A method for evaluating post-earthquake failure state of a casing and related equipment

Abstract

The embodiment of the application discloses a casing post-earthquake failure state evaluation method and related equipment. The method comprises the following steps: obtaining seismic response data according to a casing simulation model, wherein the seismic response data comprises acceleration peak values of key positions and root stress peak values; determining failure data according to target requirements; constructing sample data based on the failure data and the seismic response data to obtain a post-earthquake evaluation model; and inputting acceleration data of the key positions monitored in an earthquake into the post-earthquake evaluation model to obtain a post-earthquake failure state of the casing. The casing post-earthquake failure evaluation method is not subject to subjective interference, has high evaluation speed and accuracy, can arrange sensors according to actual conditions of the casing, can input acceleration data obtained by monitoring into the model after an earthquake to quickly obtain failure evaluation results, can guide post-earthquake maintenance, and can effectively estimate the failure state of the casing after an earthquake, thereby playing a role in quickly guiding emergency repair.

Description

Technical Field

[0001] This invention relates to the field of seismic assessment technology for electrical equipment, and in particular to a method and related equipment for assessing the post-earthquake failure status of bushings. Background Technology

[0002] In the field of seismic resistance for electrical equipment, most scholars are dedicated to research and work on seismic performance analysis and vulnerability assessment. This research and work is mainly used for pre-earthquake design, reinforcement, and vibration reduction studies to reduce the seismic response of electrical equipment and maintain its safety. However, after an earthquake, it is still necessary to detect and determine whether support-type equipment can meet operational requirements and whether cracks have appeared to ensure that the equipment can continue to operate normally.

[0003] Currently, numerous assessment methods exist in engineering projects, including expert evaluation and sensor monitoring. However, these methods present the following problems when applied to electrical equipment: expert evaluations are somewhat subjective; strain sensors are affected by the electromagnetic fields of electrical equipment; substations have numerous electrical devices, making post-earthquake simulation calculations time-consuming and complex; and support structures such as bushings are quite tall, making it difficult to adequately deploy sensors.

[0004] Therefore, it is of great significance to develop a faster and more accurate post-earthquake assessment method for casing equipment. Summary of the Invention

[0005] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0006] To provide a reliable method for assessing the post-earthquake failure state of casing, in a first aspect, the present invention proposes a method for assessing the post-earthquake failure state of casing, the method comprising:

[0007] Seismic response data is obtained from the casing simulation model, wherein the seismic response data includes peak acceleration at key locations and peak stress at the root.

[0008] Determine the failure data based on the target requirements;

[0009] Based on the failure data and the seismic response data, sample data is constructed to obtain a post-earthquake assessment model;

[0010] Acceleration data from key locations monitored during the earthquake are input into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

[0011] Optionally, obtaining seismic response data based on the casing simulation model includes:

[0012] A casing simulation model was constructed based on finite element method.

[0013] Seismic response data for each seismic action was obtained by using a batch of natural earthquake actions as input to the simulation model.

[0014] Optionally, the target requirements include response specifications, response standards, and engineering requirements;

[0015] The process of determining failure data according to target requirements includes:

[0016] The safety factor is determined based on the response specifications, the response standards, and the engineering requirements.

[0017] The failure threshold value is determined based on the aforementioned safety factor;

[0018] Failure data is determined based on the aforementioned failure threshold.

[0019] Optionally, the step of constructing sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model includes:

[0020] The peak acceleration and its corresponding failure data constitute the sample data;

[0021] A support vector machine is trained based on the sample data to form adaptive hyperparameters;

[0022] The effectiveness of the adaptive hyperparameters was verified using the sample data to obtain a post-earthquake assessment model.

[0023] Optionally, the sample data includes a large proportion of sample data and a small proportion of sample data;

[0024] The step of training a support vector machine based on the sample data to form adaptive hyperparameters includes:

[0025] The large-scale sample data is used to train the support vector machine to improve the internal structure of the support vector machine algorithm and form adaptive hyperparameters;

[0026] Optionally, the step of using the sample data to verify the effectiveness of the adaptive hyperparameters to obtain the post-earthquake assessment model includes:

[0027] The effectiveness of the adaptive hyperparameters was verified using the small-scale sample data to obtain a post-earthquake assessment model.

[0028] Optionally, inputting acceleration data from key locations monitored during the earthquake into the post-earthquake assessment model to obtain the post-earthquake failure status of the casing includes:

[0029] Obtain the peak acceleration data of key monitoring locations during the earthquake;

[0030] The peak acceleration is input into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

[0031] Secondly, the present invention also proposes a casing post-earthquake failure state assessment device, comprising:

[0032] The first acquisition unit is used to acquire seismic response data based on the casing simulation model, wherein the seismic response data includes peak acceleration at key locations and peak stress at the root.

[0033] The determination unit is used to determine failure data according to target requirements;

[0034] The second acquisition unit is used to construct sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model.

[0035] The third acquisition unit is used to input the acceleration data of key locations monitored during the earthquake into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

[0036] Thirdly, the present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of any of the bushing post-earthquake failure state assessment methods of the first aspect described above.

[0037] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the bushing post-earthquake failure state assessment method of any of the above claims in the first aspect.

[0038] Implementing the embodiments of the present invention will have the following beneficial effects:

[0039] The method provided in this application uses finite element method (FEM) technology to establish a casing simulation model and inputs a large number of seismic ground motions to calculate the seismic response, including peak acceleration response and peak stress response at key locations. A safety factor is determined according to relevant specifications and engineering requirements. Each peak stress response is considered to form a failure index, resulting in a large amount of 0 / 1 failure data. A set of ground motions corresponds to a set of peak accelerations and one failure data point (0 / 1), forming a sample. When constructing the database, all samples are divided into two parts: a large-scale sample and a small-scale sample. The large-scale sample is used to construct a new support vector machine (SVM) algorithm structure, while the small-scale sample is used for detection, thus forming a post-earthquake assessment model. Using this method, after an earthquake, the acceleration data obtained from monitoring can be input into this model to quickly obtain failure assessment results, which can then guide post-earthquake maintenance. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] in:

[0042] Figure 1 A schematic flowchart of a method for assessing the post-earthquake failure state of a casing is provided in an embodiment of this application;

[0043] Figure 2 This is a schematic diagram of the process for batch acquisition of earthquake response data;

[0044] Figure 3 A schematic diagram illustrating the formation of a post-earthquake assessment model for casing failure.

[0045] Figure 4 Flowchart of post-earthquake assessment for casing failure status assessment model;

[0046] Figure 5 This is a schematic diagram of a typical monitoring position of the casing in one embodiment;

[0047] Figure 6 A schematic diagram of a casing post-earthquake failure state assessment device provided in this application embodiment;

[0048] Figure 7 A schematic diagram of an electronic device for assessing the post-earthquake failure state of a bushing, provided in an embodiment of this application;

[0049] The meanings of the labels in the attached figures are as follows: 1-Seismic response analysis process; 2-Batch data acquisition; 31-Large-scale data; 32-Small-scale data; 33-Support vector machine hyperparameters; 34-Evaluation model; 41-Acceleration data monitoring during earthquakes; 42-Failure state assessment results; M1-M4, monitoring locations. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0052] The purpose of this invention is to provide a method for rapid assessment of the failure state of bushings after an earthquake, in order to solve the problems of traditional earthquake resistance research and assessment mentioned in the background art: expert evaluation has a certain degree of subjectivity; strain sensors are affected by the electromagnetic field of electrical equipment, and the monitoring data cannot fully reflect the actual response; substation equipment has a large number of electrical devices, and post-earthquake simulation calculations are time-consuming and complex; bushings and other support-type equipment are relatively tall, making it difficult to fully arrange sensors.

[0053] like Figure 1 As shown, the method proposed in this application includes:

[0054] S110. Obtain seismic response data based on the casing simulation model, wherein the seismic response data includes peak acceleration at key locations and peak stress at the root.

[0055] Step S110 includes steps S1101 and S1102:

[0056] S1101. Constructing a sleeve simulation model based on finite element technology;

[0057] S1102. Using a batch of natural earthquake actions as input to the simulation model, the seismic response data under each earthquake action is obtained respectively.

[0058] For example, such as Figure 2Figure 1 illustrates the seismic response analysis process. A casing simulation model is constructed using finite element technology, such as ANSYS or CAE, to create a 1:1 scale model. Seismic actions from a batch of natural earthquakes collected from different regions are used as input to the simulation model. Analysis of the simulation model yields the seismic response data output by the casing simulation model under seismic action. This data includes peak acceleration at key locations and peak stress at the root. It should be noted that key locations can be, for example,... Figure 5 The four points are M1, M2, M3 and M4.

[0059] S120. Determine the failure data according to the target requirements;

[0060] The target requirements include response specifications, response standards, and engineering requirements. Step S120 includes steps S1201 to S1203:

[0061] S1201. Determine the safety factor based on the response specification, the response standard, and the engineering requirements;

[0062] S1202. Determine the failure threshold value based on the safety factor;

[0063] S1203. Determine failure data based on the failure threshold.

[0064] For example, such as Figure 2 As shown in Figure 2, the process of batch data acquisition involves determining a safety factor based on response specifications, standards, or engineering requirements, determining a failure threshold based on this safety factor, and then converting the stress response data into 0 / 1 failure data based on this failure threshold.

[0065] S130. Construct sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model;

[0066] Step S130 includes steps S1301 to S1303:

[0067] S1301. The peak acceleration and its corresponding failure data are used to form sample data;

[0068] S1302. Train a support vector machine based on the sample data to form adaptive hyperparameters;

[0069] S1303. Use the sample data to verify the effectiveness of the adaptive hyperparameters to obtain a post-earthquake assessment model.

[0070] In some examples, the sample data includes a large proportion of sample data and a small proportion of sample data; step S1202 includes S12021:

[0071] The large-scale sample data is used to train the support vector machine to improve the internal structure of the support vector machine algorithm and form adaptive hyperparameters;

[0072] Step S1203 includes S12031:

[0073] S12301. Validate the effectiveness of the adaptive hyperparameters using the small-scale sample data to obtain a post-earthquake assessment model.

[0074] For example, such as Figure 3 As shown, the corresponding peak acceleration response and failure data are used to form sample data, with one set of ground motions corresponding to one sample. The samples are divided into a large proportion and a small proportion. The former is used to improve the internal structure of the support vector machine algorithm and form adaptive hyperparameters, while the latter is used to verify the effectiveness of these hyperparameters. Based on this, the algorithm incorporating these hyperparameters is used to form a post-earthquake assessment model.

[0075] S140. Input the acceleration data of key locations monitored during the earthquake into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

[0076] Step S140 includes steps S1401 and S1402:

[0077] S1401. Obtain the peak acceleration data of the key monitoring locations during the earthquake;

[0078] S1402. Input the peak acceleration value into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

[0079] For example, by means of Figure 5 The casing is shown, and the corresponding key monitoring locations are used to acquire the acceleration at these locations during the earthquake. The peak value is extracted and input into the post-earthquake assessment model to obtain a failure state assessment result of 0 / 1, which represents the failure state of the casing after the earthquake. The acceleration response monitoring points can be selected from locations with varying cross-sections of the casing or locations convenient for deploying acceleration monitoring instruments.

[0080] In summary, the casing failure assessment method proposed in this application is free from subjective interference; it offers fast and accurate assessment; sensors can be arranged according to the actual conditions of the casing; and after an earthquake, the acceleration data obtained from monitoring can be input into the model to quickly obtain failure assessment results, which can then guide post-earthquake maintenance. Therefore, the scheme proposed in this application can effectively estimate the failure state of the casing after an earthquake and play a rapid guiding role in emergency repairs.

[0081] Please see Figure 6 One embodiment of the casing post-earthquake failure state assessment device in this application may include:

[0082] The first acquisition unit 21 is used to acquire seismic response data based on the casing simulation model, wherein the seismic response data includes peak acceleration at key locations and peak stress at the root.

[0083] Determining unit 22 is used to determine failure data according to target requirements;

[0084] The second acquisition unit 23 is used to construct sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model.

[0085] The third acquisition unit 24 is used to input the acceleration data of key locations monitored during the earthquake into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

[0086] like Figure 7 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned methods for assessing the post-earthquake failure status of the bushing.

[0087] Since the electronic device described in this embodiment is the device used to implement the bushing post-earthquake failure state assessment device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0088] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0089] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The flowchart of the casing post-earthquake failure status assessment method in the corresponding embodiment.

[0095] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for assessing the post-earthquake failure state of a casing, characterized in that, include: Seismic response data is obtained based on the casing simulation model, wherein the seismic response data includes peak acceleration and peak root stress at key locations, and the key locations include the locations of casing cross-section variations; Determining failure data based on target requirements, including response specifications, response standards, and engineering requirements; determining failure data based on target requirements includes: determining a safety factor based on the response specifications, response standards, and engineering requirements; determining a failure threshold based on the safety factor; and determining failure data based on the failure threshold. Constructing sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model, the step of constructing sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model includes: constructing sample data from the peak acceleration and its corresponding failure data; training a support vector machine based on the sample data to form adaptive hyperparameters; and using the sample data to verify the effectiveness of the adaptive hyperparameters to obtain a post-earthquake assessment model. The sample data includes a large proportion of sample data and a small proportion of sample data; The step of training a support vector machine based on the sample data to form adaptive hyperparameters includes: The large-scale sample data is used to train a support vector machine to improve the internal structure of the support vector machine algorithm and form adaptive hyperparameters; Acceleration data from key locations monitored during the earthquake are input into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

2. The method as described in claim 1, characterized in that, The process of obtaining seismic response data based on the casing simulation model includes: A casing simulation model was constructed based on finite element method. Seismic response data for each seismic action was obtained by using a batch of natural earthquake actions as input to the simulation model.

3. The method as described in claim 1, characterized in that, The process of using the sample data to verify the effectiveness of the adaptive hyperparameters to obtain a post-earthquake assessment model includes: The effectiveness of the adaptive hyperparameters was verified using the small-scale sample data to obtain a post-earthquake assessment model.

4. The method as described in claim 1, characterized in that, The step of inputting acceleration data from key locations monitored during the earthquake into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake includes: Obtain the peak acceleration data of key monitoring locations during the earthquake; The peak acceleration is input into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

5. A device for assessing the post-earthquake failure status of a casing, characterized in that: include: The first acquisition unit is used to acquire seismic response data based on the casing simulation model, wherein the seismic response data includes peak acceleration and peak root stress at key locations, and the key locations include the locations of casing variable cross sections; A determination unit is used to determine failure data according to target requirements, which include response specifications, response standards, and engineering requirements. The determination of failure data according to the target requirements includes: determining a safety factor based on the response specifications, response standards, and engineering requirements; determining a failure threshold based on the safety factor; and determining failure data based on the failure threshold. The second acquisition unit is used to construct sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model. The construction of sample data based on the failure data and the seismic response data to obtain a post-earthquake assessment model includes: constructing sample data from the peak acceleration and its corresponding failure data; training a support vector machine based on the sample data to form adaptive hyperparameters; and using the sample data to verify the effectiveness of the adaptive hyperparameters to obtain a post-earthquake assessment model. The sample data includes a large proportion of sample data and a small proportion of sample data; The step of training a support vector machine based on the sample data to form adaptive hyperparameters includes: The large-scale sample data is used to train a support vector machine to improve the internal structure of the support vector machine algorithm and form adaptive hyperparameters; The third acquisition unit is used to input the acceleration data of key locations monitored during the earthquake into the post-earthquake assessment model to obtain the failure status of the casing after the earthquake.

6. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: the processor executes the computer program stored in the memory to implement the steps of the casing post-earthquake failure state assessment method as described in any one of claims 1-4.

7. 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 casing post-earthquake failure state assessment method as described in any one of claims 1-4.

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