Transmission tower damage assessment method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of power transmission tower damage assessment, and discloses a power transmission tower damage assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: employing a reverse finite element method to construct a global inversion model of tower damage, and employing discrete strain data collected by a small number of sensors through a discrete variation thought to obtain a power transmission tower damage assessment result; external load and material attribute information are not needed in the inversion stage, but damage judgment needs to be conducted in combination with material performance data in the evaluation stage, and efficient solving of structural displacement and a stress field is successfully achieved. The method has the beneficial technical effects that the limitation of traditional strain monitoring is broken through, the stress, displacement and inclination angle parameters at the position where a sensor is not deployed can be inverted, the monitoring capability of the stress distribution of the whole tower is improved, the comprehensive evaluation effect on the safety of the tower is remarkably improved, and important support is provided for safety management of electric power infrastructures.

Description

Technical Field

[0001] This invention relates to the field of power transmission tower damage assessment technology, and in particular to a method, apparatus, electronic device, and storage medium for power transmission tower damage assessment. Background Technology

[0002] Currently, as a key infrastructure for power transmission, the safety of transmission towers is directly related to the stable operation of the power system. However, the accuracy of existing assessment models is not high. They are usually based on simplified assumptions and historical data to infer the actual situation, resulting in insufficient accuracy in predicting the response of towers under geological disaster conditions. This uncertainty will pose a greater risk to managers when making decisions and bring hidden dangers to power grid safety. Especially in areas where natural disasters occur frequently, the safe operation of towers is of paramount importance. Summary of the Invention

[0003] Based on this, it is necessary to propose a method, device, electronic equipment and storage medium for assessing the damage of transmission towers, which addresses the existing problem of damage assessment of transmission towers.

[0004] A method for assessing damage to power transmission towers, wherein the power transmission tower comprises multiple first members and multiple second members, and sensors are installed on the first members; the method includes: Acquire sensor data on each first member of a specified transmission tower; Based on the single-member deformation identification method of inverse finite element method and the sensor data, the deformation data of each first member is calculated. An initial three-dimensional model of the specified transmission tower is obtained, and a three-dimensional reconstructed model is obtained based on the initial three-dimensional model and the deformation data of the first member. The sensor data of each of the first pole members are input into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower. The damage assessment of the specified transmission tower is performed using the inversion model.

[0005] Furthermore, the step of assessing the damage to the designated transmission tower using the inversion model includes: Multiple preset damage index values ​​are obtained through the inversion model; The preset damage index value is input into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower.

[0006] Furthermore, before the step of inputting the preset damage index value into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower, the method further includes: Acquire multiple sets of sample data; one set of sample data includes damage index values ​​obtained through historical inversion models and corresponding actual tower damage records; Multiple sets of sample data are divided into training datasets and validation datasets; An initial model is trained based on the training dataset to obtain a temporary model; The temporary model is validated using the validation dataset to obtain validation results; If the verification result is deemed satisfactory, the temporary model will be recorded as the damage assessment model.

[0007] Furthermore, before the step of inputting the preset damage index value into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower, the method further includes: Obtain the historical inversion model, historical actual damage records, and initial damage assessment model of the specified transmission tower; The initial damage assessment model is modified using the historical inversion model and the historical actual damage records to obtain the damage assessment model.

[0008] Furthermore, the step of acquiring multiple sets of sample data includes: Obtain the current environmental data of the specified transmission tower; Calculate the similarity between the environmental data and the historical environmental data corresponding to each historical sample data in the preset sample database; wherein, the preset sample database contains multiple historical sample data and their corresponding historical environmental data; A preset number of sample data are selected from each of the historical sample data based on the magnitude of the similarity.

[0009] Furthermore, the step of calculating the deformation data of each of the first members using the single-member deformation identification method based on inverse finite element method and the sensor data includes: Based on the sensor data, an objective function is established using the least squares principle; The first rod is discretized to obtain multiple discrete units, and the unit matrix equation of each discrete unit is established based on the target functional. The element matrix equation for each discrete element is solved to obtain the deformation data of the corresponding first member.

[0010] Furthermore, after the step of assessing the damage to the designated transmission tower using the inversion model, the method further includes: Determine whether the result of the damage assessment has reached a preset risk threshold; If the damage assessment result reaches the preset risk threshold, the preset damage index value and the damage assessment result are sent to the designated terminal.

[0011] A damage assessment device for power transmission towers, the power transmission tower comprising multiple first members and multiple second members, wherein sensors are installed on the first members, the device comprising: The acquisition module is used to acquire sensor data on each first member of a specified transmission tower. The calculation module is used to calculate the deformation data of each of the first members based on the single member deformation identification method of inverse finite element method and the sensor data. The reconstruction module is used to obtain the initial three-dimensional model of the specified transmission tower and reconstruct it based on the initial three-dimensional model and the deformation data of the first member to obtain a three-dimensional reconstruction model. The input module is used to input the sensor data of each of the first poles into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower; An assessment module is used to assess the damage to the specified transmission tower using the inversion model.

[0012] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Acquire sensor data on each first member of a specified transmission tower; Based on the single-member deformation identification method of inverse finite element method and the sensor data, the deformation data of each first member is calculated. An initial three-dimensional model of the specified transmission tower is obtained, and a three-dimensional reconstructed model is obtained based on the initial three-dimensional model and the deformation data of the first member. The sensor data of each of the first pole members are input into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower. The damage assessment of the specified transmission tower is performed using the inversion model.

[0013] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps: Acquire sensor data on each first member of a specified transmission tower; Based on the single-member deformation identification method of inverse finite element method and the sensor data, the deformation data of each first member is calculated. An initial three-dimensional model of the specified transmission tower is obtained, and a three-dimensional reconstructed model is obtained based on the initial three-dimensional model and the deformation data of the first member. The sensor data of each of the first pole members are input into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower. The damage assessment of the specified transmission tower is performed using the inversion model.

[0014] The beneficial effects of this invention are as follows: A global inversion model of tower damage is constructed using the inverse finite element method. Through discrete variational thinking, it relies only on discrete strain data collected by a small number of sensors. No external load or material property information is required during the inversion stage, but damage assessment requires the integration of material performance data. This successfully achieves efficient solutions for structural displacement and stress fields. It overcomes the limitations of traditional strain monitoring, enabling the inversion of force, displacement, and tilt angle parameters at locations without deployed sensors. This improves the monitoring capability of the entire tower's stress distribution, significantly enhancing the comprehensive assessment of tower safety and providing crucial support for the safety management of power infrastructure. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] in: Figure 1 This is an application environment diagram of the power transmission tower damage assessment method in one embodiment; Figure 2 This is a flowchart of a method for assessing damage to transmission towers in one embodiment; Figure 3 This is a structural block diagram of a power transmission tower damage assessment device in one embodiment; Figure 4 This is a structural block diagram of an electronic device in one embodiment. Detailed Implementation

[0017] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Figure 1This is a diagram illustrating the application environment for transmission tower damage assessment in one embodiment. (Refer to...) Figure 1 This method for assessing transmission tower damage is applied to a transmission tower damage assessment system. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; a mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. The terminal 110 is used to collect sensor data, and the server 120 is used to perform damage assessments on specified transmission towers.

[0019] like Figure 2 As shown, in one embodiment, a method for assessing damage to power transmission towers is provided. This method can be applied to both terminals and servers; this embodiment illustrates its application to terminals. The power transmission tower includes multiple first members and multiple second members, with sensors installed on the first members. The method for assessing damage to power transmission towers specifically includes the following steps: S1: Obtain sensor data on each first member of a specified transmission tower; S2: Based on the single-member deformation identification method of inverse finite element method and the sensor data, calculate the deformation data of each first member; S3: Obtain the initial three-dimensional model of the specified transmission tower, and reconstruct it based on the initial three-dimensional model and the deformation data of the first member to obtain a three-dimensional reconstructed model; S4: Input the sensor data of each of the first pole members into the three-dimensional reconstruction model to obtain the global structural damage inversion model of the specified transmission tower; S5: Perform a damage assessment on the specified transmission tower using the inversion model.

[0020] As described in step S1 above, sensor data is acquired from each of the first members of the designated transmission tower. This sensor data is fiber optic sensor data, collected from multiple first members of the transmission tower. These sensors are typically deployed at key locations on the tower to monitor strain, displacement, and other potential external influences in real time. By collecting strain values, the sensors can help assess the load conditions experienced by the members during actual operation. These key locations are pre-defined by relevant personnel. The collected data displays the operating status of each member, including normal operation, overload, or abnormal conditions. Furthermore, to ensure data validity, the system needs to be calibrated and maintained regularly to ensure timely and accurate data transmission from the sensors. The data collection process should also consider the impact of environmental factors (such as temperature changes, wind speed, and humidity) on sensor readings. Failure to consider these factors may lead to data errors, thus affecting the results of subsequent evaluations.

[0021] As described in step S2 above, the deformation data of each first member is calculated using the single-member deformation identification method based on the inverse finite element method and the sensor data. The deformation of each first member is calculated using the acquired sensor data based on the inverse finite element method. Inverse finite element analysis is a powerful tool that, through known structural responses (such as strain and displacement) and an ideal physical model, inversely infers the true deformation state of a member under load. In practice, the strain data collected from the sensors is first input into the inverse finite element model. The model, based on this strain information, estimates the displacement and rotation of the member using the least squares method by establishing an objective functional. This process relies on the established constitutive model and physical properties of the member, helping to identify the current stress state and deformation mode of each member. It allows for a detailed and accurate assessment of the specific deformation of each member, including its local and overall deformation. Inverse finite element analysis effectively reduces variations caused by measurement errors and maintains high deformation identification accuracy even with insufficient data or complex conditions, providing a reliable foundation for subsequent steps.

[0022] As described in step S3 above, an initial 3D model of the specified transmission tower is obtained, and a 3D reconstructed model is obtained based on the initial 3D model and the deformation data of the first members. The initial 3D model of the transmission tower can be obtained using computer-aided design (CAD) software, building information modeling (BIM) technology, or based on actual measurements, ensuring that the model accurately represents the geometry and structural features of the tower. The previously calculated deformation data of each first member is applied to the model for reconstruction. This reconstruction process involves applying the deformation data of each member to the corresponding 3D model location to achieve a comprehensive reconstruction of the structure. This process requires interpolation and numerical calculation methods, superimposing the deformation effects of each member to ultimately form a 3D reconstructed model reflecting the current damage state. The reconstructed 3D model should accurately reflect the stress changes and deformation processes experienced by the tower during monitoring, laying the foundation for subsequent global structural damage assessment.

[0023] As described in step S4 above, the sensor data of each of the first members is input into the three-dimensional reconstruction model to obtain the global structural damage inversion model of the specified transmission tower. The calculated sensor data of each first member is integrated into the three-dimensional reconstruction model to form the global structural inversion model. Since the geometry of the tower has been obtained, it is now necessary to further improve the detail and accuracy of the overall model based on the deformation data of each member. When inputting the sensor data into the three-dimensional reconstruction model, the deformation and strain data of each member can be mapped into the model one by one to ensure that the latest state of each member is reflected in the model. During this process, the nodes and connections in the model need to be corrected to ensure the consistency and accuracy of the data. For example, the strain data of a member may reflect the actual axial and bending loads it is subjected to, thus affecting the nodes at its connection points. Through this data integration and mapping, the resulting global structural damage inversion model can comprehensively analyze the damage situation of the entire tower, revealing possible hidden dangers and risk areas, and providing a reference for subsequent assessment and decision-making.

[0024] As described in step S5 above, the specified transmission tower is assessed for damage using the inversion model. The purpose of using the previously established inversion model to assess the damage to the specified transmission tower is to determine the tower's condition and safety during the monitoring period. The inversion model can obtain information such as stress, strain, displacement, and potential damage distribution of various parts of the tower. The damage assessment process combines structural response data, environmental factors, and geological disaster history, using mechanical analysis methods to calculate the stress state and safety indicators of each member, such as bearing capacity and failure risk level. The inversion model can assess the safety of the entire tower structure by comparing and analyzing the differences between the estimated data and normal values ​​in unmonitored areas. Furthermore, by comparing the assessment results with preset risk threshold standards, clear recommendations for inspection, maintenance, and reinforcement can be provided to operation and maintenance personnel. If the assessment results show that some members have reached or exceeded the warning line, measures should be taken promptly to ensure the overall safety of the tower. This comprehensive assessment can effectively reduce the risk of accidents caused by structural failure and ensure the stable operation of the power system.

[0025] In one embodiment, step S5, which involves assessing the damage to the designated transmission tower using the inversion model, includes: S501: Obtain multiple preset damage index values ​​through the inversion model; S502: Input the preset damage index value into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower.

[0026] As described in step S501 above, the first step is to extract multiple preset damage index values ​​based on the established inversion model. These index values ​​are key parameters used to assess the structural health and safety of transmission towers. Common damage index values ​​include stress, strain, displacement, and plastic deformation of the members. These indicators reflect the actual performance of the tower under dynamic loads (such as wind, earthquakes, and geological disasters). Using the inversion model, the specific damage state of each first member can be obtained. This process usually involves further analysis of the displacement and strain fields obtained from the inversion. Specifically, firstly, the micro-strain and macro-strain of each member are calculated using the combination of sensor data and the three-dimensional reconstruction model in the inversion model, and the stress level is derived based on this. Secondly, these stress and strain values ​​are compared with the material property data of the members (such as yield strength and ultimate strength) to determine the degree of damage to each member. By summarizing these key indicators, a comprehensive damage dataset can be formed, representing the damage risk of the entire tower under different working conditions. This not only helps to monitor the tower's status in real time but also provides data support for subsequent assessment and decision-making.

[0027] As described in step S502 above, the preset damage index values ​​are input into a preset damage assessment model to obtain the damage assessment result of the specified transmission tower. Multiple extracted damage index values ​​are input into a preset damage assessment model to obtain the overall damage assessment result of the transmission tower. This step is a further analysis of the tower assessment, using a systematic method to transform the collected damage parameters into effective assessment output. The damage assessment model can be based on machine learning algorithms or a system model based on a state-space model. They are trained using historical data and field test data to form an assessment system, interface, and decision rules. The key is how to map the stress and deformation values ​​obtained from the inversion model to the actual damage level, involving comparison with historical accident samples, physical performance standards, and preset damage thresholds. These have all been scientifically and reasonably defined in the model. After inputting these damage indicators, the assessment model quickly calculates the safety level or damage level of the tower through its built-in logic and algorithms. The result usually provides a clear assessment report, including the current status, deviation from the ideal state, key information prompts (such as the need for maintenance / reinforcement), and risk warnings. Meanwhile, this process also provides a basis for subsequent risk control and maintenance, ensuring that maintenance personnel can respond quickly to abnormal situations of the towers at the first moment. This not only improves the safety monitoring efficiency of transmission towers, but also provides a guarantee for the stable operation of the power system.

[0028] In one embodiment, before step S502 of inputting the preset damage index value into a preset damage assessment model to obtain the damage assessment result of the specified transmission tower, the method further includes: S5021: Obtain multiple sets of sample data; one set of sample data includes damage index values ​​obtained through historical inversion models and corresponding actual tower damage records; S5022: Divide multiple sets of sample data into training datasets and validation datasets; S5023: Train an initial model based on the training dataset to obtain a temporary model; S5024: Validate the temporary model using the validation dataset to obtain the validation results; S5025: If the verification result is determined to be passed, then the temporary model is recorded as the damage assessment model.

[0029] As described in step S5021 above, multiple sets of sample data are collected to provide a foundation for subsequent model training. Each set of sample data mainly includes damage index values ​​calculated through the historical inversion model, as well as corresponding actual tower damage records. Damage index values ​​may include, but are not limited to, strain, stress, displacement, and deformation of the members, while actual tower damage records represent specific damage conditions that occurred in the past, such as structural damage, deformation, and buckling of the tower. This is achieved through cross-validation with on-site monitoring, historical maintenance records, and accident logs. Efforts should be made to ensure the diversity of sample data, covering different geological conditions, weather changes, and external force factors, so that the trained damage assessment model can adapt to various changes in the actual environment. By constructing a rich and diverse sample dataset, a solid foundation is laid for subsequent model training, ensuring that the model has strong generalization ability, thereby improving the accuracy and reliability of the assessment.

[0030] As described in step S5022 above, the acquired sample data is divided into training and validation datasets. Typically, the ratio is 70% to 80% of the data for training and 20% to 30% for validation. This allocation is based on principles in machine learning and statistics, effectively improving the model's learning ability and evaluation level. The main purpose of the training dataset is to allow the model to learn and adjust its parameters, training a model capable of effective prediction. The validation dataset is used to evaluate the model's performance on unseen samples, ensuring that the model has not overfitted the training data; that is, the model not only performs well on the training set but also maintains high prediction accuracy when new data is input. To ensure the effectiveness of this partitioning, random selection is usually used to reduce sample selection bias. This partitioning method not only helps improve the model's generalization ability but also further ensures the model's reliability and stability in practical applications, providing fundamental data support for model training and validation in subsequent steps.

[0031] As described in step S5023 above, the initial model is trained using the pre-defined training dataset. The purpose of training the model is to enable it to learn the patterns and relationships present in the sample data, so as to predict the damage state of unseen samples based on these patterns. The initial model can be chosen from various machine learning algorithms such as linear regression, support vector machines, decision trees, random forests, or deep learning; the specific choice should consider factors such as data characteristics and complexity. During training, the model continuously adjusts its internal parameters to minimize the error between the predicted values ​​generated based on the training data and the actual damage records. This process requires setting a loss function and solving for the optimal parameters on a given sample using an optimization algorithm (such as gradient descent). Importantly, this step must ensure that the model can fully learn all input features (such as damage indicators) to improve the model's accuracy; at the same time, it must avoid overfitting, i.e., the model learning too deeply from the training data, which would negatively impact its generalization ability to new data. After training is complete, the resulting model is called a "temporary model," which forms the basis for subsequent validation steps.

[0032] As described in step S5024 above, the temporary model is validated using the validation dataset to obtain validation results. The main purpose of validation is to evaluate the model's performance on unseen sample data, ensuring that the model has good generalization ability and accuracy. By inputting the validation data into the temporary model, the model generates prediction results (such as the damage level of the entire tower). These prediction results are then compared with the actual damage in the validation dataset to calculate the model's prediction accuracy, precision, recall, and other evaluation metrics. Based on these metrics, the reliability level of the model in handling actual damage assessment tasks can be determined. If the model's performance on the validation dataset is unsatisfactory, it is usually necessary to return to the training phase to adjust the model parameters or select other algorithms for retraining. After generating valid validation results, this stage will further provide a basis for model adjustment and improvement. Through sufficient validation, it can be confirmed that the model's performance is stable under various conditions, laying a solid foundation for future applications.

[0033] As described in step S5025 above, if the verification result is deemed satisfactory, the temporary model is recorded as the damage assessment model. After confirming that the temporary model has passed verification, it is recorded as the final damage assessment model. At this stage, a passing standard is typically set for the critical assessment indicators, such as a specific value indicating an accuracy rate higher than 70% or 90%, to ensure the model's effectiveness and reliability. This step is based not only on quantitative assessment results but also considers the model's stability and adaptability to practical applications. Once the model is confirmed to be error-free, it will be directly used in actual operations and decision-making, possessing full-link application capabilities from data acquisition to damage assessment. This enables the model to perform rapid and accurate analysis in future safety monitoring and assessment of transmission towers, helping managers and engineers make more scientific maintenance decisions. This accelerates the entire process from data acquisition to potential risk warning, enhances the intelligence level of power equipment management, and thus provides strong technical support and guarantees for the safe and stable operation of the power system.

[0034] In one embodiment, before step S502 of inputting the preset damage index value into a preset damage assessment model to obtain the damage assessment result of the specified transmission tower, the method further includes: S5121: Obtain the historical inversion model, historical actual damage records, and initial damage assessment model of the specified transmission tower; S5122: The initial damage assessment model is modified using the historical inversion model and the historical actual damage records to obtain the damage assessment model.

[0035] As described in step S5121 above, the historical inversion model, historical actual damage records, and initial damage assessment model of the designated transmission tower are obtained. The historical inversion model, historical actual damage records, and initial damage assessment model related to the designated transmission tower are collected. These data provide the foundation for subsequent model revision and construction. The historical inversion model refers to the model obtained through inversion analysis technology during previous monitoring periods, typically including information on the tower's stress, deformation, strain, etc., under different conditions. These models have been verified in practice and can reflect the tower's true performance under different external forces. The historical actual damage records refer to detailed records of past repairs, reinforcements, and actual damage to the transmission tower. This data provides important historical references for establishing the assessment model, helping to understand past damage patterns and their impact mechanisms. The initial damage assessment model is a previously established model constructed based on theoretical and empirical methods, aiming to assess the tower's damage through certain parameters and assumptions. The process of obtaining this information relies on a robust database and information storage system to ensure the reliability and integrity of various data. Based on this, data support will be provided for subsequent model revisions, making the overall evaluation model more in line with the actual situation and improving its accuracy and applicability.

[0036] As described in step S5122 above, the initial damage assessment model is modified using the historical inversion model and the historical actual damage records to obtain the final damage assessment model. This makes the assessment model closer to the actual situation, thereby improving its reliability and effectiveness. First, the data containing the historical inversion results and the corresponding actual damage records are compared and analyzed to identify shortcomings in the model. For example, the historical strain records of some tower components may not match the damage predicted by the initial assessment model. In this case, it is necessary to adjust the damage threshold or modify the calculation method of the damage index. Such modifications can be made using various methods, such as introducing weighting factors or fine-tuning the model parameters through regression analysis. Second, machine learning algorithms can also be considered during the modification process to establish a new algorithm that uses historical data as a training set to discover potential patterns and relationships. By incorporating new data into the assessment criteria based on the patterns learned by the model, the reliability of the assessment results can be improved. Finally, after these modifications, the resulting damage assessment model will have higher adaptability and accuracy, be able to respond promptly to new detection data, and provide a reliable basis for future tower damage assessments. This not only improves the scientific rigor and effectiveness of the assessment, but also enhances the level of intelligent monitoring of tower safety, providing more solid technical support and guarantee for the stable operation of the power system.

[0037] In one embodiment, step S5021 of acquiring multiple sets of sample data includes: S50211: Obtain the current environmental data of the specified transmission tower; S50212: Calculate the similarity between the environmental data and the historical environmental data corresponding to each historical sample data in the preset sample database; wherein, the preset sample database contains multiple historical sample data and their corresponding historical environmental data; S50213: Select a preset number of sample data from each of the historical sample data according to the similarity.

[0038] As described in step S50211 above, it is first necessary to acquire the current environmental data of the specified transmission tower. This environmental data typically includes factors such as temperature, humidity, wind speed, rainfall, and radiation intensity. Various methods can be used to acquire this data, including sensors, weather stations, drone monitoring, or other forms of remote data acquisition equipment. For transmission towers, changes in environmental conditions, such as strong winds and thunderstorms, can directly affect their structural safety and stability; therefore, real-time monitoring of this environmental data is crucial. Obtaining accurate and real-time environmental data lays the foundation for subsequent similarity calculations, ensuring the validity and accuracy of the analysis results.

[0039] As described in step S50212, calculating the similarity between the current environmental data and the historical environmental data corresponding to each historical sample data in the preset sample database is crucial to this process. The preset sample database contains multiple historical sample data and their corresponding historical environmental data, which are used to compare the current environmental state. Commonly used methods in similarity calculation include Euclidean distance, cosine similarity, and Pearson correlation coefficient. These methods quantify the current environmental data and historical environmental data using mathematical formulas, obtaining a similarity score. The higher the score, the greater the similarity between the current environmental state and a certain historical sample data. This effectively identifies which historical sample data are similar to the current environmental conditions, thus providing a basis for selecting appropriate historical samples. The similarity calculation not only helps determine the relationship between the current environment and historical conditions but also provides a scientific basis for subsequently selecting a preset number of samples. This process plays a vital bridging role in data-driven decision-making.

[0040] As described in step S50213, based on the similarity results calculated in the previous steps, a predetermined number of sample data are selected from various historical sample data. Typically, this selection process requires setting a predetermined number k, for example, 500, to ensure that the selected samples are appropriately sized. Furthermore, it is important to note that the selected samples should not rely solely on the k most similar samples; sample diversity should also be considered to avoid bias caused by the uniformity of sample selection. In this way, while ensuring similarity, the selected samples can be guaranteed to be representative. This step is the final step in the entire data acquisition process. The selected samples will be used for subsequent analysis and model building, directly affecting the accuracy and reliability of the analysis results. Therefore, when selecting samples, the principles of similarity and diversity must be comprehensively considered to provide a solid foundation for subsequent decision-making.

[0041] In one embodiment, step S2, which involves calculating the deformation data of each of the first members using the inverse finite element method for single-member deformation identification and the sensor data, includes: S201: Based on the sensor data, establish the objective function using the least squares principle; S202: Discretize the first rod to obtain multiple discrete units, and establish the unit matrix equation for each discrete unit based on the target functional; S203: Solve the element matrix equation for each discrete element to obtain the deformation data of the corresponding first member.

[0042] As described in step S201, the data collected by sensors is first needed. This data is typically obtained by measuring displacement, strain, and other information of the members under stress or deformation. Based on the least squares principle, an objective functional is established for each first member. The core idea of ​​the least squares principle is to find the optimal solution by minimizing the error between the observed data and the model's predicted values. Specifically, the objective functional is usually a mathematical expression that includes the sum of squares of the differences between the variables in the model and the observed values, representing a measure of the error during the change process. The mathematical form of the least squares principle can be expressed as: Given observation data , Define the sum of squared errors as: , minimize The goal is to find parameters a and b such that: Typically, the normal equations are derived by differentiation to solve for a and b. This method effectively guides subsequent calculations, ensuring optimized fit and thus making the deformation data obtained through back-derived data more accurate. Establishing the correct objective functional is crucial for the success of the entire deformation identification process, as it directly affects the solution accuracy and computational stability of subsequent steps.

[0043] As described in step S202 above, the discretization of the first member involves transforming a continuous object into a finite number of discrete elements, making simulation and analysis on a computer feasible. First, the designer divides the member into multiple discrete elements based on its actual geometry and characteristics, with each element considered as a simplified component. For each discrete element, the corresponding element matrix equation is obtained by applying the established objective functional. This equation, typically in the form of the finite element method (FEM), describes the relationships between elements and their response under specific loads. Solving this matrix equation involves not only considering the physical parameters of the elements (such as stiffness and mass) but also the applied external loads and boundary conditions. By establishing these element matrix equations, the computation of data can effectively approximate real physical phenomena, providing a reliable model foundation.

[0044] As described in step S203 above, the element matrix equations for each discrete element are solved. The purpose of solving the element matrix equations is to obtain the physical quantities such as displacement and strain of each element under given boundary conditions and external loads. Typically, these equations are solved using numerical methods, such as Gaussian elimination or Jacobi iteration, or by using readily available finite element analysis software for numerical simulation. By solving each discrete element individually and then integrating the results, the deformation data of the entire member is obtained. This deformation data reflects the member's response to the applied new environment and provides important information for engineering design. After processing, a comprehensive understanding of the response characteristics of the entire member is obtained, laying the foundation for engineering implementation and subsequent optimization design.

[0045] In one embodiment, after step S5 of assessing the damage to the designated transmission tower using the inversion model, the method further includes: S601: Determine whether the result of the damage assessment has reached a preset risk threshold; S602: If the result of the damage assessment reaches the preset risk threshold, then the preset damage index value and the result of the damage assessment are sent to the designated terminal.

[0046] As described in step S601 above, a preset risk assessment standard needs to be established. This typically includes assessment thresholds for the tower's load-bearing capacity, durability, and structural integrity under different environmental and load conditions. When the obtained damage assessment results (such as damage index values) are compared with this preset standard, researchers can determine whether the tower is in a safe state. If the assessment results exceed the preset threshold, it indicates that the tower may have potential hidden dangers or safety risks, and may require repair or replacement. This judgment process is a crucial step in risk management and safety assessment, as it concerns not only the reliability of the power transmission system but also the safety of public life and property. Once it is confirmed that the damage assessment results have reached the preset risk threshold, the damage index values ​​and corresponding damage assessment results are promptly sent to a designated terminal. This terminal can be a monitoring center, a maintenance personnel's workstation, or even a mobile device. The sent data typically includes basic tower information, damage indicators (such as displacement, strain, and degree of damage), assessment results, and timestamps to facilitate subsequent processing and analysis by the recipient. Real-time data transmission typically relies on modern information technology, such as the Internet of Things (IoT) and cloud computing platforms, to ensure that data is delivered efficiently and accurately to relevant personnel. Furthermore, as data is transmitted, subsequent recording and analysis can be performed, supporting long-term monitoring and maintenance decisions and contributing to a closed loop of scientific management and decision-making.

[0047] Reference Figure 3The present invention also provides a damage assessment device for transmission towers, wherein the transmission tower includes multiple first members and multiple second members, and sensors are installed on the first members. The device includes: The acquisition module 902 is used to acquire sensor data on each first member of a specified transmission tower; The calculation module 904 is used to calculate the deformation data of each of the first rods based on the single rod deformation identification method based on the inverse finite element method and the sensor data. The reconstruction module 906 is used to obtain the initial three-dimensional model of the specified transmission tower and reconstruct it based on the initial three-dimensional model and the deformation data of the first member to obtain a three-dimensional reconstruction model. Input module 908 is used to input sensor data of each of the first poles into the three-dimensional reconstruction model to obtain the global structural damage inversion model of the specified transmission tower; The evaluation module 910 is used to perform a damage assessment on the specified transmission tower using the inversion model.

[0048] In one embodiment, the evaluation module 910 includes: A preset damage index value acquisition submodule is used to acquire multiple preset damage index values ​​through the inversion model; The preset damage index value input submodule is used to input the preset damage index value into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower.

[0049] In one embodiment, the evaluation module 910 further includes: The sample data acquisition submodule is used to acquire multiple sets of sample data; one set of sample data includes damage index values ​​obtained through historical inversion models and corresponding actual tower damage records. The sample data partitioning submodule is used to divide multiple sets of sample data into training datasets and validation datasets. The initial model training submodule is used to train an initial model based on the training dataset to obtain a temporary model; The temporary model validation submodule is used to validate the temporary model using the validation dataset and obtain the validation result; The damage assessment model labeling submodule is used to record the temporary model as the damage assessment model if the verification result is determined to be passed.

[0050] In one embodiment, the evaluation module 910 further includes: The historical inversion model acquisition submodule is used to acquire the historical inversion model, historical actual damage records, and initial damage assessment model of the specified transmission tower. The initial damage assessment model correction submodule is used to correct the initial damage assessment model using the historical inversion model and the historical actual damage records to obtain the damage assessment model.

[0051] In one embodiment, the sample data acquisition submodule includes: An environmental data acquisition unit is used to acquire the current environmental data of the designated transmission tower. A similarity calculation unit is used to calculate the similarity between the environmental data and the historical environmental data corresponding to each historical sample data in a preset sample database; wherein, the preset sample database contains multiple historical sample data and their corresponding historical environmental data; A preset number of sample data are selected from each of the historical sample data based on the magnitude of the similarity.

[0052] In one embodiment, the computing module 904 includes: The objective functional establishment submodule is used to establish an objective function based on the sensor data using the least squares principle; The discretization submodule is used to discretize the first bar to obtain multiple discrete units, and to establish the unit matrix equation of each discrete unit based on the target functional. The solver submodule is used to solve the element matrix equation for each discrete element to obtain the deformation data of the corresponding first member.

[0053] In one embodiment, the transmission tower damage assessment device further includes: The result judgment module is used to determine whether the result of the damage assessment has reached a preset risk threshold. The result sending module is used to send the preset damage index value and the damage assessment result to a designated terminal if the damage assessment result reaches a preset risk threshold.

[0054] Figure 4 An internal structural diagram of an electronic device in one embodiment is shown. This electronic device can specifically be a terminal or a server, and more specifically, a computer device. Figure 4 As shown, the electronic device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a method for assessing damage to power transmission towers. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the method for assessing damage to power transmission towers. Those skilled in the art will understand that… Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0055] In one embodiment, an electronic device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Acquire sensor data on each first member of a specified transmission tower; Based on the single-member deformation identification method of inverse finite element method and the sensor data, the deformation data of each first member is calculated. An initial three-dimensional model of the specified transmission tower is obtained, and a three-dimensional reconstructed model is obtained based on the initial three-dimensional model and the deformation data of the first member. The sensor data of each of the first pole members are input into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower. The damage assessment of the specified transmission tower is performed using the inversion model.

[0056] A global inversion model of tower damage was constructed using the inverse finite element method. Employing a discrete variational approach, it relies solely on discrete strain data collected by a limited number of sensors. During the inversion phase, no external load or material property information is required, but damage assessment must be performed in conjunction with material performance data. This method successfully achieves efficient solutions for structural displacement and stress fields. It overcomes the limitations of traditional strain monitoring, enabling the inversion of force, displacement, and tilt angle parameters at locations without deployed sensors. This enhances the monitoring capability of the entire tower's stress distribution, significantly improving the comprehensive assessment of tower safety and providing crucial support for the safety management of power infrastructure.

[0057] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following steps: Acquire sensor data on each first member of a specified transmission tower; Based on the single-member deformation identification method of inverse finite element method and the sensor data, the deformation data of each first member is calculated. An initial three-dimensional model of the specified transmission tower is obtained, and a three-dimensional reconstructed model is obtained based on the initial three-dimensional model and the deformation data of the first member. The sensor data of each of the first pole members are input into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower. The damage assessment of the specified transmission tower is performed using the inversion model.

[0058] A global inversion model of tower damage was constructed using the inverse finite element method. Employing a discrete variational approach, it relies solely on discrete strain data collected by a limited number of sensors. During the inversion phase, no external load or material property information is required, but damage assessment must be performed in conjunction with material performance data. This method successfully achieves efficient solutions for structural displacement and stress fields. It overcomes the limitations of traditional strain monitoring, enabling the inversion of force, displacement, and tilt angle parameters at locations without deployed sensors. This enhances the monitoring capability of the entire tower's stress distribution, significantly improving the comprehensive assessment of tower safety and providing crucial support for the safety management of power infrastructure.

[0059] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for assessing damage to transmission towers, characterized in that, The transmission tower includes multiple first members and multiple second members, and sensors are installed on the first members. The method includes: Acquire sensor data on each first member of a specified transmission tower; Based on the single-member deformation identification method of inverse finite element method and the sensor data, the deformation data of each first member is calculated. An initial three-dimensional model of the specified transmission tower is obtained, and a three-dimensional reconstructed model is obtained based on the initial three-dimensional model and the deformation data of the first member. The sensor data of each of the first pole members are input into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower. The damage assessment of the specified transmission tower is performed using the inversion model.

2. The method for assessing damage to transmission towers according to claim 1, characterized in that, The step of assessing the damage to the designated transmission tower using the inversion model includes: Multiple preset damage index values ​​are obtained through the inversion model; The preset damage index value is input into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower.

3. The method for assessing damage to transmission towers according to claim 2, characterized in that, Before the step of inputting the preset damage index value into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower, the method further includes: Acquire multiple sets of sample data; one set of sample data includes damage index values ​​obtained through historical inversion models and corresponding actual tower damage records; Multiple sets of sample data are divided into training datasets and validation datasets; An initial model is trained based on the training dataset to obtain a temporary model; The temporary model is validated using the validation dataset to obtain validation results; If the verification result is deemed satisfactory, the temporary model will be recorded as the damage assessment model.

4. The method for assessing damage to transmission towers according to claim 2, characterized in that, Before the step of inputting the preset damage index value into the preset damage assessment model to obtain the damage assessment result of the specified transmission tower, the method further includes: Obtain the historical inversion model, historical actual damage records, and initial damage assessment model of the specified transmission tower; The initial damage assessment model is modified using the historical inversion model and the historical actual damage records to obtain the damage assessment model.

5. The method for assessing damage to transmission towers according to claim 3, characterized in that, The step of obtaining multiple sets of sample data includes: Obtain the current environmental data of the specified transmission tower; Calculate the similarity between the environmental data and the historical environmental data corresponding to each historical sample data in the preset sample database; wherein, the preset sample database contains multiple historical sample data and their corresponding historical environmental data; A preset number of sample data are selected from each of the historical sample data based on the magnitude of the similarity.

6. The method for assessing damage to transmission towers according to claim 1, characterized in that, The method for identifying the deformation of a single member based on inverse finite element method and the sensor data, and the steps for calculating the deformation data of each of the first members, include: Based on the sensor data, an objective function is established using the least squares principle; The first rod is discretized to obtain multiple discrete units, and the unit matrix equation of each discrete unit is established based on the target functional. The element matrix equation for each discrete element is solved to obtain the deformation data of the corresponding first member.

7. The method for assessing damage to transmission towers according to claim 1, characterized in that, Following the step of assessing the damage to the designated transmission tower using the inversion model, the method further includes: Determine whether the result of the damage assessment has reached a preset risk threshold; If the damage assessment result reaches the preset risk threshold, the preset damage index value and the damage assessment result are sent to the designated terminal.

8. A device for assessing damage to power transmission towers, characterized in that, The transmission tower includes multiple first poles and multiple second poles, and sensors are installed on the first poles. The device includes: The acquisition module is used to acquire sensor data on each first member of a specified transmission tower. The calculation module is used to calculate the deformation data of each of the first members based on the single member deformation identification method of inverse finite element method and the sensor data. The reconstruction module is used to obtain the initial three-dimensional model of the specified transmission tower and reconstruct it based on the initial three-dimensional model and the deformation data of the first member to obtain a three-dimensional reconstruction model. The input module is used to input the sensor data of each of the first poles into the three-dimensional reconstruction model to obtain the inversion model of the global structural damage of the specified transmission tower; An assessment module is used to assess the damage to the specified transmission tower using the inversion model.

9. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, causes the processor to perform the steps of the transmission tower damage assessment method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the transmission tower damage assessment method as described in any one of claims 1 to 7.

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