Method and related device for risk assessment based on three-dimensional point cloud model of power transmission line

Abstract

The invention provides a method and a related device for risk assessment based on a three-dimensional point cloud model of a power transmission line. The method comprises the following steps: constructing an actual three-dimensional model of the power transmission line and constructing a standard three-dimensional model template of the power transmission line; comparing the structural features of the two models to obtain feature deviation information and operation dynamic information; obtaining position relation information of the power transmission line according to the position of the power transmission line in the power network topological structure, and obtaining vegetation coverage information according to the vegetation coverage condition of the power transmission line; performing comprehensive quantification on various types of information to obtain a line evaluation coefficient and an environment evaluation coefficient; and performing prediction and evaluation by using the risk evaluation model to obtain a risk evaluation result of the power transmission line in the to-be-evaluated area. According to the invention, comprehensive, accurate and efficient assessment of the power transmission line risk can be realized, a scientific basis is provided for power transmission line operation and maintenance decisions, and the operation stability and reliability of a power system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of transmission line risk assessment, and particularly relates to a method and related device for risk assessment based on a three-dimensional point cloud model of a transmission line. Background Art

[0002] The safe and stable operation of transmission lines is crucial to the power system and directly affects social production and life. Once the power supply is interrupted, industrial production will stagnate and residents' lives will also be affected. Therefore, risk assessment of transmission lines and timely detection of potential hazards are the keys to ensuring the normal operation of the power system.

[0003] Traditional transmission line risk assessment relies on manual inspections and simple monitoring data, with low efficiency and being greatly affected by terrain and weather, making it impossible to comprehensively assess line risks. With the development of three-dimensional modeling and point cloud data processing technologies, risk assessment methods based on three-dimensional point cloud models of transmission lines have emerged. By obtaining three-dimensional point cloud data to construct a model, more accurate data can be provided for assessment.

[0004] Existing risk assessment methods based on three-dimensional point cloud models of transmission lines have deficiencies. In terms of risk assessment models, existing models often only consider some characteristics of transmission lines, such as the geometric structure and operating parameters of the lines, while ignoring the impact of surrounding environmental factors on line risks, such as vegetation coverage and topography. In addition, existing risk assessment methods lack effective integration of multi-source data and cannot make full use of the correlation information between various data, resulting in the accuracy and reliability of risk assessment needing to be improved. Summary of the Invention

[0005] In view of this, the present invention aims to provide a method and related device for risk assessment based on a three-dimensional point cloud model of a transmission line to solve the above deficiencies existing in the prior art.

[0006] To achieve the above object, the technical solutions provided by the present invention are as follows:

[0007] In the first aspect, the present invention provides a method for risk assessment based on a three-dimensional point cloud model of a transmission line, including the following steps:

[0008] Construct an actual three-dimensional model of the transmission line and a standard three-dimensional model template of the transmission line according to the point cloud data of the transmission line and the surrounding environment in the area to be evaluated;

[0009] Compare the structural characteristics of the actual three-dimensional model and the three-dimensional model template to obtain the characteristic deviation information of the transmission line, and obtain the operation dynamic information according to the operation dynamic situation of the transmission line;

[0010] Obtain the location relationship information of the transmission line according to the location of the transmission line in the power network topology structure, and obtain the vegetation coverage information according to the vegetation coverage of the transmission line;

[0011] Comprehensively quantify the feature deviation information, operation dynamic information, location relationship information, and vegetation coverage information respectively to obtain the line evaluation coefficient and the environment evaluation coefficient;

[0012] Input the line evaluation coefficient and the environment evaluation coefficient into the pre-trained risk assessment model for prediction and evaluation to obtain the risk assessment result of the transmission line in the area to be evaluated; the risk assessment model is a model constructed and trained using a neural network based on the line evaluation coefficient, environment evaluation coefficient, and actual risk situation of transmission lines in different regions.

[0013] Furthermore, compare the structural features of the actual 3D model and the 3D model template to obtain the feature deviation information of the transmission line, including:

[0014] Compare each structural feature of the transmission line in the actual 3D model with the corresponding structural feature in the 3D model template, and calculate the deviation value of each structural feature;

[0015] Based on the deviation value, calculate the structural feature deviation coefficient of the transmission line as follows:

[0016]

[0017] In the formula, is the structural feature deviation coefficient, which is used to quantitatively represent the feature deviation information; is the product of the deviation values of each structural feature; is the sum of each structural feature value in the actual 3D model.

[0018] Furthermore, obtain the operation dynamic information according to the operation dynamic situation of the transmission line, including:

[0019] Obtain the swing amplitude data and tension data of the transmission line within the preset monitoring interval;

[0020] According to the relationship between the swing amplitude data, the tension data, and time, respectively fit the conductor motion function and the tension change function;

[0021] Based on the conductor motion function and the tension change function, calculate the operation anomaly accumulation coefficient as follows:

[0022]

[0023] In the formula, is the operation anomaly accumulation coefficient, which is used to quantitatively represent the operation dynamic information; and They are the wire movement function and the tension change function respectively; It is the time period when the swing amplitude is greater than the swing amplitude threshold; It is the time period when the wire tension is greater than the tension threshold.

[0024] Furthermore, the comprehensive quantification calculation formula of the line evaluation coefficient is as follows:

[0025]

[0026] In the formula, is the line evaluation coefficient; and are the quantification values of the feature deviation information and the operation dynamic information respectively; is the proportionality coefficient of the feature deviation information, is the proportionality coefficient of the operation dynamic information.

[0027] Furthermore, the position relationship information of the transmission line is obtained according to the position of the transmission line in the power network topology structure, including:

[0028] Based on the position coordinates of all transmission lines in the area to be evaluated, all transmission lines are divided into several clusters by using the K-means clustering algorithm;

[0029] The topological importance coefficient of the transmission line is calculated according to the position of the transmission line in the cluster it belongs to, as follows:

[0030]

[0031] In the formula, is the topological importance coefficient, which is used to quantitatively represent the position relationship information; is the distance between the transmission line and the cluster center of the cluster it belongs to; is the average distance of all transmission lines in the cluster where the transmission line belongs to the cluster center.

[0032] Furthermore, the vegetation coverage information is obtained according to the vegetation coverage of the transmission line, including:

[0033] The ray tracing simulation is carried out on the actual three-dimensional model, and the number of rays reaching the transmission line and the total number of rays are counted; the number of rays reaching the transmission line is determined by the vegetation coverage;

[0034] Calculate the shielding hazard concealment coefficient, as follows:

[0035]

[0036] In the formula, is the shielding hazard concealment coefficient, which is used to quantitatively represent the vegetation coverage information; is the number of light rays reaching the transmission line, is the total number of light rays.

[0037] Furthermore, the comprehensive quantification calculation formula of the environmental assessment coefficient is as follows:

[0038]

[0039] In the formula, is the environmental assessment coefficient; and are the quantification values of the position relationship information and the vegetation coverage information respectively; is the proportionality coefficient of the position relationship information, is the proportionality coefficient of the vegetation coverage information.

[0040] In a second aspect, the present invention provides a device for risk assessment based on a three-dimensional point cloud model of a transmission line, including:

[0041] A three-dimensional model construction module, configured to construct an actual three-dimensional model of the transmission line and construct a standard three-dimensional model template of the transmission line according to the point cloud data of the transmission line and the surrounding environment in the area to be evaluated;

[0042] A line information acquisition module, configured to compare the structural features of the actual three-dimensional model and the three-dimensional model template to obtain the feature deviation information of the transmission line, and obtain the operation dynamic information according to the operation dynamic situation of the transmission line;

[0043] An environmental information acquisition module, configured to obtain the position relationship information of the transmission line according to the position of the transmission line in the power network topology structure, and obtain the vegetation coverage information according to the vegetation coverage situation of the transmission line;

[0044] A comprehensive evaluation module, configured to comprehensively quantify the feature deviation information, the operation dynamic information, the position relationship information, and the vegetation coverage information respectively to obtain a line evaluation coefficient and an environmental evaluation coefficient;

[0045] A risk assessment module, configured to input the line evaluation coefficient and the environmental evaluation coefficient into a pre-trained risk assessment model for prediction and evaluation to obtain the risk assessment result of the transmission line in the area to be evaluated; the risk assessment model is a model constructed and trained by using a neural network according to the line evaluation coefficient, the environmental evaluation coefficient, and the actual risk situation of transmission lines in different regions.

[0046] In a third aspect, the present invention provides a computer device, which includes a processor and a memory:

[0047] The memory is used to store a computer program and send the instructions of the computer program to the processor;

[0048] The processor executes a method for risk assessment based on a three-dimensional point cloud model of a transmission line as described in the first aspect according to the instructions of a computer program.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for risk assessment based on a three-dimensional point cloud model of a transmission line as described in the first aspect.

[0050] In summary, the present invention provides a method and related device for risk assessment based on a three-dimensional point cloud model of a transmission line, including constructing an actual three-dimensional model of the transmission line and a standard three-dimensional model template of the transmission line according to the point cloud data of the transmission line and the surrounding environment in the area to be evaluated; comparing the structural features of the actual three-dimensional model and the three-dimensional model template to obtain the characteristic deviation information of the transmission line, and obtaining the operation dynamic information according to the operation dynamic situation of the transmission line; obtaining the position relationship information of the transmission line according to the position of the transmission line in the power network topology structure, and obtaining the vegetation coverage information according to the vegetation coverage situation of the transmission line; comprehensively quantifying the characteristic deviation information, the operation dynamic information, the position relationship information, and the vegetation coverage information respectively to obtain a line evaluation coefficient and an environment evaluation coefficient; inputting the line evaluation coefficient and the environment evaluation coefficient into a pre-trained risk assessment model for prediction and evaluation to obtain the risk assessment result of the transmission line in the area to be evaluated; the risk assessment model is a model constructed and trained by using a neural network according to the line evaluation coefficient, the environment evaluation coefficient, and the actual risk situation of transmission lines in different regions. The present invention can achieve a comprehensive, accurate, and efficient assessment of the risk of transmission lines, provide a scientific basis for the operation and maintenance decision-making of transmission lines, and improve the operation stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 It is a flowchart of a method for risk assessment based on a three-dimensional point cloud model of a transmission line provided by an embodiment of the present invention;

[0053] Figure 2 It is a block diagram of the composition of a device for risk assessment based on a three-dimensional point cloud model of a transmission line provided by an embodiment of the present invention;

[0054] Figure 3A block diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0055] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Please refer to Figure 1 , an embodiment of the present invention provides a method for risk assessment based on a three-dimensional point cloud model of a transmission line, including the following steps:

[0057] S1: Construct an actual three-dimensional model of the transmission line and a standard three-dimensional model template of the transmission line according to the point cloud data of the transmission line and the surrounding environment in the area to be evaluated.

[0058] It should be noted that an actual three-dimensional model of the transmission line is constructed using the point cloud data of the transmission line and the surrounding environment in the area to be evaluated, and at the same time, a standard three-dimensional model template for comparison is constructed. Point cloud data is a dataset composed of a large number of points, and each point contains spatial position information. Through this information, a three-dimensional model can be constructed. The standard three-dimensional model template is a three-dimensional model constructed based on the relevant design specifications, industry standards, and ideal operating conditions of the transmission line. It represents the ideal state of the transmission line in terms of shape, size, position, and other structural features theoretically or under the requirements of the specifications.

[0059] S2: Compare the structural features of the actual three-dimensional model and the three-dimensional model template to obtain the feature deviation information of the transmission line, and obtain the operation dynamic information according to the operation dynamic situation of the transmission line.

[0060] It should be noted that the structural features (such as the shape, trend, height, etc. of the line) of the actual three-dimensional model and the standard three-dimensional model template are compared and analyzed to find the differences between the two and obtain the feature deviation information. At the same time, the operation dynamic situation of the transmission line, such as the swing amplitude, tension, etc., is collected to obtain the operation dynamic information.

[0061] S3: Obtain the position relationship information of the transmission line according to the position of the transmission line in the power network topology structure, and obtain the vegetation coverage information according to the vegetation coverage situation of the transmission line.

[0062] It should be noted that the position of the transmission line in the entire power network topology is analyzed to determine its connection relationship and relative position with other lines, and the position relationship information is obtained. At the same time, the vegetation coverage around the transmission line is evaluated.

[0063] S4: The characteristic deviation information, operation dynamic information, position relationship information, and vegetation coverage information are respectively comprehensively quantified to obtain a line evaluation coefficient and an environment evaluation coefficient.

[0064] It should be noted that the obtained characteristic deviation information, operation dynamic information, position relationship information, and vegetation coverage information are quantified. Through a certain algorithm or rule, these information are converted into numerical forms, and the line evaluation coefficient and the environment evaluation coefficient are respectively calculated.

[0065] S5: The line evaluation coefficient and the environment evaluation coefficient are input into a pre-trained risk assessment model for prediction and evaluation to obtain the risk assessment result of the transmission line in the area to be evaluated; the risk assessment model is a model constructed and trained using a neural network based on the line evaluation coefficient, environment evaluation coefficient, and actual risk situation of transmission lines in different regions.

[0066] It should be noted that the calculated line evaluation coefficient and environment evaluation coefficient are input into a pre-trained risk assessment model. The model makes a prediction based on the input coefficients and outputs the risk assessment result of the transmission line in the area to be evaluated.

[0067] This embodiment provides a method for risk assessment based on a three-dimensional point cloud model of a transmission line. By constructing an actual three-dimensional model and a standard three-dimensional model template, comparing the structural characteristics of the two, and obtaining the structural deviation information of the line; combining the operation dynamic information of the transmission line, the position relationship information in the power network topology, and the surrounding vegetation coverage information, these information are quantified to obtain a line evaluation coefficient and an environment evaluation coefficient. Finally, using a risk assessment model trained based on a neural network, taking the evaluation coefficient as the input and outputting the risk assessment result, so as to realize a comprehensive and accurate assessment of the risk of the transmission line.

[0068] Compared with the existing assessment methods, the method provided in this embodiment adopts a multi-source information fusion method. Compared with the traditional single-factor assessment method, it can more comprehensively reflect the risk status of the transmission line. At the same time, by using point cloud data to construct the actual three-dimensional model and the standard three-dimensional model template of the transmission line, and obtaining the deviation information by comparing the model structural characteristics, it can intuitively and accurately reflect the actual state of the line and the difference from the standard state, improving the accuracy of the assessment.

[0069] In one embodiment, a method for constructing a three-dimensional model and a three-dimensional model template is provided. The construction of the actual three-dimensional model of a transmission line relies on collecting point cloud data in the transmission line area by using a laser scanning device or LiDAR carried by a drone. The detailed three-dimensional data of the provided transmission line and the surrounding environment is constructed as follows:

[0070] S11: Before collecting the point cloud data, it is necessary to plan the flight path for the transmission line area to ensure that the drone or laser scanning device covers the entire target area, including towers, conductors, infrastructure, and the surrounding natural environment (such as vegetation, mountains, water areas, etc.);

[0071] S12: The drone is usually equipped with a high-precision LiDAR system. The LiDAR system uses the method of laser pulse ranging, emitting hundreds of thousands to millions of laser pulses per second, and records the time it takes for the light beam to hit the object surface and reflect back to the sensor. These pulses can generate millions of three-dimensional points (i.e., point cloud), representing the object surface within the scanned area;

[0072] S13: Each collected point cloud data point is accurately positioned in the real three-dimensional space to ensure that the generated model is aligned with the actual geographical location;

[0073] S14: Remove the abnormal data points caused by laser pulse interference or aircraft vibration, filter the original point cloud data, remove high-frequency noise, and smooth the data to improve the clarity of the point cloud. Convert the data coordinate system to a coordinate system that conforms to engineering standards for subsequent fusion with other GIS data;

[0074] S15: According to the preprocessed point cloud data, use modeling software to perform three-dimensional reconstruction of objects such as towers, conductors, and insulators, and generate a fine transmission line model. For areas with severe occlusion or incomplete data, use semi-automated algorithms or standard templates to complete the missing parts, thereby obtaining a continuous three-dimensional model.

[0075] The three-dimensional model template of the transmission line is constructed by customizing the transmission line coding rules, which can enable the three-dimensional model template of the transmission line to adapt to different environmental characteristics. The customized coding rules can provide standardized numbers or labels for different types of transmission line components (such as towers, conductors, insulators, etc.), thereby clarifying the relative positions and types of each component in space;

[0076] The coding rules establish preset templates for different types of lines. For example, by analyzing data such as the common tower shapes, heights, and line spacings in a specific area, the custom coding can quickly locate similar templates, so that existing templates can be directly referenced during three-dimensional modeling, reducing the modeling time;

[0077] For the occluded or missing parts in the point cloud data, the custom encoding provides an effective "reference library", which can automatically fill in the missing parts in the model based on the structural features of the same encoding.

[0078] It should be noted that the templated 3D model is established according to existing design specifications and standards, which can ensure that the component positions, sizes, and distances of different lines meet the regulations, reduce the risk of design deviation. By directly using the template, the design cycle is significantly shortened. Moreover, the template has been verified multiple times and conforms to industry standards. Using the template directly in the design can avoid structural errors that may occur in the design stage. By directly applying the template, designers do not have to build the model from scratch, reducing repetitive work.

[0079] In one embodiment, the structural features of the actual 3D model and the 3D model template are compared to obtain the characteristic deviation information of the transmission line, including:

[0080] S21: Compare each structural feature of the transmission line in the actual 3D model with the corresponding structural feature in the 3D model template, and calculate the deviation value of each structural feature.

[0081] S22: Calculate the structural feature deviation coefficient of the transmission line based on the deviation value as follows:

[0082]

[0083] In the formula, is the structural feature deviation coefficient, which is used to quantitatively represent the characteristic deviation information; is the product of the deviation values of each structural feature; is the sum of the values of each structural feature in the actual 3D model.

[0084] In this embodiment, by comparing the 3D model template of the transmission line with the actual 3D model template of the transmission line, the deviations and potential risks in the transmission line structure are identified, the characteristic information and operation information of the transmission line are collected. After collection, the characteristic information is represented by the structural feature deviation coefficient.

[0085] Compare the 3D model of the transmission line determined by the point cloud data with the 3D model template of the transmission line defined by the custom encoding to obtain the structural features of the 3D model of the transmission line determined by the point cloud data, and obtain the structural features of the 3D model template of the transmission line defined by the custom encoding. Among them, the structural features of the 3D model of the transmission line include tower pole height, conductor inclination, conductor spacing, and suspension point height, etc.

[0086] It should be noted that the height of the tower pole affects the distance between the wire and the ground, vegetation, and buildings, ensuring that the line will not short-circuit or catch fire due to contact with the ground or obstacles. If the inclination angle of the wire is too large, it may cause the line to be unstable, and it is more likely to vibrate and sway under the influence of external forces such as strong winds and snow loads, increasing the risk of damage. The wire spacing directly affects the safety distance between phases of the line, avoiding short-circuit and discharge problems caused by insufficient line distance. The height of the suspension point determines the position of the wire on the tower pole, ensuring uniform stress on the wire and avoiding deviation caused by excessive stress on one side.

[0087] Taking these structural features as examples, the structural features of the three-dimensional model of the transmission line determined from the point cloud data are respectively marked as: GD, QX, JJ, XG, where GD is the tower pole height determined from the point cloud data, QX is the wire inclination determined from the point cloud data, JJ is the wire spacing determined from the point cloud data, and XG is the suspension point height determined from the point cloud data;

[0088] Compare the structural features determined from the point cloud data with the structural features of the three-dimensional model template of the transmission line, determine the deviation of the structural features, and mark the deviation of the structural features as: 、 、 、 ,where, is the deviation of the tower pole height, is the deviation of the wire inclination, is the deviation of the wire spacing, is the deviation of the suspension point height;

[0089] Calculate the structural feature deviation coefficient, and the calculation formula is: ; where, is the structural feature deviation coefficient.

[0090] As can be seen from the formula, the larger the structural feature deviation coefficient, the greater the deviation between the three-dimensional model constructed by using the laser scanning device or the LiDAR carried by the drone and the three-dimensional model template of the transmission line, indicating that there are certain differences between the actual situation of the transmission line and the preset template situation, and there may be relatively complex situations with low reliability.

[0091] In one embodiment, operating dynamic information is obtained according to the operating dynamic situation of the transmission line, including:

[0092] S23: Obtain the swing amplitude data and tension data of the transmission line within the preset monitoring interval;

[0093] S24: Respectively fit the wire motion function and the tension change function according to the relationship between the swing amplitude data, the tension data, and time;

[0094] S25: Calculate the operation anomaly accumulation coefficient based on the wire movement function and the tension change function as follows:

[0095]

[0096] In the formula, is the operation anomaly accumulation coefficient, which is used to quantitatively represent the operation dynamic information; and are the wire movement function and the tension change function respectively; is the time period when the swing amplitude is greater than the swing amplitude threshold; is the time period when the wire tension is greater than the tension threshold.

[0097] In this embodiment, the operation information is represented by the operation anomaly accumulation coefficient. The acquisition logic of the operation anomaly accumulation coefficient is as follows: Set the monitoring interval. According to the operation conditions of the actual transmission line monitored by the sensor, obtain the swing amplitude data of the transmission line within the monitoring interval. Describe the movement of the wire through simple harmonic vibration, and define the equation of the wire movement changing with time as: ; where is the displacement of the wire at time t, A is the swing amplitude, is the initial phase of the wire, is the angular frequency of the wire swing;

[0098] The sensor monitors the operation conditions of the actual transmission line, obtains the tension data of the transmission line within the monitoring interval, obtains the time series of the tension data by continuously recording the tension data, uses the least square method to fit the tension data, obtains the function of the wire tension changing with time, and marks the function of the wire tension changing with time as: ;

[0099] It should be noted that the monitoring interval is a specific time length, which is set by the staff in the professional field.

[0100] Calculate the operation anomaly accumulation coefficient, and the calculation formula is: ; where is the operation anomaly accumulation coefficient, is the time period when the swing amplitude is greater than the swing amplitude threshold, is the time period when the wire tension is greater than the tension threshold.

[0101] It should be noted that quantifying the accumulation degree of risk through integration helps to evaluate the accumulation of wire fatigue. The settings of the swing amplitude threshold and the tension threshold are set by the staff in the professional field, and will not be elaborated here.

[0102] As can be seen from the formula, the larger the operation anomaly accumulation coefficient is, the more certain risks exist in the actual transmission line, indicating that the possibility of causing fatigue and material damage may be greater, increasing the risk of conductor failure.

[0103] In one embodiment, through comprehensive analysis of the characteristic information and operation information of the transmission line, the structure feature deviation coefficient and the operation anomaly accumulation coefficient are weighted and calculated to construct a transmission line evaluation model, and a transmission line evaluation coefficient is generated. The expression of the transmission line evaluation coefficient is:

[0104]

[0105] Wherein, is the transmission line evaluation coefficient, is the proportional coefficient of the structure feature deviation coefficient, is the proportional coefficient of the operation anomaly accumulation coefficient, and are both greater than 0.

[0106] As can be seen from the formula, the larger the structure feature deviation coefficient and the operation anomaly accumulation coefficient are, the larger the transmission line evaluation coefficient is, indicating that the difference between the actual structure features of the transmission line and the design standard may be relatively large, and the line may bear stress exceeding the design load for a long time, and the safety of the transmission line is relatively low.

[0107] In one embodiment, the position relationship information of the transmission line is obtained according to the position of the transmission line in the power network topology structure, including:

[0108] S31: Based on the position coordinates of all transmission lines in the area to be evaluated, the K-means clustering algorithm is used to divide all transmission lines into several clusters;

[0109] S32: Calculate the topological importance coefficient of the transmission line according to the position of the transmission line in the cluster it belongs to, as follows:

[0110]

[0111] In the formula, is the topological importance coefficient, which is used to quantitatively represent the position relationship information; is the distance between the transmission line and the cluster center of the cluster it belongs to; is the average distance of all transmission lines in the cluster where the transmission line belongs to from the cluster center.

[0112] In this embodiment, first, all transmission lines in the area are analyzed based on GIS data to analyze the possible impacts of the environment on the transmission lines, including:

[0113] Collect the transmission line data within the region from authoritative data sources (such as government agencies, energy companies, professional surveying and mapping companies), including the spatial location, structural characteristics, and relevant attribute information of the lines.

[0114] Establish topological relationships to determine the spatial relationships between lines and nodes, ensuring a reasonable topological structure for the lines (such as wire intersections, grounding, etc.).

[0115] It should be noted that when analyzing all transmission lines within the region based on GIS data, each node represents the location of a transmission line tower, and the line represents the wire between the towers. By determining the actual coordinate positions of all towers within the region, the relationships between transmission lines can be determined.

[0116] Collect the position relationship information and vegetation coverage information of the transmission lines from the GIS data of the transmission lines, and represent the position relationship information of the transmission lines through the topological importance coefficient.

[0117] The acquisition logic of the topological importance coefficient is as follows: Obtain the position coordinates of all transmission lines within the region, and use the K-means clustering algorithm based on the position coordinates of the transmission lines, with the position coordinates of the transmission lines as the input of the K-means clustering algorithm.

[0118] It should be noted that the division of the region is set by staff in the professional field. The region contains multiple transmission lines. Due to different electricity consumption demands and geographical characteristics within the region, the distribution of transmission lines may be irregular.

[0119] Use K-means clustering in the region to initialize K initial clustering centers, assign the position coordinates of each transmission line to the nearest clustering center to form K clusters, recalculate the center point of each cluster as the new clustering center, and repeat the steps until the clustering center no longer changes or changes very little.

[0120] Determine the cluster where the transmission line is located and the clustering center of the cluster where the transmission line is located, and mark the clustering center of the cluster where the transmission line is located as: , where is the longitude of the clustering center, is the latitude of the clustering center, and i is the number of the cluster where the transmission line is located.

[0121] It should be noted that by analyzing each transmission line in the region, the cluster where each transmission line is located and the clustering center of the corresponding cluster can be determined. Therefore, by observing the distribution of transmission lines in different clusters, line-dense areas and sparse areas can be identified.

[0122] Mark the position coordinates of the transmission line as: ; where JD is the longitude of the specified transmission line, WD is the latitude of the specified transmission line, calculate the distance between the specified transmission line and the cluster center, and mark the distance between the specified transmission line and the cluster center as: , where ;

[0123] Obtain the average distance between all transmission lines within the cluster and the cluster center, and mark the average distance between all transmission lines within the cluster and the cluster center as: , where , , m = 1, 2, 3, ……, M, M is a positive integer, and m is the number of the transmission line within the cluster;

[0124] Calculate the topological importance coefficient, and the calculation formula is: ; where is the topological importance coefficient.

[0125] It can be seen from the formula that the larger the topological importance coefficient, the closer the distance between the transmission line and the cluster center, and the higher the importance in the entire network. It may be a key connection point in the network, responsible for supporting a large amount of current or connecting multiple important power supply areas.

[0126] In one embodiment, obtain the vegetation coverage information according to the vegetation coverage of the transmission line, including:

[0127] S33: Perform ray tracing simulation on the actual 3D model, and count the number of rays reaching the transmission line and the total number of rays; the number of rays reaching the transmission line is determined by the vegetation coverage;

[0128] S34: Calculate the shielding hazard concealment coefficient as follows:

[0129]

[0130] In the formula, is the shielding hazard concealment coefficient, which is used to quantitatively represent the vegetation coverage information; is the number of rays reaching the transmission line, is the total number of rays.

[0131] In this embodiment, the vegetation coverage information is represented by the shielding hazard concealment coefficient. The acquisition logic of the shielding hazard concealment coefficient is: determine the 3D model of the transmission line according to the point cloud data, simulate the propagation of light by determining the light source position, perform ray tracing simulation, for each ray, check its interaction with the transmission line model, record the intersection points between the ray and the transmission line, and count the number of rays reaching the transmission line and the total number of rays;

[0132] Calculate the shielding hazard concealment coefficient, and the calculation formula is:

[0133] ;

[0134] Wherein, is the shielding danger concealment coefficient, is the number of light rays reaching the transmission line, is the total number of light rays.

[0135] It can be seen from the formula that the larger the shielding danger concealment coefficient, the larger the area where the transmission line is blocked by vegetation, indicating that there is a certain risk for the transmission line. Among them, a high degree of occlusion means that the transmission line may be more difficult to detect and repair in a timely manner, increasing the risk of line faults. For example, if a certain section of the transmission line is blocked by tall trees, potential faults or damages may not be detected by the staff during inspection.

[0136] In one embodiment, through comprehensive analysis of the position relationship information of the transmission line and the vegetation coverage information, the topological importance coefficient and the shielding danger concealment coefficient are weighted and calculated to construct a transmission environment evaluation model, and a transmission environment evaluation coefficient is generated. The expression of the transmission environment evaluation coefficient is:

[0137]

[0138] Wherein, is the transmission environment evaluation coefficient, is the proportional coefficient of the topological importance coefficient, is the proportional coefficient of the shielding danger concealment coefficient, , are both greater than 0.

[0139] It can be seen from the formula that the larger the topological importance coefficient and the shielding danger concealment coefficient, the larger the transmission environment evaluation coefficient, indicating that the criticality of the transmission line in the power network is relatively high. If a fault occurs in this line, it may have a greater impact on power transmission, increasing the necessity of priority monitoring and maintenance. Moreover, there are many environmental factors such as vegetation or terrain around the transmission line, with deep hidden dangers and being difficult to detect or maintain.

[0140] In one embodiment, by analyzing from the perspectives of the characteristics of the transmission line itself and the transmission line environment, the transmission line evaluation coefficient and the transmission environment evaluation coefficient of the transmission line are determined. The transmission line evaluation coefficient and the transmission environment evaluation coefficient are used as the inputs for training the neural network model, and the transmission line warning coefficient is used as the output of the neural network model training. And it is assigned 0 or 1 to train the model, where 0 indicates that the transmission line warning coefficient is less than the transmission line warning coefficient threshold, and 1 indicates that the transmission line warning coefficient is greater than the transmission line warning coefficient threshold.

[0141] It should be noted that the warning coefficient of the transmission line is set by the staff in the professional field, aiming to identify potential risks in a timely manner. Through the warning coefficient of the transmission line, the transmission lines can be screened, that is, the transmission lines with values greater than the transmission warning coefficient are marked. When facing the maintenance and monitoring of the transmission line, it can help the staff quickly locate the transmission line with potential problems, prioritize the inspection and maintenance of this transmission line, improve the monitoring efficiency, and ensure the safe and stable operation of the power supply system.

[0142] The present invention analyzes the transmission line from two perspectives: the characteristics of the transmission line itself and the environment of the transmission line. The characteristics of the transmission line itself are subdivided into the characteristic information and operation information of the transmission line, and the environment of the transmission line is subdivided into the positional relationship information and vegetation coverage information of the transmission line. By customizing the transmission line coding rule, the point cloud data is converted into an accurate three-dimensional line model, and combined with the geographic information system (GIS) data, the three-dimensional model is combined with the geographical location information. The present invention helps to provide more comprehensive data support for the planning and management of the transmission line, improve the monitoring efficiency of the transmission line, and ensure the safe and stable operation of the power supply system.

[0143] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0144] Based on the same inventive concept, the embodiment of the present application also provides a device for risk assessment based on a three-dimensional point cloud model of a transmission line for implementing the method for risk assessment based on a three-dimensional point cloud model of a transmission line involved above. The solution provided by this device to solve the problem is similar to the solution recorded in the above method. Therefore, the specific limitations in the embodiment of the device for risk assessment based on a three-dimensional point cloud model of a transmission line provided below can refer to the limitations on the method for risk assessment based on a three-dimensional point cloud model of a transmission line in the above text, and will not be repeated here.

[0145] Please refer to Figure 2 , the embodiment of the present invention provides a device for risk assessment based on a three-dimensional point cloud model of a transmission line, including:

[0146] A three-dimensional model construction module, configured to construct an actual three-dimensional model of the transmission line and construct a standard three-dimensional model template of the transmission line according to the point cloud data of the transmission line and the surrounding environment in the area to be evaluated;

[0147] A line information acquisition module, configured to compare the structural characteristics of the actual three-dimensional model and the three-dimensional model template to obtain the characteristic deviation information of the transmission line, and obtain the operation dynamic information according to the operation dynamic situation of the transmission line;

[0148] An environmental information acquisition module, configured to obtain the location relationship information of a transmission line according to the position of the transmission line in the power network topology structure, and obtain the vegetation coverage information according to the vegetation coverage condition of the transmission line;

[0149] A comprehensive evaluation module, configured to comprehensively quantify the feature deviation information, the operation dynamic information, the location relationship information, and the vegetation coverage information respectively to obtain a line evaluation coefficient and an environmental evaluation coefficient;

[0150] A risk assessment module, configured to input the line evaluation coefficient and the environmental evaluation coefficient into a pre-trained risk assessment model for predictive evaluation to obtain the risk assessment result of the transmission line in the area to be evaluated; the risk assessment model is a model constructed and trained by using a neural network according to the line evaluation coefficient, the environmental evaluation coefficient, and the actual risk situation of transmission lines in different regions.

[0151] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above-mentioned system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0152] Referring to Figure 3 , an embodiment of the present invention further provides a computer device, including: a memory, a processor, and a computer program stored on the memory. When the computer program is executed on the processor, it implements a method for risk assessment based on a three-dimensional point cloud model of a transmission line as described in any one of the above methods.

[0153] The computer device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 merely an example of a computer device, which does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0154] The so-called processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0155] In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device. Further, the memory may also include both the internal storage unit and the external storage device of the computer device. The memory is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0156] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements a method for risk assessment based on a three-dimensional point cloud model of a transmission line as described in any one of the above methods.

[0157] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0158] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0159] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0160] In the embodiments disclosed in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0161] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for risk assessment based on a three-dimensional point cloud model of a power transmission line, characterized in that: The steps include: Construct an actual 3D model of the transmission line based on the point cloud data of the transmission line and the surrounding environment in the area to be evaluated and construct a standard 3D model template of the transmission line; Comparing the structural features of the actual three-dimensional model with the three-dimensional model template to obtain characteristic deviation information of the transmission line, and obtaining operation dynamic information according to the operation dynamic situation of the transmission line; Obtaining positional relationship information of the transmission line according to the position of the transmission line in the topological structure of the power network, and obtaining vegetation coverage information according to the vegetation coverage of the transmission line; The characteristic deviation information, the operation dynamic information, the position relationship information, and the vegetation coverage information are respectively comprehensively quantified to obtain a line evaluation coefficient and an environmental evaluation coefficient; Inputting the line assessment coefficient and the environmental assessment coefficient into a pre-trained risk assessment model for predictive assessment to obtain a risk assessment result of the power transmission line in the area to be assessed; The risk assessment model is a model constructed and trained using a neural network based on line assessment coefficients, environmental assessment coefficients and actual risk conditions of power transmission lines in different regions.

2. The method for risk assessment based on a three-dimensional point cloud model of a power transmission line according to claim 1, characterized in that: Comparing the structural features of the actual three-dimensional model and the three-dimensional model template to obtain characteristic deviation information of the transmission line includes: Comparing each structural feature of the power transmission line in the actual three-dimensional model with the corresponding structural feature in the three-dimensional model template, and calculating the deviation value of each structural feature; The structural characteristic deviation coefficient of the transmission line is calculated based on the deviation value, as follows: ； In the formula, is the structural feature deviation coefficient, used to quantitatively represent the feature deviation information; is the product of the deviation values ​​of each structural feature; It is the sum of the structural characteristic values ​​in the actual three-dimensional model.

3. The method for risk assessment based on a three-dimensional point cloud model of a power transmission line according to claim 1, characterized in that: According to the operation dynamics of the transmission line, the operation dynamics information is obtained, including: Obtaining the swing amplitude data and tension data of the transmission line within the preset monitoring interval; According to the relationship between the swing amplitude data and the tension data and time, respectively fitting a wire motion function and a tension change function; The operation abnormality accumulation coefficient is calculated based on the wire movement function and the tension change function as follows: ； In the formula, is the operation abnormality accumulation coefficient, which is used to quantitatively represent the operation dynamic information; and are the wire motion function and the tension variation function respectively; is the time period during which the swing amplitude is greater than the swing amplitude threshold; It is the time period during which the wire tension is greater than the tension threshold.

4. The method for risk assessment based on a three-dimensional point cloud model of a power transmission line according to claim 1, characterized in that: The comprehensive quantitative calculation formula of the line evaluation coefficient is as follows: ； In the formula, is the line evaluation coefficient; and are respectively the quantized value of the characteristic deviation information and the quantized value of the operation dynamic information; is the proportionality coefficient of the characteristic deviation information, is the proportionality coefficient of the running dynamic information.

5. The method for risk assessment based on a three-dimensional point cloud model of a power transmission line according to claim 1, characterized in that: According to the position of the transmission line in the power network topology, the position relationship information of the transmission line is obtained, including: Based on the location coordinates of all transmission lines in the area to be evaluated, a K-means clustering algorithm is used to divide all transmission lines into several clusters; The topological importance coefficient of the transmission line is calculated according to its position in the cluster to which it belongs, as follows: ； In the formula, is the topological importance coefficient, used to quantitatively represent the position relationship information; is the distance between the transmission line and the center of the cluster to which it belongs; It is the average distance between all transmission lines in the cluster to which the transmission line belongs and the cluster center.

6. The method for risk assessment based on a three-dimensional point cloud model of a power transmission line according to claim 1, characterized in that: The vegetation coverage information is obtained according to the vegetation coverage of the transmission line, including: Performing a ray tracing simulation on the actual three-dimensional model, and counting the number of light rays reaching the transmission line and the total number of light rays; the number of light rays reaching the transmission line is determined by the vegetation coverage; Calculate the concealment coefficient of the hazard as follows: ； In the formula, is the concealment risk concealment coefficient, which is used to quantitatively represent vegetation coverage information; is the amount of light reaching the transmission line, is the total number of rays.

7. The method for risk assessment based on a three-dimensional point cloud model of a power transmission line according to claim 1, characterized in that: The comprehensive quantitative calculation formula of the environmental assessment coefficient is as follows: ； In the formula, is the environmental assessment coefficient; and are respectively the quantized value of the position relationship information and the quantized value of the vegetation coverage information; is the proportional coefficient of the position relationship information, is the proportional coefficient of the vegetation coverage information.

8. A device for risk assessment based on a three-dimensional point cloud model of a power transmission line, characterized in that: include: A 3D model building module, used to build an actual 3D model of the transmission line and a standard 3D model template of the transmission line based on point cloud data of the transmission line and the surrounding environment in the area to be evaluated; A line information acquisition module, used to compare the structural features of the actual three-dimensional model and the three-dimensional model template to obtain characteristic deviation information of the transmission line, and obtain operation dynamic information according to the operation dynamic situation of the transmission line; An environmental information acquisition module is used to obtain positional relationship information of the transmission line according to the position of the transmission line in the power network topology structure, and to obtain vegetation coverage information according to the vegetation coverage of the transmission line; A comprehensive evaluation module, used to comprehensively quantify the characteristic deviation information, the operation dynamic information, the position relationship information, and the vegetation coverage information, respectively, to obtain a line evaluation coefficient and an environmental evaluation coefficient; A risk assessment module, used for inputting the line assessment coefficient and the environmental assessment coefficient into a pre-trained risk assessment model for predictive assessment, and obtaining a risk assessment result of the power transmission line in the area to be assessed; The risk assessment model is a model constructed and trained using a neural network based on line assessment coefficients, environmental assessment coefficients and actual risk conditions of power transmission lines in different regions.

9. A computer device, characterized in that: The device comprises a processor and a memory: The memory is used to store a computer program and send instructions of the computer program to the processor; The processor executes a method for risk assessment based on a three-dimensional point cloud model of a transmission line according to any one of claims 1 to 7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for risk assessment based on a three-dimensional point cloud model of a transmission line is implemented as described in any one of claims 1 to 7.

Images