality evaluation coefficient, and optimize the construction quality. Step S2 includes: Real-time data collection of construction layout points is performed to obtain the actual coordinate values ​​of each layout point. Calculate the deviation value of each layout point and compare it with the design value to obtain the deviation error; The environmental impact factor is calculated to correct deviations, adjust the accuracy of the layout data, and analyze the impact of the correction on the deviations. Based on the analysis results, calculate the deviation anomaly coefficient for the laying out. The process for obtaining the abnormal coefficient of the laying deviation is as follows: During construction, the actual coordinate values ​​of each layout point are collected in real time: ； in Number the layout points. It is a positive integer greater than 0. The dimension of the coordinates. It is a positive integer greater than 0. Indicates the first The actual coordinates of each laying point; Obtain the design coordinates of each layout point: ； in, Indicates the first Design coordinates of each layout point; Calculate the deviation between each layout point and its corresponding design coordinate. The calculation expression is as follows: ； in, Indicates the first Deviation value of each laying point; An isolated forest model is constructed by building multiple trees and randomly partitioning them to calculate the isolation depth of each layout point. The isolation depth of each tree The isolated path length of the line-laying point on the corresponding tree is given by the average isolation depth calculated from multiple trees. The calculation expression is as follows: ； Where, This indicates the number of trees in an isolated forest. Represents a tree in an isolated forest. Indicates the first The first tree The isolation depth of each point Indicates the average isolation depth; Based on the average isolation depth, the anomaly score for each survey point is calculated using the following expression: ； in, It is a constant. Indicates the first Anomaly score for each laying point; ； Where, Indicates the first Abnormal coefficient of layout deviation at each layout point This represents the maximum value of the deviation values ​​for all layout points.

2. The engineering supervision quality assessment method based on big data according to claim 1, characterized in that, The identification of abnormal points in the wire laying accuracy specifically includes: Determine whether the abnormal coefficient of the laying deviation is greater than or equal to the preset threshold. If it is, record it as an abnormal point of laying accuracy; otherwise, record it as a non-abnormal point of laying accuracy.

3. The engineering supervision quality assessment method based on big data according to claim 2, characterized in that, The process for obtaining the environmental impact coefficient is as follows: Obtain real-time environmental data, including: ambient humidity data. air pressure data Land type and ambient temperature data ; Establish a Bayesian network model, in which environmental humidity data is used. air pressure data Land type and ambient temperature data As an input node, construction deviation As an output node, the environmental impact coefficient As the target variable; Based on environmental data, define the conditional probability distribution among nodes, specifically including: Conditional probability of ambient humidity at a given temperature Conditional probability of air pressure at a given temperature and humidity Conditional probability of soil type under given humidity, air pressure and temperature The conditional probability of construction deviation under given humidity, air pressure, temperature and soil type. ; The joint probability of all environmental factors and construction deviations is calculated using the following expression: ； Where, Represents the joint probability; The environmental impact coefficient is calculated using joint probability, and the expression is as follows: ； in, Indicates the environmental impact coefficient. This represents the weighting function.

4. The engineering supervision quality assessment method based on big data according to claim 1, characterized in that, The analysis of excavation depth data, the calculation of deviations from the excavation depth data, and the calculation of the cumulative deviation value based on the numerical difference between the excavation depth and the design depth specifically include: During construction, excavation depth data and design depth data for each construction point are collected in real time. For each construction point, calculate the deviation between the actual excavation depth and the design depth. The calculation expression is as follows: ； Where, Indicates the first Deviation value at each point Indicates the first The actual excavation depth at each point Indicates the first The design excavation depth at each point, Indicates the excavation point; Construct a training dataset, using the design depth data of each construction point as input features. Deviation value As the target variable, a training dataset is formed; The model is trained using a random forest regression algorithm. Based on the designed deep data features, the bias value is predicted using multiple decision trees, where the output bias prediction value of each tree is: ; Where, Indicates the first tree to the first Prediction of deviation at a single point Indicates the first Input features of points, Indicates the first A decision tree, Indicates the number of the decision tree; The final bias prediction value is obtained by averaging the prediction results of all decision trees. The calculation expression is as follows: ； In the formula, Indicates the first The final predicted deviation value for each point. Indicates the total number of decision trees; Calculate the sum of the predicted deviations for all construction points to obtain the cumulative deviation value.

5. The engineering supervision quality assessment method based on big data according to claim 1, characterized in that, The process of comprehensively evaluating the accuracy of construction layout data and the deviation data of excavation depth, and analyzing the impact of layout accuracy on excavation depth, specifically includes: Obtain the abnormal coefficient of the layout deviation at each layout point and the corresponding cumulative deviation value at each excavation point; Build the input dataset and target output dataset ; in, This represents the cumulative deviation of the excavation depth. This indicates the total number of data collection points. This represents the deviation coefficient of the laying-out line. This represents the actual preset influence coefficient of the layout accuracy on the excavation depth; The collection points refer to the line surveying points and the corresponding excavation collection points; Construct a support vector machine regression model, and calculate the expression as follows: ； Where, Represents the support vector coefficients. Represents the kernel function. Indicates the bias term. Let represent the data collection point, where the kernel function is calculated as follows: ； Where, These represent the adjustment parameters of the kernel function; The influence of the layout accuracy and excavation depth is predicted using the trained support vector machine regression model. The calculation expression is as follows: ； Where, Indicates the first Predicted impact value of each collection point; Based on the predicted impact value, the overall correlation coefficient is calculated using the following expression: ； Where, Indicates the overall correlation coefficient; The overall correlation coefficient is used to evaluate the impact of layout accuracy on excavation depth.

6. The engineering supervision quality assessment method based on big data according to claim 1, characterized in that, The process of identifying anomalies in the construction process based on the comprehensive impact analysis results and adjusting the construction to address deviations includes: A dynamic monitoring data matrix is ​​established by real-time collection of the abnormal coefficient of layout deviation, the overall correlation influence coefficient, and the cumulative deviation value at each excavation point. Based on the numerical fluctuation characteristics during construction, the weights of the abnormal coefficient of layout deviation and the cumulative deviation value are set as follows: and The comprehensive anomaly coefficient is obtained by combining the overall correlation coefficient and the overall correlation coefficient. The calculation expression is as follows: ； Where, Represents the comprehensive anomaly coefficient. This indicates the total number of data collection points. Indicates the collection point. Indicates the first The cumulative deviation of the excavation depth corresponding to each collection point. Indicates the first Abnormal coefficient of layout deviation at each collection point This represents the overall correlation coefficient. and Indicates the preset weighting factor; Determine whether the comprehensive anomaly coefficient is greater than or equal to the preset threshold. If it is, record it as a construction anomaly point; otherwise, record it as a construction normal point. For identified anomalies, the impact of cumulative deviations is reduced by adjusting the coordinate values ​​of the layout and re-excavating.

7. The engineering supervision quality assessment method based on big data according to claim 1, characterized in that, The method of dynamically monitoring deviation trends during construction based on time series analysis and triggering an early warning mechanism through a comprehensive quality evaluation coefficient to optimize construction quality specifically includes: During construction, the abnormal coefficient of construction layout deviation, the cumulative value of excavation depth deviation, and the overall correlation influence coefficient are collected in real time, and the dynamic changes of the data are recorded in the form of time series. By continuously calculating the comprehensive anomaly coefficient of each construction point, the deviation trend during the construction process is analyzed. When the overall anomaly coefficient exceeds the preset threshold, the system triggers an early warning mechanism, marks the abnormal area, and identifies potential quality problems during construction. Based on the results of the early warning mechanism, the construction team immediately took optimization measures, including: calibrating the construction layout points and adjusting the excavation depth.

8. A big data-based engineering supervision quality assessment system, characterized in that: The method for assessing engineering supervision quality based on big data as described in any one of claims 1-7 includes: The data acquisition module is used to collect construction layout point data and excavation depth data in real time during the construction process. The outlining anomaly identification module is used to analyze the construction outlining point data, determine the error between the outlining point and the design value through deviation calculation, and identify outlining accuracy anomalies in combination with the characteristics of the construction area. The construction deviation and impact assessment module is used to analyze excavation depth data, calculate deviations from the excavation depth data, calculate the cumulative deviation value based on the numerical difference between the excavation depth and the design depth, comprehensively evaluate the accuracy of the construction layout data and the deviation data of the excavation depth, and analyze the impact of layout accuracy on the excavation depth. The comprehensive analysis and dynamic monitoring and evaluation module identifies anomalies in the construction process based on the comprehensive impact analysis results, adjusts the construction process to address deviations, dynamically monitors deviation trends based on time series analysis, and triggers an early warning mechanism through a comprehensive quality evaluation coefficient to optimize construction quality.

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