Underground pipe network geophysical prospecting device and method based on GIS

Abstract

The invention discloses an underground pipe network geophysical prospecting device and method based on a GIS, and relates to the technical field of underground pipe network geophysical prospecting, the underground pipe network geophysical prospecting device comprises an electromagnetic induction detection unit, an elastic wave detection unit and a data processing unit, and further comprises a coupling interference regulation and control assembly, a heat exchange regulation module, an elastic modulus compensation module and a data processing unit; the coupling interference regulation and control assembly is arranged between the electromagnetic induction detection unit and the elastic wave detection unit, and the coupling interference regulation and control assembly comprises a heat exchange regulation module and an elastic modulus compensation module; in the invention, the heat effect is actively controlled through the heat exchange regulation module in the coupling interference regulation assembly fundamentally, and the elastic modulus compensation module compensates the change of the elastic wave propagation characteristic in real time, so that the condition that the elastic wave propagation deviates from the expected path and characteristic due to the interaction of different physical quantities such as electromagnetic induction and elastic waves is avoided; and the accuracy of detection data is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of underground pipe network geophysical exploration, and in particular to an underground pipe network geophysical exploration device and method based on GIS. Background Art

[0002] With the rapid development of urban construction, accurate detection of underground pipe networks is of vital importance for the maintenance and renewal of urban infrastructure and the planning and construction of new projects. Geophysical exploration technology for underground pipe networks based on GIS (Geographic Information System) has emerged to achieve accurate positioning, information collection and comprehensive management of underground pipe networks, and to provide strong support for the rational use of urban underground space.

[0003] In the existing underground pipe network geophysical exploration methods, in order to overcome the limitations of a single detection method and improve the comprehensiveness and accuracy of detection, multiple detection physical quantities are often used at the same time, such as electromagnetic induction, elastic waves, thermal infrared and other methods for joint detection. However, in actual applications, when these detection physical quantities are used at the same time, complex coupling interference problems will arise between different physical quantities.

[0004] Taking the joint detection of electromagnetic induction and elastic wave as an example, when the alternating magnetic field generated by electromagnetic induction acts on underground metal pipes, it will induce a thermal effect. This process involves the conversion of electromagnetic energy into thermal energy. The elastic modulus of the underground medium is a key factor in determining the propagation characteristics of elastic waves. The thermal effect changes the elastic modulus, which in turn affects the propagation speed, direction and other characteristics of the elastic wave, ultimately causing the propagation of the elastic wave to deviate from its original expected propagation path and characteristics based on the normal elastic modulus state of the underground medium. This mutual influence between different detection physical quantities is not a simple linear relationship, but under the joint action of the complex underground geological structure and multiple material components, multiple physical processes are intertwined, forming an extremely complex coupling interference situation.

[0005] From the perspective of technical response, conventional technical means are incapable of solving this coupling interference problem. The parameter adjustment of a single detection method is only based on the optimization of its own physical quantity, and cannot fully consider the correlation with other physical quantities, so it is difficult to effectively eliminate coupling interference. Similarly, conventional isolation measures, such as setting up physical shielding, etc., because different physical quantities act simultaneously in the same underground space, and their mutual influence may be transmitted through multiple media, it is difficult to accurately block and isolate the interference paths between different physical quantities, and it is impossible to fundamentally solve the coupling interference problem.

[0006] In view of this, a GIS-based underground pipe network geophysical exploration device and method are provided to overcome the above problems. Summary of the invention

[0007] The object of the present invention is to provide an underground pipe network geophysical exploration device and method based on GIS to solve the problems raised in the above background technology.

[0008] In order to solve the above technical problems, the present invention provides a GIS-based underground pipe network geophysical exploration device, including an electromagnetic induction detection unit, an elastic wave detection unit and a data processing unit, and also includes: Coupling interference control component: The coupling interference control component is arranged between the electromagnetic induction detection unit and the elastic wave detection unit, and the coupling interference control component includes a heat exchange adjustment module and an elastic modulus compensation module; Heat exchange regulation module: used to actively adjust the heat exchange rate around the underground metal pipeline according to the alternating magnetic field strength and frequency generated by the electromagnetic induction detection unit; Elastic modulus compensation module: used to monitor the change of elastic modulus of underground medium in real time, and dynamically adjust the frequency and wavelength of elastic waves emitted by the elastic wave detection unit according to the change, so as to compensate for the change of elastic wave propagation characteristics caused by coupling interference; Data processing unit: connected to the electromagnetic induction detection unit, elastic wave detection unit and coupling interference control component respectively, used to receive and process the data collected by each unit, and transmit the processed data to the GIS system.

[0009] Furthermore, the heat exchange regulation module includes a heat exchange pipe network and a temperature regulation medium. The heat exchange pipe network surrounds the detection coil of the electromagnetic induction detection unit and is connected to the temperature regulation medium supply device.

[0010] Furthermore, the temperature regulating medium is a liquid medium with high specific heat capacity and low thermal conductivity.

[0011] Furthermore, the elastic modulus compensation module includes an elastic modulus sensor array and an elastic wave parameter adjustment device, the elastic modulus sensor array is distributed in the underground detection area, and the elastic wave parameter adjustment device receives elastic modulus change data.

[0012] Furthermore, the sensors in the elastic modulus sensor array use high-precision pressure sensors based on micro-electromechanical system technology to sensitively sense tiny changes in the elastic modulus of the underground medium and convert them into electrical signals for transmission.

[0013] Furthermore, the data processing unit includes a data preprocessing module, a data fusion module and a data correction module. The data preprocessing module is used for denoising, amplifying and analog-to-digital conversion of data collected by the electromagnetic induction detection unit and the elastic wave detection unit; the data fusion module is used for fusing the preprocessed data and adopting a deep learning-based algorithm to establish a nonlinear mapping relationship between the data; the data correction module is used for correcting the fused data according to the information fed back by the coupling interference control component.

[0014] A GIS-based underground pipe network geophysical exploration method comprises the following steps: First, the electromagnetic induction detection unit is turned on to generate an alternating magnetic field for preliminary detection of underground metal pipelines, and the initial alternating magnetic field intensity and frequency information are recorded; Next, the heat exchange regulation module in the coupling interference regulation component is turned on to adjust the flow rate and temperature of the temperature regulation medium in the heat exchange pipeline network according to the previously recorded alternating magnetic field strength and frequency, actively control the heat exchange rate around the underground metal pipeline, and stabilize the elastic modulus of the underground medium; Subsequently, the elastic wave detection unit is turned on to emit elastic waves, and the elastic modulus compensation module in the coupling interference control component is turned on. The elastic modulus sensor array is used to monitor the change of the elastic modulus of the underground medium in real time, and the change data is transmitted to the elastic wave parameter adjustment device to dynamically adjust the frequency and wavelength of the elastic wave. Then, the data processing unit receives the data from the electromagnetic induction detection unit, elastic wave detection unit and coupling interference control component, and performs denoising, amplification and analog-to-digital conversion in turn through the data preprocessing module. Then, the data fusion module fuses the data using an algorithm based on deep learning. Finally, the data correction module corrects the data according to the feedback of the coupling interference control component, and transmits the processed data to the GIS system. During the entire detection process, the changes in the underground environment and the working status of each unit are continuously monitored, and the working parameters of each module in the coupling interference control component are automatically and dynamically adjusted according to the preset rules and actual detection conditions; Moreover, when the detection area changes or an abnormal situation occurs, different detection modes and parameter configurations are automatically switched.

[0015] Furthermore, after the data processing unit transmits the data to the GIS system, the method further includes the steps of real-time updating and verifying the pipe network data in the GIS system, specifically: At regular intervals, the key features of the pipe network data obtained from this detection are automatically extracted, and compared and analyzed in detail with the corresponding features in the historical data; Using machine learning-based algorithms, through learning and training of a large amount of historical data and actual detection data, a correlation model between data features and errors and abnormal conditions is established; Based on this model, the possible errors and abnormal data in the detection data are automatically and accurately identified, and the corresponding correction algorithm is used to correct them; The corrected data are cross-validated with historical data again.

[0016] Compared with the prior art, the present invention has the following beneficial effects: First, the heat exchange adjustment module in the coupling interference control component actively controls the thermal effect from the source, and the elastic modulus compensation module compensates for the change in elastic wave propagation characteristics in real time, avoiding the situation where the elastic wave propagation deviates from the expected path and characteristics due to the interaction of different physical quantities such as electromagnetic induction and elastic waves, greatly improving the accuracy of the detection data. The positioning accuracy of the underground pipeline network is improved from the traditional meter level to the decimeter level or even the centimeter level, and the depth judgment error is reduced from ±20% to within ±5%. The pipeline network morphology is more detailed and accurate, providing reliable basic data for urban infrastructure maintenance, renewal and new project planning and construction, avoiding pipeline accidents caused by inaccurate data, reducing economic losses and the impact on residents' lives, and ensuring the safe and stable operation of urban infrastructure. It solves the problem that traditional technology is difficult to accurately obtain real information about underground pipelines.

[0017] The data processing unit uses advanced deep learning algorithms for data fusion and correction, and implements data update and verification mechanisms in the GIS system. Different physical quantity data complement and coordinate with each other, which improves the integrity of the comprehensive information of the pipeline network by about 30%. The accuracy of the corrected data has been stable at more than 90% for a long time, overcoming the limitations of traditional data processing methods in the face of complex coupled interference data, ensuring the accuracy and effectiveness of underground pipeline network information at all stages and working conditions, meeting the needs of urban construction for accurate detection and scientific management of pipeline networks, improving the scientific nature of urban underground space planning and the efficiency of land resource utilization, and ensuring the reasonable connection between new facilities and existing pipeline networks.

[0018] The dynamic adjustment mechanism of the geophysical method can automatically switch modes and adjust parameters according to the complex and changeable underground environment and detection conditions. Compared with the traditional fixed and lack of adaptability detection method, the number of detection interruptions is reduced by about 50%, and the overall detection efficiency is improved by about 40%. It can stably obtain high-quality detection data, effectively respond to different geological conditions and changes in the surrounding interference environment, promote the development of underground pipe network geophysical exploration technology, meet the high standards of urban construction for pipe network detection, and comprehensively improve the performance and effect of multi-physical quantity joint detection of underground pipe networks. Provide strong technical support for the sustainable development of urban underground pipe network detection, management and infrastructure construction, help urban construction move towards a scientific, efficient and sustainable direction, overcome the coupling interference problem that is difficult to solve with conventional technical means, and show excellent advantages and application value in complex underground pipe network detection scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a schematic diagram of a GIS-based underground pipe network geophysical exploration device and method thereof. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 , the present invention provides a technical solution: See also Figure 1 As shown, an embodiment of an underground pipe network geophysical exploration device and method based on GIS: Example scenario: At present, we are going to carry out detection and update work on the underground pipe network in an old urban area of ​​a large city. After years of development, the underground pipe network layout of this old urban area is extremely complex, covering various types of water supply and drainage pipes laid in different eras (including cast iron, PVC, etc.), power cable pipes (in different forms such as steel pipes and PVC protective pipes), and communication pipes (mostly plastic materials, some with metal shielding layers) and other types. In addition, the underground geological conditions are diverse, including different material components such as clay, sand, and local rock layers. At the same time, there are many operating municipal facilities in the surrounding area, such as substations, traffic light systems, etc. The electromagnetic signals generated by these facilities form a strong electromagnetic interference environment, which brings great challenges to the accurate detection of underground pipe networks.

[0022] The specific composition and functions of the underground pipe network geophysical exploration device based on GIS: Electromagnetic induction detection unit: A high-sensitivity electromagnetic induction detector is selected as the electromagnetic induction detection unit. Its detection coil has an adjustable working frequency range (for example, the frequency adjustment range is between 1kHz-100kHz) and can emit alternating magnetic fields of different frequencies.

[0023] Underground metal pipes of different materials and diameters have different response characteristics to alternating magnetic fields. For common cast iron water supply and drainage pipes, their electrical conductivity and magnetic permeability are relatively good, but because the pipe diameter may be large and the buried depth varies, a lower frequency (such as 5kHz-15kHz) alternating magnetic field is required to better penetrate the underground medium and produce clear and effective electromagnetic induction signals with the pipe; while for power cable steel pipes with smaller diameters, a higher frequency (such as 30kHz-50kHz) alternating magnetic field is more conducive to accurately detecting their position and direction. By setting a suitable low-frequency or high-frequency alternating magnetic field, it can meet the detection needs of different materials and pipe diameters.

[0024] With the function of flexibly adjusting the working frequency, it is possible to carry out preliminary detection and positioning of different types of metal pipes in a targeted manner, accurately delineate the approximate area where the pipes may exist, and provide reliable basic data for subsequent more detailed comprehensive detection, thus avoiding blind detection and improving overall detection efficiency.

[0025] Elastic wave detection unit: Equipped with professional elastic wave transmitting and receiving devices, the elastic wave transmitting device can generate elastic wave signals of different frequencies (the frequency range can be set to 100Hz-10kHz) and intensities, propagate underground and generate reflected waves when encountering obstacles such as pipelines. The receiving device can accurately capture these reflected wave signals.

[0026] According to elastic wave propagation theory, the propagation characteristics of elastic waves will change when facing pipelines of different depths and materials. For pipelines at deeper locations, due to the enhanced absorption and scattering of elastic wave energy by the underground medium, low-frequency elastic waves (such as 100Hz-500Hz) have stronger penetration ability than high-frequency elastic waves, and can more effectively propagate to the target pipeline location and generate detectable reflected waves; for pipelines of different materials, such as rigid cast iron pipelines and relatively flexible plastic pipelines, the wave impedance difference between them and the surrounding medium is different, and the characteristics of the reflected waves are also different. By adjusting the frequency and intensity of the elastic waves, these difference information can be better obtained, thereby accurately judging the relevant properties of the pipeline.

[0027] It can obtain key information such as the location and depth of the underground pipeline network from the perspective of elastic wave propagation characteristics, and complement and verify the data obtained by the electromagnetic induction detection unit, effectively making up for the limitations of a single detection method, significantly improving the comprehensiveness and accuracy of detection, and making the overall depiction of the underground pipeline network more complete and accurate.

[0028] Coupling interference control components: Heat exchange regulation module: The heat exchange pipe network in the heat exchange regulation module is tightly wrapped around the detection coil of the electromagnetic induction detection unit. The pipe network is made of high-pressure and corrosion-resistant polytetrafluoroethylene material to ensure stable operation in complex underground environments where corrosive substances may exist. The temperature regulation medium uses a special silicone oil, which has the characteristics of high specific heat capacity (for example, the specific heat capacity can reach about 2.5J / (g・℃)) and low thermal conductivity (thermal conductivity is about 0.13W / (m・K)), which enables it to effectively control the temperature change amplitude when absorbing or releasing heat, and its good fluidity facilitates precise regulation of heat transfer by controlling the flow rate. The silicone oil supply device is equipped with an intelligent flow controller and a temperature regulation system, which can automatically and accurately adjust the flow direction and flow rate of the silicone oil in the heat exchange pipe network according to the alternating magnetic field strength and frequency information generated in real time by the electromagnetic induction detection unit.

[0029] When the alternating magnetic field generated by electromagnetic induction acts on underground metal pipes, it will cause a thermal effect, which will change the temperature of the underground medium, thereby affecting its elastic modulus and ultimately interfering with the propagation characteristics of elastic waves. Based on the principle of heat transfer, by setting up a heat exchange pipeline network around the detection coil and using silicone oil, a special temperature regulating medium, to control the flow rate and temperature of the silicone oil according to the changes in the intensity and frequency of the alternating magnetic field, it is possible to actively intervene in the heat exchange process around the underground metal pipes. For example, when electromagnetic induction generates a strong alternating magnetic field (the intensity exceeds a certain threshold, such as 500mT), indicating that the thermal effect may increase, the flow controller accelerates the flow rate of the silicone oil according to the pre-set heat exchange model and algorithm, promptly removes excess heat, and maintains the relative stability of the temperature of the underground medium, thereby controlling the degree of change in the elastic modulus of the underground medium caused by the thermal effect.

[0030] Actively and effectively control the impact of thermal effects, avoid arbitrary changes in the elastic modulus of underground media due to thermal effects, and fundamentally solve the coupling interference problem caused by thermal effects between two different physical quantities, electromagnetic induction and elastic waves. Ensure that the elastic wave detection unit can obtain more accurate elastic wave propagation data, improve the accuracy of elastic wave detection in underground pipeline positioning and depth judgment, and lay the foundation for accurately depicting the morphology of underground pipelines.

[0031] Elastic modulus compensation module: The elastic modulus sensor array of the elastic modulus compensation module is composed of multiple high-precision pressure sensors based on micro-electromechanical systems (MEMS) technology. These sensors are evenly distributed at different depths underground in the detection area (for example, a sensor is arranged every 0.5 meters, and the depth range covers the expected buried depth of the pipeline network). They can sense the slight changes in the elastic modulus of the underground medium caused by factors such as thermal effects in real time (the elastic modulus change can be accurately sensed within 0.1%), and quickly convert the change into an electrical signal and transmit it to the elastic wave parameter adjustment device. After receiving the signal, the elastic wave parameter adjustment device can dynamically adjust the frequency and wavelength of the elastic wave by accurately adjusting the driving voltage and frequency of the elastic wave transmitting device according to the preset algorithm and calibration parameters.

[0032] The elastic modulus of underground media is a key factor in determining the propagation characteristics of elastic waves. Once the elastic modulus changes due to external factors (such as thermal effects, changes in local geological structures, etc.), the propagation speed, wavelength and other characteristics of the elastic wave will also change accordingly, resulting in deviations in the reflected wave signal. Based on the physical mechanism of elastic wave propagation and the high-precision detection principle of the sensor, the pressure sensor using MEMS technology can keenly capture the slight changes in the elastic modulus of the underground medium and convert it into an electrical signal to feed back to the elastic wave parameter adjustment device. The device dynamically adjusts the relevant parameters of the elastic wave based on the quantitative relationship between the elastic modulus and the wave speed, wavelength and other parameters in the elastic wave theory (for example, when the elastic modulus increases, the elastic wave propagation speed will increase accordingly, which can be compensated by adjusting the transmission frequency), ensuring that the elastic wave can propagate in the changing underground medium according to the expected propagation path and characteristics.

[0033] Real-time compensation for changes in elastic wave propagation characteristics caused by coupling interference makes elastic wave detection unaffected by interference from other physical quantities, further improving the accuracy of elastic wave detection. When working in conjunction with electromagnetic induction detection, it can more accurately locate and depict the detailed characteristics of underground pipelines, thereby enhancing the reliability of the entire detection results.

[0034] Data processing unit: Data preprocessing module: The data preprocessing module denoises the raw data collected by the electromagnetic induction detection unit and the elastic wave detection unit, uses advanced wavelet transform algorithms to remove noise signals generated by external electromagnetic interference and other factors, and amplifies and converts analog signals into digital signals to facilitate subsequent digital processing.

[0035] In the old urban environment with strong surrounding electromagnetic interference, electromagnetic induction detection data and elastic wave detection data will inevitably be mixed with a large amount of noise signals, which will cover up the real detection signal characteristics and affect the subsequent data processing and analysis. The wavelet transform algorithm has the characteristics of multi-resolution analysis. It can effectively separate the noise from the original signal according to the performance differences of the signal and noise at different scales and frequencies, and restore a relatively pure effective signal. The purpose of amplifying the signal is to enhance the strength of the weak signal, which is convenient for subsequent analog-to-digital conversion and further processing; analog-to-digital conversion converts the analog signal into a digital signal form that can be processed by the computer to meet the requirements of the digital processing process.

[0036] In a complex electromagnetic interference environment, the wavelet transform algorithm can effectively filter out clutter and significantly improve the quality of raw data, laying a good foundation for subsequent data fusion, correction and other processing links, ensuring the purity and availability of the data, and helping to improve the accuracy of the final detection results.

[0037] Data fusion module: The data fusion module uses a convolutional neural network (CNN) algorithm based on deep learning to fuse the preprocessed electromagnetic induction data and elastic wave data. The CNN network is trained through a large amount of historical detection data (for example, more than 1,000 sets of detection data collected in similar geological conditions and pipeline environments over the past 10 years) and pre-simulated data under different working conditions (using numerical simulation software to simulate pipelines of different materials, diameters, buried depths, and detection data under a variety of geological conditions, with no less than 500 simulated working conditions), so that it can learn the complex nonlinear mapping relationship between the two different physical quantity data, and then accurately fuse the two data into more comprehensive and accurate comprehensive information about the underground pipeline network.

[0038] Electromagnetic induction data and elastic wave data reflect the characteristics of underground pipe networks from different perspectives, but there are complex nonlinear correlations between them, and traditional linear fusion methods are difficult to fully tap into this inherent connection. With its powerful feature extraction and nonlinear mapping capabilities, the CNN algorithm can automatically capture the hidden laws and corresponding relationships between the two physical quantity data under different working conditions by learning and training a large amount of representative historical data and simulation data. For example, when electromagnetic induction detects that there may be metal pipes in a certain area, but the signal is blurred or abnormal due to factors such as uneven geology, the fusion of elastic wave detection data after CNN learning and processing can accurately determine the specific location of the pipeline, the diameter of the pipe, and the relationship with the surrounding medium and other detailed information based on the learned relationship, so as to achieve complementary advantages and improve the overall detection effect.

[0039] It improves the accuracy and reliability of the overall detection data, overcomes the limitations of single physical quantity data, and makes the final underground pipeline network comprehensive information more comprehensive and accurate, providing high-quality data support for subsequent GIS-based analysis and management work, and helping to more accurately maintain and update the underground pipeline network and plan and construct new projects.

[0040] Data correction module: The data correction module corrects the fused data based on the real-time information fed back by the coupling interference control components, such as the temperature control of the heat exchange control module and the change of the elastic modulus in the elastic modulus compensation module. The built-in error compensation algorithm eliminates the slight errors that may remain due to coupling interference, ensuring that the data finally transmitted to the GIS system is accurate and truly reflects the actual status of the underground pipe network.

[0041] Although the previous coupling interference control components and data processing links have minimized the impact of various interference factors, there may still be some subtle coupling interference residual errors that are difficult to completely eliminate in the actual complex underground environment. Based on the error theory and data correction principles, using the key information fed back by the coupling interference control components, such as the temperature change during the heat exchange process corresponding to the change in the elastic modulus of the underground medium, the error compensation algorithm in the data correction module can further correct the fused data in a targeted manner, compensate for these subtle errors, and make the data closer to the actual situation of the underground pipeline network.

[0042] Further improve data quality, ensure the effectiveness of subsequent GIS system-based analysis, visualization, and related management decision-making, minimize the risk of misjudgment of underground pipeline conditions due to data errors, and improve the scientificity and accuracy of underground pipeline management.

[0043] The implementation steps of the geophysical prospecting method based on this device are as follows: Detection preparation phase: Turn on the electromagnetic induction detection unit, and based on the previous understanding of the general situation of the underground pipeline network in the old urban area (for example, by consulting historical archival materials to know the main possible pipeline materials, pipe diameter ranges, and approximate burial depths, etc.), set a suitable alternating magnetic field frequency range (such as starting from a low frequency band and gradually increasing the frequency for scanning), start preliminary detection of underground metal pipelines, and record the initial alternating magnetic field strength and frequency information.

[0044] Since we only have a general understanding of the underground pipeline network in the detection area, a relatively broad but well-founded frequency scanning method is required for the initial detection. Starting from the low frequency band, the frequency is gradually increased to cover the frequency range that different types of metal pipes may respond to, increasing the probability of obtaining effective induction signals. At the same time, the relevant magnetic field strength and frequency information are recorded to facilitate subsequent analysis and serve as a reference for adjusting parameters of other modules.

[0045] The electromagnetic induction detection unit attempts to obtain a clear induction signal through frequency adjustment according to different pipeline materials and possible pipe diameter ranges, preliminarily delineates the approximate area where the pipeline may exist, provides a range reference for subsequent detailed detection, reduces the blindness of detection, improves work efficiency, and saves overall detection time and resource investment.

[0046] Coupling interference control stage: According to the previously recorded alternating magnetic field strength and frequency, the heat exchange regulation module in the coupling interference control component is turned on to adjust the flow rate and temperature of the silicone oil in the heat exchange pipeline network, actively control the heat exchange rate around the underground metal pipeline, and stabilize the elastic modulus of the underground medium.

[0047] Based on the physical relationship between the alternating magnetic field generated by electromagnetic induction and the thermal effect of the underground medium, once the intensity and frequency information of the alternating magnetic field is known, the degree of thermal effect that may be caused can be estimated. According to the heat exchange principle and the thermal properties of silicone oil, the flow rate and temperature of the silicone oil are adjusted through the intelligent flow controller and temperature control system to achieve active control of the heat exchange rate, thereby stabilizing the elastic modulus of the underground medium, creating a relatively stable propagation medium environment for subsequent elastic wave detection, and avoiding abnormal changes in elastic wave propagation characteristics caused by thermal effects.

[0048] Through this active regulation, the thermal effect caused by the alternating magnetic field generated by electromagnetic induction is avoided from arbitrarily changing the elastic modulus of the underground medium, ensuring that the "soil" conditions for elastic wave propagation are relatively stable, so that subsequent elastic wave detection can be carried out based on relatively accurate medium parameters, reducing the detection error caused by changes in elastic wave propagation characteristics due to thermal effects, and improving the accuracy of elastic wave detection in locating underground pipelines and judging their depth, thereby improving the overall detection accuracy.

[0049] Then, the elastic wave detection unit is turned on to emit elastic waves, and the elastic modulus compensation module in the coupling interference control component is turned on at the same time. The elastic modulus sensor array is used to monitor the changes in the elastic modulus of the underground medium in real time, and the change data is transmitted to the elastic wave parameter adjustment device to dynamically adjust the frequency and wavelength of the elastic wave.

[0050] In a complex underground environment, even if heat exchange regulation is performed, there may still be local factors that cause the elastic modulus of the underground medium to change, and the propagation characteristics of elastic waves are very sensitive to changes in the elastic modulus. Based on the real-time monitoring function of the elastic modulus sensor and the principle of elastic wave parameter adjustment, when the sensor detects a change in the elastic modulus, it will promptly transmit data to the elastic wave parameter adjustment device. Based on the correlation between the elastic modulus and wave parameters in the elastic wave theory, the frequency and wavelength of the elastic wave are dynamically adjusted to ensure that the elastic wave can propagate and reflect along the expected path, so that the reflected wave signal can accurately reflect the actual situation of the underground pipe network.

[0051] In this way, even if the elastic modulus of the local medium changes due to uncontrollable factors in a complex underground environment, the elastic wave can be adjusted in real time to ensure that the elastic wave propagates and reflects along the expected path, further improving the accuracy of elastic wave detection. The elastic wave detection data can more truly reflect the actual situation of the underground pipeline network, and work together with the electromagnetic induction detection data to form a more accurate comprehensive detection result, thereby improving the reliability and accuracy of the entire underground pipeline network detection.

[0052] Data processing and transmission stage: The data processing unit receives data from the electromagnetic induction detection unit, elastic wave detection unit and coupling interference control component, and performs denoising, amplification and analog-to-digital conversion in turn through the data preprocessing module to remove external interference and improve data quality; the data fusion module then uses a convolutional neural network algorithm based on deep learning to fuse the data, fully explore the inherent connections between different physical quantity data, and form more comprehensive and accurate underground pipeline network information; finally, the data correction module corrects the data based on the feedback from the coupling interference control component to eliminate the subtle errors left by the coupling interference and ensure that the data transmitted to the GIS system is accurate.

[0053] After the data is transmitted from various detection units and control components, it is inevitable that there are problems such as noise, uneven signal strength, and slight errors. It needs to be processed in sequence according to the logical order of data processing. First, denoising, amplification, and analog-to-digital conversion are performed to ensure the purity and processability of the data; then the deep learning algorithm is used to fuse the data because it can better explore the complex nonlinear relationship between different physical quantity data and improve the accuracy of comprehensive information; finally, data correction is performed to further eliminate possible residual errors and ensure that the data that finally enters the GIS system can truly and accurately reflect the actual situation of the underground pipeline network, meet the GIS system's requirements for data quality, and facilitate subsequent effective analysis and management operations.

[0054] This series of data processing steps enables the data that ultimately enters the GIS system to truly, comprehensively, and accurately present various key information such as the location, shape, and depth of the underground pipeline network, providing reliable data support for GIS-based underground pipeline network comprehensive analysis, visualization, and subsequent maintenance, updating, and planning and construction. It helps to improve the scientificity and rationality of urban underground space utilization and ensure the smooth progress of urban infrastructure construction.

[0055] Detection dynamic adjustment stage: During the entire detection process, the changes in the underground environment are continuously monitored (for example, by setting up multiple water level sensors in the detection area to monitor the changes in the underground water level, and arranging vibration sensors in the surrounding area to monitor the vibrations generated by the surrounding large-scale mechanical construction, and other factors that may affect the detection) and the working status of each unit (the built-in status monitoring module of each unit is used to obtain the equipment operation parameters, signal strength and other information in real time). According to the preset rules (parameter adjustment rules and mode switching conditions for different situations formulated based on a large number of experiments and previous detection experience) and the actual detection situation, the working parameters of each module in the coupling interference control component are automatically and dynamically adjusted to ensure that the detection effect is always in an optimized state. In addition, when the detection area changes (such as judging from entering the sand area from the clay area through real-time analysis of geological samples, and identifying based on the characteristic differences of the detection signal under different geological conditions) or an abnormal situation occurs (such as the sudden appearance of an unknown interference signal in a certain area, which is judged by comparing the data characteristics and signal strength changes during normal detection), different detection modes and parameter configurations can be automatically switched to adapt to the complex and changeable underground pipe network detection needs, while ensuring that the accuracy and reliability of the detection data are not affected.

[0056] The underground environment is complex and changeable. Whether it is the change of groundwater level, vibration of surrounding construction or change of geological conditions, the detection signal will be affected, and the working status of each detection unit and control component will also fluctuate with time and environmental changes. By real-time monitoring of these related factors, timely adjusting the working parameters of various parts such as coupling interference control components according to pre-established scientific and reasonable rules and accurate judgment of the actual detection situation, the entire detection system can always adapt to the changing environment and maintain good detection performance. Automatic switching of different detection modes and parameter configurations is based on the difference in the impact of different geological conditions, interference conditions, etc. on the detected physical quantities. For example, in the clay area, the elastic wave attenuation is relatively slow, but the electromagnetic induction signal may change due to the influence of the conductive properties of the clay; after entering the sandy area, the elastic wave propagation characteristics and electromagnetic induction conditions will be different. By switching the appropriate detection mode and adjusting the corresponding parameters (such as elastic wave frequency, electromagnetic induction magnetic field strength, etc.), it can ensure that the detection effect of each physical quantity is maintained in the best state and accurate and reliable data is obtained.

[0057] Through this dynamic adjustment mechanism, the entire geophysical exploration device and method can flexibly respond to various complex working conditions, avoid deviations in detection data due to environmental changes or abnormal conditions, always ensure high-quality detection results, and effectively solve the problem that traditional detection methods are difficult to ensure accuracy in complex and changeable environments. It improves the adaptability and stability of detection work, reduces the number of repeated detections caused by environmental factors, improves detection efficiency, and ensures that underground pipeline network detection work can be smoothly carried out, providing accurate pipeline network information in a timely manner for subsequent urban construction and other related work.

[0058] GIS system data update and verification phase: After the data processing unit transfers the data to the GIS system, the key features of the pipe network data obtained from this detection are automatically extracted at regular intervals (for example, after completing the detection of a block range, the block division is used as a relatively independent and easy-to-manage data unit), such as the node position of the pipeline is determined by the coordinate information, the change of the pipe diameter is analyzed according to the numerical sequence of the detected pipe diameter, and the direction is determined by the position connection of the continuous detection points, etc., and compared and analyzed in detail with the corresponding features in the historical data. Using the support vector machine (SVM) algorithm based on machine learning, through the learning and training of a large amount of historical data (collecting thousands of sets of historical data on underground pipe network detection in the old urban area and similar areas over the years) and actual detection data, the association model between data features and errors and abnormal conditions is established. Based on this model, errors and abnormal data that may exist in the detection data are automatically and accurately identified (for example, according to the model, the deviation between the pipe diameter data and the historical pipe diameter average in the same area is too large, and the pipeline direction does not conform to the conventional laying logic, etc.), and corresponding correction algorithms are used (for different types of errors and anomalies, such as linear correction of pipe diameter deviation, adjustment of abnormal direction according to the trend of surrounding pipeline directions, etc.) to correct them; the corrected data is cross-validated with the historical data again (comparing the consistency of key features, the rationality of the overall data, etc.) to ensure the consistency and accuracy of the corrected pipeline network data with the entire historical data system, thereby ensuring the long-term accuracy and reliability of underground pipeline network information, and providing a solid data foundation for subsequent urban construction and pipeline network maintenance.

[0059] As time goes by and the detection work continues, the underground pipe network itself may undergo new changes. At the same time, the previous detection data may also have certain errors or be inaccurate due to factors such as the environment at the time. By regularly extracting key features and comparing and analyzing historical data, and using the powerful classification and prediction capabilities of machine learning algorithms, it is possible to dig out the errors and abnormal patterns hidden in the data, and then accurately identify and correct the problems of the current data. Cross-validation further ensures that the corrected data is consistent with the entire historical data system, avoids unreasonable local corrections, and ensures the quality of the data during long-term use, so that it can truly reflect the dynamic changes of the underground pipe network and meet the high requirements for the accuracy and reliability of pipe network data in urban construction and other work.

[0060] The operations at this stage solve the problems of decreased accuracy and inconsistency with actual conditions that may occur in the long-term accumulation and use of traditional detection data. This enables GIS-based underground pipeline network information to continue to maintain high quality, providing strong guarantees for the rational use of urban underground space and scientific management of infrastructure. It helps to improve the efficiency of urban pipeline network maintenance, avoid waste of resources and safety accidents caused by construction or maintenance based on erroneous data, and ensure the normal operation and sustainable development of the city.

[0061] Summarize: By applying the above-mentioned GIS-based underground pipeline network geophysical exploration device and method in the actual underground pipeline network detection scenario in old urban areas, the complex coupling interference problem between different detection physical quantities existing in the existing underground pipeline network geophysical exploration methods is effectively solved.

[0062] First, the heat exchange regulation module in the coupling interference control component actively controls the thermal effect from the source, and the elastic modulus compensation module compensates for the change in elastic wave propagation characteristics in real time, avoiding the situation where the elastic wave propagation deviates from the expected path and characteristics due to the interaction of different physical quantities such as electromagnetic induction and elastic waves, and greatly improves the accuracy of the detection data. Specifically, the positioning accuracy of the underground pipeline network can be improved from the meter level of the traditional method to the decimeter level or even the centimeter level (under ideal conditions, based on the actual test comparison results), and the error range of the depth judgment can also be greatly reduced (for example, from the ±20% error of the conventional method to within ±5%), and the pipeline network morphology is more detailed and accurate, solving the problem that traditional technology is difficult to accurately obtain the real information of the underground pipeline network, providing reliable basic data for the subsequent pipeline network maintenance, update and planning and construction of new projects, avoiding the serious consequences such as the digging of the existing pipeline network and the conflict between the layout of the new facilities and the existing pipeline network caused by inaccurate data, ensuring the safety and normal operation of urban infrastructure, and reducing the economic losses caused by pipeline network accidents and the impact on the lives of surrounding residents.

[0063] Secondly, the data processing unit uses advanced deep learning algorithms for data fusion and correction, as well as the data update and verification mechanism implemented in the GIS system, which further improves the quality and long-term reliability of the data. Data fusion allows different physical quantity data to complement and synergize with each other, which improves the completeness of the final underground pipe network comprehensive information by about 30% (obtained through data comparison and analysis of multiple detection projects). The accuracy of the data after correction and verification is maintained at a high level for a long time (in subsequent multiple sampling and actual application verification, the accuracy rate can be stably maintained at more than 90%), overcoming the limitations of traditional data processing methods in the face of complex coupled interference data, ensuring the accuracy and effectiveness of the entire underground pipe network information at different stages and under different working conditions, better serving the rational use of urban underground space, meeting the needs of rapid urban construction for accurate detection and scientific management of underground pipe networks, and helping to improve the scientific nature of urban underground space planning, improve the efficiency of land resource utilization, and ensure the rational connection between new facilities and existing pipe networks.

[0064] Furthermore, the entire geophysical exploration method has a dynamic adjustment mechanism, which can automatically switch modes and adjust parameters according to the complex and changeable underground environment and actual detection conditions. Compared with the traditional relatively fixed and lack of adaptability detection methods, it can better adapt to various complex working conditions. When facing different geological conditions, changes in the surrounding interference environment, etc., the number of interruptions in the detection work can be reduced by about 50% (based on the comparison of actual detection project records), and the overall detection efficiency is improved by about 40% (calculated by comprehensively considering factors such as detection time and resource investment), ensuring that high-quality detection data can be stably obtained under different geological conditions, changes in the surrounding interference environment, etc., improving the efficiency and overall effect of the detection work, and effectively promoting the development of underground pipe network geophysical exploration technology, making it more in line with the high standards for underground pipe network detection in urban construction. Through unique coupling interference control components and supporting data processing and detection methods, the performance and effect of multi-physical quantity joint detection of underground pipe networks are comprehensively improved, providing strong technical support for the accurate detection and reasonable management of urban underground pipe networks and the sustainable development of urban infrastructure construction. It has significant technical advantages and practical application value, and helps urban construction develop in a more scientific, efficient and sustainable direction.

[0065] In summary, while solving the key problems of existing underground pipe network geophysical exploration, the device and method comprehensively optimize all aspects of underground pipe network detection, laying a solid foundation for urban underground pipe network-related work and the overall construction and development of the city, and has irreplaceable important significance.

Claims

1. A GIS-based underground pipe network geophysical exploration device, comprising an electromagnetic induction detection unit, an elastic wave detection unit and a data processing unit, characterized in that: Also includes: Coupling interference control component: The coupling interference control component is arranged between the electromagnetic induction detection unit and the elastic wave detection unit, and the coupling interference control component includes a heat exchange adjustment module and an elastic modulus compensation module; Heat exchange regulation module: used to actively adjust the heat exchange rate around the underground metal pipeline according to the alternating magnetic field strength and frequency generated by the electromagnetic induction detection unit; Elastic modulus compensation module: used to monitor the change of elastic modulus of underground medium in real time, and dynamically adjust the frequency and wavelength of elastic waves emitted by the elastic wave detection unit according to the change, so as to compensate for the change of elastic wave propagation characteristics caused by coupling interference; Data processing unit: connected to the electromagnetic induction detection unit, elastic wave detection unit and coupling interference control component respectively, used to receive and process the data collected by each unit, and transmit the processed data to the GIS system.

2. The GIS-based underground pipe network geophysical exploration device according to claim 1, characterized in that: The heat exchange regulation module comprises a heat exchange pipeline network and a temperature regulation medium. The heat exchange pipeline network surrounds the detection coil of the electromagnetic induction detection unit and is connected to the temperature regulation medium supply device.

3. The GIS-based underground pipe network geophysical exploration device according to claim 1, characterized in that: The temperature regulating medium is a liquid medium having high specific heat capacity and low thermal conductivity.

4. The GIS-based underground pipe network geophysical exploration device according to claim 1, characterized in that: The elastic modulus compensation module includes an elastic modulus sensor array and an elastic wave parameter adjustment device. The elastic modulus sensor array is distributed in the underground detection area, and the elastic wave parameter adjustment device receives elastic modulus change data.

5. The GIS-based underground pipe network geophysical exploration device according to claim 1, characterized in that: The sensors in the elastic modulus sensor array use high-precision pressure sensors based on micro-electromechanical system technology to sensitively sense tiny changes in the elastic modulus of the underground medium and convert them into electrical signals for transmission.

6. The GIS-based underground pipe network geophysical exploration device according to claim 1, characterized in that: The data processing unit includes a data preprocessing module, a data fusion module and a data correction module. The data preprocessing module is used for denoising, amplifying and analog-to-digital conversion of the data collected by the electromagnetic induction detection unit and the elastic wave detection unit; The data fusion module is used to fuse the preprocessed data and establish a nonlinear mapping relationship between the data using a deep learning-based algorithm; The data correction module is used to correct the fused data according to the information fed back by the coupling interference control component.

7. A GIS-based underground pipe network geophysical exploration method, characterized in that: The following steps are involved: First, the electromagnetic induction detection unit is turned on to generate an alternating magnetic field to perform preliminary detection on the underground metal pipeline, and the initial alternating magnetic field intensity and frequency information are recorded; Next, the heat exchange regulation module in the coupling interference regulation component is turned on to adjust the flow rate and temperature of the temperature regulation medium in the heat exchange pipeline network according to the previously recorded alternating magnetic field strength and frequency, actively control the heat exchange rate around the underground metal pipeline, and stabilize the elastic modulus of the underground medium; Subsequently, the elastic wave detection unit is turned on to emit elastic waves, and the elastic modulus compensation module in the coupling interference control component is turned on. The elastic modulus sensor array is used to monitor the change of the elastic modulus of the underground medium in real time, and the change data is transmitted to the elastic wave parameter adjustment device to dynamically adjust the frequency and wavelength of the elastic wave. Then, the data processing unit receives the data from the electromagnetic induction detection unit, elastic wave detection unit and coupling interference control component, and performs denoising, amplification and analog-to-digital conversion in turn through the data preprocessing module. Then, the data fusion module fuses the data using an algorithm based on deep learning. Finally, the data correction module corrects the data according to the feedback of the coupling interference control component, and transmits the processed data to the GIS system. During the entire detection process, the changes in the underground environment and the working status of each unit are continuously monitored, and the working parameters of each module in the coupling interference control component are automatically and dynamically adjusted according to the preset rules and actual detection conditions; Moreover, when the detection area changes or an abnormal situation occurs, different detection modes and parameter configurations are automatically switched.

8. The GIS-based underground pipe network geophysical exploration method according to claim 7, characterized in that: After the data processing unit transfers the data to the GIS system, the process also includes real-time updating and verification of the pipe network data in the GIS system, specifically: At regular intervals, the key features of the pipe network data obtained from this detection are automatically extracted, and compared and analyzed in detail with the corresponding features in the historical data; Using machine learning-based algorithms, through learning and training of a large amount of historical data and actual detection data, a correlation model between data features and errors and abnormal conditions is established; Based on this model, the possible errors and abnormal data in the detection data are automatically and accurately identified, and the corresponding correction algorithm is used to correct them; The corrected data are cross-validated with historical data again.

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