Iron tower safety evaluation method and device, computer equipment and storage medium

Abstract

The application relates to a tower safety evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring the displacement of each tower leg of a tower in the direction of each coordinate axis of a three-dimensional rectangular coordinate system; processing the displacement of each tower leg by using a trained safety evaluation model to obtain the tower head displacement and the maximum stress value of the tower rod of the tower; the safety evaluation model is trained by using the tower leg sample displacement of each tower leg of the tower; and the safety state corresponding to the tower is determined based on the tower head displacement and the maximum stress value of the tower rod. The method uses the objective data of tower displacement to determine the tower safety, can reduce the influence of human subjective experience, can reduce the time required for evaluation by using the safety evaluation model instead of manual evaluation, can improve the efficiency of tower safety evaluation, can increase the reaction time of supervisors, and can reduce disaster events caused by tower accidents.

Description

Technical Field

[0001] This application relates to the field of electrical equipment condition monitoring technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for assessing the safety of iron towers. Background Technology

[0002] Transmission towers are a crucial component of power systems, serving as carriers of high-load electrical energy and vital lifeline engineering structures. Overhead transmission line towers are often built on hillsides, mountaintops, or steep locations with complex natural slopes. Under the influence of heavy rainfall and human activities, the slopes of mountainous transmission towers are highly susceptible to instability and geological disasters, significantly impacting tower safety. With the development of power grid construction, the terrain along transmission line routes is becoming increasingly complex, making slope issues for mountainous towers more prominent. Furthermore, damage to transmission towers under catastrophic loads not only causes substantial economic losses but also triggers regional power outages. Therefore, it is necessary to conduct safety assessments of damaged towers affected by slope deformation to provide quantitative references for understanding their safety status and preventing collapses that could lead to regional power outages.

[0003] Currently, the methods for assessing the safety of transmission line towers mostly employ principal component analysis and hierarchical analysis. These methods involve manually assigning values ​​to influencing factors based on engineering experience. Alternatively, assessments can be conducted through on-site measurements, which are too subjective, inefficient, and time-consuming, failing to quickly determine whether the tower's safety is normal and hindering rapid response. Summary of the Invention

[0004] Therefore, it is necessary to address the technical problems of the above-mentioned methods, such as low efficiency, long cycle time, inability to quickly detect whether the safety of the tower is normal, and inability to respond quickly, by providing a tower safety assessment method, device, computer equipment, computer-readable storage medium, and computer program product.

[0005] Firstly, this application provides a method for assessing the safety of iron towers. The method includes:

[0006] Obtain the displacement of each leg of the iron tower in each coordinate axis direction of a three-dimensional rectangular coordinate system;

[0007] The safety assessment model, after training, is used to process the displacement of each tower leg to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained using the tower leg sample displacements of each tower leg of the tower.

[0008] Based on the tower head displacement and the maximum stress value of the tower members, the safety status of the tower is determined.

[0009] In one embodiment, the method further includes:

[0010] Obtain a training dataset; the training dataset includes multiple sets of tower leg sample displacements of the iron tower, as well as tower head displacement labels and tower member maximum stress labels corresponding to each pair of tower leg sample displacements. Each set of tower leg sample displacements includes the displacements of each tower leg of the iron tower in each coordinate axis direction of the three-dimensional rectangular coordinate system.

[0011] The support vector machine model is trained using the training dataset and the improved sparrow search algorithm to obtain the trained support vector machine model, which serves as the security evaluation model.

[0012] In one embodiment, training the support vector machine model using the training dataset and the improved sparrow search algorithm to obtain the trained support vector machine model includes:

[0013] The improved sparrow search algorithm iteratively determines the minimum fitness value and optimal fitness position of the global sparrows, and determines the penalty parameters and kernel function parameters of the support vector machine model based on the optimal fitness position.

[0014] The target support vector machine model is obtained based on the penalty parameters, the kernel function parameters, and the minimum fitness value.

[0015] The target support vector machine model is trained using the training dataset to obtain the trained support vector machine model.

[0016] In one embodiment, the step of iteratively determining the minimum fitness value and optimal fitness position of all sparrows using the improved sparrow search algorithm includes:

[0017] Determine the current sparrow population, calculate the fitness value of each sparrow and the current optimal fitness position;

[0018] Based on the fitness values, a new sparrow population is determined by sorting, and the latest positions of the discoverers, joiners, and watchers are determined by the position iteration formula of the discoverers, joiners, and watchers.

[0019] The fitness values ​​of each sparrow in the new sparrow population are recalculated and a new optimal fitness position is determined. The new optimal fitness position is compared with the previous optimal fitness position, and the better fitness is retained to continue updating until the preset maximum number of iterations is reached, so as to obtain the minimum fitness value and optimal fitness position of the global sparrow population.

[0020] In one embodiment, the method further includes:

[0021] The sparrow search algorithm was optimized using Sin chaotic sequence and Lévy flight to obtain the improved sparrow search algorithm.

[0022] In one embodiment, obtaining the displacement of each leg of the tower in the directions of each coordinate axis of the three-dimensional Cartesian coordinate system includes:

[0023] The displacement of each leg of the tower in the direction of each coordinate axis in a three-dimensional rectangular coordinate system is obtained by sensors.

[0024] Secondly, this application also provides a tower safety assessment device. The device includes:

[0025] The acquisition module is used to acquire the displacement of each leg of the tower in the direction of each coordinate axis in the three-dimensional rectangular coordinate system;

[0026] The processing module is used to process the displacement of each tower leg through a trained safety assessment model to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained by the tower leg sample displacement of each tower leg of the tower.

[0027] The evaluation module is used to determine the safety status of the tower based on the tower head displacement and the maximum stress value of the tower members.

[0028] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0029] Obtain the displacement of each leg of the iron tower in each coordinate axis direction of a three-dimensional rectangular coordinate system;

[0030] The safety assessment model, after training, is used to process the displacement of each tower leg to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained using the tower leg sample displacements of each tower leg of the tower.

[0031] Based on the tower head displacement and the maximum stress value of the tower members, the safety status of the tower is determined.

[0032] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0033] Obtain the displacement of each leg of the iron tower in each coordinate axis direction of a three-dimensional rectangular coordinate system;

[0034] The safety assessment model, after training, is used to process the displacement of each tower leg to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained using the tower leg sample displacements of each tower leg of the tower.

[0035] Based on the tower head displacement and the maximum stress value of the tower members, the safety status of the tower is determined.

[0036] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0037] Obtain the displacement of each leg of the iron tower in each coordinate axis direction of a three-dimensional rectangular coordinate system;

[0038] The safety assessment model, after training, is used to process the displacement of each tower leg to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained using the tower leg sample displacements of each tower leg of the tower.

[0039] Based on the tower head displacement and the maximum stress value of the tower members, the safety status of the tower is determined.

[0040] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for assessing the safety of steel towers use the displacements of the four tower legs along the three coordinate axes of a three-dimensional Cartesian coordinate system as input. Through a safety assessment model, it determines the tower head displacement and the maximum stress values ​​of the tower members, and finally determines the safety status of the tower based on these displacements. This method utilizes objective data of tower displacement to determine tower safety, reducing the influence of subjective human experience. The assessment using a safety assessment model is more time-efficient than manual assessment, improving the efficiency of tower safety assessment, increasing the reaction time of regulatory personnel, and reducing disasters caused by tower accidents. Attached Figure Description

[0041] Figure 1 This is an application environment diagram of the tower safety assessment method in one embodiment;

[0042] Figure 2 This is a flowchart illustrating a method for assessing the safety of iron towers in one embodiment;

[0043] Figure 3 This is a flowchart illustrating the training steps of a tower safety assessment model in one embodiment.

[0044] Figure 4 This is a flowchart illustrating the tower safety assessment method in another embodiment;

[0045] Figure 5 This is a structural block diagram of a tower safety assessment device in one embodiment;

[0046] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] The tower safety assessment method provided in this application can be applied to, for example... Figure 1 In the application environment shown, sensor 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on other network servers. In the application scenario of this application, sensor 102 monitors the displacement of each leg of the tower along each coordinate axis in a three-dimensional Cartesian coordinate system and sends the obtained displacement data to server 104 via the network. Server 104 processes the displacement of each leg using a trained safety assessment model to obtain the tower head displacement and the maximum stress value of the tower members; based on the tower head displacement and the maximum stress value of the tower members, the safety status of the tower is determined. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0049] In one embodiment, such as Figure 2 As shown, a method for assessing the safety of iron towers is provided, and this method is applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0050] Step S210: Obtain the displacement of each leg of the tower in the direction of each coordinate axis of the three-dimensional rectangular coordinate system.

[0051] In practice, sensors can be used to monitor the displacement d of the four legs of the tower in the XYZ directions. AX d AY d AZ d BX , ...d DZ In this context, A, B, C, and D represent the four legs of the tower. The displacement data of the four legs are transmitted wirelessly to the server's safety assessment model, so that the safety status of the tower can be determined through the safety assessment model.

[0052] Step S220: The displacement of each tower leg is processed by the trained safety assessment model to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained by the tower leg sample displacement of each tower leg of the tower.

[0053] In practice, the safety assessment model can be trained in advance using the displacement samples of each tower leg as input and the tower head displacement and the maximum stress value of the tower members as output. This results in a trained safety assessment model, which can then be used to process the displacement of each tower leg after the actual monitoring of the tower's displacement, thereby obtaining accurate tower head displacement and maximum stress value of the tower members.

[0054] Step S230: Determine the safety status of the tower based on the tower head displacement and the maximum stress value of the tower members.

[0055] In practical implementation, multiple safety states of the tower and the corresponding tower head displacement range and maximum stress value range of the tower members can be predetermined. For example, the safety states of the tower can be divided into severe state, abnormal state, warning state, and normal state, and correspondingly denoted as levels I, II, III, and IV. Then, after obtaining the actual tower head displacement and maximum stress value of the tower members, the target tower head displacement range and the target tower member maximum stress value range can be determined. Thus, the safety state corresponding to the target tower head displacement range and the target tower member maximum stress value range is determined as the safety state of the tower.

[0056] The aforementioned method for assessing the safety of steel towers uses the displacements of the four tower legs along the three axes of a three-dimensional Cartesian coordinate system as input. A safety assessment model determines the tower head displacement and the maximum stress values ​​of the tower members. Finally, the safety status of the tower is determined based on these displacements and stress values. This method utilizes objective data on tower displacement to determine safety, reducing the influence of subjective human experience. The assessment using a safety assessment model is more time-efficient than manual assessment, improving the efficiency of tower safety evaluation, increasing the reaction time for regulatory personnel, and reducing disasters caused by tower accidents.

[0057] In one exemplary embodiment, the method further includes:

[0058] Step S211: Obtain the training dataset; The training dataset includes multiple sets of tower leg sample displacements of the iron tower, as well as the tower head displacement label and the maximum stress label of the tower member corresponding to each pair of tower leg sample displacements. Each set of tower leg sample displacements includes the displacement of each tower leg of the iron tower in each coordinate axis direction of the three-dimensional rectangular coordinate system.

[0059] Step S212: Using the training dataset and the improved sparrow search algorithm, the support vector machine model is trained to obtain the trained support vector machine model, which serves as the security assessment model.

[0060] In practical implementation, numerical tests are conducted on the transmission towers. By changing the displacements of the four tower legs in the XYZ directions, the input d... AX d AY d AZ d BX , ...d DZ , (d AX (This represents the displacement variable of tower leg A in the X direction; other parameters are derived similarly.) The tower head displacement and maximum stress values ​​of the tower members are obtained. Simultaneously, the tower safety assessment standards are divided into: severe state, abnormal state, warning state, and normal state, denoted as levels I, II, III, and IV. The numerical test results are combined with the assessment standards, and the normalized data is used as the training dataset. The improved sparrow search algorithm is then used to train the support vector machine model, resulting in a trained support vector machine model, which serves as the safety assessment model.

[0061] In this embodiment, a support vector machine is trained as a safety assessment model, which reduces the time required for manual assessment and improves the efficiency of assessing the safety of iron towers.

[0062] In one exemplary embodiment, such as Figure 3 As shown, step S212 above uses the training dataset and the improved sparrow search algorithm to train the support vector machine model, resulting in a trained support vector machine model, including:

[0063] Step S310: Using the improved sparrow search algorithm, the minimum fitness value and optimal fitness position of the global sparrows are determined iteratively, and the penalty parameters and kernel function parameters of the support vector machine model are determined based on the optimal fitness position;

[0064] Step S320: Obtain the target support vector machine model based on the penalty parameters, kernel function parameters, and minimum fitness value;

[0065] Step S330: Train the target support vector machine model using the training dataset to obtain the trained support vector machine model.

[0066] In an exemplary embodiment, step S310 above, through an improved sparrow search algorithm, iteratively determines the minimum fitness value and optimal fitness position of the global sparrows, including:

[0067] Step S311: Determine the current sparrow population, calculate the fitness value of each sparrow and the current optimal fitness position;

[0068] Step S312: Based on the fitness values, sort and determine the new sparrow population, and determine the latest positions of the discoverer, joiner and watcher according to the position iteration formula of the discoverer, joiner and watcher;

[0069] Step S313: Recalculate the fitness value of each sparrow in the new sparrow population and determine the new optimal fitness position. Compare the new optimal fitness position with the previous optimal fitness position, retain the better fitness and continue to update until the preset maximum number of iterations is reached, and obtain the minimum fitness value and optimal fitness position of the global sparrow population.

[0070] In the specific implementation, the sparrow population is first initialized, and the fitness function is set. The equation for the fitness function is as follows:

[0071]

[0072] In the formula: D represents the dataset; T i Represents the actual value of the sample; The training values ​​represent the sample.

[0073] Using the fitness function, each sparrow is updated. During the sparrow population update process, the fitness value of each sparrow and its current optimal position are calculated. The algorithm stops updating when it reaches the maximum number of iterations. The minimum fitness value of all sparrows and their corresponding optimal positions are output, and these values ​​are assigned to the penalty parameter C and kernel function parameter G in the Support Vector Machine (SVM).

[0074] In the activity of a sparrow colony, the finder's task is to locate food, while the watcher is responsible for monitoring the area around the food source. The finder's position update formula during the iteration process is as follows:

[0075]

[0076] In equation (2): α is a random number ranging from (0,1]; t is the current iteration number; X i，j Let T be the position of the i-th sparrow in dimension j, where j = 1, 2, 3, ..., 12 represents the dimension; max= 500 is the maximum number of iterations; Q is a random number conforming to the normal distribution; L is a 1×12 matrix with all elements being 1; R is a random number with a value range of [0, 1]; ST∈[0.5, 1] is the warning threshold. When R < ST, it means the environment is safe, the predator poses no threat to the population, and the sparrow population can safely perform the task of searching for food. When R ≥ ST, it means that a predator has appeared around the environment, threatening the safety of the population. Some sparrows have discovered the predator and issued warnings to other sparrows, and the population will then fly to other areas to search for food.

[0077] For the followers, the purpose of the followers is to follow the direction of the discoverers in order to obtain food. However, some followers will compete with the discoverers for food in order to obtain more energy, and some followers will fly to other high-energy areas to look for food. The identities of the followers and the discoverers will also continuously interchange with iterations, but the proportions of the discoverers and the followers in the sparrow population remain unchanged. Their position update formulas are as follows:

[0078]

[0079] In Equation (3), n = 100 is the sparrow population size; A + = A T (AA T ), A is a 1×12 matrix, and the elements in the matrix are randomly assigned as 1 or -1; X worst is the worst position of the current sparrow; is the optimal position of the sparrow currently; i > n / 2 means that some follower sparrows are hungry because they have not found food, and they will leave the existing space and go to other spaces to look for food; i ≤ n / 2 means that the i-th follower sparrow will look for food in the nearby position.

[0080] For the scouts, the scouts are responsible for the warning task of the population. When the population encounters a predator, the scouts will lead the population to fly to other areas to look for food. The proportion of the scouts in the population is 10% to 20%. Their position update formula is:

[0081]

[0082] In Equation (4), X best is the optimal position of the scout; β is a random number with a normal distribution with a mean of 0 and a variance of 1; K is a random number from -1 to 1; f i is the fitness value of the current sparrow; f g and f w are the current global optimal and worst fitness values; ε is a very small constant to avoid the denominator being 0.

[0083] In an exemplary embodiment, the method further includes: optimizing the sparrow search algorithm using a Sin chaotic sequence and Lévy flight to obtain an improved sparrow search algorithm.

[0084] In the Sparrow Search Algorithm (SSA), initial population diversity can effectively expand the search range of the algorithm, thereby improving the optimization accuracy and convergence speed. Sin chaos is frequently used in optimization problems. Its basic principle is to generate a chaotic sequence in the chaotic variable space [0,1] through mapping relationships, and then transform it into the individual optimization variable space, as expressed below:

[0085]

[0086] In equation (5): X n It is a sequence with values ​​of (-1, 1) and the initial value cannot be set to 0.

[0087] When the discoverer iterates a certain number of times and its fitness value remains unchanged, the joiner becomes the discoverer. To avoid the algorithm getting trapped in local optima, the Lévy flight strategy is introduced into the joiner formula (3) to improve the global search capability. The improved formula is as follows:

[0088]

[0089] In formula (6): This is the best position currently occupied by the discoverer. Lévy's flight mechanics are as follows:

[0090]

[0091] In equation (7): r3 and r4 are both random numbers in the range [0,1], the value of ξ can be 1.5, and σ is calculated as follows:

[0092]

[0093] In equation (8): Γ(x)=(x-1)!

[0094] In this embodiment, the sparrow search algorithm is improved by using Sin chaotic sequence and Lévy flight strategy to avoid the algorithm result getting trapped in local optimum.

[0095] Furthermore, the improved Sparrow Search Algorithm (ISSA) is used to optimize the Support Vector Machine (SVM), resulting in the improved Sparrow Search Algorithm-Optimized Support Vector Machine model (ISSA-SVM). Within the given original data space, the objective function for the SVM classification line can be expressed by the following formula:

[0096]

[0097] sty i (ω T x i +b)≥1-ε i ,ε i ≥0, i=1,2,3,...,n

[0098] In equation (9), min represents the point closest to the classification line; C is the penalty parameter; ε i It is a relaxation factor.

[0099] Another important part of SVM is the kernel function and its parameters. Radial basis function (RBF) is chosen as the kernel function because it reflects the data in a relatively high dimension, making it well-suited for processing nonlinear data. Its expression is as follows:

[0100]

[0101] In equation (10), x1 is the center of the RBF; G is the kernel function parameter, representing the coverage of the constraint function; K() represents the kernel function; K(x1,x2) represents the maximum position from the center point x1.

[0102] The displacements d of the four legs of the tower in the XYZ directions are given in the training set. AX d AY d AZ d BX , ...d DZ The initial support vector machine (SVM) is input and used as the initial condition for the fitness formula. The initial fitness value (fitness) is calculated using formula (1). The initial fitness value is then imported into the improved sparrow search algorithm (ISSA) model to solve for the penalty parameter C, kernel function parameter G, and minimum fitness value in the support vector machine (SVM). The fitness value of each sparrow in each iteration and its corresponding C and G parameters are updated according to the number of iterations from formula (2) to (8). When the algorithm reaches the maximum number of iterations T, the fitness value of each sparrow in each iteration and its corresponding C and G parameters are updated. max When the program ends, the global optimal position and minimum fitness value are output, and the optimal parameters of C and G are obtained. The SVM prediction model is constructed by formulas (9) to (10) to obtain the final ISSA-SVM as a security assessment model.

[0103] In the above embodiments, the sparrow search algorithm is used to optimize the penalty parameter C and kernel function parameter G in the support vector machine, thereby improving the accuracy of the support vector machine model in assessing the safety of iron towers.

[0104] In an exemplary embodiment, step S210 above, obtaining the displacement of each leg of the tower in each coordinate axis direction of the three-dimensional Cartesian coordinate system, includes: obtaining the displacement of each leg of the tower in each coordinate axis direction of the three-dimensional Cartesian coordinate system through a sensor.

[0105] In this embodiment, sensors and wireless transmission are used to collect data on the displacement of the four tower legs of China Railway Tower in the XYZ directions, so as to save the time and cost of manual measurement and improve the evaluation efficiency.

[0106] In one embodiment, to facilitate understanding of the embodiments of this application by those skilled in the art, specific examples will be described below in conjunction with the accompanying drawings. References Figure 4 The diagram illustrates a flowchart of a method for assessing the safety of iron towers. In this embodiment, the method includes the following steps:

[0107] Step 1: Conduct numerical tests on the transmission towers. By changing the displacement of the four tower legs in the XYZ directions, the tower head displacement and the maximum stress value of the tower members are obtained. At the same time, the tower safety assessment standard is divided into: severe state, abnormal state, warning state and normal state, denoted as I, II, III and IV. The above numerical test results are combined with the assessment standard, and the normalized data is used as training samples.

[0108] Step 2: Data preprocessing, sparrow population initialization, and setting the fitness function.

[0109] Step 3: The Sparrow Search Algorithm (SSA) is optimized using Sin chaotic sequence and Lévy flight to obtain the Improved Sparrow Algorithm (ISSA).

[0110] Step 4: Using steps 2 and 3, update each sparrow. During the sparrow population update process, calculate the fitness value of each sparrow and its current optimal position. Stop updating when the algorithm reaches the maximum number of iterations, output the minimum fitness value of all sparrows and its corresponding optimal position, and assign these values ​​to the penalty parameter C and kernel function parameter G in the Support Vector Machine (SVM).

[0111] Step 5: Construct an SVM evaluation model using the optimal C and G parameters and minimum fitness value obtained through iteration. Combine the numerical experimental data from Step 1 with the improved evaluation algorithm to obtain the evaluation model of the improved sparrow algorithm-optimized support vector machine (ISSA-SVM) after training.

[0112] Step Six: Use sensors to monitor the displacement of the four tower legs in the XYZ directions, and transmit the data wirelessly to the backend of the evaluation model. Input this data into the ISSA-SVM prediction model for evaluation, and the evaluation model will calculate and display the safety status of the tower.

[0113] The tower safety assessment model provided in this application has the following beneficial effects:

[0114] (1) The safety status of the transmission tower is evaluated by the displacement of the four tower legs in the XYZ directions. Compared with the principal component analysis, hierarchical analysis and other methods that assign values ​​to the influencing factors based on engineering experience, this method reduces the interference of human subjective factors and reduces the workload and cost of traditional manual measurement.

[0115] (2) Improve the sparrow search algorithm by using Sin chaotic sequence and Lévy flight strategy to avoid the algorithm result from getting trapped in local optimum.

[0116] (3) The improved sparrow search algorithm is used to optimize the penalty parameter C and kernel function parameter G in the support vector machine, thereby improving the accuracy of the support vector machine model in assessing the safety of iron towers.

[0117] (4) The improved sparrow algorithm optimized support vector machine evaluation model (ISSA-SVM) is used as the tower safety evaluation model to evaluate the safety of the tower. This helps decision-makers to evaluate the overall safety of the tower after it is damaged by slope deformation. Compared with manual evaluation, it greatly reduces the time and can quickly and efficiently evaluate the safety of the tower. It increases the responsiveness of transmission line managers and reduces disasters caused by tower accidents.

[0118] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0119] Based on the same inventive concept, this application also provides a tower safety assessment device for implementing the tower safety assessment method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more tower safety assessment device embodiments provided below can be found in the limitations of the tower safety assessment method described above, and will not be repeated here.

[0120] In one embodiment, such as Figure 5As shown, a tower safety assessment device is provided, comprising: an acquisition module 510, a processing module 520, and an assessment module 530, wherein:

[0121] The acquisition module 510 is used to acquire the displacement of each leg of the tower in the direction of each coordinate axis of the three-dimensional rectangular coordinate system;

[0122] The processing module 520 is used to process the displacement of each tower leg through the trained safety assessment model to obtain the tower head displacement and the maximum stress value of the tower members; wherein, the safety assessment model is trained by the tower leg sample displacement of each tower leg of the tower.

[0123] The evaluation module 530 is used to determine the safety status of the tower based on the tower head displacement and the maximum stress value of the tower members.

[0124] In one embodiment, the above-mentioned device further includes a training module for acquiring a training dataset; the training dataset includes multiple sets of tower leg sample displacements of the iron tower and tower head displacement labels and maximum stress labels of tower members corresponding to each pair of tower leg sample displacements, and each set of tower leg sample displacements includes the displacements of each tower leg of the iron tower in each coordinate axis direction of the three-dimensional rectangular coordinate system; the support vector machine model is trained using the training dataset and the improved sparrow search algorithm to obtain the trained support vector machine model, which serves as a safety assessment model.

[0125] In one embodiment, the training module is further configured to iteratively determine the minimum fitness value and optimal fitness position of the global sparrows using the improved sparrow search algorithm, determine the penalty parameters and kernel function parameters of the support vector machine model based on the optimal fitness position, obtain the target support vector machine model based on the penalty parameters, kernel function parameters and minimum fitness value, and train the target support vector machine model using the training dataset to obtain the trained support vector machine model.

[0126] In one embodiment, the training module is further configured to determine the current sparrow population, calculate the fitness value of each sparrow and the current optimal fitness position; sort and determine a new sparrow population based on the fitness values, and update the positions of the discoverer, joiner and watcher according to the position update formula of the discoverer, joiner and watcher; recalculate the fitness value of each sparrow in the new sparrow population and determine the new optimal fitness position, compare the new optimal fitness position with the previous optimal fitness position, retain the better fitness and continue to update until the preset maximum number of iterations is reached, and obtain the minimum fitness value and optimal fitness position of the global sparrow population.

[0127] In one embodiment, the training module is further used to optimize the sparrow search algorithm using Sin chaotic sequences and Lévy flight to obtain an improved sparrow search algorithm.

[0128] In one embodiment, the acquisition module 510 is further configured to acquire the displacement of each leg of the tower in the direction of each coordinate axis of the three-dimensional rectangular coordinate system through a sensor.

[0129] Each module in the aforementioned tower safety assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0130] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data used in the tower safety assessment process. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a tower safety assessment method.

[0131] Those skilled in the art will understand that Figure 6 The 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 computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0132] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0133] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0134] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0135] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0137] 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.

[0138] 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 application should be determined by the appended claims.

Claims

1. A method for assessing the safety of iron towers, characterized in that, The method includes: Obtain the displacement of each leg of the iron tower in each coordinate axis direction of a three-dimensional rectangular coordinate system; The safety assessment model, once trained, processes the displacements of each tower leg to obtain the tower head displacement and the maximum stress value of the tower members. The safety assessment model is obtained by training a support vector machine model using a training dataset and an improved sparrow search algorithm. The training dataset includes sample displacements of each tower leg along each coordinate axis in a three-dimensional Cartesian coordinate system. The improved sparrow search algorithm is optimized using Sin chaotic sequences and Lévy flight. Based on the tower head displacement and the maximum stress value of the tower members, the safety status of the tower is determined.

2. The method according to claim 1, characterized in that, The training dataset includes multiple sets of tower leg sample displacements of the iron tower. Each set of tower leg sample displacements has a corresponding tower head displacement label and tower member maximum stress label.

3. The method according to claim 1, characterized in that, The support vector machine model is trained using a training dataset and an improved sparrow search algorithm, including: The improved sparrow search algorithm iteratively determines the minimum fitness value and optimal fitness position of the global sparrows, and determines the penalty parameters and kernel function parameters of the support vector machine model based on the optimal fitness position. The target support vector machine model is obtained based on the penalty parameters, the kernel function parameters, and the minimum fitness value. The target support vector machine model is trained using the training dataset to obtain a trained support vector machine model, which serves as the security evaluation model.

4. The method according to claim 3, characterized in that, The improved sparrow search algorithm iteratively determines the minimum fitness value and optimal fitness position of all sparrows globally, including: Determine the current sparrow population, calculate the fitness value of each sparrow and the current optimal fitness position; Based on the fitness values, a new sparrow population is determined by sorting, and the latest positions of the discoverers, joiners, and watchers are determined by the position iteration formula of the discoverers, joiners, and watchers. The fitness values ​​of each sparrow in the new sparrow population are recalculated and a new optimal fitness position is determined. The new optimal fitness position is compared with the previous optimal fitness position, and the better fitness is retained to continue updating until the preset maximum number of iterations is reached, so as to obtain the minimum fitness value and optimal fitness position of the global sparrow population.

5. The method according to claim 1, characterized in that, The determination of the safety status of the tower based on the tower head displacement and the maximum stress value of the tower members includes: Obtain multiple safety states of the tower, as well as the range of tower head displacement and the range of maximum stress values ​​of the tower members corresponding to each safety state; Determine the target tower head displacement range corresponding to the tower head displacement and the target tower member maximum stress value range corresponding to the maximum stress value of the tower member; The safety state corresponding to the target tower head displacement range and the maximum stress value range of the target tower members is determined as the safety state of the tower.

6. The method according to claim 1, characterized in that, The process of obtaining the displacements of each leg of the tower in the directions of each coordinate axis in a three-dimensional Cartesian coordinate system includes: The displacement of each leg of the tower in the direction of each coordinate axis in a three-dimensional rectangular coordinate system is obtained by sensors.

7. A tower safety assessment device, characterized in that, The device includes: The acquisition module is used to acquire the displacement of each leg of the tower in the direction of each coordinate axis in the three-dimensional rectangular coordinate system; The processing module is used to process the displacement of each tower leg using a trained safety assessment model to obtain the tower head displacement and the maximum stress value of the tower members. The safety assessment model is obtained by training a support vector machine model using a training dataset and an improved sparrow search algorithm. The training dataset includes sample displacements of each tower leg in the three-dimensional Cartesian coordinate system along each coordinate axis. The improved sparrow search algorithm is optimized using Sin chaotic sequences and Lévy flight. The evaluation module is used to determine the safety status of the tower based on the tower head displacement and the maximum stress value of the tower members.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the tower safety assessment method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the tower safety assessment method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the tower safety assessment method according to any one of claims 1 to 6.

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