Power transmission line icing intelligent identification method and system based on multi-source data

Abstract

The invention discloses a power transmission line icing intelligent identification method and system based on multi-source data, and relates to the technical field of icing intelligent identification, and the method comprises the steps: estimating the rough sag of a lead, carrying out the differential interference processing of an SAR complex image, calculating a winding gradient, constructing a target function, and obtaining an optimal integer flow field; and calculating the bus density of the wire after icing, generating the icing weight on the wire, and converting the icing weight into the equivalent icing thickness. According to the method, the quantitative inversion capability of the continuous distribution icing thickness along the power transmission line is improved through the combination of the low-orbit satellite SAR differential interference deformation inversion result and the fiber grating strain and span data, and the wrapping gradient network flow optimization unwrapping method is combined with the expected vertical deformation field constructed based on the rough sag and atmospheric phase correction. And the robustness of interferometric phase unwrapping and the inversion precision of the deformation quantity are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent icing identification technology, and in particular to an intelligent identification method and system for icing of transmission lines based on multi-source data. Background Technology

[0002] Transmission lines are prone to icing in high-altitude, frigid, and humid regions. Icing can significantly increase conductor load, alter sag and stress distribution, and in severe cases may induce serious accidents such as galloping, tripping, line breakage, and tower collapse. To ensure the safe operation of the power grid, the industry has successively proposed a variety of icing monitoring technologies based on manual inspection, fixed monitoring devices, meteorological numerical forecasting, and remote sensing observation.

[0003] Existing icing monitoring and identification technologies still have shortcomings. Methods based on manual inspections and video images are limited by weather, lighting, and terrain conditions, resulting in limited spatial coverage. Moreover, they often remain at the level of "whether there is icing" or coarse-level identification, making it difficult to obtain quantitative parameters such as the icing thickness that is continuously distributed along the line. Monitoring methods based on single sensors (such as single-point strain, single-tower tilt, or single-point weather) can only reflect local operating conditions and cannot establish a stable one-to-one correspondence with the overall sag shape and linear density changes of the line. Summary of the Invention

[0004] In view of the aforementioned existing problems, the inventors have proposed the present invention.

[0005] Therefore, this invention provides a method and system for intelligent identification of icing on transmission lines based on multi-source data. This solves the problems that methods based on manual inspection and video images are limited by weather, lighting and terrain conditions, have limited spatial coverage, and are mostly limited to "whether there is icing" or coarse-level identification, making it difficult to obtain quantitative parameters such as the icing thickness continuously distributed along the line. Monitoring methods based on single sensors (such as single-point strain, single-tower tilt or single-point weather) can only reflect local operating conditions and cannot establish a stable one-to-one correspondence with the overall sag shape and linear density changes of the line.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for intelligent identification of icing on transmission lines based on multi-source data, comprising, Multi-source data is collected and preprocessed, strain is filtered, the average strain value at the center of the span is extracted, and the coarse sag of the conductor is estimated by combining the average strain value and the span. Differential interferometry is performed on the SAR complex image, the winding gradient is calculated, the objective function is constructed, and the optimal integer flow field is obtained. The optimal integer flow field is summed to calculate the initial deformation phase. The final unwrapped phase is converted into the physical deformation of the radar line of sight. The chord connecting the two suspension points is calculated to generate the equivalent sag depth. The bus density of the conductor after icing is calculated to generate the icing weight on the conductor. The icing weight is converted into the equivalent icing thickness. The equivalent icing thickness is subjected to state detection, and a visual interface is constructed to display the state detection results.

[0007] As a preferred embodiment of the intelligent identification method for transmission line icing based on multi-source data described in this invention, the following steps are included: filtering the strain, extracting the average strain value at the center of the span, combining the average strain value and the span to estimate the coarse sag of the conductor, performing differential interferometry processing on the SAR complex image, calculating the winding gradient, constructing the objective function, and obtaining the optimal integer flow field, including: The strain is filtered, the low-frequency strain signal is arithmetically averaged, and a quasi-static strain field is generated that is spatially distributed along the conductor. The average strain value at the center of the span is extracted from the quasi-static strain field. By combining the average strain value and span, the rough sag of the conductor is estimated; Differential interferometry is performed on SAR complex images to generate a wrapped differential interferogram. The wrapping gradients of the differential interferograms in the x and y directions are calculated, the curl of the wrapping gradients is calculated, and residual points are identified. Each pixel in the interferogram is treated as a network node, and a supply value is assigned to each network node based on the residual point. The connection between adjacent pixel nodes is regarded as a network edge. A directed graph is generated, and an objective function is constructed based on the entangled phase gradient. The optimal integer flow field is obtained by solving the network simplex method.

[0008] As a preferred embodiment of the intelligent identification method for transmission line icing based on multi-source data described in this invention, the steps of summing the optimal integer flow field, calculating the preliminary deformation phase, converting the final unwrapped phase into the physical deformation along the radar line of sight, calculating the chord connecting the two suspension points, and generating the equivalent sag depth include: The optimal integer flow field is summed to generate the integer ambiguity of each pixel, and the preliminary deformation phase is calculated. Based on the rough sag, the expected vertical deformation field is constructed, and the expected vertical deformation field is projected onto the radar line of sight to calculate the expected unwrapping phase. The initial deformation phase is subtracted from the expected unwrapped phase pixel by pixel to obtain the residual field. The residual field is then processed by Gaussian filtering to generate the atmospheric delay phase. The atmospheric delay phase is then subtracted from the initial deformation phase to generate the final unwrapped phase. The final unwrapped phase is converted into the physical deformation of the radar line of sight, generating the deformation field of the radar line of sight, and the sag distribution curve along the transmission line path is extracted from the deformation field of the radar line of sight. Extract the suspension points from the sag distribution curve, calculate the chord connecting the two suspension points, convert it to the sag at the current position, and select the maximum sag as the equivalent sag depth.

[0009] As a preferred embodiment of the intelligent identification method for transmission line icing based on multi-source data described in this invention, the step of calculating the bus density of the conductor after icing, generating the icing weight on the conductor, and converting the icing weight into an equivalent icing thickness includes: Numerical integration is performed on the sag distribution curve of the conductor along the span direction to generate the geometric area of ​​the deformation field. The bus density of the conductor after icing is calculated by combining the geometric area of ​​the deformation field and the span. Based on the bus density, the weight of ice accretion on the conductor is calculated, and the weight of ice accretion is converted into the equivalent ice thickness.

[0010] As a preferred embodiment of the intelligent identification method for transmission line icing based on multi-source data described in this invention, the step of performing state detection on the equivalent icing thickness includes: Set a detection threshold and compare the equivalent icing thickness with the detection threshold. If the equivalent icing thickness is greater than or equal to the detection threshold, the line is judged to be in a warning state. If the equivalent icing thickness is less than the detection threshold, the line is judged to be in a safe state.

[0011] As a preferred embodiment of the intelligent identification method for transmission line icing based on multi-source data described in this invention, the step of constructing a visual interface to display the status detection results includes: A visualization interface was built using the visualization tool Matplotlib to display the identified equivalent ice thickness and the analysis results in real time.

[0012] As a preferred embodiment of the intelligent identification method for transmission line icing based on multi-source data described in this invention, the step of collecting and preprocessing multi-source data includes: Smart sensors are used to collect multi-source data from power transmission lines, and noise reduction and standardization processes are performed. The intelligent sensors include low-orbit satellites, fiber optic gratings, and GNSS receiver sensors; The multi-source data includes SAR complex images, radar wavelength, radar incident angle, strain, and span data.

[0013] Secondly, this invention provides an intelligent identification system for icing on transmission lines based on multi-source data, comprising: The data collection and processing module is used to collect multi-source data and perform noise reduction and standardization processing. The sag construction module is used to estimate the coarse sag of the conductor, perform differential interferometry processing on SAR complex images, calculate the winding gradient, construct the objective function, and obtain the optimal integer flow field. The phase recognition module is used to calculate the initial deformation phase, generate the equivalent sag depth, calculate the bus density of the conductor after icing, generate the icing weight on the conductor, and convert the icing weight into the equivalent icing thickness. The detection and display module is used to detect the equivalent ice thickness and build a visual interface to display the detection results.

[0014] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the intelligent identification method for transmission line icing based on multi-source data as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent identification method for transmission line icing based on multi-source data as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: This invention improves the quantitative inversion capability of continuously distributed icing thickness along transmission lines by combining the differential interferometric deformation inversion results of low-orbit satellite SAR with fiber optic strain and span data. It overcomes the limitations of traditional manual inspection and video images, which can only provide "whether there is icing" or a rough level and are difficult to obtain continuous thickness parameters. The winding gradient network flow optimization unwrapping method, combined with the expected vertical deformation field constructed based on the rough sag and atmospheric phase correction, improves the robustness of interferometric phase unwrapping and the accuracy of deformation inversion. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0018] Figure 1 This is a flowchart of the intelligent identification method for icing of transmission lines based on multi-source data in Example 1.

[0019] Figure 2 This is a schematic diagram of the intelligent identification system for icing of transmission lines based on multi-source data in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for intelligent identification of icing on transmission lines based on multi-source data, including the following steps: S1. Collect and preprocess multi-source data, filter the strain, extract the average strain value at the center of the span, combine the average strain value and the span to estimate the rough sag of the conductor, perform differential interferometry processing on the SAR complex image, calculate the winding gradient, construct the objective function, and obtain the optimal integer flow field. Specifically, this involves collecting and preprocessing multi-source data, including: Smart sensors are used to collect multi-source data from power transmission lines, and noise reduction and standardization processes are performed. The intelligent sensors include low-orbit satellites, fiber optic gratings, and GNSS receiver sensors; The multi-source data includes SAR complex images, radar wavelength, radar incident angle, strain, and span data.

[0024] Low-Earth orbit satellites possess high spatiotemporal resolution and all-weather imaging capabilities, enabling them to acquire SAR complex images and phase information of power transmission corridors. By denoising and standardizing multi-source data, they ensure consistency in amplitude scale, time synchronization, and spatial registration among various data sources, thereby reducing systematic errors caused by differences between different sources.

[0025] Furthermore, the strain is filtered to extract the average strain value at the center of the span. Combining the average strain value and the span, the coarse sag of the conductor is estimated. Differential interferometry is performed on the SAR complex image, and the winding gradient is calculated. An objective function is constructed to obtain the optimal integer flow field, including: Using a low-pass filter, such as a Butterworth filter, to filter the strain, the low-frequency strain signal is arithmetically averaged to generate a quasi-static strain field distributed along the conductor space. The average strain value at the center of the span is then extracted from the quasi-static strain field. Based on the principles of conductor mechanics, an approximate relationship between quasi-static strain and sag is established. Combining the average strain value and span, a rough sag of the conductor is estimated using the following formula: , in Let L be the approximate sag of the conductor, and L be the conductor span. This is a strain reference value, set based on the historical average strain. This represents the average strain value at the center of the span. Differential interferometry (DI-I) was used to perform differential interferometry processing on SAR complex images to generate a wrapped differential interferogram. The wrapping gradients in the x and y directions of the differential interferogram were calculated, and the wrapping gradient was defined as the amount of time it takes for the phase difference between adjacent pixels to be wrapped in the image. Within the interval, the formula is: , , in and Let be the gradients of the winding phase in the x and y directions at pixel (i, j), respectively. For the wrapper operator, This represents the phase value at pixel (i,j) in the differential interferogram. Calculate the curl of the winding gradient and identify the residual point using the following formula: , , in Let the curl of the gradient at pixel (i,j) be the curvature of the gradient. Let be the residual value at pixel (i,j); like Then pixel (i,j) is marked as a positive residual point. If the pixel (i,j) is a negative residual, then the pixel (i,j) is marked as a negative residual. Each pixel in the interferogram is considered a network node. A supply value is assigned to each network node based on the residual point: +1 for a positive residual point and -1 for a negative residual point. Connections between adjacent pixel nodes are considered network edges, generating a directed graph. Based on the wrapped phase gradient, an objective function is constructed, with the following formula: , , , Where J is the objective function value. Let be the set of edges. Let be the decision variable, representing the flow of integers through the directed edge (i,j). Let (i,j) be the unit flow cost on the directed edge (i,j). Let V be the supply value of node i, and V be the set of nodes. It is a very small constant. and These are the phase values ​​of the pixels corresponding to nodes i and j in the differential interferogram, respectively; The optimal integer flow field is obtained by using the network simplex method.

[0026] It effectively suppresses environmental noise, improves the stability and repeatability of strain signals, and forms a spatially continuous strain field, providing a high-resolution data foundation for judging the distribution of icing along the line. It transforms the distributed strain monitoring results into the overall geometric deformation index of the conductor, serving as a deformation prior before SAR unwrapping. This effectively limits the phase solution space, reduces error propagation, and the winding gradient calculation can identify local phase discontinuities, providing input for residual point location. It reduces noise interference on the overall trend of the phase field, enhances stability before unwrapping, and transforms SAR data from two-dimensional amplitude information into resolvable phase geometric information, improving spatial resolution, accurately locating the source of phase discontinuities, improving the reliability of phase unwrapping, avoiding the error propagation problems that are prone to occur in traditional region growing or least squares unwrapping, and improving the stability and anti-interference ability of SAR phase unwrapping results in complex power corridor environments.

[0027] S2. Sum the optimal integer flow field, calculate the preliminary deformation phase, convert the final unwrapped phase into the physical deformation of the radar line of sight, calculate the chord connecting the two suspension points, generate the equivalent sag depth, calculate the bus density of the conductor after icing, generate the icing weight on the conductor, and convert the icing weight into the equivalent icing thickness. Specifically, the optimal integer flow field is summed to calculate the preliminary deformation phase, the final unwrapped phase is converted into the physical deformation along the radar line of sight, the chord connecting the two suspension points is calculated, and the equivalent sag depth is generated, including: The optimal integer flow field is summed to generate the integer blur of each pixel, and the preliminary deformation phase is calculated using the following formula: , in For the initial deformation phase, For differential interferograms, Integer blur for each pixel; Based on a rough sag, the expected vertical deformation field is constructed, and the formula is: , in For the expected vertical deformation field, These are the traverse coordinates corresponding to the SAR complex image pixel (x,y); Projecting the expected vertical deformation field onto the radar line of sight, the expected unwrapping phase is calculated using the following formula: , in For the expected untangling phase, The radar incident angle, The radar wavelength; The initial deformation phase is subtracted from the expected unwrapped phase pixel by pixel to obtain the residual field. The residual field is then processed by Gaussian filtering to generate the atmospheric delay phase. The atmospheric delay phase is then subtracted from the initial deformation phase to generate the final unwrapped phase. Based on the principle of radar interferometry, the final unwrapped phase is converted into physical deformation along the radar line of sight, generating a deformation field along the radar line of sight, as shown in the formula: , in For the deformation field along the radar line of sight, This is the final untangling phase; The sag distribution curve along the transmission line path is extracted from the radar line of sight to the deformation field using the path resampling method. Extract the suspension points connecting the conductor and the tower from the sag distribution curve, and calculate the chord connecting the two suspension points using the following formula: , in The equation of the chord connecting the two suspension points is: The independent variable is denoted by , representing any horizontal coordinate position in a two-dimensional vertical plane coordinate system. and These are the horizontal and vertical coordinates of the suspension point A, respectively. and These are the horizontal and vertical coordinates of the suspension point B, respectively. It is then converted to the sag at the current position, and the maximum sag is selected as the equivalent sag depth. The formula is: , in Let be the sag at the o-th measurement point. and Let x and y be the horizontal and vertical coordinates of the o-th measurement point in a two-dimensional vertical plane coordinate system, respectively.

[0028] By accumulating and summing the optimal integer flow field, a globally consistent phase solution is obtained, enabling direct inversion of physical deformation. This transforms phase unwrapping from a local propagation process into a global accumulation process, avoiding error propagation, improving the overall consistency of the SAR phase field, and giving the results higher physical reliability. By establishing spatial constraints on deformation through sag priors, the accuracy of subsequent residual identification is improved, forming a "theoretical template" for the phase field. This helps distinguish between real deformation and atmospheric and ground object interference, providing a direct mapping relationship from the mechanical model to radar observations. It effectively separates atmospheric disturbances from real structural deformation, eliminates interference from non-physical signals, improves the spatiotemporal stability of radar line-of-sight deformation, and transforms SAR two-dimensional deformation information into a one-dimensional sag curve along the traverse direction, facilitating physical interpretation, avoiding perspective projection errors, and achieving accurate mapping from the radar line-of-sight coordinate system to the traverse geometric coordinate system. This avoids local sag errors caused by uneven distribution of measurement points.

[0029] Furthermore, the bus density of the conductor after icing is calculated, the icing weight on the conductor is generated, and the icing weight is converted into an equivalent icing thickness, including: The sag distribution curve of the conductor is numerically integrated along the span direction to generate the geometric area of ​​the deformation field, as shown in the formula: , in Let be the geometric area enclosed by the deformation field and the string. The sag distribution curve of the conductor represents the sag distribution at position. Verticality at the location; The analytical relationship derived from the variational principle of potential energy, combined with the geometric area of ​​the deformation field and the span, is used to calculate the bus density of the conductor after icing. The formula is as follows: , in This represents the bus density of the conductor after it becomes icy; Based on the bus density, the weight of ice accumulation on the conductor is calculated using the following formula: , in The weight of the ice covering the conductor. S and S are the density and cross-sectional area of ​​the conductor material, respectively, provided by the supplier; The formula for converting icing weight to equivalent icing thickness is: , Where h is the equivalent icing thickness and r is the radius of the conductor, provided by the supplier. This is the density of ice.

[0030] By establishing an analytical relationship between the deformation area and the span, the linear density is transformed from geometric deformation to mass parameters, reflecting the overall stress state of the conductor under different icing loads. This facilitates dynamic load assessment, avoids the limitations of traditional tension measurement methods, and can invert the linear density without contacting the conductor. It can obtain the icing thickness curve continuously distributed along the line, making it suitable for ice distribution analysis and early warning systems. Compared with image-based classification or empirical models, the accuracy is significantly improved and it is not limited by lighting or weather conditions.

[0031] S3. Perform state detection on the equivalent icing thickness and build a visual interface to display the state detection. Specifically, the equivalent icing thickness is subjected to state detection, including: The historical ice thickness was calculated using statistical analysis, with the mean plus three times the variance, and a detection threshold was set accordingly. The equivalent icing thickness is compared with the detection threshold. If the equivalent icing thickness is greater than or equal to the detection threshold, the line is judged to be in a warning state. If the equivalent icing thickness is less than the detection threshold, the line is judged to be in a safe state.

[0032] pass The principle automatically adapts to the characteristics of different regions, seasons, or routes, avoiding misjudgments caused by fixed thresholds. It adopts an unsupervised statistical modeling approach, without relying on external meteorological models or manual settings, reducing system complexity and constructing a clear binary state space, making the icing state determinable and logically closed, suitable for parallel monitoring of multiple routes.

[0033] Furthermore, a visual interface is constructed to display the state detection results, including: A visualization interface was built using the visualization tool Matplotlib to display the identified equivalent ice thickness and the analysis results in real time.

[0034] It can interface with geographic information systems to map the icing thickness to the spatial location of the line, realizing a two-dimensional display of space and time. It can be used as the front-end display module of the icing disaster early warning system and form a linkage response with meteorological and remote sensing subsystems.

[0035] This embodiment also provides an intelligent identification system for icing on transmission lines based on multi-source data, including: The data collection and processing module is used to collect multi-source data and perform noise reduction and standardization processing. The sag construction module is used to estimate the coarse sag of the conductor, perform differential interferometry processing on SAR complex images, calculate the winding gradient, construct the objective function, and obtain the optimal integer flow field. The phase recognition module is used to calculate the initial deformation phase, generate the equivalent sag depth, calculate the bus density of the conductor after icing, generate the icing weight on the conductor, and convert the icing weight into the equivalent icing thickness. The detection and display module is used to detect the equivalent ice thickness and build a visual interface to display the detection results.

[0036] This embodiment also provides a computer device applicable to the intelligent identification method for icing of transmission lines based on multi-source data, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent identification method for icing of transmission lines based on multi-source data as proposed in the above embodiment.

[0037] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0038] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the intelligent identification method for transmission line icing based on multi-source data as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0039] In summary, this invention improves the quantitative inversion capability of continuously distributed icing thickness along transmission lines by combining low-orbit satellite SAR differential interferometric deformation inversion results with fiber optic strain and span data. It overcomes the limitations of traditional manual inspections and video images, which can only provide "whether there is icing" or a rough level and are difficult to obtain continuous thickness parameters. The winding gradient network flow optimization unwrapping method, combined with the expected vertical deformation field constructed based on the rough sag and atmospheric phase correction, improves the robustness of interferometric phase unwrapping and the accuracy of deformation inversion.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent identification of icing on transmission lines based on multi-source data, characterized in that: include, Multi-source data is collected and preprocessed, strain is filtered, the average strain value at the center of the span is extracted, and the coarse sag of the conductor is estimated by combining the average strain value and the span. Differential interferometry is performed on the SAR complex image, the winding gradient is calculated, the objective function is constructed, and the optimal integer flow field is obtained. The optimal integer flow field is summed to calculate the initial deformation phase. The final unwrapped phase is converted into the physical deformation of the radar line of sight. The chord connecting the two suspension points is calculated to generate the equivalent sag depth. The bus density of the conductor after icing is calculated to generate the icing weight on the conductor. The icing weight is converted into the equivalent icing thickness. The equivalent icing thickness is subjected to state detection, and a visual interface is constructed to display the state detection results.

2. The intelligent identification method for icing of transmission lines based on multi-source data as described in claim 1, characterized in that: The strain is filtered to extract the average strain value at the center of the span. Combining the average strain value and the span, a rough sag of the conductor is estimated. Differential interferometry is performed on the SAR complex image, and the winding gradient is calculated. An objective function is constructed to obtain the optimal integer flow field, including: The strain is filtered, the low-frequency strain signal is arithmetically averaged, and a quasi-static strain field is generated that is spatially distributed along the conductor. The average strain value at the center of the span is extracted from the quasi-static strain field. By combining the average strain value and span, the rough sag of the conductor is estimated; Differential interferometry is performed on SAR complex images to generate a wrapped differential interferogram. The wrapping gradients of the differential interferograms in the x and y directions are calculated, the curl of the wrapping gradients is calculated, and residual points are identified. Each pixel in the interferogram is treated as a network node, and a supply value is assigned to each network node based on the residual point. The connection between adjacent pixel nodes is regarded as a network edge. A directed graph is generated, and an objective function is constructed based on the entangled phase gradient. The optimal integer flow field is obtained by solving the network simplex method.

3. The intelligent identification method for icing of transmission lines based on multi-source data as described in claim 2, characterized in that: The process of summing the optimal integer flow field, calculating the preliminary deformation phase, converting the final unwrapped phase into the physical deformation along the radar line of sight, calculating the chord connecting the two suspension points, and generating the equivalent sag depth includes: The optimal integer flow field is summed to generate the integer ambiguity of each pixel, and the preliminary deformation phase is calculated. Based on the rough sag, the expected vertical deformation field is constructed, and the expected vertical deformation field is projected onto the radar line of sight to calculate the expected unwrapping phase. The initial deformation phase is subtracted from the expected unwrapped phase pixel by pixel to obtain the residual field. The residual field is then processed by Gaussian filtering to generate the atmospheric delay phase. The atmospheric delay phase is then subtracted from the initial deformation phase to generate the final unwrapped phase. The final unwrapped phase is converted into the physical deformation of the radar line of sight, generating the deformation field of the radar line of sight, and the sag distribution curve along the transmission line path is extracted from the deformation field of the radar line of sight. Extract the suspension points from the sag distribution curve, calculate the chord connecting the two suspension points, convert it to the sag at the current position, and select the maximum sag as the equivalent sag depth.

4. The intelligent identification method for icing of transmission lines based on multi-source data as described in claim 3, characterized in that: The calculation of the bus density of the conductor after icing, the generation of the icing weight on the conductor, and the conversion of the icing weight into an equivalent icing thickness include: Numerical integration is performed on the sag distribution curve of the conductor along the span direction to generate the geometric area of ​​the deformation field. The bus density of the conductor after icing is calculated by combining the geometric area of ​​the deformation field and the span. Based on the bus density, the weight of ice accretion on the conductor is calculated, and the weight of ice accretion is converted into the equivalent ice thickness.

5. The intelligent identification method for icing of transmission lines based on multi-source data as described in claim 4, characterized in that: The condition detection of the equivalent icing thickness includes: Set a detection threshold and compare the equivalent icing thickness with the detection threshold. If the equivalent icing thickness is greater than or equal to the detection threshold, the line is judged to be in a warning state. If the equivalent icing thickness is less than the detection threshold, the line is judged to be in a safe state.

6. The intelligent identification method for icing of transmission lines based on multi-source data as described in claim 5, characterized in that: The construction of a visual interface to display the status detection results includes: A visualization interface was built using the visualization tool Matplotlib to display the identified equivalent ice thickness and the analysis results in real time.

7. The intelligent identification method for icing of transmission lines based on multi-source data as described in claim 1, characterized in that: The collection and preprocessing of multi-source data includes: Smart sensors are used to collect multi-source data from power transmission lines, and noise reduction and standardization processes are performed. The intelligent sensors include low-orbit satellites, fiber optic gratings, and GNSS receiver sensors; The multi-source data includes SAR complex images, radar wavelength, radar incident angle, strain, and span data.

8. A smart identification system for icing on transmission lines based on multi-source data, based on the smart identification method for icing on transmission lines based on multi-source data according to any one of claims 1 to 7, characterized in that: include, The data collection and processing module is used to collect multi-source data and perform noise reduction and standardization processing. The sag construction module is used to estimate the coarse sag of the conductor, perform differential interferometry processing on SAR complex images, calculate the winding gradient, construct the objective function, and obtain the optimal integer flow field. The phase recognition module is used to calculate the initial deformation phase, generate the equivalent sag depth, calculate the bus density of the conductor after icing, generate the icing weight on the conductor, and convert the icing weight into the equivalent icing thickness. The detection and display module is used to detect the equivalent ice thickness and build a visual interface to display the detection results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent identification method for transmission line icing based on multi-source data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent identification method for icing of transmission lines based on multi-source data as described in any one of claims 1 to 7.

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