CN-122020347-A - Detection system and method capable of monitoring various heavy metal ions on line in real time

CN122020347ACN 122020347 ACN122020347 ACN 122020347ACN-122020347-A

Abstract

The invention discloses a detection system and a detection method capable of monitoring various heavy metal ions on line in real time, and relates to the technical field of water quality detection, wherein the detection system comprises the steps of measuring the temperature of a water sample by using a thermoelectric coupler temperature detection method and carrying out real-time temperature correction by using a resistance compensation method to obtain a target water sample with stable temperature; the method comprises the steps of exciting a target water sample with stable temperature by adopting an electrochemical impedance spectrum method, collecting a composite electric response signal by adopting a capacitance response measurement method, decomposing and denoising the composite electric response signal by adopting a time-frequency combined wavelet transformation method to obtain multiple electric characteristic data, and learning and reasoning the multiple electric characteristic data by utilizing a deep convolutional neural network to obtain heavy metal ion types and estimating a concentration quantitative result. The invention improves the detection sensitivity and the resolution capability of weak response ions, further realizes high-precision identification and concentration estimation of various heavy metal ions, and effectively supports the real-time detection requirement in complex water environments.

Inventors

LI XIAOHUI
Mei Xiaorui
LIANG XIAOFEI
ZHAO TIEZHOU
LI GUOFANG
CHANG BOBO
LI HONG
LI LI
YANG SHUAI
QIAN XING
SUN YANLEI
WEI YANRUI
LI ZHIBIN

Assignees

中铁十局集团有限公司

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. A detection method capable of monitoring various heavy metal ions on line in real time is characterized by comprising the following steps of, Measuring the temperature of a water sample by using a thermal coupler temperature detection method, and carrying out real-time temperature correction by using a resistance compensation method to obtain a target water sample with stable temperature; exciting a target water sample with stable temperature by adopting an electrochemical impedance spectrum method, and acquiring a composite electric response signal by utilizing a capacitance response measurement method; Decomposing and denoising the composite electric response signal by adopting a time-frequency combined wavelet transformation method to obtain multiple electric characteristic data; The deep convolutional neural network is utilized to learn and infer multiple electrical characteristic data, so that heavy metal ion types are obtained, and concentration quantitative results are estimated; And carrying out real-time environment interference correction on the quantitative results of the heavy metal ion types and the concentration through online dynamic calibration to form heavy metal ion detection results.
2. The method for detecting heavy metal ions on line in real time according to claim 1, wherein the method comprises measuring the temperature of the water sample by using a thermal coupler temperature detection method and correcting the temperature in real time by using a resistance compensation method to obtain a target water sample with stable temperature, Continuously measuring the temperature of the inflow water sample by using a thermoelectric coupler temperature detection method to form temperature original data; comparing the temperature original data with a set reference temperature to obtain a temperature difference offset value; According to the temperature difference offset value, adopting a resistance compensation control method to adjust the resistance value of the constant temperature element, and generating a temperature correction control signal; and dynamically regulating and controlling the thermal state of the water sample according to the temperature correction control signal to obtain a target water sample with stable temperature.
3. The method for detecting multiple heavy metal ions on line in real time according to claim 2, wherein the method comprises exciting a target water sample with stable temperature by electrochemical impedance spectroscopy, collecting a composite electric response signal by capacitance response measurement method, Based on a target water sample with stable temperature, establishing an electrolysis environment to be detected, applying a multi-frequency alternating voltage signal, and exciting ions in the target water sample to generate frequency domain response so as to form initial electrochemical impedance data; And capturing transient current and polarization response of the initial electrochemical impedance data by using a high-precision capacitance response measurement method, and generating a composite electrical response signal.
4. The method for detecting multiple heavy metal ions on line in real time according to claim 3, wherein the method for detecting multiple heavy metal ions on line is characterized in that the method for detecting multiple heavy metal ions on line comprises the steps of, Performing time-frequency analysis on the composite electric response signal to obtain different frequency band sub-signals after multi-scale decomposition, and performing multi-scale wavelet decomposition to obtain different frequency band sub-signals; And removing high-frequency random noise from the sub-signals in different frequency bands by using a soft threshold denoising algorithm, and reconstructing the denoised sub-signals in each frequency band into a time-frequency composite response to obtain multiple electrical characteristic data.
5. The method for detecting multiple heavy metal ions on line in real time according to claim 4, wherein the method for detecting multiple heavy metal ions on line is characterized by learning and reasoning multiple electrical characteristic data by using a deep convolutional neural network to obtain heavy metal ion types and estimating concentration quantitative results, and comprises the following steps of, Inputting the multiple electrical characteristic data into a pre-trained deep convolutional neural network, performing multi-layer convolution and pooling operation to extract multiple electrical characteristic representations, and generating a probability distribution map of heavy metal ion types; based on the extracted multiple electrical characteristic representation, carrying out regression calculation by using the full-connection layer, and estimating the concentration value of the heavy metal ions; And combining the probability distribution map of the heavy metal ion species with the concentration value of the heavy metal ion, and analyzing and reading through a post-processing algorithm to obtain the heavy metal ion species and concentration quantitative result.
6. The method for detecting heavy metal ions on line in real time according to claim 5, wherein the concentration value of heavy metal ions is estimated by performing regression calculation using a full connection layer based on the extracted multiple electrical characteristic representation, Inputting multiple electrical characteristic representations into a first layer of the full-connection layer, carrying out weighted summation and activation to generate an initial concentration mapping vector; inputting the initial concentration mapping vector into a second layer full-connection network, further compressing the characteristic dimension and enhancing the nonlinear expression, and outputting a concentration estimation intermediate result; And inputting the intermediate result of the concentration estimation into a final output layer, and calculating the concentration values of various heavy metal ions through a regression function.
7. The method for detecting heavy metal ions on line in real time as set forth in claim 5, wherein the method comprises performing real-time environmental interference correction on quantitative results of heavy metal ion types and concentrations by on-line dynamic calibration to form heavy metal ion detection results, Taking the quantitative result of the heavy metal ion type and the concentration as initial detection data; synchronously collecting current environmental parameter data to form an interference factor data set; fusing the interference factor data set with the initial detection data to obtain a calibration factor reflecting environmental interference; And adjusting initial detection data by using the calibration factors, correcting the influence of environmental interference, obtaining the dynamically calibrated heavy metal ion types and concentration values, and forming a heavy metal ion detection result.
8. The detection system capable of monitoring a plurality of heavy metal ions on line in real time is based on the detection method capable of monitoring a plurality of heavy metal ions on line in real time as claimed in any one of claims 1 to 7, and is characterized by comprising a temperature correction module, a response acquisition module, a signal decomposition and denoising module, a concentration reasoning module and a result correction module; The temperature correction module is used for measuring the temperature of the water sample by using a thermocouple temperature detection method and carrying out real-time temperature correction by using a resistance compensation method to obtain a target water sample with stable temperature; The response acquisition module is used for exciting a target water sample with stable temperature by adopting an electrochemical impedance spectrum method and acquiring a composite electric response signal by utilizing a capacitance response measurement method; the signal decomposition and denoising module is used for decomposing and denoising the composite electric response signal by adopting a time-frequency combined wavelet transformation method to obtain multiple electric characteristic data; the concentration reasoning module is used for learning and reasoning the multiple electrical characteristic data by using the deep convolutional neural network to obtain heavy metal ion types and estimating a concentration quantitative result; And the result correction module is used for carrying out real-time environment interference correction on the quantitative result of the heavy metal ion type and concentration through online dynamic calibration to form a heavy metal ion detection result.
9. A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that the processor realizes the steps of the detection method capable of monitoring a plurality of heavy metal ions on line in real time according to any one of claims 1-7 when executing the computer program.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the method for detecting multiple heavy metal ions in real time on line according to any one of claims 1 to 7.

Description

Detection system and method capable of monitoring various heavy metal ions on line in real time Technical Field The invention relates to the technical field of water quality detection, in particular to a detection system and method capable of monitoring various heavy metal ions on line in real time. Background Under the background of the continuous development of water quality safety monitoring technology, the detection of heavy metal ions becomes the research focus in the current environmental science and public health fields. The traditional detection method mainly comprises an atomic absorption spectrometry, an inductively coupled plasma mass spectrometry, a spectrophotometry and the like, has higher detection sensitivity and selectivity, but generally depends on a complex laboratory instrument, has a strict pretreatment flow, and is difficult to realize on-site rapid response and synchronous analysis of various ions. In recent years, electrochemical detection technology is widely applied to in-situ detection research of heavy metal ions due to the advantages of portability, high response speed, low cost and the like. The method based on the Electrochemical Impedance Spectroscopy (EIS) principle can detect the polarization behavior of ions at different frequencies, and is a nondestructive testing means with a relatively development prospect. Meanwhile, with the introduction of signal processing methods and artificial intelligence technologies, researchers try to further analyze detection signals by using pattern recognition, feature extraction and machine learning algorithms so as to improve the accuracy and the intelligent level of heavy metal ion species discrimination and concentration prediction. The detection method based on the electrochemical signal has advanced to a certain extent in the aspect of theoretical research, but the detection method still faces key problems in the actual deployment process, and is mainly reflected in the lack of a dynamic compensation mechanism for environmental interference factors (such as water sample temperature fluctuation), so that the stability and accuracy of a detection result are insufficient. The existing researches mostly adopt constant temperature control or laboratory static conditions for detection, and lack an adaptive mechanism for external environment change. Disclosure of Invention The present invention has been made in view of the above-described problems occurring in the prior art. Therefore, the invention provides a detection method capable of monitoring various heavy metal ions on line in real time, which solves the problem that the detection result in the prior art is insufficient in accuracy under the condition of environmental interference. In order to solve the technical problems, the invention provides the following technical scheme: The invention provides a detection method capable of monitoring various heavy metal ions on line in real time, which comprises the steps of measuring the temperature of a water sample by a thermal coupler temperature detection method and carrying out real-time temperature correction by a resistance compensation method to obtain a target water sample with stable temperature, exciting the target water sample with stable temperature by an electrochemical impedance spectrum method and collecting a composite electric response signal by a capacitance response measurement method, decomposing and denoising the composite electric response signal by a time-frequency combined wavelet transformation method to obtain multiple electrical characteristic data, learning and reasoning the multiple electrical characteristic data by a deep convolution neural network to obtain heavy metal ion types and estimating a concentration quantitative result, and carrying out real-time environmental interference correction on the heavy metal ion types and the concentration quantitative result by on-line dynamic calibration to form the heavy metal ion detection result. As a preferable scheme of the detection method capable of monitoring various heavy metal ions on line in real time, the method for detecting the temperature of the water sample by using the thermoelectric coupler temperature detection method and carrying out real-time temperature correction by using the resistance compensation method to obtain a target water sample with stable temperature comprises the following specific steps of, Continuously measuring the temperature of the inflow water sample by using a thermoelectric coupler temperature detection method to form temperature original data; comparing the temperature original data with a set reference temperature to obtain a temperature difference offset value; According to the temperature difference offset value, adopting a resistance compensation control method to adjust the resistance value of the constant temperature element, and generating a temperature correction control signal; and dynamically regulating and controlling t