Tower bearing capacity data modeling method

Abstract

This invention discloses a method for modeling the load-bearing capacity of power poles. The method includes: acquiring meteorological data, power pole inspection data, power pole ledger information, conductor parameter information, and terrain data; inputting the meteorological data and the power pole inspection data into a scene recognition model to identify the corresponding working scene of the power pole; inputting the power pole ledger information, conductor parameter information, and terrain data into a large-scale load-bearing capacity calculation model corresponding to the working scene to calculate the power pole's load-bearing capacity range; and determining whether the power pole meets the load-bearing capacity conditions corresponding to the working scene based on the power pole's load-bearing capacity range. This technical solution utilizes large-scale model technology to calculate the load-bearing capacity of power poles for different working scenes, with high accuracy in the calculation results.

Description

Technical Field

[0001] This invention relates to the field of power distribution network load-bearing capacity assessment technology, and more specifically, to a method for modeling data on tower load-bearing capacity. Background Technology

[0002] Distribution network towers are a major component of the power system. Problems with their operation can negatively impact power supply and distribution, such as causing power outages, affecting residential and industrial electricity use, and posing significant safety risks. Therefore, assessing the load-bearing capacity of distribution network towers is a crucial task for power systems. By assessing the load-bearing capacity of distribution network towers, power system planning can be optimized, fault rates reduced, and sustainable power supply achieved. Summary of the Invention

[0003] In view of the above problems, the purpose of this invention is to provide a method for modeling tower bearing capacity data, so as to overcome the shortcomings of the prior art and improve the accuracy of tower bearing capacity calculation results.

[0004] According to one embodiment of the present invention, a method for modeling tower bearing capacity data is provided, the method comprising:

[0005] Acquire meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data;

[0006] The meteorological data and the tower inspection data are fed into the scene recognition model to identify the working scene corresponding to the tower.

[0007] The tower ledger information, the conductor parameter information and the terrain data are sent into the bearing capacity calculation model corresponding to the working scenario to calculate the tower bearing capacity range.

[0008] Determine whether the tower meets the load-bearing capacity conditions corresponding to the working scenario based on the tower's load-bearing capacity range.

[0009] In the above-mentioned method for modeling tower bearing capacity data, the meteorological data includes weather, temperature, wind speed, icing thickness and / or humidity.

[0010] In the above-mentioned method for modeling data on the load-bearing capacity of poles and towers, the pole and tower inspection data includes image information and / or video information captured by drones during pole and tower inspections.

[0011] In the above-mentioned data modeling method for tower bearing capacity, the tower ledger information includes tower type, tower material, tower model, geometric information, number of circuits, bolt diagram and guy wire information.

[0012] In the above-mentioned method for modeling tower bearing capacity data, the conductor parameter information includes conductor type, span, sag, conductor material and / or safety factor.

[0013] The above-mentioned data modeling method for tower bearing capacity also includes:

[0014] If the tower does not meet the load-bearing capacity requirements corresponding to the working scenario, a handling suggestion will be output.

[0015] According to one embodiment of the present invention, a data modeling device for tower bearing capacity is provided, the device comprising:

[0016] The acquisition module is used to acquire meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data;

[0017] The work scene recognition module is used to send the meteorological data and the tower inspection data into the scene recognition model to identify the work scene corresponding to the tower.

[0018] The load-bearing capacity calculation module is used to input the tower ledger information, the conductor parameter information and the terrain data into the large load-bearing capacity calculation model corresponding to the working scenario, and calculate the tower load-bearing capacity range.

[0019] The determination module is used to determine whether the tower meets the bearing capacity conditions corresponding to the working scenario based on the tower bearing capacity range.

[0020] According to one embodiment of the present invention, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described tower bearing capacity data modeling method.

[0021] According to one embodiment of the present invention, an electronic device is provided, the electronic device including at least a memory and at least a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the above-described tower bearing capacity data modeling method.

[0022] According to one embodiment of the present invention, a computer-readable storage medium is provided, wherein a computer program used in the above-described electronic device is stored in the computer-readable storage medium.

[0023] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0024] This invention discloses a method for modeling the load-bearing capacity of power poles. The method includes: acquiring meteorological data, power pole inspection data, power pole ledger information, conductor parameter information, and terrain data; inputting the meteorological data and power pole inspection data into a scene recognition model to identify the corresponding working scene of the power pole; using multi-dimensional data to identify the corresponding working scene of the power pole, resulting in higher recognition accuracy; inputting the power pole ledger information, conductor parameter information, and terrain data into a large-scale load-bearing capacity calculation model corresponding to the working scene to calculate the power pole's load-bearing capacity range; determining whether the power pole meets the load-bearing capacity conditions corresponding to the working scene based on the power pole's load-bearing capacity range; intelligently calculating the power pole's load-bearing capacity through large-scale load-bearing capacity calculation models corresponding to different working scenes, resulting in more accurate calculation results; and utilizing the generalization ability of the large-scale model, it can generalize to different working scenes, making it more widely applicable.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of protection of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a method for modeling data on the bearing capacity of a pole according to an embodiment of the present invention is shown.

[0028] Figure 2 A flowchart illustrating another method for modeling tower bearing capacity data provided in an embodiment of the present invention is shown.

[0029] Figure 3 A schematic diagram of a data modeling device for tower bearing capacity provided in an embodiment of the present invention is shown. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0031] Example 1

[0032] Figure 1 The diagram shows a flowchart of a tower bearing capacity data modeling method provided by an embodiment of the present invention.

[0033] Distribution network towers are structures used to support conductors, insulators, hardware, and other equipment in overhead lines of a distribution network, and are one of the infrastructures of the distribution system.

[0034] The method for modeling the load-bearing capacity of this tower includes the following steps:

[0035] In step S110, meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data are acquired.

[0036] In this embodiment, the meteorological data includes weather, temperature, wind speed, icing thickness, and / or humidity.

[0037] It can obtain meteorological data issued by the meteorological bureau, as well as through third-party websites, weather apps, weather APIs, news and / or social media.

[0038] In this embodiment, the tower inspection data includes image information and / or video information captured by the drone during tower inspection.

[0039] The image information and / or video information refers to the images and / or videos of the pole and its surrounding environment captured by the drone during inspection.

[0040] In this embodiment, the pole and tower ledger information includes pole type, pole and tower material, pole and tower model, geometric information, number of circuits, bolt diagram, and guy wire information.

[0041] The pole type, or pole tower type, includes straight-line pole towers, tension pole towers, angle pole towers, and terminal pole towers (T-joint pole towers), etc. Different pole tower types correspond to different specifications of load-bearing capacity.

[0042] Among them, straight-line towers are used on straight sections of the line and only bear the weight of the conductor and wind force, not the tension of the conductor.

[0043] Tension towers are used for line segmentation to withstand conductor tension and prevent accidents from escalating. They are commonly used at the start, end, or turning points of a line.

[0044] Angle towers are used at bends in power lines and are divided into two types: suspension angle towers and tension angle towers.

[0045] Terminal towers are used at the beginning or end of a line to bear the full tension of the conductor on one side.

[0046] T-junction towers are used at line branch points to enable one line to supply power to another substation or user.

[0047] The material of a pole or tower refers to the material itself, such as concrete poles, steel pipe poles, angle steel poles, and composite material poles. Different materials result in different load-bearing capacities. Among them, concrete poles are the most common and are often placed in open outdoor areas; steel pipe poles are often used in cities, narrow corridors, or areas with high stress; angle steel poles are often used for crossings, mountainous areas, or areas with special stress, requiring high strength and large spans; composite material poles are relatively lightweight and corrosion-resistant, and are often placed in special environments, such as highly corrosive or high-lightning-prone areas.

[0048] The geometric information of the tower includes parameters such as the top diameter, root diameter, wall thickness, and burial depth.

[0049] The number of circuits can be single or double.

[0050] Bolt drawings include bolt CAD drawings, which are CAD drawings designed to ensure that the bolt's size, shape, or mechanical properties meet the design specifications.

[0051] Guy information includes whether there is a guy wire, such as a cantilever without a guy wire, or one with a guy wire.

[0052] In this embodiment, the conductor parameter information includes conductor type, span, sag, conductor material and / or safety factor.

[0053] The span refers to the distance between two support points in an overhead line, directly affecting the line's sag, tension, and safe operation. Span length includes horizontal and vertical spans. Sag refers to the degree of sag of the conductor under neutral load. Conductor materials include bare conductors and insulated conductors. Bare conductors are primarily round, concentric stranded overhead conductors, commonly made of steel-cored aluminum stranded wire. The safety factor refers to the conductor's safe load-bearing capacity under normal operating conditions, typically used to assess the conductor's strength and reliability.

[0054] In this embodiment, the terrain data refers to data describing the shape of the Earth's surface, including ground elevation, slope, direction, and topographic relief. This data can be acquired through ground surveying, aerial photography, satellite remote sensing, and other methods. Digital terrain models (DTMs) and digital elevation models (DEMs) are common ways to represent terrain data. A DTM includes not only surface elevation information but also other physical characteristics of the terrain, such as slope and direction. A DEM, on the other hand, primarily contains surface elevation information.

[0055] In step S120, the meteorological data and the tower inspection data are sent to the scene recognition model to identify the working scene corresponding to the tower.

[0056] In this embodiment, the scene recognition model can be a large model, which can be a multimodal large model that can process different types of input data, such as text information, image information and / or video information.

[0057] In this embodiment, meteorological data and pole inspection data are fed into the scene recognition model. Through multi-dimensional input parameters and the scene recognition model, the working scene corresponding to the pole is identified, resulting in higher recognition accuracy.

[0058] The scene recognition model is pre-trained. During training, multimodal meteorological data samples and tower inspection data samples are used as inputs. Training stops when the scene loss function or gradient descent value meets the conditions. The trained scene recognition model is then used to detect tower working scenes.

[0059] In this embodiment, in order to improve the accuracy of bearing capacity calculation, the working environment of the tower is divided into different working scenarios, and the bearing capacity calculation model corresponding to each working scenario is different (obtained by training through sample parameters corresponding to each working scenario), which further improves the accuracy of bearing capacity calculation.

[0060] In this embodiment, the working scenarios include icing working scenarios, thunderstorm working scenarios, strong wind working scenarios, strong corrosion working scenarios, and windless and rainless working scenarios. The above working scenarios can be customized by the user to distinguish the different capacity requirements of the tower under different working scenarios.

[0061] In step S130, the tower ledger information, the conductor parameter information, and the terrain data are sent to the bearing capacity calculation model corresponding to the working scenario to calculate the tower bearing capacity range.

[0062] In this embodiment, the load-bearing capacity calculation models are different for different working scenarios. Each load-bearing capacity calculation model is trained using sample data from its corresponding working scenario, which can accurately and efficiently identify the load-bearing capacity of the tower under different working scenarios.

[0063] For example, the large-scale model for calculating the load-bearing capacity of poles in icing working scenarios is trained using sample data from icing working scenarios. It can accurately and efficiently identify the load-bearing capacity of poles in icing working scenarios, but its accuracy in calculating the load-bearing capacity of poles in non-icing working scenarios is generally lower.

[0064] In some other embodiments, the scene recognition model and the load-bearing capacity calculation model are in the same overall model. A portion of the network layers in the overall model constitutes the scene recognition model, while another portion constitutes the load-bearing capacity calculation model.

[0065] Specifically, the overall large-scale model comprises M+1+N layers. The M layers constitute the scene recognition model, and the N layers contain all the bearing capacity calculation models. The bearing capacity models for different work scenarios include different layers (e.g., the bearing capacity model for an icing work scenario includes layer S of the N layers, where S is less than N; the bearing capacity model for a thunderstorm work scenario includes layer P of the N layers, where P is less than N; and so on). Layer 1 is the decision layer, used to decide which bearing capacity calculation model to send the tower register information, conductor parameter information, and terrain data to based on the work scenario identified by the scene recognition model—that is, which layers in the subsequent N layers. This allows for the implementation of work scenario recognition and bearing capacity calculation through a single overall large-scale model. Furthermore, the bearing capacity calculation model and the scene model can share some layers, reducing redundant layers. Additionally, other work scenarios can be added to the overall large-scale model by selecting certain layers from the N layers for training.

[0066] The decision-making layer can make decisions based on the correspondence between the work scenarios and the large-scale load-bearing capacity calculation models. Specifically, the correspondence can include each work scenario and the network layer corresponding to each work scenario.

[0067] In this embodiment, the overall large model may include an input layer, multiple multi-head attention layers, multiple network layers, multiple decision layers, multiple normalization layers, and an output layer.

[0068] The input layer maps meteorological data, pole inspection data, pole ledger information, conductor parameter information, and terrain data into a high-dimensional vector containing location codes. The multi-head attention layer identifies the correlation information between different parameters in the high-dimensional vector. The network layer processes the correlation information nonlinearly to obtain structured information and identify the working scenario. The decision layer makes decisions based on the correspondence between the working scenario and each large-scale bearing capacity calculation model, identifying which large-scale bearing capacity calculation model to send the pole ledger information, conductor parameter information, and terrain data to, i.e., which layers in the subsequent N-layer network (after sending to which layers, those layers can perform bearing capacity interval inference based on the received parameters). The normalization layer standardizes the data from the previous layer to obtain standardized data. The output layer outputs the bearing capacity interval based on the standardized data.

[0069] The large-scale scene recognition model of the M-layer may include a multi-head attention layer, a network layer, and a normalization layer in some layers; the large-scale capacity calculation model of the N-layer may include multi-head attention layers, network layers, and normalization layers in other layers.

[0070] It is worth noting that the aforementioned partial layers and other partial layers can be overlapping network layers or non-overlapping, independent network layers.

[0071] In this embodiment, a normalization layer can be embedded after each of the aforementioned input layer, multi-head attention layer, network layer, and decision layer. By stacking multiple normalization layers after each network layer, the problem of "gradient vanishing" during model training can be avoided by having excessively large values ​​in each layer. In addition, the input of each layer can be superimposed on the output of that layer, providing a shortcut for gradient propagation and preventing the gradient from decaying to 0 during the training of deep, large models.

[0072] It is worth noting that the large-scale load-bearing capacity calculation model corresponding to each work scenario can have the same network layer. The difference between different work scenarios lies in the different values ​​of their corresponding parameter matrices. Therefore, in this embodiment, the parameter matrices of the large-scale load-bearing capacity calculation model corresponding to different work scenarios can also be stored in a preset location. At the decision layer, the parameter matrix corresponding to the identified work scenario is called to participate in the nonlinear calculation of the subsequent network layer.

[0073] In step S140, it is determined whether the tower meets the load-bearing capacity conditions corresponding to the working scenario based on the tower load-bearing capacity range.

[0074] In this embodiment, the tower bearing capacity range may include the current status of the tower, the ultimate bearing capacity of the tower in the working scenario, the remaining bearing capacity of the tower in the working scenario, and optional maintenance suggestions.

[0075] For example, the tower bearing capacity range includes the following parameters: the ultimate bearing capacity coefficient of the tower system at XX wind speed, and the buckling of the main material of the tower leg under the control condition of XX degrees wind direction. It is recommended to upgrade the tower type / material to XX, which can improve the ultimate bearing capacity coefficient to XX.

[0076] In this embodiment, the bearing capacity range is calculated by the large bearing capacity calculation model, and the bearing capacity range is compared with the bearing capacity conditions corresponding to the working scenario of the tower. The bearing capacity conditions include the bearing capacity conditions corresponding to the safe operation of the tower.

[0077] If the load-bearing capacity range of the tower meets the load-bearing capacity condition, the tower can be operated / used safely; if the load-bearing capacity range of the tower does not meet the load-bearing capacity condition, the tower has a safety risk, which can be confirmed manually or corresponding maintenance measures can be implemented.

[0078] For example, if there are safety risks associated with a power pole, options include building a new power pole, assessing whether to continue using it, emergency repairs, or capacity expansion.

[0079] This embodiment utilizes multi-dimensional input parameters and artificial intelligence models to identify different working scenarios of the tower. Furthermore, it deploys large-scale load-bearing capacity calculation models corresponding to each specific working scenario to calculate the load-bearing capacity, resulting in higher accuracy and greater generalization.

[0080] Figure 2 A flowchart illustrating another method for modeling tower bearing capacity data provided by an embodiment of the present invention is shown.

[0081] The method for modeling the load-bearing capacity of this tower includes the following steps:

[0082] In step S210, meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data are acquired.

[0083] This step is the same as step S110, and will not be repeated here.

[0084] In step S220, the meteorological data and the tower inspection data are sent to the scene recognition model to identify the working scene corresponding to the tower.

[0085] This step is the same as step S120, and will not be repeated here.

[0086] In step S230, the tower ledger information, the conductor parameter information, and the terrain data are sent to the bearing capacity calculation model corresponding to the working scenario to calculate the tower bearing capacity range.

[0087] This step is the same as step S130, and will not be repeated here.

[0088] In step S240, it is determined whether the tower meets the load-bearing capacity conditions corresponding to the working scenario based on the tower load-bearing capacity range.

[0089] This step is the same as step S140, and will not be repeated here.

[0090] In step S250, if the tower does not meet the bearing capacity conditions corresponding to the working scenario, a handling suggestion is output.

[0091] In this embodiment, when the tower does not meet the load-bearing capacity conditions corresponding to the working scenario, the most suitable or cost-effective handling suggestion can be evaluated. Unlike the conservative blind spot of "taking the most unfavorable value" in traditional specifications, the data obtained in the above steps are used for refined calculations to ensure that the load-bearing capacity of each distribution network tower is utilized and repaired just right.

[0092] For example, the ultimate bearing capacity of the pole in this working scenario can be used to deduce the required pole type, avoiding over-design and increasing the cost of the power distribution network.

[0093] For example, the remaining load-bearing capacity of the pole in the working scenario can be used to assess whether the pole should continue to be used, thus avoiding the risk of pole collapse and line breakage caused by not replacing the pole when it should be replaced, and also avoiding the financial waste caused by replacing the pole when it should not be replaced.

[0094] For example, in post-disaster emergency repair scenarios, the ultimate bearing capacity of the towers in that scenario can be used to prioritize the repair of truly dangerous towers and prevent secondary tower collapses.

[0095] For example, the ultimate load-bearing capacity of the tower in this working scenario can be used to assess whether to replace the conductor, so as to avoid the conductor load exceeding the limit and causing the tower to collapse.

[0096] Figure 3 A schematic diagram of a tower bearing capacity data modeling device according to an embodiment of the present invention is shown. This tower bearing capacity data modeling device 300 corresponds to the tower bearing capacity data modeling method in the above embodiments, and the tower bearing capacity data modeling method in the above embodiments is also applicable to this tower bearing capacity data modeling device 300, which will not be described again here.

[0097] The tower bearing capacity data modeling device 300 includes an acquisition module 310, a working scene recognition module 320, a bearing capacity calculation module 330, and a determination module 340.

[0098] The acquisition module 310 is used to acquire meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data.

[0099] The work scene recognition module 320 is used to send the meteorological data and the tower inspection data into the scene recognition model to identify the work scene corresponding to the tower.

[0100] The load-bearing capacity calculation module 330 is used to input the tower ledger information, the conductor parameter information and the terrain data into the load-bearing capacity calculation model corresponding to the working scenario, and calculate the tower load-bearing capacity range.

[0101] The determination module 340 is used to determine whether the tower meets the bearing capacity conditions corresponding to the working scenario based on the tower bearing capacity range.

[0102] This embodiment utilizes multi-dimensional input parameters and artificial intelligence models to identify different working scenarios of the tower. Furthermore, it deploys large-scale load-bearing capacity calculation models corresponding to each specific working scenario to calculate the load-bearing capacity, resulting in higher accuracy and greater generalization.

[0103] Another embodiment of the present invention provides an electronic device, the electronic device including at least one memory and at least one processor, the memory for storing a computer program, the processor running the computer program to enable the electronic device to perform the functions of each module in the above-described tower bearing capacity data modeling method or the above-described tower bearing capacity data modeling device.

[0104] The memory may include a stored program area and a stored data area. The stored program area may store the operating system, applications required for at least one function, etc.; the stored data area may store data created based on the use of the computer device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0105] This invention also provides a computer storage medium for storing the tower bearing capacity data modeling method used in the above-mentioned electronic equipment.

[0106] This invention also provides a computer program product, which includes a computer program / instruction that, when executed by a processor, implements the steps of the above-described tower bearing capacity data modeling method.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0108] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0109] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned computer-readable storage medium can include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0110] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for modeling data on the bearing capacity of towers, characterized in that, The method includes: Acquire meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data; The meteorological data and the tower inspection data are fed into the scene recognition model to identify the working scene corresponding to the tower. The tower ledger information, the conductor parameter information and the terrain data are sent into the bearing capacity calculation model corresponding to the working scenario to calculate the tower bearing capacity range. Determine whether the tower meets the load-bearing capacity conditions corresponding to the working scenario based on the tower's load-bearing capacity range.

2. The method for modeling tower bearing capacity data according to claim 1, characterized in that, The meteorological data includes weather, temperature, wind speed, icing thickness, and / or humidity.

3. The method for modeling tower bearing capacity data according to claim 1, characterized in that, The tower inspection data includes image and / or video information captured by drones during tower inspections.

4. The method for modeling tower bearing capacity data according to claim 1, characterized in that, The information in the pole and tower ledger includes pole type, pole and tower material, pole and tower model, geometric information, number of circuits, bolt diagrams, and guy wire information.

5. The method for modeling tower bearing capacity data according to claim 1, characterized in that, The conductor parameter information includes conductor type, span, sag, conductor material and / or safety factor.

6. The method for modeling tower bearing capacity data according to any one of claims 1, characterized in that, Also includes: If the tower does not meet the load-bearing capacity requirements corresponding to the working scenario, a handling suggestion will be output.

7. A data modeling device for tower bearing capacity, characterized in that, The device includes: The acquisition module is used to acquire meteorological data, tower inspection data, tower ledger information, conductor parameter information, and terrain data; The work scene recognition module is used to send the meteorological data and the tower inspection data into the scene recognition model to identify the work scene corresponding to the tower. The load-bearing capacity calculation module is used to input the tower ledger information, the conductor parameter information and the terrain data into the large load-bearing capacity calculation model corresponding to the working scenario, and calculate the tower load-bearing capacity range. The determination module is used to determine whether the tower meets the bearing capacity conditions corresponding to the working scenario based on the tower bearing capacity range.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the tower bearing capacity data modeling method according to any one of claims 1-6.

9. An electronic device, characterized in that, The electronic device includes at least one memory and at least one processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the tower bearing capacity data modeling method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores the computer program used in the electronic device of claim 9.

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