Visual-monitoring-based method for enabling water supply pipeline to penetrate through ultrahigh-voltage power transmission line downwards

Abstract

The invention relates to the technical field of water supply pipeline construction, and discloses a visual monitoring-based method for enabling a water supply pipeline to pass through an ultrahigh-voltage power transmission line, and the method comprises a multi-mode sensing step, a dynamic early warning step, a BIM positioning step and a visual monitoring step. According to the method, electromagnetic field distribution between the ultrahigh-voltage power transmission line and the water supply pipeline can be accurately sensed, the shielding effectiveness can be evaluated, dynamic safety protection is achieved through real-time transmission of electromagnetic field data and a threshold triggering mechanism, and electromagnetic compatibility verification of a construction path is completed by combining electromagnetic simulation and BIM model fusion. Meanwhile, the scene sensing precision is improved through fusion of all-dimensional visual monitoring data and multi-source data, accurate sensing, reliable early warning, scientific planning and efficient monitoring of construction in a strong electromagnetic field environment are integrally achieved, the construction safety, accuracy and efficiency are effectively improved, and the core requirement for construction of water supply pipelines around the ultrahigh-voltage power transmission line is met.

Description

Technical Field

[0001] This invention relates to the field of water supply pipeline construction technology, specifically a method for a water supply pipeline to pass under an ultra-high voltage transmission line based on visual monitoring. Background Technology

[0002] With the continuous expansion of urban water supply networks, the demand for construction of large-diameter water supply pipelines (diameter ≥ 2m) passing under ultra-high voltage transmission lines (≤ 750kV) is becoming increasingly urgent. The core challenge of such projects lies in the interference and safety threats posed by the strong power frequency electromagnetic fields generated by the operation of ultra-high voltage transmission lines to the entire construction process. Existing construction technologies have significant shortcomings in their ability to integrate perception, protection, planning, and monitoring in strong electromagnetic field environments, making it difficult to meet the core requirements of safety, efficiency, and accuracy for these projects. Existing technologies largely rely on data collection from single electromagnetic field sensing devices, neglecting the spatial relationship between pipelines and their surrounding environment. This leads to an inability to accurately assess pipeline shielding effectiveness and identify high-interference areas, resulting in malfunctions of construction equipment due to electromagnetic interference. Furthermore, the long-term effects of strong electromagnetic fields accelerate the aging of pipeline corrosion protection layers, shortening pipeline lifespan. Secondly, existing technologies do not consider the impact of real-time parameters such as ambient temperature and humidity when determining electromagnetic interference, easily leading to false alarms or delayed warnings. Active electromagnetic shielding devices cannot adjust their shielding positions in conjunction with electromagnetic field distribution data, and shielding effectiveness monitoring relies solely on simple current detection, lacking scientific calculation and self-checking mechanisms, resulting in frequent exceedances of leakage current. In addition, traditional BIM technology in construction path planning only uses spatial positioning, ignoring electromagnetic simulation data and lacking sufficient electromagnetic compatibility verification dimensions. This makes it impossible to comprehensively assess the radiation interference from construction equipment and the risk of pipeline corrosion protection layer failure, easily causing delays due to repeated path adjustments.

[0003] Therefore, traditional construction relies on manual calibration and temporary insulation measures, which are cumbersome and cannot meet the requirements of high efficiency, safety and precision for the construction of large-diameter water supply pipelines under ultra-high pressure environments. Summary of the Invention

[0004] The purpose of this invention is to provide a method for visually monitoring water supply pipelines passing under ultra-high voltage transmission lines, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention discloses the following technical solution: a method for a water supply pipeline passing under an ultra-high voltage transmission line based on visual monitoring, comprising:

[0006] Multimodal sensing steps: Electromagnetic field data around the ultra-high voltage transmission line is collected by electromagnetic field sensing equipment, spatial location correlation data between the pipeline and the surrounding environment is collected by distance sensing equipment, a three-dimensional electromagnetic field distribution model between the transmission line and the pipeline is constructed based on the electromagnetic field data and spatial location correlation data, and the shielding effectiveness of the pipeline against the electromagnetic field is evaluated by the three-dimensional electromagnetic field distribution model.

[0007] Dynamic early warning steps: The electromagnetic field data is transmitted in real time through a wireless communication network. An electromagnetic interference threshold is preset. When the electromagnetic field data reaches or exceeds the electromagnetic interference threshold, the device control command, audible and visual warning actions, and the activation operation of the active electromagnetic shielding device are triggered simultaneously.

[0008] BIM positioning steps: Obtain the BIM model of the transmission line, overlay and fuse the electromagnetic simulation data with the BIM model of the transmission line, and before the construction operations of water supply pipeline excavation, pipeline main body installation, interface welding and trench backfilling are carried out, the electromagnetic compatibility verification of the construction path is completed based on the fused model and data.

[0009] Visual monitoring steps: Deploy several visual monitoring devices to collect omnidirectional images of the area where the pipeline passes under the power transmission line to obtain visual monitoring data. Analyze the visual monitoring data in real time to identify the pipeline's location, status, and relative positional relationship with the power transmission line. Then, fuse the visual monitoring data with the electromagnetic field data, spatial location correlation data, and BIM model data to improve the accuracy of scene perception.

[0010] Preferably, the three-dimensional electromagnetic field distribution model is constructed in the following manner:

[0011] Data preprocessing: The electromagnetic field data is subjected to noise reduction and filtering, and the spatial location correlation data is subjected to coordinate calibration to remove outliers and redundant information, so as to obtain the preprocessed electromagnetic field data and the preprocessed spatial location correlation data respectively.

[0012] Parameter integration: The voltage level, operating current parameters, and line laying direction of ultra-high voltage transmission lines, as well as the material properties, pipe diameter, and burial depth of water supply pipelines are extracted as multi-dimensional parameters. The multi-dimensional parameters are then correlated and mapped with the pre-processed electromagnetic field data and the pre-processed spatial location correlation data.

[0013] Modeling and Calculation: Based on the multidimensional parameters and preprocessed electromagnetic field data and spatial location correlation data, numerical calculation methods are used to model and calculate the distribution law of electromagnetic field between transmission lines and pipelines;

[0014] Model generation: The modeling calculation results are fused with the preprocessed spatial location correlation data and imported into the 3D visualization engine to generate a dynamically displayable 3D electromagnetic field distribution model.

[0015] Preferably, the modeling calculation includes the following steps:

[0016] Using the finite element analysis method, the area where the power transmission line and pipeline are located is divided into several three-dimensional mesh units. The size of the three-dimensional mesh units is dynamically adjusted according to the electromagnetic field gradient distribution. Specifically, the size of the three-dimensional mesh units is reduced in the area around the power transmission line conductor and the outer wall of the pipeline where the electromagnetic field changes drastically, and the size of the three-dimensional mesh units is increased in the area where the electromagnetic field changes gently.

[0017] Electromagnetic field control equations are established based on Maxwell's equations. The voltage level and operating current parameters of the transmission line are transformed into boundary conditions. The relative permeability and conductivity parameters of the pipeline material are substituted into the electromagnetic field control equations to construct an electromagnetic field numerical calculation model suitable for transmission line-pipeline coupling scenarios.

[0018] The electromagnetic field numerical calculation model is solved by an iterative solution algorithm. The convergence condition of the iteration is set to ensure that the electromagnetic field strength error between two adjacent calculations meets the engineering accuracy requirements. After the solution is completed, the electromagnetic field strength, direction and phase information of each three-dimensional mesh element are output as the modeling calculation results.

[0019] Preferably, the electromagnetic interference threshold is determined in the following manner:

[0020] The basic threshold range is determined based on the rated operating parameters of ultra-high voltage transmission lines, the electromagnetic immunity level of water supply pipeline supporting equipment, and the current national or industry safety protection standards for transmission lines and pipelines.

[0021] Through a real-time environmental parameter compensation mechanism, when the ambient temperature or air humidity exceeds the range of conventional engineering environments, the basic threshold is automatically and adaptively corrected.

[0022] Preferably, the electromagnetic compatibility verification includes:

[0023] The electromagnetic radiation prediction model is used to calculate the radiation field strength of excavators, welding machines and cranes at different construction distances during pipeline construction. The radiation field strength is then compared with the electromagnetic tolerance threshold of the transmission line to determine the interference risk.

[0024] By using accelerated aging test data, a correlation model between electromagnetic field strength and pipeline anti-corrosion coating life is established. Based on the correlation model, the failure time of pipeline anti-corrosion coating under long-term electromagnetic field action of transmission line is predicted. If the predicted failure time does not meet the pipeline design service life requirements, the electromagnetic compatibility verification is deemed to have failed.

[0025] Based on the spatial measurement function of the BIM model, the minimum spatial distance between each point on the construction path and the transmission line conductor is calculated. At the same time, the model data of the three-dimensional electromagnetic field distribution model is superimposed. If the electromagnetic field strength corresponding to the minimum spatial distance exceeds the immunity level of the pipeline equipment, the coordinates of the construction path are automatically adjusted until the safety requirements are met.

[0026] Preferably, the visual monitoring device includes:

[0027] An ultra-high-definition camera is used to collect image data of the area where the pipeline passes under the power transmission line, and the ultra-high-definition camera is equipped with a laser ranging unit for real-time acquisition of distance measurement data between the ultra-high-definition camera and the pipeline and between the ultra-high-definition camera and the power transmission line.

[0028] The AI ​​image recognition algorithm unit adopts a convolutional neural network architecture. After being trained with a large number of pipeline construction scene image samples, it analyzes and recognizes the image data collected by the ultra-high-definition camera to obtain abnormal states such as pipeline displacement, deformation, interface leakage, and power transmission line conductor galloping and foreign object attachment.

[0029] The edge computing module performs preprocessing and preliminary identification of the image data collected by the ultra-high-definition camera locally on the visual monitoring device, and only uploads abnormal image data and identification results through the wireless communication network.

[0030] Preferably, the active electromagnetic shielding device includes:

[0031] The shielding body has a multi-layer composite structure, wherein the inner layer is a high magnetic permeability material layer to enhance the magnetic field shielding effect; the middle layer is a high electrical conductivity material layer to shield the electric field; and the outer layer is an anti-corrosion coating to adapt to harsh outdoor environments.

[0032] A shielding drive unit is provided, wherein the shielding body is disposed at the output end of the shielding drive unit, and the shielding drive unit is used to adjust the spatial position of the shielding body according to the high interference area coordinates output in real time by the three-dimensional electromagnetic field distribution model.

[0033] The performance monitoring module is used to collect electromagnetic field strength data in front of and behind the shield in real time, and to analyze the data using a formula. Calculate the shielding effectiveness, where E1 is the electromagnetic field strength before shielding, E2 is the electromagnetic field strength after shielding, and lg is the common logarithm; if the shielding effectiveness does not meet the design requirements, a maintenance warning will be issued if damage to the shielding body is detected.

[0034] Preferably, the data fusion is achieved through the following steps:

[0035] Establish a unified coordinate system based on satellite positioning, and calibrate the distance measurement data of visual monitoring devices, the position sensor data of multimodal sensing devices, and the geographic coordinates of the BIM model of power transmission lines to achieve precise coordinate alignment;

[0036] By using a multi-source data filtering and fusion algorithm, the pipeline location data from visual monitoring is used as the observation value, the electromagnetic field data is used as the state variable, and the static parameters of the BIM model of the transmission line are used as prior information. Data noise is eliminated through filtering iteration.

[0037] A data confidence assessment mechanism is constructed to score the reliability of data from different sources in real time. When the confidence of a data source fails to meet the set standard, the weight of that data source in the fusion computing is automatically reduced.

[0038] Preferably, the method further includes an intelligent decision optimization step, which includes:

[0039] Based on historical construction data and electromagnetic interference event data, including historical electromagnetic field anomaly data and visual monitoring anomaly records, a construction plan-electromagnetic interference risk association database is constructed. After inputting new construction parameters, the K-nearest neighbor algorithm is used to automatically match similar cases in the association database and output the interference risk level and countermeasure suggestions.

[0040] By using reinforcement learning algorithms, with the dual objective functions of maximizing construction efficiency and minimizing interference risk, the optimal construction plan is generated by optimizing the parameters of construction schedule, equipment selection, and activation timing of active electromagnetic shielding devices.

[0041] A feedback mechanism for scheme execution is established to collect actual interference data during the construction process in real time. The actual interference data includes real-time electromagnetic field data and visual monitoring anomaly data. The data is compared with the predicted data of the optimal construction scheme, and the deviation value is calculated. The reward function of the reinforcement learning algorithm is corrected based on the comparison results.

[0042] Preferably, the reinforcement learning algorithm is designed in the following manner:

[0043] The state space is defined as a combination of construction environment parameters, transmission line operation parameters, and pipeline construction parameters;

[0044] The operational space includes adjusting the operating parameters of construction equipment, changing the shielding status of active electromagnetic shielding devices, and switching the operation type of construction procedures;

[0045] The reward function is designed according to the following rules: Let R be the reward value. When the construction progress deviation is less than 5% and there are no interference events, R = 100; when there is a slight interference but it does not affect safe operation, R = 50; when there is a serious interference that causes shutdown, R = -50. Through continuous iterative training, the reinforcement learning algorithm converges to the optimal decision strategy.

[0046] Beneficial Effects: Compared with existing technologies, the method for constructing water supply pipelines under ultra-high voltage transmission lines based on visual monitoring in this invention can accurately perceive the electromagnetic field distribution between the ultra-high voltage transmission line and the water supply pipeline and assess the shielding effectiveness. It achieves dynamic safety protection by transmitting electromagnetic field data in real time and using a threshold triggering mechanism. It also completes electromagnetic compatibility verification of the construction path by combining electromagnetic simulation and BIM model fusion. At the same time, it improves the scene perception accuracy by fusing comprehensive visual monitoring data and multi-source data. Overall, it realizes accurate perception, reliable early warning, scientific planning and efficient monitoring of construction in strong electromagnetic field environments, effectively improving construction safety, accuracy and efficiency, and meeting the core needs of water supply pipeline construction around ultra-high voltage transmission lines. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method for a water supply pipeline to pass under an ultra-high voltage transmission line based on visual monitoring, as provided in an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0051] This embodiment provides a method for constructing water supply pipelines under ultra-high voltage transmission lines based on visual monitoring. It aims to address issues in existing construction projects involving water supply pipelines under ultra-high voltage transmission lines, such as inaccurate perception of strong electromagnetic fields, rigid dynamic early warning mechanisms, incomplete electromagnetic compatibility verification of construction paths, and limited visual monitoring information with insufficient integration of multi-source data. Figure 1 As shown, the method includes:

[0052] Multimodal sensing steps: Electromagnetic field data around the ultra-high voltage transmission line is collected by electromagnetic field sensing equipment, spatial location correlation data between the pipeline and the surrounding environment is collected by distance sensing equipment, a three-dimensional electromagnetic field distribution model between the transmission line and the pipeline is constructed based on the electromagnetic field data and spatial location correlation data, and the shielding effectiveness of the pipeline against the electromagnetic field is evaluated by the three-dimensional electromagnetic field distribution model.

[0053] Dynamic early warning steps: Electromagnetic field data is transmitted in real time through a wireless communication network. An electromagnetic interference threshold is preset. When the electromagnetic field data reaches or exceeds the electromagnetic interference threshold, the device control command, audible and visual warning actions, and active electromagnetic shielding device activation are triggered simultaneously.

[0054] BIM positioning steps: Obtain the BIM model of the transmission line, overlay and fuse the electromagnetic simulation data with the BIM model of the transmission line, and perform electromagnetic compatibility verification of the construction path based on the fused model and data before carrying out construction operations such as water supply pipeline excavation, pipeline main body installation, interface welding, and trench backfilling.

[0055] Visual monitoring steps: Deploy several visual monitoring devices to collect images of the area where the pipeline passes under the power transmission line in all directions to obtain visual monitoring data. Analyze the visual monitoring data in real time to identify the location, status, and relative positional relationship of the pipeline with the power transmission line. In addition, integrate the visual monitoring data with electromagnetic field data, spatial location correlation data, and BIM model data to improve the accuracy of scene perception.

[0056] Based on the above, the method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring in this embodiment utilizes electromagnetic field sensing devices and distance sensing devices to collect data and construct a three-dimensional electromagnetic field distribution model between the transmission line and the pipeline. This achieves accurate perception of strong electromagnetic fields and evaluation of shielding effectiveness, effectively solving the problem of insufficient understanding of electromagnetic field distribution in traditional technologies. Through a dynamic early warning step, electromagnetic field data is transmitted in real time via a wireless communication network, and device control, audible and visual warnings, and activation of active electromagnetic shielding devices are triggered based on preset thresholds, improving dynamic safety protection capabilities in strong electromagnetic field environments and avoiding false alarms or delayed warnings. With the help of a BIM positioning step, electromagnetic simulation data is integrated with the BIM model of the transmission line, and electromagnetic compatibility verification of the path is completed before construction, overcoming the limitation of traditional BIM being used only for spatial positioning and ensuring the safety and compliance of the construction path. Through a visual monitoring step, omnidirectional image data is collected using visual monitoring devices and integrated with electromagnetic field data, spatial location correlation data, and BIM model data, improving scene perception accuracy and solving the problem of incomplete information from a single visual monitoring system. Overall, this invention integrates precise sensing, dynamic early warning, scientific planning, and efficient monitoring for water supply pipeline underpass construction in strong electromagnetic field environments, effectively improving the safety, accuracy, and efficiency of construction and meeting the core needs of water supply pipeline construction around ultra-high voltage transmission lines.

[0057] In this embodiment, the three-dimensional electromagnetic field distribution model is constructed in the following way:

[0058] The first step is data preprocessing: noise reduction and filtering are performed on the electromagnetic field data, coordinate calibration is performed on the spatial location correlation data, outliers and redundant information are removed, and the preprocessed electromagnetic field data and preprocessed spatial location correlation data are obtained respectively.

[0059] The second step is parameter integration: extract the voltage level, operating current parameters, and line laying direction of the ultra-high voltage transmission line, as well as the material properties, pipe diameter, and burial depth of the water supply pipeline as multi-dimensional parameters, and then map the multi-dimensional parameters with the pre-processed electromagnetic field data and the pre-processed spatial location correlation data.

[0060] The third step is modeling and calculation: Based on multidimensional parameters and preprocessed electromagnetic field data and spatial location correlation data, numerical calculation methods are used to model and calculate the distribution law of electromagnetic field between transmission lines and pipelines.

[0061] The fourth step is model generation: the model calculation results are fused with the preprocessed spatial location correlation data and imported into a 3D visualization engine to generate a dynamically displayable 3D electromagnetic field distribution model.

[0062] Based on the above, the method for constructing a three-dimensional electromagnetic field distribution model for water supply pipelines passing under ultra-high voltage transmission lines based on visual monitoring in this embodiment clarifies the complete steps of constructing the model. First, the electromagnetic field data and spatial location-related data are preprocessed to remove outliers and redundant information. Then, the multi-dimensional key parameters of the transmission line and pipeline are integrated and associated with the preprocessed data. Finally, a three-dimensional model is formed through modeling calculation and model generation. This makes the model construction process more standardized and the data foundation more accurate, effectively avoiding model deviations caused by data clutter and missing parameters. It significantly improves the accuracy of the three-dimensional electromagnetic field distribution model in depicting the electromagnetic field distribution between the transmission line and pipeline, providing reliable model support for subsequent shielding effectiveness evaluation.

[0063] Furthermore, the modeling calculation includes the following steps:

[0064] S1: Using the finite element analysis method, the area where the transmission line and pipeline are located is divided into several three-dimensional mesh units. The size of the three-dimensional mesh unit is dynamically adjusted according to the electromagnetic field gradient distribution. The size of the three-dimensional mesh unit is reduced in the area around the transmission line conductor and the outer wall of the pipeline where the electromagnetic field changes drastically, and the size of the three-dimensional mesh unit is increased in the area where the electromagnetic field changes gently.

[0065] S2: Based on Maxwell's equations, establish electromagnetic field control equations, transform the voltage level and operating current parameters of the transmission line into boundary conditions, substitute the relative permeability and conductivity parameters of the pipe material into the electromagnetic field control equations, and construct an electromagnetic field numerical calculation model suitable for the transmission line-pipe coupling scenario.

[0066] S3: Solve the electromagnetic field numerical calculation model through an iterative solution algorithm. Set the convergence condition of the iteration as the electromagnetic field strength error between two adjacent calculation results meeting the engineering accuracy requirements (error less than 0.5%). After the solution is completed, output the electromagnetic field strength, direction and phase information of each three-dimensional mesh element.

[0067] S4: Import the obtained 3D mesh cell electromagnetic field data into the 3D visualization engine, and combine it with the preprocessed spatial location correlation data to generate a 3D electromagnetic field distribution model that can dynamically display the electromagnetic field distribution state. The 3D electromagnetic field distribution model supports color rendering according to the electromagnetic field intensity level (red for intensity ≥100μT, yellow for 50-100μT, and green for <50μT) to intuitively present the distribution of high interference areas.

[0068] Based on the above, the method for visually monitoring water supply pipelines passing under ultra-high voltage transmission lines in this embodiment improves calculation accuracy in critical areas with drastic electromagnetic field changes and balances calculation efficiency in areas with gentler changes by dynamically adjusting the size of the three-dimensional mesh cells. It combines Maxwell's equations to construct a numerical calculation model that fits the coupling scenario of the transmission line and pipeline, and ensures that the results meet engineering accuracy through iterative solution. Finally, a visual model that can be rendered in color is generated, which achieves a balance between the accuracy and efficiency of electromagnetic field distribution calculation and can intuitively present high interference areas. This further improves the calculation reliability and practical application value of the three-dimensional electromagnetic field distribution model and facilitates the subsequent accurate location of high interference risk points.

[0069] In this embodiment, the electromagnetic interference threshold is determined in the following way:

[0070] Based on the rated operating parameters of ultra-high voltage transmission lines, the electromagnetic immunity level of water supply pipeline supporting equipment (such as the level 3 immunity specified in GB / T17626.3-2016), and the current national or industry safety protection standards for transmission lines and pipelines (such as DL / T5445-2010), the basic threshold range (80-120μT) is determined.

[0071] Simultaneously, a real-time environmental parameter compensation mechanism is introduced. When the ambient temperature (exceeding -10℃ to 40℃) or air humidity (exceeding 40% to 60%RH) exceeds the range of conventional engineering environments, the basic threshold is automatically and adaptively corrected according to the compensation coefficient (5% compensation for every 10℃ increase in temperature and 3% compensation for every 10%RH increase in humidity), forming a dynamic and adaptive electromagnetic interference threshold system.

[0072] Based on the above, the method for water supply pipelines passing under ultra-high voltage transmission lines based on visual monitoring in this embodiment determines the basic threshold based on the rated parameters of the ultra-high voltage transmission line, the immunity level of pipeline equipment, and industry safety standards. At the same time, it introduces an environmental temperature and humidity compensation mechanism to correct the threshold in real time, breaking the limitations of traditional fixed thresholds and forming a dynamic and adaptive electromagnetic interference threshold system. This effectively avoids false alarms or delays in early warnings caused by environmental changes, making electromagnetic interference early warnings more consistent with the actual construction environment. It significantly improves the accuracy and timeliness of the dynamic early warning steps in judging electromagnetic interference risks and reduces safety hazards caused by threshold mismatch.

[0073] In this embodiment, electromagnetic compatibility verification includes the following dimensions:

[0074] First dimension: Using an electromagnetic radiation prediction model (based on the Hertz dipole radiation formula), calculate the radiation field strength of commonly used equipment in pipeline construction such as excavators, welding machines, and cranes at different construction distances. Compare the radiation field strength with the electromagnetic tolerance threshold (50μT) of the transmission line. If the radiation field strength is ≥ the tolerance threshold, it is determined that there is an interference risk.

[0075] The second dimension: By using accelerated aging test data, a correlation model between electromagnetic field strength and pipeline corrosion protection layer life is established. Let B be the pipeline corrosion protection layer life (unit: days) and E be the electromagnetic field strength (unit: μT). Then the correlation model formula is "B=1000 / (E×0.01)". Based on the correlation model, the failure time of pipeline corrosion protection layer under the long-term action of transmission line electromagnetic field is predicted. If the predicted failure time does not meet the pipeline design service life (30 years, equivalent to 10950 days), then the electromagnetic compatibility verification is deemed to have failed.

[0076] The third dimension: Combining the spatial measurement function of the BIM model of the transmission line, the minimum spatial distance between each point on the construction path and the transmission line conductor is calculated. At the same time, the three-dimensional electromagnetic field distribution model data is superimposed. If the electromagnetic field strength corresponding to the minimum spatial distance exceeds the immunity level of the pipeline equipment (level 3 immunity corresponds to 80μT), the coordinates of the construction path are automatically adjusted (each adjustment range ±0.5m) until the safety requirements are met.

[0077] Based on the above, the method for constructing water supply pipelines under ultra-high voltage transmission lines based on visual monitoring in this embodiment conducts electromagnetic compatibility verification from three dimensions: radiation interference from construction equipment, lifespan of pipeline anti-corrosion layer, and construction path space and electromagnetic safety. It can not only determine the interference risk of construction equipment to the transmission line, but also predict the failure time of pipeline anti-corrosion layer under long-term electromagnetic field action. Furthermore, it can adjust the path by combining path space distance and electromagnetic field strength, avoiding the omissions of single-dimensional verification, and ensuring that the construction path meets the requirements in terms of electromagnetic safety, equipment tolerance, and long-term pipeline lifespan. This significantly improves the comprehensiveness and safety of construction path planning in the BIM positioning step.

[0078] In this embodiment, the visual monitoring device includes:

[0079] The core monitoring component is an ultra-high-definition camera (4K resolution) that integrates a laser ranging module (measurement accuracy ±2mm). The ultra-high-definition camera is used to collect image data (25fps) of the area where the pipeline passes under the power transmission line, and the image data is the core component of the visual monitoring data. The laser ranging module is used to measure the distance between the ultra-high-definition camera and the pipeline and power transmission line in real time and generate distance measurement data.

[0080] The AI ​​image recognition algorithm unit employs a convolutional neural network architecture (ResNet50). After training on a large number of pipeline construction scene image samples (100,000+ images), it analyzes and recognizes image data captured by ultra-high-definition cameras (recognition accuracy ≥95%). It can identify pipeline displacement (displacement amount ≥5mm), deformation (deformation rate ≥2%), and interface leakage (leakage area ≥1cm). 2And conductor galloping on transmission lines (amplitude ≥ 1m), foreign object attachment (area ≥ 0.5m²) 2 Abnormal states such as )

[0081] The edge computing module (equipped with an ARM Cortex-A72 processor) can perform preprocessing (noise reduction, sharpening) and preliminary recognition of image data captured by ultra-high-definition cameras locally on the visual monitoring device. Only abnormal image data and recognition results are uploaded through wireless communication networks (4G / 5G) to reduce data transmission volume (by more than 80%) and reduce network bandwidth usage.

[0082] Based on the above, the method for visual monitoring of water supply pipelines passing under ultra-high voltage transmission lines in this embodiment uses an ultra-high-definition camera to collect clear image data, a laser ranging module to simultaneously acquire distance data, an AI image recognition unit to accurately identify abnormal states of the pipeline and transmission line, and an edge computing module to preprocess data locally and upload only key information. This enables the visual monitoring device to collect multiple types of high-precision monitoring data and accurately capture abnormal situations, while reducing data transmission volume and network bandwidth usage. This significantly improves the monitoring accuracy, anomaly identification capability, and data transmission efficiency of the visual monitoring steps, providing high-quality data support for improving scene perception accuracy.

[0083] Furthermore, the active electromagnetic shielding device includes:

[0084] The shielding body has a multi-layer composite structure. The inner layer is a high magnetic permeability material layer (permalloy, 1mm thick) to enhance the magnetic field shielding effect; the middle layer is a high electrical conductivity material layer (copper, 0.5mm thick) to shield the electric field; and the outer layer is an anti-corrosion coating (polytetrafluoroethylene, 0.3mm thick) to adapt to harsh outdoor environments.

[0085] The shielding drive unit has a shielding body located at its output end. It should be noted that this drive unit can be any of the existing technologies, such as a combination of a motor (stepper motor, step angle 1.8°) and a guide rail (linear guide rail, positioning accuracy ±0.1mm). It supports multi-directional movement adjustment and can automatically adjust the spatial position of the shielding body according to the high interference area coordinates output in real time by the three-dimensional electromagnetic field distribution model.

[0086] The shielding effectiveness monitoring module is built into the active electromagnetic shielding device and is used to collect electromagnetic field strength data before and after the shielding body in real time (sampling frequency 10Hz). Let P be the shielding effectiveness (unit: dB), E1 be the electromagnetic field strength before shielding (unit: μT), E2 be the electromagnetic field strength after shielding (unit: μT), and lg be the common logarithm (logarithm to base 10). Then the formula for calculating the shielding effectiveness is: When the calculated value of P is less than the design requirement (P≥30dB), the shielding structure self-check is automatically triggered (checking the integrity of the shielding coating and the conductivity of the material). If damage to the shielding is detected (the damaged area of ​​the coating ≥1cm²), the self-check will be triggered. 2 If the conductivity decreases by ≥10%, a maintenance warning (audio-visual alarm + remote push) will be issued to ensure the stability of the shielding effect.

[0087] Based on the above, the method for water supply pipelines passing under ultra-high voltage transmission lines based on visual monitoring in this embodiment uses a multi-layer composite shielding body to enhance magnetic field shielding, electric field shielding, and corrosion resistance through different materials. The shielding drive unit dynamically adjusts the position of the shielding body according to the coordinates of the high-interference area, and the shielding effectiveness monitoring module calculates the shielding effectiveness in real time and performs self-checks and maintenance. This ensures that the shielding device can accurately cover the high-interference area, stably achieve the designed shielding effect, and promptly detect and warn of shielding damage. This effectively improves the targeting, effectiveness, and stability of active electromagnetic shielding in the dynamic warning step, and further reduces the risk of interference from ultra-high voltage electromagnetic fields to construction.

[0088] In this embodiment, data fusion is achieved through the following steps:

[0089] Step 1: Establish a unified coordinate system based on satellite positioning (GPS + Beidou dual-mode, positioning accuracy ±1m), and calibrate the distance measurement data of the visual monitoring device, the position sensor data of the multimodal sensing device, and the geographic coordinates of the BIM model of the transmission line to achieve accurate coordinate alignment (deviation less than 0.3m).

[0090] The second step involves using a multi-source data filtering and fusion algorithm (Kalman filter algorithm) to take the pipeline location data from visual monitoring (obtained by analyzing image data from the visual monitoring device) as the observation value, the electromagnetic field data as the state variable, and the static parameters of the BIM model of the transmission line (such as conductor height and span) as prior information. Through filtering iterations (10-20 iterations), data noise is eliminated (the noise is reduced to less than 10% of the original amplitude) to improve the accuracy of the fused data.

[0091] Step 3: Construct a data confidence assessment mechanism. Let C be the data source confidence score (out of 100) and Δ be the data error (unit: %). The confidence score formula is "C = 100 - 2 × Δ". The reliability of data from different sources (including electromagnetic field data, spatial location correlation data, visual monitoring data, and BIM model data) is scored in real time according to this formula. When the C of a certain data source does not reach the set standard (C ≥ 80 points), the weight of that data source in the fusion calculation is automatically reduced (the weight coefficient is reduced from 0.25 to 0.1) to avoid low-quality data affecting the overall perception accuracy.

[0092] Based on the above, the method for visually monitored water supply pipelines passing under ultra-high voltage transmission lines in this embodiment establishes a unified coordinate system through satellite positioning to align the coordinates of multi-source data, eliminates data noise by employing a multi-source data filtering and fusion algorithm, and adjusts the weight of data sources by combining a confidence assessment mechanism to avoid low-quality data affecting the fusion results. This enables efficient and accurate fusion of multi-source information such as visual monitoring data, electromagnetic field data, and BIM model data, significantly improving the accuracy and reliability of the fused data. It provides a scientific and efficient fusion method for improving scene perception accuracy through the fusion of visual monitoring data and multi-source data, further enhancing the accuracy of scene perception.

[0093] Feasibly, the method also includes an intelligent decision optimization step, which is executed through the following process:

[0094] The first step: Based on historical construction data and electromagnetic interference event data (including historical electromagnetic field anomaly data and visual monitoring anomaly records), a construction plan-electromagnetic interference risk association database is constructed. After inputting new construction parameters, the K-nearest neighbor algorithm is used to automatically match similar cases in the association database (similarity ≥ 85%), and output the interference risk level (high / medium / low) and corresponding countermeasures.

[0095] The second step involves using a reinforcement learning algorithm (DQN algorithm) with the dual objective function of "highest construction efficiency - lowest interference risk" (let F be the comprehensive objective function, F1 be the construction efficiency target value, F2 be the interference risk target value, and the weight coefficients ω1 = 0.6 and ω2 = 0.4, then F = ω1 × F1 + ω2 × F2). This optimizes the construction schedule (e.g., avoiding peak electricity consumption periods), equipment selection (e.g., using low-radiation welding machines), and the activation timing parameters of the active electromagnetic shielding device (activating when the electromagnetic field strength reaches 80% of the threshold) to generate the optimal construction plan.

[0096] The third step: Establish a feedback mechanism for scheme execution. Let D be the deviation between the actual interference data and the predicted data, D1 be the actual interference data, and D2 be the predicted interference data. Then the deviation calculation formula is "D=|D1-D2| / D2×100%". Collect D1 and D2 in real time during the construction process and calculate D. Based on D, correct the reward function of the reinforcement learning algorithm (for every 5% increase in D, the reward function coefficient is corrected by -0.1) to continuously improve the accuracy of scheme optimization.

[0097] Based on the above, the method for constructing water supply pipelines under ultra-high voltage transmission lines based on visual monitoring in this embodiment provides case references by building an associated database based on historical data. It optimizes the construction plan with the dual objectives of "highest construction efficiency and lowest interference risk" through reinforcement learning algorithms, and then corrects the algorithm through a feedback mechanism. This ensures that the construction plan is supported by historical experience and achieves a balance between efficiency and safety. Moreover, the optimization accuracy of the plan continues to improve with construction feedback, providing better decision support for the entire construction process. This effectively improves construction efficiency while reducing interference risk and makes up for the limitations of traditional manual decision-making.

[0098] Furthermore, the reinforcement learning algorithm is designed in the following way:

[0099] The state space is defined as a combination of construction environment parameters (including ambient temperature and air humidity), transmission line operation parameters (including voltage level and operating current), and pipeline construction parameters (including construction equipment type and construction procedure).

[0100] The action space covers operation types such as adjusting the working parameters of construction equipment (e.g., the current adjustment range of electric welding machine is 50-300A), changing the shielding status of active electromagnetic shielding device (opening / closing / adjusting the shielding strength), and switching construction procedures (e.g., backfilling before welding / welding before backfilling);

[0101] The reward function is designed according to the following rules: Let R be the reward value. When the construction progress deviation is less than 5% and there are no interference events (no electromagnetic field exceeding the threshold, no visual monitoring anomalies), R = 100; when there is slight interference (electromagnetic field close to the threshold but not exceeding it, slight visual monitoring anomalies) and it does not affect safe operation, R = 50; when there is severe interference (electromagnetic field exceeding the threshold, severe visual monitoring anomalies) leading to shutdown, R = -50; through continuous iterative training (when the fluctuation range of the reward function value is less than 5% for 100 consecutive iterations, training is stopped), the reinforcement learning algorithm converges to the optimal decision strategy.

[0102] Based on the above, the method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring in this embodiment clearly defines the state space, action space, and reward function of the reinforcement learning algorithm. The state space covers key construction parameters, the action space covers core adjustment operations, and the reward function is set according to the construction progress and interference situation. This enables the algorithm to accurately perceive the construction status, execute effective adjustments, and iteratively optimize based on actual results, ensuring that the algorithm can stably converge to the optimal decision strategy. This provides accurate and efficient algorithmic support for intelligent decision optimization steps, ensuring the quality of the generated optimal construction plan and its continuous optimization capability.

[0103] In the embodiments provided by this invention, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0104] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring water supply pipelines passing under ultra-high voltage transmission lines based on visual monitoring, characterized in that, include: Multimodal sensing steps: Electromagnetic field data around the ultra-high voltage transmission line is collected by electromagnetic field sensing equipment, spatial location correlation data between the pipeline and the surrounding environment is collected by distance sensing equipment, a three-dimensional electromagnetic field distribution model between the transmission line and the pipeline is constructed based on the electromagnetic field data and spatial location correlation data, and the shielding effectiveness of the pipeline against the electromagnetic field is evaluated by the three-dimensional electromagnetic field distribution model. Dynamic early warning steps: The electromagnetic field data is transmitted in real time through a wireless communication network. An electromagnetic interference threshold is preset. When the electromagnetic field data reaches or exceeds the electromagnetic interference threshold, the device control command, audible and visual warning actions, and the activation operation of the active electromagnetic shielding device are triggered simultaneously. BIM positioning steps: Obtain the BIM model of the transmission line, overlay and fuse the electromagnetic simulation data with the BIM model of the transmission line, and before the construction operations of water supply pipeline excavation, pipeline main body installation, interface welding and trench backfilling are carried out, the electromagnetic compatibility verification of the construction path is completed based on the fused model and data. Visual monitoring steps: Deploy several visual monitoring devices to collect omnidirectional images of the area where the pipeline passes under the power transmission line to obtain visual monitoring data. Analyze the visual monitoring data in real time to identify the pipeline's location, status, and relative positional relationship with the power transmission line. Then, fuse the visual monitoring data with the electromagnetic field data, spatial location correlation data, and BIM model data to improve the accuracy of scene perception.

2. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 1, characterized in that, The three-dimensional electromagnetic field distribution model is constructed in the following way: Data preprocessing: The electromagnetic field data is subjected to noise reduction and filtering, and the spatial location correlation data is subjected to coordinate calibration to remove outliers and redundant information, so as to obtain the preprocessed electromagnetic field data and the preprocessed spatial location correlation data respectively. Parameter integration: The voltage level, operating current parameters, and line laying direction of ultra-high voltage transmission lines, as well as the material properties, pipe diameter, and burial depth of water supply pipelines are extracted as multi-dimensional parameters. The multi-dimensional parameters are then correlated and mapped with the pre-processed electromagnetic field data and the pre-processed spatial location correlation data. Modeling and Calculation: Based on the multidimensional parameters and preprocessed electromagnetic field data and spatial location correlation data, numerical calculation methods are used to model and calculate the distribution law of electromagnetic field between transmission lines and pipelines; Model generation: The modeling calculation results are fused with the preprocessed spatial location correlation data and imported into the 3D visualization engine to generate a dynamically displayable 3D electromagnetic field distribution model.

3. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 2, characterized in that, The modeling and calculation includes the following steps: Using the finite element analysis method, the area where the power transmission line and pipeline are located is divided into several three-dimensional mesh units. The size of the three-dimensional mesh units is dynamically adjusted according to the electromagnetic field gradient distribution. Specifically, the size of the three-dimensional mesh units is reduced in the area around the power transmission line conductor and the outer wall of the pipeline where the electromagnetic field changes drastically, and the size of the three-dimensional mesh units is increased in the area where the electromagnetic field changes gently. Electromagnetic field control equations are established based on Maxwell's equations. The voltage level and operating current parameters of the transmission line are transformed into boundary conditions. The relative permeability and conductivity parameters of the pipeline material are substituted into the electromagnetic field control equations to construct an electromagnetic field numerical calculation model suitable for transmission line-pipeline coupling scenarios. The electromagnetic field numerical calculation model is solved by an iterative solution algorithm. The convergence condition of the iteration is set to ensure that the electromagnetic field strength error between two adjacent calculations meets the engineering accuracy requirements. After the solution is completed, the electromagnetic field strength, direction and phase information of each three-dimensional mesh element are output as the modeling calculation results.

4. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 1, characterized in that, The electromagnetic interference threshold is determined in the following way: The basic threshold range is determined based on the rated operating parameters of ultra-high voltage transmission lines, the electromagnetic immunity level of water supply pipeline supporting equipment, and the current national or industry safety protection standards for transmission lines and pipelines. Through a real-time environmental parameter compensation mechanism, when the ambient temperature or air humidity exceeds the range of conventional engineering environments, the basic threshold is automatically and adaptively corrected.

5. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 1, characterized in that, The electromagnetic compatibility verification includes: The electromagnetic radiation prediction model is used to calculate the radiation field strength of excavators, welding machines and cranes at different construction distances during pipeline construction. The radiation field strength is then compared with the electromagnetic tolerance threshold of the transmission line to determine the interference risk. By using accelerated aging test data, a correlation model between electromagnetic field strength and pipeline anti-corrosion coating life is established. Based on the correlation model, the failure time of pipeline anti-corrosion coating under long-term electromagnetic field action of transmission line is predicted. If the predicted failure time does not meet the pipeline design service life requirements, the electromagnetic compatibility verification is deemed to have failed. Based on the spatial measurement function of the BIM model, the minimum spatial distance between each point on the construction path and the transmission line conductor is calculated. At the same time, the model data of the three-dimensional electromagnetic field distribution model is superimposed. If the electromagnetic field strength corresponding to the minimum spatial distance exceeds the immunity level of the pipeline equipment, the coordinates of the construction path are automatically adjusted until the safety requirements are met.

6. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 1, characterized in that, The visual monitoring device includes: An ultra-high-definition camera is used to collect image data of the area where the pipeline passes under the power transmission line, and the ultra-high-definition camera is equipped with a laser ranging unit for real-time acquisition of distance measurement data between the ultra-high-definition camera and the pipeline and between the ultra-high-definition camera and the power transmission line. The AI ​​image recognition algorithm unit adopts a convolutional neural network architecture. After being trained with a large number of pipeline construction scene image samples, it analyzes and recognizes the image data collected by the ultra-high-definition camera to obtain abnormal states such as pipeline displacement, deformation, interface leakage, and power transmission line conductor galloping and foreign object attachment. The edge computing module performs preprocessing and preliminary identification of the image data collected by the ultra-high-definition camera locally on the visual monitoring device, and only uploads abnormal image data and identification results through the wireless communication network.

7. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 6, characterized in that, The active electromagnetic shielding device includes: The shielding body has a multi-layer composite structure, wherein the inner layer is a high magnetic permeability material layer to enhance the magnetic field shielding effect; the middle layer is a high electrical conductivity material layer to shield the electric field; and the outer layer is an anti-corrosion coating to adapt to harsh outdoor environments. A shielding drive unit is provided, wherein the shielding body is disposed at the output end of the shielding drive unit, and the shielding drive unit is used to adjust the spatial position of the shielding body according to the high interference area coordinates output in real time by the three-dimensional electromagnetic field distribution model. The performance monitoring module is used to collect electromagnetic field strength data in front of and behind the shield in real time, and to analyze the data using a formula. The shielding effectiveness is calculated, where E1 is the electromagnetic field strength before shielding, E2 is the electromagnetic field strength after shielding, and lg is the common logarithm. If the shielding effectiveness does not meet the design requirements, a maintenance warning is issued if damage to the shielding body is detected.

8. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 1, characterized in that, The data fusion is achieved through the following steps: Establish a unified coordinate system based on satellite positioning, and calibrate the distance measurement data of visual monitoring devices, the position sensor data of multimodal sensing devices, and the geographic coordinates of the BIM model of power transmission lines to achieve precise coordinate alignment; By using a multi-source data filtering and fusion algorithm, the pipeline location data from visual monitoring is used as the observation value, the electromagnetic field data is used as the state variable, and the static parameters of the BIM model of the transmission line are used as prior information. Data noise is eliminated through filtering iteration. A data confidence assessment mechanism is constructed to score the reliability of data from different sources in real time. When the confidence of a data source fails to meet the set standard, the weight of that data source in the fusion computing is automatically reduced.

9. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 1, characterized in that, It also includes an intelligent decision optimization step, which includes: Based on historical construction data and electromagnetic interference event data, including historical electromagnetic field anomaly data and visual monitoring anomaly records, a construction plan-electromagnetic interference risk association database is constructed. After inputting new construction parameters, the K-nearest neighbor algorithm is used to automatically match similar cases in the association database and output the interference risk level and countermeasure suggestions. By using reinforcement learning algorithms, with the dual objective functions of maximizing construction efficiency and minimizing interference risk, the optimal construction plan is generated by optimizing the parameters of construction schedule, equipment selection, and activation timing of active electromagnetic shielding devices. A feedback mechanism for scheme execution is established to collect actual interference data during the construction process in real time. The actual interference data includes real-time electromagnetic field data and visual monitoring anomaly data. The data is compared with the predicted data of the optimal construction scheme, and the deviation value is calculated. The reward function of the reinforcement learning algorithm is corrected based on the comparison results.

10. The method for constructing a water supply pipeline under an ultra-high voltage transmission line based on visual monitoring according to claim 9, characterized in that, The reinforcement learning algorithm is designed in the following way: The state space is defined as a combination of construction environment parameters, transmission line operation parameters, and pipeline construction parameters; The operational space includes adjusting the operating parameters of construction equipment, changing the shielding status of active electromagnetic shielding devices, and switching the operation type of construction procedures; The reward function is designed according to the following rules: Let R be the reward value. When the construction progress deviation is less than 5% and there are no interference events, R = 100; when there is a slight interference but it does not affect safe operation, R = 50; when there is a serious interference that causes shutdown, R = -50. Through continuous iterative training, the reinforcement learning algorithm converges to the optimal decision strategy.

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