Power transmission line diagnosis method and system based on physical information generation and causal analysis

Abstract

The application discloses a power transmission line diagnosis method and system based on physical information generation and causal analysis, and belongs to the technical field of power transmission line component state diagnosis. The method comprises the following steps: acquiring multi-source data of a target power transmission line component, wherein the multi-source data comprises equipment attribute parameters, environmental parameters and real observation images obtained through an image acquisition device; inputting the equipment attribute parameters and the environmental parameters into a physical information generation model to generate multi-modal reference images; calculating difference information between the real observation images and the multi-modal reference images; performing causal analysis on the difference information based on a preset causal diagram model, inferring equipment state reasons causing the difference information, and outputting a diagnosis result. The multi-modal reference images conforming to physical laws are generated by combining the equipment attribute parameters and the environmental parameters through the physical information generation model, the imaging degradation problem caused by environmental interference in real observation is compensated, and the robustness of diagnosis under complex environments is significantly improved.

Description

Transmission line diagnostic method and system based on physical information generation and causal analysis Technical Field

[0001] This invention belongs to the field of power transmission line component condition diagnosis technology, specifically relating to a power transmission line diagnosis method and system based on physical information generation and causal analysis. Background Technology

[0002] As a crucial component of the power system, transmission lines bear the critical function of transmitting electrical energy. Their operational status directly affects the reliability and security of the power grid. Due to long-term exposure to complex natural environments such as wind, rain, snow, temperature variations, and ultraviolet radiation, the transmission line itself and its fittings, such as pre-stretched wires, tension clamps, and suspension clamps, are prone to defects such as mechanical fatigue, corrosion, loosening, and breakage. If these defects are not detected and addressed in a timely manner, they may lead to serious accidents such as line breaks and tower collapses, causing widespread power outages and socio-economic losses.

[0003] To ensure the safety of power transmission lines, regular inspections are one of the core tasks of power operation and maintenance. Traditional inspections mainly rely on manual visual inspection or ground-based telescope observation, which has limitations such as low efficiency, high risk, and significant susceptibility to subjective factors. In recent years, with the popularization of inspection methods such as drones, helicopters, and fixed cameras, image-based automated and semi-automated inspection technologies have developed rapidly, significantly improving the coverage and frequency of data collection. Correspondingly, image intelligent analysis technology has become a key support for identifying line defects and assessing operational status.

[0004] Currently, image analysis methods for power transmission lines are mainly divided into two categories: methods based on traditional image processing, which primarily rely on algorithms such as edge detection, threshold segmentation, morphological operations, and template matching to locate and identify simple defects in components such as insulators, conductors, and fittings. These methods are effective under simple backgrounds and good lighting conditions, but are significantly affected by image quality and are sensitive to interference from noise, occlusion, lighting changes, and complex backgrounds, exhibiting poor robustness. Methods based on deep learning, especially convolutional neural networks, have shown strong advantages in tasks such as object detection and semantic segmentation. They can automatically learn features and identify more complex defect patterns, such as insulator bursts, bird nests, and hanging foreign objects. However, deep learning methods rely on a large number of high-quality labeled samples for training. In practical engineering, the scarcity of defect samples and the high cost of labeling are significant problems. Furthermore, existing methods are mostly "black box" models, whose recognition logic is disconnected from the physical failure mechanisms of line components, making it difficult to maintain stable performance under insufficient sample conditions or poor imaging conditions. More importantly, existing methods generally ignore a fundamental challenge: the imaging degradation problem under complex environments. Power transmission lines are often located in remote, mountainous, and coastal areas, where images are easily affected by fog, rain, snow, backlighting, and motion blur. This can lead to loss of detail in key components, decreased contrast, and increased noise, severely impacting the accuracy of subsequent analysis. Neither traditional nor deep learning methods possess mechanisms for specifically enhancing image quality to address these degradations, limiting their recognition accuracy and generalization capabilities in real-world scenarios.

[0005] Therefore, in real-world operation and maintenance environments where samples are scarce and imaging conditions are variable, there is an urgent need for an imaging enhancement and condition diagnosis method that can integrate the advantages of physical priors and data-driven approaches to improve image quality, enhance defect identification capabilities, and provide stable and reliable visual input for subsequent intelligent analysis. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method and system for diagnosing power transmission lines based on physical information generation and causal analysis.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for diagnosing transmission lines based on physical information generation and causal analysis, comprising the following steps:

[0009] Acquire multi-source data of the target transmission line components, including equipment attribute parameters, environmental parameters, and real observation images;

[0010] The device attribute parameters and environmental parameters are input into a pre-trained physical information generative model to generate a multimodal reference image of the target transmission line component under corresponding conditions; wherein, the physical information generative model is trained by introducing physical law constraint terms so that the generated multimodal reference image conforms to the physical characteristics of the target transmission line component in a healthy state;

[0011] Calculate the difference information between the multimodal reference image and the actual observed image;

[0012] Based on a pre-defined cause-effect graph model, causal analysis is performed on most of the discrepancies to infer the cause of the device status that led to the discrepancies and obtain a diagnostic result.

[0013] In the step of inputting equipment attribute parameters and environmental parameters into a pre-trained physical information generative model to generate a multimodal reference image of the target transmission line component under corresponding conditions, the training process of the physical information generative model is as follows:

[0014] Use the cGAN model as the initial generative model;

[0015] The initial generative model is pre-trained using a dataset generated based on physical simulation, wherein the physical simulation simulates the electrical, thermal, and mechanical behavior of the target transmission line components under different equipment attribute parameters and environmental parameters.

[0016] During the pre-training process, a loss function is constructed using physical law constraints to obtain a well-trained physical information generative model.

[0017] The physical constraint terms include thermodynamic constraints. The multimodal reference image is an infrared reference image. Thermodynamic constraints ensure that the pixel value distribution of the infrared reference image satisfies the temperature field distribution law of the heat conduction equation. The specific formula is expressed as follows:

[0018]

[0019] in, Indicates curl, T represents the thermal conductivity of the material, and T represents the temperature. Where c is density and c is specific heat capacity. This is an internal heat source item.

[0020] The physical law constraint term includes electromagnetic field constraint. The multimodal reference image is an electromagnetic distribution reference image. The electromagnetic field constraint is used to make the pixel intensity distribution of the generated electromagnetic distribution reference image conform to the spatial variation law of electromagnetic field described by Maxwell's equations.

[0021] The physical constraints include optical imaging constraints. The multimodal reference image is a visible light reference image. The optical imaging constraints, by introducing a physical model of the rendering equation, ensure that the illumination, shadow, and reflection characteristics of the generated visible light reference image conform to the optical laws of the actual scene.

[0022] The physical model of the rendering equation is as follows:

[0023]

[0024] in, This indicates the direction of emission at point p. The brightness of the emitted light, It is self-illuminating. For reflected light, Represents the hemispherical space The points, The surface describes how the incident light at point p is directed from the direction... Reflected in the direction of emission ability, The actual incident radiance from the environment or light source. The term represents the effect of the incident angle on the effective received light energy.

[0025] In the step of calculating the difference information between the multimodal reference image and the real observed image, the difference information includes pixel-level residuals, structural similarity deviations, and spectral response differences.

[0026] In the step of performing causal analysis on the discrepancy information based on a preset causal graph model, inferring the equipment state cause of the discrepancy information, and outputting diagnostic results, the causal analysis includes intervening in environmental disturbance variables and equipment state variables to make attribution judgments. The intervention process includes:

[0027] Set ideal conditions, conduct simulated intervention based on the ideal conditions to obtain the intervention structure, and analyze the intervention results to obtain the intervention analysis results;

[0028] Adjust one of the device state variables while keeping the other variables constant, analyze the changing trend of the difference information, and obtain the adjustment analysis results;

[0029] A diagnostic report is generated based on the results of intervention analysis and regulation analysis.

[0030] It also includes the following steps:

[0031] Receive a counterfactual query request, the counterfactual query request containing future settings for device attribute parameters and / or environmental parameters, based on the settings in the counterfactual query request, generate a predicted image of future time points through a physical information generative model, and output a state prediction report based on the predicted image.

[0032] Secondly, the present invention provides a transmission line diagnostic system based on physical information generation and causal analysis, comprising:

[0033] The data acquisition module is used to acquire multi-source data of the target transmission line components, including equipment attribute parameters, environmental parameters, and real observation images;

[0034] A multimodal reference image generation module is used to input the device attribute parameters and environmental parameters into a pre-trained physical information generative model to generate a multimodal reference image of the target transmission line component under corresponding conditions; wherein, the physical information generative model is trained by introducing physical law constraint terms so that the generated multimodal reference image conforms to the physical characteristics of the target transmission line component in a healthy state;

[0035] The difference information calculation module is used to calculate the difference information between the multimodal reference image and the real observed image;

[0036] The causal analysis module is used to perform causal analysis on the difference information based on a preset causal graph model, infer the cause of the device status that led to the difference information, and obtain a diagnostic result.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This invention provides a transmission line diagnostic method based on physical information generation and causal analysis. Instead of directly identifying defects in potentially degraded real-world observation images, it utilizes a physical information generation model to generate a multimodal reference image representing the "ideal healthy state" under corresponding conditions, based on known equipment attributes and environmental parameters. Anomalies are located by calculating the difference between the generated multimodal reference image and the real-world observation image. This method shifts the focus of analysis from interpreting degraded images to identifying deviations from the multimodal reference image, significantly reducing reliance on the quality of the original image and effectively overcoming the interference of complex environments such as fog, rain, and backlighting on diagnostic results, thus significantly improving the robustness of diagnosis in complex environments.

[0039] Furthermore, embedding physical laws as constraints into the training process of the physical information generative model means that the generated multimodal baseline images are not simply statistical extrapolations of data, but rather images that conform to physical principles. Subsequently, by using a causal graph model to analyze the discrepancies, the root causes of the differences can be inferred. This provides maintenance personnel with a clear chain of physical evidence, significantly improving the credibility and acceptability of the diagnostic results.

[0040] Furthermore, the multimodal benchmark images in this scheme are generated by a physical information generative model, rather than relying on a large number of labeled healthy / defective samples for training, thus fundamentally reducing the requirement for a large number of defective samples. For rare defects or entirely new types of parts, only the device attribute parameters need to be adjusted and input into the physical information generative model to generate the corresponding multimodal benchmark images, thereby enabling state diagnosis in cases with small or even zero samples, and solving the pain point of weak generalization ability of deep learning models in scenarios with scarce samples.

[0041] Furthermore, through causal analysis of the difference information, this solution can further infer the type, severity, and even development trend of the defect, achieving a deeper level of status diagnosis and risk assessment. Attached Figure Description

[0042] Figure 1 is a flowchart of the method of the present invention;

[0043] Figure 2 is a structural diagram of the system composition of the present invention. Detailed Implementation

[0044] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0045] Example 1

[0046] As shown in Figure 1, a transmission line diagnosis method based on physical information generation and causal analysis includes the following steps:

[0047] S1: Acquire multi-source data of the target transmission line components, including equipment attribute parameters, environmental parameters, and real observation images;

[0048] S2: Input the device attribute parameters and environmental parameters into a pre-trained physical information generative model to generate a multimodal reference image of the target transmission line component under corresponding conditions; wherein, the physical information generative model is trained by introducing physical law constraint terms so that the generated multimodal reference image conforms to the physical characteristics of the target transmission line component in a healthy state;

[0049] S3: Calculate the difference information between the multimodal reference image and the actual observed image;

[0050] S4: Based on a preset cause-effect graph model, perform causal analysis on the difference information, infer the cause of the device status that led to the difference information, and obtain a diagnostic result.

[0051] Specifically, in S1, multi-source data of the target transmission line components is acquired. This multi-source data includes at least equipment attribute parameters, environmental parameters, and real observation images obtained through image acquisition equipment.

[0052] During the inspection of power transmission line components, drones or other inspection equipment are typically used to collect data from the components via onboard acquisition devices. However, current technologies usually only collect image data and then optimize and analyze it. While this method can analyze power transmission line components to some extent, it relies heavily on labeled data for defect identification. For example, when using deep learning models for defect identification, a large number of labeled samples are required to ensure accuracy. However, in actual inspections, defect samples are scarce and labeling costs are high, which can easily lead to insufficient samples and inaccurate defect identification. Furthermore, over-reliance on image data makes it difficult to effectively address imaging degradation under complex weather conditions.

[0053] In this embodiment, in addition to acquiring image data, the device attribute parameters and real-time environmental parameters of the transmission line components are also acquired simultaneously. The actual observation images acquired by the image acquisition device are used as multi-source data, and then analysis is performed based on this multi-source data.

[0054] The equipment attribute parameters include, but are not limited to, the geometric dimensions, material properties, electrical parameters, and spatial information of transmission line components, such as the insulator disc diameter, disc thickness, creepage distance, etc., the conductor diameter, cross-sectional area, etc., and the standard dimensions of fittings, etc.; material properties include the insulator material, conductive material, emissivity, etc.; spatial information refers to the coordinate position, installation angle, and relative distance of the transmission line components in three-dimensional space, etc.; environmental parameters refer to the dynamically changing parameters of the external conditions of the transmission line components, including but not limited to real-time ambient temperature, humidity, wind speed, light intensity, electrical load, background interference, etc., where electrical load refers to the real-time load current passing through the conductor, and background interference refers to the temperature of background objects (displayed as an infrared image) and the presence or absence of obstructions;

[0055] To improve the efficiency of subsequent analysis, the real observation images obtained by the image acquisition equipment can be restricted by conditions including but not limited to size and resolution, such as uniformly adjusting to a fixed resolution and pixel format. The real observation images can also be preprocessed, and the preprocessing process can refer to the methods of denoising, enhancing and registering images in the existing technology to obtain the preprocessed real observation images.

[0056] Specifically, in S2, the acquired equipment attribute parameters and real-time environmental parameters are input into a pre-trained physical generative model to generate a multimodal reference image of the target transmission line component under corresponding conditions. The physical information generative model is trained by introducing physical law constraint terms to make the generated multimodal reference image conform to the physical characteristics of the target transmission line component in a healthy state.

[0057] Furthermore, the physical information generative model is a generative artificial intelligence model based on deep learning and trained by introducing physical law constraints. In the existing technology, the physical information generative model can combine physical laws and data-driven methods to ensure that its output data conforms to physical laws, such as heat conduction equations, electromagnetic field distribution laws and optical imaging models, thereby generating sample data that conforms to real physical characteristics under specific environmental and device parameters.

[0058] In a preferred embodiment, the training process of the physical information generative model includes at least the following steps:

[0059] 1) Use the cGAN model as the initial generative model;

[0060] Preferably, the initial generative model can be one of the deep generative models such as Generative Adversarial Network (GAN), Variational Autoencoder (VAE), or Diffusion model. Conditional Generative Adversarial Network (cGAN) is preferred, which uses device attribute parameters and environmental parameters as conditional inputs to guide the generation of multimodal reference images.

[0061] 2) The initial generative model was pre-trained using a dataset generated based on physical simulation, where the physical simulation simulated the electrical, thermal, and mechanical behavior of the target transmission line components under different equipment attribute parameters and environmental parameters.

[0062] In the training process of the physical information generative model, pre-training is required first. This pre-training includes using a dataset generated based on physical simulation to pre-train the initial generative model so that the initial generative model can initially learn the physical response characteristics of transmission line components under different operating conditions. This physical simulation needs to be based on the electrical, thermal, and mechanical behavior of the target transmission line components under different equipment attribute parameters and environmental parameters. For example, finite element analysis software is used to simulate the electric field distribution and leakage current of insulators under specific voltage levels and ambient humidity; computational fluid dynamics and thermodynamics simulation is used to calculate the steady-state temperature field of conductors under specific load current, ambient temperature, and wind speed; and an optical rendering engine is used to generate visible light images with correct lighting effects based on the optical properties of materials (such as reflectivity) and real-time lighting conditions.

[0063] Based on the simulation data above, a paired sample set containing multimodal inputs (device attribute parameters, environmental parameters) and corresponding physical response images is constructed for supervised training, thereby forming a training sample mapping relationship between parameters and images.

[0064] 3) During the pre-training process, a loss function is constructed using physical law constraints. These physical law constraints are used to ensure that the output of the physical information generative model conforms to the preset physical laws.

[0065] Furthermore, the physical constraints include thermodynamic constraints, electromagnetic field constraints, and optical imaging constraints.

[0066] The physical constraint term includes a thermodynamic constraint. The multimodal reference image can be an infrared reference image. The thermodynamic constraint is used to ensure that the pixel value distribution of the generated infrared reference image satisfies the temperature field distribution law described by the heat conduction equation, and to ensure that the temperature gradient of each region in the infrared image is consistent with the actual physical conditions.

[0067] The temperature field distribution described by the heat conduction equation is determined by both Fourier's law and the energy conservation equation, specifically expressed as heat divergence:

[0068]

[0069] in: Indicates curl, T represents the thermal conductivity of the material, and T represents the temperature. Where c is density and c is specific heat capacity. For internal heat source terms; under steady-state conditions, The equation simplifies to It is used to constrain the rationality of the spatial distribution of temperature in the generated image, ensure that the high-temperature area matches the input parameters such as current load and heat dissipation conditions, and avoid the phenomenon of heat backflow that violates the second law of thermodynamics.

[0070] The physical law constraint includes electromagnetic field constraint. The multimodal reference image can be an electromagnetic distribution reference image. This electromagnetic field constraint is used to make the pixel intensity distribution of the generated electromagnetic distribution reference image conform to the electromagnetic field spatial variation law described by Maxwell's equations, thereby ensuring that the electromagnetic field hot spot location is highly consistent with the actual corona discharge area.

[0071] The spatial variation law of electromagnetic field described by Maxwell's equations ensures that the generated electromagnetic field distribution is not only accurate in spatial shape, but also truly reflects the influence of voltage, current and material dielectric properties on the electric field strength.

[0072] The physical constraints include optical imaging constraints. The multimodal reference image can be a visible light reference image. By introducing a physical model into the rendering equation, this constraint ensures that the lighting, shadow, and reflection characteristics of the generated visible light reference image conform to the optical laws of the actual scene, especially maintaining the realism of the surface texture and three-dimensional deformation of the parts under complex lighting conditions.

[0073] The physical model that introduces rendering equations simulates various processes of light. These rendering equations can be referenced from existing rendering processes that simulate the interaction between light and materials based on physical laws. The detailed formula can be expressed as follows:

[0074]

[0075] This equation describes the situation at point p along the exit direction. luminance of emitted light Self-luminescent It consists of two parts: the reflected light and the integral term. Indicates from all incident directions incident light Surface bidirectional reflection distribution function The contribution after the action, and is weighted by the cosine of the angle between the normal n and the incident direction; where, Represents the hemispherical space The integral ensures that the light contribution from all possible incident directions is fully considered; the formula contains... Determined by material properties, it describes how a surface directs incident light from a direction at point p. Reflected in the direction of emission ability, The actual incident radiance from the environment or light source. This item reflects the impact of the incident angle on the effective received light energy, ensuring that the transition between shadows and light and dark conforms to the real physical lighting model. Thus, it serves as a geometric attenuation factor to reflect the impact of the incident angle on the effective lighting area. When the angle between the incident light and the surface normal increases, the energy received per unit area decreases accordingly, which conforms to the basic physical principle of Lambert's law.

[0076] Loss functions established using physical constraint terms can be used to guide the generator to output multimodal reference images that conform to physical laws during generative adversarial networks (GANs). This loss function calculates the deviation between the generated multimodal reference images and the generative model based on real physical information, applying the aforementioned physical constraint terms to various multimodal reference images. For example, using discharge physics constraints, electromagnetic field distribution laws, and optical imaging models as regularization terms, a multiphysics coupling loss function can be constructed. Using this loss function to train the initial generative model is a common deep learning optimization technique. The formula for the loss function can be found by referring to... ,in It represents the physical deviation loss in the heat conduction process, used to constrain the rationality of the temperature field distribution during the discharge process; This corresponds to the electromagnetic field distribution regularization term derived from Maxwell's equations. For optical imaging loss based on rendering equations, each physical term is weighted by coefficients. , , Balance the data to ensure that the generated multimodal reference images still satisfy the true physical laws under various conditions.

[0077] The loss function constructed using physical law constraints can train the physical information generative model, enabling it to not only rely on data-driven statistical laws when generating multimodal reference images, but also follow various physical laws. This ensures that the generated multimodal reference images maintain physical consistency under different acquisition environments, reducing the risk of physical distortion caused by environmental parameter disturbances or changes in equipment status.

[0078] Specifically, in S3, based on the real observation data obtained by the image acquisition equipment, the difference information is calculated with the multimodal reference image. This difference information includes pixel-level residuals, structural similarity deviations, and spectral response differences.

[0079] After generating the multimodal reference image, the difference information between it and the real observation image can be calculated. This difference information includes at least pixel-level residuals, structural similarity deviations, and spectral response differences.

[0080] By comparing the infrared reference image with the measured thermal image pixel by pixel, the residual distribution of the temperature field is obtained. The deviation of the thermal distribution morphology on the surface of the device is evaluated by combining the structural similarity index. The color fidelity of the visible light image under different lighting conditions is quantified by using the spectral angle mapping method.

[0081] It should be noted that the various types of difference information obtained from the above difference information calculation are all existing technologies. Their calculation methods and application logic have clear technical implementation paths in this field and can be flexibly selected according to specific detection needs. They are not limited or described in detail in this embodiment.

[0082] Specifically, in S4, a causal graph model is preset, and causal analysis is performed on the obtained difference information based on the causal graph model. The cause of the equipment state that caused the difference information is inferred, and a diagnostic result is obtained. The causal analysis includes at least intervening in environmental disturbance variables and equipment state variables to make attribution judgments.

[0083] A causal graphical model is a probabilistic graphical model used to represent causal relationships between variables. It describes the transmission path of environmental disturbance variables (such as temperature, humidity, and light intensity) and equipment state variables (such as insulation aging and poor contact) to the observed differences through a directed acyclic graph. The application process of the causal graphical model can refer to existing technologies, such as the causal graphical model processing method and apparatus disclosed in Chinese patent application CN117521816A. In this embodiment, the detailed usage method is not explained, but only the intervention process is described.

[0084] Interventions on environmental disturbance variables and equipment state variables should at least include:

[0085] Set ideal conditions, conduct simulated interventions based on these ideal conditions, obtain intervention results, analyze the intervention results, and obtain intervention analysis results.

[0086] Ideal conditions could be as follows: ambient temperature constant at 25℃, relative humidity maintained at 40%RH, light intensity stable at 500 lux and no external electromagnetic interference. If the difference information is significantly reduced under these conditions, it indicates that the actual difference is mainly caused by environmental interference variables. This is because under these ideal conditions, the environmental variables have been intervened to ensure that there are no extreme or adverse disturbance factors. This means that most of the differences observed in the actual inspection environment are due to environmental interference such as temperature fluctuations, humidity changes, and uneven lighting, rather than abnormal equipment condition.

[0087] If the discrepancy information still exists significantly or increases under ideal conditions, it indicates that the discrepancy information is likely caused by the inherent abnormality of the equipment itself. In this case, further intervention of the equipment state variables is required for precise positioning. The intervention of equipment state variables includes at least: adjusting one of the equipment state variables while keeping other variables unchanged, analyzing the trend of the discrepancy information, and obtaining the adjustment analysis results; for example, simulating the degradation process of the dielectric constant of the insulating material and observing its impact on the consistency of infrared radiation characteristics and visible light texture. If the discrepancy information increases significantly, it can be determined that insulation aging is the main cause of the discrepancy information.

[0088] The above example of the intervention process is only one possible implementation method. In practical applications, similar intervention analysis can be performed on other equipment state variables based on the same principle. Based on the results of the intervention analysis and the adjustment analysis, the specific equipment state causes that produce the difference information can be determined, and a diagnostic result can be obtained.

[0089] It should be noted that a significant increase in the above-mentioned difference information can be judged by a threshold. This threshold can be obtained based on historical data statistical analysis. For example, if the range of difference information under normal operating conditions is set to within ±15%, then when the difference information exceeds this range, it is judged as a significant increase. This threshold can be dynamically adjusted according to the equipment type, operating years and operating conditions, and is not limited by a specific value.

[0090] A diagnostic report is generated based on the results of intervention analysis and regulation analysis.

[0091] Furthermore, the diagnostic method also includes step S5: receiving a counterfactual query request, the counterfactual query request containing future settings for device attribute parameters and / or environmental parameters, generating a predicted image of future time points through a physical information generative model based on the settings in the counterfactual query request, and outputting a state prediction report based on the predicted image.

[0092] To determine the future state of transmission line components based on existing data, counterfactual reasoning can be used to simulate image predictions under different parameter settings, and the predicted images can be analyzed. The counterfactual query request sets hypothetical modifications to the equipment attribute parameters and / or environmental parameters at a future point in time. For example, it is assumed that the slight contamination level of the currently discovered transmission line components will increase by 20% in the next three months. Based on this hypothetical modification, the physical information generative model generates a new multimodal baseline image.

[0093] Based on the newly generated multimodal reference image, it is compared with the current actual observation image, the difference information is extracted, and the analysis results of the difference information and the state prediction report based on the analysis results are output.

[0094] Preferably, the analysis results of the difference information include at least the trend, magnitude and spatial distribution characteristics of the difference changes, and the state prediction report based on the analysis results includes at least the potential risk areas, key influencing factors and possible failure modes of future state changes.

[0095] Based on the above embodiments, one possible scenario is: if the generated multimodal reference image deviates from the current real observation image in the insulator string area in terms of infrared and visible light characteristics, and the difference is increased by a certain percentage compared to the current state, then it is determined that there is a risk of hot spot deterioration in the insulator string area. The key influencing factors are the coupling effect of pollution accumulation and partial discharge, which may cause flashover faults. It is recommended to carry out cleaning in advance or install anti-pollution flashover coating, and dynamically adjust the maintenance cycle according to the equipment operating environment.

[0096] If the thermal characteristics of the conductor splice area are significantly enhanced in the generated multimodal reference image, combined with changes in environmental parameters such as wind speed and load current, it can be inferred that there is a risk of poor contact or metal fatigue, which may develop into strand breakage or fracture fault. It is necessary to arrange live fastening or reinforcement measures in a timely manner.

[0097] If the generated multimodal reference image shows an abnormal temperature field distribution in the tower foundation area, accompanied by changes in humidity and vibration parameters, there may be a risk of settlement or tilting. The key influencing factors are the superposition of geological loosening and external loads, which can easily lead to structural instability. It is recommended to verify the data by combining deformation monitoring data and implement reinforcement treatment.

[0098] Based on the above embodiments, the present invention has at least the following technical effects: by integrating physical information with a data-driven generative model, it can predict the future state of transmission lines; by using counterfactual reasoning and image difference analysis, it can improve the predictive ability of transmission lines and effectively identify potential fault risk areas and key influencing factors.

[0099] Example 2

[0100] This invention provides a power transmission line diagnostic system based on physical information generation and causal analysis, comprising:

[0101] The data acquisition module is used to acquire multi-source data of the target transmission line components, including equipment attribute parameters, environmental parameters, and real observation images;

[0102] A multimodal reference image generation module is used to input the device attribute parameters and environmental parameters into a pre-trained physical information generative model to generate a multimodal reference image of the target transmission line component under corresponding conditions; wherein, the physical information generative model is trained by introducing physical law constraint terms so that the generated multimodal reference image conforms to the physical characteristics of the target transmission line component in a healthy state;

[0103] The difference information calculation module is used to calculate the difference information between the multimodal reference image and the real observed image;

[0104] The causal analysis module is used to perform causal analysis on most discrepancies based on a preset causal graph model, infer the cause of the device status that leads to the discrepancies, and obtain diagnostic results.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for diagnosing transmission lines based on physical information generation and causal analysis, characterized in that, The process includes the following steps: acquiring multi-source data of the target transmission line component, including equipment attribute parameters, environmental parameters, and real observation images; inputting the equipment attribute parameters and environmental parameters into a pre-trained physical information generative model to generate multimodal reference images of the target transmission line component under corresponding conditions; wherein the physical information generative model is trained by introducing physical law constraint terms; calculating the difference information between the multimodal reference images and real observation images; performing causal analysis on the difference information based on a preset causal graph model to infer the equipment state causes that lead to the difference information and obtain diagnostic results; wherein the pre-trained physical information generative model is as follows: using a cGAN model as the initial generative model; pre-training the initial generative model using a dataset generated based on physical simulation, wherein the physical simulation simulates the electrical, thermal, and mechanical behavior of the target transmission line component under different equipment attribute parameters and environmental parameters; during the pre-training process, constructing a loss function through physical law constraint terms to obtain the trained physical information generative model.

2. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 1, characterized in that, The physical constraint terms include thermodynamic constraints. The multimodal reference image is an infrared reference image. Thermodynamic constraints ensure that the pixel value distribution of the infrared reference image satisfies the temperature field distribution law of the heat conduction equation. The specific formula is expressed as follows: in, Indicates curl, T represents the thermal conductivity of the material, and T represents the temperature. Where c is density and c is specific heat capacity. This is an internal heat source item.

3. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 1, characterized in that, The physical law constraint term includes electromagnetic field constraint. The multimodal reference image is an electromagnetic distribution reference image. The electromagnetic field constraint is used to make the pixel intensity distribution of the generated electromagnetic distribution reference image conform to the spatial variation law of electromagnetic field described by Maxwell's equations.

4. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 1, characterized in that, The physical constraints include optical imaging constraints. The multimodal reference image is a visible light reference image. The optical imaging constraints, by introducing a physical model of the rendering equation, ensure that the illumination, shadow, and reflection characteristics of the generated visible light reference image conform to the optical laws of the actual scene.

5. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 4, characterized in that, The physical model of the rendering equation is as follows: in, This indicates the direction of emission at point p. The brightness of the emitted light, It is self-illuminating. For reflected light, Represents the hemispherical space The points, The surface describes how the incident light at point p is directed from the direction... Reflected in the direction of emission ability, The actual incident radiance from the environment or light source. The term represents the effect of the incident angle on the effective received light energy.

6. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 1, characterized in that, In the step of calculating the difference information between the multimodal reference image and the real observed image, the difference information includes pixel-level residuals, structural similarity deviations, and spectral response differences.

7. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 1, characterized in that, In the step of performing causal analysis on the difference information based on a preset causal graph model to infer the cause of the equipment status that leads to the difference information and obtain a diagnostic result, the causal analysis includes intervening in environmental interference variables and equipment status variables to make attribution judgments. The intervention process includes: setting ideal conditions and performing simulated intervention based on the ideal conditions to obtain intervention results; analyzing the intervention results to obtain intervention analysis results; adjusting one of the equipment status variables while keeping other variables unchanged, analyzing the changing trend of the difference information to obtain adjustment analysis results; and outputting a diagnostic report based on the intervention analysis results and adjustment analysis results.

8. The transmission line diagnosis method based on physical information generation and causal analysis according to claim 1, characterized in that, It also includes the following steps: receiving a counterfactual query request, the counterfactual query request containing future settings for device attribute parameters and / or environmental parameters; generating a predicted image of future time points based on the settings in the counterfactual query request through a physical information generative model; and outputting a state prediction report based on the predicted image.

9. A transmission line diagnostic system based on physical information generation and causal analysis, based on the transmission line diagnostic method based on physical information generation and causal analysis according to any one of claims 1 to 8, characterized in that, include: The data acquisition module is used to acquire multi-source data of the target transmission line components, including equipment attribute parameters, environmental parameters, and real observation images; A multimodal reference image generation module is used to input the equipment attribute parameters and environmental parameters into a pre-trained physical information generative model to generate multimodal reference images of the target transmission line component under corresponding conditions; wherein, the physical information generative model is trained by introducing physical law constraint terms; a difference information calculation module is used to calculate the difference information between the multimodal reference image and the actual observed image; a causal analysis module is used to perform causal analysis on the difference information based on a preset causal graph model, infer the equipment state cause of the difference information, and obtain a diagnostic result.

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