Water conservancy project construction plane flatness detection method based on intelligent sensor

Abstract

The invention relates to the technical field of water conservancy project construction quality detection, in particular to a water conservancy project construction plane flatness detection method based on an intelligent sensor, and the method comprises the steps: obtaining the flatness data of a construction site through the intelligent sensor, collecting the construction terrain and temperature and humidity environment parameters, and transmitting the parameters to a data processing unit in real time; preprocessing the collected original data, including de-noising, error correction and standardization processing, to obtain corrected flatness data; based on the corrected data, the flatness of the construction plane is calculated through a flatness analysis algorithm, deviation is recognized, and a construction quality analysis report is generated; and a flatness analysis result is uploaded to a cloud platform, a real-time adjustment report is generated in combination with historical data and real-time data and is fed back to construction management personnel, and the construction process is optimized. According to the invention, the precision and efficiency of hydraulic engineering construction flatness detection are improved, and intelligent control and dynamic adjustment of construction quality are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project construction quality inspection, and particularly to a method for detecting the flatness of the construction plane of a water conservancy project based on intelligent sensors. Background Art

[0002] During the construction process of water conservancy projects, the flatness of the construction plane directly affects the overall quality of the project and the safety and stability of subsequent construction. However, traditional flatness detection methods mainly rely on manual measurement or simple tools. This method is not only inefficient but also easily affected by human factors, resulting in large errors in the detection results. In addition, the construction site environment is complex and changeable, and factors such as terrain undulation and temperature and humidity changes will further increase the uncertainty of measurement. Especially in large-scale water conservancy projects, how to achieve real-time detection and dynamic adjustment of the flatness of the construction site has become an important problem in the current technical field.

[0003] In recent years, the development of intelligent sensor technology has provided new ideas for solving the above problems. By using high-precision sensors to collect the terrain and environmental data of the construction site in real time and combining data processing algorithms to correct the collected data, the accuracy and efficiency of flatness detection can be significantly improved. At the same time, the introduction of cloud computing and big data technology makes it possible to perform real-time analysis and dynamic adjustment of construction data. However, there is still a lack of a complete technical solution in the existing technology that can achieve precise monitoring, deviation identification, and dynamic optimization adjustment of construction flatness, so as to meet the high-quality requirements of water conservancy project construction. Therefore, there is an urgent need for a method for detecting the flatness of the construction plane of a water conservancy project based on intelligent sensors to achieve efficient and accurate construction quality control. Summary of the Invention

[0004] The present invention provides a method for detecting the flatness of the construction plane of a water conservancy project based on intelligent sensors to solve the problem of how to use intelligent sensors to collect, process, and analyze the construction flatness data in real time based on the terrain data and environmental parameters of the construction site, and generate dynamic adjustment suggestions to optimize the construction quality of water conservancy projects.

[0005] To solve the above technical problems, the present invention provides a method for detecting the flatness of the construction plane of a water conservancy project based on intelligent sensors, including:

[0006] Obtain the flatness data of the construction site, collect the construction terrain, temperature and humidity environmental parameters through intelligent sensors, and transmit them to the data processing unit in real time;

[0007] Preprocess the collected original data, automatically correct the data error based on environmental changes, and obtain the corrected flatness data;

[0008] Based on the corrected flatness data, use the flatness analysis algorithm to calculate the flatness of the construction plane, identify flatness deviations, and generate a construction quality analysis report;

[0009] Upload the flatness analysis results to the cloud platform, combine historical data with real-time data, generate a real-time adjustment report and feedback it to the construction management personnel for on-site adjustment to optimize the construction process.

[0010] Furthermore, the steps of obtaining the flatness data of the construction site include:

[0011] Use a laser rangefinder and a GPS sensor to collect the height data and position data of the construction ground;

[0012] Collect the temperature and humidity environment parameters of the construction site through a temperature sensor and a humidity sensor.

[0013] Furthermore, the steps of preprocessing the collected raw data include:

[0014] Perform denoising processing on the raw data to eliminate invalid or abnormal data points;

[0015] Automatically correct data errors based on the temperature and humidity environment parameters and adjust the error values of the sensors;

[0016] Perform standardization processing on the corrected data to generate a sequence of corrected flatness data.

[0017] Furthermore, the denoising processing includes using the Kalman filter algorithm to eliminate the noise in the raw data.

[0018] Furthermore, the error correction calculates the correction value based on the regression model of environmental parameter changes and adjusts the deviation of the data collected by the sensors.

[0019] Furthermore, the steps of using the flatness analysis algorithm to calculate the flatness of the construction plane include:

[0020] Based on the sequence of corrected flatness data, use the least squares method or the B-spline curve fitting method to calculate the flatness value of the construction plane;

[0021] Identify flatness deviations and generate deviation data.

[0022] Furthermore, the deviation data is classified according to the magnitude of the deviation value, and the positions of the deviation points are marked.

[0023] Furthermore, the steps of uploading the flatness analysis results include:

[0024] Upload the flatness analysis results to the cloud platform through a wireless communication module;

[0025] Classify and store the data according to the timestamp and geographical location.

[0026] Furthermore, the flatness analysis result includes the corrected flatness data sequence, deviation data and its classification information.

[0027] Furthermore, the step of generating a real-time adjustment report by combining historical data and real-time data includes:

[0028] Perform trend analysis on the data using big data analysis algorithms;

[0029] Generate construction adjustment suggestions based on the trend analysis results and feedback the adjustment suggestions to the construction management personnel.

[0030] The key innovation points of the present invention include:

[0031] (1) Intelligent sensor data acquisition and transmission: Combine laser rangefinders, GPS sensors, and temperature and humidity sensors to collect terrain data and environmental parameters in real time and synchronously transmit them to the data processing unit, ensuring the comprehensiveness and real-time nature of the data.

[0032] (2) Environmentally adaptive error correction: Based on the temperature and humidity environment parameters collected in real time, dynamically adjust the measurement error of the sensor, and combine regression models and standardization algorithms to improve the accuracy and consistency of the data.

[0033] (3) Application of flatness analysis algorithms: Use complex mathematical formulas (such as plane fitting, least squares method) to calculate the flatness of the construction plane, classify and identify deviations, and generate an efficient and reliable construction quality analysis report.

[0034] The following are its main beneficial effects:

[0035] The present invention collects the flatness data of the construction site through intelligent sensors, including terrain height, position, and temperature and humidity environment parameters, and transmits them to the data processing unit in real time. Compared with traditional manual detection or simple tool measurement methods, the present invention can significantly reduce human errors and environmental interference, and improve the accuracy and real-time nature of data collection. Through denoising processing of the original data, automatic error correction based on environmental changes, and standardization processing, the consistency and accuracy of the data in a complex construction environment are ensured, providing a reliable data basis for subsequent flatness analysis.

[0036] The present invention uses a flatness analysis algorithm to process the corrected flatness data, which can quickly calculate the flatness of the construction plane, identify deviations and generate a construction quality analysis report. Compared with traditional methods, this algorithm combines complex mathematical models (such as surface fitting, least squares method) with dynamic environmental parameters, significantly improving the efficiency and accuracy of flatness deviation identification. In addition, by uploading the analysis results to the cloud platform, combining historical data and real-time data for big data analysis and trend prediction, generating construction adjustment suggestions and real-time feedback to construction management personnel, the dynamic optimization and efficient management of the construction process are realized.

[0037] The present invention is particularly applicable to complex environments in water conservancy project construction. It can monitor the construction quality in real time, provide dynamic adjustment schemes, optimize the construction process, reduce rework and resource waste, and improve project quality and construction efficiency. Brief Description of the Drawings

[0038] Figure 1 It is a flow chart of a method for detecting the flatness of a construction plane in a water conservancy project based on intelligent sensors provided by an embodiment of the present application;

[0039] Figure 2 It is a structural block diagram of a method for detecting the flatness of a construction plane in a water conservancy project based on intelligent sensors provided by an embodiment of the present application. Detailed Embodiments

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments, and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects, not to describe a specific order.

[0041] Referring to "embodiment" herein means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0042] Embodiment 1: Refer to Figure 1, is a schematic flowchart of a method for detecting the flatness of a construction plane in a water conservancy project based on intelligent sensors provided by an embodiment of the present invention. This process can at least include steps S100 - S400:

[0043] S100. Obtain the flatness data of the construction site, collect the construction terrain, temperature and humidity environment parameters through intelligent sensors, and transmit them to the data processing unit in real time;

[0044] S200. Preprocess the collected original data, automatically correct data errors based on environmental changes, and obtain the corrected flatness data;

[0045] S300. Based on the corrected flatness data, use the flatness analysis algorithm to calculate the flatness of the construction plane, identify flatness deviations, and generate a construction quality analysis report;

[0046] S400. Upload the flatness analysis results to the cloud platform, combine historical data with real - time data, generate a real - time adjustment report, and feedback it to the construction management personnel for on - site adjustment to optimize the construction process.

[0047] Step S100 at least includes steps S110 - S130:

[0048] S110: Obtain the terrain data of the construction site.

[0049] Specifically, the ground height and position parameters of the construction site are collected in real time through the intelligent sensors (such as laser rangefinders, GPS sensors, etc.). The ground height data is H ground , and the position data is P location . During the collection process, the laser rangefinder provides H ground data by measuring the distance to the ground, while the GPS sensor obtains P location through satellite positioning, thereby determining the specific location and ground elevation of the construction site.

[0050] Furthermore, the terrain data will be transmitted to the data processing unit in real time through a wireless communication module for storage and processing. During this process, the accuracy of the collected terrain data will directly affect the accuracy of subsequent flatness calculations.

[0051] The real - time transmission of the ground height and position data provides basic data for subsequent steps and supports the terrain factors in flatness analysis.

[0052] S120: Obtain the temperature and humidity environment parameters of the construction site through intelligent sensors.

[0053] In this step, the temperature and humidity environment parameters of the construction site are collected through the intelligent sensors. The temperature and humidity parameters include temperature T env and humidity Henv Specifically, the temperature sensor measures the real-time temperature at the construction site, while the humidity sensor records the ambient humidity.

[0054] The data will be transmitted in real time between the sensor and the data processing unit via wireless communication.

[0055] The ambient data T env and H env will play an important role in error correction in subsequent flatness analysis. Specifically, temperature and humidity changes may cause systematic errors in the sensor, so the data needs to be combined with terrain data to ensure more accurate error correction in flatness analysis.

[0056] More accurate.

[0057] S130: Synchronously transmit the acquired terrain data and environmental parameter data to the data processing unit.

[0058] In this step, the terrain data H ground and the position data P location along with the environmental data T env and H env will be synchronously transmitted to the data processing unit. This synchronization process ensures the real-time association of environmental changes and terrain data, thus enabling precise tracking of the flatness of the construction site.

[0059] Furthermore, the data is fused in the data processing unit to obtain a sequence of flatness data for the real-time construction site, denoted as D floor (t), where t represents the time dimension. This sequence of flatness data D floor (t) is generated by integrating terrain data and environmental parameter data and can accurately reflect the changes in the flatness of the construction site.

[0060] The described sequence of flatness data D floor (t) will be passed as input data to the subsequent step S200 for data preprocessing and error correction.

[0061] Explanation of association and connection:

[0062] In step S110, the collection of the ground height H ground and the position data P location provides the basic data for subsequent flatness calculation. The data will be combined with temperature and humidity parameters in step S130 and finally form the complete sequence of flatness data D floor (t) for the construction site.

[0063] In step S120, the collected temperature and humidity data T env and H envIt is to provide input for data error correction in step S200. Temperature and humidity changes will affect the correction algorithm of sensor errors to ensure the accuracy of the final flatness data.

[0064] Step S130 realizes the synchronous transmission of terrain data and environmental data, ensuring the consistency and accuracy of data in subsequent steps. The synchronized flatness data sequence D floor (t) is the basic input for subsequent analysis and correction.

[0065] Step S200 includes at least steps S210 - S230:

[0066] S210: Denoise the original data and eliminate invalid or abnormal data points.

[0067] Specifically, the original data includes terrain data H ground and position data P location collected by a laser rangefinder, GPS sensor, etc., env and temperature and humidity data T env obtained by an environmental sensor, as well as H

[0068] In this step, the data is processed by a denoising algorithm to eliminate invalid or abnormal data points. The data denoising process can use filtering methods such as Kalman filtering or median filtering to remove noise data generated by external interference or sensor errors.

[0068] Specifically, by setting a threshold range [T min , T max and [H min , H max , the temperature and humidity data is screened. If the temperature and humidity values at a certain time point exceed the preset range, they are regarded as abnormal data points and eliminated. Similarly, for terrain data H ground and position data P location If they deviate from the normal fluctuation range, they are determined as abnormal points and eliminated.

[0069] The denoised data will become the data input for subsequent steps, laying a foundation for data correction and standardization processing.

[0070] S220: Automatically correct data errors based on environmental changes (temperature and humidity fluctuations), and use an algorithm to adjust the error value of the sensor to obtain the corrected flatness data.

[0071] Furthermore, in this step, according to the collected temperature and humidity environmental parameters T env and H env, the error value of the sensor is adjusted through a specific error correction algorithm. Since fluctuations in temperature and humidity may cause errors in sensor measurements, for example, the errors of laser rangefinders and GPS sensors change with the environment. Therefore, an error correction algorithm, such as a regression model or Kalman filter, needs to be adopted to correct the terrain data in combination with temperature and humidity data.

[0072] The core formula of the correction algorithm is:

[0073] H corrected = H raw + ΔH(T env , H env )

[0074] where H corrected is the corrected ground height data, H raw is the original ground height data, and ΔH(T env , H env ) is the deviation value corrected based on environmental parameters (temperature T env and humidity H env ).

[0075] Specifically, ΔH(T env , H env ) can be obtained through a pre-trained model or empirical formula, representing the impact of temperature and humidity changes on sensor errors. This correction process will ensure more accurate terrain data and eliminate measurement errors caused by environmental changes.

[0076] In this step, the corrected flatness data will be used as the input for subsequent standardization processing.

[0077] S230: Standardize the corrected flatness data to obtain a sequence of corrected flatness data.

[0078] In this step, the corrected flatness data H corrected and other relevant terrain and environmental data will be standardized to obtain a sequence of corrected flatness data. The purpose of standardization processing is to eliminate differences in different dimensions and magnitudes, enabling the data to be analyzed within a unified standard range.

[0079] Specifically, the standardization processing is carried out through the following formula:

[0080]

[0081] where D floor is the corrected flatness data, μ(D floor ) is the mean of the data sequence, and σ(D floor ) is the standard deviation of the data sequence. The standardized data It can remove data bias, making subsequent flatness analysis and deviation detection more accurate.

[0082] The standardized flatness data sequence will be used as the input for the subsequent flatness analysis algorithm, ensuring the accuracy and reliability of flatness deviation calculation.

[0083] Association and connection description:

[0084] The denoising process in step S210 ensures the accuracy of the input data. Remove invalid or abnormal data points H ground and T env provides reliable original data for the error correction in step S220.

[0085] The error correction in step S220 adjusts the sensor error through environmental parameters T env and H env to obtain the corrected terrain data H corrected and provides the basic data for the subsequent standardization step.

[0086] Step S230 makes the corrected data suitable for further flatness analysis through standardization and the data will be used for flatness calculation and deviation analysis in step S300.

[0087] The above steps are interconnected and interdependent in the entire data processing flow. The output of each step provides the necessary data support for the next step, ensuring the accuracy of the finally corrected flatness data.

[0088] Step S300 at least includes steps S310 - S330:

[0089] S310: Based on the corrected flatness data, use the flatness analysis algorithm to calculate the construction plane flatness, identify flatness deviations and generate deviation data.

[0090] Specifically, in this step, first, it is necessary to calculate based on the corrected flatness data through the flatness analysis algorithm. This algorithm will evaluate the flatness of the entire construction plane, identify possible flatness deviations, and generate corresponding deviation data. The corrected flatness data has been standardized through the previous process, removing errors caused by factors such as temperature and humidity changes, so it can be used for accurate flatness analysis.

[0091] Specific steps include:

[0092] According to the flatness analysis algorithm, calculate the construction plane flatness P flatness, an algorithm based on surface fitting, such as least squares fitting or B-spline curve fitting, etc., is adopted to obtain the plane fitting result. Let the flatness calculation formula be:

[0093]

[0094] where P flatness is the flatness of the construction plane, is the standardized ground height data of the i-th point, and F surface (P location,i ) is the fitting surface model, and the fitting value at the position P location,i .

[0095] Through this formula, the calculated flatness data can evaluate whether the construction plane meets the design standards, and the positions with large deviations are the flatness deviation points.

[0096] When the flatness P flatness is calculated, the deviation positions D deviation existing on the construction plane are identified, that is, the data points where the flatness deviation exceeds the set threshold ΔP threshold . The deviation calculation formula is:

[0097]

[0098] where ΔP threshold is the set flatness deviation threshold.

[0099] The generated deviation data D deviation includes all points where the flatness deviation exceeds the set threshold, serving as the basis for subsequent quality analysis.

[0100] S320: Classify the flatness deviation data of the construction plane, identify the existing quality problems, and mark the deviation positions.

[0101] Furthermore, in this step, the deviation data D deviation generated in step S310 will be classified. Specifically, the deviation data will be marked according to the severity, position, and possible quality problems of the deviation.

[0102] Classification processing: According to the magnitude of the deviation value, the deviation data is divided into different categories by using the grading method. For example, the deviation value D deviation is divided into three grades: mild, moderate, and severe. Set the classification thresholds as ΔP l ight , ΔP moderate , and ΔP severe , and the specific classification formula is as follows:

[0103]

[0104] Based on the classification results, further mark the deviations as quality problems. For example, if the deviation at a certain position is severe, it can be marked as "urgent rectification required"; if it is a moderate deviation, it is marked as "further inspection required". The marking of quality problems will provide a guiding basis for subsequent construction adjustments.

[0105] Through this classification step, the quality problems existing in the construction can be clearly identified, and various problems can be accurately located.

[0106] S330: Generate a construction quality analysis report based on the analysis results, which details the types, locations, and severities of the flatness deviations, serving as the basis for construction quality control.

[0107] In this step, integrate the analysis results of steps S310 and S320, and generate a construction quality analysis report according to the results. The report will detail the types, locations, and severities of the flatness deviations for construction management personnel to make on-site adjustments.

[0108] According to the aforementioned deviation data D deviation and the classification results, generate a construction quality analysis report. The report should include the following content:

[0109] Deviation location: The location P of each deviation point location,i , that is, the specific location on the construction plane.

[0110] Deviation type and severity: By classifying the deviation data, indicate whether each deviation point belongs to mild, moderate, or severe, and give corresponding construction suggestions.

[0111] Rectification suggestions: For severe deviations, the report will give detailed rectification suggestions; for mild or moderate deviations, the report will give suggestions for inspection or optimization.

[0112] The report can be output in the following format:

[0113] Flatness data table: List the flatness data of all construction points and the deviation categories of each point. Deviation location map: Based on the terrain and location data of the construction site, mark the spatial locations of the deviation points.

[0114] Problem analysis: According to the deviation types and locations, analyze the existing problems and propose corresponding rectification measures.

[0115] The generated quality analysis report will serve as the basis for construction quality control and guide construction management personnel in on-site adjustments and optimizations.

[0116] Explanation of association and connection

[0117] The construction plane flatness P calculated by the flatness analysis algorithm in step S310 flatness and the deviation data D deviation are used in step S320 for deviation classification and quality problem marking to ensure that the severity of the deviation is correctly identified and classified.

[0118] The deviation classification results and quality problem marking in step S320 provide a detailed basis for generating a construction quality analysis report in step S330, making the content of the report more accurate and comprehensive.

[0119] The construction quality analysis report generated in step S330 will provide data support for subsequent construction adjustment and optimization, further improving the entire construction quality control system.

[0120] The above steps ensure the real-time monitoring, accurate identification, and effective management of the construction plane flatness through data transfer and analysis.

[0121] Step S400 includes at least steps S410 - S430:

[0122] S410: Upload the flatness analysis results of the construction site to the cloud platform and store the data.

[0123] According to the aforementioned flatness analysis process, all flatness data obtained from the construction site including the corrected flatness data, deviation data, and classified quality marks need to be uploaded to the cloud platform. Specifically, the upload process includes:

[0124] Upload the flatness data and the generated deviation data D deviation to the data storage system of the cloud platform through encryption or compression technology.

[0125] When uploading the data, it includes, but is not limited to, the geographical location P of the collection point location,i , flatness value deviation level Category(i), analysis results, etc., to ensure the integrity and timeliness of the analysis results.

[0126] The uploaded data will be stored in the data management module of the cloud platform and stored and classified according to the timestamp. For example, the data will be marked and stored according to time, location, and deviation type for subsequent query and comparison. The data storage format may include:

[0127] Timestamp: Record the time of each data upload.

[0128] Location data: The geographical coordinates of each measurement point.

[0129] Flatness and deviation data: including the corrected value of flatness, deviation value, and classification information.

[0130] The data will serve as the basis for subsequent data analysis, real-time adjustment report generation, and construction management optimization.

[0131] S420: Combine historical data with real-time data, and use big data analysis to generate a real-time adjustment report, providing specific construction adjustment suggestions.

[0132] In this step, the flatness data of the construction site uploaded to the cloud platform and the deviation data D deviation will be integrated with historical data. Historical data includes: flatness data of past construction sites, records of completed construction quality adjustments, construction results under different environmental conditions, etc. Through big data analysis technology, comprehensive analysis of real-time data and historical data is carried out.

[0133] Combine real-time data and historical data, and use big data analysis techniques such as regression analysis, clustering analysis, or machine learning algorithms to generate a construction adjustment report. Specifically, the following steps can be adopted:

[0134] By comparing historical data and current real-time data, identify the change trend of the construction plane flatness. For example, the seasonal changes and the impact of climate change on flatness in historical data can be combined with the current real-time temperature T env and humidity H env data to analyze and predict the future flatness trend.

[0135] Based on the corrected flatness data and the deviation data D deviation , use a machine learning model to predict possible future deviation areas and generate a deviation data trend report.

[0136] The formula description is as follows:

[0137]

[0138] where Adjustments predict is the predicted construction adjustment suggestion, and f(·) represents the function mapping of the big data analysis model (such as a regression model or neural network) to real-time data and historical data.

[0139] Combine trend analysis and deviation trend prediction to generate a construction adjustment report. The report content includes:

[0140] The future trend of the construction plane flatness.

[0141] Anticipated possible quality problems and their locations.

[0142] Optimized construction plan and specific adjustment suggestions, such as more precise flatness adjustment for certain construction areas if needed.

[0143] S430: Feed the generated real-time adjustment report back to the construction management personnel, provide construction adjustment instructions or automated adjustment plans to optimize the construction process.

[0144] Based on the real-time adjustment report generated in step S420, the cloud platform will automatically feed back the adjustment report to the construction management personnel. The content of the report includes:

[0145] Detailed construction adjustment suggestions: For example, for flatness deviations at specific locations, give suggestions on increasing or decreasing the construction intensity, or suggest using specific construction equipment.

[0146] Automated adjustment plan: If the system has an automated adjustment function, adjustment instructions can be directly sent to the construction machinery and equipment through the cloud platform to automatically execute the adjustment tasks.

[0147] Construction progress and quality monitoring: Real-time tracking of the construction progress, monitoring of the construction quality in combination with the adjustment suggestions, and timely feedback on the adjustment effects.

[0148] After receiving the real-time adjustment report, the construction management personnel can perform on-site operations according to the report content. Specifically, the management personnel will operate on the received adjustment instructions, or manually adjust the construction plan to optimize the construction process when automated adjustment is not possible. For example, adjust the construction methods in certain areas or change the working mode of the equipment to optimize the flatness and reduce quality deviations.

[0149] After the report is fed back, the construction management personnel can continue to optimize the construction process based on the latest adjustment results. The system will also continuously monitor the subsequent construction, provide real-time feedback on new data, and continuously update and optimize the construction quality control plan.

[0150] Description of association and connection:

[0151] The flatness analysis results in step S410 and the deviation data D deviation are uploaded to the cloud platform and become the basis for big data analysis in step S420.

[0152] The big data analysis and trend prediction in step S420 are based on the combination of historical data and real-time data. Through algorithms, a real-time adjustment report is generated and future possible flatness problems are predicted.

[0153] In step S430, by feeding back the real-time adjustment report, construction adjustment instructions or automated adjustment plans are provided for the construction management personnel, ultimately optimizing the construction process and ensuring the accuracy and efficiency of construction quality control.

[0154] The above steps are closely integrated to ensure a closed-loop from data collection, upload, analysis to final construction management, supporting real-time monitoring and optimization adjustment of the flatness during the construction process of water conservancy projects.

[0155] The key innovative points of the present invention include:

[0156] (1) Intelligent sensor data collection and transmission: Combining laser rangefinders, GPS sensors, and temperature and humidity sensors to collect terrain data and environmental parameters in real time and synchronously transmit them to the data processing unit, ensuring the comprehensiveness and real-time nature of the data.

[0157] (2) Environmentally adaptive error correction: Based on the real-time collected temperature and humidity environmental parameters, dynamically adjust the measurement errors of the sensors. Combining regression models and standardization algorithms to improve the accuracy and consistency of the data.

[0158] (3) Application of flatness analysis algorithms: Use complex mathematical formulas (such as plane fitting, least squares method) to calculate the flatness of the construction plane, classify and identify deviations, and generate efficient and reliable construction quality analysis reports.

[0159] The following are its main beneficial effects:

[0160] The present invention collects the flatness data of the construction site through intelligent sensors, including terrain height, position, and temperature and humidity environmental parameters, and transmits them to the data processing unit in real time. Compared with traditional manual detection or simple tool measurement methods, the present invention can significantly reduce human errors and environmental interference, and improve the accuracy and real-time nature of data collection. Through denoising processing of the original data, automatic error correction based on environmental changes, and standardization processing, the consistency and accuracy of the data in complex construction environments are ensured, providing a reliable data basis for subsequent flatness analysis.

[0161] The present invention uses flatness analysis algorithms to process the corrected flatness data, which can quickly calculate the flatness of the construction plane, identify deviations, and generate construction quality analysis reports. Compared with traditional methods, this algorithm combines complex mathematical models (such as surface fitting, least squares method) with dynamic environmental parameters, significantly improving the efficiency and accuracy of flatness deviation identification. In addition, by uploading the analysis results to the cloud platform, combining historical data and real-time data for big data analysis and trend prediction, generating construction adjustment suggestions and real-time feedback to construction management personnel, realizing the dynamic optimization and efficient management of the construction process.

[0162] The present invention is particularly suitable for complex environments in the construction of water conservancy projects. It can monitor the construction quality in real time, provide dynamic adjustment plans, optimize the construction process, reduce rework and resource waste, and improve the project quality and construction efficiency.

[0163] Example Two:Figure 2 FIG. 2 is a block diagram showing a method for detecting the flatness of a hydraulic engineering construction plane based on an intelligent sensor according to an embodiment of the present invention. Figure 2 As shown, the structure may include:

[0164] Function of data acquisition module 10: obtain terrain data and environmental parameter data of the construction site in real time through intelligent sensors.

[0165] Laser rangefinders and GPS sensors are used to collect construction ground height data and location information to generate terrain data.

[0166] Environmental parameters, including real-time temperature and humidity, are collected through temperature and humidity sensors.

[0167] The terrain data and environmental data are synchronously transmitted to the data processing unit to form a complete construction site data input.

[0168] The data processing module 20 performs preprocessing on the original collected data, including denoising, error correction and standardization.

[0169] De-noising: Use filtering algorithm (Kalman filtering) to remove noise data to ensure data accuracy.

[0170] Error correction: Based on the collected environmental parameters (temperature, humidity), an algorithm is used to dynamically adjust the sensor's measurement error.

[0171] Standardization processing: Standardize the corrected data to eliminate dimensional differences and generate a standardized flatness data series.

[0172] The flatness analysis module 30 calculates the flatness of the construction plane using an analysis algorithm, identifies deviations and generates a construction quality analysis report.

[0173] Flatness calculation: Based on the corrected data, the flatness value of the construction plane is calculated by a surface fitting method (such as the least squares method).

[0174] Deviation identification and classification: Based on the flatness calculation results, the deviation points are identified and classified and marked to distinguish between mild, moderate and severe deviations.

[0175] Quality analysis report generation: Integrate flatness calculation and deviation classification data to generate a construction quality report, indicate the deviation location and type, and provide correction suggestions.

[0176] The cloud platform and feedback module 40 uploads the flatness analysis results to the cloud platform, generates an adjustment report based on historical data and real-time data, and feeds back to the construction management personnel.

[0177] Data Upload and Storage: The flatness analysis results (including deviation data, location markers, and classification information) are uploaded to the cloud platform through a wireless communication module and stored classified by time and location.

[0178] Real-time Adjustment Report Generation: Combining historical construction data and real-time environmental data, using big data analysis algorithms to predict future trends and generate construction adjustment suggestions.

[0179] Feedback and Automatic Adjustment: Through the generated adjustment report, specific adjustment instructions or automatic adjustment schemes are provided to the construction site to optimize the construction process.

[0180] Through the system structure design of the present invention, the following advantages are achieved:

[0181] 1. Efficient and accurate data collection and processing: Using a variety of intelligent sensors to collect key parameters of the construction site in real time, and ensuring the accuracy and consistency of the data through a data processing module.

[0182] 2. Intelligent flatness analysis: Combining the corrected data, accurately calculating the flatness of the construction plane, quickly identifying deviation points, and generating a high-quality construction quality analysis report.

[0183] 3. Real-time adjustment and optimization: Based on the cloud platform and big data analysis technology, providing real-time adjustment suggestions and optimizing the construction quality control process through an automatic function.

[0184] 4. Closed-loop construction management: Forming a complete closed-loop from data collection to analysis, feedback, and adjustment, improving the efficiency and accuracy of construction management.

[0185] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The accompanying drawings show preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing the embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements for some of the technical features. Any equivalent structures directly or indirectly using the content of the specification and drawings of the present application in other related technical fields are equally within the scope of the patent protection of the present application.

Claims

1. A method for detecting the flatness of a hydraulic engineering construction plane based on an intelligent sensor, characterized in that: The method comprises: Obtain the flatness data of the construction site, collect the construction terrain, temperature and humidity environmental parameters through intelligent sensors, and transmit them to the data processing unit in real time; Pre-process the collected raw data, automatically correct the data error based on environmental changes, and obtain the corrected flatness data; Based on the corrected flatness data, the flatness of the construction plane is calculated using the flatness analysis algorithm, the flatness deviation is identified and a construction quality analysis report is generated; The flatness analysis results are uploaded to the cloud platform, and historical data is combined with real-time data to generate a real-time adjustment report and feedback to construction management personnel for on-site adjustments to optimize the construction process.

2. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 1, characterized in that: The step of obtaining the flatness data of the construction site comprises: Use laser rangefinders and GPS sensors to collect height and location data of the construction ground; The temperature and humidity environmental parameters of the construction site are collected through temperature sensors and humidity sensors.

3. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 1, characterized in that: The step of preprocessing the collected raw data includes: Performing denoising on the raw data to remove invalid or abnormal data points; Automatically perform data error correction based on temperature and humidity environmental parameters to adjust the sensor error value; The corrected data is standardized to generate a corrected flatness data sequence.

4. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 3, characterized in that: The denoising process includes using a Kalman filter algorithm to remove noise from the original data.

5. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 3, characterized in that: The error correction calculates a correction value based on a regression model of environmental parameter changes to adjust the deviation of the sensor collected data.

6. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 1, characterized in that: The step of calculating the flatness of the construction plane using a flatness analysis algorithm comprises: Based on the corrected flatness data sequence, the flatness value of the construction plane is calculated using the least square method or B-spline curve fitting method; Identify flatness deviations and generate deviation data.

7. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 6, characterized in that: The deviation data are classified according to the size of the deviation value, and the position of the deviation point is marked.

8. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 1, characterized in that: The step of uploading the flatness analysis result comprises: Uploading the flatness analysis result to the cloud platform via a wireless communication module; The data is stored in categories according to timestamp and geographic location.

9. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 8, characterized in that: The flatness analysis result includes a corrected flatness data sequence, deviation data and classification information thereof.

10. The method for detecting the flatness of a hydraulic engineering construction plane according to claim 1, characterized in that: The step of combining historical data with real-time data to generate a real-time adjustment report includes: Performing trend analysis on the data using big data analysis algorithms; A construction adjustment suggestion is generated according to the trend analysis result, and the adjustment suggestion is fed back to the construction management personnel.

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