CN117973131A - Finite element model-based power transmission tower geological disaster failure prediction method and system - Google Patents

Abstract

The invention discloses a method and a system for predicting geological disaster failure of a power transmission tower based on a finite element model, which relate to the technical field of power engineering and comprise the following steps: collecting rainfall data and related data of a power transmission tower, and establishing a finite element model; establishing a coupling action model of the power transmission tower based on the finite element model; predicting rainfall landslide geological disasters based on a coupling action model of the power transmission tower; and taking corresponding measures according to rainfall landslide geological disaster prediction results. According to the invention, the finite element model is built by collecting rainfall data and related data of the power transmission tower, the coupling action model is built, and geological disasters such as rainfall landslide and the like are predicted. The steps are jointly acted on warning potential risks in advance, so that the possibility of damage to the power transmission tower is reduced, and the stability of a power grid and the safety of personnel are ensured.

Description

Failure prediction method and system for transmission tower geological disasters based on finite element model

Technical Field

The present invention relates to the technical field of electric power engineering, and in particular to a method and system for predicting geological disaster failure of a transmission tower based on a finite element model.

Background technique

In the field of power engineering, the stability of transmission towers is crucial for the reliable operation of power systems. In recent years, with climate change and environmental degradation, geological disasters such as rainfall landslides have increasingly threatened transmission towers. Traditionally, the stability analysis of transmission towers mainly relies on empirical judgment and simple static calculation models. These methods are often insufficient when dealing with complex geological conditions and dynamic environmental factors such as rainfall. In recent years, the development of the finite element method (FEM) has provided new solutions to such problems. The finite element method can provide more accurate structural stress and deformation analysis, but its application in geological disaster prediction is still relatively limited, especially in integrating complex geological data and meteorological data.

Existing technologies mainly focus on predicting the stability of transmission towers using static models, while ignoring the impact of dynamic environmental factors. In addition, these technologies often fail to fully utilize deep learning algorithms to optimize model parameters, resulting in limited prediction accuracy. On the other hand, existing methods are inefficient in processing large amounts of complex data related to transmission towers and cannot respond to environmental changes in real time. For example, when rainfall changes, existing systems often cannot quickly adjust their prediction results. In addition, existing technologies are also insufficient in preventive measures before disasters occur and emergency responses after disasters occur. In these aspects, the development of finite element models and the integration of deep learning algorithms provide new possibilities for improving prediction accuracy and response speed.

In view of these shortcomings, the present invention proposes a method and system for predicting the failure of transmission towers due to geological disasters based on a finite element model. This method integrates rainfall data, transmission tower structure design, rock and soil geological data, and slope data, and uses a finite element model combined with a deep learning algorithm for optimization, thereby improving the prediction accuracy of the stability of transmission towers in complex geological environments. In addition, the system can dynamically adjust the prediction model according to real-time environmental changes and quickly respond to the possibility of geological disasters such as rainfall landslides. The innovation of this method is that it not only improves the accuracy of the prediction, but also enhances the flexibility and response speed of the system.

The present invention also includes a complete set of risk assessment and emergency response mechanisms. According to the results of the prediction model, the system can classify and judge the possibility of geological disasters, and take corresponding measures according to different risk levels, such as routine monitoring, public warning release, and activation of emergency evacuation plans. This mechanism not only enhances the effectiveness of preventive measures, but also provides reliable decision-making support for emergency management when disasters occur. In addition, the design of the system takes into account the compatibility with the existing power system and the implementation cost, ensuring its feasibility and economy in practical applications. In general, the present invention shows significant advantages in improving the accuracy and response speed of geological disaster prediction of transmission towers, and provides an innovative and practical solution for the stable operation of power systems and disaster risk management.

Summary of the invention

In view of the above-mentioned problems, the present invention is proposed.

Therefore, the technical problem solved by the present invention is: how to improve the accuracy and response speed of stability prediction of transmission towers in complex geological environments, especially in geological disasters such as rainfall landslides.

In order to solve the above technical problems, the present invention provides the following technical solutions: a method for predicting failure of transmission tower geological disasters based on a finite element model, which comprises the following steps:

Collect rainfall data and transmission tower related data to establish a finite element model; establish a coupling model of the transmission tower based on the finite element model; predict rainfall-induced landslide geological disasters based on the coupling model of the transmission tower; take corresponding measures based on the prediction results of rainfall-induced landslide geological disasters.

As a preferred solution of the transmission tower geological disaster failure prediction method based on the finite element model described in the present invention, the transmission tower related data includes the transmission tower structure design and location information, rock and soil geological data and slope data, and the establishment of the finite element model includes optimizing the parameter settings of the finite element model through a deep learning algorithm.

The finite element model is expressed as,

Where D represents the transmission tower data, T represents rainfall data, G represents geotechnical data, S represents slope data, M represents historical disaster records, α represents the adjustment parameter, f(G, s) represents the function defined according to geotechnical data and slope data, a and b represent the upper and lower limits of the integral, and n represents the number of rainfall hours.

As a preferred solution of the transmission tower geological disaster failure prediction method based on the finite element model described in the present invention, the deep learning algorithm is expressed as:

Where W represents the weight parameter of the deep learning model, X represents the input data set, represents the parameters in the finite element model, Y represents the geological data, σ represents the activation function, g(X _i ) represents the preprocessing function for the input data X, h(Y, t) represents the integral processing of Y, t represents the integral variable, and z represents the number of input data.

As a preferred solution of the transmission tower geological disaster failure prediction method based on the finite element model described in the present invention, wherein: the establishment of the coupling effect model of the transmission tower includes, after the finite element model of the transmission tower is established, a soil model is established according to the rock and soil geological data of the transmission tower foundation, and the inclination angle of the slope is set to establish the coupling effect model of the transmission tower, which is expressed as:

Among them, Y represents the risk assessment function, x represents the location, P represents the finite element model, G(x) represents the rock and soil geological data function, δ represents the adjustment parameter, which controls the influence of the transmission tower structure parameters on risk assessment, β represents the adjustment parameter, θ represents the slope inclination, and R(x) represents the rainfall function.

As a preferred solution of the transmission tower geological disaster failure prediction method based on the finite element model described in the present invention, the prediction of rainfall landslide geological disasters includes: if the prediction function Y is less than 0.3, it means that the rainfall landslide geological disaster will not occur, recorded as A1; if the prediction function 0.3≤Y≤0.7, it means that the rainfall landslide geological disaster may occur, recorded as A2; if the prediction function Y＞0.7, it means that the rainfall landslide geological disaster will definitely occur, recorded as A3.

When rainfall-landslide geological disasters are likely to occur, they are further determined through risk assessment models.

As a preferred solution of the transmission tower geological disaster failure prediction method based on the finite element model described in the present invention, the risk assessment model is expressed as:

Among them, Q represents the risk assessment value of rainfall landslide geological disasters, T(u) represents the rainfall function, and H represents the geological stability index.

If Q is greater than the threshold, it is further determined that the rainfall landslide geological disaster will occur, which is recorded as B1.

If Q is less than the threshold, it is further determined that the rainfall landslide geological disaster will not occur, which is recorded as B2.

As a preferred solution of the transmission tower geological disaster failure prediction method based on the finite element model described in the present invention, the taking of corresponding measures includes, if the current state is A1, continuing to use advanced sensors or satellite data to perform conventional geological and meteorological monitoring in real time, while checking and maintaining existing safety measures and early warning systems, collecting and analyzing geological, meteorological and structural data for future analysis and prediction model optimization.

If the current status is A2 and the status is further confirmed to be B1, an early warning will be immediately issued to the public through multiple channels including broadcasting, mobile phone applications, and social media, the evacuation plan will be activated, emergency rescue teams and supplies will be deployed, and the situation will be continuously monitored and information will be updated in real time.

If the current status is A2 and it is further determined that the status is B2, the collected data will be deeply analyzed to determine the accuracy of the risk assessment model. Based on the latest assessment, the latest assessment results will be issued to the public, the issued warning will be lifted, and the existing emergency plans and strategies will be reviewed and adjusted based on the latest risk assessment results.

If the current status is A3, emergency measures will be taken immediately, the evacuation plan will be implemented immediately, residents in high-risk areas will be evacuated first, the safety of the evacuation routes will be ensured, and assembly points for rescue teams and equipment will be set in advance.

Another object of the present invention is to provide a transmission tower geological disaster failure prediction system based on a finite element model, which can solve the problems of the prior art in terms of dynamic environmental factor response, data processing efficiency and disaster warning accuracy by integrating a finite element model optimized by a deep learning algorithm, real-time environmental data processing capabilities and a comprehensive risk assessment mechanism.

To solve the above technical problems, the present invention provides the following technical solutions: a transmission tower geological disaster failure prediction system based on a finite element model, comprising a data acquisition module, a finite element model construction module, a disaster prediction module and a risk assessment module.

The data acquisition module is responsible for collecting transmission tower related data, including structural design, location information, geotechnical data, slope data and rainfall data.

The finite element model building module is responsible for building a finite element model using the collected data.

The disaster prediction module predicts the possibility of rainfall landslide geological disasters.

The risk assessment module is responsible for using the risk assessment model to further determine the possibility of the disaster when a possible rainfall landslide geological disaster is predicted.

A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for predicting failure of a transmission tower geological disaster based on a finite element model as described above are implemented.

A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting failure of a transmission tower geological disaster based on a finite element model as described above.

Beneficial effects of the invention: The invention collects rainfall data and transmission tower related data to establish a finite element model, builds a coupling model, and predicts geological disasters such as rainfall landslides. The invention not only improves the accuracy and dynamic response capability of geological disaster prediction, but also provides comprehensive and integrated technical support for the stable operation of the power system and disaster risk management. These steps work together to warn of potential risks in advance and reduce the possibility of damage to transmission towers, thereby ensuring the stability of the power grid and the safety of personnel.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

FIG1 is an overall flow chart of a method for predicting failure of transmission tower geological disasters based on a finite element model provided in a first embodiment of the present invention.

FIG2 is an overall framework diagram of a transmission tower geological disaster failure prediction system based on a finite element model provided in a second embodiment of the present invention.

FIG3 is a flowchart of failure model analysis software for a method for predicting failure of transmission tower geological disasters based on a finite element model according to a third embodiment of the present invention.

FIG. 4 is a main interface of failure analysis software for a method for predicting failure of transmission tower geological disasters based on a finite element model provided in a third embodiment of the present invention.

FIG5 is a key point interface of a method for predicting failure of transmission tower geological disasters based on a finite element model provided in a third embodiment of the present invention.

FIG6 is an interface of inputting relevant parameters of a method for predicting failure of transmission tower geological disasters based on a finite element model provided in a third embodiment of the present invention.

FIG. 7 is an interface of input related parameters of a method for predicting failure of transmission tower geological disasters based on a finite element model provided in a third embodiment of the present invention.

FIG8 is a diagram showing two base tower selections for a method for predicting transmission tower geological disaster failure based on a finite element model according to a third embodiment of the present invention.

FIG. 9 is a graph showing daily rainfall and total precipitation of a transmission tower geological disaster failure prediction method based on a finite element model provided in a third embodiment of the present invention.

FIG. 10 is a diagram showing the numerical analysis results of a method for predicting failure of transmission tower geological disasters based on a finite element model according to a third embodiment of the present invention.

FIG. 11 is a displacement and strain cloud diagram of a transmission tower geological disaster failure prediction method based on a finite element model provided in a third embodiment of the present invention.

Detailed ways

In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

Example 1

1 , which is an embodiment of the present invention, provides a method for predicting failure of transmission tower geological disasters based on a finite element model, which is characterized by:

S1: Collect rainfall data and transmission tower related data to establish a finite element model.

The transmission tower related data includes the transmission tower structure design and location information, rock and soil geological data and slope data. The establishment of the finite element model includes optimizing the parameter settings of the finite element model through a deep learning algorithm.

The finite element model is expressed as,

Where D represents the transmission tower data, T represents rainfall data, G represents geotechnical data, S represents slope data, M represents historical disaster records, α represents the adjustment parameter, f(G, s) represents the function defined according to geotechnical data and slope data, a and b represent the upper and lower limits of the integral, and n represents the number of rainfall hours.

The deep learning algorithm is represented as,

Where W represents the weight parameter of the deep learning model, X represents the input data set, represents the parameters in the finite element model, Y represents the geological data, σ represents the activation function, g(X _i ) represents the preprocessing function for the input data X, h(Y, t) represents the integral processing of Y, t represents the integral variable, and z represents the number of input data.

It is further added that the finite element model in the present invention uses the collected data to analyze the response of the transmission tower under different geological conditions through computer simulation technology. The model parameters are optimized using a deep learning algorithm to improve the accuracy and applicability of the model. The innovation of this step is to combine the traditional finite element analysis method with modern machine learning technology to provide a new perspective for the stability analysis of the transmission tower. The specific steps of optimization through the deep learning algorithm include collecting data related to the finite element model, preprocessing the collected data, and determining the key features that affect the output of the finite element model, thereby constructing a deep learning model. Through the deep learning model, the characteristics of the optimized material of the transmission tower are predicted, and the most appropriate boundary conditions and load settings are determined.

S2: Establish the coupling effect model of transmission tower based on finite element model.

The coupling effect model of the transmission tower is established by establishing the finite element model of the transmission tower, establishing the soil model according to the rock and soil geological data of the transmission tower foundation, setting the slope angle, and establishing the coupling effect model of the transmission tower, which is expressed as:

Among them, Y represents the risk assessment function, x represents the location, P represents the finite element model, G(x) represents the rock and soil geological data function, δ represents the adjustment parameter, which controls the influence of the transmission tower structure parameters on risk assessment, β represents the adjustment parameter, θ represents the slope inclination, and R(x) represents the rainfall function.

Furthermore, this step establishes a more comprehensive coupled model by considering the interaction between the transmission tower structure and its foundation rock and soil. This model not only includes static structural analysis, but also considers dynamic environmental factors (such as rainfall), thus providing a more comprehensive perspective for prediction. The establishment of this coupled model can more accurately predict the response of transmission towers in specific environments, especially under extreme weather conditions.

S3: Prediction of rainfall-induced landslide geological disasters based on the coupling effect model of transmission towers.

The prediction of rainfall landslide geological disasters includes: if the prediction function Y is less than 0.3, it means that rainfall landslide geological disasters will not occur, which is recorded as A1; if the prediction function is 0.3≤Y≤0.7, it means that rainfall landslide geological disasters may occur, which is recorded as A2; if the prediction function Y＞0.7, it means that rainfall landslide geological disasters will definitely occur, which is recorded as A3.

When rainfall-landslide geological disasters are likely to occur, they are further determined through risk assessment models.

The risk assessment model is expressed as,

Among them, Q represents the risk assessment value of rainfall landslide geological disasters, T(u) represents the rainfall function, and H represents the geological stability index.

If Q is greater than the threshold, it is further determined that the rainfall landslide geological disaster will occur, which is recorded as B1.

If Q is less than the threshold, it is further determined that the rainfall landslide geological disaster will not occur, which is recorded as B2.

It is further added that after predicting possible geological disasters, the present invention further determines the possibility and severity of the disaster through a risk assessment model. This model combines the geological stability index and rainfall data to provide a basis for making more accurate risk judgments. This has great practical application value for disaster emergency response and resource allocation.

S4: Take corresponding measures based on the prediction results of rainfall-induced landslide geological disasters.

The corresponding measures include, if the current status is A1, continuing to use advanced sensors or satellite data for regular geological and meteorological monitoring in real time, checking and maintaining existing safety measures and early warning systems, and collecting and analyzing geological, meteorological and structural data for future analysis and prediction model optimization.

If the current status is A2 and the status is further confirmed to be B1, an early warning will be immediately issued to the public through multiple channels including broadcasting, mobile phone applications, and social media, the evacuation plan will be activated, emergency rescue teams and supplies will be deployed, and the situation will be continuously monitored and information will be updated in real time.

If the current status is A2 and it is further determined that the status is B2, the collected data will be deeply analyzed to determine the accuracy of the risk assessment model. Based on the latest assessment, the latest assessment results will be issued to the public, the issued warning will be lifted, and the existing emergency plans and strategies will be reviewed and adjusted based on the latest risk assessment results.

If the current status is A3, emergency measures will be taken immediately, the evacuation plan will be implemented immediately, residents in high-risk areas will be evacuated first, the safety of the evacuation routes will be ensured, and assembly points for rescue teams and equipment will be set in advance.

Example 2

Referring to Figure 2, an embodiment of the present invention provides a system for a method for predicting geological disaster failure of a transmission tower based on a finite element model. The system for predicting geological disaster failure of a transmission tower based on a finite element model includes a data acquisition module, a finite element model construction module, a disaster prediction module and a risk assessment module.

The data acquisition module is responsible for collecting transmission tower related data, including structural design, location information, geotechnical data, slope data and rainfall data.

The finite element model building module is responsible for building a finite element model using the collected data.

The disaster prediction module predicts the possibility of rainfall-induced landslide geological disasters.

The risk assessment module is responsible for using the risk assessment model to further determine the possibility of disasters when a possible rainfall landslide geological disaster is predicted.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

The logic and/or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Example 3

In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment. The present invention is simulated by high-voltage transmission line iron tower foundation geological disaster failure model analysis software, which collects the structure and geological data of the Nanning tower (Nanning Nanli II line 220kV 42# base tower) and Laibin tower (Laibin Pengqing line 220kV 35# base tower) in Guangxi, and simulates and calculates the stability of the transmission tower foundation under the action of rainfall-induced landslide disasters for these two towers and their foundations and slopes. This software can be expanded to other towers, as long as the tower body and geological condition data are obtained, and the corresponding working condition library is established according to the data.

Failure model analysis software can be divided into three modules:

1. Input module

In the input module, you need to select the tower to be analyzed, enter parameters such as rainfall time and rainfall rate, set the rainfall rate to 1mm/h-16mm/h, and the rainfall time to 1h-72h.

2. Database module

In the database module, the structural design data of the target transmission tower is first collected, and a finite element model of the transmission tower is established in ABAQUS finite element according to the structural drawings of the target tower. Then, a soil model is established according to the rock and soil geological data of the transmission tower foundation, and the inclination angle of the slope is set to establish a transmission tower structure-foundation-rock and soil coupling model. Considering different rainfall rates and rainfall durations, the displacement and deformation of the transmission tower structure and foundation under different working conditions, as well as the stress-strain cloud map, are simulated to construct a database of concrete foundation instability of high-voltage transmission lines under the action of rainfall-induced landslide geological disasters.

3. Data output module

In the data output module, according to the parameter input conditions, the displacement deformation and stress-strain data of the transmission tower structure and the surrounding control nodes of the foundation under the corresponding working conditions are selected. Not only can the data be presented in the form of EXCEL, but also the data can be visualized, as shown in Figure 3.

The failure model analysis software can simulate the stress state and displacement deformation of the target transmission tower structure foundation and surrounding geotechnical control nodes under the action of rainfall-induced landslide geological disasters. The software takes into account the rainfall rate and rainfall duration. For different towers, based on the provided geotechnical survey data and considering different slope angles, a transmission tower structure-foundation-rock and soil coupling model is established to simulate the displacement deformation and stress state of the tower structure-foundation-rock and soil under different rainfall conditions, providing a reference for the safe operation of the transmission tower structure.

Failure model analysis software can output the following.

1. Displacement and deformation data of the transmission tower foundation (JD1-JD4) and the tower top (TD) (but not limited to these points).

2. Horizontal and vertical displacement of rock and soil around the transmission tower foundation.

3. Transmission tower-foundation-rock and soil stress-strain cloud diagram.

Figure 4 shows the main interface of the failure analysis software, with a menu bar on the top, parameter input on the left, calculation result area on the lower left, and graphic display area on the right, which display key point graph, strain cloud graph, displacement x cloud graph, and displacement z cloud graph respectively.

The failure analysis software database is based on ABAQUS finite element software. The deformation calculation of the tower-foundation-rock-soil coupling model under different slope angles and different rainfall amounts is carried out, and the calculation results are stored in the database. According to the on-site survey and related information, the soil parameters of the transmission tower foundation are obtained, and the unit parameters have been input according to the data obtained from the actual engineering project to improve the accuracy of the analysis.

The results calculated by the failure analysis software are displayed according to the key points. The key points are shown in Figure 5. It can be seen from Figure 5 that JD1-JD4 are the four key points of the transmission tower foundation. The failure analysis software can calculate the horizontal displacement x and vertical displacement z of the four foundation points under different working conditions.

TD is the key point on the top of the transmission tower and is used to analyze the displacement of the tower top.

In the parameter input area, select the corresponding transmission tower and enter parameters such as rainfall rate and rainfall duration for calculation.

The rainfall rate is the rainfall per hour, which can be selected from the range of 1 mm/h to 16 mm/h, corresponding to the daily precipitation of 24 mm/d to 384 mm/d.

The rainfall duration is input in hours. Since the calculated results show that there will be almost no sudden change in the soil during light rainfall, it is recommended to input at least 24 hours, 48 hours, or 72 hours. The time is encrypted after 72 hours.

The parameter input interface is shown in Figure 6 and Figure 7:

For tower selection, the geological data and tower body data currently obtained are for Nanning Tower and Bin Tower, so this software can currently calculate the models of these two towers.

Nanning tower corresponds to the 42# base tower (220kV) of Nanning Nanli II line

Laibin tower corresponds to Laibin Pengqing line 35# base tower (110kV) tower

The interface is shown in Figure 8.

After entering the parameters, click Execute and the software will automatically calculate and display the results.

The daily rainfall and total precipitation are displayed in the input area, as shown in Figure 9 below.

As shown in FIG10 , the numerical analysis results are displayed in the result area.

According to the key point diagram and the corresponding key point positions, the corresponding maximum stress, the maximum displacement of the four foundations of the tower base (JD1x, JD1z, JD2x, JD2z, JD3x, JD3z, JD4x, JD4z), and the displacement of the tower top (TDx, TDz) are calculated.

In the graphic display area, except for the key point graph which does not change, the other three graphs display the strain cloud graph, the horizontal displacement x cloud graph and the vertical displacement z cloud graph, as shown in Figure 11.

The strain contour shows the Von Mises stress in MPa.

Displacement X shows the displacement cloud along the horizontal direction of the slope, in meters.

Displacement Z shows the vertical displacement cloud map, unit is m.

It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims (10)

1. A method for predicting failure of transmission tower geological disasters based on a finite element model, characterized by comprising: Collect rainfall data and transmission tower related data to build a finite element model; Establish the coupling effect model of transmission tower based on finite element model; Prediction of rainfall-induced landslide geological disasters based on the coupling model of transmission towers; Take corresponding measures based on the prediction results of rainfall-landscape geological disasters. 2. The method for predicting failure of a transmission tower according to claim 1, wherein the transmission tower related data includes transmission tower structure design and location information, rock and soil geological data, and slope data; and the establishment of the finite element model includes optimizing the parameter settings of the finite element model by a deep learning algorithm; The finite element model is expressed as, Where D represents the transmission tower data, T represents rainfall data, G represents geotechnical data, S represents slope data, M represents historical disaster records, α represents the adjustment parameter, f(G, s) represents the function defined according to geotechnical data and slope data, a and b represent the upper and lower limits of the integral, and n represents the number of rainfall hours. 3. The method for predicting failure of transmission tower geological disasters based on finite element model according to claim 2, characterized in that: the deep learning algorithm is expressed as: Where W represents the weight parameter of the deep learning model, X represents the input data set, represents the parameters in the finite element model, Y represents the geological data, σ represents the activation function, g(X _i ) represents the preprocessing function for the input data X, h(Y, t) represents the integral processing of Y, t represents the integral variable, and z represents the number of input data. 4. The method for predicting failure of a transmission tower due to geological disasters based on a finite element model as claimed in claim 3 is characterized in that: the establishment of the coupling action model of the transmission tower comprises: after establishing the finite element model of the transmission tower, establishing a soil model according to the rock and soil geological data of the foundation of the transmission tower, and setting the inclination angle of the slope to establish the coupling action model of the transmission tower, which is expressed as: Among them, Y represents the risk assessment function, x represents the location, P represents the finite element model, G(x) represents the rock and soil geological data function, δ represents the adjustment parameter, which controls the influence of the transmission tower structure parameters on risk assessment, β represents the adjustment parameter, θ represents the slope inclination, and R(x) represents the rainfall function. 5. The method for predicting failure of a transmission tower due to geological disasters based on a finite element model as claimed in claim 4, wherein the prediction of a landslide due to rainfall includes: if the prediction function Y is less than 0.3, it indicates that the landslide due to rainfall will not occur, which is recorded as A1; if the prediction function is 0.3≤Y≤0.7, it indicates that the landslide due to rainfall may occur, which is recorded as A2; if the prediction function Y＞0.7, it indicates that the landslide due to rainfall will occur, which is recorded as A3; When rainfall-landslide geological disasters are likely to occur, they are further determined through risk assessment models. 6. The method for predicting failure of transmission tower geological disasters based on finite element model according to claim 5, characterized in that: the risk assessment model is expressed as: Among them, Q represents the risk assessment value of rainfall landslide geological disasters, T(u) represents the rainfall function, and H represents the geological stability index; If Q is greater than the threshold, it is further determined that the rainfall landslide geological disaster will occur, which is recorded as B1; If Q is less than the threshold, it is further determined that the rainfall landslide geological disaster will not occur, which is recorded as B2. 7. The method for predicting failure of transmission towers due to geological disasters based on a finite element model as claimed in claim 6, characterized in that: the taking of corresponding measures includes, if the current state is A1, continuing to use advanced sensors or satellite data to conduct conventional geological and meteorological monitoring in real time, while checking and maintaining existing safety measures and early warning systems, collecting and analyzing geological, meteorological and structural data for future analysis and prediction model optimization; If the current status is A2 and it is further confirmed that the status is B1, an alert will be immediately issued to the public through multiple channels including broadcasting, mobile phone applications, and social media, and an evacuation plan will be initiated, emergency rescue teams and supplies will be deployed, and the situation will be continuously monitored and updated in real time; If the current status is A2 and it is further determined that the status is B2, the collected data will be analyzed in depth to determine the accuracy of the risk assessment model. Based on the latest assessment, the latest assessment results will be issued to the public, the issued warning will be lifted, and the existing emergency plans and strategies will be reviewed and adjusted based on the latest risk assessment results; If the current status is A3, emergency measures will be taken immediately, the evacuation plan will be implemented immediately, residents in high-risk areas will be evacuated first, the safety of the evacuation routes will be ensured, and assembly points for rescue teams and equipment will be set in advance. 8. A system using the transmission tower geological disaster failure prediction method based on the finite element model as claimed in any one of claims 1 to 7, characterized in that it comprises a data acquisition module, a finite element model construction module, a disaster prediction module and a risk assessment module; The data acquisition module is responsible for collecting transmission tower related data, including structural design, location information, geotechnical data, slope data and rainfall data; The finite element model building module is responsible for building a finite element model using the collected data; The disaster prediction module predicts the possibility of rainfall landslide geological disasters; The risk assessment module is responsible for using the risk assessment model to further determine the possibility of the disaster when a possible rainfall landslide geological disaster is predicted. 9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method for predicting failure of transmission tower geological disasters based on a finite element model as described in any one of claims 1 to 7 when executing the computer program. 10. A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for predicting failure of a transmission tower geological disaster based on a finite element model as described in any one of claims 1 to 7 are implemented.