Integrated corrosion monitoring sensing device and corrosion monitoring method

Abstract

The invention discloses an integrated corrosion monitoring sensing device and a corrosion monitoring method. The device comprises a resistance sensor, a QCM quartz crystal sensor, a TOW humidity sensor, an EIS sensor, a signal conditioning circuit, a data storage and transmission device and a data monitoring processor. The resistance sensor is used for measuring a resistance value sequence of a to-be-measured material; the QCM quartz crystal sensor is used for measuring to obtain a mass change value sequence of a to-be-measured material; the TOW humidity sensor is used for measuring to obtain a wetting time sequence of the surface of the to-be-measured material; the data storage and transmission device is connected with the signal conditioning circuit and is used for storing the signal sequences which are output by the various sensors and subjected to level conversion and sending the stored signal sequences of the various sensors to the data monitoring processor; and the data monitoring processor is connected with the data storage and transmission device and is used for carrying out corrosion monitoring on the received signal sequences of the various sensors to obtain a corrosion monitoring comprehensive value of the material to be detected.

Description

Technical Field

[0001] The present invention relates to the fields of corrosion monitoring and data processing technology, and in particular to an integrated corrosion monitoring sensor device and a corrosion monitoring method. Background Art

[0002] In many fields such as industrial production and infrastructure construction, the corrosion of materials has always been an important factor affecting the normal operation of equipment, shortening its service life, and causing safety hazards. Traditional corrosion monitoring methods often only use a single type of sensor for measurement. For example, a resistance sensor is used to measure the change in material resistance to reflect the degree of corrosion. However, the single sensor monitoring method has many limitations and cannot fully consider the various influencing factors in the material corrosion process. In addition, the existing corrosion monitoring devices have problems in signal processing and data transmission, such as signal conversion mismatch, single data storage and transmission methods, etc., which makes it difficult to ensure the accuracy and real-time performance of the monitoring data. At the same time, when analyzing and calculating the monitoring data, there is a lack of scientific and effective comprehensive processing methods, making it difficult to accurately obtain the comprehensive status of material corrosion, which greatly reduces the effectiveness of corrosion monitoring and cannot meet the needs of accurate monitoring and timely warning of material corrosion conditions in actual production. Summary of the Invention

[0003] The present invention mainly solves the problem of how to perform multi-dimensional rapid integrated monitoring of material corrosion. The present invention discloses an integrated corrosion monitoring sensor device and a corrosion monitoring method.

[0004] In a first aspect of an embodiment of the present invention, an integrated corrosion monitoring sensor device is disclosed, comprising: a resistance sensor, a QCM quartz crystal sensor, a TOW wetness sensor, an EIS sensor, a signal conditioning circuit, a data storage device, and a data monitoring processor;

[0005] The resistance sensor is used to measure and obtain a resistance value sequence of the material to be tested;

[0006] The QCM quartz crystal sensor is used to measure and obtain a sequence of mass change values of the material to be measured;

[0007] The TOW wettability sensor is used to measure the wettability time series of the surface of the material to be tested;

[0008] The EIS sensor is used to measure and obtain a sequence of electrical impedance values of the material to be tested;

[0009] The signal conditioning circuit is connected to the resistance sensor, QCM quartz crystal sensor, TOW wetness sensor, and EIS sensor respectively, and is used to convert the signal levels of the signal sequences output by various sensors into signal levels that match the data storage device;

[0010] The data storage device is connected to the signal conditioning circuit and is used to store the signal sequences output by the various sensors after level conversion, and send the stored signal sequences of the various sensors to the data monitoring processor;

[0011] The data monitoring processor is connected to the data storage device and is used to perform corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0012] The data storage device includes a memory, a wired communication module and a wireless communication module; the memory is used to store the signal sequences output by the various sensors after level conversion; the wired communication module is used to send the stored signal sequences of the various sensors to the data monitoring processor through the RS485 bus or USB bus; the wireless communication module is used to send the stored signal sequences of the various sensors to the data monitoring processor through Lora wireless communication.

[0013] The data monitoring processor performs corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested, including:

[0014] The data monitoring processor preprocesses the signal sequences received from various sensors to obtain a preprocessed signal set;

[0015] Corrosion monitoring is performed on the preprocessed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0016] The preprocessing of the received signal sequences of various sensors to obtain a preprocessed signal set includes:

[0017] Performing data type discrimination processing on the signal sequences received from various sensors to obtain a first signal set;

[0018] Performing pattern check processing on the first signal set to obtain a preprocessed signal set.

[0019] A second aspect of the embodiments of the present invention discloses a corrosion monitoring method, which is implemented using the integrated corrosion monitoring sensor device, comprising:

[0020] S1, using the resistance sensor to measure and obtain a resistance value sequence of the material to be tested; using the QCM quartz crystal sensor to measure and obtain a mass change value sequence of the material to be tested; using the TOW wettability sensor to measure and obtain a wetting time sequence of the surface of the material to be tested; using the EIS sensor to measure and obtain a resistance value sequence of the material to be tested;

[0021] S2, using the signal conditioning circuit to convert the signal levels of the signal sequences output by various sensors into signal levels that match the data storage device;

[0022] S3, using the data storage device to store the signal sequences output by the various sensors after level conversion, and sending the stored signal sequences of the various sensors to the data monitoring processor;

[0023] S4, using the data monitoring processor to perform corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0024] The corrosion monitoring is performed on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested, including:

[0025] S41, preprocessing the signal sequences received from various sensors to obtain a preprocessed signal set;

[0026] S42, performing corrosion monitoring on the preprocessed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0027] The preprocessing of the received signal sequences of various sensors to obtain a preprocessed signal set includes:

[0028] S411, performing data type discrimination processing on the signal sequences received from various sensors to obtain a first signal set;

[0029] S412: Perform pattern check processing on the first signal set to obtain a preprocessed signal set.

[0030] The performing corrosion monitoring on the pre-processed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested includes:

[0031] S421, obtaining a standard signal value set; the standard signal value set includes a resistance standard value, a mass change standard value, a humidity standard value, and an electrical impedance standard value;

[0032] S422, subtracting each numerical sequence of the preprocessed signal set from the corresponding standard value to obtain a corresponding difference sequence; and normalizing each difference sequence;

[0033] S423, using all difference sequences as row vectors to construct a difference measurement matrix;

[0034] S424, performing weight vector calculation on the difference measurement matrix to obtain a weight vector;

[0035] S425, calculating a difference eigenvector on the difference measurement matrix to obtain a difference eigenvector;

[0036] S426, performing vector dot product on the weight vector and the difference characteristic vector to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0037] The calculating the difference eigenvector of the difference measurement matrix to obtain the difference eigenvector includes:

[0038] S4251, performing statistical eigenvalue calculation on the difference measurement matrix to obtain statistical eigenvalues;

[0039] S4252, performing an ITD transformation on each row vector of the difference measurement matrix to obtain a corresponding transformation vector;

[0040] S4253, using all the transformation vectors as row vectors to construct a transformation matrix;

[0041] S4254: Perform cross-correlation calculations on the difference measurement matrix and the transformation matrix, respectively, to obtain a first cross-correlation matrix and a second cross-correlation matrix; the elements in the i-th row and j-th column of the first cross-correlation matrix are cross-correlation values between the i-th row vector and the j-th row vector of the difference measurement matrix; the elements in the i-th row and j-th column of the second cross-correlation matrix are cross-correlation values between the i-th row vector and the j-th row vector of the transformation matrix;

[0042] S4255, performing matrix cross-correlation calculation on the difference measurement matrix and the transformation matrix to obtain a third cross-correlation matrix; the elements in the i-th row and j-th column of the third cross-correlation matrix are cross-correlation values between the i-th row vector of the difference measurement matrix and the j-th row vector of the transformation matrix;

[0043] S4256, performing fusion calculation processing on the first mutual correlation matrix, the second mutual correlation matrix, and the third mutual correlation matrix to obtain a fusion matrix;

[0044] S4257, performing singular value decomposition on the fusion matrix to obtain a singular value vector;

[0045] S4258, performing difference feature calculation on the singular value vector and the statistical eigenvalue to obtain a difference feature vector.

[0046] The expression of the fusion calculation process is:

[0047]

[0048] Where A is the fusion matrix, R1, R2 and R3 are the first mutual correlation matrix, the second mutual correlation matrix and the third mutual correlation matrix respectively.

[0049] The beneficial effects of the present invention are:

[0050] The integrated corrosion monitoring sensor device provided by the present invention integrates a resistance sensor, a QCM quartz crystal sensor, a TOW wetness sensor, and an EIS sensor. This device can comprehensively monitor material corrosion across multiple dimensions, including resistance, mass change, surface moisture, and electrical impedance. Compared to traditional single-sensor monitoring methods, this significantly improves the comprehensiveness and accuracy of monitoring data. The signal conditioning circuitry adapts the output signal levels of various sensors, ensuring effective signal connection with the data storage and transmission device, and guaranteeing stable signal transmission.

[0051] In terms of data processing, the data monitoring processor effectively improves the quality and reliability of data by pre-processing various sensor signal sequences, including data type discrimination and pattern verification; by constructing a difference measurement matrix and combining the calculation of weight vectors and difference eigenvectors, the pre-processed signals are deeply analyzed to accurately obtain the comprehensive corrosion monitoring value of the material to be tested. Compared with traditional data processing methods, it significantly improves the accuracy and scientificity of corrosion monitoring results, provides a reliable basis for material corrosion prevention and maintenance, and effectively ensures the safe and stable operation of equipment and facilities.

[0052] The multi-technology integrated sensor system of the present invention can comprehensively cover the environmental conditions (TOW), real-time dynamics (QCM), mechanism analysis (EIS) and quantitative evaluation (TER) of corrosion monitoring, significantly improving monitoring reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flowchart of the firmware design for the integrated corrosion monitoring sensor system of the present invention;

[0054] Figure 2 Schematic diagram of the composition of the integrated corrosion monitoring sensor device of the present invention;

[0055] Figure 3 4 is an implementation flow chart of the method of the present invention. DETAILED DESCRIPTION

[0056] In order to better understand the content of the present invention, an embodiment is given here.

[0057] Figure 1 A flowchart of the firmware design for the integrated corrosion monitoring sensor system of the present invention; Figure 2 Schematic diagram of the composition of the integrated corrosion monitoring sensor device of the present invention; Figure 3 4 is an implementation flow chart of the method of the present invention.

[0058] In a first aspect of an embodiment of the present invention, an integrated corrosion monitoring sensor device is disclosed, comprising: a resistance sensor, a QCM quartz crystal sensor, a TOW wetness sensor, an EIS sensor, a signal conditioning circuit, a data storage device, and a data monitoring processor;

[0059] The resistance sensor is used to measure and obtain a resistance value sequence of the material to be tested;

[0060] The QCM quartz crystal sensor is used to measure and obtain a sequence of mass change values of the material to be measured;

[0061] The TOW wettability sensor is used to measure the wettability time series of the surface of the material to be tested;

[0062] The EIS sensor is used to measure and obtain a sequence of electrical impedance values of the material to be tested.

[0063] The signal conditioning circuit is connected to the resistance sensor, QCM quartz crystal sensor, TOW wetness sensor, and EIS sensor respectively, and is used to convert the output signal levels of various sensors into signal levels that match the data storage device.

[0064] The data storage device is connected to the signal conditioning circuit and is used to store the signal sequences output by the various sensors after level conversion, and send the stored signal sequences of the various sensors to the data monitoring processor;

[0065] The data monitoring processor is connected to the data storage device and is used to perform corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0066] The data storage device includes a dual-channel 64Mbit memory, a wired communication module and a wireless communication module; the dual-channel 64Mbit memory is used to store the signal sequences output by the various sensors after level conversion; the wired communication module is used to send the stored signal sequences of the various sensors to the data monitoring processor through the RS485 bus or USB bus; the wireless communication module is used to send the stored signal sequences of the various sensors to the data monitoring processor through Lora wireless communication.

[0067] The data storage and transmission device adopts dual-channel 64Mbit memory in conjunction with wired communication module and wireless communication module, which not only realizes the efficient storage of large amounts of monitoring data, but also provides a variety of data transmission methods that can be flexibly selected according to actual needs, thereby improving the flexibility and applicability of data transmission.

[0068] The data monitoring processor performs corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested, including:

[0069] The data monitoring processor preprocesses the signal sequences received from various sensors to obtain a preprocessed signal set;

[0070] Corrosion monitoring is performed on the preprocessed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0071] The preprocessing of the received signal sequences of various sensors to obtain a preprocessed signal set includes:

[0072] Performing data type discrimination processing on the signal sequences received from various sensors to obtain a first signal set;

[0073] Performing pattern check processing on the first signal set to obtain a preprocessed signal set.

[0074] The signal conditioning circuit includes an excitation signal source (sine wave / multi-frequency sweep), a constant potential instrument / constant current instrument (to control the electrochemical interface), a current-voltage converter (I / V), a lock-in amplifier (LIA) or a Fourier analyzer (to extract impedance amplitude / phase), and a data acquisition and processor (ADC+equivalent circuit fitting).

[0075] The present invention integrates four types of corrosion sensors: an electrical resistance sensor, a QCM (Quartz Crystal Microbalance) quartz crystal sensor, a TOW (Time of Wetness) wetness sensor, and an EIS (Electrochemical Impedance Spectroscopy) sensor. This forms an integrated corrosion monitoring sensor capable of high-sensitivity electronic metal material corrosion monitoring, medium-sensitivity structural metal material corrosion monitoring, and surface wetness monitoring. This makes it convenient to place the sensor inside the packaging box envelope at the storage site.

[0076] The upper cover of the integrated corrosion monitoring sensor is equipped with a 4-channel resistance probe, a QCM quartz crystal microbalance, a TOW moisture sensor, and a temperature and humidity detection probe. A stainless steel porous sieve plate is installed on its surface to provide real-time protection for the probe.

[0077] The integrated corrosion monitoring sensor device integrates four measurement circuits, including TER, QCM, EIS and TOW, in its circuit design, and performs timed cycle measurements under the control of the MCU. After each measurement is completed, the integrated corrosion sensor automatically turns off all analog power supplies and enters a sleep state to save energy to the greatest extent.

[0078] The integrated corrosion monitoring sensor can measure the ohmic resistance of four channels (four metal sheets) and calculate the atmospheric corrosion rate of up to four materials through the resistance increment. It can also measure dual-channel electrochemical impedance measurement, which is used to measure the material corrosion rate and surface wetting time based on electrochemical methods; the built-in QCM frequency measurement circuit can measure the corrosion weight gain of precision electronic materials (Cu, Ag, Sn). The main components of the entire integrated sensor include communication conversion module, power conversion module, precision resistance probe measurement module, electrochemical impedance measurement module, temperature and humidity sensor, QCM frequency measurement module and system management software. All modules are integrated on the same circuit board to save sensor space and power consumption. The main block diagram of its software measurement is as follows Figure 1 .

[0079] For the integrated corrosion monitoring sensor, since four sensors are integrated in the circuit, it is necessary to ensure the isolation of each measurement circuit. To this end, low-power relays are used in the circuit to switch channels to prevent mutual interference.

[0080] Since the operation of multiple sensors consumes a lot of current, this design adopts a time-sharing measurement mode, which turns on and off each sensor measurement in a time sequence to avoid excessive current consumption.

[0081] During PCB layout and routing, all leads should be protected against leakage current. The areas around electrodes should be cleaned, grooves should be dug to increase insulation impedance, and protective equipotential lines should be added. High-frequency digital devices should be separated from precision analog devices to prevent crosstalk.

[0082] The data storage and transmission device is used for data storage and power control. It features a built-in dual-channel 64Mbit memory, capable of storing at least 100,000 historical data records, supporting data backup and enhancing data security. It also features built-in RS485 and USB wired communication, with a maximum speed of 10 Mbps. It supports LoRa wireless communication and ad hoc networking, with a maximum communication distance of 3.5 km where conditions permit.

[0083] The instrument is designed for ultra-low power consumption, with a quiescent current of less than 80μA in sleep mode. It can automatically wake up in sleep mode.

[0084] The built-in sensor interface provides controllable power to the integrated sensor. External status indicators or buzzer alarms provide real-time feedback on environmental quality.

[0085] The data storage and transmission device has a built-in Zigbee or Lora wireless module or a TCP / IP wired communication module.

[0086] The integrated corrosion sensor is designed through three aspects: PCB design process, PCB processing process, and structural design process.

[0087] PCB design primarily consists of circuit design, PCB routing, and layout design. PCB fabrication primarily consists of soldering, cleaning, testing, and anti-corrosion treatment. Structural design primarily consists of structural design, fabrication, and assembly.

[0088] The integrated corrosion sensor has no built-in battery, and the data collector provides it with a 7V to 24V power supply. Communication uses the RS485 communication bus. It has a built-in 64Mbit memory to store sensor measurement parameters. It supports single-channel quartz crystal sensor quality monitoring, dual-channel EIS electrode wetness monitoring, and four-channel corrosion rate measurement of resistance corrosion sensors of different materials. EIS impedance measurement uses a 24-bit Sigma-delta analog-to-digital converter with a maximum resolution of 0.1μV. The EIS measurement excitation current is 500mA, and the built-in 24-bit analog-to-digital converter can accurately collect changes in mΩ resistance. The design block diagram of the integrated corrosion sensor is as follows: Figure 2 shown.

[0089] A second aspect of the embodiments of the present invention discloses a corrosion monitoring method, which is implemented using the integrated corrosion monitoring sensor device, comprising:

[0090] S1, using the resistance sensor to measure and obtain a resistance value sequence of the material to be tested; using the QCM quartz crystal sensor to measure and obtain a mass change value sequence of the material to be tested; using the TOW wettability sensor to measure and obtain a wetting time sequence of the surface of the material to be tested; using the EIS sensor to measure and obtain a resistance value sequence of the material to be tested.

[0091] S2, using the signal conditioning circuit to convert the output signal levels of various sensors into signal levels that match the data storage device.

[0092] S3, using the data storage device to store the signal sequences output by the various sensors after level conversion, and sending the stored signal sequences of the various sensors to the data monitoring processor;

[0093] S4, using the data monitoring processor to perform corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0094] The corrosion monitoring is performed on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested, including:

[0095] S41, preprocessing the signal sequences received from various sensors to obtain a preprocessed signal set;

[0096] S42, performing corrosion monitoring on the preprocessed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0097] The preprocessing of the received signal sequences of various sensors to obtain a preprocessed signal set includes:

[0098] S411, performing data type discrimination processing on the signal sequences received from various sensors to obtain a first signal set;

[0099] S412: Perform pattern check processing on the first signal set to obtain a preprocessed signal set.

[0100] The data type discrimination process is to use a preset data category to discriminate whether the category of data in the signal sequence of each sensor is consistent with the preset data category, and to delete inconsistent data from the signal sequence to obtain a first signal sequence; the first signal set includes the first signal sequence;

[0101] The performing pattern check processing on the first signal set to obtain a preprocessed signal set includes:

[0102] S4121: Perform a cross-correlation calculation on all signal sequences in the first signal set to obtain a cross-correlation matrix; the element in the i-th row and j-th column of the cross-correlation matrix is a cross-correlation value between the i-th signal sequence and the j-th signal sequence in the first signal set;

[0103] S4122, performing eigenvalue calculation processing on the mutual correlation matrix to obtain an eigenvalue vector; the eigenvalue vector is a vector constructed by all eigenvalues;

[0104] S4123, constructing a fusion polynomial using the eigenvalue vector as a polynomial coefficient;

[0105] The eigenvalue vector is used as a polynomial coefficient, which includes: when the number of eigenvalue vectors is N, the first element of the eigenvalue vector is the N-1 order coefficient of the polynomial, and the last element is the constant term of the polynomial.

[0106] S4124: Using the fused polynomial, calculate and process the element sequence value of each signal sequence in the first signal set to obtain a corresponding approximate value;

[0107] S4125: Determine whether the difference between an element of each signal sequence in the first signal set and the corresponding approximate value is greater than a preset threshold; if so, replace the value of the element with the average of the two adjacent elements before and after the element;

[0108] S4126: Execute S4125 on each signal sequence in the first signal set to obtain a preprocessed signal set.

[0109] The performing corrosion monitoring on the pre-processed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested includes:

[0110] S421, obtaining a standard signal value set; the standard signal value set includes a resistance standard value, a mass change standard value, a humidity standard value, and an electrical impedance standard value;

[0111] S422, subtracting each numerical sequence of the preprocessed signal set from the corresponding standard value to obtain a corresponding difference sequence; and normalizing each difference sequence;

[0112] S423, using all difference sequences as row vectors to construct a difference measurement matrix;

[0113] S424, performing weight vector calculation on the difference measurement matrix to obtain a weight vector;

[0114] S425, calculating a difference eigenvector on the difference measurement matrix to obtain a difference eigenvector;

[0115] S426, performing vector dot product on the weight vector and the difference characteristic vector to obtain a comprehensive corrosion monitoring value of the material to be tested.

[0116] The step of performing weight vector calculation on the difference measurement matrix to obtain a weight vector includes:

[0117] S4241, performing weight coefficient calculation processing on the difference sequence of each row number in the difference measurement matrix and all other difference sequences to obtain a weight value corresponding to each difference sequence;

[0118] S4242: Utilize the weight values of all difference sequences to construct a weight vector.

[0119] The expression for the weight coefficient calculation process is:

[0120]

[0121] Where N is the number of difference sequences, t i represents the weight value of the ith difference sequence, t1=1, r ijis the mutual correlation coefficient between the ith difference sequence and the jth difference sequence, r i2,1 is the first-order partial correlation coefficient of the ith difference sequence, and so on, r i3,12 is the secondary partial correlation coefficient of the ith difference sequence, r iN,1234…(N-1) is the N-1 level partial correlation coefficient of the ith difference sequence;

[0122] The calculation expression for the partial correlation coefficient at each level is:

[0123]

[0124] The calculation of other partial correlation coefficients is similar.

[0125] The calculating the difference eigenvector of the difference measurement matrix to obtain the difference eigenvector includes:

[0126] S4251, performing statistical eigenvalue calculation on the difference measurement matrix to obtain statistical eigenvalues;

[0127] S4252, performing an ITD transformation on each row vector of the difference measurement matrix to obtain a corresponding transformation vector;

[0128] S4253, using all the transformation vectors as row vectors to construct a transformation matrix;

[0129] S4254: Perform cross-correlation calculations on the difference measurement matrix and the transformation matrix, respectively, to obtain a first cross-correlation matrix and a second cross-correlation matrix; the elements in the i-th row and j-th column of the first cross-correlation matrix are cross-correlation values between the i-th row vector and the j-th row vector of the difference measurement matrix; the elements in the i-th row and j-th column of the second cross-correlation matrix are cross-correlation values between the i-th row vector and the j-th row vector of the transformation matrix;

[0130] S4255, performing matrix cross-correlation calculation on the difference measurement matrix and the transformation matrix to obtain a third cross-correlation matrix; the elements in the i-th row and j-th column of the third cross-correlation matrix are cross-correlation values between the i-th row vector of the difference measurement matrix and the j-th row vector of the transformation matrix;

[0131] S4256, performing fusion calculation processing on the first mutual correlation matrix, the second mutual correlation matrix, and the third mutual correlation matrix to obtain a fusion matrix;

[0132] S4257, performing singular value decomposition on the fusion matrix to obtain a singular value vector;

[0133] S4258, performing difference feature calculation on the singular value vector and the statistical eigenvalue to obtain a difference feature vector.

[0134] The expression of the fusion calculation process is:

[0135]

[0136] Where A is the fusion matrix, R1, R2 and R3 are the first mutual correlation matrix, the second mutual correlation matrix and the third mutual correlation matrix respectively;

[0137] The ITD transform is an intrinsic time scale decomposition transform.

[0138] The expression for calculating the statistical characteristic value is:

[0139]

[0140]

[0141] Among them, C ij represents the element in the i-th row and j-th column of the difference measurement matrix, represents the mean of all elements of the difference measurement matrix, represents the mean of the i-th row vector of the difference measurement matrix, M1 and N1 are the row dimension and column dimension of the difference measurement matrix respectively, a1 and a2 are the first intermediate quantity and the second intermediate quantity respectively, and α is the statistical eigenvalue.

[0142] The expression for calculating the difference feature is:

[0143]

[0144] Among them, t ij is the jth element of the i-th singular value vector, is the mean of the i-th singular value vector, M2 is the number of elements contained in the singular value vector, b i is the i-th element of the difference eigenvector.

[0145] The algorithms involved in the above steps, such as fusion calculation processing, ITD transformation, statistical eigenvalue calculation, and difference feature calculation, work closely with the overall technical solution and have significant advantages, as follows:

[0146] In terms of fusion calculation processing, the expression achieves a deep fusion of cross-correlation information of different dimensions through specific operations on the first cross-correlation matrix, the second cross-correlation matrix, and the third cross-correlation matrix. This algorithm can effectively integrate the correlation characteristics between the difference measurement matrix and its transformation matrix, fully explore the corrosion information association contained in different matrices, and avoid information omission or deviation caused by single matrix analysis. Through normalization operations between matrices, the robustness of the fusion result to noise and interference is enhanced, so that the final fusion matrix A can more accurately and comprehensively reflect the comprehensive characteristics of the corrosion signal of the material to be tested, providing a high-quality data foundation for subsequent singular value decomposition and corrosion monitoring comprehensive value calculation.

[0147] The advantage of applying the ITD transform (intrinsic time-scale decomposition transform) to the difference measurement matrix lies in its ability to adaptively decompose complex corrosion signal sequences into components of varying time scales, eliminating the need for pre-defined basis functions and aligning with the dynamic, variable, and nonlinear nature of corrosion signals. By performing an ITD transform on the row vectors of the difference measurement matrix, it is possible to effectively extract characteristic information about the corrosion signal at different time scales, revealing the temporal evolution of the corrosion process. Compared to traditional signal decomposition methods, this is more suitable for processing complex signals caused by factors such as environmental changes and differences in material properties during corrosion monitoring, providing transformation vectors with greater physical significance and diagnostic value for subsequent cross-correlation calculations and feature extraction.

[0148] The statistical eigenvalue calculation expression calculates the degree of dispersion of the elements of the difference measurement matrix (a1) and the degree of dispersion of the elements within the row vector (a2), and constructs the ratio α as the statistical eigenvalue, which can quantitatively describe the distribution characteristics of the difference measurement matrix. This algorithm captures the statistical characteristics of the corrosion signal from both the global and local levels of the matrix. a1 reflects the degree to which the overall data deviates from the mean, while a2 is refined to the fluctuation within the row vector. The combined value α can keenly reflect the abnormal change trend of the corrosion signal. Compared with simple statistics such as the mean and variance, this method characterizes the characteristics of the corrosion signal more comprehensively and meticulously, providing an effective statistical basis for the subsequent calculation of the difference eigenvector, which helps to improve the accuracy and reliability of corrosion monitoring results.

[0149] The differential feature calculation expression constructs a differential feature vector by operating on the elements of the singular value vector using trigonometric and exponential functions. This algorithm fully utilizes the corrosion signal characteristic information contained in the singular value vector, highlights the differences between different singular value elements through nonlinear transformation, and combines the statistical eigenvalue α for normalization, thereby enhancing the sensitivity of the differential feature vector to changes in the corrosion state. Trigonometric functions can effectively amplify the relative differences between singular value elements, while exponential functions link singular value elements with statistical eigenvalues. This allows the calculated differential feature vector to accurately reflect subtle changes in the corrosion state of the material under test, providing key feature input for the calculation of the final corrosion monitoring comprehensive value, effectively improving the accuracy and sensitivity of corrosion monitoring.

[0150] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. An integrated corrosion monitoring sensor device, characterized in that: include: Resistance sensor, QCM quartz crystal sensor, TOW humidity sensor, EIS sensor, signal conditioning circuit, data storage device, data monitoring processor; The resistance sensor is used to measure and obtain a resistance value sequence of the material to be tested; The QCM quartz crystal sensor is used to measure and obtain a sequence of mass change values of the material to be measured; The TOW wettability sensor is used to measure the wettability time series of the surface of the material to be tested; The EIS sensor is used to measure and obtain a sequence of electrical impedance values of the material to be tested; The signal conditioning circuit is connected to the resistance sensor, QCM quartz crystal sensor, TOW wetness sensor, and EIS sensor respectively, and is used to convert the signal levels of the signal sequences output by various sensors into signal levels that match the data storage device; The data storage device is connected to the signal conditioning circuit and is used to store the signal sequences output by the various sensors after level conversion, and send the stored signal sequences of the various sensors to the data monitoring processor; The data monitoring processor is connected to the data storage device and is used to perform corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested.

2. The integrated corrosion monitoring sensor device according to claim 1, characterized in that: The data storage device includes a memory, a wired communication module and a wireless communication module; the memory is used to store the signal sequences output by the various sensors after level conversion; the wired communication module is used to send the stored signal sequences of the various sensors to the data monitoring processor through the RS485 bus or USB bus; the wireless communication module is used to send the stored signal sequences of the various sensors to the data monitoring processor through Lora wireless communication.

3. The integrated corrosion monitoring sensor device according to claim 1, characterized in that: The data monitoring processor performs corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested, including: The data monitoring processor preprocesses the signal sequences received from various sensors to obtain a preprocessed signal set; Corrosion monitoring is performed on the preprocessed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested.

4. The integrated corrosion monitoring sensor device according to claim 3, characterized in that: The preprocessing of the received signal sequences of various sensors to obtain a preprocessed signal set includes: Performing data type discrimination processing on the signal sequences received from various sensors to obtain a first signal set; Performing pattern check processing on the first signal set to obtain a preprocessed signal set.

5. A corrosion monitoring method, characterized in that: The method is implemented by using the integrated corrosion monitoring sensor device according to any one of claims 1 to 4, comprising: S1, using the resistance sensor to measure and obtain a resistance value sequence of the material to be tested; using the QCM quartz crystal sensor to measure and obtain a mass change value sequence of the material to be tested; using the TOW wettability sensor to measure and obtain a wetting time sequence of the surface of the material to be tested; using the EIS sensor to measure and obtain a resistance value sequence of the material to be tested; S2, using the signal conditioning circuit to convert the signal levels of the signal sequences output by various sensors into signal levels that match the data storage device; S3, using the data storage device to store the signal sequences output by the various sensors after level conversion, and sending the stored signal sequences of the various sensors to the data monitoring processor; S4, using the data monitoring processor to perform corrosion monitoring on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested.

6. The corrosion monitoring method according to claim 5, wherein: The corrosion monitoring is performed on the signal sequences received from various sensors to obtain a comprehensive corrosion monitoring value of the material to be tested, including: S41, preprocessing the signal sequences received from various sensors to obtain a preprocessed signal set; S42, performing corrosion monitoring on the preprocessed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested.

7. The corrosion monitoring method according to claim 6, wherein: The preprocessing of the received signal sequences of various sensors to obtain a preprocessed signal set includes: S411, performing data type discrimination processing on the signal sequences received from various sensors to obtain a first signal set; S412: Perform pattern check processing on the first signal set to obtain a preprocessed signal set.

8. The corrosion monitoring method according to claim 6, wherein: The performing corrosion monitoring on the pre-processed signal set to obtain a comprehensive corrosion monitoring value of the material to be tested includes: S421, obtaining a standard signal value set; the standard signal value set includes a resistance standard value, a mass change standard value, a humidity standard value, and an electrical impedance standard value; S422, subtracting each numerical sequence of the preprocessed signal set from the corresponding standard value to obtain a corresponding difference sequence; and normalizing each difference sequence; S423, using all difference sequences as row vectors to construct a difference measurement matrix; S424, performing weight vector calculation on the difference measurement matrix to obtain a weight vector; S425, calculating a difference eigenvector on the difference measurement matrix to obtain a difference eigenvector; S426, performing vector dot product on the weight vector and the difference characteristic vector to obtain a comprehensive corrosion monitoring value of the material to be tested.

9. The corrosion monitoring method according to claim 8, wherein: The calculating the difference eigenvector of the difference measurement matrix to obtain the difference eigenvector includes: S4251, performing statistical eigenvalue calculation on the difference measurement matrix to obtain statistical eigenvalues; S4252, performing an ITD transformation on each row vector of the difference measurement matrix to obtain a corresponding transformation vector; S4253, using all the transformation vectors as row vectors to construct a transformation matrix; S4254: Perform cross-correlation calculations on the difference measurement matrix and the transformation matrix, respectively, to obtain a first cross-correlation matrix and a second cross-correlation matrix; the elements in the i-th row and j-th column of the first cross-correlation matrix are cross-correlation values between the i-th row vector and the j-th row vector of the difference measurement matrix; the elements in the i-th row and j-th column of the second cross-correlation matrix are cross-correlation values between the i-th row vector and the j-th row vector of the transformation matrix; S4255, performing matrix cross-correlation calculation on the difference measurement matrix and the transformation matrix to obtain a third cross-correlation matrix; the elements in the i-th row and j-th column of the third cross-correlation matrix are cross-correlation values between the i-th row vector of the difference measurement matrix and the j-th row vector of the transformation matrix; S4256, performing fusion calculation processing on the first mutual correlation matrix, the second mutual correlation matrix, and the third mutual correlation matrix to obtain a fusion matrix; S4257, performing singular value decomposition on the fusion matrix to obtain a singular value vector; S4258, performing difference feature calculation on the singular value vector and the statistical eigenvalue to obtain a difference feature vector.

10. The corrosion monitoring method according to claim 9, wherein: The expression of the fusion calculation process is: Where A is the fusion matrix, R1, R2 and R3 are the first mutual correlation matrix, the second mutual correlation matrix and the third mutual correlation matrix respectively.

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