A sewer environmental protection detection method based on data processing

Abstract

The application relates to the field of sewer environmental protection detection technology, and discloses a sewer environmental protection detection method based on data processing. Four probe electrodes are arranged at equal intervals along the axis inside the pipeline, the parasitic impedance of the instrument and the lead is automatically calculated and compensated through open circuit and short circuit tests, meanwhile, under preset temperature and humidity conditions, sine signals are sequentially output according to a set frequency range and discrete frequency points, and after waiting for a steady state, multiple voltage and current data are collected, abnormal values lower than a threshold value are removed, average and variance are counted, and the impedance is corrected by applying compensation parameters; then, the corrected impedance is linearly smoothed at three points and the consistency of adjacent points is verified, an orthogonal basis function is generated by using a self-created discrete Gram-Schmidt algorithm, the expansion coefficients are obtained multiple times by using a non-polluted baseline medium, the health state is dynamically calibrated, and a maximum deviation threshold value is set; during actual detection, real-time impedance is projected on the orthogonal basis, the deviation is calculated, and pollution or blockage can be quickly and accurately determined.

Description

Technical Field

[0001] This invention relates to the field of environmental protection testing technology for sewers, specifically a sewer environmental protection testing method based on data processing. Background Technology

[0002] With the acceleration of urbanization, sewer systems bear the important functions of sewage discharge and environmental protection. Traditional sewer environmental monitoring relies heavily on periodic manual sampling and testing or online chemical sensor monitoring, such as measuring indicators like pH, dissolved oxygen, turbidity, and chemical oxygen demand. These methods suffer from long sampling cycles, insufficient spatial coverage, and difficulty in achieving real-time online monitoring. Furthermore, while technologies based on ultrasonic echo and acoustic impedance detection can reflect structural changes or larger particle blockages within pipes, they have low sensitivity to trace pollutants or early signs of blockage in the water flow.

[0003] In the field of impedance detection, existing studies have used single-frequency or limited-frequency measurements to determine the concentration of contaminants or the degree of blockage in pipelines by measuring impedance amplitude or phase changes. However, single-frequency or limited-frequency schemes do not clearly differentiate the responses to different contaminants and blockages, and are easily affected by factors such as temperature, pipe wall material, and electrode spacing changes, resulting in poor measurement stability and difficulty in providing reliable judgments of pipeline conditions. To improve detection accuracy and anti-interference capabilities, some scholars have proposed multi-frequency impedance spectroscopy analysis, which identifies abnormal states by performing principal component analysis or template matching on multi-frequency impedance data. However, these methods usually rely on pre-trained models or preset thresholds and lack adaptive online correction capabilities. In practical applications, the parasitic impedance of axially arranged electrodes and the additional impedance of instruments and wires have a significant impact on measurement results; without real-time correction, baseline drift and misjudgment can occur. Furthermore, while existing orthogonalization or decomposition algorithms (such as Fourier transform and wavelet analysis) have certain advantages in multi-frequency impedance signal processing, they often have high requirements for basis function selection and high computational complexity, making rapid deployment in sewer systems difficult. Especially for pipeline online monitoring, it is necessary to ensure the efficient and stable operation of the closed-loop process of sampling, calibration, signal processing and decision-making under limited computing resources and harsh environmental conditions.

[0004] Therefore, this case aims to propose a data processing-based environmental monitoring method for sewer systems. By deploying four probe electrodes at equal intervals inside the pipe section, combined with parasitic impedance compensation, multi-point signal acquisition and threshold filtering, three-point linear smoothing, discrete orthogonal basis function construction, and baseline coefficient statistics and real-time deviation determination under a reference medium, this method achieves high-precision and robust detection of pollutants and blockages within the pipe. This method solves the problems of existing technologies in complex fluid environments, which struggle to obtain stable and reliable detection results due to high measurement noise, transient response interference, and the lack of effective baseline comparisons. Furthermore, by recording real-time data and storing historical trends, it provides a basis for subsequent maintenance decisions and trend analysis. Summary of the Invention

[0005] This invention provides a data processing-based environmental monitoring method for sewer systems, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: a sewer environmental protection detection method based on data processing, comprising:

[0007] S1. Four-electrode probes are evenly spaced along the axial direction inside the target pipe section, and the parasitic impedance of the instrument and wires is measured by voltage and current tests under open and short circuit conditions; the zero offset compensation value and gain compensation coefficient are calculated based on the measured parasitic impedance data; at the same time, the temperature and humidity of the medium inside the pipe are maintained within the preset range.

[0008] S2. Set the measurement frequency range and discrete frequency points, output sine signals sequentially and set the amplitude, and wait for the predetermined steady-state time after each frequency switch;

[0009] S3. Repeatedly acquire voltage and current data at each measurement frequency point, remove abnormal readings below the preset threshold, re-acquire, calculate the average value and standard deviation of voltage and current, and then correct the original impedance according to the compensation parameters.

[0010] S4. Perform three-point linear smoothing on the impedance data after correction at each frequency point, and verify the consistency between adjacent data. If the consistency is not met, re-acquire and smooth; otherwise, continue with subsequent processing.

[0011] S5. Apply the discrete Gram-Schmidt orthogonalization algorithm to the smoothed impedance sequence to generate a set of orthogonal basis functions;

[0012] S6. Collect orthogonal expansion coefficients multiple times using a pollution-free baseline medium, calculate the average baseline value of each order, and determine the maximum deviation threshold.

[0013] S7. In actual testing, the real-time impedance data is expanded onto orthogonal basis functions, the deviation from the baseline threshold is calculated, and the pollution or blockage status is determined based on the magnitude of the deviation.

[0014] S8. Store each measurement frequency, smoothed impedance value, orthogonal expansion coefficient, deviation and judgment result into the database in batches, and ensure that at least the most recent 100 test records are retained.

[0015] Optionally, the step involves arranging four electrode probes at equal intervals along the axial direction inside the target pipe section and measuring the parasitic impedance of the instrument and conductors through voltage and current tests under open-circuit and short-circuit conditions; calculating the zero-offset compensation value and gain compensation coefficient based on the measured parasitic impedance data; and simultaneously maintaining the temperature and humidity of the medium inside the pipe within a preset range, specifically including:

[0016] Four probe electrodes are evenly spaced inside the target pipe section, with a probe spacing error. Simultaneously adjust the probe position to ensure the probe is coaxial with the tube wall. ;

[0017] The probe is suspended and does not contact the medium; the parasitic impedance of the measuring instrument and the wire is measured. ;in, The voltage measured under open-circuit conditions; The current measured under open-circuit conditions; This is the parasitic impedance measured during open-circuit calibration;

[0018] Short-circuit probe; measure short-circuit parasitic impedance: ;in, The voltage was measured under short-circuit conditions; The current was measured under short-circuit conditions; This is the parasitic impedance measured during short-circuit calibration;

[0019] Calculate zero offset compensation: ;in, This is the zero offset compensation value;

[0020] Two standard resistors Connect probes to measure impedance. ;in, Corresponding standard resistor serial number; For access The impedance was measured at that time;

[0021] Calculate the impedance measurement after removing parasitic offset. ;

[0022] like If so, the standard resistor needs to be replaced; otherwise, continue.

[0023] Construct a linear model: ;

[0024] Solving , ;in, This is the gain compensation coefficient; This is the offset compensation coefficient;

[0025] Will , and Write into the measurement system;

[0026] Maintain the temperature of the medium inside the pipe humidity ;

[0027] Inject non-contamination baseline medium and let stand. Then, subsequent baseline data collection will be conducted.

[0028] Optionally, the step of setting the measurement frequency range and discrete frequency points, sequentially outputting sinusoidal signals and setting the amplitude, and waiting for a predetermined steady-state time after each frequency switch specifically includes:

[0029] Set the measurement frequency range to Set the total number of discrete measurement frequency points to ;in, Minimum measurement frequency; Maximum measurement frequency;

[0030] Calculate frequency spacing ;

[0031] right :

[0032] Set the sine wave output frequency: ;in, Number the frequency points; For the first There are several frequency points; among them,

[0033] Set peak-peak amplitude value ;

[0034] Wait after switching frequencies until steady state; where, This represents the steady-state waiting time after frequency switching.

[0035] Optionally, the step of repeatedly acquiring voltage and current data at each measurement frequency point, removing abnormal readings below a preset threshold, re-acquiring data, calculating the average value and standard deviation of voltage and current, and then correcting the original impedance according to compensation parameters, specifically includes:

[0036] At each frequency point Location, collection Group data , ;in, The number of times the measurement is repeated at each frequency point; For the first The voltage was measured once; For the first The current was measured once;

[0037] Requirements for each reading , If the conditions are not met, discard the measurement and take a new one.

[0038] in, This is the minimum acceptable voltage amplitude; This is the minimum acceptable current amplitude;

[0039] Calculate the average: , ;

[0040] Calculate the standard deviation:

[0041] ; ;

[0042] in, For the first The average voltage at the point; For the first The average current at the point; For the first The sample standard deviation of point voltage; For the first The sample standard deviation of the point current;

[0043] Require: , Otherwise, data will be collected again; among which, The threshold for relative stability of voltage readings; The threshold for relative stability of current readings;

[0044] Calculate the original impedance: ;

[0045] Apply offset and gain compensation: ;in, For the corrected first Secondary impedance;

[0046] Calculate the first Point average impedance .

[0047] Optionally, the impedance data corrected at each frequency point is subjected to three-point linear smoothing, and the consistency between adjacent data points is verified. If the consistency is not met, the data is re-acquired and smoothed; otherwise, subsequent processing continues. Specifically, this includes:

[0048] Calculate the first Impedance after point smoothing:

[0049] ;

[0050] Jump check occurs: If not satisfied, only for that... Return to step S3 to resample and resmooth; otherwise, continue.

[0051] Optionally, the step of applying the discrete Gram-Schmidt orthogonalization algorithm to the smoothed impedance sequence to generate a set of orthogonal basis functions specifically includes:

[0052] For order 0: Construct a zeroth-order intermediate function ;

[0053] Zero-order normalization factor Zero-order normalized orthogonal basis ;in, For the first A normalized frequency, ; For normalized frequency spacing, ;

[0054] For order :

[0055] Calculate the first The first basis pair Projection coefficients of the basis , ;

[0056] Construct the first orthogonalization of intermediate functions ;

[0057] Calculate the first normalization factor ;

[0058] Calculate the first Normalized orthogonal basis ;

[0059] S510, Calculate the elements of the inner product matrix:

[0060] For all ,calculate ;in, For the first basis functions With the basis functions The inner product at all discrete points;

[0061] S520. Determine orthogonality: Assume tolerance. If for any , or If the orthogonality is not satisfied, then it is considered that the orthogonality is not satisfied.

[0062] S530. If orthogonality is not satisfied, then perform reorthogonalization:

[0063] S531, Correction: For each Execute in sequence:

[0064] ;

[0065] S532, Renormalization: , ;

[0066] Repeat steps S510 to S530 for a maximum of [number] iterations. Second-rate;

[0067] If still or If orthogonalization fails, you can choose one of the following:

[0068] Reduce the order: Let End of the first Order calculation;

[0069] Increase After selecting more frequencies, return to step S2;

[0070] Adjusting tolerance : Tolerance and tolerance;

[0071] When all At that time, it is considered the first Orthogonalization successful, proceed to the next one. .

[0072] Optionally, the step of repeatedly acquiring orthogonal expansion coefficients using a pollution-free baseline medium, calculating the average baseline values ​​for each order, and determining the maximum deviation threshold specifically includes:

[0073] No contaminant media standing ;

[0074] Loop collection :

[0075] S601, Repeat steps S2 to S4 to obtain ;

[0076] S602, Calculate the first Second baseline order expansion quantity ;in, This is the sequence number of the current baseline acquisition; Number of baseline measurements;

[0077] Calculate the first Baseline average ;

[0078] Construct the first Maximum deviation threshold .

[0079] Optionally, in actual detection, the real-time impedance data is expanded onto orthogonal basis functions, the deviation from the baseline threshold is calculated, and the contamination or blockage status is determined based on the magnitude of the deviation. Specifically, this includes:

[0080] Calculate the real-time first order expansion quantity ;

[0081] Constructing the real-time first Order deviation ;

[0082] If any make If it is positive, it is determined to be either contaminated or blocked; otherwise, it is determined to be normal.

[0083] Optionally, the step of storing each measurement frequency point, smoothed impedance value, orthogonal expansion coefficient, deviation, and judgment result into the database in batches, and ensuring that at least the most recent one hundred test records are retained, specifically includes:

[0084] frequency point set Smoothing impedance , expansion amount , deviation amount The judgment results are stored in the database in batches;

[0085] At the same time, retain no fewer than 100 test records.

[0086] The present invention has the following beneficial effects:

[0087] 1. Four probe electrodes are evenly spaced along the axial direction inside the target pipe section. Combined with voltage and current tests under open-circuit and short-circuit conditions, the parasitic impedance of the instrument and conductors is accurately measured, and the zero-offset and gain compensation coefficients are calculated. This approach simultaneously considers probe coaxiality and geometric layout errors, avoids contact noise by using suspended probes, and corrects parasitic effects bidirectionally under open-circuit and short-circuit conditions, ensuring the accuracy and stability of subsequent impedance data. Compared to traditional compensation methods that only use a single test state, this solution reduces system errors and signal drift risks. This process not only provides a precise instrument calibration benchmark but also provides technical assurance for measurement consistency under different pipe materials and environmental conditions, enabling subsequent multi-frequency measurements and data expansion based on true, offset-free impedance values, thus improving overall detection reliability.

[0088] 2. A comprehensive design was implemented for the selection of measurement frequency and the steady-state preparation of the system. Discrete measurement points were divided within a preset frequency range, and sufficient steady-state waiting time was set after each frequency switch to ensure that the probe and fluid environment reached a stable response before data acquisition. Combining the fluid dynamics and circuit response characteristics within the pipe, the optimal waiting time was determined through experience and experimentation. A trade-off was struck between spectral resolution and scanning speed, enabling the capture of detailed impedance variations with frequency while controlling the total on-site detection time. Compared to traditional fast scanning methods that rely solely on fixed frequencies or lack waiting time verification, this solution effectively avoids transient response errors, improves the consistency of multi-frequency measurement data, provides a more stable input for subsequent data processing, and enhances the accuracy and repeatability of online detection.

[0089] 3. At each measurement frequency point, voltage and current data are repeatedly collected. Abnormal readings below the preset minimum acceptable amplitude are discarded before re-collection. The average and standard deviation are calculated to assess signal stability, and the original impedance is corrected using a compensation coefficient. Combining threshold filtering with stability assessment avoids errors introduced by noise, transient drift, or weak signal amplification. Simultaneously, the sample standard deviation constraint ensures that only sufficiently stable data is accepted. Compared to existing technologies that rely on simple one-time readings or manual interpretation, this scheme automatically identifies and discards unreliable data, improving measurement repeatability and data reliability. This process reduces the impact of noise interference on the detection results, providing high-quality input for subsequent smoothing and orthogonalization analysis.

[0090] 4. The impedance data corrected at each frequency point is subjected to three-point linear smoothing, and abnormal transitions are identified through adjacent data consistency checks. If the transition exceeds a predetermined tolerance, the system automatically returns to resampling and resmoothing until the data becomes coherent before proceeding to the next stage. By combining the smoothing algorithm with the anomaly detection logic, real-time adaptive correction of abrupt noise on the curve is achieved. This method differs from traditional simple moving averages or manual screening; instead, it uses an automatic iterative feedback mechanism to ensure distortion-free transitions after data smoothing. This approach preserves the main characteristics of the impedance spectrum while eliminating sharp noise, improving the accuracy of data smoothing, providing a smoother and more continuous input curve for subsequent orthogonal expansion of basis functions, and enhancing the system's sensitivity to real contamination or blockage signals.

[0091] 5. Based on the smoothed impedance sequence, an improved discrete orthogonalization algorithm is applied to construct a set of orthogonal basis functions for subsequent signal expansion and deviation calculation. The orthogonalization process is tailored to discrete impedance data, balancing numerical stability and computational efficiency. Multiple iterative re-orthogonalization and tolerance adjustment strategies are introduced in the basis function construction to ensure the orthogonality and normalization characteristics between basis functions. Unlike traditional orthogonalization methods that assume continuous data or fixed polynomials, this scheme is more robust to the discreteness of actual measurement points and noise distribution. Therefore, the generated orthogonal basis not only approximates complex impedance change curves well but also provides high-resolution signal feature extraction in subsequent deviation determination, laying a solid foundation for pollution source indication and blockage location.

[0092] 6. By repeatedly executing the aforementioned measurement and orthogonal expansion process using a contaminated baseline medium, and statistically analyzing the average value and maximum deviation of the expansion coefficients at each order, a robust baseline threshold model is constructed. Repeated data collection under actual field conditions in a "contaminated state" is incorporated into the baseline calibration. Through data accumulation, a more reliable initial coefficient distribution is obtained, and thresholds at each order are determined accordingly. This avoids the risks of false alarms and missed alarms caused by baseline instability during a single calibration. Compared to traditional laboratory calibration or empirical threshold setting, this solution achieves real-time, field-adaptive dynamic baseline calibration, making subsequent deviation assessment more targeted and reliable, and improving detection accuracy and field applicability.

[0093] 7. In actual online detection, real-time smoothed impedance data is mapped to the previously constructed orthogonal basis function space. The deviation between each order expansion coefficient and the corresponding baseline threshold is calculated, and intelligent identification of pollution or blockage status is achieved based on preset judgment rules. Utilizing orthogonal eigenvalue decomposition to achieve multi-dimensional anomaly detection can not only quickly identify minute deviations but also distinguish the contributions of anomalies in different frequency bands, providing more detailed pollution or blockage indications. Compared with traditional methods that rely solely on a single frequency threshold for judgment, this scheme effectively reduces the false positive and false negative rates through joint judgment of multi-order coefficients, improves detection sensitivity and resolution, and better meets the needs of online monitoring under complex operating conditions.

[0094] 8. All smoothed impedance values, orthogonal expansion coefficients, deviations, and final judgment results for all measured frequencies are stored in the database in batches, with at least one hundred complete test records continuously retained to form time-series data. Batch archiving and historical data retention provide rich data support for subsequent trend analysis, fault warning, and maintenance decisions. Compared to methods that rely solely on single test results or simple log records, this solution establishes a traceable, multi-dimensional monitoring archive, facilitating long-term performance evaluation and a closed-loop experience feedback mechanism. Simultaneously, systematic storage provides a foundation for data mining and algorithm optimization, offering crucial digital support for pipeline network operation and management. Attached Figure Description

[0095] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0096] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0097] Example, refer to Figure 1 A data processing-based environmental monitoring method for sewer systems includes:

[0098] S1. Four-electrode probes are evenly spaced along the axial direction inside the target pipe section, and the parasitic impedance of the instrument and wires is measured by voltage and current tests under open and short circuit conditions; the zero offset compensation value and gain compensation coefficient are calculated based on the measured parasitic impedance data; at the same time, the temperature and humidity of the medium inside the pipe are maintained within the preset range.

[0099] S2. Set the measurement frequency range and discrete frequency points, output sine signals sequentially and set the amplitude, and wait for the predetermined steady-state time after each frequency switch;

[0100] S3. Repeatedly acquire voltage and current data at each measurement frequency point, remove abnormal readings below the preset threshold, re-acquire, calculate the average value and standard deviation of voltage and current, and then correct the original impedance according to the compensation parameters.

[0101] S4. Perform three-point linear smoothing on the impedance data after correction at each frequency point, and verify the consistency between adjacent data. If the consistency is not met, re-acquire and smooth; otherwise, continue with subsequent processing.

[0102] S5. Apply the discrete Gram-Schmidt orthogonalization algorithm to the smoothed impedance sequence to generate a set of orthogonal basis functions;

[0103] S6. Collect orthogonal expansion coefficients multiple times using a pollution-free baseline medium, calculate the average baseline value of each order, and determine the maximum deviation threshold.

[0104] S7. In actual testing, the real-time impedance data is expanded onto orthogonal basis functions, the deviation from the baseline threshold is calculated, and the pollution or blockage status is determined based on the magnitude of the deviation.

[0105] S8. Store each measurement frequency, smoothed impedance value, orthogonal expansion coefficient, deviation and judgment result into the database in batches, and ensure that at least the most recent 100 test records are retained.

[0106] By arranging electrode probes at equal intervals inside the pipe and measuring, calculating, and applying parasitic impedance compensation under no-load and short-circuit conditions, the measurement deviation problem caused by wire and instrument system errors was solved. Transient response errors were avoided by outputting sinusoidal signals at multiple discrete frequency points and waiting for steady state after frequency switching. Noise and weak signal interference were eliminated and data stability was ensured by repeatedly acquiring and removing abnormal data below a threshold, statistically calculating the average and standard deviation, and correcting the impedance according to compensation parameters. Sharp jumps and glitches were eliminated by performing three-point linear smoothing and consistency verification on the corrected data. The steps of using orthogonal basis functions and repeatedly calculating the average and maximum deviation threshold in a pollution-free baseline enable dynamic calibration of health status, solving the problem that fixed thresholds are difficult to adapt to changes in the field. By mapping monitoring data to an orthogonal space in real time and comparing the deviation with the baseline threshold, rapid and accurate determination of pollution and blockage status is achieved, avoiding the defects of traditional single-frequency or single-threshold judgments that are prone to misjudgment or omission. By batch storing complete detection data and retaining historical records, traceable detection archives are provided, meeting the needs of trend analysis and decision support during pipeline maintenance, thus outperforming existing technologies in terms of accuracy, robustness, and practicality.

[0107] The process involves arranging four electrode probes at equal intervals along the axial direction inside the target pipe section, and measuring the parasitic impedance of the instrument and conductors through voltage and current tests under open-circuit and short-circuit conditions; calculating the zero-offset compensation value and gain compensation coefficient based on the measured parasitic impedance data; and simultaneously maintaining the temperature and humidity of the medium inside the pipe within a preset range, specifically including:

[0108] Four probe electrodes are evenly spaced inside the target pipe section, with a probe spacing error. Simultaneously adjust the probe position to ensure the probe is coaxial with the tube wall. Ensure geometric consistency in measurements and reduce systematic errors;

[0109] The probe is suspended and does not contact the medium; the parasitic impedance of the measuring instrument and the wire is measured. ;in, The voltage measured under open-circuit conditions; The current measured under open-circuit conditions; The parasitic impedance measured during open-circuit calibration; obtain the open-circuit portion of the system's parasitic impedance;

[0110] Short-circuit probe; measure short-circuit parasitic impedance: ;in, The voltage was measured under short-circuit conditions; The current was measured under short-circuit conditions; The parasitic impedance measured during short-circuit calibration; obtain the short-circuit portion of the system's parasitic impedance;

[0111] Calculate zero offset compensation: ;in, Zero offset compensation value; establish parasitic impedance compensation benchmark;

[0112] Two standard resistors Connect probes to measure impedance. ;in, Corresponding standard resistor serial number; For access The impedance was measured at the specified time; the standard impedance measurement value was obtained;

[0113] Calculate the impedance measurement after removing parasitic offset. Remove parasitic shifts;

[0114] like If the result is negative, the standard resistor needs to be replaced; otherwise, continue; this is to prevent the denominator from being zero during gain correction.

[0115] Construct a linear model: ;

[0116] Solving , ;in, This is the gain compensation coefficient; The offset compensation coefficient is used to obtain the system gain and offset compensation coefficient.

[0117] Will , and Write it into the measurement system; enabling automatic correction in subsequent measurements;

[0118] Maintain the temperature of the medium inside the pipe humidity ;

[0119] Inject non-contamination baseline medium and let stand. Then, subsequent baseline data collection will be conducted to ensure environmental consistency and obtain reliable baseline data.

[0120] By employing steps such as equally spaced probe placement and ensuring coaxiality between the probe and the pipe wall, performing step-by-step measurements under both no-load and short-circuit conditions, and introducing standard resistance values ​​for multiple rounds of calibration, the measurement errors caused by probe position deviation, parasitic effects of the wires and instruments, and system linear mismatch were resolved. Furthermore, by injecting a pollution-free baseline medium into the pipe and allowing it to stand under constant temperature and humidity, the interference of environmental fluctuations on the accuracy of baseline data was eliminated. Finally, by directly writing the compensation coefficient into the measurement system and automatically applying it, real-time correction of all subsequent impedance measurements was achieved, avoiding the inconvenience of repeated manual calibration. Ultimately, this ensured high accuracy and consistency of impedance data in multi-frequency testing, laying a solid foundation for reliable judgment of pipeline contamination and blockage, and improving the repeatability and field applicability of the testing.

[0121] The process of setting the measurement frequency range and discrete frequency points, sequentially outputting sinusoidal signals and setting their amplitudes, and waiting for a predetermined steady-state time after each frequency switch specifically includes:

[0122] Set the measurement frequency range to Set the total number of discrete measurement frequency points to ;in, Minimum measurement frequency; Maximum measurement frequency;

[0123] Impact of value: Resolution: , The larger, The smaller the value, the higher the spectral resolution, allowing for a more precise capture of impedance variations with frequency. Measurement time: Each additional point requires steady-state waiting and multiple sampling times; the total time is approximately proportional to the frequency. Data volume and computational burden: greater This brings in more data, increasing the burden on online processing, storage, and subsequent orthogonalization calculations. Recommended value: If the goal is to quickly detect and focus on overall trends, consider using... If fine-grained feature analysis is required, you can take... The maximum scanning rate of the instrument, the allowable measurement time on site, and the required spectral resolution are all considered together.

[0124] Calculate frequency spacing Generate discrete test frequency sequences;

[0125] right :

[0126] Set the sine wave output frequency: ;in, Number the frequency points; For the first There are several frequency points; among them,

[0127] Set peak-peak amplitude value ;

[0128] Wait after switching frequencies until steady state; where, This is the steady-state waiting time after frequency switching; ensuring that measurements at each frequency point are performed under steady-state conditions.

[0129] This is the time it takes for the system (probe, circuitry, fluid environment) to reach steady state after each switch to a new frequency. Impact of this value: Too short, and it may capture transient responses, resulting in "transitional" errors in the measurement; too long, while ensuring steady-state reliability, significantly increases the total measurement time. Recommended value: Based on the response time constants of the LCR meter and probe system. Generally take If the system response time is unknown, it can be determined experimentally: after introducing a step frequency change, monitor the duration for which the voltage / current reaches and remains in the new steady state; common values ​​are approximately... .

[0130] By precisely dividing the measurement frequency points and implementing steady-state waiting after each frequency switch, the problems of transient response error and insufficient spectral resolution caused by excessively rapid frequency switching are solved. By optimizing the number of frequency points in combination with instrument performance and on-site time constraints, the risks of overly coarse measurements or excessively long measurement times are avoided. By performing multi-frequency sampling under steady-state conditions, it is ensured that the obtained impedance data truly reflects the steady-state characteristics of the medium inside the tube at different frequencies. These steps together improve the accuracy and reliability of the spectral data, providing high-quality input for subsequent smoothing, orthogonal expansion, and accurate determination.

[0131] The process of repeatedly acquiring voltage and current data at each measurement frequency point, removing abnormal readings below a preset threshold, re-acquiring data, calculating the average value and standard deviation of voltage and current, and then correcting the original impedance according to compensation parameters specifically includes:

[0132] At each frequency point Location, collection Group data , ;in, The number of times the measurement is repeated at each frequency point; For the first The voltage was measured once; For the first The current was measured multiple times to improve data stability.

[0133] Requirements for each reading , If the conditions are not met, discard the measurement and resample; discard data with extremely low signal to avoid distortion.

[0134] in, This is the minimum acceptable voltage amplitude; This is the minimum acceptable current amplitude;

[0135] and These are the lower threshold values ​​for voltage and current measurements, used to filter out excessively weak signals and avoid distortion caused by noise amplification or division by near-zero values. The magnitude of these values ​​has an impact: if the threshold is too low, noise or drift data is allowed to enter, increasing the error; if the threshold is too high, it may filter out true small-amplitude signals, leading to missing or biased data. Recommended values: depending on the instrument's noise level and resolution, they are typically set to approximately 3 to 5 times the standard deviation of the signal noise (obtained when measuring no-load noise); for example, if the instrument's voltage noise... Then we can set Similarly, if current noise ,but .

[0136] Calculate the average: , ;

[0137] Calculate the standard deviation:

[0138] ; ;

[0139] in, For the first The average voltage at the point; For the first The average current at the point; For the first The sample standard deviation of point voltage; For the first The sample standard deviation of the point current;

[0140] Require: , Otherwise, data will be collected again; among which, The threshold for relative stability of voltage readings; This serves as a threshold for the relative stability of the current reading; ensuring the stability of the measurement signal.

[0141] and These are the relative standard deviation thresholds for voltage / current readings, used to determine measurement stability: , The impact of the threshold value: If the threshold is too loose, measurements with poor stability will be accepted, resulting in high data noise; if the threshold is too tight, even slight fluctuations will require resampling, increasing processing time. Recommended value: Generally, use [value missing]. For instruments with higher performance or in more stable environments, additional settings can be configured. If the measurement environment fluctuates greatly, the restrictions can be appropriately relaxed to... .

[0142] Calculate the original impedance: ;

[0143] Apply offset and gain compensation: ;in, For the corrected first Secondary impedance; after eliminating parasitic and gain errors, the true impedance is obtained;

[0144] Calculate the first Point average impedance To reduce noise, the average impedance at each frequency point is calculated.

[0145] By repeatedly acquiring readings at the same frequency and removing abnormal data below a threshold, the problem of data distortion caused by weak signal amplification and noise interference is solved. By calculating the average and standard deviation and using them to assess data fluctuations and decide whether to re-acquire data, the impact of transient drift and occasional interference on the measurement results is avoided. By applying compensation parameters to the impedance correction step, parasitic offsets are eliminated, improving the accuracy of the measured values. These measures improve the stability and reliability of impedance data, provide reliable input for subsequent smoothing and feature extraction, and reduce misjudgments and interference from data fluctuations on the detection results.

[0146] The impedance data after correction at each frequency point is then subjected to three-point linear smoothing, and the consistency between adjacent data points is verified. If the consistency is not met, the data is re-acquired and smoothed; otherwise, subsequent processing continues. Specifically, this includes:

[0147] Calculate the first Impedance after point smoothing:

[0148] Remove sharp noise and smooth the impedance curve;

[0149] Jump check occurs: If not satisfied, only for that... Return to step S3 to resample and resmooth; otherwise, continue; ensure that the smoothed curve is continuous and without abnormal jumps.

[0150] By combining three-point linear smoothing with consistency checks and abnormal resampling, this method solves the problem that traditional moving averages cannot completely remove sharp noise and spurious jumps. This method can not only smooth the impedance curve, but also eliminate occasional jumps through automatic feedback resampling, ensuring the continuity and structural integrity of the curve. It reduces the impact of spurious interference on subsequent feature extraction, improves the accuracy of smoothed data, provides a more reliable input for orthogonal decomposition processing, and thus improves the robustness of pollution and blockage determination.

[0151] The process of applying the discrete Gram-Schmidt orthogonalization algorithm to the smoothed impedance sequence to generate a set of orthogonal basis functions specifically includes:

[0152] For order 0: Construct a zeroth-order intermediate function ;

[0153] Zero-order normalization factor Zero-order normalized orthogonal basis ;in, For the first A normalized frequency, ; For normalized frequency spacing, Construct a constant basis;

[0154] For order :

[0155] Calculate the first The first basis pair Projection coefficients of the basis , ;

[0156] Construct the first orthogonalization of intermediate functions ;

[0157] Calculate the first normalization factor ;

[0158] Calculate the first Normalized orthogonal basis ;

[0159] Generate a mutually orthogonal and normalized polynomial basis;

[0160] S510, Calculate the elements of the inner product matrix:

[0161] For all ,calculate ;in, For the first basis functions With the basis functions The inner product at all discrete points;

[0162] S520. Determine orthogonality: Assume tolerance. If for any , or If the orthogonality is not satisfied, then it is considered that the orthogonality is not satisfied.

[0163] Tolerance It is the maximum allowable absolute value of the inner product for orthogonality determination, requiring... Only then are the basis functions considered orthogonal. Impact of value selection: tolerance. If the value is too large, there will be significant overlap between basis functions, which will worsen orthogonality and reduce the accuracy of subsequent expansion; tolerance If the value is too small, numerical iteration may fail to meet the requirements, easily leading to multiple "re-orthogonalization" or "failure" branches, increasing the computational burden. Value recommendation: For regular double-precision floating-point operations, This is sufficient to guarantee that numerical orthogonality and simultaneous iteration convergence are achieved; if high precision is used or extremely high orthogonality is required, alternative methods can be selected. .

[0164] S530. If orthogonality is not satisfied, then perform reorthogonalization:

[0165] S531, Correction: For each Execute in sequence:

[0166] ;

[0167] S532, Renormalization: , ;

[0168] Repeat steps S510 to S530 for a maximum of [number] iterations. Second-rate;

[0169] This represents the maximum allowed number of iterations for reorthogonalization during iterative verification. The impact of this value: If the value is too small, and the problem is abandoned after one correction that fails to meet the target, it may prematurely enter the "downgrade" or "increase" phase. "Branches may affect efficiency or require frequent reruns of the entire process." If the value is too large, it will waste a lot of computing resources when convergence is difficult, leading to performance degradation. Recommended value: Generally set to... This allows the basis functions to converge rapidly in most double-precision environments; if environmental noise or large deviation steps are known, the value can be appropriately increased to [value missing]. If continuous If the second-order orthogonality still fails, then "reducing the order" or "increasing the order" should be used instead. "and other safety net strategies."

[0170] If still or If orthogonalization fails, you can choose one of the following:

[0171] Reduce the order: Let End of the first Order calculation;

[0172] Increase After selecting more frequencies, return to step S2;

[0173] Adjusting tolerance : Tolerance and tolerance;

[0174] When all At that time, it is considered the first Orthogonalization successful, proceed to the next one. .

[0175] By constructing basis functions, verifying inner products, and performing automatic reorthogonal iterations, this algorithm solves the problem of numerical instability or insufficient orthogonality when directly applying traditional orthogonalization methods to discrete noise data. The algorithm can adaptively adjust to the actual distribution of measurement points, ensuring that the generated basis functions are both strictly orthogonal and maintain normalization characteristics. It provides a high-precision and robust feature decomposition basis for mapping complex impedance sequences to orthogonal space, enhances signal feature extraction capabilities, and further improves the accuracy and reliability of subsequent deviation determination.

[0176] The process of repeatedly collecting orthogonal expansion coefficients using a pollution-free baseline medium, calculating the average baseline values ​​for each order, and determining the maximum deviation threshold specifically includes:

[0177] No contaminant media standing ;

[0178] Loop collection :

[0179] S601, Repeat steps S2 to S4 to obtain Obtain smoothed impedance data in a "pollution-free" state;

[0180] S602, Calculate the first Second baseline order expansion quantity ;in, This is the sequence number of the current baseline acquisition, used for each measurement. Perform numbering and accumulation; The baseline measurement count represents the number of times a complete data collection was repeated under "contamination-free" conditions, in order to statistically determine the stability threshold.

[0181] Calculate the first Baseline average ;

[0182] Construct the first Maximum deviation threshold Determine the average value and maximum deviation threshold for each level of expansion.

[0183] By performing multiple measurements and orthogonal expansions under pollution-free conditions and calculating the average and maximum deviation thresholds for each order of coefficients, this method solves the problem that baseline calibration relying solely on a single measurement is susceptible to occasional errors or minor environmental changes. It can determine more scientific thresholds based on the distribution and fluctuation range of baseline coefficients collected on-site, improving the reliability of anomaly detection. This provides a precise and dynamic reference standard for subsequent real-time deviation detection, reducing the risk of false alarms or missed alarms due to unreasonable thresholds, and enhancing the system's adaptability and on-site applicability.

[0184] In actual detection, the real-time impedance data is expanded onto orthogonal basis functions, the deviation from the baseline threshold is calculated, and the contamination or blockage status is determined based on the magnitude of the deviation. Specifically, this includes:

[0185] Calculate the real-time first order expansion quantity Map the real-time smoothed impedance onto the orthogonal basis;

[0186] Constructing the real-time first Order deviation ; Calculate the deviation between real-time and baseline;

[0187] If any make If the threshold is not met, it is determined to be contaminated or blocked; otherwise, it is determined to be normal; anomaly detection is achieved based on thresholds.

[0188] By performing orthogonal expansion of smoothed impedance data in real time and comparing the deviation with multi-order baseline thresholds, this method solves the problems of lag and misjudgment that are common in traditional methods that require manual analysis or rely on a single indicator. This method can perform multi-dimensional collaborative judgment on characteristic coefficients of each order, quickly capture minute abnormal signals, and improve detection sensitivity and accuracy. It realizes online and automated rapid pollution and blockage identification, meets the application requirements of real-time monitoring and immediate early warning, and provides reliable protection for pipeline operation and maintenance.

[0189] The process of storing each measurement frequency point, smoothed impedance value, orthogonal expansion coefficient, deviation, and judgment result into the database in batches, and ensuring that at least the most recent one hundred test records are retained, specifically includes:

[0190] frequency point set Smoothing impedance , expansion amount , deviation amount The results and judgments are stored in the database in batches; complete test data and results are saved.

[0191] It also retains no fewer than 100 test records; and supports subsequent trend analysis and source tracing.

[0192] By batch storing all key data and retaining a large volume of historical records, this method overcomes the limitations of traditional single-inspection methods, which are prone to omissions or lack of traceability. It enables systematic management and long-term archiving of inspection data, supporting subsequent fault retrospection, trend analysis, and intelligent operation and maintenance decision-making. It also improves data traceability and availability, providing a solid data foundation for pipeline network operation status monitoring, maintenance planning, and big data analysis.

[0193] Example 2: A sewer environmental monitoring method based on data processing, comprising:

[0194] Example Environment and Parameter Settings: Pipeline: 200mm diameter ring pipe section, clean and free of contaminants on the inner wall. Environment: Temperature humidity Calibrate the resistor: Frequency range discrete points Peak-to-peak voltage Number of samples Voltage and current thresholds: Stability threshold Smoothness coherence threshold: Steady-state waiting Orthogonal basis order Number of baseline measurements ;

[0195] Normalized frequency and interval: ,

[0196] , ;

[0197] S1. Precision calibration:

[0198] Parasitic impedance measured in open circuit: ;

[0199] Parasitic impedance measured during short circuit: ;

[0200] Zero offset: ;

[0201] Access , measured ;

[0202] ;

[0203] Access , measured ;

[0204] ;

[0205] Calculate the correction factor:

[0206] ;

[0207] .

[0208] S2. Frequency Point Allocation and Steady-State Preparation: Frequency Point Generation And output; pending Signals are then acquired to ensure steady-state operation.

[0209] S3. Multiple measurements, inspections, and calibrations:

[0210] With the third frequency For example:

[0211] Three sets of data were collected:

[0212] , ;

[0213] Amplitude lower limit test: minimum voltage Minimum current ;

[0214] Stability test:

[0215] ,

[0216] , ;

[0217] Original impedance and correction: ;

[0218] ;

[0219] Similarly, other frequency points are processed to obtain... .

[0220] S4, Three-point linear smoothing:

[0221] According to the formula, , , , ,

[0222] ;

[0223] Maximum jump If an anomaly is detected, the test can be repeated; in this example, it is considered acceptable.

[0224] S5. Discrete Gram-Schmidt orthogonalization:

[0225] ;

[0226] Order 0: ;

[0227] Order 1:

[0228] ;

[0229] , ,

[0230] .

[0231] S6. Baseline Acquisition and Threshold Calculation:

[0232] Repeat the baseline measurement three times to obtain the expansion amount: , ;

[0233] Calculate the average and threshold: ; .

[0234] S7. Real-time Deployment and Judgment:

[0235] New round of measurements Same as above, calculate:

[0236] , ;

[0237] , ;

[0238] because The system determines whether the pollution or blockage is "contaminated / blocked".

[0239] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0240] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A sewer environmental monitoring method based on data processing, characterized in that, include: S1. Four electrode probes are arranged at equal intervals along the axial direction inside the target pipe section, and the parasitic impedance of the instrument and wires is measured by voltage and current tests under open and short circuit conditions. The zero offset compensation value and gain compensation coefficient are calculated based on the measured parasitic impedance data; at the same time, the temperature and humidity of the medium inside the pipe are maintained within the preset range. S2. Set the measurement frequency range and discrete frequency points, output sine signals sequentially and set the amplitude, and wait for the predetermined steady-state time after each frequency switch; S3. Repeatedly acquire voltage and current data at each measurement frequency point, remove abnormal readings below the preset threshold, re-acquire, calculate the average value and standard deviation of voltage and current, and then correct the original impedance according to the compensation parameters. S4. Perform three-point linear smoothing on the impedance data after correction at each frequency point, and verify the consistency between adjacent data. If the consistency is not met, re-acquire and smooth; otherwise, continue with subsequent processing. S5. Apply the discrete Gram-Schmidt orthogonalization algorithm to the smoothed impedance sequence to generate a set of orthogonal basis functions; S6. Collect orthogonal expansion coefficients multiple times using a pollution-free baseline medium, calculate the average baseline value of each order, and determine the maximum deviation threshold. S7. In actual testing, the real-time impedance data is expanded onto orthogonal basis functions, the deviation from the baseline threshold is calculated, and the pollution or blockage status is determined based on the magnitude of the deviation. S8. Store each measurement frequency, smoothed impedance value, orthogonal expansion coefficient, deviation and judgment result into the database in batches, and ensure that at least the most recent 100 test records are retained.

2. The sewer environmental protection monitoring method based on data processing according to claim 1, characterized in that, The method involves arranging four electrode probes at equal intervals along the axial direction inside the target pipe section, and measuring the parasitic impedance of the instrument and wires by testing the voltage and current under open and short circuit conditions. The zero-offset compensation value and gain compensation coefficient are calculated based on the measured parasitic impedance data; simultaneously, the temperature and humidity of the medium inside the pipe are maintained within a preset range, specifically including: Four probe electrodes are evenly spaced inside the target pipe section, with a probe spacing error. Simultaneously adjust the probe position to ensure the probe is coaxial with the tube wall. ; The probe is suspended and does not contact the medium; the parasitic impedance of the measuring instrument and the wire is measured. ;in, The voltage measured under open-circuit conditions; The current measured under open-circuit conditions; This is the parasitic impedance measured during open-circuit calibration; Short-circuit probe; measure short-circuit parasitic impedance: ;in, The voltage was measured under short-circuit conditions; The current was measured under short-circuit conditions; This is the parasitic impedance measured during short-circuit calibration; Calculate zero offset compensation: ;in, This is the zero offset compensation value; Two standard resistors Connect probes to measure impedance. ;in, Corresponding standard resistor serial number; For access The impedance was measured at that time; Calculate the impedance measurement after removing parasitic offset. ; like If so, the standard resistor needs to be replaced; otherwise, continue. Construct a linear model: ; Solving , ;in, This is the gain compensation coefficient; This is the offset compensation coefficient; Will , and Write into the measurement system; Maintain the temperature of the medium inside the pipe humidity ; Inject non-contamination baseline medium and let stand. Then, subsequent baseline data collection will be conducted.

3. The sewer environmental protection monitoring method based on data processing according to claim 2, characterized in that, The process of setting the measurement frequency range and discrete frequency points, sequentially outputting sinusoidal signals and setting their amplitudes, and waiting for a predetermined steady-state time after each frequency switch specifically includes: Set the measurement frequency range to Set the total number of discrete measurement frequency points to ;in, Minimum measurement frequency; Maximum measurement frequency; Calculate frequency spacing ; right : Set the sine wave output frequency: ;in, Number the frequency points; For the first There are several frequency points; among them, Set peak-peak amplitude value ; Wait after switching frequencies until steady state; where, This represents the steady-state waiting time after frequency switching.

4. The sewer environmental protection monitoring method based on data processing according to claim 3, characterized in that, The process of repeatedly acquiring voltage and current data at each measurement frequency point, removing abnormal readings below a preset threshold, re-acquiring data, calculating the average value and standard deviation of voltage and current, and then correcting the original impedance according to compensation parameters specifically includes: At each frequency point Location, collection Group data , ;in, The number of times the measurement is repeated at each frequency point; For the first The voltage was measured once; For the first The current was measured once; Requirements for each reading , If the conditions are not met, discard the measurement and take a new one. in, This is the minimum acceptable voltage amplitude; This represents the minimum acceptable current amplitude. Calculate the average: , ; Calculate the standard deviation: ； ； in, For the first The average voltage at the point; For the first The average current at the point; For the first The sample standard deviation of point voltage; For the first The sample standard deviation of the point current; Require: , Otherwise, data will be collected again; among which, The threshold for relative stability of voltage readings; The threshold for relative stability of current readings; Calculate the original impedance: ; Apply offset and gain compensation: ;in, For the corrected first Secondary impedance; Calculate the first Point average impedance .

5. The sewer environmental protection monitoring method based on data processing according to claim 4, characterized in that, The impedance data after correction at each frequency point is then subjected to three-point linear smoothing, and the consistency between adjacent data points is verified. If the consistency is not met, the data is re-acquired and smoothed; otherwise, subsequent processing continues. Specifically, this includes: Calculate the first Impedance after point smoothing: ； Jump check occurs: If not satisfied, only for that... Return to step S3 to resample and resmooth; otherwise, continue.

6. The sewer environmental protection monitoring method based on data processing according to claim 5, characterized in that, The process of applying the discrete Gram-Schmidt orthogonalization algorithm to the smoothed impedance sequence to generate a set of orthogonal basis functions specifically includes: For order 0: Construct a zeroth-order intermediate function ; Zero-order normalization factor Zero-order normalized orthogonal basis ;in, For the first A normalized frequency, ; For normalized frequency spacing, ; For order : Calculate the first The first basis pair Projection coefficients of the basis , ; Construct the first orthogonalization of intermediate functions ; Calculate the first normalization factor ; Calculate the first Normalized orthogonal basis ; S510, Calculate the elements of the inner product matrix: For all ,calculate ;in, For the first order basis functions With the order basis functions The inner product at all discrete points; S520. Determine orthogonality: Assume tolerance. If for any , or If the orthogonality is not satisfied, then it is considered that the orthogonality is not satisfied. S530. If orthogonality is not satisfied, then perform reorthogonalization: S531, Correction: For each Execute in sequence: ； S532, Renormalization: , ; Repeat steps S510 to S530 for a maximum of [number] iterations. Second-rate; If still or If orthogonalization fails, you can choose one of the following: Reduce the order: Let End of the first Order calculation; Increase After selecting more frequencies, return to step S2; Adjusting tolerance : Tolerance and tolerance; When all At that time, it is considered the first Orthogonalization successful, proceed to the next step. .

7. The sewer environmental protection monitoring method based on data processing according to claim 6, characterized in that, The process of repeatedly collecting orthogonal expansion coefficients using a pollution-free baseline medium, calculating the average baseline values ​​for each order, and determining the maximum deviation threshold specifically includes: Pollution-free media standing ; Loop collection : S601, Repeat steps S2 to S4 to obtain ; S602, Calculate the first Second baseline order expansion quantity ;in, This is the sequence number of the current baseline acquisition; Number of baseline measurements; Calculate the first Baseline average ; Construct the first Maximum deviation threshold .

8. The sewer environmental protection monitoring method based on data processing according to claim 7, characterized in that, In actual detection, the real-time impedance data is expanded onto orthogonal basis functions, the deviation from the baseline threshold is calculated, and the contamination or blockage status is determined based on the magnitude of the deviation. Specifically, this includes: Calculate the real-time first order expansion quantity ; Constructing the real-time first Order deviation ; If any make If it is positive, it is determined to be either contaminated or blocked; otherwise, it is determined to be normal.

9. The sewer environmental protection monitoring method based on data processing according to claim 8, characterized in that, The process of storing each measurement frequency point, smoothed impedance value, orthogonal expansion coefficient, deviation, and judgment result into the database in batches, and ensuring that at least the most recent one hundred test records are retained, specifically includes: frequency point set Smoothing impedance , expansion amount , deviation amount The judgment results are stored in the database in batches; At the same time, retain no fewer than 100 test records.

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