Real-time dynamic monitoring method and system for stray current of buried pipeline

Abstract

The invention discloses a buried pipeline stray current real-time dynamic monitoring method and a buried pipeline stray current real-time dynamic monitoring system, and relates to the technical field of buried pipeline monitoring and protection. The influence degrees of different interference sources on pipeline corrosion are evaluated for the electrical interference signals, risk evaluation results of the different interference sources on the pipeline corrosion are obtained, finally, targeted protection parameters are generated according to the risk evaluation results, and a cathode protection system is adjusted in real time, so that the characteristics of real-time and dynamic monitoring of external interference in a complex environment are achieved. And the source and the influence of the source on the pipeline corrosion are accurately identified, so that accurate evaluation and dynamic protection of the pipeline corrosion risk are realized, and the safe operation level and the service life of the buried pipeline are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of buried pipeline monitoring and protection, and in particular to a method and system for real-time dynamic monitoring of stray current in buried pipelines. Background Art

[0002] As a vital component of urban infrastructure, buried pipelines play a crucial role in energy transmission and resource allocation, placing immense importance on their safe operation. However, electrical interference from the external environment often poses a significant corrosion threat to pipelines, severely impacting their service life and reliability. Therefore, developing effective monitoring and protection methods for buried pipelines is urgent.

[0003] Currently, although there are some monitoring methods for pipeline corrosion, most of them have problems such as insufficient coverage and delayed response. Especially in complex urban environments, the sources of interference are diverse and dynamically changing. Existing methods are difficult to fully capture the instantaneous characteristics of interference, and cannot accurately distinguish the impact of different sources, resulting in a lack of targeted and precise protective measures.

[0004] Against this backdrop, the core challenges facing this field are gradually becoming apparent. First, the sources of external electrical interference are extremely complex, encompassing a wide range of scenarios, including subway operations and industrial equipment. These sources vary significantly in their characteristics, with varying interference intensities and durations, making identification more challenging. The inability to effectively distinguish and locate the characteristics of these sources makes it impossible to establish a clear correlation between interference and pipeline corrosion risk. Consequently, protective strategies often remain superficial, failing to address the root causes.

[0005] Therefore, how to dynamically monitor the characteristics of external interference in real time in a complex environment and accurately identify its source and its impact on pipeline corrosion has become a key issue in ensuring the safe operation of buried pipelines. Summary of the Invention

[0006] The present invention provides a real-time dynamic monitoring method and system for stray currents in buried pipelines, which can realize the real-time dynamic monitoring of the characteristics of external interference in complex environments, accurately identify its source and its impact on pipeline corrosion, realize accurate assessment and dynamic protection of pipeline corrosion risks, and effectively improve the safe operation level and service life of buried pipelines.

[0007] The present invention provides a real-time dynamic monitoring method for stray current in buried pipelines, comprising: Obtain electrical interference signals from the surrounding environment of buried pipelines; Determining interference characteristics of the electrical interference signal based on the electrical interference signal, wherein the interference characteristics include interference source, interference intensity, and action time corresponding to the electrical interference signal; Inputting the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk, and obtaining a risk assessment result output by the corrosion risk association model; Based on the risk assessment result, generating target protection parameters corresponding to the electrical interference signal; Determining a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; The dynamic protection configuration is used by the pipeline cathodic protection system to perform electrochemical dynamic protection operations on buried pipelines, and the corrosion risk association model is obtained by training interference feature data sets corresponding to different interference sources.

[0008] According to a method for real-time dynamic monitoring of stray current in buried pipelines provided by the present invention, the corrosion risk association model is obtained by the following method: Obtain electrical interference signals from the surrounding environment of buried pipelines, use multi-channel sensors to collect voltage and current data, and obtain the original interference data set; Decomposing the signal spectrum using a fast Fourier transform algorithm, extracting the frequency and amplitude characteristics of the original interference data set, and obtaining an interference characteristic data set; Determining a subway interference subset and an industrial interference subset according to the interference feature data set; According to the subway interference subset and the industrial interference subset, a support vector machine algorithm is used to classify the interference intensity and action time to obtain an interference classification result; According to the interference classification results, a data association modeling method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate to obtain a corrosion risk association model; The interference classification result includes the correlation between the interference source, interference intensity and action time.

[0009] According to a method for real-time dynamic monitoring of stray current in buried pipelines provided by the present invention, the correlation coefficient between interference intensity and pipeline corrosion rate is calculated using a data association modeling method based on the interference classification result to obtain a corrosion risk association model, including: Based on the interference classification results and pipeline corrosion rate data, extracting characteristic values ​​of interference intensity and corrosion rate from a pre-established database to obtain an initial data set; The principal component analysis method is used to extract features from the initial data set, reduce the data dimension, and obtain a reduced-dimensionality feature set; The Pearson correlation coefficient method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate in the dimensionality reduction feature set, and the correlation coefficient matrix is ​​obtained; According to the correlation coefficient matrix and the interference classification result, the feature pairs of interference intensity and corrosion rate in the dimensionality reduction feature set are marked as high-correlation feature pairs to obtain a high-correlation feature set; According to the highly correlated feature set, a linear regression algorithm is used to train a preset training model to obtain the corrosion risk association model.

[0010] According to a method for real-time dynamic monitoring of stray current in buried pipelines provided by the present invention, determining a subway interference subset and an industrial interference subset based on the interference feature data set includes: If the frequency characteristics in the interference characteristic data set match the preset subway operation frequency band, then determining the interference source as subway operation through time domain analysis to obtain a subway interference subset; If the frequency characteristics in the interference characteristic data set match the preset industrial equipment frequency band, the interference source is determined to be industrial equipment through waveform analysis to obtain an industrial interference subset.

[0011] According to a method for real-time dynamic monitoring of stray current in buried pipelines provided by the present invention, determining a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system includes: According to the target protection parameters, a digital signal processor is used to adjust the current output of the pipeline cathodic protection system to obtain protection optimization parameters; By using protection optimization parameters, the operating status of the pipeline cathodic protection system is updated in real time, and the real-time operating status fed back by the target protection parameters acting on the pipeline cathodic protection system is determined; Based on the real-time operating status, a dynamic protection configuration is determined.

[0012] According to a method for real-time dynamic monitoring of stray current in buried pipelines provided by the present invention, the corrosion risk association model includes a corrosion rate prediction model, a corrosion risk level prediction model, and a corrosion risk result prediction model. The interference characteristics are input into the corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk, and the risk assessment result output by the corrosion risk association model is obtained, including: Inputting the interference characteristics into the corrosion rate prediction model to obtain a corrosion rate result output by the corrosion rate prediction model, wherein the corrosion rate result reflects the effects of different interference sources on pipeline corrosion; Inputting the corrosion rate result into the corrosion risk level prediction model to determine the degree of influence of different interference sources on pipeline corrosion risk, and obtaining a risk level list output by the corrosion risk level prediction model, wherein the risk level list is determined by marking interference features whose influence weights of interference sources exceed a preset threshold as high-risk factors; Inputting the risk level list into the corrosion risk result prediction model to obtain a risk assessment result output by the corrosion risk result prediction model; The risk assessment results include the pipeline corrosion risk level, the time required for pipeline perforation, the pipeline risk trend and the pipeline protection strategy.

[0013] According to a method for real-time dynamic monitoring of stray current in buried pipelines provided by the present invention, the corrosion risk result prediction model is used to: Based on the risk level list, in-depth data mining is conducted to determine the action paths of high-risk factors on pipeline corrosion and the corrosion risk change trends corresponding to the action paths; Based on the corrosion risk change trend and historical records of pipeline status, conditional judgment logic is used to determine the key monitoring objects in the risk level list; Based on the key monitoring objects, in-depth data mining is carried out to determine the target action path of the key monitoring objects and the target corrosion risk change trend corresponding to the target action path; Determining the risk assessment result based on the target action path and the target corrosion risk change trend; If the corrosion risk change trend matches the historical high-risk pattern in the historical record data, the key monitoring object is determined.

[0014] The present invention also provides a real-time dynamic monitoring system for stray current in buried pipelines, comprising: A signal acquisition module is used to acquire electrical interference signals from the surrounding environment of the buried pipeline; an interference feature extraction module, configured to determine, based on the electrical interference signal, an interference feature of the electrical interference signal, wherein the interference feature includes an interference source, interference intensity, and action time corresponding to the electrical interference signal; A result prediction module inputs the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk and obtain a risk assessment result output by the corrosion risk association model; A parameter generation module, configured to generate target protection parameters corresponding to the electrical interference signal based on the risk assessment result; A dynamic protection configuration module is used to determine a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; The dynamic protection configuration is used by the pipeline cathodic protection system to perform electrochemical dynamic protection operations on buried pipelines, and the corrosion risk association model is obtained by training interference feature data sets corresponding to different interference sources.

[0015] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for real-time dynamic monitoring of stray current in buried pipelines as described above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for real-time dynamic monitoring of stray current in buried pipelines as described in any one of the above is implemented.

[0017] The real-time dynamic monitoring method and system for stray current in buried pipelines provided by the present invention accurately identify the interference characteristics of electrical interference signals in the pipeline environment, evaluate the degree of influence of different interference sources on pipeline corrosion based on the corrosion risk association model and interference characteristics, and obtain risk assessment results of pipeline corrosion from different interference sources. Finally, targeted protection parameters are generated based on the risk assessment results and the cathodic protection system is adjusted in real time to achieve real-time dynamic monitoring of the characteristics of external interference in complex environments, accurately identify its source and its influence on pipeline corrosion, realize accurate assessment and dynamic protection of pipeline corrosion risks, and effectively improve the safe operation level and service life of buried pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is one of the flow charts of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 2 This is the second flow chart of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 3 This is the third flow chart of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 4 This is the fourth flow chart of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 5 This is the fifth flow chart of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 6 This is the sixth flow chart of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 7 This is the seventh flow chart of the method for real-time dynamic monitoring of stray current in buried pipelines provided by an embodiment of the present invention; Figure 8 1 is a schematic structural diagram of a real-time dynamic monitoring system for stray current in buried pipelines provided by an embodiment of the present invention; Figure 9It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Reference Figure 1 The embodiment of the present invention provides a method for real-time dynamic monitoring of stray current in buried pipelines, comprising the following steps: Step 100, obtaining an electrical interference signal from the surrounding environment of the buried pipeline; A multi-channel sensor can be installed on a buried pipeline to collect real-time electrical interference signals from the environment surrounding the buried pipeline. This generates an electrical interference signal. The multi-channel sensor can be deployed on a corrosion probe to collect the electrical signal. The electrical interference signal is a physical magnetic field signal within the buried pipeline environment, reflecting the effects of electrical interference on the buried pipeline. Therefore, predicting the corrosion risk of a buried pipeline using the electrical interference signal allows for analysis of the source of interference within the buried pipeline, enabling targeted protection of the buried pipeline at its source.

[0021] Step 200: determining interference characteristics of the electrical interference signal based on the electrical interference signal, wherein the interference characteristics include interference source, interference intensity, and action time corresponding to the electrical interference signal; Furthermore, the interference characteristics of electrical interference signals can be extracted using the following methods: using a fast Fourier transform algorithm to decompose the signal spectrum and determine the frequency and amplitude characteristics of the electrical interference signal; and determining the interference characteristics of the electrical interference signal based on the frequency and amplitude characteristics. This method uses a multi-channel sensor to collect electrical interference signals from the surrounding environment of buried pipelines, uses a fast Fourier transform algorithm to extract interference characteristics, and then combines them with preset frequency band matching to determine the interference source. The interference is then classified using a support vector machine algorithm. This method can improve the accuracy of interference feature identification, enhance the accuracy of interference source identification, and improve the accuracy of pipeline corrosion risk prediction.

[0022] Step 300: Input the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk, and obtain a risk assessment result output by the corrosion risk association model; After collecting electrical interference signals from multiple interference sources, the influencing factors of pipeline corrosion can be obtained by extracting the interference features of the electrical interference signals from multiple interference sources, wherein the interference features are environmental features that affect pipeline corrosion. The interference features related to pipeline corrosion can be classified and sorted in advance, and missing values ​​and outliers therein can be cleaned through data preprocessing technology to obtain the interference features after preliminary processing. Based on the interference features after preliminary processing, a linear regression algorithm, a decision tree algorithm, or a random forest algorithm can be used to construct a corrosion risk association model. It should be noted that the algorithm for constructing the corrosion risk association model can be selected as needed and is not limited to the algorithm proposed in this embodiment. Among them, the corrosion risk association model is used to extract features of the relationship between interference sources and pipeline corrosion, determine the weight distribution of key influencing factors, and thus quantitatively analyze the impact of different interference sources through the constructed risk association model to obtain risk assessment results.

[0023] Risk assessment results can include the pipeline's corrosion risk level, the time required for pipeline perforation, pipeline risk trends, and pipeline protection strategies. Therefore, the risk association model can predict the corrosion risk level of interference sources affecting pipeline corrosion, the time required for buried pipeline perforation under current interference factors, whether the pipeline corrosion risk trend is increasing or decreasing under current interference factors, and the corresponding pipeline protection strategies. These strategies can include electrochemical methods, coating the pipeline with a protective layer, patching with special materials, or adding a mechanical protective layer.

[0024] Step 400: generating target protection parameters corresponding to the electrical interference signal based on the risk assessment result; The risk assessment results of the buried pipeline are obtained through the pre-established corrosion risk association model. Then, the protection demand distribution of the buried pipeline is determined based on the risk assessment results. According to the protection demand distribution, target protection parameters corresponding to the electrical interference signal are generated for the key areas of pipeline protection. The target protection parameters are the configuration parameters of the protection measures or protection devices. Therefore, in this step, the target protection parameters can be input into the protection device so that the protection device can execute the target configuration operation corresponding to the target protection parameters, thereby realizing the corresponding protection operation of the buried pipeline based on the risk assessment results.

[0025] For example, based on the risk assessment results, targeted protection parameters are generated to obtain the corresponding target protection parameters. First, the electrical interference signal to be predicted in the pipeline is obtained through the data acquisition system. The interference characteristics are determined based on the electrical interference signal. The interference characteristics include soil resistivity of 1000 ohm·cm, pipeline potential of -0.85 volts, and stray current density of 0.5 mA / m2. Using the risk assessment algorithm, a weighted scoring model is used to calculate the risk index. The risk assessment results include the risk index. The calculation formula of the risk index is as follows: Among them, R is the risk index, is the soil resistivity factor, is the potential deviation, is the stray current factor, The soil resistivity factor reflects the corrosiveness of the soil. The higher the soil resistivity factor, the lower the corrosiveness. The potential deviation is the deviation between the pipeline potential and the protection potential, which is used to measure the effectiveness of cathodic protection. The stray current factor is the intensity of stray current, which is commonly found near electrified railways or high-voltage lines. is the degree of pipeline aging, When the value is 0, the pipeline is brand new. When the value is 1, the pipeline is completely aged. Assuming the material aging factor is 0.2, it is calculated that R=400+0.3×|-0.85-(-0.85)|+0.2×0.5+0.1×0.2=400.12. When the risk index is higher than 350, the protection parameters need to be optimized. Then, a digital signal processor (DSP) is used to adjust the current output of the cathodic protection system. Based on the PI control algorithm, the target protection potential is set to -0.85 volts. The deviation between the actual potential and the target is used as input, and the proportional coefficient is used as the input. =0.8, integration time = 0.5 seconds, calculate the output current adjustment amount, where the calculation formula of the current adjustment amount is as follows: in, is the error signal, is the proportionality coefficient, is the deviation, is the integral coefficient. By substituting the above variables into the above formula, we can know =0.8 / 0.5=1.6. Assuming the current deviation is 0.05V, we can calculate =0.8×0.05+1.6×0.05×0.5=0.08 amps. Based on this, the digital signal processor (DSP) adjusts the rectifier output current from 1.5 amps to 1.58 amps. The optimized parameters are verified by a real-time monitoring system, which collects data on the adjusted potential. For example, at -0.86 volts, the deviation is reduced to 0.01 volt, meeting the protection standard. The system records the optimized parameters and stores them in a database, generating a log file containing a timestamp, the adjustment amount (0.08 amps), and the new potential (-0.86 volts) for subsequent analysis. The entire process is automated by an embedded control system, ensuring a closed-loop logic loop and closely linking parameter adjustments to risk assessment results.

[0026] Step 500: determining a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; The dynamic protection configuration is used by the pipeline cathodic protection system to perform electrochemical dynamic protection operations on buried pipelines, and the corrosion risk association model is obtained by training interference feature data sets corresponding to different interference sources.

[0027] Among them, electrochemical dynamic protection operation is used to guide the real-time regulation of the pipeline cathodic protection system. It uses the configuration parameters of the input dynamic protection configuration or the electrochemical parameters fed back by the buried pipeline to dynamically adjust the protection of the buried pipeline through electrochemical methods to prevent the pipeline from rusting. Electrochemical dynamic protection operation includes sacrificial anode operation and impressed current operation. The principle of sacrificial anode operation is to release a corrosive metal of corresponding size around the pipeline according to the configuration parameters or electrochemical parameters, so that the corrosive metal acts as an anode instead of the pipeline to corrode. Impressed current operation is to apply a corresponding current to the pipeline according to the configuration parameters or electrochemical parameters through a power supply device, forcing the pipeline to become a cathode to prevent corrosion.

[0028] By collecting real-time data from the pipeline cathodic protection system, the real-time operating status, such as actual output current / voltage, pipeline potential, and system temperature, is obtained. The current real-time operating status is analyzed to obtain preliminary status feedback information. Based on this preliminary status feedback information, the target protection parameters are analyzed and compared using preset thresholds to determine whether any deviation exists, resulting in a deviation analysis result. If the deviation analysis result exceeds the preset range, the target protection parameters are optimized and adjusted to determine the adjusted parameter values. The adjusted parameter values ​​are then used to update the operating status of the cathodic protection system, obtaining updated status feedback information. Based on the updated status feedback information, the protection effect of the adjusted parameter values ​​is analyzed in conjunction with the real-time monitoring mechanism to obtain effect evaluation information. Based on the effect evaluation information, if the protection effect does not meet expectations, the dynamic configuration mechanism is triggered to adjust the system update strategy and determine the final protection configuration plan, resulting in a dynamic protection configuration. This embodiment thus enables continuous monitoring of the pipeline system's operating status through the final protection configuration plan, obtaining long-term status feedback data, and achieving dynamic, real-time protection for buried pipelines.

[0029] The real-time dynamic monitoring method and system for stray current in buried pipelines provided by the present invention accurately identify the interference characteristics of electrical interference signals in the pipeline environment, evaluate the degree of influence of different interference sources on pipeline corrosion based on the corrosion risk association model and interference characteristics, and obtain risk assessment results of pipeline corrosion from different interference sources. Finally, targeted protection parameters are generated based on the risk assessment results and the cathodic protection system is adjusted in real time to achieve real-time dynamic monitoring of the characteristics of external interference in complex environments, accurately identify its source and its influence on pipeline corrosion, realize accurate assessment and dynamic protection of pipeline corrosion risks, and effectively improve the safe operation level and service life of buried pipelines.

[0030] In one embodiment, see Figure 2 , the corrosion risk association model is obtained in the following way: Step 301: Acquire electrical interference signals from the surrounding environment of the buried pipeline, use a multi-channel sensor to collect voltage and current data, and obtain an original interference data set; Using multi-channel sensors, electrical interference signals are acquired from the surrounding environment of the buried pipeline, and voltage and current data are collected to form an initial dataset. Based on the initial dataset, signal preprocessing methods are used to denoise the voltage and current data, resulting in a processed clean dataset. The clean dataset is then subjected to frequency domain analysis using the fast Fourier transform method to determine the primary frequency components of the signal. If the primary frequency components exceed the preset threshold, further time domain feature extraction is performed on the clean dataset to obtain time domain feature parameters. Based on the time domain feature parameters, the interference signals are classified using a support vector machine algorithm to determine their type and source. Based on the classification results, the clean dataset is optimized using an adaptive filtering method for different types of interference signals, resulting in the final optimized dataset, which serves as the original interference dataset. If the interference signal characteristics in the original interference dataset still exhibit anomalies, a secondary frequency domain analysis is performed on the original interference dataset to determine the specific distribution characteristics of the anomaly signals.

[0031] Specifically, to acquire electrical interference signals from the surrounding environment of buried pipelines and process the data, a multi-channel sensor system is first deployed to collect voltage and current data. Assume that a sensor node is set up every 100 meters along the pipeline. Each node is equipped with four channels, collecting voltage signals (ranging from 0 to 50V) and current signals (ranging from 0 to 10A), respectively. The sampling frequency is 1000Hz to ensure that high-frequency interference signals are captured. The data is transmitted to a central server via a wireless network, generating approximately 1GB of raw data per hour. Subsequently, a fast Fourier transform (FFT) algorithm is used to perform frequency domain analysis on the collected raw interference data set, decomposing the time domain signal into frequency components and extracting the main interference frequencies. For example, 50Hz and its harmonics (100Hz and 150Hz) are the main interference sources. The power spectral density shows a peak of 3.2W at 50Hz, indicating possible power frequency interference. Next, the signal was denoised using a wavelet transform. The Daubechies wavelet basis (db4) was selected, the decomposition level was set to 5, and a soft threshold of 0.05 was set. After filtering out high-frequency noise, the signal-to-noise ratio (SNR) increased from an initial 12.5 dB to 18.7 dB, validating the denoising effect. Finally, a classification model based on a support vector machine was constructed to identify the interference type. Time domain features (e.g., mean 0.8V, variance 0.2) and frequency domain features (e.g., main frequency 50Hz) were extracted from the signal. The training dataset contained 5,000 samples, and the test set achieved an accuracy of 92.3%. This allowed the system to determine whether the interference source was a power line or a natural factor such as lightning.

[0032] Step 302: Decompose the signal spectrum using a fast Fourier transform algorithm to extract the frequency and amplitude characteristics of the original interference data set to obtain an interference characteristic data set; Fast Fourier transform is used to decompose the original interference signal in the original interference data set to obtain spectrum data. Through spectrum data analysis, the frequency information and amplitude information in the original interference data set are separated to generate a spectrum feature vector. If there is periodic change in the frequency information, the frequency information of the original interference data set is normalized to obtain a standardized frequency feature; if there is no periodic change, the original frequency feature in the original interference data set is retained. After the original interference data set is processed as above, a standardized data set is obtained. Based on the standardized frequency features in the standardized data set, principal component analysis is used to extract the dominant frequency components and determine the key interference features. For the key interference features and the amplitude information corresponding to the key interference features, the K-Means clustering algorithm is used to divide the interference patterns and obtain the interference categories. The pattern distribution features are extracted from the interference categories, and the probability distribution between each category is calculated to generate an interference characteristic data set.

[0033] Specifically, a series of information technology techniques can be used to decompose the signal spectrum and extract features from the original interference dataset. The specific implementation method is as follows. Assume an interference signal dataset containing 1000 sampling points at a sampling rate of 10 kHz. First, the signal spectrum is decomposed using a fast Fourier transform (FFT) algorithm. The algorithm used is based on the Cooley-Tukey FFT method, which converts the time domain signal into the frequency domain. The amplitude and phase information of each frequency component are calculated. For example, the calculations reveal a significant peak at 500 Hz with an amplitude of 2.5 V, while the amplitude at 1000 Hz is 1.8 V, indicating that the signal has a strong interference component in the low-frequency range. Next, when extracting frequency and amplitude features, an amplitude threshold of 0.5 V is set, and only frequency components with amplitudes above this threshold are retained. Ultimately, 10 key frequency points are screened out, of which 500 Hz and 1000 Hz are identified as key interference frequencies. Subsequently, through statistical analysis of these frequency and amplitude data, the energy proportion of each frequency component is calculated. For example, the energy proportion of 500 Hz is 35%, and that of 1000 Hz is 20%. In this way, an interference characteristic data set is constructed, which contains a three-dimensional feature matrix of frequency, amplitude, and energy proportion.

[0034] Step 303: determining a subway interference subset and an industrial interference subset based on the interference feature dataset; After determining the interference signature dataset, we perform spectrum analysis on it using spectrum analysis techniques to separate it into a subway interference subset and an industrial interference subset. This step is based on the fact that the subway and industrial interference subsets correspond to different frequency bands. Therefore, by analyzing the frequency bands of each data point in the interference signature dataset based on the difference in their spectra, we can distinguish the subway and industrial interference subsets.

[0035] The determining of the subway interference subset and the industrial interference subset according to the interference feature data set includes: a) Frequency domain discrimination For each set of frequency-amplitude features in the interference characteristic data set, the following judgment is performed: If its main frequency Meet 10Hz≤ ≤80Hz, and second harmonic 2 Amplitude and the main frequency amplitude The ratio is 0.2≤ ≤0.6, it is marked as “subway candidate”; If its main frequency Meet 40Hz≤ ≤120 Hz, and 50 Hz energy proportion The proportion of total energy E ≥0.7, then marked as “industrial candidate”; b) Time domain discrimination For the time domain signal segments marked as "subway candidate" or "industrial candidate", calculate their short-time average amplitude M and short-time variance σ²: If M / σ²≥3 and the pulse width τ satisfies 20ms≤τ≤200ms, it is finally determined to be the subway interference subset; If M / σ²≤1.5 and the waveform is a continuous sinusoidal envelope, it is ultimately determined to be an industrial interference subset; c) Joint discrimination When the same signal segment satisfies the corresponding conditions of a) and b) at the same time, it is classified into the corresponding subset; if it does not meet both conditions, it is marked as other interference sources and eliminated.

[0036] Step 304: Based on the subway interference subset and the industrial interference subset, a support vector machine algorithm is used to classify the interference intensity and action time to obtain an interference classification result; After determining the interference signature dataset, spectrum analysis is performed on the dataset using spectrum analysis techniques to divide it into a subway interference subset and an industrial interference subset. An initial interference dataset is determined by obtaining raw data records from the subway and industrial interference datasets. Feature extraction methods are used to isolate the characteristic values ​​of interference intensity and duration from the initial interference dataset to obtain a characterized dataset. A support vector machine algorithm is used to train a model on the characterized dataset to obtain a trained classification model. The trained classification model is used to classify the subway and industrial interference subsets based on interference intensity and duration, respectively. The characterized dataset is classified using the trained classification model to determine the category distribution of interference intensity and duration, generating preliminary classification results. If the preliminary classification results show an imbalance in category distribution, the data subsets are resampled to obtain adjusted classification results. Based on the adjusted classification results and combined with information on interference sources, a final interference classification map is determined. The final interference classification map generates a correlation between interference sources and interference intensity and duration, allowing for evaluation of the completeness of the classification results.

[0037] Specifically, when classifying the subway and industrial interference subsets, data preprocessing was first performed to clean and extract features from the raw interference data. Assuming the subway interference subset contained 1,000 data points, each recording the interference intensity (in decibels, ranging from 30.5 to 80.5) and duration (in seconds, ranging from 10 to 300). The industrial interference subset contained 800 data points, with intensity ranging from 40.5 to 90.5 and duration from 15 to 400. A normalization method was used to normalize the data to a range of 0 to 1 to ensure that features of different dimensions had a consistent impact on the model. Next, a support vector machine model was constructed using a radial basis function (RBF) kernel, with the penalty parameter C set to 1.0 and the kernel parameter gamma set to 0.1. Model performance was evaluated using five-fold cross-validation, resulting in an average accuracy of 85.3%, demonstrating the model's strong ability to distinguish between the two types of interference data. The normalized data was then fed into the model for training. The training and test sets were split at an 8:2 ratio, with 1,440 data points in the training set and 360 data points in the test set. The model output classification results, showing 87.2% of subway interference data correctly classified, and 83.5% of industrial interference data correctly classified. Finally, analysis of the classification results revealed that samples with intensities above 70.5 decibels and durations exceeding 200 seconds were more likely to be classified as industrial interference, while samples with intensities below 50.5 decibels and durations less than 100 seconds were more likely to be classified as subway interference. Feature importance analysis revealed that intensity had a weight of 0.65 on the classification results, while duration had a weight of 0.35. Further model optimization suggests prioritizing adjustment of the threshold setting for the intensity feature.

[0038] To form business associations, the classification results can be combined with the interference source positioning system. Assuming that the positioning system adjusts the detection frequency based on the classification results, industrial interference has a higher priority and the detection frequency is increased to 5 times per minute, while subway interference is 3 times per minute, thereby achieving resource allocation optimization. The entire process is completed through automated algorithms and system interfaces to ensure rigorous logic and data-driven.

[0039] Step 305: Based on the interference classification results, a data association modeling method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate to obtain a corrosion risk association model; The interference classification result includes the correlation between the interference source, interference intensity and action time.

[0040] Interference classification results and pipeline corrosion rate data are obtained. A linear regression algorithm is used for data association modeling. The corrosion risk association model is trained and the correlation coefficient between interference intensity and pipeline corrosion rate is calculated to obtain a trained corrosion risk model. This model can then be used to input real-time interference characteristics and predict pipeline corrosion rates, yielding predicted corrosion rate values. Based on these predicted corrosion rate values ​​and pre-defined risk assessment rules, the pipeline's corrosion risk level is determined, resulting in a corrosion risk assessment result.

[0041] This implementation uses multi-channel sensors to collect electrical interference signals from the surrounding environment of buried pipelines, uses the fast Fourier transform algorithm to extract interference features, combines preset frequency band matching to determine the source of interference, and uses the support vector machine algorithm to classify the interference. Then, a corrosion risk association model is established, thereby realizing the prediction of pipeline corrosion rate by inputting real-time interference features through the corrosion risk model and obtaining the corrosion rate prediction value, thereby improving the accuracy of pipeline corrosion risk prediction and achieving accurate assessment of pipeline corrosion risk.

[0042] In one embodiment, see Figure 3 According to the interference classification results, the data association modeling method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate to obtain the corrosion risk association model, including: Step 3051: Based on the interference classification results and pipeline corrosion rate data, characteristic values ​​of interference intensity and corrosion rate are extracted from a pre-established database to obtain an initial data set; Step 3052: extract features from the initial data set using principal component analysis to reduce the data dimension and obtain a reduced-dimensionality feature set. Step 3053: Using the Pearson correlation coefficient method, the correlation coefficient between the interference intensity and the pipeline corrosion rate in the dimensionality reduction feature set is calculated to obtain a correlation coefficient matrix. Step 3054: Based on the correlation coefficient matrix and the interference classification result, the feature pairs of interference intensity and corrosion rate in the dimensionality reduction feature set are marked as high-correlation feature pairs to obtain a high-correlation feature set. Step 3055: Based on the highly correlated feature set, a linear regression algorithm is used to train a preset training model to obtain the corrosion risk association model.

[0043] Interference classification results and pipeline corrosion rate data are obtained, and characteristic values ​​of interference intensity and corrosion rate are extracted from a pre-established database to obtain an initial dataset. Principal component analysis is then used to extract features from the initial dataset to reduce its data dimensionality and obtain a reduced-dimensionality feature set. The Pearson correlation coefficient method is used to calculate the correlation coefficient between interference intensity and pipeline corrosion rate in the reduced-dimensionality feature set, resulting in a correlation coefficient matrix. If the absolute value of a correlation coefficient in the correlation coefficient matrix is ​​greater than a preset threshold, the feature pair of interference intensity and corrosion rate is marked as a highly correlated feature pair, and the feature data corresponding to the highly correlated feature pair in the reduced-dimensionality feature set is used as a highly correlated feature set to obtain a highly correlated feature set. Based on the highly correlated feature set, a linear regression algorithm is used to train a preset linear regression model to obtain a trained corrosion risk association model. Using the corrosion risk association model, real-time interference intensity data is input to predict the pipeline corrosion rate and obtain a predicted corrosion rate value. The advantage of using a linear regression model training method is that it can train a linear model to determine which features of the electrical interference source have the greatest impact on pipeline corrosion. For example, by inputting different frequency band values, temperature values, or humidity values ​​into the trained linear regression model, that is, inputting different interference features, the linear regression model outputs the pipeline corrosion rate, which is used to predict the pipeline corrosion rate under different interference conditions, thereby realizing the prediction of which interference source has the greatest impact on the pipeline corrosion risk.

[0044] Specifically, based on the interference classification results, the data association modeling method is used to calculate the correlation coefficient between interference intensity and pipeline corrosion rate. The implementation method of the corrosion risk association model is as follows: First, historical data on the pipeline system is collected, including interference intensity (such as electromagnetic interference intensity, in μT) and corrosion rate (in mm / year). Assume that the dataset contains 1,000 records, with interference intensity ranging from 0.5 to 50 μT and corrosion rate ranging from 0.01 to 2.5 mm / year. A support vector machine (SVM) algorithm is used to classify interference intensity into three levels: low (0.5-10 μT), medium (10-30 μT), and high (30-50 μT). The SVM uses a radial basis function (RBF) kernel with parameters C = 1.0 and γ = 0.1, and the classification accuracy is 85%. Next, the Pearson correlation coefficient is used to calculate the correlation between interference intensity and corrosion rate. The correlation coefficient is calculated as follows: Where r is the correlation coefficient, is the interference intensity, is the corrosion rate, and The calculation results show that the correlation coefficient between interference intensity and corrosion rate is r=0.78, indicating a strong positive correlation. Subsequently, a multiple linear regression model was constructed, where the model formula of the multiple linear regression model is: in, is the corrosion rate, is the interference intensity, is the hardness of the pipe material (assuming the hardness range is 100-300HB), 、 and is the regression coefficient. By fitting with the least square method, we can get =0.02, =0.045, =-0.001, the explanatory coefficient of the linear regression model is R²=0.82, indicating that the model has strong explanatory power. Finally, based on the regression model, the formula for the corrosion risk index is: in, is the corrosion rate, is the interference intensity, with a risk index ranging from 0 to 1. When R > 0.7, it is marked as high risk. Assuming a pipeline with an interference intensity of 40 μT and a hardness of 200 HB, substituting y into the model formula yields y = 0.02 + 0.045 × 40 - 0.001 × 200 = 1.62 mm / year, and R = 0.6 × 1.62 + 0.4 × 40 / 50 = 1.292. After normalization, R = 0.81, indicating a high risk.

[0045] In one embodiment, see Figure 4 , determining the subway interference subset and the industrial interference subset according to the interference feature data set includes: Step 3031: If the frequency characteristics in the interference characteristic data set match the preset subway operation frequency band, then determine through time domain analysis that the interference source is subway operation, and obtain a subway interference subset; Signal data is obtained from the interference characteristic dataset and frequency domain analysis is used to extract frequency features. Preliminary matching results are determined to determine whether these features match the preset operating frequency band. If the preliminary matching results indicate that the frequency features match the operating frequency band, time domain analysis is performed on the extracted frequency features to determine the temporal variation patterns of the signals. Classification is performed based on the temporal variation patterns obtained from the time domain analysis to determine whether the interference is caused by subway operation, thereby obtaining a classified interference subset. For each classified interference subset, the corresponding time-frequency feature distribution is obtained. A support vector machine algorithm is used to perform pattern recognition on the feature distribution to determine the characteristic pattern of the interference subset. If the characteristic pattern matches the preset subway operation interference pattern, comparative analysis is performed to further extract key time period features from the interference subset, resulting in a refined interference feature set. Based on the refined interference feature set, signals are screened in combination with preset criteria to determine whether abnormal interference signals exist, thereby obtaining the subway interference subset.

[0046] Specifically, frequency domain analysis is first performed to extract frequency features from the interference characteristic dataset. Assume the dataset contains electromagnetic signals spanning one hour, with a sampling rate of 1000 Hz and a total of 3,600,000 samples. Using the Fast Fourier Transform (FFT) algorithm, the time domain signal is converted to the frequency domain, resulting in a spectrum with a resolution of 0.001 Hz. Characteristic peaks are extracted from the frequency range of 10 Hz to 100 Hz. Assume that significant peaks are found at 15 Hz, 30 Hz, and 60 Hz, which closely match the predefined subway operating frequency range (10 Hz to 80 Hz, with a main frequency of 30 Hz). The Pearson correlation coefficient is used to calculate the match, resulting in a coefficient of 0.92, which exceeds the threshold of 0.85, confirming that the frequency features conform to subway operation characteristics. Next, time domain analysis is performed. For the subset of frequency domain matches, the original signal for the corresponding time period is extracted. Assume that the signal amplitude fluctuates between 0.1 V and 0.5 V. The signal periodicity is analyzed using the autocorrelation function. The calculated period shows a period of approximately 33.3 ms, consistent with a main frequency of 30 Hz, further confirming that the interference source is subway operation. To generate subway interference subsets, a bandpass filter (10Hz to 80Hz) is used to remove irrelevant frequencies while retaining matching signals. The filtered signal is then segmented using threshold detection (amplitude > 0.2V) to identify interference fragments. Assume 100 interference subsets are generated, each approximately 0.5s long. The final subsets are saved as a matrix of timestamp and amplitude pairs for subsequent interference suppression. This process, through a logical progression from the frequency domain to the time domain, ensures accurate identification of interference sources and ensures that subset generation meets service requirements.

[0047] Step 3032: If the frequency characteristics in the interference characteristic data set match the preset industrial equipment frequency band, the interference source is determined to be industrial equipment through waveform analysis, and an industrial interference subset is obtained.

[0048] Signal processing extracts frequency features from the interference characteristic dataset, and a fast Fourier transform (FFT) algorithm is used to calculate spectral features to obtain a frequency feature set. If the feature values ​​in the frequency feature set match the preset industrial equipment frequency band, spectral analysis is used to determine the degree of match, resulting in a matching feature subset. Waveform analysis is then used to perform joint time and frequency domain processing on the matching feature subset, and short-time Fourier transform is used to extract interference waveform features to obtain an interference waveform set. Similarity is calculated between the interference waveform set and a preset industrial equipment waveform template, and a dynamic time warping algorithm is used to determine the interference source, resulting in a preliminary interference source set. Interference features related to industrial equipment are extracted from the preliminary interference source set, and cluster analysis is used to divide the interference subsets into industrial interference subsets. Furthermore, spectral denoising is performed on the industrial interference subset, and adaptive filtering techniques are used to separate the interference signals to obtain a pure interference signal set. Pattern matching is performed between the pure interference signal set and a preset industrial equipment signal library to determine the device type of the interference source, resulting in the final industrial interference subset.

[0049] Specifically, when processing an interference characteristic dataset, frequency features are first extracted through spectrum analysis. Assuming the dataset contains multiple signal samples, each sample has a sampling rate of 10 kHz and a duration of 1 second. Using the Fast Fourier Transform (FFT) algorithm, the time-domain signal is converted into a frequency-domain signal, obtaining the frequency components of each sample. The main frequency peaks are calculated. For example, if the dominant frequencies of a sample are found to be 50 Hz and 100 Hz, these frequency features are then matched with the preset frequency band of industrial equipment. Assuming the typical frequency band of industrial equipment is 40 Hz to 120 Hz, a matching algorithm is written with a frequency error tolerance of ±5 Hz. If the dominant frequency of a sample falls within this range, it is marked as a potential industrial interference signal. For example, if both 50 Hz and 100 Hz meet the criteria, the sample is marked as suspected industrial interference. Next, waveform analysis is used to further identify the source of interference. Wavelet transforms are used to perform multi-resolution decomposition of the signal and extract its time-frequency characteristics. Assuming a five-level decomposition, the energy proportion of low-frequency components (e.g., 20 Hz to 60 Hz) associated with industrial equipment operation in each layer is analyzed. If the proportion exceeds 60%, such as 75% for a sample with low-frequency energy, the interference source is confirmed to be industrial equipment. Finally, all qualified samples are classified as an industrial interference subset. Assuming a total of 1,000 samples, a subset of 200 samples with a dominant frequency between 40 Hz and 120 Hz and a low-frequency energy proportion exceeding 60% is selected. The data storage module saves this subset to a database and generates a spectrum feature report containing the dominant frequency value and energy proportion data for each sample, forming a complete data processing chain and ensuring traceability for subsequent analysis.

[0050] In this embodiment, the fast Fourier transform algorithm is used to extract interference features, combined with preset frequency band matching to determine the interference source, and the support vector machine algorithm is used to classify the interference, so that the feature data in the interference feature data set can be accurately classified into subway interference subsets and industrial interference subsets, thereby improving the prediction effect of different interference sources on the corrosion risk of buried pipelines.

[0051] In one embodiment, see Figure 5 The determining of the dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system includes: Step 501: According to the target protection parameters, a digital signal processor is used to adjust the current output of the pipeline cathodic protection system to obtain protection optimization parameters; Step 502: updating the operating status of the pipeline cathodic protection system in real time by using the protection optimization parameters, and determining the real-time operating status fed back by the target protection parameters acting on the pipeline cathodic protection system; Step 503: Determine a dynamic protection configuration based on the real-time operating status.

[0052] When it is detected that the deviation between the real-time operating status and the target protection parameter exceeds a preset threshold, the closed-loop adjustment of the current output is completed within 30 seconds, and the adjustment result is written into the real-time control register of the cathodic protection system.

[0053] Based on the target protection parameters and the real-time operating status of the pipeline cathodic protection system, the target protection parameters are adjusted to obtain a new current output scheme and determine the dynamic protection configuration. Specifically, the target protection parameters are input into the pipeline cathodic protection system to obtain the current output value of the pipeline cathodic protection system. The current output value of the pipeline cathodic protection system is dynamically corrected to determine whether the current output value meets the preset threshold range. If it exceeds the threshold range, the current output value is recalculated until the current output value meets the conditions. Based on the corrected current output value, the protection optimization parameters are obtained. The protection optimization parameters are input into the pipeline cathodic protection system to obtain the real-time operating status feedback of the pipeline cathodic protection system. Based on the protection optimization parameters and the real-time operating status feedback of the pipeline protection, the changing trend of the protection effect is analyzed to obtain the effect evaluation results. Based on the effect evaluation results, if the protection effect does not meet expectations, the generation logic of the target protection parameters is adjusted to determine the final protection parameter configuration and obtain the dynamic protection configuration. Through the system adjustment function, the final protection parameter configuration, i.e., the dynamic protection configuration, is applied to the pipeline cathodic protection system to obtain continuous protection effect monitoring data, completing the closed-loop parameter optimization process.

[0054] In another embodiment, the protection optimization parameters are determined according to the target protection parameters; thereafter, the protection optimization parameters are run, and the current operating status is analyzed by collecting real-time data of the pipeline cathodic protection system to obtain preliminary status feedback information. The preliminary status feedback information is compared with a preset threshold value to determine whether there is a deviation in the preliminary status feedback information, and a deviation analysis result is obtained. If the deviation analysis result exceeds the preset range, the protection optimization parameters are optimized and adjusted to determine the adjusted parameter value. The operating status of the cathodic protection system is updated through the protection optimization parameters, and the updated system operating data is obtained. For the updated system operating data, the protection effect is analyzed in combination with the real-time monitoring mechanism to obtain effect evaluation information. According to the effect evaluation information, if the protection effect does not meet expectations, the dynamic configuration mechanism is triggered, the system update strategy is adjusted, and the final protection configuration scheme is determined to obtain a dynamic protection configuration. Through the final protection configuration scheme, the operating status of the pipeline system is continuously monitored to obtain long-term status feedback data.

[0055] In this embodiment, the protection optimization parameters are continuously optimized by combining the real-time operating status of the target protection parameters fed back by the pipeline cathodic protection system. Through the dynamic configuration mechanism, the system update strategy is adjusted to obtain the final protection configuration scheme, namely the dynamic protection configuration, to realize the dynamic protection configuration of the pipeline cathodic protection system, and to achieve precise protection of buried pipelines, thereby improving the protection effect of the pipeline.

[0056] In one embodiment, see Figure 6 The corrosion risk association model includes a corrosion rate prediction model, a corrosion risk level prediction model, and a corrosion risk result prediction model. The interference characteristics are input into the corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk, and the risk assessment results output by the corrosion risk association model are obtained, including: Step 311: input the interference feature into the corrosion rate prediction model to obtain a corrosion rate result output by the corrosion rate prediction model, wherein the corrosion rate result reflects the effects of different interference sources on pipeline corrosion; Step 312: Input the corrosion rate result into the corrosion risk level prediction model to determine the degree of influence of different interference sources on pipeline corrosion risk, and obtain a risk level list output by the corrosion risk level prediction model. The risk level list is determined by marking interference features whose influence weights of interference sources exceed a preset threshold as high-risk factors. Step 313: input the risk level list into the corrosion risk result prediction model to obtain a risk assessment result output by the corrosion risk result prediction model; The risk assessment results include the pipeline corrosion risk level, the time required for pipeline perforation, the pipeline risk trend and the pipeline protection strategy.

[0057] The corrosion risk association model includes a corrosion rate prediction model, a corrosion risk level prediction model, and a corrosion risk result prediction model. The output of the corrosion rate prediction model is connected to the input of the corrosion risk level prediction model, and the output of the corrosion risk level prediction model is connected to the input of the corrosion risk result prediction model. Specifically, the interference characteristics are input into the corrosion rate prediction model to obtain corrosion rate results. For example, the corrosion rate of the pipeline can be predicted under subway interference signals or industrial interference signals, or under different temperature and humidity conditions. The corrosion rate results corresponding to different interference sources are then input into the corrosion risk level prediction model, which outputs risk level results corresponding to different interference sources. If the impact weight of a particular interference source exceeds a preset threshold, it is marked as a high-risk factor, and a classified risk level list is obtained. The risk level list is then input into the corrosion risk result prediction model. Based on the classified risk level list, in-depth data mining is performed on high-risk factors to determine their specific impact paths on pipeline corrosion, determine potential corrosion risk trends, predict the time required for pipeline perforation, and obtain risk assessment results for different interference sources. In one embodiment, the corrosion rate prediction model may be a linear regression model, the corrosion risk level prediction model may be a decision tree model, and the corrosion risk result prediction model may be a random forest model.

[0058] In this embodiment, the corrosion rate prediction model is used to predict the corrosion rate of the pipeline caused by different interference sources, the corrosion risk level prediction model is used to predict the level of corrosion risk of different interference sources under the conditions of their corresponding corrosion rates, and the corrosion risk result prediction model is used to predict the risk assessment results of pipeline corrosion under the conditions of corresponding corrosion rates and corrosion risk levels for different interference sources, including the corrosion risk trend of the pipeline, the time required for pipeline perforation, and the pipeline protection strategy, thereby achieving an accurate assessment of the pipeline corrosion risk, greatly reducing the risk of pipeline corrosion, and improving the protection level of buried pipelines.

[0059] In one embodiment, see Figure 7 , the corrosion risk result prediction model is used to: Step 3131: Based on the risk level list, in-depth data mining is performed to determine the action paths of high-risk factors on pipeline corrosion and the corrosion risk change trends corresponding to the action paths; Step 3132: Based on the corrosion risk change trend and historical records of pipeline status, conditional judgment logic is used to determine the key monitoring objects in the risk level list; Step 3133: Based on the key monitoring object, in-depth data mining is performed to determine the target action path of the key monitoring object and the target corrosion risk change trend corresponding to the target action path; Step 3134: determining the risk assessment result based on the target action path and the target corrosion risk change trend; If the corrosion risk change trend matches the historical high-risk pattern in the historical record data, the key monitoring object is determined.

[0060] Specifically, in the corrosion risk prediction model, in-depth data mining is conducted based on the high-risk factors in the risk level list to predict their specific action paths on pipeline corrosion. Based on the specific action paths of pipeline corrosion, the potential corrosion risk change trend under these action paths is determined. For example, under a high-risk level, the action paths are predicted to be high humidity and coating aging. It can be predicted that the corresponding pipeline corrosion risk change trend will become increasingly serious. Therefore, it can be known that under high-risk level and action paths of high humidity and coating aging, the corresponding interference source and pipeline will be determined as key monitoring targets. Under low-risk level conditions, the action path is predicted to be low humidity. It can be predicted that the corresponding pipeline corrosion is less severe and its risk change trend will become increasingly lower. Therefore, it can be known that under low-risk level and action path of low humidity, the corresponding interference source and pipeline will be determined as ordinary monitoring targets. By analyzing the corrosion risk change trend and combining it with historical pipeline safety data, conditional judgment logic is adopted. If a trend matches the historical high-risk pattern, it is prioritized and the key monitoring target is determined. Afterwards, based on the key monitoring objects, in-depth data mining is conducted on the high-risk factors reflected by the key monitoring objects. The target action path of the key monitoring objects on pipeline corrosion and the target corrosion risk change trend under this target action path are predicted, thereby obtaining the target action path and target corrosion risk change trend corresponding to the key monitoring objects. Finally, based on the target action path and target corrosion risk change trend corresponding to the key monitoring objects, a targeted risk assessment strategy is generated, and a comparative analysis is obtained between the real-time monitoring data and the model prediction results to determine whether there is any deviation and adjust the model parameters. With the adjusted model parameters, the decision tree algorithm is re-run to continuously analyze the dynamic changes in pipeline corrosion risk and obtain updated assessment results.

[0061] In this embodiment, the corrosion risk result prediction model is used to predict the interference sources and the objects that need to be focused on prevention and control in the pipeline under the corresponding corrosion rate and corrosion risk level conditions for different interference sources, as well as the corrosion risk trend of the key monitored objects, the time required for pipeline perforation, and the pipeline protection strategy, etc., to obtain the pipeline corrosion risk assessment results, thereby achieving an accurate assessment of the pipeline corrosion risk, greatly reducing the risk of pipeline corrosion, and improving the protection level of buried pipelines.

[0062] The following describes the buried pipeline stray current real-time dynamic monitoring system provided by the present invention. The buried pipeline stray current real-time dynamic monitoring system described below and the buried pipeline stray current real-time dynamic monitoring method described above can be referenced to each other.

[0063] Please refer to Figure 8 The present invention provides a real-time dynamic monitoring system for stray current in buried pipelines, comprising: The signal acquisition module 810 is used to acquire electrical interference signals from the surrounding environment of the buried pipeline; An interference feature extraction module 820 is configured to determine an interference feature of the electrical interference signal based on the electrical interference signal, wherein the interference feature includes an interference source, interference intensity, and action time corresponding to the electrical interference signal; The result prediction module 830 inputs the interference characteristics into the corrosion risk association model to determine the impact of different interference sources on pipeline corrosion risk and obtain the risk assessment result output by the corrosion risk association model; A parameter generation module 840 is configured to generate target protection parameters corresponding to the electrical interference signal based on the risk assessment result; A dynamic protection configuration module 850 is configured to determine a dynamic protection configuration based on the target protection parameters and the real-time operating status of the pipeline cathodic protection system as a function of the target protection parameters; The dynamic protection configuration is used by the pipeline cathodic protection system to perform electrochemical dynamic protection operations on buried pipelines, and the corrosion risk association model is obtained by training interference feature data sets corresponding to different interference sources.

[0064] Figure 9 An example of a physical structure diagram of an electronic device is shown below. Figure 9As shown, the electronic device may include: a processor (processor) 910, a communication interface (Communications Interface) 920, a memory (memory) 930 and a communication bus 940, wherein the processor 910, the communication interface 920, and the memory 930 communicate with each other through the communication bus 940. The processor 910 can call the logic instructions in the memory 930 to execute a real-time dynamic monitoring method for stray currents in buried pipelines, the method comprising: obtaining an electrical interference signal from the surrounding environment of the buried pipeline; determining the interference characteristics of the electrical interference signal based on the electrical interference signal, wherein the interference characteristics include the interference source, interference intensity and action time corresponding to the electrical interference signal; inputting the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on the pipeline corrosion risk, and obtaining a risk assessment result output by the corrosion risk association model; generating a target protection parameter corresponding to the electrical interference signal based on the risk assessment result; determining a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; wherein the dynamic protection configuration is used for the pipeline cathodic protection system to perform electrochemical dynamic protection operations on the buried pipeline, and the corrosion risk association model is obtained by training the interference feature data sets corresponding to different interference sources.

[0065] Furthermore, the logic instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0066] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the real-time dynamic monitoring method for stray current in buried pipelines provided by the above-mentioned methods, the method comprising: obtaining an electrical interference signal from the surrounding environment of the buried pipeline; determining the interference characteristics of the electrical interference signal based on the electrical interference signal, wherein the interference characteristics include the interference source, interference intensity and action time corresponding to the electrical interference signal; inputting the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on the pipeline corrosion risk, and obtaining a risk assessment result output by the corrosion risk association model; generating a target protection parameter corresponding to the electrical interference signal based on the risk assessment result; determining a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; wherein the dynamic protection configuration is used for the pipeline cathodic protection system to perform electrochemical dynamic protection operations on the buried pipeline, and the corrosion risk association model is obtained by training the interference feature data set corresponding to different interference sources.

[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0068] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0070] Definition of terms To enable those skilled in the art to accurately understand and implement the present invention, the following definitions are given for the key terms used in this specification. Unless otherwise specified, the following terms have the following meanings: 1.Buried Pipeline Refers to metal or non-metal pipelines and their ancillary facilities laid below the surface for transporting gas, oil, water or other media, usually including main pipelines, valves, pipe fittings, insulating joints and cathodic protection systems.

[0071] 2. Stray Current Also known as stray current, it refers to the DC or AC current that unexpectedly flows through buried metal structures (such as pipelines). It comes from urban rail transit, DC transmission systems, industrial electrolysis equipment, high-voltage AC line induction, lightning or ground faults, etc.

[0072] 3. Electrical Interference Signal In the context of the present invention, it specifically refers to the instantaneous value and spectral components of voltage, current or magnetic field collected by sensors that can reflect stray current and its changes. This signal is the raw data for subsequent interference feature extraction.

[0073] 4. Interference Feature Refers to key quantitative indicators extracted from electrical interference signals to characterize interference sources and corrosion risks, including but not limited to: • Interference sources (subway, industrial rectifier equipment, high-voltage AC lines, lightning, etc.); • Interference intensity (current density mA / m², magnetic induction intensity μT, potential offset mV); • Action time (disturbance duration, periodicity, pulse width); • Frequency domain characteristics (dominant frequency, harmonic distribution, power spectral density).

[0074] 5. Corrosion Risk Association Model A mathematical model developed using machine learning or statistical methods (such as support vector machines, random forests, linear regression, neural networks, or a combination thereof). This model uses interference signatures as input and corrosion rates or corrosion risk levels as output. It is used to quantify the impact of different interference sources on buried pipeline corrosion. The model is trained using historical interference signature datasets and corresponding corrosion rate datasets.

[0075] 6. Corrosion Rate In the present invention, it refers to the amount of metal thickness loss in the pipeline caused by stray current per unit time, and the common unit is millimeter per year (mm / a). It can be measured in real time or periodically through coupon test, electrochemical probe or ultrasonic thickness gauge.

[0076] 7. Risk Assessment Result The comprehensive assessment information output by the corrosion risk association model shall at least include: • Current corrosion risk level (high, medium, low); • Predicting time to perforation (Time-to-Perforation); • Corrosion risk trend (increasing, stable, decreasing); • Recommended protection strategies (e.g., current augmentation, coating repair, drain grounding, polarity drain switch switching).

[0077] 8. Target Protection Parameter Based on the risk assessment results, the algorithm generates control instructions or setpoints for real-time adjustment of the cathodic protection system, such as output current (A), protection potential setpoint (V vs. Cu / CuSO4), duty cycle (%), or pulse frequency (Hz).

[0078] 9. Dynamic Protection Configuration It refers to a closed-loop control strategy that is calculated and issued in real time by a digital signal processor (DSP) or microcontroller (MCU) based on the target protection parameters and real-time feedback from the cathodic protection system (actual output current, pipeline potential, system power consumption, etc.), so that the cathodic protection system can continuously maintain the optimal protection level under the scenario of stray current changes.

[0079] 10. Multi-Channel Sensor A sensor array arranged along the buried pipeline with ≥2-way synchronous acquisition capability may include any one or a combination of Hall current sensors, fluxgate sensors, reference electrodes, resistance probes or electromagnetic induction coils to obtain electrical interference signals.

[0080] 11. Real-Time Operating Status The current set of operating points of the cathodic protection system, including but not limited to: • Actual output current / voltage; • The instantaneous potential of the pipe relative to the reference electrode; • System temperature, power consumption, fault codes; • Deviation from target protection parameters.

[0081] 12. Non-Transitory Computer-Readable Storage Medium Refers to a tangible and non-transitory medium that can store computer program instructions, such as flash memory, solid-state drives, read-only memory (ROM), random-access memory (RAM), magnetic disks, optical disks, USB flash drives, cloud storage servers, etc., used to implement the software deployment and distribution of the method described in the present invention.

[0082] Unless otherwise specified, the above terms apply to the entire contents of this specification, claims and drawings.

Claims

1. A real-time dynamic monitoring method for stray current in buried pipelines, characterized in that: Executed by a computer, including: Obtain electrical interference signals from the surrounding environment of buried pipelines; Determining interference characteristics of the electrical interference signal based on the electrical interference signal, wherein the interference characteristics include interference source, interference intensity, and action time corresponding to the electrical interference signal; Inputting the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk, and obtaining a risk assessment result output by the corrosion risk association model; Based on the risk assessment result, generating target protection parameters corresponding to the electrical interference signal; Determining a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; The dynamic protection configuration is used by the pipeline cathodic protection system to perform electrochemical dynamic protection operations on buried pipelines, and the corrosion risk association model is obtained by training interference feature data sets corresponding to different interference sources.

2. The method for real-time dynamic monitoring of stray current in buried pipelines according to claim 1, characterized in that: The corrosion risk association model is obtained in the following way: Obtain electrical interference signals from the surrounding environment of buried pipelines, use multi-channel sensors to collect voltage and current data, and obtain the original interference data set; Decomposing the signal spectrum using a fast Fourier transform algorithm, extracting the frequency and amplitude characteristics of the original interference data set, and obtaining an interference characteristic data set; Determining a subway interference subset and an industrial interference subset according to the interference feature data set; According to the subway interference subset and the industrial interference subset, a support vector machine algorithm is used to classify the interference intensity and action time to obtain an interference classification result; According to the interference classification results, a data association modeling method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate to obtain a corrosion risk association model; The interference classification result includes the correlation between the interference source, interference intensity and action time.

3. The method for real-time dynamic monitoring of stray current in buried pipelines according to claim 2, characterized in that: According to the interference classification results, a data association modeling method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate to obtain a corrosion risk association model, including: Based on the interference classification results and pipeline corrosion rate data, extracting characteristic values ​​of interference intensity and corrosion rate from a pre-established database to obtain an initial data set; The principal component analysis method is used to extract features from the initial data set, reduce the data dimension, and obtain a reduced-dimensionality feature set; The Pearson correlation coefficient method is used to calculate the correlation coefficient between the interference intensity and the pipeline corrosion rate in the dimensionality reduction feature set, and the correlation coefficient matrix is ​​obtained; According to the correlation coefficient matrix and the interference classification result, the feature pairs of interference intensity and corrosion rate in the dimensionality reduction feature set are marked as high-correlation feature pairs to obtain a high-correlation feature set; According to the highly correlated feature set, a linear regression algorithm is used to train a preset training model to obtain the corrosion risk association model.

4. The method for real-time dynamic monitoring of stray current in buried pipelines according to claim 2 is characterized in that The determining of the subway interference subset and the industrial interference subset based on the interference feature data set includes: If the frequency characteristics in the interference characteristic data set match the preset subway operation frequency band, then determining the interference source as subway operation through time domain analysis to obtain a subway interference subset; If the frequency characteristics in the interference characteristic data set match the preset industrial equipment frequency band, the interference source is determined to be industrial equipment through waveform analysis to obtain an industrial interference subset.

5. The method for real-time dynamic monitoring of stray current in buried pipelines according to claim 1, characterized in that: Determining the dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system includes: According to the target protection parameters, a digital signal processor is used to adjust the current output of the pipeline cathodic protection system to obtain protection optimization parameters; By using protection optimization parameters, the operating status of the pipeline cathodic protection system is updated in real time, and the real-time operating status fed back by the target protection parameters acting on the pipeline cathodic protection system is determined; Based on the real-time operating status, a dynamic protection configuration is determined.

6. The method for real-time dynamic monitoring of stray current in buried pipelines according to claim 1, characterized in that: The corrosion risk association model includes a corrosion rate prediction model, a corrosion risk level prediction model, and a corrosion risk result prediction model. The interference characteristics are input into the corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk, and the risk assessment results output by the corrosion risk association model are obtained, including: Inputting the interference characteristics into the corrosion rate prediction model to obtain a corrosion rate result output by the corrosion rate prediction model, wherein the corrosion rate result reflects the effects of different interference sources on pipeline corrosion; Inputting the corrosion rate result into the corrosion risk level prediction model to determine the degree of influence of different interference sources on pipeline corrosion risk, and obtaining a risk level list output by the corrosion risk level prediction model, wherein the risk level list is determined by marking interference features whose influence weights of interference sources exceed a preset threshold as high-risk factors; Inputting the risk level list into the corrosion risk result prediction model to obtain a risk assessment result output by the corrosion risk result prediction model; The risk assessment results include the pipeline corrosion risk level, the time required for pipeline perforation, the pipeline risk trend and the pipeline protection strategy.

7. The method for real-time dynamic monitoring of stray current in buried pipelines according to claim 6, characterized in that: The corrosion risk result prediction model is used to: Based on the risk level list, in-depth data mining is conducted to determine the action paths of high-risk factors on pipeline corrosion and the corrosion risk change trends corresponding to the action paths; Based on the corrosion risk change trend and historical records of pipeline status, conditional judgment logic is used to determine the key monitoring objects in the risk level list; Based on the key monitoring objects, in-depth data mining is carried out to determine the target action path of the key monitoring objects and the target corrosion risk change trend corresponding to the target action path; Determining the risk assessment result based on the target action path and the target corrosion risk change trend; If the corrosion risk change trend matches the historical high-risk pattern in the historical record data, the key monitoring object is determined.

8. A system for real-time dynamic monitoring of stray current in buried pipelines, used to implement the method for real-time dynamic monitoring of stray current in buried pipelines according to any one of claims 1 to 7, characterized in that: include: A signal acquisition module is used to acquire electrical interference signals from the surrounding environment of the buried pipeline; an interference feature extraction module, configured to determine, based on the electrical interference signal, an interference feature of the electrical interference signal, wherein the interference feature includes an interference source, interference intensity, and action time corresponding to the electrical interference signal; A result prediction module inputs the interference characteristics into a corrosion risk association model to determine the degree of influence of different interference sources on pipeline corrosion risk and obtain a risk assessment result output by the corrosion risk association model; A parameter generation module, configured to generate target protection parameters corresponding to the electrical interference signal based on the risk assessment result; A dynamic protection configuration module is used to determine a dynamic protection configuration based on the target protection parameter and the real-time operating status fed back by the target protection parameter acting on the pipeline cathodic protection system; The dynamic protection configuration is used by the pipeline cathodic protection system to perform electrochemical dynamic protection operations on buried pipelines, and the corrosion risk association model is obtained by training interference feature data sets corresponding to different interference sources.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for real-time dynamic monitoring of stray current in buried pipelines according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for real-time dynamic monitoring of stray current in buried pipelines according to any one of claims 1 to 7 is implemented.

Images