CN-121998513-A - Hydraulic engineering construction quality detection method and system

CN121998513ACN 121998513 ACN121998513 ACN 121998513ACN-121998513-A

Abstract

The invention discloses a hydraulic engineering construction quality detection method and system, which relate to the technical field of hydraulic engineering quality monitoring and comprise the steps of collecting multi-source heterogeneous data in the hydraulic engineering construction process through a distributed sensor network and preprocessing; the method comprises the steps of carrying out process risk assessment on a construction unit by adopting a process-quality association model, calculating a process risk index and marking a potential risk area, dynamically calculating a quality characteristic parameter threshold value based on a quality evolution model, carrying out space reconstruction of sub-physical quantities on sensor monitoring data to obtain a space distribution result of each physical quantity, carrying out quality anomaly analysis and structure defect judgment on the construction area to obtain a quality anomaly area and a defect area, and generating a quality state identification result based on construction process characteristics and the potential risk area, the quality anomaly area and the defect area. According to the invention, the accuracy and the reliability of construction quality detection can be improved by establishing a quality characteristic parameter dynamic threshold calculation mechanism and a multidimensional cross verification system.

Inventors

BAI JIACHUAN
GONG YIBIN
ZHANG FAN
WANG JI
CAI CHAO
YU JIAHONG

Assignees

昆山市水务工程质量与安全监督站

Dates

Publication Date: 20260508
Application Date: 20260211

Claims (10)

1. The hydraulic engineering construction quality detection method is characterized by comprising the following steps of: the method comprises the steps of collecting multi-source heterogeneous data in the construction process of hydraulic engineering through a distributed sensor network, and preprocessing, wherein the multi-source heterogeneous data comprise sensor monitoring data, construction process parameters and environment parameters; Based on construction process parameters, performing process risk assessment on the construction unit by adopting a process-quality association model, calculating a process risk index and marking a potential risk area; Dynamically calculating a quality characteristic parameter threshold based on a quality evolution model according to the construction process parameters and the environment parameters; carrying out space reconstruction processing of the sub-physical quantity on the sensor monitoring data to obtain a space distribution result of each physical quantity; based on the spatial distribution result of each physical quantity and the quality characteristic parameter threshold value, carrying out quality anomaly analysis and structural defect judgment on the spatial position in the construction area to obtain a quality anomaly area and a defect area; And comprehensively analyzing the construction area based on the potential risk area, the quality abnormal area and the defect area to generate a quality state identification result of the construction area.
2. The method for detecting the construction quality of the hydraulic engineering according to claim 1, wherein the process risk assessment of the construction unit by using the process-quality association model comprises: Acquiring construction process parameters corresponding to each construction unit, screening out the process parameters participating in coupling analysis according to the construction process types, and obtaining an effective process parameter set; and constructing a process parameter coupling relation based on the effective process parameter set.
3. The method for detecting the construction quality of the hydraulic engineering according to claim 1, wherein the process risk assessment of the construction unit by using the process-quality association model further comprises: Calculating a process risk index corresponding to each construction unit based on the process-quality association model and the process parameter coupling relation; And marking the space region corresponding to the construction unit meeting the preset risk judging condition as a potential risk region according to the process risk index, and generating process early warning information corresponding to the potential risk region.
4. The hydraulic engineering construction quality detection method according to claim 1, wherein the dynamic calculation of the quality feature parameter threshold based on the quality evolution model comprises: combining the construction units with the same construction procedure type and construction age differences within a preset time window into an evaluation unit according to the construction procedure type and the construction age recorded by each construction unit; For each evaluation unit, determining an initial parameter set of a quality evolution model according to the construction procedure type of the evaluation unit, wherein the initial parameter set comprises a target quality characteristic parameter type and an evolution reference curve thereof; Calculating the representative age of each construction unit in the evaluation unit according to the construction age of the evaluation unit, and determining a corresponding quality characteristic theoretical value based on the representative age and the initial parameter set; Correcting the quality characteristic theoretical value according to the environmental parameters corresponding to the evaluation unit; judging environmental stability according to fluctuation characteristics of environmental parameters corresponding to the evaluation unit, and dynamically adjusting tolerance ranges of the quality characteristic parameters based on an environmental stability judging result; And calculating an upper limit value and a lower limit value of the quality characteristic parameter threshold value of the evaluation unit based on the corrected quality characteristic theoretical value and the tolerance range.
5. The hydraulic engineering construction quality detection method according to claim 1, wherein the spatial reconstruction processing of the sensor monitoring data by the separated physical quantity includes: Classifying the sensor monitoring data according to the evaluation units and the physical quantity types according to the evaluation unit division results to form a sub-physical quantity monitoring data set of each evaluation unit; determining a space reconstruction parameter according to the space range of the construction unit contained in each evaluation unit, wherein the space reconstruction parameter comprises an interpolation method, a space resolution and a boundary constraint condition; Performing spatial interpolation processing on the sub-physical quantity monitoring data set of each evaluation unit to obtain the spatial distribution result of each physical quantity of each evaluation unit; And spatially splicing the physical quantity spatial distribution results of each evaluation unit to form the spatial distribution results of each physical quantity of the complete construction area.
6. The method for detecting the quality of hydraulic engineering construction according to claim 1, wherein the step of performing quality anomaly analysis and structural defect determination on the spatial position in the construction area comprises the steps of: Comparing the spatial distribution result of each physical quantity with a corresponding quality characteristic parameter threshold value, and marking the spatial position exceeding the threshold value range as a preliminary abnormal position; judging whether the physical quantity deviation belongs to normal fluctuation of a construction process according to construction process parameters of a construction unit to which the preliminary abnormal position belongs; Determining a preliminary abnormal position which does not belong to normal fluctuation of a construction process as a quality abnormal space position; carrying out space connectivity analysis on the quality abnormal space position to form a quality abnormal region; Acquiring image sensor data of the quality abnormal region, and extracting defect structural features; And judging the quality abnormal region in which the physical quantity abnormality and the defect structural feature exist simultaneously as a defect region.
7. The method for detecting the construction quality of the hydraulic engineering according to claim 1, wherein the comprehensive analysis of the construction area comprises: determining the region crossing type of each spatial position according to the attribution condition of each spatial position in the potential risk region, the quality abnormal region and the defect region; Acquiring region intersection type change sequences of each spatial position in different construction advancing stages, and judging quality evolution trend according to the change sequences; And generating a quality state identification result of the construction area based on the area crossing type and the quality evolution trend.
8. The utility model provides a hydraulic engineering construction quality detecting system which characterized in that includes: The data acquisition preprocessing module is used for acquiring multi-source heterogeneous data in the hydraulic engineering construction process through the distributed sensor network and preprocessing the multi-source heterogeneous data; The process risk assessment module is used for carrying out process risk assessment on the construction unit by adopting a process-quality association model based on construction process parameters, calculating a process risk index and marking a potential risk area; The quality threshold calculating module is used for dynamically calculating a quality characteristic parameter threshold based on the quality evolution model according to the construction process parameters and the environment parameters; The physical quantity space reconstruction module is used for carrying out space reconstruction processing of the physical quantity of the sensor monitoring data to obtain a space distribution result of each physical quantity; the quality anomaly defect judging module is used for carrying out quality anomaly analysis and structural defect judgment on the space position in the construction area based on the spatial distribution result of each physical quantity and the quality characteristic parameter threshold value to obtain a quality anomaly area and a defect area; The quality state identification module is used for comprehensively analyzing the construction area based on the potential risk area, the quality abnormal area and the defect area to generate a quality state identification result of the construction area.
9. An electronic device, comprising: A memory for storing a computer program; a processor for executing the computer program to implement the hydraulic engineering construction quality detection method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the hydraulic engineering construction quality detection method according to any one of claims 1 to 7.

Description

Hydraulic engineering construction quality detection method and system Technical Field The invention relates to the technical field of hydraulic engineering quality monitoring, in particular to a hydraulic engineering construction quality detection method and system. Background The construction quality of the hydraulic engineering is influenced by various factors such as material performance, construction process, environmental conditions and the like, and the dynamic evolution characteristic is presented. The construction quality monitoring method based on the distributed sensor network is applied to the field of hydraulic engineering, and the construction quality is evaluated by distributing sensors in a construction area to collect physical quantity data such as temperature, strain, displacement and the like and combining a data processing algorithm. The typical technical scheme includes that a fixed threshold value is adopted to conduct abnormality judgment on monitoring data, a spatial interpolation method is utilized to conduct spatial reconstruction on discrete sensor data, and quality abnormal areas are identified based on statistical analysis of the monitoring data. The technical scheme has certain limitation in practical application. In the construction process, the quality characteristic parameters dynamically change along with the construction age and the environmental conditions, and the quality evolution difference under different construction stages and the environmental conditions is difficult to adapt to by adopting a fixed threshold value. The spatial geometrical characteristics and the sensor distribution density of different positions in the construction area are different, and when uniform spatial interpolation parameters are adopted for processing, the reconstruction accuracy is affected. In addition, the fluctuation of the construction process parameters can cause the normal change of the monitoring data, and when the abnormal judgment is carried out only based on the comparison of the monitoring data and the threshold value, the normal fluctuation of the process can be misjudged as the quality abnormality, and the real quality defect can be omitted. Disclosure of Invention The invention mainly aims to provide a hydraulic engineering construction quality detection method and system, and aims to solve the technical problems of insufficient construction quality detection accuracy and reliability in the prior art. In a first aspect, the present invention provides a hydraulic engineering construction quality detection method, including: the method comprises the steps of collecting multi-source heterogeneous data in the construction process of hydraulic engineering through a distributed sensor network, and preprocessing, wherein the multi-source heterogeneous data comprise sensor monitoring data, construction process parameters and environment parameters; Based on construction process parameters, performing process risk assessment on the construction unit by adopting a process-quality association model, calculating a process risk index and marking a potential risk area; Dynamically calculating a quality characteristic parameter threshold based on a quality evolution model according to the construction process parameters and the environment parameters; carrying out space reconstruction processing of the sub-physical quantity on the sensor monitoring data to obtain a space distribution result of each physical quantity; based on the spatial distribution result of each physical quantity and the quality characteristic parameter threshold value, carrying out quality anomaly analysis and structural defect judgment on the spatial position in the construction area to obtain a quality anomaly area and a defect area; And comprehensively analyzing the construction area based on the potential risk area, the quality abnormal area and the defect area to generate a quality state identification result of the construction area. Optionally, performing the process risk assessment on the construction unit using the process-quality correlation model includes: Acquiring construction process parameters corresponding to each construction unit, screening out the process parameters participating in coupling analysis according to the construction process types, and obtaining an effective process parameter set; And constructing a process parameter coupling relation based on the effective process parameter set. Optionally, performing the process risk assessment on the construction unit using the process-quality correlation model further comprises: Calculating a process risk index corresponding to each construction unit based on the process-quality association model and the process parameter coupling relation; and marking the space region corresponding to the construction unit meeting the preset risk judging condition as a potential risk region according to the process risk index, and generating process early warning in