CN117973131A - Finite element model-based power transmission tower geological disaster failure prediction method and system - Google Patents
Finite element model-based power transmission tower geological disaster failure prediction method and system Download PDFInfo
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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
Technical Field
The invention relates to the technical field of power engineering, in particular to a method and a system for predicting geological disaster failure of a power transmission tower based on a finite element model.
Background
In the field of power engineering, the stability of a transmission tower is critical for reliable operation of a power system. In recent years, with climate change and environmental deterioration, geological disasters such as rainfall landslide have increasingly threatened power transmission towers. Traditionally, stability analysis of transmission towers has relied primarily on empirical judgment and simple static computational models. These methods tend to be inadequate in 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 a new solution to such problems. The finite element method can provide more accurate structural stress and deformation analysis, but its application in geological disaster prediction is also relatively limited, especially in integrating complex geological data and meteorological data.
The prior art mainly focuses on predicting the stability of a power transmission tower by using a static model, and ignores the influence of dynamic environmental factors. Furthermore, these techniques often fail to fully utilize deep learning algorithms to optimize model parameters, resulting in limited prediction accuracy. On the other hand, the existing method has low efficiency in processing a large amount of complex data related to the power transmission tower, and cannot respond to environmental changes in real time. For example, existing systems often fail to quickly adjust their predictions when rainfall changes. In addition, the prior art has shortcomings in preventive measures before occurrence of disasters and emergency response after occurrence of disasters. In these respects, the development of finite element models and the integration of deep learning algorithms provide new possibilities for improving prediction accuracy and response speed.
Aiming at the defects, the invention provides a method and a system for predicting geological disaster failure of a power transmission tower based on a finite element model. According to the method, rainfall data, power transmission tower structural design, rock-soil geological data and slope data are integrated, and finite element models are used for optimization in combination with a deep learning algorithm, so that the prediction accuracy of the stability of the power transmission tower in a complex geological environment is improved. In addition, the system can dynamically adjust the prediction model according to the real-time environmental change, and rapidly respond to the possibility of geological disasters such as rainfall landslide and the like. The innovation of the method is that the method not only improves the accuracy of prediction, but also enhances the flexibility and response speed of the system.
The invention also comprises a complete set of risk assessment and emergency response mechanisms. According to the result 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 conventional monitoring, public early warning release, emergency evacuation plan starting and the like. The mechanism not only enhances the effectiveness of preventive measures, but also provides reliable decision support for emergency management when disasters occur. In addition, the design of the system considers the compatibility with the existing power system and the implementation cost, and ensures the feasibility and the economy of the system in practical application. In general, the invention has obvious advantages in improving accuracy and response speed of geological disaster prediction of the power transmission tower, and provides an innovative and practical solution for stable operation and disaster risk management of the power system.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: how to improve the accuracy and response speed of stability prediction of the power transmission tower under complex geological environments, particularly under the condition of geological disasters such as rainfall landslide and the like.
In order to solve the technical problems, the invention provides the following technical scheme: the method for predicting the geological disaster failure of the power transmission tower based on the finite element model comprises 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.
As a preferable scheme of the power transmission tower geological disaster failure prediction method based on the finite element model, the invention comprises the following steps: the power transmission tower related data comprise power transmission tower structural design, position information, rock-soil geological data and slope data, and the finite element model establishment comprises the step of optimizing parameter setting of the finite element model through a deep learning algorithm.
The finite element model is represented as,
Wherein D represents transmission tower data, T represents rainfall data, G represents geotechnical geological data, S represents slope data, M represents historical disaster records, α represents adjustment parameters, f (G, S) represents functions defined according to the geotechnical geological data and the slope data, a, b represents upper and lower limits of integration, and n represents rainfall time.
As a preferable scheme of the power transmission tower geological disaster failure prediction method based on the finite element model, the invention comprises the following steps: the deep learning algorithm is represented as,
Wherein W represents a weight parameter of the deep learning model, X represents an input dataset, represents a parameter in the finite element model, Y represents geological data, σ represents an activation function, g (X i) represents a preprocessing function for the input data X, h (Y, t) represents an integration process for Y, t represents an integration variable, and z represents the number of input data.
As a preferable scheme of the power transmission tower geological disaster failure prediction method based on the finite element model, the invention comprises the following steps: the establishment of the coupling action model of the power transmission tower comprises the steps of establishing a finite element model of the power transmission tower, establishing a soil body model according to rock-soil geological data of a foundation of the power transmission tower, setting the inclination angle of a side slope, establishing the coupling action model of the power transmission tower, and representing as,
Wherein Y represents a risk evaluation function, x represents a position, P represents a finite element model, G (x) represents a geotechnical geological data function, delta represents an adjustment parameter, beta represents the adjustment parameter, theta represents a slope inclination angle, and R (x) represents a rainfall function.
As a preferable scheme of the power transmission tower geological disaster failure prediction method based on the finite element model, the invention comprises the following steps: the rainfall landslide geological disaster prediction method comprises the steps that if a prediction function Y is smaller than 0.3, the rainfall landslide geological disaster is indicated to be unnecessary, A1 is marked, if the prediction function Y is more than or equal to 0.3 and less than or equal to 0.7, the rainfall landslide geological disaster is indicated to be possible to occur, A2 is marked, and if the prediction function Y is more than 0.7, the rainfall landslide geological disaster is indicated to be necessary to occur, and A3 is marked.
And when rainfall landslide geological disasters can occur, further determining through a risk assessment model.
As a preferable scheme of the power transmission tower geological disaster failure prediction method based on the finite element model, the invention comprises the following steps: the risk assessment model is expressed as,
Wherein Q represents a risk assessment value of rainfall landslide geological disaster, T (u) represents a rainfall function, and H represents a geological stability index.
If Q is larger than the threshold value, further determining that the rainfall landslide geological disaster must occur, and marking as B1.
If Q is smaller than the threshold value, further determining that the rainfall landslide geological disaster does not occur, and recording as B2.
As a preferable scheme of the power transmission tower geological disaster failure prediction method based on the finite element model, the invention comprises the following steps: and if the current state is A1, continuing to use advanced sensor or satellite data to perform conventional geological and meteorological monitoring in real time, checking and maintaining the existing safety measures and early warning system, and collecting and analyzing geological, meteorological and structural data for future analysis and prediction model optimization.
If the current state is A2 and the state is B1, immediately sending early warning to the public through various channels including broadcasting, mobile phone application and social media, starting an evacuation plan, deploying emergency rescue teams and materials, continuously monitoring the condition development, and updating information in real time.
If the current state is A2 and the state is B2, carrying out deep analysis on the acquired data to determine the accuracy of a risk assessment model, sending a latest assessment result to the public according to the latest assessment, removing the sent early warning, and reviewing and adjusting the existing emergency plan and strategy according to the latest risk assessment result.
If the current state is A3, immediately taking emergency measures, immediately implementing evacuation plan, preferentially evacuating residents in the high-risk area, ensuring the safety of evacuation routes, and presetting a rescue team and equipment collecting point.
The invention also aims to provide a power transmission tower geological disaster failure prediction system based on a finite element model, which can solve the problems of the prior art in the aspects of dynamic environment factor response, data processing efficiency and disaster early warning accuracy by integrating a finite element model optimized by a deep learning algorithm, real-time environment data processing capability and a comprehensive risk assessment mechanism.
In order to solve the technical problems, the invention provides the following technical scheme: the power transmission tower geological disaster failure prediction system based on the finite element model 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 relevant data of the power transmission tower, including structural design, position information, geotechnical and geological 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 further determining the likelihood of a rainfall landslide geological disaster using a risk assessment model when it is predicted that the disaster will occur.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the power transmission tower geological disaster failure prediction method based on a finite element model as described above when the computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a power transmission tower geological disaster failure prediction method based on a finite element model as described above.
The invention has the beneficial effects that: 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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a method for predicting geological disaster failure of a power transmission tower based on a finite element model according to a first embodiment of the present invention.
Fig. 2 is an overall frame diagram of a power transmission tower geological disaster failure prediction system based on a finite element model according to a second embodiment of the present invention.
Fig. 3 is a flowchart of failure model analysis software of a method for predicting failure of geological disaster of a power transmission tower 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 of a power transmission tower geological disaster failure prediction method based on a finite element model according to a third embodiment of the present invention.
Fig. 5 is a key point interface of a power transmission tower geological disaster failure prediction method based on a finite element model according to a third embodiment of the present invention.
Fig. 6 is an input-related parameter interface of a power transmission tower geological disaster failure prediction method based on a finite element model according to a third embodiment of the present invention.
Fig. 7 is an input-related parameter interface of a power transmission tower geological disaster failure prediction method based on a finite element model according to a third embodiment of the present invention.
Fig. 8 is two base tower selection diagrams of a method for predicting geological disaster failure of a power transmission tower 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 rainfall of a method for predicting geological disaster failure of a power transmission tower based on a finite element model according to a third embodiment of the present invention.
Fig. 10 is a graph of a numerical analysis result of a method for predicting geological disaster failure of a power transmission tower based on a finite element model according to a third embodiment of the present invention.
Fig. 11 is a displacement and strain cloud chart of a power transmission tower geological disaster failure prediction method based on a finite element model according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, a method for predicting geological disaster failure of a power transmission tower based on a finite element model is provided, which is characterized in that:
s1: and collecting rainfall data and related data of the power transmission tower, and establishing a finite element model.
The power transmission tower related data comprise power transmission tower structural design, position information, rock-soil geological data and slope data, and the finite element model establishment comprises the step of optimizing parameter setting of the finite element model through a deep learning algorithm.
The finite element model is represented as,
Wherein D represents transmission tower data, T represents rainfall data, G represents geotechnical geological data, S represents slope data, M represents historical disaster records, α represents adjustment parameters, f (G, S) represents functions defined according to the geotechnical geological data and the slope data, a, b represents upper and lower limits of integration, and n represents rainfall time.
The deep learning algorithm is represented as,
Wherein W represents a weight parameter of the deep learning model, X represents an input dataset, represents a parameter in the finite element model, Y represents geological data, σ represents an activation function, g (X i) represents a preprocessing function for the input data X, h (Y, t) represents an integration process for Y, t represents an integration variable, and z represents the number of input data.
Further, the finite element model in the invention utilizes the collected data to analyze the response of the power transmission tower under different geological conditions through a computer simulation technology. And model parameters are optimized by using a deep learning algorithm, so that the accuracy and applicability of the model are improved. The innovation of the step is that a novel visual angle is provided for stability analysis of the power transmission tower by combining a traditional finite element analysis method and a modern machine learning technology, the specific step of optimizing through a deep learning algorithm comprises the steps of collecting data related to a finite element model, preprocessing the collected data, determining key characteristics affecting output of the finite element model, constructing a deep learning model, predicting characteristics of optimized materials of the power transmission tower through the deep learning model, and determining most suitable boundary conditions and load setting.
S2: and establishing a coupling action model of the power transmission tower based on the finite element model.
Establishing a coupling action model of the power transmission tower comprises establishing a finite element model of the power transmission tower, establishing a soil body model according to rock-soil geological data of a foundation of the power transmission tower, setting an inclination angle of a side slope, establishing a coupling action model of the power transmission tower, which is expressed as,
Wherein Y represents a risk evaluation function, x represents a position, P represents a finite element model, G (x) represents a geotechnical geological data function, delta represents an adjustment parameter, beta represents the adjustment parameter, theta represents a slope inclination angle, and R (x) represents a rainfall function.
Further, this step creates a more comprehensive coupling model by taking into account the interactions between the structure of the transmission tower and its underlying earth. The model not only comprises static structural analysis, but also considers dynamic environmental factors (such as rainfall), thereby providing a more comprehensive view angle for prediction. The coupling model can be established to more accurately predict the response of the power transmission tower in a specific environment, particularly in extreme weather conditions.
S3: and predicting rainfall landslide geological disasters based on the coupling action model of the power transmission tower.
Predicting the rainfall landslide geological disaster comprises the steps of marking A1 when the prediction function Y is smaller than 0.3, marking A2 when the prediction function Y is larger than or equal to 0.3 and smaller than or equal to 0.7, and marking A3 when the prediction function Y is larger than 0.7.
And when rainfall landslide geological disasters can occur, further determining through a risk assessment model.
The risk assessment model is represented as,
Wherein Q represents a risk assessment value of rainfall landslide geological disaster, T (u) represents a rainfall function, and H represents a geological stability index.
If Q is larger than the threshold value, further determining that the rainfall landslide geological disaster must occur, and marking as B1.
If Q is smaller than the threshold value, further determining that the rainfall landslide geological disaster does not occur, and recording as B2.
Further, after predicting a possible geological disaster, the invention further determines the possibility and severity of the disaster through a risk assessment model. The model combines the geological stability index and rainfall data, and provides a basis for more accurate risk judgment. The method has great practical application value for disaster emergency response and resource allocation.
S4: and taking corresponding measures according to rainfall landslide geological disaster prediction results.
Taking corresponding measures includes, if the current state is A1, continuing to use advanced sensor or satellite data to conduct conventional geological and meteorological monitoring in real time, checking and maintaining the existing safety measures and early warning systems, collecting and analyzing geological, meteorological and structural data, and optimizing future analysis and prediction models.
If the current state is A2 and the state is B1, immediately sending early warning to the public through various channels including broadcasting, mobile phone application and social media, starting an evacuation plan, deploying emergency rescue teams and materials, continuously monitoring the condition development, and updating information in real time.
If the current state is A2 and the state is B2, carrying out deep analysis on the acquired data to determine the accuracy of a risk assessment model, sending a latest assessment result to the public according to the latest assessment, removing the sent early warning, and reviewing and adjusting the existing emergency plan and strategy according to the latest risk assessment result.
If the current state is A3, immediately taking emergency measures, immediately implementing evacuation plan, preferentially evacuating residents in the high-risk area, ensuring the safety of evacuation routes, and presetting a rescue team and equipment collecting point.
Example 2
Referring to fig. 2, for one embodiment of the present invention, a system for predicting a geological disaster failure of a power transmission tower based on a finite element model is provided, where the power transmission tower geological disaster failure prediction system based on the 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 relevant data of the power transmission tower, including structural design, position information, geotechnical and geological 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 further determining the likelihood of a rainfall landslide geological disaster using a risk assessment model when it is predicted that the disaster will occur.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 3
In this embodiment, in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments. According to the simulation method, simulation is carried out through high-voltage transmission line iron tower foundation geological disaster failure model analysis software, the software collects structures and geological data of a Nanning tower (Nanning Nanli II line 220kV 42# base tower) and a guest tower (guest Kangqing line 220kV 35# base tower) in Guangxi region, and simulation and calculation analysis of transmission tower foundation stability under the action of rainfall landslide disasters are carried out on the two towers and the foundation and side slopes of the two towers. The software can be extended to other towers, and can be realized by only obtaining the tower body and geological condition data and establishing a corresponding working condition library according to the data.
The failure model analysis software can be divided into three modules:
1. Input module
In the input module, a base tower to be analyzed is required to be selected, parameters such as rainfall time, rainfall rate and the like are input, the rainfall rate is set to be 1-16 mm/h, and the rainfall time is set to be 1-72 h.
2. Database module
And in a database module, firstly, collecting target power transmission tower structure design data, establishing a power transmission tower finite element model in an ABAQUS finite element according to a target tower structure drawing, then establishing a soil model according to rock-soil geological data of a power transmission tower foundation, setting the inclination angle of a side slope, establishing a power transmission tower structure-foundation-rock-soil coupling action model, and simulating displacement deformation of the power transmission tower structure and foundation under different working conditions under different rainfall rates and rainfall duration conditions and constructing a high-voltage power transmission line concrete foundation instability database under the action of rainfall induced landslide geological disasters by considering stress strain cloud pictures.
3. Data output module
And in the data output module, displacement deformation of the power transmission tower structure and the basic peripheral control nodes under corresponding working conditions and stress strain data are selected according to parameter input conditions, so that the data can be displayed in the form of EXCEL and the like, and the data can be visualized as shown in fig. 3.
The failure model analysis software can simulate the stress state and displacement deformation of a target power transmission tower structure foundation and surrounding rock-soil control nodes under the action of rainfall induced landslide geological disasters, the software considers rainfall rate and rainfall duration, and according to provided rock-soil geological investigation data and different slope angles, a power transmission tower structure-foundation-rock-soil coupling action model is established to simulate the displacement deformation and stress state of the power transmission tower structure-foundation-rock-soil under different rainfall conditions according to different provided rock-soil geological investigation data, so that a reference basis is provided for safe operation of the power transmission tower structure.
The failure model analysis software may output the following.
1. Displacement deformation data of the transmission tower foundations (JD 1-JD 4) and the tower Top (TD), but not limited to these points.
2. And (3) horizontal displacement and vertical displacement of rock soil around the foundation of the power transmission tower.
3. Power transmission tower-foundation-geotechnical stress strain cloud picture.
Fig. 4 shows a main interface of failure analysis software, wherein the upper part is a menu bar, the left side is a parameter input, the left lower side is a calculation result area, the right side is a graphic display area, and key point diagrams, strain cloud diagrams, displacement x cloud diagrams and displacement z cloud diagrams are respectively displayed.
The failure analysis software database is based on ABAQUS finite element software, deformation calculation is performed on tower-foundation-rock-soil coupling action models under different slope angles and different rainfall capacities, and calculation results are stored in the database. According to the on-site investigation and related data, soil parameters of the foundation of the power transmission tower are obtained, and unit parameters are input according to the data obtained from the actual engineering project, so that the analysis accuracy is improved.
The calculated result of the failure analysis software is displayed according to key points, the key points are shown in fig. 5, the JD1-JD4 are 4 key points of the foundation of the power transmission tower, and the failure analysis software can calculate the horizontal displacement x and the vertical displacement z of four foundation points under different working conditions.
TD is a key point of the top of the power transmission tower and is used for analyzing the displacement of the top of the power transmission tower.
And selecting a corresponding power transmission tower in the parameter input area, and inputting parameters such as rainfall rate, rainfall duration and the like for calculation.
The rainfall rate is a rainfall per hour, selected from the range of 1mm/h to 16mm/h, corresponding to a daily rainfall of 24mm/d to 384mm/d.
The rainfall time is input in hours, and the calculation result hardly causes the mutation of soil body in small rainfall, so the minimum input is recommended to be 24 hours, 48 hours and 72 hours, and the encryption treatment is carried out after 72 hours.
The parameter input interfaces are shown in fig. 6 and 7:
The tower is selected, and the geological data and tower body data obtained at present are Nanning tower and Bining tower, so that the software can calculate models of the two towers at present.
The Nanning tower corresponds to Nanning Nanli II line 42# base tower (220 kV) tower
The guest tower corresponds to a guest awning celebration line 35# base tower (110 kV) tower
The interface is as in fig. 8.
Clicking and executing after the parameter input is finished, and automatically calculating and displaying a result by software.
The daily rainfall and total precipitation are displayed in the input area as shown in fig. 9 below.
As shown in fig. 10, the numerical analysis results are displayed in the result area.
According to the key point diagram, the corresponding maximum stress, the maximum displacement (JD 1x, JD1z, JD2x, JD2z, JD3x, JD3z, JD4x, JD4 z) of the tower foundation 4 bases, and the displacement (TDx, TDz) of the tower top are calculated.
In the graphic display area, except for the key point diagram, the other 3 diagrams show strain cloud diagrams, horizontal displacement x cloud diagrams and vertical displacement z cloud diagrams, and see fig. 11.
Strain cloud shows Von Mises stress in MPa.
Displacement X shows the displacement of the cloud along the slope horizontal direction, in m.
The displacement Z shows the vertical displacement cloud in m.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The method for predicting geological disaster failure of the power transmission tower based on the finite element model is characterized by comprising the following steps of:
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.
2. The method for predicting geological disaster failure of power transmission tower based on finite element model as claimed in claim 1, wherein the method comprises the following steps: the power transmission tower related data comprise power transmission tower structural design and position information, rock-soil geological data and slope data, and the finite element model establishment comprises the step of optimizing parameter setting of the finite element model through a deep learning algorithm;
The finite element model is represented as,
Wherein D represents transmission tower data, T represents rainfall data, G represents geotechnical geological data, S represents slope data, M represents historical disaster records, α represents adjustment parameters, f (G, S) represents functions defined according to the geotechnical geological data and the slope data, a, b represents upper and lower limits of integration, and n represents rainfall time.
3. The method for predicting geological disaster failure of power transmission tower based on finite element model as claimed in claim 2, wherein the method comprises the following steps: the deep learning algorithm is represented as,
Wherein W represents a weight parameter of the deep learning model, X represents an input dataset, represents a parameter in the finite element model, Y represents geological data, σ represents an activation function, g (X i) represents a preprocessing function for the input data X, h (Y, t) represents an integration process for Y, t represents an integration variable, and z represents the number of input data.
4. A method for predicting geological disaster failure of power transmission tower based on finite element model as claimed in claim 3, wherein: the establishment of the coupling action model of the power transmission tower comprises the steps of establishing a finite element model of the power transmission tower, establishing a soil body model according to rock-soil geological data of a foundation of the power transmission tower, setting the inclination angle of a side slope, establishing the coupling action model of the power transmission tower, and representing as,
Wherein Y represents a risk evaluation function, x represents a position, P represents a finite element model, G (x) represents a geotechnical geological data function, delta represents an adjustment parameter, beta represents the adjustment parameter, theta represents a slope inclination angle, and R (x) represents a rainfall function.
5. The method for predicting geological disaster failure of power transmission tower based on finite element model as claimed in claim 4, wherein the method comprises the following steps: the rainfall landslide geological disaster prediction method comprises the steps that if a prediction function Y is smaller than 0.3, the rainfall landslide geological disaster is indicated to be unnecessary, A1 is marked, if the prediction function Y is more than or equal to 0.3 and less than or equal to 0.7, the rainfall landslide geological disaster is indicated to be possible to occur, A2 is marked, and if the prediction function Y is more than 0.7, the rainfall landslide geological disaster is indicated to be necessary to occur, and A3 is marked;
And when rainfall landslide geological disasters can occur, further determining through a risk assessment model.
6. The method for predicting geological disaster failure of power transmission tower based on finite element model as claimed in claim 5, wherein the method comprises the following steps: the risk assessment model is expressed as,
Wherein Q represents a risk assessment value of rainfall landslide geological disasters, T (u) represents a rainfall function, and H represents a geological stability index;
if Q is greater than the threshold value, determining that the rainfall landslide geological disaster is necessary to occur, and marking as B1;
If Q is smaller than the threshold value, further determining that the rainfall landslide geological disaster does not occur, and recording as B2.
7. The method for predicting geological disaster failure of power transmission tower based on finite element model as claimed in claim 6, wherein the method comprises the following steps: if the current state is A1, continuing to use advanced sensor or satellite data to perform conventional geological and meteorological monitoring in real time, checking and maintaining the 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 state is A2 and the state is B1, immediately sending early warning to the public through various channels including broadcasting, mobile phone application and social media, starting an evacuation plan, deploying emergency rescue teams and materials, continuously monitoring the condition development, and updating information in real time;
If the current state is A2 and the state is B2, carrying out in-depth analysis on the acquired data to determine the accuracy of a risk assessment model, sending a latest assessment result to the public according to the latest assessment, removing the sent early warning, and reviewing and adjusting the existing emergency plan and strategy according to the latest risk assessment result;
If the current state is A3, immediately taking emergency measures, immediately implementing evacuation plan, preferentially evacuating residents in the high-risk area, ensuring the safety of evacuation routes, and presetting a rescue team and equipment collecting point.
8. A system employing the finite element model-based power transmission tower geological disaster failure prediction method as claimed in any one of claims 1to 7, characterized in that: the system 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 relevant data of the power transmission tower, including structural design, position information, geotechnical and geological 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 further determining the likelihood of a rainfall landslide geological disaster using a risk assessment model when it is predicted that the disaster will occur.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the finite element model-based transmission tower geological disaster failure prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the finite element model-based power transmission tower geological disaster failure prediction method according to any one of claims 1 to 7.
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