Data processing method and system for traffic engineering construction and storage medium

Abstract

The application relates to the technical field of data processing, and discloses a data processing method and system for traffic engineering construction and a storage medium. The method comprises the following steps: according to multi-source data of a construction site, processing construction equipment and environmental data through adaptive filtering to obtain a quality score table and a preprocessed data set; performing segmented noise reduction and time-frequency analysis based on the data to obtain engineering indexes containing equipment vibration, structural response and other characteristics; generating a quality parameter set and a process feature chain through feature recognition; optimizing model parameters by using a case library to obtain evaluation criteria; analyzing a construction process by fusing a model and the parameter set to obtain quality characteristics; and finally optimizing resource allocation to form a decision scheme. The application solves the technical problems of insufficient data processing, inaccurate quality evaluation and unreasonable resource scheduling in traditional construction quality control methods.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a data processing method, system and storage medium for traffic engineering construction. Background Technology

[0002] In the field of transportation engineering construction, with the continuous increase in project scale and complexity, construction quality control faces higher requirements. Existing construction quality control technologies mainly rely on manual inspections and single-point monitoring, assessing construction quality through regular checks and sampling tests. Simultaneously, various sensors are deployed at the construction site to collect construction data, including parameters such as structural displacement, vibration, and temperature, and a construction quality assessment system is established. Regarding resource allocation, traditional methods primarily rely on experience for manual allocation, while project management software is used to assist in construction progress control and resource allocation.

[0003] However, existing technologies have the following shortcomings: First, there is a lack of effective processing methods for multi-source heterogeneous data collected at construction sites, resulting in inconsistent data quality and making it difficult to provide a reliable basis for construction quality control; second, construction quality assessment relies too much on human experience and lacks scientific data analysis methods and evaluation standards; third, construction resource scheduling mainly relies on manual decision-making, failing to fully consider the correlation between resource allocation and construction quality, which can easily lead to resource waste or quality hazards; fourth, quality risk identification during construction is not timely, and quality control measures are lagging behind, affecting construction efficiency and quality. Summary of the Invention

[0004] This application provides a data processing method, system, and storage medium for traffic engineering construction, which solves the technical problems of insufficient data processing, inaccurate quality assessment, and unreasonable resource scheduling in traditional construction quality control methods.

[0005] Firstly, this application provides a data processing method for traffic engineering construction. The method includes: preprocessing dynamic parameters of construction equipment and construction environment status data using adaptive data filtering based on multi-source data collected at the construction site to obtain a construction data quality scoring table and a preprocessed dataset; performing time-frequency domain processing on the traffic engineering construction data through segmented noise reduction based on the preprocessed dataset and the construction data quality scoring table to obtain engineering construction characteristic indicators; wherein the engineering construction characteristic indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment influencing factors, engineering material performance indicators, and construction process monitoring data; based on the engineering... Construction characteristic indicators are extracted through multi-dimensional process mapping and dynamic feature recognition to generate a time-series correlated set of engineering construction quality status parameters and a spatiotemporal feature chain of construction processes. Based on the set of engineering construction quality status parameters and the spatiotemporal feature chain of construction processes, the construction model is dynamically calibrated and its parameters are optimized using a construction case knowledge base to obtain a standard model for evaluating traffic engineering construction. The standard model for evaluating traffic engineering construction is then fused with the set of engineering construction quality status parameters, and the construction process is analyzed using a quality indicator extractor to obtain a set of construction quality features. Using the set of construction quality features, the construction plan is dynamically optimized through a construction resource scheduling system to obtain an optimized construction decision scheme.

[0006] Secondly, this application provides a data processing system for traffic engineering construction, the data processing system for traffic engineering construction comprising:

[0007] The data acquisition module is used to preprocess the dynamic parameters of construction equipment and the status data of the construction environment based on multi-source data collected at the construction site through adaptive data filtering, so as to obtain a construction data quality scoring table and a preprocessed dataset.

[0008] The processing module is used to perform time-frequency domain processing on the traffic engineering construction data through segmented noise reduction based on the preprocessed dataset and the construction data quality scoring table to obtain engineering construction characteristic indicators; wherein, the engineering construction characteristic indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment impact factors, engineering material performance indicators and construction process monitoring data;

[0009] The extraction module is used to extract the features of the construction process based on the engineering construction feature indicators through multi-dimensional process mapping and dynamic feature recognition, and generate a time-series associated set of engineering construction quality status parameters and a spatiotemporal feature chain of construction processes.

[0010] The optimization module is used to dynamically calibrate and optimize the construction model based on the set of engineering construction quality status parameters and the spatiotemporal feature chain of construction procedures, and obtain a standard model for evaluating traffic engineering construction.

[0011] The fusion module is used to fuse the traffic engineering construction evaluation standard model with the engineering construction quality status parameter set, and to analyze the construction process through the quality index extractor to obtain the construction quality feature set.

[0012] The optimization module is used to dynamically optimize the construction plan through the construction resource scheduling system using the construction quality feature set to obtain an optimized construction decision plan.

[0013] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the aforementioned data processing method for traffic engineering construction.

[0014] The technical solution provided in this application preprocesses the dynamic parameters of construction equipment and the status data of the construction environment through adaptive data filtering, significantly improving the quality of the original data and providing a reliable data foundation for subsequent analysis. It employs segmented noise reduction to process the traffic engineering construction data in the time and frequency domain, obtaining engineering construction characteristic indicators including vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment influencing factors, engineering material performance indicators, and construction process monitoring data, achieving comprehensive extraction of multi-dimensional characteristics of the construction process. Through multi-dimensional process mapping and dynamic feature recognition, it extracts construction process characteristics, generating a time-series correlated set of engineering construction quality status parameters and a spatiotemporal feature chain of construction processes, establishing a mapping relationship between construction quality and process progress. It utilizes a construction case knowledge base to dynamically calibrate and optimize the construction model, obtaining a standard model for traffic engineering construction evaluation, improving the accuracy of quality assessment. It fuses the standard model for traffic engineering construction evaluation with the set of engineering construction quality status parameters, and analyzes the construction process through a quality indicator extractor, achieving accurate assessment of construction quality. Finally, it uses a construction resource scheduling system to dynamically optimize the construction plan, obtaining an optimized construction decision scheme, realizing the rational allocation of construction resources and effective control of construction quality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of one embodiment of the data processing method for traffic engineering construction in this application.

[0017] Figure 2 This is a distribution map of environmental and material characteristics in the embodiments of this application;

[0018] Figure 3 This is a schematic diagram of one embodiment of a data processing system for traffic engineering construction in this application. Detailed Implementation

[0019] This application provides a data processing method, system, and storage medium for traffic engineering construction. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the data processing method for traffic engineering construction in this application includes:

[0021] Step S101: Based on the multi-source data collected at the construction site, the dynamic parameters of the construction equipment and the status data of the construction environment are preprocessed through adaptive data filtering to obtain the construction data quality scoring table and the preprocessed dataset.

[0022] Step S102: Based on the preprocessed dataset and the construction data quality scoring table, the traffic engineering construction data is processed in the time and frequency domain through segmented noise reduction to obtain the engineering construction characteristic indicators; among them, the engineering construction characteristic indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment impact factors, engineering material performance indicators and construction process monitoring data.

[0023] Step S103: Based on engineering construction characteristic indicators, extract construction process characteristics through multi-dimensional process mapping and dynamic feature recognition to generate a time-series associated engineering construction quality status parameter set and construction process spatiotemporal feature chain;

[0024] Step S104: Based on the engineering construction quality status parameter set and the spatiotemporal characteristic chain of construction procedures, the construction model is dynamically calibrated and the parameters are optimized through the construction case knowledge base to obtain the standard model for evaluating traffic engineering construction.

[0025] Step S105: The standard model for evaluating traffic engineering construction is fused with the set of engineering construction quality status parameters. The construction process is analyzed through a quality index extractor to obtain a set of construction quality features.

[0026] Step S106: Using the construction quality feature set, the construction plan is dynamically optimized through the construction resource scheduling system to obtain the construction optimization decision plan.

[0027] It is understood that the executing entity of this application can be a data processing system used for traffic engineering construction, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0028] Specifically, in the data preprocessing stage, multi-source data is collected from the construction site, including construction equipment sensor data, environmental monitoring data, and structural response data. This raw data undergoes adaptive data filtering preprocessing, which involves dividing the data into time series segments, segmenting the continuous data stream according to fixed time windows, with the length of each window dynamically adjusted based on the data sampling frequency. For dynamic parameters of the construction equipment, data quality is assessed by calculating the mean, standard deviation, skewness, and kurtosis, generating a data quality score. For construction environmental status data, scores are awarded based on three dimensions: completeness, consistency, and validity, forming a construction data quality scoring table.

[0029] Time-frequency domain processing was performed based on the preprocessed dataset and construction data quality scoring table. A segmented noise reduction method was used, dividing the data into segments according to process characteristics, and frequency domain analysis was performed on each segment. Fourier transform was used to convert the time-domain signal to the frequency domain, extracting spectral features to obtain the vibration frequency characteristic values ​​of the construction equipment. Simultaneously, phase analysis was performed on the structural dynamic response data to obtain structural dynamic response parameters. For the construction environment data, temperature, humidity, and wind speed variation characteristics were extracted to form construction environment influencing factors. Statistical analysis was performed on the engineering material performance testing data to establish engineering material performance indicators. Furthermore, data completion and outlier handling were performed on the real-time monitoring data of the construction process to generate construction process monitoring data. Multi-dimensional process mapping was performed based on engineering construction characteristic indicators. This process establishes a process-feature correspondence, organizing different characteristic indicators according to construction processes. Construction process features were extracted using a dynamic feature recognition algorithm, which includes three steps: data standardization, feature dimensionality reduction, and feature selection. Data standardization transforms features of different dimensions to a unified scale, feature dimensionality reduction reduces data redundancy, and feature selection retains key feature parameters. After processing, a time-series correlated set of engineering construction quality status parameters is formed, which includes key control indicators for construction quality. Simultaneously, a time-space feature chain of construction procedures is established through spatiotemporal feature correlation analysis, describing the logical relationships and spatiotemporal dependencies between procedures. A construction case knowledge base stores historical construction case data, including engineering features, quality parameters, and construction plans. Similar cases are retrieved from the knowledge base through feature matching, feature similarity is calculated, and cases with high matching degrees are selected as references. Dynamic feature calibration is performed on the construction model, adjusting model parameters based on actual construction data to optimize model performance. Through parameter optimization, a standard model for evaluating traffic engineering construction is obtained, which can accurately assess the construction quality status. The standard model for evaluating traffic engineering construction is fused with the set of engineering construction quality status parameters using a weighted fusion method, with weight coefficients determined through data correlation analysis. A quality indicator extractor analyzes the fused features, extracts key quality indicators, and establishes a quality assessment indicator system. After data analysis, a construction quality feature set is formed, including quality control parameters, anomaly warning indicators, and risk assessment results.

[0030] Construction scheme optimization is performed based on a construction quality feature set. A construction resource scheduling system is used to rationally allocate construction resources, considering resource constraints, process dependencies, and quality control requirements. The construction scheme is dynamically optimized by adjusting construction procedures, resource allocation, and schedule to obtain the optimal construction optimization decision scheme.

[0031] Taking elevated bridge construction as an example, construction data is collected during processes such as rebar processing, concrete pouring, scaffold installation, and formwork installation. Outliers are removed through data preprocessing to assess data quality. Spectral analysis is performed on the vibration data from concrete vibrating equipment to extract vibration frequency characteristics. Temperature and humidity changes during concrete pouring are monitored to analyze the impact of environmental factors on concrete strength. A process quality status assessment model is established to dynamically evaluate construction quality based on monitoring data. Based on historical case experience, construction plans are optimized, and construction resources are rationally allocated to ensure construction quality control.

[0032] In this embodiment, adaptive data filtering is used to preprocess the dynamic parameters of construction equipment and the status data of the construction environment, significantly improving the quality of the original data and providing a reliable data foundation for subsequent analysis. Segmented noise reduction is employed to process the traffic engineering construction data in the time-frequency domain, obtaining engineering construction characteristic indicators including vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment influencing factors, engineering material performance indicators, and construction process monitoring data, achieving comprehensive extraction of multi-dimensional characteristics of the construction process. Multi-dimensional process mapping and dynamic feature recognition are used to extract construction process features, generating a time-series correlated set of engineering construction quality status parameters and a spatiotemporal feature chain of construction processes, establishing a mapping relationship between construction quality and process progress. A construction case knowledge base is used to dynamically calibrate and optimize the construction model, obtaining a standard model for traffic engineering construction evaluation, improving the accuracy of quality assessment. The standard model for traffic engineering construction evaluation is fused with the set of engineering construction quality status parameters, and the construction process is analyzed using a quality indicator extractor, achieving accurate assessment of construction quality. Finally, a construction resource scheduling system is used to dynamically optimize the construction plan, obtaining an optimized construction decision scheme, achieving reasonable allocation of construction resources and effective control of construction quality.

[0033] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0034] (1) Based on the multi-source data collected at the construction site, the multi-source data is segmented into time series using a data preprocessing analyzer to form a time series data sequence;

[0035] (2) Based on the time series data sequence, the dynamic parameters of construction equipment and the status data of construction environment are evaluated and classified to generate a construction data classification table;

[0036] (3) Input the construction data classification table into the data quality scoring system, score it according to the scoring rules, and obtain the construction data quality scoring table;

[0037] (4) Based on the construction data quality scoring table, filter valid data through data filters to generate a preprocessed dataset;

[0038] The time-series data includes equipment operating status data, environmental monitoring data, and engineering parameter data;

[0039] (5) The construction data classification table is classified and labeled according to the data source and data characteristics;

[0040] (6) The construction data quality scoring table includes data integrity score, data consistency score and data validity score.

[0041] Specifically, the data preprocessing analyzer performs time-series segmentation on multi-source data collected from the construction site. This multi-source data includes raw data from various sensing devices at the construction site. The time-series segmentation uses a sliding time window method, dividing the continuous data stream into multiple data segments at fixed time intervals. The size of the time window is determined based on the data sampling frequency; a smaller time window, such as 1 minute, is used for high-frequency sampled equipment operating status data, while a larger time window, such as 10 minutes, is used for low-frequency sampled environmental monitoring data. Through time-series segmentation, a time-series data sequence is formed, containing equipment operating status data, environmental monitoring data, and engineering parameter data. After obtaining the time-series data sequence, the dynamic parameters of the construction equipment and the construction environment status data undergo quality assessment and classification. The quality assessment checks the validity of the data, removing obviously abnormal data points, such as zero values ​​or out-of-range values ​​caused by sensor malfunctions. Then, statistical characteristic analysis is performed on the data, calculating statistics such as mean, standard deviation, skewness, and kurtosis to evaluate the data's distribution characteristics. The classification process is based on the data's source and characteristics, dividing the data into different categories. For example, dynamic parameters of construction equipment are categorized by equipment type, including vibration data, temperature data, and pressure data; environmental status data are categorized by monitoring object, including air quality data, meteorological data, and noise data. Through quality assessment and classification, a construction data classification table is generated.

[0042] After the construction data classification table is input into the data quality scoring system, the system scores the data according to preset scoring rules. Data integrity score primarily examines the amount of missing data, scored by calculating the missing data rate; the lower the missing data rate, the higher the integrity score. Data consistency score primarily examines the continuity and stability of the data, scored by calculating the coefficient of variation and the degree of abrupt changes. Data validity score examines the accuracy and reliability of the data, scored through data range checks and outlier detection. The scores for each category are weighted and averaged to obtain a comprehensive score, forming the construction data quality scoring table. Based on the construction data quality scoring table, data is filtered. The data filter sets multi-level filtering conditions, setting thresholds based on the comprehensive data quality score to remove data with excessively low scores. Then, it filters based on the temporal continuity of the data, removing data with time jumps or repetitions. Finally, it filters based on the physical meaning of the data, removing data that does not conform to physical laws. After multi-level filtering, a preprocessed dataset is obtained. The classification and labeling process in the construction data classification table adopts a multi-level classification system. The first level classifies data according to its source, distinguishing between sensor data, detection equipment data, and manually recorded data. The second layer categorizes data based on its characteristics, including time-series data, spatial data, and attribute-based data. Each data record is labeled with its source category and characteristic category to facilitate subsequent processing.

[0043] The three scores in the construction data quality scoring table each have their own specific calculation methods. The data integrity score is obtained by calculating the ratio of the actual number of data records to the theoretical number. The data consistency score is obtained by calculating the autocorrelation coefficient and trend indicators of the data series. The data validity score is obtained by calculating the signal-to-noise ratio and accuracy indicators of the data.

[0044] For example, sensing devices collect data such as concrete vibration frequency, concrete temperature, and ambient temperature and humidity. A data preprocessing analyzer divides the continuously collected data into time periods according to the pouring process, forming a time-series data sequence. The quality of the vibration equipment operation data and environmental monitoring data is assessed, revealing drift in some temperature sensor data. This data is corrected and categorized. A data quality scoring system scores each type of data. Concrete vibration frequency data, due to stable sampling, receives a high score for completeness and consistency; ambient temperature and humidity data, affected by weather conditions, receives a lower consistency score. A data filter selects data based on the scoring results, retaining the higher-quality data to form a preprocessed dataset.

[0045] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0046] (1) Based on the preprocessed dataset, the traffic engineering construction data is segmented according to the construction process time nodes to form process data segments; the process data segments are transformed into equal interval time series through resampling processing to generate construction time series;

[0047] (2) Based on the data integrity score in the construction data quality scoring table, local linear interpolation is used to complete the missing values ​​in the construction time series; median filtering is used to process values ​​that exceed the normal range to obtain the corrected dataset.

[0048] (3) Perform time-frequency domain analysis on the corrected dataset, convert the continuous signal into a time spectrum, extract the frequency band energy distribution characteristics, and construct a time-frequency feature matrix;

[0049] (4) Based on the time-frequency feature matrix, feature extraction is performed for different data types: the vibration frequency feature value of the construction equipment is obtained through amplitude analysis, the dynamic response parameters of the structure are obtained through displacement calculation, the construction environment impact factor is extracted based on the monitoring data, the performance index of engineering materials is calculated based on the detection data, and the monitoring data of the construction process is organized based on the real-time monitoring data.

[0050] (5) Divide the vibration frequency characteristic values ​​of construction equipment into frequency bands of 0-10Hz, 10-50Hz, and 50-100Hz according to the frequency range, calculate the energy value of each frequency band, and extract the main frequency characteristics; establish the displacement-time relationship based on the structural dynamic response parameters, and mark the abnormal response interval by comparing with the set threshold; construct an environmental parameter correlation map using construction environmental impact factors; establish a material performance evaluation sequence based on the engineering material performance indicators; sort the construction process monitoring data according to the collection time and correspond it with the construction progress to generate a construction monitoring data stream;

[0051] (6) By combining the main frequency features, response anomaly intervals, environmental parameter correlation maps, material performance evaluation sequences and construction monitoring data streams through feature recombination, engineering construction feature indicators are formed.

[0052] Specifically, the data is divided according to the time nodes of the construction process, with each process corresponding to a data segment, such as pile foundation construction, beam and slab pouring, and road paving. For process data segments with different sampling frequencies, a unified sampling interval is achieved through resampling, downsampling high-frequency data, and interpolation of low-frequency data to transform them into an evenly spaced time series. To address the data missing issues in the construction time series, the data intervals requiring completion are determined based on the data integrity score in the construction data quality scoring table. A local linear interpolation method is used, calculating interpolation based on valid data before and after the missing points. For outliers exceeding the normal range, such as data jumps caused by sudden sensor malfunctions, median filtering is used for smoothing, and the median within the data window is used to replace the outlier, resulting in a corrected dataset.

[0053] The corrected dataset undergoes time-frequency domain analysis, employing a short-time Fourier transform to decompose the time-domain signal into a time-frequency representation, obtaining the frequency components of the signal at different time points. By calculating the energy magnitude of each frequency component, the energy distribution characteristics of the frequency band are obtained, and these characteristics are organized into a time-frequency feature matrix.

[0054] Multidimensional feature extraction is performed based on the time-frequency feature matrix. The structural dynamic response parameters are obtained through displacement calculation.

[0055]

[0056] in, Indicates dynamic response parameters. Let be the weight coefficient for the i-th measurement point. The vibration frequency, For horizontal displacement, The vertical displacement is n, and the number of measuring points is n.

[0057] The extraction of construction environmental impact factors was carried out using:

[0058]

[0059] in, Indicates environmental impact factors, The comprehensive environmental impact coefficient. These are the weighting coefficients for temperature, humidity, and wind speed, respectively. denoted as temperature, humidity, and wind speed at the j-th time point, respectively, and m represents the number of time points.

[0060] The formula for calculating the performance indicators of engineering materials is as follows:

[0061]

[0062] in, Indicates material performance indicators, These are the weighting coefficients for strength, density, and moisture content, respectively. These represent the strength, density, and moisture content values ​​of the k-th material, respectively. This refers to the types and quantities of materials.

[0063] The vibration frequency characteristics of construction equipment are divided into low-frequency (0-10Hz), mid-frequency (10-50Hz), and high-frequency (50-100Hz) bands, and the energy value of each band is calculated. Displacement-time curves are plotted based on the structural dynamic response parameters, displacement thresholds are set, and abnormal intervals exceeding the thresholds are marked. The correlation between construction environmental influencing factors is represented by a correlation graph, showing the mutual influence between various environmental parameters. Engineering material performance indicators are arranged in time series to form a performance evaluation sequence. Construction process monitoring data is correlated with the construction schedule to generate a monitoring data stream. Various features are combined through feature recombination. The main frequency characteristics, abnormal response intervals, environmental parameter correlation graphs, material performance evaluation sequences, and construction monitoring data streams are aligned in chronological order to establish the correspondence between features, forming engineering construction characteristic indicators.

[0064] For example, during the paving of elevated bridge decks, vibration data from the asphalt paver, road surface temperature data, and asphalt mixture performance data are segmented according to the paving sections. High-frequency data collected by vibration sensors are downsampled and synchronized with temperature sensor data to a 1-minute time series. Missing values ​​in the temperature data due to poor sensor contact are supplemented using local linear interpolation. Vibration characteristics of the paver are extracted through time-frequency analysis, revealing that the main energy is concentrated in the 20-40Hz frequency band. Combined with displacement monitoring data, areas of uneven compaction are identified. Furthermore, by incorporating parameters such as ambient temperature, asphalt temperature, and oil content, a correlation between material properties and construction quality is established.

[0065] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0066] (1) Based on the engineering construction characteristic index, the vibration frequency characteristic value of the construction equipment and the dynamic response parameter of the structure are decomposed by Fourier transform, the frequency domain characteristics are extracted, the frequency-amplitude relationship matrix is ​​established, and the equipment-structure characteristic association table is generated.

[0067] (2) Based on the environmental impact factors and the performance indicators of engineering materials, a spatial distribution network of monitoring points is constructed, spatial correlation coefficients are calculated, and interpolation is used to supplement the monitoring blind area data to form an environmental-material characteristic distribution map;

[0068] (3) The monitoring data of the construction process is divided into segments according to the process nodes. Statistical features are extracted for each segment of data, including the calculation of mean, variance, skewness and kurtosis, and a process-feature correspondence table is established.

[0069] (4) Through multidimensional feature mapping, the equipment-structure feature association table and the environment-material feature distribution map are spatiotemporally registered to generate a time-series associated set of engineering construction quality status parameters;

[0070] (5) Based on the process-feature correspondence table, conduct time series analysis on the construction quality status, establish the correlation between processes, and form a spatiotemporal feature chain of construction processes.

[0071] Specifically, the vibration frequency characteristic values ​​of construction equipment and the dynamic response parameters of the structure are subjected to spectral decomposition, and the time-domain signals are transformed to the frequency domain using Fourier transform. Amplitude and phase spectrum features are extracted from the transformed frequency-domain signals to establish a frequency-amplitude relationship matrix. This matrix reflects the correspondence between equipment vibration and structural response, thereby generating an equipment-structure characteristic association table. In terms of environmental and material data processing, a spatial distribution network of monitoring points is constructed at the construction site based on construction environmental influencing factors and engineering material performance indicators. Spatial correlation coefficients are calculated using the distance between monitoring points and data similarity, and Kriging interpolation is used to supplement data for monitoring blind spots. This process forms an environmental-material characteristic distribution map, reflecting the spatial distribution patterns of environmental factors and material properties. Figure 2 The figure shown is an environmental-material characteristic distribution map of an embodiment of this application, illustrating the monitoring network layout and data distribution at the construction site. Solid dots P1-P6 represent the actual monitoring points, dashed dots represent supplementary monitoring points obtained through interpolation, dashed lines indicate the spatial correlation between monitoring points, and curves represent the contour lines of environmental and material characteristics. The figure provides legends for monitoring points, interpolation points, spatial correlation, and characteristic contour lines, intuitively reflecting the spatial distribution patterns of environmental factors and material properties at the construction site.

[0072] The following formula is used to extract statistical features from the construction process monitoring data:

[0073]

[0074] in, Represents statistical characteristic values. Let be the weighting coefficient for the i-th process. The monitoring value at time point j of the i-th process is... These are the weighting coefficients for variance, skewness, and kurtosis, respectively. Let be the mean of the i-th process. Let k be the standard deviation, k be the number of time points, and n be the number of processes.

[0075] Feature spatiotemporal registration uses the following formula:

[0076]

[0077] in, For the registration matrix, and For registration weighting coefficients, For equipment-structural features, For environmental and material characteristics, and They are time and space coordinates, respectively. Indicates feature fusion operation, This represents the coordinate matching operation, where m and n are the number of features, respectively.

[0078] For example, during the installation of bridge bearings in elevated bridge construction, vibration data of the bearing installation equipment and structural response data of the bearing locations are collected. Spectral analysis reveals the correspondence between the dominant vibration frequency of the equipment and the structural response frequency, identifying abnormal vibrations during bearing installation. Simultaneously, temperature and humidity sensors and material performance testing points are deployed around the bearings to form a monitoring network. The monitoring data is divided according to the bearing installation sequence, and the statistical characteristics of each sequence are extracted. Spatiotemporal registration maps the equipment vibration, structural response, environmental conditions, and material performance data to a unified spatiotemporal coordinate system, establishing a set of process quality state parameters. Based on the logical sequence and quality dependencies between processes, a spatiotemporal feature chain of construction processes is constructed.

[0079] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0080] (1) Based on the set of engineering construction quality status parameters, data matching is performed in the construction case knowledge base. By calculating parameter similarity, spatiotemporal feature correlation and process logic relationship, the feature set of historical construction cases is selected.

[0081] (2) For the spatiotemporal feature chain of construction procedures, conduct procedure analysis based on the feature set of historical construction cases, extract parameters such as procedure time nodes, resource consumption, construction progress, and quality control points, and construct a construction feature correlation matrix;

[0082] (3) Divide the construction feature correlation matrix into multiple time windows according to the process flow, perform statistical analysis on the feature data in each window, calculate the feature mean, standard deviation and data distribution characteristics, and obtain the feature calibration parameter table;

[0083] (4) Based on the characteristic calibration parameter table, the construction sequence data is dynamically calibrated, the calibration coefficient is calculated, the parameters that deviate from the standard range are corrected, and the construction parameter optimization table is generated;

[0084] (5) By combining the construction parameter optimization table with the process calibration parameters, the feature sequence is reconstructed according to the construction process sequence to generate a standard model for evaluating traffic engineering construction.

[0085] Specifically, based on the set of engineering construction quality status parameters, data matching is performed on the construction case knowledge base. Similarity calculations are performed on engineering parameters, including the matching degree assessment of key features such as project scale, structural type, and construction technology. Then, the correlation of spatiotemporal features is analyzed, comparing the similarity of factors such as construction environmental conditions, geographical location characteristics, and construction season. Simultaneously, the logical relationships of construction processes are examined, assessing the comparability of the organization methods and connection patterns of construction processes. Through multi-dimensional matching analysis, a set of cases similar to the current project characteristics is selected from the historical case database. Analysis of the spatiotemporal feature chain of construction processes is fundamental to a deep understanding of the construction process. The historical construction case feature set is used to analyze the processes, extracting process time node information, including the start time, end time, duration, and key milestones of each process. Next, the resource consumption of each process is statistically analyzed, involving data such as human resource allocation, equipment usage time, and material usage. Construction progress information is also recorded, including planned progress, actual progress, and progress deviations. Quality control points are set for each process, recording quality inspection data and quality anomalies. These features are organized into a construction feature association matrix, describing the correlation and dependence between processes.

[0086] The analysis of the construction feature correlation matrix employs a time window method, dividing the entire construction process into several time windows according to the natural division of construction procedures. Within each time window, statistical analysis is performed on the feature data. The mean is calculated to reflect the central tendency of the features, the standard deviation characterizes the dispersion of the data, and the quantiles describe the distribution characteristics of the data. A feature calibration parameter table is obtained through statistical analysis, containing the standard range and fluctuation range of each feature parameter. Dynamic calibration is performed based on the feature calibration parameter table, calculating calibration coefficients for each parameter in the construction procedure time-series data. The calibration coefficients reflect the degree of deviation between the actual parameters and the standard parameters. For parameters deviating from the standard range, corrections are made according to the calibration coefficients. The correction process considers the mutual influence and constraint relationships between parameters to avoid a chain reaction of other parameters caused by the correction of a single parameter. After parameter correction, a construction parameter optimization table is generated, containing the corrected construction parameter values.

[0087] The construction parameter optimization table and process calibration parameters are integrated through feature combination. The integration process reconstructs the feature sequence according to the logical order of construction procedures, establishing correspondences and conversion rules between features. This feature recombination forms a standard model for evaluating traffic engineering construction, which accurately reflects the construction quality status and guides construction process control.

[0088] In the installation of steel box girders for the cross-sea bridge, similar engineering cases were retrieved from the construction case database. By comparing characteristics such as bridge span, structural form, and construction technology, historical cases with high similarity were selected. The installation process of the steel box girders was analyzed, extracting time node data for processes such as hoisting preparation, main girder lifting, temporary fixing, and welding connections. Resource consumption data, including the usage time of large lifting equipment, welding material consumption, and the number of professional welders, were statistically analyzed for each process. The comparison between actual construction progress and planned progress was recorded, and quality control points such as weld quality and geometric dimensions were set. The process data was divided into time windows according to construction stages, and the variation patterns of characteristic parameters were analyzed within each window. Abnormal data, such as the working parameters of hoisting equipment and welding process parameters, were corrected through parameter calibration. The calibrated parameters were then reorganized according to the construction process to form a construction quality assessment model.

[0089] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0090] (1) Based on the standard model for evaluating the construction of traffic engineering, the data of the construction quality status parameter set is standardized, the mean and standard deviation of the parameters are calculated, the construction quality evaluation index system is established, and the quality evaluation parameter table is formed.

[0091] (2) Calculate the feature correlation degree of the indicator data in the quality evaluation parameter table through correlation analysis, including the correlation coefficient, contribution degree and influence weight between parameters, and generate the quality indicator correlation matrix;

[0092] (3) The quality index correlation matrix is ​​segmented according to the construction progress nodes, the quality control parameters of each construction stage are calculated, the key quality control points are extracted, and the construction quality control sequence is constructed.

[0093] (4) Perform deviation analysis between the construction quality control sequence and the standard value, calculate the quality deviation value, mark the quality abnormality interval, and form a quality assessment report;

[0094] (5) The key indicators in the quality assessment report are classified and summarized by feature recombination, and the features are combined according to the construction procedure sequence to obtain the construction quality feature set.

[0095] Specifically, the original parameter data is normalized to eliminate the influence of dimensions and orders of magnitude. Statistical characteristics are calculated for different types of parameters, including the mean reflecting the average level of the parameter and the standard deviation characterizing the degree of fluctuation. Based on this, a construction quality evaluation index system is established, covering multiple dimensions such as construction technology indicators, material performance indicators, structural quality indicators, and environmental impact indicators, forming a quality evaluation parameter table. The index data in the quality evaluation parameter table undergoes correlation analysis to calculate the correlation coefficient matrix between parameters. The correlation coefficient reflects the strength of the linear association between parameters, and principal component analysis is used to determine the contribution of each parameter to the overall quality. Simultaneously, the analytic hierarchy process (AHP) is used to calculate the influence weights of the parameters, considering a comprehensive evaluation of expert experience and engineering practice. The correlation coefficients, contribution rates, and influence weights are combined to form a quality index correlation matrix, which describes the intrinsic relationships between quality parameters.

[0096] The following formulas are used to calculate the construction quality control parameters:

[0097]

[0098] in, These are quality control parameter values. Let be the weighting coefficient for the i-th construction stage. and These are the weights of the process parameters and material parameters, respectively. These are process control parameters. For material quality parameters, For environmental impact weighting, These are environmental condition parameters. denoted as the environmental impact index, where n is the number of construction stages, m is the number of control parameters, and p is the number of environmental parameters.

[0099] Based on the calculation results of the quality control parameters, a construction quality control sequence is obtained. Deviation analysis is performed between this sequence and the quality standard values ​​to calculate the differences between the actual values ​​and the standard values. By setting deviation thresholds, quality anomaly intervals exceeding the allowable range are marked. The analysis results are summarized to form a quality assessment report, which details the changing trends of quality parameters, anomalies, and their causes. Key indicators in the quality assessment report are categorized and organized. Following the logical sequence of construction procedures, relevant indicators are recombined to establish correspondences between them. Through feature recombination, a construction quality feature set is obtained, which comprehensively reflects the quality status of the construction process.

[0100] For example, during highway pavement construction, parameters such as asphalt mixture temperature, compaction degree, and smoothness are standardized, and an evaluation index system is established based on specifications. Correlation analysis reveals a significant correlation between compaction degree and temperature, indicating that ambient temperature has a significant impact on construction quality. Quality control points are set up at key processes such as paving and compaction to monitor construction parameters in real time. When the compaction degree of a certain section of pavement is found to be lower than the standard requirement, the cause of the quality anomaly is identified by analyzing the temperature field distribution and construction process parameters, and corresponding adjustment measures are taken. All quality indicators are organized according to the construction sequence to form a quality feature set.

[0101] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0102] (1) Decompose the construction quality feature set into data, extract key quality control parameters and construction process node parameters, and establish a process quality correlation sequence;

[0103] (2) Conduct a correlation analysis on the construction resource demand and construction process time in the process quality correlation sequence, calculate the resource allocation weight coefficient, and obtain the construction resource allocation table;

[0104] (3) Through resource scheduling analysis, perform inter-process resource balance calculations on the construction resource allocation table, identify resource conflict points, mark key time nodes for resource allocation, and generate a resource scheduling optimization table;

[0105] (4) Based on the resource scheduling optimization table, conduct quality constraint analysis on the construction process, establish a mapping relationship diagram between construction resources and process quality, and form a construction plan quality evaluation sequence;

[0106] (5) Optimize the construction plan quality assessment sequence according to the construction progress requirements, adjust the resource allocation plan, and obtain the construction resource optimization matrix;

[0107] (6) The construction resource optimization matrix is ​​dynamically adjusted through comprehensive evaluation of the scheme, and the process resource allocation is reconstructed according to the quality control requirements to obtain the construction optimization decision scheme.

[0108] Specifically, data decomposition is performed to extract key quality control parameters from the construction quality characteristics set, including material performance indicators, process parameters, and quality inspection data. Simultaneously, construction process node parameters are extracted, recording the start and end times, duration, and quality control points of each process. These parameters are arranged according to the construction process sequence to establish a process quality correlation sequence, reflecting the correspondence between construction quality and process progress. Based on this sequence, the correlation between construction resource requirements and process time is analyzed. The human resources, equipment resources, and material resources required for each process are statistically analyzed, and the ratio of total resources to process workload is calculated. Correlation analysis determines the importance of each type of resource, and resource allocation weight coefficients are calculated. These coefficients reflect the degree of impact of resource input on process quality, generating a construction resource allocation table that lists the specific resource allocation schemes required for each process.

[0109] Resource scheduling analysis is a crucial step in optimizing resource allocation. Based on the construction resource allocation table, a resource balance calculation method is used to check for conflicts in resource usage between different work processes. When multiple processes require the same resources within the same time period, these are marked as resource conflict points. Simultaneously, key time nodes for resource allocation are determined, including resource arrival time, transfer time, and withdrawal time. By optimizing the resource usage sequence, resource conflicts are eliminated, resulting in a resource scheduling optimization table. Based on this table, quality constraint analysis is conducted to study the impact of resource allocation schemes on construction quality. A mapping relationship diagram between construction resources and process quality is established, illustrating the correspondence between resource input levels and quality control indicators. For example, the relationship between the quantity of machinery and equipment and construction efficiency, and the relationship between construction personnel allocation and quality control accuracy. By analyzing these mapping relationships, a quality assessment sequence for the construction scheme is formed.

[0110] The construction plan's quality assessment sequence is compared with the construction schedule, and the work sequence arrangement and resource allocation plan are adjusted. While ensuring quality requirements, resource utilization efficiency is optimized to avoid resource idleness or over-concentration. The adjusted plan is recorded in the construction resource optimization matrix, which contains the optimal resource allocation plan for each work sequence. Through comprehensive plan evaluation, the construction resource optimization matrix is ​​dynamically adjusted. Based on quality control requirements, the resource allocation for each work sequence is fine-tuned to ensure that resource allocation meets quality standards. The optimized resource allocation plan is then integrated to form the construction optimization decision plan.

[0111] Taking the construction of a bridge pile foundation as an example, quality control parameters such as concrete strength, rebar cage processing accuracy, and pile verticality were extracted from the quality characteristic set, along with process node parameters such as drilling rig positioning, rebar cage installation, and concrete pouring. The demand for equipment resources such as drilling rigs, cranes, and concrete pump trucks for each process was analyzed, and the relationship between equipment usage time and workload was calculated. Resource balance calculations revealed a conflict in equipment usage between rebar cage hoisting and concrete pouring, necessitating adjustments to the process arrangement. Based on quality requirements, the concrete pouring speed and pumping equipment configuration standards were determined, optimizing the construction plan. The resulting decision-making plan clearly specifies the resource allocation quantity and scheduling sequence for each process, satisfying quality requirements while avoiding resource waste.

[0112] In one specific embodiment, the process of performing quality constraint analysis on construction procedures based on a resource scheduling optimization table may specifically include the following steps:

[0113] (1) Based on the resource scheduling optimization table, the correlation analysis of resource allocation between processes is carried out, and the process quality constraint matrix is ​​generated by calculating key parameters;

[0114] (2) Divide the process quality constraint matrix into multiple quality control blocks according to the construction sequence, calculate the quality index threshold of each block, and form a process quality control sequence;

[0115] (3) Conduct a construction resource matching degree analysis on the process quality control sequence, establish the correspondence between resource input and quality output, and obtain a resource-quality mapping table;

[0116] (4) Weight the parameters in the resource-quality mapping table by correlation calculation, identify key influencing factors, and construct a quality influence factor matrix;

[0117] (5) Based on the quality impact factor matrix, perform quality assessment calculations for each construction process node, mark quality risk points, and generate a process quality assessment table;

[0118] (6) Reorganize the evaluation results in the process quality evaluation table according to the time sequence, integrate the quality evaluation data according to the construction progress, and form a construction plan quality evaluation sequence.

[0119] Specifically, based on the resource scheduling optimization table, the resource allocation of each process is analyzed to extract key parameters reflecting resource utilization efficiency, including equipment utilization rate, personnel allocation efficiency, and material consumption rate. By calculating the correlation between these parameters, a process quality constraint matrix is ​​established, which describes the constraint relationship between resource allocation and process quality. The process quality constraint matrix is ​​divided according to the construction sequence, dividing the continuous construction process into multiple quality control blocks. Each block corresponds to one or more related processes with similar quality control requirements. For each quality control block, threshold values ​​for quality indicators are calculated, including acceptable standard values ​​and warning values. These thresholds are determined based on specification requirements and engineering experience, forming a process quality control sequence.

[0120] A resource matching degree analysis was conducted on the process quality control sequence to study the correspondence between the quantity and quality of resource input and the output quality of construction. The analysis included the correspondence between equipment model and construction precision, the relationship between personnel skill level and construction quality, and the relationship between material properties and project quality. A resource-quality mapping table was established through data analysis, reflecting the quality output level under different resource input conditions. Based on the resource-quality mapping table, the weights of each parameter were determined through correlation calculations. The impact of different resource types on quality was analyzed, and the key factors with the most significant impact on quality were identified. Based on the correlation analysis results, a quality influence factor matrix was constructed, which quantifies the intensity of the impact of each resource factor on construction quality.

[0121] Based on the quality impact factor matrix, quality assessments are conducted at each stage of the construction process. The assessments include whether resource input meets quality requirements, whether construction techniques are standardized, and whether quality control measures are in place. Stages with high quality risks are identified through assessment calculations, forming a process quality assessment table. This table records the quality assessment results and risk levels for each stage in detail. The assessment results in the process quality assessment table are reorganized chronologically and aligned with the construction schedule. Quality assessment data from various time periods are integrated, and quality change trends are analyzed to form a construction plan quality assessment sequence. This sequence comprehensively reflects the changes in quality status throughout the construction process.

[0122] Taking tunnel construction as an example, this paper conducts a correlation analysis on resource allocation for tunnel excavation, support, and lining processes. The impact of different types of support materials and construction equipment on tunnel deformation control is calculated, generating a process quality constraint matrix. The construction process is divided into quality control blocks such as excavation, initial support, and secondary lining, with thresholds set for quality indicators such as surrounding rock deformation, support thickness, and concrete strength for each block. The correlation between tunneling machine performance and excavation quality, shotcrete machine parameters and concrete strength, and steel support specifications and support effect is analyzed to establish a resource-quality mapping relationship. Data analysis reveals that support timing and support strength are key influencing factors, and these parameters are the focus of control. Quality assessments are conducted for each construction section, marking risk points such as changes in surrounding rock grade and groundwater inrush. The quality assessment results of each section are integrated to form a construction quality assessment sequence to guide quality control during the construction process.

[0123] The data processing method for traffic engineering construction in the embodiments of this application has been described above. The data processing system for traffic engineering construction in the embodiments of this application is described below. Please refer to [link / reference]. Figure 3 One embodiment of the data processing system for traffic engineering construction in this application includes:

[0124] The data acquisition module 201 is used to preprocess the dynamic parameters of construction equipment and the status data of construction environment through adaptive data filtering based on multi-source data collected at the construction site, so as to obtain a construction data quality scoring table and a preprocessed dataset.

[0125] The processing module 202 is used to perform time-frequency domain processing on the traffic engineering construction data through segmented noise reduction based on the preprocessed dataset and the construction data quality scoring table to obtain engineering construction characteristic indicators; wherein, the engineering construction characteristic indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment impact factors, engineering material performance indicators and construction process monitoring data.

[0126] Extraction module 203 is used to extract construction process features based on the engineering construction feature indicators through multi-dimensional process mapping and dynamic feature recognition, and generate a time-series associated engineering construction quality status parameter set and construction process spatiotemporal feature chain;

[0127] The optimization module 204 is used to dynamically calibrate and optimize the construction model based on the set of engineering construction quality status parameters and the spatiotemporal feature chain of construction procedures, and obtain a standard model for evaluating traffic engineering construction.

[0128] The fusion module 205 is used to fuse the traffic engineering construction evaluation standard model with the engineering construction quality status parameter set, and analyze the construction process through the quality index extractor to obtain the construction quality feature set.

[0129] The optimization module 206 is used to dynamically optimize the construction plan through the construction resource scheduling system using the construction quality feature set to obtain the construction optimization decision plan.

[0130] Through the collaborative efforts of the aforementioned components, adaptive data filtering was used to preprocess the dynamic parameters of construction equipment and the status data of the construction environment, significantly improving the quality of the original data and providing a reliable data foundation for subsequent analysis. Segmented noise reduction was employed to process the traffic engineering construction data in the time and frequency domain, yielding engineering construction characteristic indicators including vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment influencing factors, engineering material performance indicators, and construction process monitoring data, achieving comprehensive extraction of multi-dimensional characteristics of the construction process. Multi-dimensional process mapping and dynamic feature recognition were used to extract construction process features, generating a time-series correlated set of engineering construction quality status parameters and a spatiotemporal feature chain of construction processes, establishing a mapping relationship between construction quality and process progress. A construction case knowledge base was used to dynamically calibrate and optimize the construction model, obtaining a standard model for traffic engineering construction evaluation, improving the accuracy of quality assessment. The standard model for traffic engineering construction evaluation was fused with the set of engineering construction quality status parameters, and the construction process was analyzed using a quality indicator extractor, achieving accurate assessment of construction quality. Finally, a construction resource scheduling system was used to dynamically optimize the construction plan, obtaining an optimized construction decision scheme, achieving rational allocation of construction resources and effective control of construction quality.

[0131] This application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the data processing method for traffic engineering construction.

[0132] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method for traffic engineering construction, characterized in that, The data processing method used for traffic engineering construction includes: Based on multi-source data collected at the construction site, adaptive data filtering is used to preprocess the dynamic parameters of construction equipment and the status data of the construction environment to obtain a construction data quality scoring table and a preprocessed dataset. Based on the preprocessed dataset and the construction data quality scoring table, the traffic engineering construction data is processed in the time and frequency domain through segmented noise reduction to obtain engineering construction characteristic indicators; wherein, the engineering construction characteristic indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment impact factors, engineering material performance indicators and construction process monitoring data; Based on the aforementioned engineering construction characteristic indicators, construction process features are extracted through multi-dimensional process mapping and dynamic feature recognition, generating a time-series correlated set of engineering construction quality state parameters and a spatiotemporal feature chain of construction processes. This includes: based on the aforementioned engineering construction characteristic indicators, performing spectral decomposition on the vibration frequency characteristic values ​​of construction equipment and the dynamic response parameters of the structure using Fourier transform to extract frequency domain features, establishing a frequency-amplitude relationship matrix, and generating an equipment-structure feature correlation table; and constructing a spatial distribution network of monitoring points based on the aforementioned construction environmental impact factors and engineering material performance indicators, calculating spatial correlation coefficients, and using... Interpolation is used to supplement data in monitoring blind spots, forming an environmental-material feature distribution map. The monitoring data of the construction process is segmented according to the process nodes, and statistical features are extracted for each segment, including the calculation of mean, variance, skewness, and kurtosis, and a process-feature correspondence table is established. Through multi-dimensional feature mapping, the equipment-structure feature association table and the environmental-material feature distribution map are spatiotemporally registered to generate a time-series associated set of engineering construction quality status parameters. Based on the process-feature correspondence table, time series analysis is performed on the construction quality status to establish the correlation between processes and form a spatiotemporal feature chain of construction processes. Based on the set of engineering construction quality status parameters and the spatiotemporal characteristic chain of construction procedures, the construction model is dynamically calibrated and optimized through the construction case knowledge base to obtain a standard model for evaluating traffic engineering construction. The construction quality status parameter set of the transportation engineering construction evaluation standard model is fused with features. The construction process is analyzed using a quality index extractor to obtain a construction quality feature set. This includes: based on the transportation engineering construction evaluation standard model, standardizing the construction quality status parameter set, calculating the parameter mean and standard deviation, establishing a construction quality evaluation index system, and forming a quality evaluation parameter table; calculating the feature correlation degree of the index data in the quality evaluation parameter table through correlation analysis, including the correlation coefficient, contribution degree, and influence weight between parameters, and generating a quality index correlation matrix; segmenting the quality index correlation matrix according to construction progress nodes, calculating the quality control parameters for each construction stage, extracting key quality control points, and constructing a construction quality control sequence; performing deviation analysis between the construction quality control sequence and standard values, calculating quality deviation values, marking quality anomaly intervals, and forming a quality assessment report; and classifying and summarizing the key indicators in the quality assessment report through feature recombination, combining features according to the construction sequence to obtain the construction quality feature set. Using the aforementioned construction quality feature set, a construction resource scheduling system is used to dynamically optimize the construction plan and obtain an optimized construction decision scheme. This includes: decomposing the construction quality feature set into data, extracting key quality control parameters and construction process node parameters, and establishing a process quality correlation sequence; performing correlation analysis on the construction resource demand and construction process time in the process quality correlation sequence, calculating resource allocation weight coefficients, and obtaining a construction resource allocation table; performing inter-process resource balance calculations on the construction resource allocation table through resource scheduling analysis, identifying resource conflict points, marking key time nodes for resource allocation, and generating a resource scheduling optimization table; performing quality constraint analysis on the construction processes based on the resource scheduling optimization table, establishing a mapping relationship between construction resources and process quality, and forming a construction plan quality assessment sequence; optimizing the construction plan quality assessment sequence according to construction progress requirements, adjusting the resource allocation scheme, and obtaining a construction resource optimization matrix; dynamically adjusting the construction resource optimization matrix through comprehensive scheme evaluation, reconstructing process resource allocation according to quality control requirements, and obtaining an optimized construction decision scheme.

2. The data processing method for traffic engineering construction according to claim 1, characterized in that, The process involves preprocessing construction equipment dynamic parameters and construction environment status data using adaptive data filtering based on multi-source data collected from the construction site, resulting in a construction data quality scoring table and a preprocessed dataset, including: Based on multi-source data collected at the construction site, the multi-source data is segmented into time series using a data preprocessing analyzer to form a time series data sequence. Based on the time series data sequence, the dynamic parameters of construction equipment and the status data of the construction environment are evaluated and classified to generate a construction data classification table. The construction data classification table is input into the data quality scoring system, and scores are assigned according to the scoring rules to obtain the construction data quality scoring table. Based on the construction data quality scoring table, valid data is filtered through data filters to generate a preprocessed dataset. The time-series data sequence includes equipment operating status data, environmental monitoring data, and engineering parameter data. The construction data classification table categorizes and labels data based on its source and characteristics; the construction data quality scoring table includes data integrity score, data consistency score, and data validity score.

3. The data processing method for traffic engineering construction according to claim 1, characterized in that, Based on the preprocessed dataset and the construction data quality scoring table, the traffic engineering construction data is processed in the time-frequency domain through segmented noise reduction to obtain engineering construction characteristic indicators. These indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environmental impact factors, engineering material performance indicators, and construction process monitoring data, including: Based on the preprocessed dataset, the traffic engineering construction data is segmented according to the construction process time nodes to form process data segments. The process data segments are transformed into equally spaced time series through resampling to generate a construction time series; based on the data integrity score in the construction data quality scoring table, the missing values ​​in the construction time series are filled in using a local linear interpolation method. Values ​​exceeding the normal range are processed using median filtering to obtain a corrected dataset; time-frequency domain analysis is performed on the corrected dataset to convert the continuous signal into a time spectrum, extract frequency band energy distribution features, and construct a time-frequency feature matrix; Based on the time-frequency feature matrix, feature extraction is performed for different data types: vibration frequency feature values ​​of construction equipment are obtained through amplitude analysis, dynamic response parameters of the structure are obtained through displacement calculation, construction environment impact factors are extracted based on monitoring data, performance indicators of engineering materials are calculated based on detection data, and monitoring data of the construction process are organized based on real-time monitoring data. The vibration frequency characteristic value of the construction equipment is divided into frequency bands of 0-10Hz, 10-50Hz, and 50-100Hz according to the frequency range. The energy value of each frequency band is calculated and the main frequency characteristics are extracted. Based on the structural dynamic response parameters, a displacement-time relationship is established, and abnormal response intervals are marked by comparison with a set threshold. An environmental parameter correlation map is constructed using the construction environment impact factors. A material performance evaluation sequence is established based on the engineering material performance indicators. The construction process monitoring data is sorted according to the collection time and correlated with the construction progress to generate a construction monitoring data stream; By combining key frequency features, response anomaly intervals, environmental parameter correlation maps, material performance evaluation sequences, and construction monitoring data streams, engineering construction characteristic indicators are formed.

4. The data processing method for traffic engineering construction according to claim 1, characterized in that, The step involves dynamically calibrating and optimizing the construction model using a construction case knowledge base, based on the set of engineering construction quality status parameters and the spatiotemporal feature chain of construction procedures, to obtain a standard model for evaluating traffic engineering construction. This includes: Based on the set of engineering construction quality status parameters, data matching is performed in the construction case knowledge base. By calculating parameter similarity, spatiotemporal feature correlation, and process logic relationship, a set of historical construction case features is selected. For the spatiotemporal feature chain of the construction process, process analysis is performed based on the feature set of historical construction cases to extract parameters such as process time nodes, resource consumption, construction progress, and quality control points, and a construction feature correlation matrix is ​​constructed. The construction feature association matrix is ​​divided into multiple time windows according to the process flow. Statistical analysis is performed on the feature data in each window to calculate the feature mean, standard deviation, and data distribution characteristics, and a feature calibration parameter table is obtained. Based on the aforementioned feature calibration parameter table, the construction sequence data is dynamically calibrated, calibration coefficients are calculated, parameters that deviate from the standard range are corrected, and a construction parameter optimization table is generated. The construction parameter optimization table and process calibration parameters are fused by feature combination, and the feature sequence is reconstructed according to the construction process order to generate a standard model for traffic engineering construction evaluation.

5. The data processing method for traffic engineering construction according to claim 1, characterized in that, Based on the resource scheduling optimization table, a quality constraint analysis is performed on the construction procedures to establish a mapping relationship between construction resources and procedure quality, forming a construction plan quality assessment sequence, including: Based on the resource scheduling optimization table, a correlation analysis is performed on the resource allocation between processes, and a process quality constraint matrix is ​​generated by calculating key parameters. The process quality constraint matrix is ​​divided into multiple quality control blocks according to the construction sequence, and the quality index threshold of each block is calculated to form a process quality control sequence. A construction resource matching degree analysis is performed on the quality control sequence of the process to establish the correspondence between resource input and quality output, and a resource-quality mapping table is obtained. By calculating the correlation, the parameters in the resource-quality mapping table are weighted, key influencing factors are identified, and a quality influence factor matrix is ​​constructed. Based on the quality impact factor matrix, a quality assessment calculation is performed for each construction process node, quality risk points are marked, and a process quality assessment table is generated. The evaluation results in the process quality evaluation table are reorganized chronologically, and the quality evaluation data are integrated according to the construction progress to form a construction plan quality evaluation sequence.

6. A data processing system for traffic engineering construction, used to implement the data processing method for traffic engineering construction as described in any one of claims 1 to 5, characterized in that, The data processing system for traffic engineering construction includes: The data acquisition module is used to preprocess the dynamic parameters of construction equipment and the status data of the construction environment based on multi-source data collected at the construction site through adaptive data filtering, so as to obtain a construction data quality scoring table and a preprocessed dataset. The processing module is used to perform time-frequency domain processing on the traffic engineering construction data through segmented noise reduction based on the preprocessed dataset and the construction data quality scoring table to obtain engineering construction characteristic indicators; wherein, the engineering construction characteristic indicators include: vibration frequency characteristic values ​​of construction equipment, structural dynamic response parameters, construction environment impact factors, engineering material performance indicators and construction process monitoring data; The extraction module is used to extract construction process features based on the engineering construction feature indicators through multi-dimensional process mapping and dynamic feature recognition, generating a time-series associated set of engineering construction quality status parameters and a spatiotemporal feature chain of construction processes. This includes: based on the engineering construction feature indicators, performing spectral decomposition on the vibration frequency characteristic values ​​of construction equipment and the dynamic response parameters of the structure using Fourier transform, extracting frequency domain features, establishing a frequency-amplitude relationship matrix, and generating an equipment-structure feature association table; and constructing a spatial distribution network of monitoring points based on the construction environment impact factors and engineering material performance indicators, and calculating spatial correlation coefficients. Interpolation is used to supplement monitoring blind spot data, forming an environmental-material feature distribution map. The monitoring data of the construction process is segmented according to the process nodes, and statistical features are extracted for each segment, including the calculation of mean, variance, skewness, and kurtosis, and a process-feature correspondence table is established. Through multi-dimensional feature mapping, the equipment-structure feature association table and the environmental-material feature distribution map are spatiotemporally registered to generate a time-series associated set of engineering construction quality status parameters. Based on the process-feature correspondence table, time series analysis is performed on the construction quality status to establish the correlation between processes and form a spatiotemporal feature chain of construction processes. The optimization module is used to dynamically calibrate and optimize the construction model based on the set of engineering construction quality status parameters and the spatiotemporal feature chain of construction procedures, and obtain a standard model for evaluating traffic engineering construction. The fusion module is used to fuse the traffic engineering construction evaluation standard model with the engineering construction quality status parameter set. It analyzes the construction process using a quality index extractor to obtain a construction quality feature set, including: standardizing the engineering construction quality status parameter set based on the traffic engineering construction evaluation standard model, calculating the parameter mean and standard deviation, establishing a construction quality evaluation index system, and forming a quality evaluation parameter table; calculating the feature correlation degree of the index data in the quality evaluation parameter table through correlation analysis, including the correlation coefficient, contribution, and influence weight between parameters, and generating a quality index correlation matrix; segmenting the quality index correlation matrix according to construction progress nodes, calculating the quality control parameters for each construction stage, extracting key quality control points, and constructing a construction quality control sequence; performing deviation analysis between the construction quality control sequence and standard values, calculating quality deviation values, marking quality anomaly intervals, and forming a quality assessment report; and classifying and summarizing the key indicators in the quality assessment report through feature recombination, combining features according to the construction sequence to obtain the construction quality feature set. The optimization module utilizes the construction quality feature set to dynamically optimize the construction plan through a construction resource scheduling system, obtaining an optimized construction decision scheme. This includes: decomposing the construction quality feature set into data, extracting key quality control parameters and construction process node parameters, and establishing a process quality correlation sequence; performing correlation analysis on the construction resource demand and construction process time in the process quality correlation sequence, calculating resource allocation weight coefficients, and obtaining a construction resource allocation table; performing inter-process resource balance calculations on the construction resource allocation table through resource scheduling analysis, identifying resource conflict points, marking key time nodes for resource allocation, and generating a resource scheduling optimization table; performing quality constraint analysis on the construction processes based on the resource scheduling optimization table, establishing a mapping relationship between construction resources and process quality, and forming a construction plan quality assessment sequence; optimizing the construction plan quality assessment sequence according to construction progress requirements, adjusting the resource allocation scheme, and obtaining a construction resource optimization matrix; dynamically adjusting the construction resource optimization matrix through comprehensive scheme evaluation, reconstructing process resource allocation according to quality control requirements, and obtaining an optimized construction decision scheme.

7. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the data processing method for traffic engineering construction as described in any one of claims 1 to 5.

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