CN118090648A - Method and system for detecting water content of engineering material - Google Patents
Method and system for detecting water content of engineering material Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting the water content of engineering materials, which belong to the technical field of water content detection and specifically comprise the following steps: collecting engineering material samples, cleaning, dividing and weighing, scanning spectra and extracting water content characteristics, drying the samples by adopting a temperature and time control algorithm, monitoring spectrum, temperature and humidity data in real time, dynamically adjusting drying conditions to optimize efficiency, setting a water content threshold value, calculating preliminary water content, repeating the drying step if the water content exceeds the threshold value, finally introducing a correction factor to correct the water content, and transmitting a result to a remote monitoring center in real time through a wireless sensor network, thereby ensuring the accuracy and the high efficiency of water content detection, and realizing remote monitoring and management.
Description
Technical Field
The invention belongs to the technical field of water content detection, and particularly relates to a method and a system for detecting the water content of engineering materials.
Background
The water content is the ratio of the mass of water in the engineering material to the dry mass of the material, usually expressed in percentage, reflects the content and distribution condition of the water in the material, and is used as a key index for measuring the physical state of the material, and the mechanical property, compressibility, stability and safety of an engineering structure of the material are directly influenced, so that the accurate and rapid measurement of the water content of the engineering material has important significance for guaranteeing the engineering quality. The traditional water content detection method mainly comprises a drying method and an alcohol combustion method, and the methods can meet the requirements of engineering practice to a certain extent, but have the defects of complicated operation, long time consumption and influence of environmental factors on precision, and along with the development of technology and test technology, the novel water content detection method, such as a microwave heating method and a calcium carbide decrement method, has the advantages of simplicity and convenience in operation, high speed and high precision. According to the method for detecting the water content of the engineering material, disclosed by the invention, the real-time monitoring and the remote control of the water content of the engineering material are realized through the integrated sensor, the data transmission, the data analysis and the spectrum analysis technology, so that the detection efficiency and the detection precision are improved.
The Chinese patent with publication number CN117129466A discloses a water content detection method of hydraulic engineering materials, which comprises the following steps: step one: conveying carrier gas to the surface of a hydraulic engineering material sample to be detected, and collecting spectral data of a position covered by the carrier gas on the hydraulic engineering material sample to be detected by using a laser-induced breakdown spectrometer; constructing a water content detection model of the hydraulic engineering material; step two: preprocessing the spectrum data of the position covered by the carrier gas; optimizing the constructed water content detection model of the hydraulic engineering material; the beneficial effects of the invention are as follows: the preprocessed data is imported into an optimal water content detection model to obtain the water content of the hydraulic engineering material sample to be detected, so that the influence caused by data deviation is reduced, and the detection accuracy is improved; the detection process is simplified, so that the detection is more convenient, and the detection efficiency is improved; the method is favorable for enhancing the spectrum signal intensity in the carrier gas environment and improving the analysis efficiency, thereby improving the accuracy of the detection result and the data stability.
The above prior art has the following problems: 1) The laser-induced breakdown spectrometer is used as a precise analysis instrument, has relatively high equipment cost and is not suitable for all-scale laboratories or engineering projects; 2) The method aims at the water content detection of the hydraulic engineering materials, is not applicable to other engineering materials, and has limited detection range; 3) The environmental adaptability is poor.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method and a system for detecting the moisture content of engineering materials, which are characterized in that engineering material samples are collected, cleaned, segmented and weighed, the spectra are scanned and moisture content characteristics are extracted, the samples are dried by adopting a temperature and time control algorithm, the spectra, temperature and humidity data are monitored in real time, the drying conditions are dynamically adjusted to optimize the efficiency, a moisture content threshold value is set, the preliminary moisture content is calculated, if the preliminary moisture content exceeds the threshold value, the drying step is repeated, a correction factor is introduced to correct the moisture content, and the results are transmitted to a remote monitoring center in real time through a wireless sensor network, so that the accuracy and the high efficiency of the moisture content detection are ensured, and the remote monitoring and management are realized.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A method for detecting the water content of engineering materials comprises the following steps:
Step S1: collecting engineering material samples from engineering sites, cleaning and dividing the collected engineering material samples, weighing the samples by using an accurate electronic scale, recording the original quality of the samples, simultaneously carrying out spectrum scanning on the engineering materials by using a spectrometer to obtain spectrum data of the engineering materials, preprocessing and analyzing the collected spectrum data by using a real-time spectrum data analysis technology, and extracting water content characteristic information;
Step S2: putting the processed engineering material sample into an oven, adopting a temperature control algorithm and a time control algorithm to carry out drying treatment on the engineering material sample according to the characteristics of the material, monitoring the changes of spectrum data and temperature and humidity sensor data in real time in the drying process, analyzing the change trend of the water content, combining a drying efficiency optimization algorithm, and dynamically adjusting the drying time and temperature according to the spectrum data and real-time feedback in the drying process;
Step S3: setting a water content threshold of the engineering material, taking out a dried engineering material sample after drying, recording the dried quality, calculating the preliminary water content according to the original quality and the dried quality, and repeating the drying treatment process of the step S2 until the water content is smaller than the water content threshold if the preliminary water content exceeds the water content threshold;
step S4: introducing correction factors, determining specific values and correction modes of the correction factors through a machine learning algorithm and experimental verification, applying the correction factors to the primary water content to obtain corrected water content results, recording the corrected water content results, and transmitting the results to a remote monitoring center in real time through a wireless sensor network to realize remote monitoring and management.
Specifically, the characteristic information of the water content in the step S1 includes: intensity of a specific wavelength band, position of absorption peak, and intensity.
Specifically, the specific step of analyzing the variation trend of the water content in the step S2 includes:
Step S201: collecting spectrum data, weighing data, temperature and humidity sensor data, and preprocessing the four data;
Step S202: from spectral data Extracting characteristic information of water contentAnd with weighing data obtained by the sensorsTemperature dataHumidity dataAnd carrying out feature fusion to obtain new water content feature information, wherein a feature fusion formula is as follows:
Wherein, New water content characteristic information after characteristic fusion is represented, r represents the number of the new water content characteristic information,Indicating the r new characteristic information of the water content,、、、Respectively representing spectral data characteristic informationWeighing dataTemperature dataHumidity dataI represents the number of spectral data characteristic information, j represents the number of weighing data, n represents the number of temperature data, q represents the number of humidity data,Representing the amount of spectral data;
Step S203: selecting a multiple linear regression model, training the multiple linear regression model by using historical data, evaluating the trained multiple linear regression model by using a verification set, adjusting parameters of the multiple linear regression model according to an evaluation result, and optimizing the performance of the multiple linear regression model;
Step S204: the trained multiple linear regression model is deployed to practical application, in the drying process, the spectrum data, weighing data and temperature and humidity sensor data which are collected in real time are input into the multiple linear regression model, the multiple linear regression model predicts the water content in real time according to the input data, and the change trend is analyzed;
Step S205: and dynamically adjusting the drying time and the temperature according to the prediction result of the model and real-time data feedback.
Specifically, the specific steps of step S203 include:
S2031: the collected spectrum data, weighing data, temperature and humidity sensor data are fused to obtain Dividing the training set, the verification set and the test set;
S2032: selecting a multiple linear regression model as a prediction model, and fitting the multiple linear regression model by using characteristic variables in a training set to obtain multiple linear regression model coefficients and intercepts;
s2033: evaluating the trained model by using the verification set, and calculating an evaluation index;
S2034: and judging whether the performance of the model meets the requirement according to the evaluation result, if not, increasing or deleting the number of the characteristic variables, and then retraining the model and evaluating.
Specifically, the calculation formula of the preliminary water content in the step S3 is as follows:
Wherein k represents the primary water content, Representing the original quality of the engineering material,The quality of the engineering material after being dried is shown,Indicating the dehydration rate of the engineering material,Representing the coefficient of performance of the engineering material,Representing the environmental impact factor(s),Representing the time adjustment factor.
Specifically, the specific steps of verifying the correction factor through the machine learning algorithm and the experiment in the step S4 include:
s401: will fuse features As an input of the machine learning model, training the model by using the selected machine learning algorithm and the data preprocessed in S201, and adjusting parameters of the model;
S402: analyzing and converting model output according to the trained model, and calculating the numerical value and correction mode of the correction factor;
s403: dividing the spectrum data, weighing data, temperature and humidity sensor data collected in the step S201 into a training set and a verification set, and inputting the verification set into a preliminary water content calculation formula to obtain a preliminary water content data value;
s404: applying the correction factors obtained in the training stage to the preliminary water content data on the verification set to obtain corrected water content results;
S405: and comparing the corrected water content result with the actual water content value, calculating an error index, and adjusting the value of the correction factor or the correction mode according to the error index value to verify again.
An engineered material moisture content detection system comprising: the device comprises a sample acquisition and preprocessing module, a water content calculation module, a verification and correction module and a remote monitoring module;
The sample acquisition and preprocessing module is used for acquiring engineering material samples from engineering sites, preprocessing the engineering material samples, performing spectrum scanning on the engineering material samples by using a spectrometer, acquiring spectrum data of the samples, and extracting information related to water content by using a real-time spectrum data analysis technology;
the water content calculating module is used for drying engineering material samples by controlling temperature and time and calculating the preliminary water content according to the quality change before and after drying;
The calibration and correction module is used for constructing a multi-dimensional water content calibration model by utilizing the spectrum data, weighing data and other sensor data monitored in real time, and performing calibration and correction on the primary water content;
The remote monitoring module is used for recording the final water content result and transmitting the result to the remote monitoring center in real time through the wireless sensor network.
Specifically, the sample acquisition and preprocessing module comprises: the system comprises an acquisition unit, a preprocessing unit, a weighing unit, a spectrum scanning unit and a data analysis unit, wherein the water content calculation module comprises: the device comprises a drying unit, a quality monitoring unit and a water content calculating unit;
The acquisition unit is used for acquiring sample data; the pretreatment unit is used for cleaning the sample to remove impurities and dividing the sample; the weighing unit is used for weighing the preprocessed sample by using an accurate electronic scale and recording the original quality; the spectrum scanning unit is used for scanning the sample by using a spectrometer to acquire spectrum data; the data analysis unit is used for preprocessing and analyzing the spectrum data and extracting the characteristic information of the water content; the drying unit is used for drying the sample through the oven and controlling drying conditions; the quality monitoring unit is used for monitoring the quality change of the sample in real time in the drying process; the water content calculating unit is used for calculating the preliminary water content according to the quality change before and after drying.
Specifically, the verification and correction module includes: the system comprises a verification model construction unit, a water content verification unit, a correction factor determination unit and a water content correction unit, wherein the remote monitoring module comprises: the system comprises a result recording unit, a data transmission unit and a remote monitoring unit;
The verification model construction unit is used for constructing a water content verification model according to the real-time monitoring data; the water content verification unit is used for verifying the preliminary water content by using a verification model; the correction factor determining unit is used for determining the specific numerical value and the correction mode of the correction factor through a machine learning algorithm and experimental verification; the water content correction unit is used for correcting the primary water content according to the correction factors to obtain a final water content result; the result recording unit is used for recording the final water content result; the data transmission unit is used for transmitting the result to a remote monitoring center in real time through a wireless sensor network; the remote monitoring unit is used for receiving and displaying the water content data in the remote monitoring center and performing remote monitoring and management.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention provides an engineering material water content detection system, which is optimized and improved in terms of architecture, operation steps and flow, and has the advantages of simple flow, low investment and operation cost and low production and working costs.
2. The invention provides a method for detecting the water content of engineering materials, which utilizes a spectrum analysis technology to acquire the spectrum characteristics of the materials, processes and analyzes the spectrum data through a machine learning algorithm, realizes the accurate prediction of the water content, overcomes the defects of complicated operation and long time consumption of the traditional method, and improves the detection efficiency; through integrating wireless sensor network, realize data acquisition, transmission and control in real time for the moisture content detects no longer to receive the region restriction, can carry out remotely.
Drawings
FIG. 1 is a diagram of an overall framework of a method for detecting the water content of an engineering material;
FIG. 2 is a flowchart of an algorithm for detecting the water content of engineering materials;
FIG. 3 is a flow chart of a water content algorithm of a method for detecting the water content of engineering materials;
FIG. 4 is a schematic diagram of a system for detecting the water content of engineering materials according to the present invention.
Detailed Description
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: the method for detecting the water content of the engineering material comprises the following steps:
Step S1: collecting engineering material samples from engineering sites, cleaning and dividing the collected engineering material samples, weighing the samples by using an accurate electronic scale, recording the original quality of the samples, simultaneously carrying out spectrum scanning on the engineering materials by using a spectrometer to obtain spectrum data of the engineering materials, preprocessing and analyzing the collected spectrum data by using a real-time spectrum data analysis technology, and extracting water content characteristic information;
common engineering materials include metallic materials, nonmetallic materials, polymeric materials, and composite materials.
The steps typically need to be considered and performed in collecting a sample of engineering material:
(1) Selecting a sampling position: selecting representative sampling positions according to the distribution condition and characteristics of engineering materials;
(2) Preparing a sampling tool: preparing appropriate sampling tools, such as scoops, drills, samplers, which should be clean and suitable for collecting the desired sample of engineering material;
(3) Collecting a sample: collecting engineering material samples at selected sampling locations using prepared tools, which may vary for different types of engineering materials, such as drilling core samples from concrete structures, or digging soil samples from the soil;
(4) Sample marking and recording: marking the acquired sample, marking sampling position and date information, and recording the sampling process in detail, wherein the sampling process comprises acquisition conditions and appearance characteristics of the sample;
(5) Preliminary treatment of the sample: for some engineering materials, preliminary treatment, such as impurity removal, crushing or mixing, is required to facilitate the subsequent detection of water content;
(6) Sample preservation and transportation: the collected sample should be preserved properly, so as to avoid pollution or water loss in the preservation and transportation processes, and if the sample needs to be transported to a laboratory for detection, the safety of the sample in the transportation process should be ensured.
Spectral data analysis is a method of studying the properties and composition of a substance by measuring and analyzing the wavelength and intensity of light emitted, absorbed or scattered by the substance, and in the present invention, spectral data is used to analyze the trend of the change in water content of a sample of engineering material.
According to the invention, the spectrometer is used for carrying out spectrum scanning on the engineering material to obtain the spectrum data of the engineering material, the data exchange can be carried out with an upper computer, the running speed is increased, and thus the rapid analysis on the water content of the engineering material is realized, meanwhile, the spectrometer adopts a high-stability excitation light source and an instrument with the advantages of high resolution, good repeatability, high sensitivity and the like, the high-precision detection result is ensured, the automation degree is high, the failure rate is low, and the working performance is extremely stable.
Step S2: putting the processed engineering material sample into an oven, adopting a temperature control algorithm and a time control algorithm to carry out drying treatment on the engineering material sample according to the characteristics of the material, monitoring the changes of spectrum data and temperature and humidity sensor data in real time in the drying process, analyzing the change trend of the water content, combining a drying efficiency optimization algorithm, and dynamically adjusting the drying time and temperature according to the spectrum data and real-time feedback in the drying process;
the temperature control algorithm and the time control algorithm are control strategies commonly used in the engineering field, and the temperature control algorithm ensures that the temperature in the oven changes according to a preset program so as to optimize the drying effect; the time control algorithm is responsible for controlling the duration of the drying process to ensure that the material is dried to a suitable degree.
The drying efficiency optimization algorithm is an algorithm for improving the efficiency and quality of the drying process, combines spectral data and real-time feedback in the drying process, and determines the optimal drying time and temperature through algorithm analysis and calculation, so that the drying efficiency is optimized.
Step S3: setting a water content threshold of the engineering material, taking out a dried engineering material sample after drying, recording the dried quality, calculating the preliminary water content according to the original quality and the dried quality, and repeating the drying treatment process of the step S2 until the water content is smaller than the water content threshold if the preliminary water content exceeds the water content threshold;
step S4: introducing correction factors, determining specific values and correction modes of the correction factors through a machine learning algorithm and experimental verification, applying the correction factors to the primary water content to obtain corrected water content results, recording the corrected water content results, and transmitting the results to a remote monitoring center in real time through a wireless sensor network to realize remote monitoring and management.
The water content characteristic information in the step S1 includes: intensity of a specific wavelength band, position of absorption peak, and intensity.
The water content characteristic information is extracted by adopting a principal component analysis method, which is the prior art content in the field and is not an inventive scheme of the application, and is not described in detail herein.
The specific step of analyzing the variation trend of the water content in the step S2 includes:
Step S201: collecting spectrum data, weighing data, temperature and humidity sensor data, and preprocessing the four data;
Step S202: from spectral data Extracting characteristic information of water contentAnd with weighing data obtained by the sensorsTemperature dataHumidity dataAnd carrying out feature fusion to obtain new water content feature information, wherein a feature fusion formula is as follows:
Wherein, New water content characteristic information after characteristic fusion is represented, r represents the number of the new water content characteristic information,Indicating the r new characteristic information of the water content,、、、Respectively representing spectral data characteristic informationWeighing dataTemperature dataHumidity dataI represents the number of spectral data characteristic information, j represents the number of weighing data, n represents the number of temperature data, q represents the number of humidity data,Representing the amount of spectral data;
Step S203: selecting a multiple linear regression model, training the multiple linear regression model by using historical data, evaluating the trained multiple linear regression model by using a verification set, adjusting parameters of the multiple linear regression model according to an evaluation result, and optimizing the performance of the multiple linear regression model;
Step S204: the trained multiple linear regression model is deployed to practical application, in the drying process, the spectrum data, weighing data and temperature and humidity sensor data which are collected in real time are input into the multiple linear regression model, the multiple linear regression model predicts the water content in real time according to the input data, and the change trend is analyzed;
Step S205: and dynamically adjusting the drying time and the temperature according to the prediction result of the model and real-time data feedback.
The specific steps of the step S203 include:
S2031: the collected spectrum data, weighing data, temperature and humidity sensor data are fused to obtain Dividing the training set, the verification set and the test set;
S2032: selecting a multiple linear regression model as a prediction model, and fitting the multiple linear regression model by using characteristic variables in a training set to obtain multiple linear regression model coefficients and intercepts;
The multiple linear regression model formula is:
Wherein, The target variable is represented by a value of the target variable,The intercept is indicated as the intercept and,Feature variables representing the fusion features,Coefficients of feature variables representing the fused features.
The coefficient formula of the multiple linear regression model is as follows:
Wherein, Representing an estimate of the coefficient vector, a represents a fused feature matrix, where each row is a sample, each column is a feature variable,Representing the transpose of the fusion feature matrix.
S2033: evaluating the trained model by using the verification set, and calculating an evaluation index;
The formula of the evaluation index is:
Wherein, Represents an evaluation index of the evaluation index,Represent the firstThe number of samples of the verification set,Representing the number of samples in the verification set,Representation model pair numberThe predicted target value of each sample is calculated,Represent the firstThe true target value of the individual samples is,Representing the mean of the target variables in the validation set.
S2034: and judging whether the performance of the model meets the requirement according to the evaluation result, if not, increasing or deleting the number of the characteristic variables, and then retraining the model and evaluating.
The calculation formula of the preliminary water content in the step S3 is as follows:
Wherein k represents the primary water content, Representing the original quality of the engineering material,The quality of the engineering material after being dried is shown,A value representing the dehydration rate of the engineering material, between 0 and 1, for reflecting the efficiency of the drying process,Representing the coefficient of performance of the engineering material,Representing the environmental impact factor(s),Representing the time adjustment factor.
The specific steps of verifying the correction factor through a machine learning algorithm and experiments in the step S4 include:
s401: will fuse features As an input of the machine learning model, training the model by using the selected machine learning algorithm and the data preprocessed in S201, and adjusting parameters of the model;
the machine learning algorithm formula is:
Wherein Y represents the target water content, b represents the bias term, 、、、The weights representing the corresponding features are part of the correction factor.
S402: analyzing and converting model output according to the trained model, and calculating the numerical value and correction mode of the correction factor;
s403: dividing the spectrum data, weighing data, temperature and humidity sensor data collected in the step S201 into a training set and a verification set, and inputting the verification set into a preliminary water content calculation formula to obtain a preliminary water content data value;
s404: applying the correction factors obtained in the training stage to the preliminary water content data on the verification set to obtain corrected water content results;
The corrected water content formula is:
Wherein, The corrected water content is represented, and w represents a correction factor.
S405: and comparing the corrected water content result with the actual water content value, calculating an error index, and adjusting the value of the correction factor or the correction mode according to the error index value to verify again.
The calculation formula of the error index is as follows:
where MSE represents the mean square error, Indicating the actual water cut rate value,Indicating the corrected water cut rate value,The smaller the MSE, which represents the number of validation set samples, the higher the accuracy of the correction factor.
Example 2
Referring to fig. 4, another embodiment of the present invention is provided: an engineered material moisture content detection system comprising:
the device comprises a sample acquisition and preprocessing module, a water content calculation module, a verification and correction module and a remote monitoring module;
The sample acquisition and preprocessing module is used for acquiring engineering material samples from engineering sites, preprocessing the engineering material samples, performing spectrum scanning on the engineering material samples by using a spectrometer, acquiring spectrum data of the samples, and extracting information related to water content by using a real-time spectrum data analysis technology;
the water content calculating module is used for drying engineering material samples by controlling temperature and time and calculating the preliminary water content according to the quality change before and after drying;
The calibration and correction module is used for constructing a multi-dimensional water content calibration model by utilizing the spectrum data, weighing data and other sensor data monitored in real time, and performing calibration and correction on the primary water content;
The remote monitoring module is used for recording the final water content result and transmitting the result to the remote monitoring center in real time through the wireless sensor network.
The sample collection and pretreatment module comprises: the system comprises an acquisition unit, a preprocessing unit, a weighing unit, a spectrum scanning unit and a data analysis unit, wherein the water content calculation module comprises: the device comprises a drying unit, a quality monitoring unit and a water content calculating unit;
The acquisition unit is used for acquiring sample data; the pretreatment unit is used for cleaning the sample to remove impurities and dividing the sample; the weighing unit is used for weighing the preprocessed sample by using an accurate electronic scale and recording the original quality; the spectrum scanning unit is used for scanning the sample by using a spectrometer to acquire spectrum data; the data analysis unit is used for preprocessing and analyzing the spectrum data and extracting the characteristic information of the water content; the drying unit is used for drying the sample through the oven and controlling drying conditions; the quality monitoring unit is used for monitoring the quality change of the sample in real time in the drying process; the water content calculating unit is used for calculating the preliminary water content according to the quality change before and after drying.
The verification and correction module comprises: the system comprises a verification model construction unit, a water content verification unit, a correction factor determination unit and a water content correction unit, wherein the remote monitoring module comprises: the system comprises a result recording unit, a data transmission unit and a remote monitoring unit;
The verification model construction unit is used for constructing a water content verification model according to the real-time monitoring data; the water content verification unit is used for verifying the preliminary water content by using a verification model; the correction factor determining unit is used for determining the specific numerical value and the correction mode of the correction factor through a machine learning algorithm and experimental verification; the water content correction unit is used for correcting the primary water content according to the correction factors to obtain a final water content result; the result recording unit is used for recording the final water content result; the data transmission unit is used for transmitting the result to a remote monitoring center in real time through a wireless sensor network; the remote monitoring unit is used for receiving and displaying the water content data in the remote monitoring center and performing remote monitoring and management.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and variations, modifications, substitutions and alterations of the above-described embodiments may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the present invention as defined by the claims, which are all within the scope of the present invention.
Claims (9)
1. The method for detecting the water content of the engineering material is characterized by comprising the following steps of:
Step S1: collecting engineering material samples from engineering sites, cleaning and dividing the collected engineering material samples, weighing the samples by using an accurate electronic scale, recording the original quality of the samples, simultaneously carrying out spectrum scanning on the engineering materials by using a spectrometer to obtain spectrum data of the engineering materials, preprocessing and analyzing the collected spectrum data by using a real-time spectrum data analysis technology, and extracting water content characteristic information;
Step S2: putting the processed engineering material sample into an oven, adopting a temperature control algorithm and a time control algorithm to carry out drying treatment on the engineering material sample according to the characteristics of the material, monitoring the changes of spectrum data and temperature and humidity sensor data in real time in the drying process, analyzing the change trend of the water content, combining a drying efficiency optimization algorithm, and dynamically adjusting the drying time and temperature according to the spectrum data and real-time feedback in the drying process;
Step S3: setting a water content threshold of the engineering material, taking out a dried engineering material sample after drying, recording the dried quality, calculating the preliminary water content according to the original quality and the dried quality, and repeating the drying treatment process of the step S2 until the water content is smaller than the water content threshold if the preliminary water content exceeds the water content threshold;
step S4: introducing correction factors, determining specific values and correction modes of the correction factors through a machine learning algorithm and experimental verification, applying the correction factors to the primary water content to obtain corrected water content results, recording the corrected water content results, and transmitting the results to a remote monitoring center in real time through a wireless sensor network to realize remote monitoring and management.
2. The method for detecting the water content of engineering materials according to claim 1, wherein the characteristic information of the water content in the step S1 comprises: intensity of a specific wavelength band, position of absorption peak, and intensity.
3. The method for detecting the water content of engineering materials according to claim 2, wherein the specific step of analyzing the trend of the water content in the step S2 comprises the following steps:
Step S201: collecting spectrum data, weighing data, temperature and humidity sensor data, and preprocessing the four data;
Step S202: from spectral data Extract water content characteristic information/>And with the weighing data/>, obtained by the sensorsTemperature data/>Humidity data/>And carrying out feature fusion to obtain new water content feature information, wherein a feature fusion formula is as follows:
；
Wherein, Representing new water content characteristic information after characteristic fusion, r representing the number of the new water content characteristic information, and/(m)Representing the r new characteristic information of water content,/>、/>、/>、/>Respectively represent the characteristic information/>, of the spectrum dataWeighing data/>Temperature data/>Humidity data/>I represents the number of spectral data characteristic information, j represents the number of weighing data, n represents the number of temperature data, q represents the number of humidity data,/>Representing the amount of spectral data;
Step S203: selecting a multiple linear regression model, training the multiple linear regression model by using historical data, evaluating the trained multiple linear regression model by using a verification set, adjusting parameters of the multiple linear regression model according to an evaluation result, and optimizing the performance of the multiple linear regression model;
Step S204: the trained multiple linear regression model is deployed to practical application, in the drying process, the spectrum data, weighing data and temperature and humidity sensor data which are collected in real time are input into the multiple linear regression model, the multiple linear regression model predicts the water content in real time according to the input data, and the change trend is analyzed;
Step S205: and dynamically adjusting the drying time and the temperature according to the prediction result of the model and real-time data feedback.
4. The method for detecting the water content of engineering materials according to claim 3, wherein the specific steps of the step S203 include:
S2031: the collected spectrum data, weighing data, temperature and humidity sensor data are fused to obtain Dividing the training set, the verification set and the test set;
S2032: selecting a multiple linear regression model as a prediction model, and fitting the multiple linear regression model by using characteristic variables in a training set to obtain multiple linear regression model coefficients and intercepts;
s2033: evaluating the trained model by using the verification set, and calculating an evaluation index;
S2034: and judging whether the performance of the model meets the requirement according to the evaluation result, if not, increasing or deleting the number of the characteristic variables, and then retraining the model and evaluating.
5. The method for detecting the water content of engineering materials according to claim 4, wherein the calculation formula of the preliminary water content in the step S3 is as follows:
；
Wherein k represents the primary water content, Representing the original quality of engineering materials,/>Representing the quality of engineering materials after drying,/>Representing the dehydration rate of engineering materials,/>Representing the characteristic coefficient of engineering material,/>Representing environmental impact factors,/>Representing the time adjustment factor.
6. The method for detecting the water content of engineering materials according to claim 5, wherein the specific step of verifying the correction factor in step S4 through a machine learning algorithm and experiments comprises:
s401: will fuse features As an input of the machine learning model, training the model by using the selected machine learning algorithm and the data preprocessed in S201, and adjusting parameters of the model;
S402: analyzing and converting model output according to the trained model, and calculating the numerical value and correction mode of the correction factor;
s403: dividing the spectrum data, weighing data, temperature and humidity sensor data collected in the step S201 into a training set and a verification set, and inputting the verification set into a preliminary water content calculation formula to obtain a preliminary water content data value;
s404: applying the correction factors obtained in the training stage to the preliminary water content data on the verification set to obtain corrected water content results;
S405: and comparing the corrected water content result with the actual water content value, calculating an error index, and adjusting the value of the correction factor or the correction mode according to the error index value to verify again.
7. An engineering material water content detection system realized based on the engineering material water content detection method according to any one of claims 1 to 6, characterized by comprising: the device comprises a sample acquisition and preprocessing module, a water content calculation module, a verification and correction module and a remote monitoring module;
The sample acquisition and preprocessing module is used for acquiring engineering material samples from engineering sites, preprocessing the engineering material samples, performing spectrum scanning on the engineering material samples by using a spectrometer, acquiring spectrum data of the samples, and extracting information related to water content by using a real-time spectrum data analysis technology;
the water content calculating module is used for drying engineering material samples by controlling temperature and time and calculating the preliminary water content according to the quality change before and after drying;
The calibration and correction module is used for constructing a multi-dimensional water content calibration model by utilizing the spectrum data, weighing data and other sensor data monitored in real time, and performing calibration and correction on the primary water content;
The remote monitoring module is used for recording the final water content result and transmitting the result to the remote monitoring center in real time through the wireless sensor network.
8. The engineered material moisture content detection system of claim 7, wherein said sample collection and pre-processing module comprises: the system comprises an acquisition unit, a preprocessing unit, a weighing unit, a spectrum scanning unit and a data analysis unit, wherein the water content calculation module comprises: the device comprises a drying unit, a quality monitoring unit and a water content calculating unit;
The acquisition unit is used for acquiring sample data; the pretreatment unit is used for cleaning the sample to remove impurities and dividing the sample; the weighing unit is used for weighing the preprocessed sample by using an accurate electronic scale and recording the original quality; the spectrum scanning unit is used for scanning the sample by using a spectrometer to acquire spectrum data; the data analysis unit is used for preprocessing and analyzing the spectrum data and extracting the characteristic information of the water content; the drying unit is used for drying the sample through the oven and controlling drying conditions; the quality monitoring unit is used for monitoring the quality change of the sample in real time in the drying process; the water content calculating unit is used for calculating the preliminary water content according to the quality change before and after drying.
9. The engineered material moisture content detection system of claim 8, wherein the verification and correction module comprises: the system comprises a verification model construction unit, a water content verification unit, a correction factor determination unit and a water content correction unit, wherein the remote monitoring module comprises: the system comprises a result recording unit, a data transmission unit and a remote monitoring unit;
The verification model construction unit is used for constructing a water content verification model according to the real-time monitoring data; the water content verification unit is used for verifying the preliminary water content by using a verification model; the correction factor determining unit is used for determining the specific numerical value and the correction mode of the correction factor through a machine learning algorithm and experimental verification; the water content correction unit is used for correcting the primary water content according to the correction factors to obtain a final water content result; the result recording unit is used for recording the final water content result; the data transmission unit is used for transmitting the result to a remote monitoring center in real time through a wireless sensor network; the remote monitoring unit is used for receiving and displaying the water content data in the remote monitoring center and performing remote monitoring and management.
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