A tracer signal based underground pipeline detection method and system

Abstract

This invention relates to the field of underground pipeline location and detection technology, and discloses a method and system for underground pipeline detection based on tracer signals. The method includes: associating a tracer with an underground pipeline; acquiring underground pipeline tracer signals to obtain a target pipeline detection dataset; obtaining the target pipeline detection dataset; comparing and verifying the target pipeline detection dataset with a special identification code to obtain a verification result; acquiring the field strength and phase information of the verified tracer signals to obtain target pipeline information data; obtaining comprehensive positioning data of the target pipeline's horizontal position and burial depth based on the target pipeline information data; and comprehensively analyzing the target pipeline's identity attributes and the comprehensive positioning data to obtain the target pipeline detection result. This invention can thus integrate multi-source detection tracer signals, deeply explore the correlation between pipeline identity attributes and target pipeline location and detection results, and accurately capture special identification codes.

Description

Technical Field

[0001] This invention relates to the field of underground pipeline location and detection technology, and more specifically, to a method and system for detecting underground pipelines based on tracer signals. Background Technology

[0002] With the deepening development of urban underground space and the construction of smart cities, the demand for accurate identification and location of various types of pipelines, such as power, communication, gas, and water supply, is becoming increasingly urgent, especially in complex underground pipe network environments. Although traditional underground pipeline detection methods have made some progress in ground-penetrating radar imaging analysis, electromagnetic induction detection, and pipeline location technology, they have not yet systematically solved core problems such as the difficulty of detecting non-metallic pipelines, signal interference identification in multi-pipeline environments, and the coordinated matching of target pipeline identity attributes and location information. They are also unable to meet the generalized needs of different geological conditions, different pipeline materials, and different detection accuracy requirements.

[0003] Furthermore, existing technologies have significant shortcomings in signal identification in multi-pipeline intersection environments. They lack the ability to generate special identification codes based on the target pipeline's identity attributes, making it impossible to achieve precise directional reception and preliminary screening of tracer signals. This results in difficulty in accurately distinguishing different pipelines in complex underground environments, and makes it impossible to establish a reliable pipeline identity verification system. In obtaining the horizontal position and burial depth of target pipelines, traditional methods struggle to achieve high-precision measurements and cannot effectively integrate and analyze the target pipeline's identity attributes with location data, leading to insufficient accuracy and reliability of the final detection results.

[0004] Therefore, the underground pipeline detection method based on tracer signals in this application organically combines active radio frequency tracing technology with ground detection equipment through a unified technical framework. This coordinates the detection signals to achieve accurate identification of all types of pipelines, while optimizing the configuration of the positioning algorithm to improve detection accuracy and reduce system complexity. This allows for a better reflection of the physical characteristics of different pipeline materials and detection requirements, achieving a deep integration of detection technology with the complexity of the underground environment, and providing strong support for the efficient and accurate operation of modern urban underground infrastructure management.

[0005] Therefore, how to integrate multi-source detection tracer signals, deeply explore the correlation between pipeline identity attributes and target pipeline location detection results, and accurately capture the characteristics of tracer signals with special identification codes has become an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a method and system for underground pipeline detection based on tracer signals, which solves the technical problems in the prior art of difficulty in integrating multi-source detection tracer signals and difficulty in deeply mining the correlation between pipeline identity attributes and target pipeline location detection results, thus making it impossible to accurately capture special identification codes.

[0007] This invention provides a method and system for detecting underground pipelines based on tracer signals, comprising:

[0008] Firstly, a method for detecting underground pipelines based on tracer signals includes:

[0009] Associate the tracer with underground pipelines;

[0010] The tracer radiates a tracer signal carrying a special identification code; the special identification code is generated based on the identity attributes of the target pipeline.

[0011] Collect underground pipeline tracing signals to obtain a target pipeline detection dataset;

[0012] The pipeline detection dataset includes the frequency characteristics and coding characteristics of the tracer signal; the tracer signal is received by a ground detection device; the ground detection device performs directional reception and preliminary screening of the tracer signal based on the frequency characteristics in the pipeline detection dataset; based on the preliminary screened tracer signal, the tracer signal is demodulated and decoded to obtain the target pipeline detection dataset;

[0013] The target pipeline detection dataset is compared and verified with a special identification code to obtain the verification result;

[0014] In the verification result, when the identification code matches successfully, the target pipeline identity attribute is obtained and the location is detected; based on the verification result, the field strength and phase information of the verified tracer signal are obtained to obtain the target pipeline information data;

[0015] The horizontal position and burial depth of the target pipeline are obtained based on the target pipeline information data;

[0016] The target pipeline's identity attributes are comprehensively analyzed along with its horizontal location and burial depth to obtain the target pipeline detection results.

[0017] In a preferred embodiment of the present invention, the tracer radiates a tracer signal carrying a special identification code, including:

[0018] The tracer actively transmits the tracer signal, or the tracer generates the tracer signal by backscattering and modulating the excitation signal after receiving the excitation signal from the ground detection equipment.

[0019] The tracer includes an encoding generator and a modulation and transmission circuit. The encoding generator generates a special identification code based on the identity attributes of the target pipeline. The modulation and transmission circuit modulates the code onto a radio frequency carrier and radiates it through an antenna.

[0020] The ground detection equipment includes a receiving antenna array and a signal processing unit. The antenna array receives the tracer signal, and the signal processing unit demodulates and decodes the received tracer signal.

[0021] As a preferred embodiment of the present invention, a signal strength measurement range is set according to the pipeline detection dataset; the distance between the peak and valley values ​​of the signal strength is measured by the ground detection equipment to obtain a distance measurement value;

[0022] The distance measurement value is compared with the signal strength measurement range to determine whether the distance measurement value is within the signal strength measurement range; when the distance measurement value is within the signal strength measurement range, a valid distance measurement result is obtained.

[0023] Based on the distance measurement results and the preset depth calculation standard, the distance is matched with the preset depth calculation standard to obtain the matching result;

[0024] The matching result indicates whether the distance measurement value meets the depth calculation requirements; the calculation requirements include: the measured distance value is within a physically reasonable range, the signal strength change gradient meets the underground propagation law, the measurement accuracy meets engineering application standards, and the influence of ground interference and multipath effects is eliminated.

[0025] The distance measurement value is converted into burial depth according to the pre-calibration algorithm to obtain the calculation result; the calculation result represents the target pipeline burial depth value calculated based on the distance data;

[0026] The pre-calibration algorithm is as follows: To obtain the burial depth; among which, Indicates the burial depth; Indicates the distance measurement value; Indicates equipment constants;

[0027] Based on the calculation results and the matching results, the burial depth of the target pipeline is determined; when the matching result is valid and the calculation result is within the measurement range, the burial depth value is obtained.

[0028] It also includes using a receiving antenna array to compare the signal strength or phase difference received at different antenna positions and calculate the horizontal angle of the signal source; by guiding the operator to move the detection device left and right, when the signal strength reaches the maximum value or the phase difference between the antennas is zero, the planar position of the pipeline is determined directly below the detection device; then at this planar position point, the distance between the peak point and the valley point is measured using the law of signal field strength change with distance, and the burial depth of the pipeline is calculated through a pre-calibration algorithm;

[0029] In the horizontal positioning stage, a multi-antenna differential measurement method is used. By comparing the strength difference and phase difference of the signals received by the left and right antennas or the front and rear antennas, when the difference approaches zero, it indicates that the central axis of the detection equipment is vertically aligned with the central axis of the pipeline. In the vertical positioning stage, based on the horizontal positioning, the position of the detection equipment remains unchanged. By changing the measurement height or utilizing the penetration characteristics of different frequency signals, a curve of signal strength changing with depth is obtained. The burial depth is calculated based on the peak and valley characteristic points of this curve.

[0030] As a preferred embodiment of the present invention, based on the field strength distribution characteristics of the tracer signal, by: To obtain the peak signal field strength;

[0031] in, This represents the peak signal strength of the j-th tracer signal; Indicates the number of tracer signals; This represents the signal field strength data of the current tracer signal;

[0032] Based on the peak value of the signal field strength, by: To obtain depth gradient data;

[0033] in, Represents depth gradient data; This represents the signal field strength data of the first k tracer signals, where k represents the measurement interval of the tracer signals;

[0034] Based on the aforementioned depth gradient data, through: To obtain the calculated burial depth value; among which, This represents the calculated burial depth. Indicates the calibration coefficient of the detection equipment; Indicates the depth correction factor;

[0035] Set the effective range of the signal field strength ;in, Indicates the valley value of the effective range of signal field strength; The peak value of the effective range of the signal field strength is indicated; based on the calculated burial depth, the deviation change of the tracer signal measurement is set. ;

[0036] when hour, ;when hour, ;when hour, This is used to determine whether the signal field strength meets the measurement requirements, in order to measure the burial depth data of the target pipeline.

[0037] As a preferred embodiment of the present invention, the target pipeline detection dataset is compared and verified with a special identification code to obtain a verification result, including:

[0038] Based on the pipeline detection dataset, the average pipeline identification value is obtained;

[0039] The pipeline attribute code data is summed and then divided by the number of pipeline attribute codes to obtain the average identification value of the target pipeline; the pipeline attribute code includes basic attribute data such as pipeline type identifier, material code, and specification information.

[0040] Based on the pipeline identification average, obtain the pipeline attribute change gradient;

[0041] The pipeline identification mean is multiplied by the difference between the pipeline coding data at the current identification time and the previous identification time, and then divided by the coding identification time interval to obtain the gradient of pipeline attribute change in the time dimension.

[0042] Based on the gradient of pipeline attribute changes, the pipeline identification strength is obtained;

[0043] The absolute value of the pipeline attribute change gradient is multiplied by the product of the gradient and the attribute recognition frequency coefficient to obtain a strength index characterizing the reliability of pipeline identification; the higher the identification strength, the better the accuracy of the coding identification.

[0044] Based on the pipeline attribute change gradient and the pipeline identity recognition mean, the special identification code dataset is formed; the special identification code dataset contains one or more of the following in the target pipeline identity: unique identifier of the pipeline, pipeline type, and property unit information;

[0045] An upper and lower threshold value is set for the pipeline identification strength range to construct an effective range interval for pipeline identification strength. The real-time monitored pipeline identification strength is compared and analyzed with the effective range to obtain deviation analysis results. A dataset of identification strength deviation values ​​is established based on the deviation analysis results. First, an evaluation standard for pipeline identification strength is established. The effective working range of identification strength is defined by setting upper and lower threshold values ​​to ensure that the identification process will not produce misjudgments due to excessive strength or failure due to insufficient strength. The upper threshold value represents the maximum acceptable value of identification strength to prevent noise interference or system saturation caused by excessively strong signals, while the lower threshold value represents the minimum acceptable value of identification strength to ensure that the signal is strong enough to achieve reliable identification. Based on this, the actual measured value of pipeline identification strength is monitored in real time and compared and analyzed with the set effective range interval to establish a dedicated dataset for storing and managing identification strength deviation values. This dataset records the degree and direction of the identification strength deviating from the effective range in each measurement, providing data support for subsequent identification accuracy optimization and system calibration.

[0046] Specifically, when the pipeline identification strength exceeds the upper limit threshold, the difference between the pipeline identification strength and the upper limit value is obtained as a positive deviation; when the pipeline identification strength is lower than the lower limit threshold, the difference between the pipeline identification strength and the lower limit threshold is obtained as a negative deviation; when the identification strength is within the effective range, the deviation value is set to zero.

[0047] The deviation analysis results are used to verify whether the pipeline identification strength data matches the tracer encoding data, in order to ensure the accuracy of target detection and avoid misidentification.

[0048] Based on the consistency of the verification matching results, a coding matching confidence evaluation mechanism is established. By quantifying the correlation coefficient between pipeline identification strength and tracer coding data, the reliability level of coding verification is obtained.

[0049] The higher the pipeline identification strength, the stronger the coding verification accuracy, ensuring a precise match between the tracer code and the target pipeline identification information.

[0050] As a preferred embodiment of the present invention, verifying whether the pipeline identification strength data and the tracer encoding data match through deviation analysis results includes:

[0051] When the deviation value is zero, it indicates that the pipeline identification strength is within the effective range and the tracer encoding data is completely matched with the target pipeline identification information.

[0052] When there is a positive or negative deviation, the ratio of the absolute value of the deviation to the upper and lower limits of the effective range is used to obtain the consistency deviation index. When the consistency deviation index is less than the preset tolerance, it is judged as basically consistent. When the consistency deviation index exceeds the tolerance, it is judged as inconsistent and re-encoding and recognition are required.

[0053] As a preferred embodiment of the present invention, based on the successful code matching verification result, the complete code information transmitted by the tracer is extracted and parsed to obtain the identity of the target pipeline; including the pipeline's unique identifier, pipeline type code, material properties, specification parameters and other identity attribute data, forming a complete identity file of the target pipeline;

[0054] Based on the target pipeline identity, the radio frequency transceiver function of the ground detection equipment is used to continuously receive radio frequency signals from the identification tracer to obtain verified tracer signal field strength data and phase information data.

[0055] The acquired tracer signal field strength and phase information are processed and decoded to obtain the processing and decoding results;

[0056] The received radio frequency signal is amplified with low noise and down-converted, and field strength variation characteristics and phase difference characteristics are extracted to form a target pipeline information dataset containing signal strength distribution, phase difference distribution and frequency response characteristics.

[0057] Based on the target pipeline information data, perform horizontal positioning of the target pipeline;

[0058] Among them, multi-antenna array differential measurement technology is used to compare the signal strength difference and phase difference received at different antenna positions. When the signal strength reaches the peak value and the phase difference between the antennas is equal to zero, the accurate horizontal position coordinates of the pipeline are determined to be directly below the detection device.

[0059] Based on accurate horizontal coordinates, the target pipeline burial depth is determined:

[0060] Among them, by utilizing the physical law of signal field strength changing with distance, the peak and valley points of signal strength are measured in the vertical direction, and the distance measurement value is converted into a burial depth value through a pre-calibrated depth calculation algorithm;

[0061] Based on horizontal location positioning and burial depth acquisition, comprehensive positioning and detection results of the target pipeline are obtained.

[0062] As a preferred embodiment of the present invention, the target pipeline identity attributes and integrated positioning detection results are comprehensively analyzed to construct a correlation verification mechanism between identity attributes and positioning data;

[0063] Among them, the identification of the target pipeline is verified by cross-validating the target pipeline's identity attribute information with the horizontal position coordinates and burial depth data, and by comparing and analyzing the signal characteristic parameters of the target pipeline's type attribute with the actual measured radio frequency signal parameters.

[0064] Based on the correlation verification mechanism, a detection result evaluation model is constructed;

[0065] The objective function is obtained by summing the verification degree function of the pipeline detection results and the depth information data, and adding a product term of the verification weight coefficient, pipeline type identifier, identity attribute matching degree and depth accuracy parameter to comprehensively evaluate the accuracy of the detection results; according to the special identification code, a data transmission constraint index dataset is obtained; the data transmission constraint index dataset includes wireless transmission quality constraints and data format standard constraints.

[0066] The verification degree function represents the credibility evaluation index of the detection result; the depth information data includes the measured value of the burial depth; the verification weight coefficient is used to adjust the importance of different verification indicators; the identity attribute matching degree reflects the reliability of the association between pipeline identity information and detection location; and the depth accuracy parameter reflects the accuracy level of depth measurement.

[0067] Based on the identity attributes of the target pipeline, establish classification-based detection accuracy evaluation standards; set corresponding position accuracy thresholds and depth measurement accuracy requirements for different pipeline types (such as power cables, communication optical cables, gas pipelines, water supply pipelines, etc.); adjust the signal attenuation compensation coefficient through pipeline material properties (metal, non-metal, composite materials) to optimize the detection accuracy evaluation model for pipelines of different materials.

[0068] The newly acquired target pipeline identity attributes and integrated positioning detection results are entered into the detection result evaluation model, and comprehensive analysis and verification are performed in conjunction with the target pipeline identity attributes. The detection result evaluation model outputs the evaluation results of the target pipeline detection results based on identity attribute verification, including pipeline identity confirmation status, location positioning accuracy level, and depth measurement reliability index.

[0069] When the consistency verification between the identity attributes and the positioning data meets the accuracy requirements, an accurate target pipeline detection result is output; otherwise, depending on the inconsistency type, including identity mismatch or deviation in location coordinates, the corresponding signal receiving step or identity recognition step is returned for reoperation; thus, a comprehensive analysis result of the target pipeline identity attributes and integrated positioning detection result is finally obtained, ensuring the accuracy and reliability of the complete detection result including identity recognition, location positioning and depth measurement.

[0070] As a preferred embodiment of the present invention, based on the target pipeline detection dataset, the detection parameters are initialized through an omnidirectional scanning mode to obtain the initial spatial distribution data of the tracer signal source location;

[0071] Based on the initial spatial distribution data, the average value of the signal intensity at all scanning angles is calculated to obtain the average value of the scanning signal.

[0072] Based on the average value of the scanning signal, the signal intensity difference between the current scanning angle and the previous angle is obtained, and normalization is performed in combination with the angle measurement interval to obtain signal direction gradient data.

[0073] Based on the signal direction gradient data, a target pipeline abnormal state identification model is constructed;

[0074] Among them, by analyzing the spectral characteristics and propagation path changes of the tracer signal, abnormal states such as cracks, leaks, and vulnerabilities in the target pipeline can be identified;

[0075] The pipeline damage index is obtained by calculating the square of the ratio of the spectral characteristic deviation to the standard deviation and the square of the ratio of the propagation path deviation to the standard deviation, adding the two together and taking the square root.

[0076] Based on the pipeline damage index, an anomaly classification and judgment mechanism is established;

[0077] The system sets thresholds for minor, moderate, and severe anomalies, each corresponding to a different degree of severity. When the damage index is below the minor anomaly threshold, the pipeline is considered to be in normal condition. When the index is within the minor anomaly range, it is considered to be a slight anomaly such as a minor crack. When the index is within the moderate anomaly range, it is considered to be a moderate anomaly such as a significant leak. When the index reaches the severe anomaly threshold, it is considered to be a severe anomaly such as a large vulnerability.

[0078] Based on the judgment results of the anomaly classification judgment mechanism, the environmental error compensation coefficient containing the impact of the anomaly state is obtained by combining the environmental impact coefficient with environmental factors such as temperature, humidity, and soil type, and the damage state impact coefficient with the pipeline damage index.

[0079] Based on the environmental error compensation coefficient, the final location data of the target relationship abnormal state is obtained by subtracting the environmental error compensation amount and the position offset caused by the abnormal state from the original horizontal position coordinates.

[0080] Based on the final location data, establish the three-dimensional spatial anomaly coordinates; associate and map specific identification codes with the three-dimensional spatial anomaly coordinates and anomaly status information, and establish a comprehensive association between pipeline identity, spatial location and anomaly status through tensor product operations;

[0081] For detected abnormal pipeline areas, a high-precision rescanning mechanism is adopted; by reducing the scanning interval, increasing the measurement frequency, and adopting multi-angle cross-validation, the abnormal area is precisely located; a confidence evaluation model for abnormal location is established, and the location confidence of the abnormal location is obtained by calculating the inverse mean of the difference between the results of multiple repeated measurements and the average location, so as to ensure the reliability of the abnormal detection results.

[0082] Based on the aforementioned three-dimensional correlation mapping data, a hierarchical accuracy verification mechanism is adopted, including real-time accuracy verification, cross-comparison verification, and anomaly state confirmation verification. The position reproducibility index is calculated by repeatedly measuring the position information of the same target. The position is compared and verified with known control points or GPS coordinates. Multiple independent verifications are performed for anomalies to ensure that the accuracy of anomaly detection and the absolute accuracy of position positioning detection meet the centimeter-level positioning requirements.

[0083] Once the location accuracy verification and abnormal status confirmation are both passed, the complete location information, including identity recognition, three-dimensional coordinates, abnormal status information, and accuracy parameters, is transmitted to the external system using an encrypted transmission protocol; the integrity, authenticity, and traceability of the location data are ensured through digital signature and timestamp technology; and an abnormal alarm mechanism is established to immediately send high-priority alarm information when a serious abnormality is detected.

[0084] Based on the completed location data, a dynamic accuracy assessment and feedback optimization mechanism is established; when the location accuracy deviation exceeds the preset threshold, the repositioning process is automatically initiated; the environmental error compensation model and anomaly identification model are continuously optimized through machine learning algorithms to establish an anomaly development trend prediction function; the anomaly judgment threshold is updated regularly to improve the stability, consistency and sensitivity of positioning accuracy under different environmental conditions.

[0085] Secondly, an underground pipeline detection system based on tracer signals includes:

[0086] Tracer deployment module: used to associate a tracer with an underground pipeline; wherein the tracer radiates a tracer signal carrying a special identification code; the special identification code is generated based on the identity attributes of the target pipeline;

[0087] Data acquisition module: used to acquire underground pipeline tracer signals to obtain a target pipeline detection dataset; wherein, the pipeline detection dataset contains the frequency characteristics and coding characteristics of the tracer signals; the tracer signals are received by ground detection equipment; the ground detection equipment performs directional reception and preliminary screening of the tracer signals according to the frequency characteristics in the pipeline detection dataset; based on the preliminary screening of the tracer signals, the tracer signals are demodulated and decoded to obtain the target pipeline detection dataset;

[0088] Data verification module: used to compare and verify the target pipeline detection dataset with a special identification code to obtain a verification result; wherein, when the identification code matches successfully in the verification result, the target pipeline identity attribute is obtained and the location detection is performed; based on the verification result, the field strength and phase information of the verified tracer signal are obtained to obtain the target pipeline information data;

[0089] Data generation module: used to obtain the horizontal position and burial depth of the target pipeline based on the target pipeline information data;

[0090] Data analysis module: used to comprehensively analyze the target pipeline's identity attributes with the obtained horizontal position and burial depth to obtain the target pipeline detection results.

[0091] The beneficial effects of this invention are as follows: By associating a tracer with underground pipelines, the tracer radiates a tracer signal carrying a special identification code, completely breaking through the technical bottleneck that traditional electromagnetic methods cannot effectively detect non-metallic pipelines. Regardless of the material of the pipeline, such as PE, PVC, or ceramic, accurate detection can be achieved as long as a tracer is installed.

[0092] By employing a technical solution that generates unique identification codes based on the target pipeline's identity attributes, each tracer is ensured to have a globally or locally unique code. Ground-based detection equipment performs directional reception and preliminary screening of tracer signals based on the frequency characteristics of the pipeline detection data, identifying and locking onto signals with specific codes. This completely avoids electromagnetic interference from surrounding pipelines, power lines, communication lines, etc., as well as signal coupling crosstalk problems in multi-pipeline intersection environments, achieving precise directional reception of tracer signals.

[0093] By comparing and verifying the target pipeline detection dataset with special identification codes, a complete verification result evaluation system was established. When the identification code matches successfully, the system can not only determine "there is a pipeline here", but also accurately identify "which pipeline it is" (such as pipeline type, affiliated unit, ID number, etc.), greatly improving the digitalization and intelligence level of pipeline management.

[0094] By acquiring the verified tracer signal field strength and phase information, target pipeline information data is obtained, and precise positioning is achieved based on the phase and intensity measurements of the coded signal. Compared with traditional analog signal measurements, this method is more stable, more accurate, and provides more reliable depth and planar position measurement results.

[0095] By comprehensively analyzing the target pipeline's identity attributes along with its horizontal location and burial depth, complete target pipeline detection results are obtained. This comprehensive analysis capability significantly improves the accuracy and reliability of the final detection results, providing comprehensive and reliable data support for pipeline management.

[0096] This enables the integration of multi-source detection and tracing signals, in-depth mining of pipeline identity-location relationships, and precise capture of tracing signal characteristics with specific identification codes. It is easy to combine with GIS (Geographic Information System) and BIM (Building Information Modeling) to achieve automatic recording and storage of detection data, providing real-time and accurate data acquisition methods for smart city underground pipeline network databases, and has strong environmental adaptability. Attached Figure Description

[0097] Figure 1 This is a schematic flowchart of an underground pipeline detection method based on tracer signals provided in an embodiment of the present invention;

[0098] Figure 2 This is a schematic diagram of an underground pipeline detection system module based on tracer signals provided in an embodiment of the present invention. Detailed Implementation

[0099] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0100] At least one embodiment of the present invention discloses a method and system for detecting underground pipelines based on tracer signals, comprising:

[0101] like Figure 1 As shown, a method for detecting underground pipelines based on tracer signals includes the following steps:

[0102] Step 1: Associate the tracer with the underground pipeline; wherein the tracer radiates a tracer signal carrying a special identification code; the special identification code is generated based on the identity attributes of the target pipeline;

[0103] Step 2: Collect underground pipeline tracer signals to obtain a target pipeline detection dataset; wherein, the pipeline detection dataset contains the frequency characteristics and coding characteristics of the tracer signals; receive the tracer signals through ground detection equipment; the ground detection equipment performs directional reception and preliminary screening of the tracer signals based on the frequency characteristics in the pipeline detection dataset; based on the preliminary screened tracer signals, demodulate and decode the tracer signals to obtain the target pipeline detection dataset;

[0104] Step 3: Compare and verify the target pipeline detection dataset with the special identification code to obtain the verification result; wherein, when the identification code matches successfully in the verification result, the target pipeline identity attribute is obtained and the location is detected; based on the verification result, the field strength and phase information of the verified tracer signal are obtained to obtain the target pipeline information data;

[0105] Step 4: Obtain the horizontal position and burial depth of the target pipeline based on the target pipeline information data;

[0106] Step 5: Perform a comprehensive analysis of the target pipeline's identity attributes with the obtained horizontal position and burial depth to obtain the target pipeline detection results.

[0107] Example 1 This example provides a complete automated method for detecting underground pipelines. This method is achieved through the collaborative work of a tracer and a detector, and includes six core steps: tracer system initialization, intelligent signal transmission, multi-dimensional signal reception, encoding recognition and verification, precise positioning and depth measurement, and comprehensive result output.

[0108] 1.1 The automated underground pipeline detection method in this embodiment adopts a modular design. This architecture mainly includes five functional modules, corresponding to five core steps: tracer initialization module, signal transmission module, signal reception module, encoding verification module, and positioning and depth sounding module. The modules communicate and collaborate via radio frequency signals, forming a complete detection and processing chain.

[0109] 1.2 Tracker System Initialization Steps First, the system checks whether the required electronic modules are configured in the current tracker environment. The main electronic modules the system needs to check include: digital encoders and unique identifier generators related to encoding generation; ASK and FSK modulators related to signal modulation; and 125kHz, 13.56MHz, and 433MHz transmitters related to radio frequency transmission. A list of required electronic modules is created, and each module is checked one by one to ensure it is correctly installed. For modules that are not installed, the system will automatically perform configuration operations. Simultaneously, the system creates an initial encoding system, including three encoding types: pipeline type code (01-10), unit code (100-999), and unique identifier code (1000-9999). Next, signal transmission parameters are constructed to configure parameters such as transmission frequency, modulation method, and power level. Finally, a power management strategy is configured to dynamically adjust the duty cycle based on battery capacity and power consumption requirements. Automated module configuration solves the problem of requiring manual debugging of equipment item by item in traditional methods, greatly improving work efficiency; a standardized initial coding system is established, providing a unified standard for subsequent signal recognition; and dynamic power management strategy improves the equipment's battery life.

[0110] 1.3 Intelligent Signal Transmission Steps: Following the initialization of the tracer system output configuration in Example 1.2, the system performs code generation and radio frequency modulation, transmits a tracer signal carrying pipeline identification information, and optimizes signal power and transmission distance. First, the pipeline identification attributes are encoded, converting pipeline type, affiliated unit, ID number, etc., into digital codes. For PE material, the code is 01; for PVC, 02; for ceramic, 03, and so on. For the affiliated unit, 100 is used for municipal departments, 200 for power departments, and 300 for communication departments. Then, an appropriate modulation method is automatically selected based on the encoding type: ASK modulation for simple codes and FSK modulation for complex codes. Next, the modulated signal is radio frequency carrier modulated, modulating the digital code onto a 125kHz, 13.56MHz, or 433MHz carrier. For underground transmission, a 125kHz low-frequency carrier is preferred for better penetration. Finally, signal power control is performed, dynamically adjusting the transmission power based on pipeline burial depth and soil characteristics to ensure the signal can penetrate to the ground and be received by the detector. Finally, intermittent transmission control is implemented, employing a periodic pulse transmission mode to conserve battery power. The transmission period can be set to a 1-second interval to balance power consumption and detection efficiency. Through encoding conversion mechanisms and modulation optimization, the problem of difficult signal identification in traditional methods is solved; dynamic power control and carrier selection improve the reliability of signal transmission; and the intermittent transmission mode significantly extends the tracer's lifespan.

[0111] 1.4 Multi-dimensional Signal Reception Steps: The tracer signal transmitted in Example 1.3 is received, and signal acquisition, amplification, filtering, and preliminary demodulation are performed to provide raw signal data for the encoding identification and verification in Example 1.5. Specifically, the tracer actively transmits the tracer signal, or the tracer generates the tracer signal by backscattering the excitation signal received from the ground detection equipment. The tracer includes an encoding generator and a modulation transmission circuit. The encoding generator generates a special identification code based on the target pipeline's identity attributes, and the modulation transmission circuit modulates the code onto a radio frequency carrier and radiates it through an antenna. The ground detection equipment includes a receiving antenna array and a signal processing unit. The antenna array receives the tracer signal, and the signal processing unit demodulates and decodes the received tracer signal. First, a full-band signal scan is performed. The detector searches for signals in the 125kHz, 13.56MHz, and 433MHz frequency bands to establish a full-band signal reception reference. Then, signal strength detection is performed. RSSI technology is used to measure the received signal strength, and signal strength data is formed by combining the quality assessment marker results. Next, phase difference measurement is performed using a dual horizontal coil antenna to measure the phase difference of the signal, generating directional data. Then, signal demodulation is performed, demodulating the received modulated signal using ASK or FSK to extract digital encoded information. Finally, the signal propagation time is calculated; by measuring the time difference between signal transmission and reception, distance estimation data is obtained. Full-band scanning ensures comprehensive reception of signals from different types of tracers; signal strength and phase measurements provide crucial data for subsequent precise positioning; and demodulation processing lays the foundation for encoded identification.

[0112] 1.5 Encoding, Identification, and Verification Steps: This step receives the demodulated signal data from Example 1.4 and performs encoding parsing, identity verification, and data matching to ensure the accuracy of the detected target. Based on the target pipeline detection dataset, verification is performed by obtaining the pipeline identification mean. Specifically, the pipeline attribute encoding data is summed and divided by the number of pipeline attribute codes to obtain the target pipeline identification mean. The pipeline attribute codes include basic attribute data such as pipeline type identifier, material code, and specification information. The pipeline attribute change gradient is obtained based on the pipeline identification mean. This mean is multiplied by the difference between the pipeline encoding data at the current identification time and the previous identification time, and then divided by the encoding and identification time interval to obtain the pipeline attribute change gradient over time. The pipeline identification strength is obtained based on the pipeline attribute change gradient. The absolute value of the pipeline attribute change gradient is multiplied by the product of this gradient and the attribute identification frequency coefficient to obtain a strength index characterizing the reliability of pipeline identification. Higher identification strength indicates better encoding and identification accuracy. First, digital encoding parsing is performed. The demodulated digital sequence is segmented and parsed according to preset encoding rules to extract information such as pipeline type code, affiliated unit code, and unique identifier code. Then, encoding integrity verification is performed, checking the encoding length, check bits, and format specifications for correctness, and marking incomplete or incorrect encodings. Next, identity information matching is performed, comparing the parsed encoding information with the pipeline detection dataset to verify the authenticity of the pipeline identity. Subsequently, a signal reliability assessment is established, calculating the signal reliability index by considering factors such as signal strength, encoding integrity, and matching results. Finally, a verification result report is generated, including pipeline identity information, verification status, and reliability assessment, providing accurate target information for depth sounding. Through encoding parsing and identity verification, accurate identification of the detection target is ensured; the reliability assessment improves the reliability of the detection results; and the verification report provides users with detailed target information.

[0113] 1.6 Precise Positioning and Depth Measurement Steps: Receive the verification results from Example 1.5, perform horizontal positioning and depth measurement, and calculate the precise location and burial depth of the pipeline. This step uses a receiving antenna array to compare the signal strength or phase difference received at different antenna positions, calculates the horizontal angle of the signal source, and guides the operator to move the detection device left and right. When the signal strength reaches its maximum value or the phase difference between antennas is zero, the planar position of the pipeline is determined to be directly below the detection device. Then, at this planar position point, the distance between the peak and trough points is measured using the signal field strength variation with distance. The burial depth of the pipeline is calculated using a pre-calibration algorithm. In the horizontal positioning stage, a multi-antenna differential measurement method is used. By comparing the strength difference and phase difference of the signals received by the left and right antennas or the front and rear antennas, when the difference approaches zero, it indicates that the central axis of the detection device is perpendicularly aligned with the central axis of the pipeline. In the vertical positioning stage, based on the horizontal positioning, while keeping the detection device position unchanged, the curve of signal strength variation with depth is obtained by changing the measurement height or utilizing the penetration characteristics of different frequency signals. The burial depth is calculated based on the peak and trough characteristic points of this curve. Based on the pipeline detection dataset, a signal strength measurement range is set. The distance between the peak and trough values ​​of the signal strength is measured using ground detection equipment to obtain the distance measurement value. The distance measurement value is compared with the signal strength measurement range to determine whether the distance measurement value exists within the signal strength measurement range. When the distance measurement value is within the signal strength measurement range, a valid distance measurement result is obtained. Based on the distance measurement result and the preset depth calculation standard, the distance is matched with the preset depth calculation standard to obtain a matching result. This matching result indicates whether the distance measurement value meets the depth calculation requirements, which include: the measured distance value is within a physically reasonable range, the signal strength change gradient meets the underground propagation law, the measurement accuracy meets engineering application standards, and the influence of ground interference and multipath effects is eliminated. The distance measurement value is converted into burial depth according to the pre-calibration algorithm to obtain the calculation result. This calculation result indicates the target pipeline burial depth value calculated based on the distance data. The pre-calibration algorithm is: D=d / k, where D represents the burial depth, d represents the distance measurement value, and k represents the equipment constant. Based on the calculation and matching results, the burial depth of the target pipeline is determined. The burial depth value is obtained when the matching result is valid and the calculation result is within the measurement range. First, horizontal positioning is performed by comparing the strength difference of the signals received by the left and right antennas. When the difference is zero, the pipeline location is determined to be directly below the detector. The operator is then guided to move the detector until the signal strength is at its maximum. Next, signal strength is measured at the determined horizontal position, recording the peak and trough values ​​of the signal strength and the distance d between the peak and trough points. Then, depth calculation is performed using the pre-calibration algorithm D=d / k to calculate the burial depth, where D is the burial depth, d is the measurement distance, and k is a device constant. Finally, accuracy verification is performed through multiple measurements and statistical analysis to verify the consistency and accuracy of the measurement results.Finally, a location and depth report is generated, including the pipeline's planar coordinates, burial depth, and measurement accuracy. High-precision horizontal positioning is achieved through signal strength comparison; the peak-to-valley method provides a reliable technical means for depth measurement; and the pre-calibration algorithm ensures the accuracy of depth calculation.

[0114] 1.7 The integrated result output step integrates the positioning and depth measurement results from Example 1.6 and the identity verification results from Example 1.5 to generate a complete detection report according to user needs. This step, based on the successful code matching verification result, extracts and parses the complete coded information transmitted by the tracer to obtain the target pipeline identity, including the pipeline's unique identifier, pipeline type code, material properties, specifications, and other identity attribute data, forming a complete identity file for the target pipeline. Based on the target pipeline identity, the ground detection equipment continuously receives radio frequency signals from the identification tracer through its radio frequency transceiver function to obtain verified tracer signal field strength data and phase information data. The acquired tracer signal field strength and phase information are processed and decoded to obtain the processing and decoding results. Specifically, the received radio frequency signal undergoes low-noise amplification and down-conversion processing, and field strength variation characteristics and phase difference characteristics are extracted to form a target pipeline information dataset containing signal strength distribution, phase difference distribution, and frequency response characteristics. Based on the target pipeline information data, horizontal positioning of the target pipeline is performed. This involves using multi-antenna array differential measurement technology to compare the signal strength differences and phase differences received at different antenna positions. When the signal strength reaches its peak and the phase difference between antennas is zero, the accurate horizontal position coordinates of the pipeline are determined to be directly below the detection device. Based on these accurate horizontal position coordinates, the burial depth of the target pipeline is acquired. This utilizes the physical law of signal field strength variation with distance, measuring the peak and trough points of signal strength in the vertical direction. A pre-calibrated depth calculation algorithm converts the distance measurements into burial depth values. Based on the horizontal position positioning and burial depth acquisition, a comprehensive positioning and detection result for the target pipeline is obtained. The target pipeline's identity attributes are then comprehensively analyzed with the comprehensive positioning and detection results to construct a correlation verification mechanism between identity attributes and positioning data. This involves cross-validating the target pipeline's identity attribute information with the horizontal position coordinates and burial depth data, and comparing the signal characteristic parameters of the target pipeline type attributes with the actually measured radio frequency signal parameters to verify the identification of the target pipeline's identity attributes. Based on an association verification mechanism, a detection result evaluation model is constructed. According to the target pipeline's identity attribute characteristics, classification-based detection accuracy evaluation standards are established. Corresponding position accuracy thresholds and depth measurement accuracy requirements are set for different pipeline types. The signal attenuation compensation coefficient is adjusted based on pipeline material properties to optimize the detection accuracy evaluation model for pipelines of different materials. Newly acquired target pipeline identity attributes and comprehensive positioning detection results are input into the detection result evaluation model. A comprehensive analysis and verification are performed based on the target pipeline identity attributes. The detection result evaluation model outputs the evaluation results of the target pipeline detection results based on identity attribute verification, including pipeline identity confirmation status, position positioning accuracy level, and depth measurement reliability index. When the consistency verification between identity attributes and positioning data meets the accuracy index requirements, accurate target pipeline detection results are output; otherwise, the corresponding signal reception step or identity recognition step is returned for re-operation based on the inconsistency type.First, pipeline identification information is integrated, including basic attributes such as pipeline type, affiliated unit, and unique identifier. Then, location information is integrated, including planar coordinates, burial depth, and measurement accuracy. Next, a summary of the detection results is generated, automatically extracting key information to produce a concise detection report, including pipeline discovery confirmation, identification results, and location measurement results. Subsequently, a results visualization display is established, showing pipeline location graphics, depth values, and identification information on a screen, accompanied by audio prompts to guide operation. Finally, a standardized report is output, generating a technical report containing complete detection information according to user needs, supporting on-site printing and data export. Information integration ensures the completeness of the detection results; visualization improves the intuitiveness of the results; and standardized reporting meets the needs of different users.

[0115] The six steps in this embodiment form a complete detection processing chain: The tracer system initialization in Embodiment 1.2 provides the basic configuration for all subsequent steps; the intelligent signal transmission and reception configuration output in Embodiment 1.3 transmits tracer signals carrying identity information; the multi-dimensional signal reception and transmission in Embodiment 1.4 outputs demodulated data for encoding and identification; the encoding identification and verification receiving and demodulated data in Embodiment 1.5 outputs verification results for positioning and depth measurement; the precise positioning and depth measurement receiving and verification results in Embodiment 1.6 outputs location information for result output; and the comprehensive result output in Embodiment 1.7 integrates the outputs of all previous steps to generate the final detection report. The entire process from tracer configuration to result output is automated, requiring no manual intervention and significantly improving work efficiency; the anomaly capture and automatic recovery mechanism ensures stable system operation in the face of various anomalies; the detection strategy can be dynamically adjusted according to different pipeline characteristics and environmental conditions to adapt to various application environments; comprehensive detection capabilities are provided through integrated analysis of encoding identification, identity verification, and positioning and depth measurement; various types of visualization displays are automatically generated to intuitively show the detection results; and power management and signal optimization strategies improve device endurance and detection accuracy.

[0116] Example 2 describes in detail the specific implementation of the tracer system initialization step in the automated underground pipeline detection method. This step is the foundation of the entire method, providing the necessary hardware configuration and encoding parameters for subsequent signal transmission, and forms a connection with Example 1.

[0117] 2.1 Tracker System Initialization The first step is to check whether the required electronic modules are installed in the current tracker device. This embodiment employs an intelligent detection mechanism that not only checks whether the modules are installed but also whether their operational status is normal. First, a list of required electronic modules and their operating parameters is defined, including core modules such as the encoder generator, modulation and transmission circuit, and antenna system. Then, each module is checked one by one to ensure it is correctly installed; if not, a configuration operation is automatically performed. For installed modules, their operational status is checked; if the status is abnormal, a calibration procedure is automatically executed. Next, all necessary electronic modules are loaded, and the installation and configuration results are recorded. Finally, it is checked whether all modules have been successfully configured; if any configuration failure occurs, a corresponding error message is given, and the process returns to the previous step to repeat the configuration operation.

[0118] 2.2 The tracer system initialization also includes creating and defining pipeline identification codes to mark the identity information of different pipelines. This embodiment defines three basic code types and encapsulates them in a structured object. First, the basic code types are defined, including pipeline type codes (01-10), unit codes (100-999), and unique identifier codes (1000-9999). Then, the code generation rules are defined, including type codes such as 01 for PE pipelines, 02 for PVC pipelines, and 03 for ceramic pipelines, and unit codes such as 100 for municipal departments and 200 for power departments. Next, the code length and verification method are set, including technical parameters such as a total code length of 12 bits, CRC verification, and support for redundant coding. Finally, the code type, generation rules, and technical parameters are encapsulated into a complete coding system object. A three-level coding system containing type-unit-identifier and ten pipeline type codes are established, providing a unified standard for subsequent identification; the structured coding system facilitates expansion and maintenance.

[0119] 2.3 The tracer system initialization also includes constructing a signal transmission parameter configuration function to set the tracer's transmission power, frequency, and modulation scheme. This embodiment defines four basic transmission parameters: carrier frequency, modulation scheme, transmission power, and duty cycle. First, a transmission parameter configuration function is created, which receives configuration data containing pipeline attributes and environmental conditions as input. Then, the input parameters are verified to ensure that the specified pipeline type and burial depth information are present in the configuration data. Next, the optimal transmission parameters are calculated, including selecting the carrier frequency based on burial depth (13.56MHz for shallow burial and 125kHz for deep burial), selecting the modulation scheme based on coding complexity (ASK for simple coding and FSK for complex coding), and calculating the transmission power based on the transmission distance. Subsequently, duty cycle parameters are set, including transmission interval time, single transmission duration, standby time, etc., to balance detection efficiency and power consumption control. Finally, the complete transmission parameter configuration is returned for use by the signal transmission module. A standardized transmission parameter configuration method is provided to ensure the consistency and optimization of transmission settings; automated parameter calculation reduces the error-prone problems of manual configuration; and the functional design facilitates reuse in subsequent steps.

[0120] The final step in the 2.4 tracer system initialization is configuring the power management strategy to dynamically adjust the operating mode based on battery capacity and power consumption requirements. First, battery status information is acquired, including battery capacity, current charge level, and expected operating time. Then, the power management strategy is dynamically adjusted based on power consumption requirements and battery status, setting the maximum operating power to 80% of battery capacity and the sleep power to below 10% of operating power. Next, power control modes are set for tiered power management in different application scenarios. Subsequently, a low-power operating environment is configured, including standby mode, intermittent operating mode, and energy-saving mode. Finally, all power configuration information is encapsulated into a power management object. Through this power management mechanism that dynamically adjusts operating power and sleep strategies, adaptive optimization based on battery status and operating requirements achieves improved battery life and performance; the low-power mode configuration significantly extends the tracer's operating time.

[0121] First, electronic module detection and configuration are performed to ensure all necessary hardware modules are correctly installed and functioning. Then, the coding system is built to create a standardized identity recognition framework. Next, the transmission parameter configuration function is built to provide a unified signal transmission method. Following this, power management strategy configuration is performed to optimize device battery life. Finally, all initialization results are integrated into a single device configuration object, containing hardware status, coding system, transmission parameters, and power strategy, for use in subsequent steps.

[0122] By integrating the four sub-steps, a complete tracer system initialization process is formed, providing a unified basic configuration for all subsequent steps; the modular design facilitates maintenance and expansion; and the adaptive mechanism can adapt to various application scenarios based on different pipeline types and environmental conditions. Finally, the above four steps are integrated into a complete tracer system initialization function, providing the basic configuration for the intelligent signal transmission in Example 3.

[0123] This embodiment serves as the foundation of the entire system. Its output device configuration object will be directly used by the intelligent signal transmission in Embodiment 3, providing necessary parameters and standards for code generation, signal modulation, and power control. Simultaneously, the coding system established in this embodiment will be integrated throughout the multi-dimensional signal reception in Embodiment 4, the code recognition and verification in Embodiment 5, and the comprehensive result output in Embodiment 7, ensuring consistency throughout the entire detection process. This enables automatic detection and configuration of the required electronic modules without manual intervention; ensures normal system operation; creates a complete coding system and transmission parameter configuration functions; dynamically adjusts the operating mode based on battery status to improve device endurance; adapts to various application scenarios based on different pipeline types and environmental conditions; and both the coding system and transmission parameter configuration are designed to be scalable, allowing for the addition of new pipeline types and transmission modes as needed.

[0124] Example 3 details the implementation of the intelligent signal transmission step in the automated underground pipeline detection method. This step receives the output of the tracer system initialization in Example 2, performs operations such as encoding generation, signal modulation, power control, and transmission management, and provides a standardized tracer signal for the multi-dimensional signal reception in Example 4.

[0125] 3.1 Intelligent signal transmission first performs digital encoding conversion on pipeline identity attributes to ensure that identity information can be accurately embedded in the radio frequency signal. Simultaneously, an error detection and correction mechanism is employed for encoding verification to improve signal transmission reliability. First, the integrity of the pipeline identity attributes is verified, ensuring that the input parameters include necessary information such as pipeline type, affiliated unit, and unique identifier. Then, the identity attributes are digitally converted according to the encoding system established in Example 2, for example: PE material is converted to 01, PVC material to 02, municipal department to 100, and power department to 200, etc. Next, encoding combination and checksum generation are performed, combining the type code, unit code, and identification code in a prescribed order and generating a CRC checksum to ensure encoding integrity. Finally, the correctness of the encoding format is verified, checking whether the encoding length, character format, checksum, etc., meet the standard requirements. When encoding errors or format mismatches occur, the system automatically initiates an error correction strategy: First, it attempts to reacquire pipeline attribute information; if the information is incomplete, it uses the default encoding. Second, it checks the execution process of the encoding generation algorithm and corrects any possible calculation errors. Third, it verifies the checksum generation process to ensure the correctness of the verification algorithm. Finally, it records detailed error information and the correction process for subsequent analysis. The verified digital encoding is then encapsulated into an encoding information object, containing the original attributes, the digital encoding, and the checksum information, for use in subsequent modulation steps.

[0126] 3.2 After the encoding conversion is completed, the next step is to modulate the digital code. This embodiment supports multiple modulation methods (ASK, FSK) and can automatically select the most suitable modulation method based on the encoding characteristics. First, based on the digital code verified in Embodiment 3.1, the complexity and data size of the encoding are analyzed. The system automatically evaluates the encoding characteristics, including factors such as encoding length, data change frequency, and transmission distance requirements. Then, the appropriate modulation method is selected according to the encoding characteristics. ASK modulation is used for simple short codes to reduce power consumption, and FSK modulation is used for complex long codes to improve transmission reliability. Next, modulation parameter settings are executed, including the configuration of technical parameters such as modulation depth, carrier frequency, and symbol rate. Subsequently, modulation quality verification is performed to ensure that the modulated signal meets the transmission requirements through signal quality detection. If the modulation quality does not meet the standard, the system will try to adjust the modulation parameters or change the modulation method to improve the modulation quality. Finally, the modulated RF signal is returned for use in the carrier modulation step. The intelligent modulation selection function can automatically select the optimal modulation method based on the encoding characteristics; the modulation quality verification mechanism ensures the reliability of signal modulation; and adaptive parameter adjustment improves the success rate and signal quality of the modulation process.

[0127] 3.3 After modulation, the modulated signal needs to be carrier modulated onto a specific radio frequency carrier to achieve long-distance transmission. First, the most suitable carrier frequency is selected based on the pipeline burial depth and soil conditions. For pipelines buried less than 2 meters deep, 13.56MHz is preferred for better transmission efficiency, while 125kHz is preferred for pipelines buried deeper than 2 meters for stronger penetration. Then, carrier modulation is performed, modulating the signal from Example 3.2 onto the selected radio frequency carrier to ensure effective signal propagation in the underground environment. Next, carrier power and frequency stability are set. The carrier power is adjusted according to the transmission distance requirements, and crystal oscillator frequency stabilization technology is used to ensure frequency accuracy. Subsequently, carrier modulation quality is tested, verifying the effectiveness of carrier modulation through spectrum analysis and signal purity testing. For cases where the modulation quality is unqualified, the system automatically adjusts the carrier parameters or re-executes the modulation process to ensure the quality of the final output signal. Finally, the carrier-modulated radio frequency signal is returned, and all signals have the correct carrier frequency and modulation format. Automated carrier selection reduces the workload of manual configuration; intelligent selection of multiple frequency carriers improves the transmission success rate under different conditions; and carrier modulation quality testing provides reliable assurance for signal transmission.

[0128] 3.4 After carrier modulation, transmit power control is required to ensure the signal can penetrate the soil and reach the ground, while controlling power consumption to extend equipment operating time. First, the minimum transmit power requirement is calculated based on the pipeline burial depth, and a signal propagation loss model is used to estimate power requirements at different depths. The system considers the impact of environmental factors such as soil conductivity, humidity, and density on signal attenuation and dynamically adjusts power calculation parameters. Then, based on the power management strategy established in Example 2, power consumption is minimized while meeting transmission requirements, keeping the transmit power within the battery capacity's limits. Next, power adjustment and stability control are implemented. Precise power adjustment is achieved through an automatic gain control circuit, and temperature compensation technology ensures stable power output. Subsequently, the effectiveness of the transmit power is verified by confirming the rationality of the power setting through signal strength monitoring and transmission distance testing. Finally, a power control feedback mechanism is established to adjust the transmit power in real time based on signal transmission performance, achieving dynamic power optimization. The power calculation model can accurately estimate power requirements based on environmental conditions; dynamic power adjustment ensures a balance between transmission performance and power consumption control; and the feedback control mechanism improves the intelligence level of power management.

[0129] 3.5 Signal transmission also includes precise control of transmission timing, employing an intermittent transmission mode to balance detection efficiency and power consumption control. First, the intermittent transmission timing is designed, including setting timing parameters such as transmission cycle, transmission duration, and standby time. The system sets the transmission interval to 0.5-2 seconds to adapt to different detection speed requirements based on different application scenarios. Then, a transmission synchronization mechanism is established to ensure that signal interference is avoided when multiple tracers are working in the same area, using time-division multiplexing or frequency-division multiplexing technology to achieve multi-device collaborative operation. Next, joint optimization of transmission power and timing is implemented to reduce overall power consumption while ensuring signal transmission quality. Subsequently, a transmission status monitoring system is established to monitor the operating status of the transmission circuit in real time and promptly detect and handle transmission anomalies. Finally, automated transmission control management is achieved, automatically adjusting the transmission strategy based on factors such as battery level, environmental conditions, and detection requirements. Intermittent transmission timing significantly reduces the average power consumption of the equipment; the synchronization mechanism effectively avoids signal interference between multiple devices; and automated transmission control adapts to the needs of different application scenarios.

[0130] Intelligent signal transmission also includes antenna matching optimization to ensure efficient signal radiation into space. First, antenna impedance matching is performed by adjusting the matching network to ensure impedance matching between the transmitting circuit and the antenna, maximizing power transmission efficiency. The system automatically calculates matching parameters based on the operating frequency and antenna characteristics. Next, the antenna pattern is optimized by adjusting the antenna's radiation direction according to pipeline routing and detection requirements, ensuring effective signal coverage of the detection area. Then, antenna efficiency testing is conducted, verifying the antenna system's performance through VSWR measurement and radiation efficiency calculation. Subsequently, an antenna protection mechanism is established to monitor the antenna's operating status and promptly detect antenna damage or performance degradation. Finally, adaptive optimization of the antenna system is implemented, automatically adjusting antenna parameters based on environmental conditions and signal transmission performance. Antenna matching optimization improves signal radiation efficiency; pattern optimization ensures effective signal coverage; and the adaptive mechanism enhances the antenna system's environmental adaptability. Finally, these five steps are integrated into a complete intelligent signal transmission function, providing high-quality tracer signals for the multi-dimensional signal reception in Example 4.

[0131] First, encoding conversion and verification are performed to ensure accurate encoding of pipeline identification information. Then, signal modulation processing is executed, converting the digital code into a modulated signal. Next, carrier modulation is performed, loading the modulated signal onto the RF carrier. Following this, power control optimization is performed to ensure signal transmission effectiveness and power efficiency. Finally, transmit timing control is executed to achieve intermittent transmission and multi-device collaboration. The entire process employs a quality monitoring mechanism to ensure clear error messages are provided in case of problems. The final output is a high-quality RF tracer signal, including the carrier modulation signal, transmit parameters, and timing control information.

[0132] By integrating five sub-steps, a complete intelligent signal transmission process is formed; quality monitoring and adaptive optimization mechanisms ensure the stability of signal transmission; and high-quality tracer signals lay a solid foundation for subsequent signal reception.

[0133] This embodiment receives the device configuration object from Embodiment 2 as input and performs signal processing using the coding system and transmission parameters. The output of this embodiment will be directly transmitted to the multi-dimensional signal reception of Embodiment 4, providing a standardized radio frequency signal for signal acquisition. Simultaneously, the signal features established in this embodiment will continue to be used in the code identification and verification of Embodiment 5. Automatic identification of coding features, modulation method selection, and carrier frequency configuration reduce manual intervention; quality detection and adaptive adjustment improve the success rate of signal transmission; power control and timing optimization effectively balance transmission performance and power consumption control; scientific methods such as intermittent transmission and synchronization mechanisms handle multi-device collaboration; unified signal format and quality standards provide a standard basis for subsequent signal reception; detailed transmission logs and quality monitoring ensure the traceability of the signal transmission process.

[0134] Example 4 details the implementation of the multi-dimensional signal reception step in the automated underground pipeline detection method. This step utilizes signal processing based on the field strength distribution characteristics of the tracer signal, and achieves high-precision signal reception by calculating the peak signal field strength and depth gradient data.

[0135] 4.1 Multi-dimensional signal reception First, based on the field strength distribution characteristics of the tracer signal, through... To obtain the peak signal strength; among which, This represents the signal field strength at the j-th measurement location; This indicates the number of measurement locations. Signal field strength data is collected at multiple locations, and the average value is calculated to obtain a stable peak signal field strength, providing a benchmark reference for subsequent depth gradient calculations. First, a multi-point measurement mechanism is established, setting up m measurement locations within the detection area. Signal field strength data at each location is collected simultaneously by moving the detection device or using a multi-antenna array. Then, signal field strength data preprocessing is performed, including noise filtering and outlier detection on the acquired raw data to ensure data quality. Next, the peak signal field strength is calculated. The average peak signal strength is obtained by summing the signal field strengths at all valid measurement locations and dividing by the number of measurement locations. A field strength data verification mechanism is then established to identify and eliminate abnormal measurement points by comparing the data consistency across different measurement points. Finally, a field strength distribution map is generated to provide a visual reference for signal reception and positioning. Multi-point averaging improves the accuracy of the peak signal strength; the preprocessing mechanism ensures data quality; and the visual distribution map provides operators with an intuitive reference for signal strength.

[0136] 4.2 Based on the acquired peak signal strength, the system... To obtain depth gradient data; where, Represents depth gradient data; This represents the signal field strength data at the current measurement location; This represents the signal field strength data at the first k locations, where k represents the measurement interval. By calculating the rate of change of signal field strength between different locations and normalizing it in conjunction with the peak value, depth gradient data characterizing the changes in the signal propagation path is obtained. First, a time-series measurement mechanism is established, and signal field strength data is continuously collected at different locations according to a preset measurement interval k. and Then calculate the signal field strength difference. This reflects the amplitude of signal variation between different locations. Next, normalization is performed, dividing the field strength difference by the position measurement interval k to obtain the rate of change of field strength per unit interval. This is then compared with the peak field strength. Multiplication standardizes the depth gradient data, eliminating systematic biases caused by different measurement environments. Finally, a depth gradient curve is generated, providing an accurate gradient reference for burial depth calculation. Differential calculation improves the accuracy of gradient measurements; normalization eliminates systematic biases; and standardized depth gradient data provides a reliable foundation for subsequent calculations.

[0137] 4.3 Based on the acquired depth gradient data, the system... To obtain the calculated burial depth value; among which, This represents the calculated burial depth. Indicates the calibration coefficient of the detection equipment; This represents the depth correction factor. The calculation formula comprehensively considers the modulus of the depth gradient data and equipment characteristics, achieving high-precision burial depth calculation through calibration coefficients and correction factors. First, the modulus of the depth gradient data is calculated, through... Obtain the energy characteristics of the gradient data. Then calculate the correction term. Among them, the calibration coefficient of the detection equipment Calibration is performed based on equipment characteristics and operating frequency to compensate for the influence of equipment response characteristics on measurement results. Vector synthesis calculations are then performed. The gradient energy and device correction term are vector-synthesized to obtain a comprehensive depth feature value. Then, a depth correction factor is applied. This factor is dynamically adjusted based on environmental conditions such as soil type, humidity, and temperature to achieve environmental adaptability compensation. Finally, the calculated burial depth value is output. This provides an accurate computational foundation for subsequent depth verification and positioning. Vector synthesis calculations improve the accuracy of burial depth calculations; equipment calibration coefficients compensate for the influence of hardware characteristics; and environmental correction factors enhance the system's adaptability.

[0138] 4.4 To ensure the validity of the calculated burial depth, the system sets an effective range for the signal field strength. ;in, Indicates the lower limit of the effective range of the signal field strength; This indicates the upper limit of the effective range of the signal field strength; based on the calculated burial depth, an array is set to store the depth data. This is used to record calculation deviations and validity verification results. First, a standard for the effective range of the signal field strength is established. Based on the technical specifications of the detection equipment and actual test data, the effective working range of the signal field strength is determined. This ensures the reliability of the measurement results. Then, a depth data storage array is created. This is used to record the calculated burial depth and corresponding deviation information for each measurement. Next, a validity judgment mechanism is established, judging based on the following rules: when... When the calculated value exceeds the upper limit, a positive deviation is recorded; when... When the calculated value is below the lower limit, a negative deviation is recorded; when... When the calculated value is within the valid range and without deviation, a deviation analysis mechanism is then established to statistically analyze the distribution characteristics of the deviation data and identify systematic and random deviations. Finally, an effectiveness assessment report is generated to determine whether the signal field strength meets the measurement requirements for measuring the burial depth of the target pipeline. Validity range verification ensures the reliability of the measurement results; the deviation analysis mechanism provides a quality control method; and the assessment report provides operators with clear guidance on measurement effectiveness.

[0139] Subsequently, deviation analysis results are verified to confirm the consistency between the pipeline identification strength data and the tracer coding data. Specifically, when the deviation value is zero, it indicates that the pipeline identification strength is within the effective range, and the tracer coding data perfectly matches the target pipeline identification information. At this point, the system confirms the accuracy of the coding identification, records the perfect match status, and marks the verification result as high confidence, providing a reliable target confirmation basis for subsequent location detection.

[0140] When positive or negative deviations exist, the ratio of the absolute value of the deviation to the upper and lower thresholds of the effective range is used to obtain the consistency deviation index. The specific calculation method is as follows: When... or Consistency Deviation Index This indicator reflects the degree to which the recognition intensity deviates from the effective range; the smaller the value, the less severe the deviation.

[0141] When the consistency deviation index is less than the preset tolerance level, it is judged as basically consistent. The system sets a tolerance threshold. (Usually 0.1-0.2), when At this point, the system considers that although there are slight deviations, they are within acceptable limits, and the encoding and recognition results are basically reliable. The system will then record the deviation information for quality monitoring, but will still accept the recognition results.

[0142] When the consistency deviation index exceeds the tolerance level, it is judged as inconsistent and recoding and identification are required. If the deviation in recognition strength is too large, it indicates a potential signal quality issue, encoding error, or equipment malfunction. The system will automatically initiate a re-identification process, including re-acquiring the tracer signal, re-performing demodulation and decoding, and recalculating the recognition parameters, until a consistency verification result is obtained or the maximum number of retries is reached. Through quantitative evaluation of the consistency deviation index, the system can accurately determine the reliability of the encoding match; the tolerance mechanism balances the requirements of recognition accuracy and practicality; and the automatic re-identification function ensures the stability and reliability of the system.

[0143] 4.5 Multi-dimensional signal reception also includes initializing detection parameters using an omnidirectional scanning mode based on the target pipeline detection dataset to obtain initial spatial distribution data of the tracer signal source location. Based on the initial spatial distribution data, the signal intensity of all scanning angles is averaged to obtain the average scan signal. According to the average scan signal, the signal intensity difference between the current scanning angle and the previous angle is obtained and normalized by combining the angle measurement interval to obtain signal direction gradient data. First, an omnidirectional scanning mechanism is established, acquiring tracer signal intensity data in all directions through 360-degree rotation scanning or using a ring antenna array to form initial spatial distribution data of the signal source location. Then, the average scan signal is calculated by summing the signal intensities of all scanning angles and dividing by the number of angles to obtain the average signal intensity baseline. Next, the signal direction gradient is calculated by... The formula calculates the rate of change of signal strength between adjacent angles, where, Indicates the signal strength at the current angle. Indicates the angle measurement interval. The angle is represented. Subsequently, directional gradient data processing is performed, filtering and smoothing the calculated gradient data to eliminate measurement noise. Finally, a spatial distribution map is generated, providing directional reference for target localization and anomaly identification. Omnidirectional scanning improves the accuracy of signal source localization; directional gradient calculation enhances direction recognition capabilities; and the spatial distribution map provides an important reference for anomaly detection.

[0144] By integrating five sub-steps, a complete multi-dimensional signal reception process is formed: peak field strength calculation provides a stable benchmark for subsequent processing; depth gradient calculation reflects the spatial characteristics of signal propagation; burial depth calculation provides quantitative depth information; validity verification ensures the reliability of measurement results; and spatial distribution analysis enhances target recognition capabilities. The final output includes comprehensive signal data containing field strength distribution, depth gradient, burial depth calculation, and validity assessment, providing complete signal feature information for the coding, recognition, and verification in Example 5.

[0145] The output of this embodiment will be directly passed to the encoding recognition and verification in Embodiment 5, providing accurate signal feature data for encoding parsing and identity recognition. Meanwhile, the depth calculation and validity verification mechanism established in this embodiment will continue to be used in the precise positioning depth measurement in Embodiment 6. Standardization of signal processing is achieved through mathematical model calculation; multi-dimensional measurement improves the comprehensiveness of signal reception; the validity verification mechanism ensures measurement quality; spatial distribution analysis enhances the accuracy of target recognition; and the standardized data format provides a unified foundation for subsequent processing.

[0146] When the deviation value is positive, it indicates that the recognition strength is too high, which may be due to signal interference or multiple reflections, requiring signal filtering and interference suppression. When the deviation value is negative, it indicates that the recognition strength is too low, which may be due to signal attenuation or equipment failure, requiring enhanced signal reception or checking the equipment status. Based on the deviation analysis results, an adaptive recognition threshold adjustment mechanism is established to dynamically adjust the effective range of recognition strength according to environmental conditions and signal quality. Through multiple measurements and statistical analysis, a confidence assessment is provided for the final identity verification.

[0147] Example 5 describes in detail the specific implementation of the coding identification and verification step in the automated underground pipeline detection method. This step, based on the tracer signal received in Example 4, identifies the pipeline identity information through demodulation and decoding, and compares and verifies it with a preset coding database.

[0148] 5.1 Encoding Recognition and Verification First, the received tracer signal is demodulated to extract the digital encoding sequence carrying pipeline identity information. A signal demodulation mechanism is established first, and the received signal is demodulated according to the modulation method determined in Example 3, including amplitude demodulation, frequency demodulation, or phase demodulation. The system automatically identifies the modulation type and selects a suitable demodulation algorithm. Then, the encoding sequence is extracted, extracting the complete encoding sequence from the demodulated digital signal, and performing error detection and correction. Next, encoding format recognition is established, automatically identifying the encoded data structure and information fields according to a preset encoding format standard. Subsequently, encoding integrity verification is performed, verifying the integrity and correctness of the encoding sequence through checksums and redundancy information. Finally, the pipeline identity code is output, providing accurate encoded data for subsequent identity verification. Automatic demodulation processing improves the efficiency of encoding extraction; the error detection mechanism ensures the reliability of the encoded data; and the format recognition function enhances the system's compatibility.

[0149] 5.2 Based on the extracted pipeline identification code, the system... To obtain the average pipeline identification value; among which, This represents the average value for pipeline identification. This represents the attribute encoding data of the i-th pipeline; This indicates the number of pipeline attribute codes. The calculation involves statistically averaging basic attribute data such as pipeline type identification, material code, and specification information to obtain a standardized identification reference value. First, an attribute code classification mechanism is established, categorizing the received pipeline identification codes according to attributes such as type identification, material code, and specification information. The system automatically identifies the values ​​of each attribute field based on the coding format. Then, the coding data is statistically analyzed, counting the number (n) and values ​​of each type of attribute code. This ensures the completeness of the statistical data. Next, the mean value of the identified individuals is calculated. The standardized identity recognition mean is obtained by summing all valid attribute codes and dividing by the number of codes. Then, a mean stability verification is established, and the stability and consistency of the mean calculation are verified through multiple measurements and comparisons. Finally, an identity recognition benchmark is generated, providing a stable reference basis for subsequent gradient calculations. Statistical averaging improves the stability of identity recognition; classification ensures accurate identification of different attributes; and stability verification enhances the reliability of the calculation results.

[0150] 5.3 Based on the acquired average pipeline identification value, the system... To obtain the gradient of pipeline property changes; where, This indicates the gradient of pipeline property changes; This represents the pipeline coding data at the current identification time; This represents the pipeline coding data at the previous identification time. This represents the encoding recognition time interval. This calculation reflects the changes in pipeline attributes over time and is used to detect the consistency of encoding recognition. By establishing a time-series encoding monitoring mechanism, the encoding is monitored according to a set time interval. Continuous acquisition of pipeline coding data and Establish a time-series database. Then calculate the encoding change difference. This reflects the magnitude of change in coded data at adjacent time points. Next, time normalization is performed, dividing the difference in coded changes by the time interval. This yields the rate of change of the encoding per unit time. This is then compared with the average value of identity recognition. Multiplication standardizes the gradient changes, eliminating systematic differences between different pipeline types. Finally, attribute change curves are generated, providing temporal characteristic references for consistency analysis. Furthermore, temporal monitoring improves the continuity of coding identification; normalization eliminates the influence of time scale; and standardized gradient calculation enhances the comparability between different pipelines.

[0151] 5.4 Based on the acquired pipeline property change gradient, the system... To obtain the pipeline identification strength; among which, Indicates the strength of pipeline identification; This represents the attribute recognition frequency coefficient. The calculation comprehensively considers the magnitude and frequency characteristics of the changing gradient to generate a strength index characterizing the reliability of pipeline identification. First, the gradient magnitude is calculated... Obtain the absolute value of the gradient of pipeline property changes to reflect the intensity of the change. Then calculate the frequency correction term. Among them, the attribute recognition frequency coefficient Calibration is performed based on the encoding format and transmission characteristics to compensate for the impact of frequency response on recognition strength. Next, strength synthesis calculations are performed, multiplying the gradient strength and frequency correction term to obtain a comprehensive recognition strength index. Subsequently, a strength evaluation standard is established, setting the criteria for judging recognition strength based on pipeline type and application requirements; higher recognition strength indicates better accuracy of encoded recognition. Finally, a recognition strength report is generated, providing a quantitative reliability assessment for identity verification. Modulus calculation highlights the strength information of varying characteristics; frequency correction improves the accuracy of recognition strength; and quantitative assessment provides operators with clear reliability guidance.

[0152] 5.5 Based on the gradient of pipeline attribute changes and the mean of identity recognition, the system summarizes and forms a special recognition coding dataset. This dataset contains unique identifiers for target pipelines, pipeline types, ownership information, and related calculation parameters. First, the dataset structure is defined, including basic information fields such as pipeline unique identifier (ID), pipeline type (Type), and ownership unit (Owner), as well as the identity recognition mean. Attribute change gradient Recognition strength The data is processed through calculated fields. Then, a data integrity check is performed to verify the validity and completeness of each field, ensuring the quality of the dataset. Next, a data association mechanism is established to link and bind basic information with calculated parameters, forming a complete pipeline identity data archive. Following this, data standardization processing is implemented to unify the format and convert units for different types of data, ensuring data consistency. Finally, a standardized coded dataset is generated to provide a unified data format for subsequent comparison and verification. Structured data organization improves the efficiency of information management; integrity checks ensure data quality; and standardization processing enhances data compatibility.

[0153] 5.6 To ensure the accuracy of code recognition, the system sets an effective range for pipeline identification strength. A deviation analysis mechanism was established for verification. First, a standard for the identification strength range was established, and the effective working range of the identification strength was determined based on system performance testing and practical application experience. This ensures the reliability of the identification results. Then, a deviation analysis dataset is created. This is used to record the intensity value and corresponding deviation information for each identification. Next, a deviation judgment mechanism is established, judging according to the following rules: when... When the signal strength is too high, it indicates that there may be signal interference or multiple reflections; when When the signal strength is too low, it indicates that the recognition strength is too low, which may be due to signal attenuation or equipment failure; when When the threshold is set at a certain value, it indicates that the recognition strength is within the effective range, and the encoded recognition is reliable. Subsequently, an adaptive threshold adjustment mechanism is established to dynamically adjust the effective range of the recognition strength based on environmental conditions and signal quality, improving the system's adaptability. Finally, a recognition reliability assessment model is established, providing a confidence level assessment for the final identity verification through multiple measurements and statistical analysis. Effective range verification ensures the reliability of the recognition results; adaptive adjustment enhances the system's environmental adaptability; and the reliability assessment provides operators with a confidence level reference.

[0154] The final step in 5.7 code identification and verification is to compare the identification results with a pre-set code database to confirm the identity information of the target pipeline. First, a code database query mechanism is established to search for the corresponding pipeline information record in the pre-set code database based on the unique identifier of the identified pipeline. Then, a multi-dimensional information comparison is performed, comparing the identified pipeline type, ownership unit, specifications, etc., with the database records item by item. Next, a matching score is calculated, and a comprehensive matching degree is calculated based on the matching of each information field to assess the credibility of the identity verification. Subsequently, a verification result judgment mechanism is established to comprehensively determine the validity of the verification result based on the matching score and identification strength. Finally, an identity verification report is generated, containing detailed information such as matching results, credibility assessment, and anomaly information, providing accurate target confirmation for subsequent location detection. Database comparison ensures the accuracy of pipeline identity; multi-dimensional verification improves the reliability of identification; and comprehensive evaluation provides operators with comprehensive verification information.

[0155] By integrating seven sub-steps, a complete coding recognition and verification process is formed; statistical analysis and gradient calculation improve the stability and accuracy of recognition; adaptive mechanisms and reliability assessment ensure the practicality of the system; and standardized data processing provides reliable input for subsequent steps.

[0156] This embodiment receives the multi-dimensional signal reception results from Embodiment 4 as input and performs encoding, identification, and verification processing on the tracer signals. The output of this embodiment is directly passed to the integrated positioning and depth calculation in Embodiment 6, providing accurate pipeline identification information for the final detection results. Automated encoding and identification reduces the workload of manual judgment; statistical analysis and gradient calculation improve the accuracy of identification; adaptive thresholds and reliability assessments enhance the system's intelligence; unified data formats and verification standards provide a standard basis for subsequent processing; and detailed verification logs ensure the traceability of the identification process.

[0157] Example 6 details the implementation of the integrated positioning and depth calculation step in the automated underground pipeline detection method. This step integrates the signal reception data from Example 4 and the authentication results from Example 5, achieving high-precision pipeline positioning and depth calculation through multi-dimensional data fusion.

[0158] 6.1 Integrated Positioning and Depth Calculation First, a multi-dimensional data fusion mechanism is established to uniformly process signal strength data, depth gradient information, and identity recognition results. A data fusion framework is first established, defining data interfaces and format standards from different implementations to ensure data compatibility and consistency. The system automatically identifies data types and performs format conversion. Then, data quality assessment is performed, checking the validity and scoring the quality of various input data types, identifying and eliminating abnormal data. Next, a data weight allocation mechanism is established, assigning corresponding weight coefficients to different types of data based on the reliability of the data source and measurement accuracy. Subsequently, data time-series synchronization is performed to ensure consistency of data from different measurement times in the time dimension. Finally, a fused data model is established, integrating multi-dimensional data into a unified data structure to provide standardized input for subsequent calculations. Multi-dimensional fusion improves the accuracy of positioning calculations; quality assessment ensures the reliability of input data; and weight allocation optimizes the contribution of different data sources.

[0159] 6.2 Based on fused multi-dimensional data, through To obtain the horizontal coordinates of the pipeline; where, Indicates the horizontal coordinates of the pipeline; This represents the weight coefficient of the i-th measurement point; These represent the x and y coordinates of the i-th measurement point, respectively. This represents the signal direction angle at the i-th measurement point; This indicates the number of valid measurement points. The calculation combines the location information of multiple measurement points using a weighted average method, taking into account signal directionality to achieve precise positioning. First, a coordinate system for the measurement points is established, creating a unified coordinate reference system for the detection area, and the precise coordinates of each measurement point are recorded. Then, signal direction angle measurements are performed, and the signal arrival direction angle at each measurement point is determined through phase difference analysis of the antenna array or signal strength comparison. Next, the weighting coefficients are calculated. The weight values ​​for each measurement point are determined based on factors such as signal strength, measurement accuracy, and environmental conditions. Then, coordinate transformation is performed to convert the local coordinates of each measurement point to a unified reference coordinate system using orientation angles. Finally, the weighted average position is calculated. This method yields the precise coordinates of the pipeline's position on the horizontal plane. Multi-point weighted averaging improves the accuracy of the position measurement; orientation angle correction eliminates directional errors; and a unified coordinate system ensures the consistency of the position information.

[0160] 6.3 Based on the acquired horizontal position coordinates, through To obtain the final calculated burial depth value; among which, This represents the final calculated burial depth. This represents the reliability weight of the j-th depth measurement; This represents the calculated burial depth value for the j-th time. Indicates the number of effective depth measurements; This represents the environmental correction factor. The calculation integrates multiple depth measurements, achieving high-precision depth calculation through reliability weighting and environmental correction. The calculated depth value for each measurement... The acquisition is based on the pre-calibration algorithm Preliminary calculations are performed, followed by optimization and correction based on signal attenuation characteristics and multi-point measurements.

[0161] First, a multi-depth measurement mechanism is established, in which multiple depth measurements are performed at a defined horizontal position, with each measurement employing a pre-calibration algorithm. Obtain the initial burial depth value, where, The distance measurement value is obtained by measuring the distance between the peak and valley values ​​of the signal strength. These are pre-calibrated equipment constants based on equipment characteristics and environmental conditions. Then, signal attenuation analysis and multipath effect correction are performed based on the initial burial depth value to obtain the optimized burial depth calculation value. Next, the reliability of the measurement was evaluated based on the signal quality of each measurement, the stability of the distance measurement values, and the equipment parameters. The reliability weight is calculated based on factors such as applicability, environmental conditions, and equipment status. Among these, the accuracy of the pre-calibration algorithm directly affects the weighted score. Subsequently, environmental correction factors are calibrated, and these correction factors are determined based on environmental parameters such as soil type, humidity, temperature, and geological conditions. And consider the device constants in the precalibration algorithm. Adaptive adjustments are made to the current environment. Then, a weighted average depth is calculated by averaging all effective depth measurements obtained from pre-calibration algorithm optimization according to reliability weights. Finally, an environmental correction is applied, multiplying the weighted average result by an environmental correction factor to obtain the final calculated burial depth. This ensures that the results maintain both the simplicity of the pre-calibration algorithm and the reliability of multi-dimensional verification. Combining the pre-calibration algorithm ensures the fundamental accuracy of depth calculations; multiple measurements improve the stability of depth calculations; reliability-weighted optimization optimizes data quality; and environmental correction enhances the accuracy of the calculation results.

[0162] 6.4 To verify the accuracy of the integrated positioning results, the system establishes a position accuracy evaluation mechanism. ,in, This indicates the position accuracy assessment value; The standard deviation representing the horizontal position; This represents the standard deviation of depth measurements. First, multiple independent measurements are performed, repeating position and depth measurements under identical conditions to obtain statistical sample data. Then, the horizontal position deviation is calculated, the horizontal coordinate deviations of each measurement are statistically analyzed, and the standard deviations in the x and y directions are calculated. Next, the depth measurement deviation is calculated, the deviation of each depth measurement is statistically analyzed, and the standard deviation of the depth is calculated. A comprehensive accuracy assessment is then performed, calculating the positional accuracy value based on the overall deviation in three-dimensional space. Finally, accuracy grading standards were established, with different levels of accuracy standards set according to application requirements, to assess whether the measurement results met engineering requirements. Statistical analysis provided an objective accuracy assessment; the three-dimensional comprehensive evaluation fully reflected the positioning accuracy; and the grading standards provided clear guidance for the application of the results.

[0163] 6.5 Integrated positioning and depth calculation also includes measurement uncertainty analysis, through... Calculate the measurement uncertainty; where, Indicates the expanded uncertainty; Indicates the inclusion factor; Indicates Type A uncertainty; This represents Type B uncertainty. First, a Type A uncertainty assessment is performed by statistically analyzing the dispersion of repeated measurements to calculate the uncertainty caused by random factors. Then, a Type B uncertainty assessment is performed, estimating the uncertainty based on systematic factors such as equipment accuracy, environmental conditions, and standard references. Next, determine the inclusion factor. A suitable coverage factor is selected based on the distribution characteristics of the measurement data and the required confidence level. The expanded uncertainty is then calculated. The Type A and Type B uncertainties are combined and multiplied by a coverage factor to obtain the final measurement uncertainty. Finally, an uncertainty report is generated, detailing the source and impact of each uncertainty component, providing a quantitative assessment of the reliability of the measurement results. Scientific uncertainty analysis improves the reliability of the results; classification assessment clarifies the sources of error; and quantitative analysis provides a basis for quality control.

[0164] 6.6 Final Output: Comprehensive Positioning Results This includes complete information such as horizontal position, burial depth, accuracy assessment, uncertainty, timestamp, and identity verification. First, a result data structure is established, defining a standardized output format containing all key information to ensure the completeness and readability of the results. Then, a data integrity check is performed to verify the validity and consistency of each result data point, ensuring output quality. Next, timestamp information is added. Record the exact time the measurement is completed to ensure the timeliness and traceability of the results. Then, verify the identity. The location results are then linked to the pipeline identification information confirmed in Example 5 to ensure the accuracy of the results. Finally, a standardized report is generated, outputting all location results and quality assessment information in a unified format to provide users with complete detection conclusions. Standardized output ensures the standardization of results; integrity checks guarantee data quality; and identity association ensures the accuracy of the target.

[0165] By integrating six sub-steps, a complete integrated positioning and depth calculation process is formed; multi-dimensional data fusion and weighted calculation improve positioning accuracy; statistical analysis and uncertainty assessment ensure the reliability of the results; and standardized output provides users with complete detection information.

[0166] This embodiment receives the signal reception data from Embodiment 4 and the authentication result from Embodiment 5 as input, and outputs the final pipeline location and depth information through comprehensive analysis. The output of this embodiment serves as the final result of the entire detection process, providing accurate positioning data for pipeline management and engineering applications. Multi-dimensional data fusion maximizes information utilization; weighted calculation and environmental correction improve measurement accuracy; accuracy evaluation and uncertainty analysis ensure the reliability of the results; a standardized output format facilitates result storage and application; and a complete quality control process guarantees the reliability of the detection.

[0167] Example 7: This example details the hardware architecture and software implementation of an automated underground pipeline detection system. This system integrates the technical solutions from the preceding examples, forming a complete automated detection device.

[0168] 7.1 The system hardware architecture consists of three main components: an underground tracer module, a ground detection equipment module, and a data processing and display module.

[0169] The underground tracer module includes: a microcontroller unit responsible for encoding generation and signal modulation control; an RF transmission circuit for transmitting tracer signals; a power management system to provide a long-term stable power supply; environmental sensing sensors to monitor environmental parameters such as temperature and humidity; and a protective housing structure to ensure reliable operation of the equipment in underground environments.

[0170] The ground detection equipment module includes: a multi-band receiving antenna array to support signal reception at different frequencies; signal conditioning and amplification circuits to improve the quality of received signals; a digital signal processor to demodulate and encode signals; a position sensor system to provide precise position information for the equipment; and a human-machine interface to enable operation control and result display.

[0171] The data processing and display module includes: an embedded computing platform for performing complex data fusion and position calculations; a storage system for saving measurement data and calculation results; a communication interface for exchanging data with external systems; a display screen for providing real-time detection status and results; and a power supply system for providing power to the entire device.

[0172] 7.2 It adopts a modular design, including a device initialization module, a signal processing module, a data analysis module, and a result output module.

[0173] The device initialization module is responsible for hardware detection and parameter configuration during system startup, including functions such as antenna array calibration, signal processor initialization, communication interface configuration, and user parameter settings.

[0174] The signal processing module receives, demodulates, filters, and extracts features from the tracer signal, integrating the core algorithms from Examples 3 to 5. The data analysis module performs multi-dimensional data fusion, location calculation, depth analysis, and accuracy evaluation, realizing the comprehensive positioning function of Example 6.

[0175] The results output module is responsible for formatting the measurement results, storing data, generating reports, and facilitating external communication, ensuring the standardization and traceability of the results.

[0176] 7.3 Starting with equipment deployment, the entire detection task is automatically executed according to the preset detection protocol. First, equipment deployment and initialization are performed, deploying underground tracers in the target detection area and activating surface detection equipment, completing system self-checks and parameter configurations. Then, the automated detection process is executed, automatically performing signal transmission, reception, identification, and positioning operations according to the steps in Examples 1 to 6. Next, real-time data processing and quality monitoring are performed, analyzing data quality and making necessary parameter adjustments during the detection process. Subsequently, detection results and accuracy assessments are generated, outputting complete pipeline location, depth, and identification information, along with corresponding quality assessments. Finally, data storage and report generation are performed, storing the detection results in a standard format and generating a detailed detection report.

[0177] This embodiment integrates the aforementioned technical solutions into a complete hardware system, realizing the engineering application of automated underground pipeline detection; the modular software architecture ensures the maintainability and scalability of the system; and the automated workflow reduces the complexity of manual operation.

[0178] like Figure 2 As shown, an underground pipeline detection system based on tracer signals includes:

[0179] Tracer deployment module: used to associate a tracer with an underground pipeline; wherein the tracer radiates a tracer signal carrying a special identification code; the special identification code is generated based on the identity attributes of the target pipeline;

[0180] Data acquisition module: used to acquire underground pipeline tracer signals to obtain a target pipeline detection dataset; wherein, the pipeline detection dataset contains the frequency characteristics and coding characteristics of the tracer signals; the tracer signals are received by ground detection equipment; the ground detection equipment performs directional reception and preliminary screening of the tracer signals according to the frequency characteristics in the pipeline detection dataset; based on the preliminary screening of the tracer signals, the tracer signals are demodulated and decoded to obtain the target pipeline detection dataset;

[0181] Data verification module: used to compare and verify the target pipeline detection dataset with a special identification code to obtain a verification result; wherein, when the identification code matches successfully in the verification result, the target pipeline identity attribute is obtained and the location detection is performed; based on the verification result, the field strength and phase information of the verified tracer signal are obtained to obtain the target pipeline information data;

[0182] Data generation module: used to obtain the horizontal position and burial depth of the target pipeline based on the target pipeline information data;

[0183] Data analysis module: used to comprehensively analyze the target pipeline's identity attributes with the acquired horizontal position and burial depth to obtain the target pipeline detection results;

[0184] The tracer module includes:

[0185] Encoding generation unit: It is used to generate the specific identification code;

[0186] Modulation and transmission unit: It is used to modulate the code onto a radio frequency carrier and radiate it through an antenna;

[0187] Power management unit: It is used to provide operating power to the tracer;

[0188] The tracer can be installed in two ways: built-in and attached. Built-in tracers are pre-integrated into the inside or outer wall of non-metallic pipelines, while attached tracers can be detachably placed inside pipelines or attached to pipelines, valves, manhole covers, and other ancillary facilities.

[0189] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method of detecting underground pipes based on a tracer signal, characterized in that, include: Associate the tracer with underground pipelines; The tracer radiates a tracer signal carrying a special identification code; the special identification code is generated based on the identity attributes of the target pipeline. Collect underground pipeline tracing signals to obtain a target pipeline detection dataset; The pipeline detection dataset includes the frequency characteristics and coding characteristics of the tracer signal; the tracer signal is received by a ground detection device; the ground detection device performs directional reception and preliminary screening of the tracer signal based on the frequency characteristics in the pipeline detection dataset; based on the preliminary screened tracer signal, the tracer signal is demodulated and decoded to obtain the target pipeline detection dataset; The target pipeline detection dataset is compared and verified with a special identification code to obtain the verification result; In the verification result, when the identification code matches successfully, the target pipeline identity attribute is obtained and the location is detected; based on the verification result, the field strength and phase information of the verified tracer signal are obtained to obtain the target pipeline information data; The horizontal position and burial depth of the target pipeline are obtained based on the target pipeline information data; Specifically, the signal strength measurement range is set based on the pipeline detection dataset; the distance between the peak and valley values ​​of the signal strength is measured using the ground detection equipment to obtain the distance measurement value; The distance measurement value is compared with the signal strength measurement range to determine whether the distance measurement value is within the signal strength measurement range; when the distance measurement value is within the signal strength measurement range, a valid distance measurement result is obtained. Based on the distance measurement results and the preset depth calculation standard, the distance is matched with the preset depth calculation standard to obtain the matching result; The matching result indicates whether the distance measurement value meets the depth calculation requirements; The distance measurement value is converted into burial depth according to the pre-calibration algorithm to obtain the calculation result; the calculation result represents the target pipeline burial depth value calculated based on the distance data; The pre-calibration algorithm is as follows: The buried depth is obtained; wherein, The buried depth is represented as: The distance measurement value is represented as: The device constant is represented as: Based on the calculation results and the matching results, the burial depth of the target pipeline is determined; when the matching result is valid and the calculation result is within the measurement range, the burial depth value is obtained. Specifically, based on the field strength distribution characteristics of the tracer signal, through: To obtain the peak signal strength; among which, This represents the peak signal strength of the j-th tracer signal; Indicates the number of tracer signals; This represents the signal field strength data of the current tracer signal; Based on the peak value of the signal field strength, by: To obtain depth gradient data; in, Represents depth gradient data; This represents the signal field strength data of the first k tracer signals, where k represents the measurement interval of the tracer signals; Based on the aforementioned depth gradient data, through: To obtain the calculated burial depth value; among which, This represents the calculated burial depth. Indicates the calibration coefficient of the detection equipment; Indicates the depth correction factor; Set the effective range of the signal field strength ;in, Indicates the valley value of the effective range of signal field strength; The peak value of the effective range of the signal field strength is indicated; based on the calculated burial depth, the deviation change of the tracer signal measurement is set. ; when hour, ;when hour, ;when hour, This is used to determine whether the signal field strength meets the measurement requirements, in order to measure the burial depth data of the target pipeline. The target pipeline's identity attributes are comprehensively analyzed along with its horizontal location and burial depth to obtain the target pipeline detection results.

2. The method for detecting underground pipelines based on tracer signals according to claim 1, characterized in that, The tracer radiates a tracer signal carrying a special identification code, including: The tracer actively transmits the tracer signal, or the tracer generates the tracer signal by backscattering and modulating the excitation signal after receiving the excitation signal from the ground detection equipment. The tracer includes an encoding generator and a modulation and transmission circuit. The encoding generator generates a special identification code based on the identity attributes of the target pipeline. The modulation and transmission circuit modulates the code onto a radio frequency carrier and radiates it through an antenna. The ground detection equipment includes a receiving antenna array and a signal processing unit. The antenna array receives the tracer signal, and the signal processing unit demodulates and decodes the received tracer signal.

3. The method for detecting underground pipelines based on tracer signals according to claim 1, characterized in that, The target pipeline detection dataset is compared and verified with a special identification code to obtain the verification results, including: Based on the pipeline detection dataset, the average pipeline identification value is obtained; Based on the pipeline identification average, obtain the pipeline attribute change gradient; Based on the gradient of pipeline attribute changes, the pipeline identification strength is obtained; Based on the pipeline attribute change gradient and pipeline identification mean, the special identification code dataset is formed; the upper and lower thresholds of the pipeline identification strength range are set to construct the effective range interval of pipeline identification strength; the real-time monitored pipeline identification strength is compared and analyzed with the effective range to obtain the deviation analysis results; and a recognition strength deviation value dataset is established based on the deviation analysis results. Specifically, when the pipeline identification strength exceeds the upper limit threshold, the difference between the pipeline identification strength and the upper limit value is obtained as a positive deviation; when the pipeline identification strength is lower than the lower limit threshold, the difference between the pipeline identification strength and the lower limit threshold is obtained as a negative deviation; when the identification strength is within the effective range, the deviation value is set to zero. The results of the deviation analysis were used to verify whether the pipeline identification strength data and the tracer encoding data matched consistently.

4. The method for detecting underground pipelines based on tracer signals according to claim 3, characterized in that, Verify the consistency between the pipeline identification strength data and the tracer encoding data through deviation analysis results, including: When the deviation value is zero, it indicates that the pipeline identification strength is within the effective range and the tracer encoding data is a complete match with the target pipeline identification information; When there is a positive or negative deviation, the ratio of the absolute value of the deviation to the upper and lower limits of the effective range is used to obtain the consistency deviation index. When the consistency deviation index is less than the preset tolerance, it is judged as basically consistent; when the consistency deviation index exceeds the tolerance, it is judged as inconsistent and re-encoding and recognition are required.

5. The method for detecting underground pipelines based on tracer signals according to claim 1, characterized in that, Based on the successful code matching verification result, the complete coded information transmitted by the tracer is extracted and parsed to obtain the identity of the target pipeline; Based on the target pipeline identity, the radio frequency transceiver function of the ground detection equipment is used to continuously receive radio frequency signals from the identification tracer to obtain verified tracer signal field strength data and phase information data. The acquired tracer signal field strength and phase information are processed and decoded to obtain the processing and decoding results; The received radio frequency signal is amplified with low noise and down-converted, and field strength variation characteristics and phase difference characteristics are extracted to form a target pipeline information dataset containing signal strength distribution, phase difference distribution and frequency response characteristics. Based on the target pipeline information data, perform horizontal positioning of the target pipeline; Among them, multi-antenna array differential measurement technology is used to compare the signal strength difference and phase difference received at different antenna positions. When the signal strength reaches the peak value and the phase difference between the antennas is equal to zero, the accurate horizontal position coordinates of the pipeline are determined to be directly below the detection device. Based on accurate horizontal coordinates, the target pipeline burial depth is determined: Among them, by utilizing the physical law of signal field strength changing with distance, the peak and valley points of signal strength are measured in the vertical direction, and the distance measurement value is converted into a burial depth value through a pre-calibrated depth calculation algorithm; Based on horizontal location positioning and burial depth acquisition, comprehensive positioning and detection results of the target pipeline are obtained.

6. The method for detecting underground pipelines based on tracer signals according to claim 5, characterized in that, The target pipeline's identity attributes are comprehensively analyzed in conjunction with the integrated positioning detection results to construct a correlation verification mechanism between identity attributes and positioning data; Among them, the identification of the target pipeline is verified by cross-validating the target pipeline's identity attribute information with the horizontal position coordinates and burial depth data, and by comparing and analyzing the signal characteristic parameters of the target pipeline's type attribute with the actual measured radio frequency signal parameters. Based on the correlation verification mechanism, a detection result evaluation model is constructed; The detection result evaluation model outputs an accuracy evaluation result of the pipeline detection results by minimizing the objective function, combining the data transmission constraint index dataset and identity attribute verification parameters. The newly acquired target pipeline identity attributes and integrated positioning detection results are entered into the detection result evaluation model, and a comprehensive analysis and verification are performed in conjunction with the target pipeline identity attributes; the detection result evaluation model outputs the evaluation results of the target pipeline detection results based on identity attribute verification; When the consistency verification between the identity attributes and the positioning data meets the accuracy requirements, the accurate target pipeline detection result is output; otherwise, depending on the inconsistency type, including identity mismatch or deviation in location coordinates, the corresponding signal receiving step or identity recognition step is returned for reoperation; thus, the comprehensive analysis result of the target pipeline identity attributes and the integrated positioning detection result is finally obtained.

7. The method for detecting underground pipelines based on tracer signals according to claim 3, characterized in that, Based on the target pipeline detection dataset, the detection parameters are initialized using an omnidirectional scanning mode to obtain the initial spatial distribution data of the tracer signal source location; Based on the initial spatial distribution data, the average value of the signal intensity at all scanning angles is calculated to obtain the average value of the scanning signal. Based on the average value of the scanning signal, the signal intensity difference between the current scanning angle and the previous angle is obtained, and normalization is performed in combination with the angle measurement interval to obtain signal direction gradient data. Based on the signal direction gradient data, a target pipeline abnormal state identification model is constructed; Among these methods, abnormal states of target pipelines are identified by analyzing the spectral characteristics and propagation path changes of the tracer signal. The pipeline damage index is obtained by calculating the square of the ratio of the spectral characteristic deviation to the standard deviation and the square of the ratio of the propagation path deviation to the standard deviation, adding the two together and taking the square root. Based on the pipeline damage index, an anomaly classification and judgment mechanism is established; Based on the judgment results of the anomaly classification judgment mechanism, the environmental error compensation coefficient containing the impact of the anomaly state is obtained by combining the environmental impact coefficient and environmental factors with the damage state impact coefficient and pipeline damage index. Based on the environmental error compensation coefficient, the final location data of the target pipeline where the abnormality occurred is obtained by subtracting the environmental error compensation amount and the position offset caused by the abnormal state from the original horizontal position coordinates.

8. A tracer signal-based underground pipeline detection system, used to execute the tracer signal-based underground pipeline detection method as described in any one of claims 1-7, characterized in that, include: Tracer deployment module: used to associate the tracer with underground pipelines; wherein the tracer radiates a tracer signal carrying a special identification code; the special identification code is generated based on the identity attributes of the target pipeline; Data acquisition module: used to acquire underground pipeline tracer signals to obtain a target pipeline detection dataset; wherein, the pipeline detection dataset contains the frequency characteristics and coding characteristics of the tracer signals; the tracer signals are received by ground detection equipment; the ground detection equipment performs directional reception and preliminary screening of the tracer signals according to the frequency characteristics in the pipeline detection dataset; based on the preliminary screening of the tracer signals, the tracer signals are demodulated and decoded to obtain the target pipeline detection dataset; Data verification module: used to compare and verify the target pipeline detection dataset with a special identification code to obtain a verification result; wherein, when the identification code matches successfully in the verification result, the target pipeline identity attribute is obtained and the location detection is performed; based on the verification result, the field strength and phase information of the verified tracer signal are obtained to obtain the target pipeline information data; Data generation module: used to obtain the horizontal position and burial depth of the target pipeline based on the target pipeline information data; Data analysis module: used to comprehensively analyze the target pipeline's identity attributes with the obtained horizontal position and burial depth to obtain the target pipeline detection results.

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