Steel structure monitoring method and device based on distributed optical fiber sensing network

Abstract

The invention discloses a steel structure monitoring method, a steel structure monitoring device, steel structure monitoring equipment and a computer readable storage medium based on a distributed optical fiber sensing network, and the method comprises the steps: carrying out the recognition of structure key points based on the model data of a steel structure, and obtaining an optical fiber sensor distribution scheme; the optical fiber sensor distribution scheme is sent to a deployment terminal, so that the deployment terminal carries out sensor deployment processing based on the optical fiber sensor distribution scheme; the physical variable quantity is converted into the variable quantity of the wavelength of an optical signal through a deployed optical fiber sensor by adopting an optical fiber grating array, and the optical signal is sent; performing structure fault prediction processing on the optical signal through a trained time sequence prediction model to obtain prediction data; wherein the trained time sequence prediction model is a prediction model obtained by training based on light signal training data; and performing monitoring and early warning processing based on the prediction data.

Description

Technical Field

[0001] The present invention belongs to the technical field of engineering monitoring, and more specifically, relates to a steel structure monitoring method, a steel structure monitoring device, a steel structure monitoring equipment, and a computer-readable storage medium based on a distributed optical fiber sensing network. Background Art

[0002] In the field of steel structure engineering, ensuring the safety and stability of steel structures is of crucial importance. With the wide application of steel structures in many fields such as buildings, bridges, and large machinery, effective monitoring of them has become a key link to ensure the safe operation of the structure and prevent accidents.

[0003] Currently, there are many limitations in steel structure monitoring technologies. In terms of sensor layout, traditional methods lack scientific and accurate strategies. In many cases, only experience or simple structural analysis is used to determine the sensor positions, making it difficult to comprehensively and accurately capture the physical change information of key parts of the structure. For example, in complex structures, some potential stress concentration points or fatigue-prone areas may be overlooked, resulting in monitoring blind spots and the inability to detect problems in these key parts in a timely manner, thus posing a potential threat to the structural safety.

[0004] Moreover, in terms of structural fault prediction, existing prediction models are mostly simple mathematical models or models trained based on a small amount of data, and they cannot fully consider the complex mechanical properties and changing operating environments of steel structures. The prediction accuracy of these models for the structural state is relatively low, and false positives and false negatives are likely to occur. When there are minor damages or the performance of the steel structure is gradually degrading, existing models are difficult to accurately predict the occurrence of faults in advance and cannot provide timely and effective support for maintenance decisions.

[0005] Therefore, the existing steel structure monitoring technologies have deficiencies in multiple key aspects, and there is an urgent need for a more scientific, accurate, and efficient monitoring method to improve the accuracy and reliability of steel structure monitoring and ensure the safe and stable operation of steel structures. Summary of the Invention

[0006] The purpose of this application is to provide a steel structure monitoring method based on a distributed optical fiber sensing network to improve the accuracy and reliability of steel structure monitoring.

[0007] In view of the above defects or improvement requirements of the prior art, the present invention provides a steel structure monitoring method based on a distributed optical fiber sensing network, including:

[0008] Identifying key structure points based on the model data of the steel structure to obtain a fiber optic sensor layout plan;

[0009] Send the fiber optic sensor layout plan to the deployment terminal so that the deployment terminal can perform sensor deployment processing based on the fiber optic sensor layout plan;

[0010] The deployed fiber optic sensor uses a fiber grating array to convert the physical change quantity into a change quantity of the wavelength of the optical signal, and sends the optical signal;

[0011] Perform structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data; wherein, the trained time series prediction model is a prediction model trained based on optical signal training data;

[0012] Perform monitoring and early warning processing based on the prediction data.

[0013] Optionally, identify the structural key points based on the model data of the steel structure to obtain the fiber optic sensor layout plan, including:

[0014] Perform structural mechanics analysis on the model data of the steel structure using finite element analysis to obtain structural mechanics characteristic data;

[0015] Based on the structural mechanics characteristic data, perform structural vibration mode identification to obtain structural vibration sensitive points and fatigue sensitive regions;

[0016] Based on the multi-objective optimization algorithm, the structural vibration sensitive points, and the fatigue sensitive regions.

[0017] Optionally, the deployment terminal performs sensor deployment processing based on the fiber optic sensor layout plan, including:

[0018] The deployment terminal generates a visual layout guidance interface based on the fiber optic sensor layout plan;

[0019] Display the visual layout guidance interface for sensor deployment processing.

[0020] Optionally, the deployed fiber optic sensor uses a fiber grating array to convert the physical change quantity into a change quantity of the wavelength of the optical signal, and sends the optical signal, including:

[0021] The deployed fiber optic sensor uses a fiber grating array to convert the physical change quantity into a change quantity of the wavelength of the optical signal;

[0022] Preprocess the optical signal to obtain the processed optical signal;

[0023] Send the processed optical signal.

[0024] Optionally, perform structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data, including:

[0025] Demodulate the optical signal to obtain a corresponding electrical signal;

[0026] Perform structural fault prediction processing on the electrical signal through the trained time series prediction model to obtain the prediction data.

[0027] Optionally, perform monitoring and early warning processing based on the prediction data, including:

[0028] Perform monitoring and early warning processing on the prediction data based on multiple urgency levels to obtain an early warning report;

[0029] Perform early warning push processing on the early warning report based on the urgency level information of the early warning report.

[0030] This application also provides a steel structure monitoring device based on a distributed optical fiber sensing network, including:

[0031] A structure recognition module for identifying structural key points based on the model data of the steel structure to obtain a fiber optic sensor layout plan;

[0032] A sensor deployment module for sending the fiber optic sensor layout plan to a deployment terminal so that the deployment terminal can perform sensor deployment processing based on the fiber optic sensor layout plan;

[0033] A signal acquisition module for converting the physical change amount into a change amount of the wavelength of the optical signal by using a fiber optic grating array through the deployed fiber optic sensors and sending the optical signal;

[0034] A structural fault prediction module for performing structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data; wherein, the trained time series prediction model is a prediction model trained based on optical signal training data;

[0035] An early warning processing module for performing monitoring and early warning processing based on the prediction data.

[0036] Optionally, the structure recognition module is specifically configured to perform structural mechanics analysis on the model data of the steel structure by using finite element analysis to obtain structural mechanics characteristic data; perform structural vibration mode identification based on the structural mechanics characteristic data to obtain structural vibration sensitive points and fatigue sensitive regions; based on a multi-objective optimization algorithm, the structural vibration sensitive points, and the fatigue sensitive regions.

[0037] This application also provides a steel structure monitoring device, including:

[0038] A memory for storing a computer program;

[0039] A processor, which is used to implement the steps of the steel structure monitoring method as described above when executing the computer program.

[0040] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the steel structure monitoring method as described above are implemented.

[0041] A steel structure monitoring method based on a distributed optical fiber sensing network provided by the present application includes: identifying structure key points based on the model data of the steel structure to obtain an optical fiber sensor layout scheme; sending the optical fiber sensor layout scheme to a deployment terminal so that the deployment terminal implements sensor deployment processing based on the optical fiber sensor layout scheme; using an optical fiber grating array through the deployed optical fiber sensors to convert a physical change quantity into a change quantity of the wavelength of an optical signal, and sending the optical signal; performing structure fault prediction processing on the optical signal through a trained time series prediction model to obtain prediction data; where the trained time series prediction model is a prediction model trained based on optical signal training data; and performing monitoring and early warning processing based on the prediction data.

[0042] It has the following beneficial effects:

[0043] By scientifically identifying structure key points to reasonably arrange sensors, physical change information can be accurately obtained. Using an optical fiber grating array to convert signals has strong anti-interference ability and high measurement accuracy. With the help of a trained time series prediction model to predict structure faults, potential problems can be detected in advance. The monitoring and early warning processing based on the prediction data can issue an alarm in time, enabling relevant personnel to respond quickly, effectively ensuring the safe and stable operation of the steel structure, and reducing maintenance costs and safety risks. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0045] Figure 1 It is a flowchart of a steel structure monitoring method based on a distributed optical fiber sensing network provided by an embodiment of the present application;

[0046] Figure 2 It is a schematic structural diagram of a steel structure monitoring device based on a distributed optical fiber sensing network provided by an embodiment of the present application;

[0047] Figure 3It is a schematic structural diagram of the steel structure monitoring device provided by the embodiment of the present application. Detailed implementation manners

[0048] The core of the present application is to provide a steel structure monitoring method based on a distributed optical fiber sensing network to improve the accuracy and reliability of steel structure monitoring.

[0049] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] The following describes, through an embodiment, a steel structure monitoring method based on a distributed optical fiber sensing network provided by the present application.

[0051] Please refer to Figure 1 , Figure 1 It is a flowchart of a steel structure monitoring method based on a distributed optical fiber sensing network provided by the embodiment of the present application.

[0052] In this embodiment, the method may include:

[0053] S101, identifying structural key points based on the model data of the steel structure to obtain a fiber optic sensor layout plan;

[0054] In the actual use of the steel structure, the stress conditions and importance of different parts are different. By analyzing the model data of the steel structure in this step, those key points that have a greater impact on the structural safety and performance can be found. When the structure is stressed, deformed or fails, the physical quantity changes more significantly at these key points. Arranging sensors at these positions can more effectively obtain the structural state information.

[0055] This step uses finite element analysis to perform structural mechanics analysis on the model data of the steel structure to obtain structural mechanics characteristic data. Based on these data, structural vibration mode identification is performed to determine the structural vibration sensitive points and fatigue sensitive regions. Then, using a multi-objective optimization algorithm, considering factors such as structural vibration sensitive points and fatigue sensitive regions, an optimal layout plan for fiber optic sensors is formulated.

[0056] In this step, the sensor layout is scientifically and reasonably determined, which improves the pertinence and effectiveness of monitoring, avoids monitoring loopholes or resource waste caused by blind layout, can more accurately obtain the information of key parts, and lays a foundation for accurately judging the structural state subsequently.

[0057] S102. Send the fiber optic sensor layout plan to the deployment terminal so that the deployment terminal can perform sensor deployment processing based on the fiber optic sensor layout plan;

[0058] Based on S101, in this step, the deployment terminal receives the layout plan and conducts sensor deployment accordingly, which is a key step in implementing the monitoring system setup. By transmitting the designed layout plan to the deployment terminal, the deployment work can proceed orderly according to the predetermined plan.

[0059] Through network communication technology, send the fiber optic sensor layout plan to the deployment terminal. After receiving the plan, the deployment terminal generates a visual layout guidance interface based on the plan and displays this interface. The staff installs and deploys the sensors according to the interface guidance.

[0060] This step realizes the standardization and visual guidance of sensor deployment, reduces the deployment difficulty, improves the deployment efficiency and accuracy, ensures that the sensors can be accurately installed at the predetermined positions according to the design plan, and guarantees the normal operation of the monitoring system.

[0061] S103. Use the fiber grating array in the deployed fiber optic sensor to convert the physical change quantity into the change quantity of the wavelength of the optical signal, and send the optical signal;

[0062] Based on S102, the fiber grating in this step has optical characteristics. When the external physical quantity (such as strain, temperature, etc.) changes, it will cause the Bragg wavelength of the fiber grating to change. Utilizing this characteristic, through the fiber grating array, the physical change quantity of the steel structure can be converted into the change of the optical signal wavelength, thereby realizing the perception of the structural state.

[0063] Among them, the fiber grating array in the deployed fiber optic sensor is tightly connected to the steel structure. When the steel structure undergoes physical changes (such as deformation, vibration, etc.), the fiber grating is subjected to corresponding effects, and its Bragg wavelength changes accordingly. The generated optical signal is processed (such as amplified, filtered, etc.) and then sent out through the fiber optic transmission line.

[0064] The fiber optic sensing technology in this step has the advantages of anti-electromagnetic interference, high precision, and distributed measurement. Using the fiber grating array to convert physical changes into optical signal wavelength changes can obtain the physical change information of the steel structure more accurately and stably, and can realize long-distance and large-scale distributed monitoring.

[0065] S104. Perform structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data; among them, the trained time series prediction model is a prediction model trained based on the optical signal training data;

[0066] Based on S103, the trained time series prediction model in this step is learned based on a large amount of optical signal training data. It can capture the variation law of optical signals over time and the potential relationship with structural failures. By analyzing and processing the currently received optical signals, it predicts the future state of the structure, determines whether a failure will occur, and the development trend of the failure.

[0067] In this step, the received optical signal is first demodulated and converted into a corresponding electrical signal. Then, the electrical signal is input into the trained time series prediction model. The model analyzes and processes the electrical signal according to the knowledge and algorithms it has learned, and outputs prediction data, which reflect the possible failure conditions of the structure in the future.

[0068] Among them, it can predict structural failures in advance, provide a basis for timely maintenance measures, avoid the further development of structural failures, reduce maintenance costs and safety risks, and ensure the safe and stable operation of steel structures.

[0069] S105, perform monitoring and early warning processing based on the prediction data.

[0070] Based on S104, in this step, the prediction data is compared with a preset threshold, and the safety state of the structure is judged according to the comparison result. When the prediction data exceeds the normal range, the early warning mechanism is triggered to notify relevant personnel to take measures in a timely manner.

[0071] In this step, grading standards with different emergency levels are set, and the prediction data is compared with these standards. According to the comparison results, an early warning report is generated. Then, according to the emergency level information in the early warning report, the early warning report is pushed to relevant personnel or systems through text messages, emails, system pop-ups, etc.

[0072] This step realizes the real-time monitoring and early warning of the state of the steel structure, can notify relevant personnel in time when there are potential failure risks in the structure, so as to quickly take countermeasures, effectively prevent accidents, and ensure the safety of personnel's lives and property.

[0073] In summary, in this embodiment, by scientifically identifying the key points of the structure, sensors are reasonably arranged to accurately obtain physical change information. The fiber Bragg grating array is used to convert signals, with strong anti-interference ability and high measurement accuracy. The trained time series prediction model is used to predict structural failures, and potential problems can be detected in advance. The monitoring and early warning processing based on the prediction data can issue alarms in time, enabling relevant personnel to respond quickly, effectively ensuring the safe and stable operation of the steel structure, and reducing maintenance costs and safety risks.

[0074] Optionally, based on the model data of the steel structure, key points of the structure are identified to obtain a fiber optic sensor layout plan, including:

[0075] Perform structural mechanics analysis on the model data of the steel structure using finite element analysis to obtain structural mechanics characteristic data;

[0076] Based on the structural mechanics characteristic data, perform structural vibration mode identification to obtain structural vibration sensitive points and fatigue sensitive regions;

[0077] Based on the multi-objective optimization algorithm, structural vibration sensitive points, and fatigue sensitive regions.

[0078] In this alternative solution, the finite element analysis discretizes the steel structure model into multiple elements, and obtains the overall structural mechanics characteristics by solving the mechanical equations of each element, providing basic data for subsequent analysis. The structural vibration mode identification is based on the principle of structural dynamics to determine the sensitive points of the structure in different vibration modes, and these points are more obvious for the structural vibration response. The multi-objective optimization algorithm comprehensively considers multiple objectives such as vibration sensitive points and fatigue sensitive regions, balances different factors, and obtains the best layout scheme of fiber optic sensors that takes into account various requirements.

[0079] In this alternative solution, use finite element analysis software to analyze the model data of the steel structure and obtain structural mechanics characteristic data such as stress and strain. Adopt modal analysis methods, such as modal identification techniques based on experiments or theoretical calculations, to determine structural vibration sensitive points and fatigue sensitive regions. Select a suitable multi-objective optimization algorithm, such as genetic algorithm, particle swarm optimization algorithm, etc., use the structural vibration sensitive points and fatigue sensitive regions as optimization objectives, set the algorithm parameters for iterative calculation, and obtain the fiber optic sensor layout scheme.

[0080] This alternative solution can improve the scientificity and accuracy of sensor layout, and avoid blind layout. It can comprehensively cover the key parts of the structure, effectively capture the structural mechanics change information, provide a reliable basis for subsequent accurate monitoring and evaluation of the steel structure state, reduce the monitoring blind area, and improve the effectiveness of the monitoring system.

[0081] Optionally, the deployment terminal performs sensor deployment processing based on the fiber optic sensor layout scheme, including:

[0082] The deployment terminal generates a visual layout guidance interface based on the fiber optic sensor layout scheme;

[0083] Display the visual layout guidance interface for sensor deployment processing.

[0084] This alternative solution uses the graphic display function of the deployment terminal to convert the fiber optic sensor layout scheme into visual graphic information. Through visual display, the staff can intuitively understand the layout position and requirements, reduce human errors, and improve the deployment efficiency.

[0085] In this alternative solution, a deployed terminal (such as a tablet computer, an industrial computer, etc.) receives the data of the fiber optic sensor layout plan, writes a program based on a graphics development library (such as OpenGL, Qt, etc.), converts the layout position information into a visual graph, and displays information such as the steel structure model and the position marks and numbers where the sensors should be installed on the screen. The staff performs the sensor installation operation according to the displayed graph.

[0086] This alternative solution greatly simplifies the sensor deployment process and improves the accuracy and efficiency of deployment. The visual guidance is convenient for construction personnel to understand and operate, reduces deployment errors caused by human understanding deviations, ensures that the sensors are correctly installed according to the design plan, and guarantees the normal operation of the monitoring system.

[0087] Optionally, the deployed fiber optic sensor uses a fiber Bragg grating array to convert the physical change quantity into a change quantity of the wavelength of the optical signal, and sends the optical signal, including:

[0088] The deployed fiber optic sensor uses a fiber Bragg grating array to convert the physical change quantity into a change quantity of the wavelength of the optical signal;

[0089] Preprocess the optical signal to obtain the processed optical signal;

[0090] Send the processed optical signal.

[0091] In this alternative solution, the Bragg wavelength of the fiber Bragg grating will change with the change of external physical quantities (such as strain, temperature, etc.). Based on this characteristic, the physical change of the steel structure is converted into a change of the optical signal wavelength. The preprocessing aims to remove the noise and interference in the optical signal, improve the signal quality, and make the subsequent transmission and analysis more accurate.

[0092] In this alternative solution, the fiber Bragg grating part of the fiber optic sensor is tightly connected to the steel structure. When the steel structure undergoes physical changes, the fiber Bragg grating generates strain or temperature changes, causing the Bragg wavelength to change, realizing the conversion of the physical quantity to the change of the optical signal wavelength. Signal processing circuits or algorithms such as filtering, amplifying, and denoising are used to preprocess the generated optical signal, remove noise, and enhance the signal strength, and then send the processed optical signal through the fiber optic transmission line.

[0093] This alternative solution ensures that the obtained optical signal can truly reflect the physical changes of the steel structure, improves the signal quality and stability. The preprocessed optical signal is more conducive to long-distance transmission and subsequent precise analysis, reduces the influence of signal distortion and interference on the monitoring results, and improves the reliability of the monitoring data.

[0094] Optionally, the trained time series prediction model is used to perform structural fault prediction processing on the optical signal to obtain prediction data, including:

[0095] Demodulate the optical signal to obtain the corresponding electrical signal;

[0096] The electrical signal is processed for structural fault prediction using the trained time series prediction model to obtain prediction data.

[0097] Optical signal demodulation in this optional solution is to convert the information carried by the optical signal into an electrical signal for subsequent electronic equipment processing. The trained time series prediction model learns from a large amount of historical optical signal (converted electrical signal) data to grasp the relationship between the optical signal change law and structural faults, thereby analyzing and predicting the input electrical signal.

[0098] In this optional solution, an optical signal demodulation device (such as a fiber Bragg grating demodulator) is used to convert the received optical signal into an electrical signal. An appropriate time series prediction model (such as deep learning models such as LSTM, GRU, or traditional time series models such as ARI MA) is selected to collect a large amount of optical signal data corresponding to the state of the steel structure and convert it into an electrical signal, perform data preprocessing (such as normalization, denoising, etc.), divide the training set and test set, and train and optimize the model. The trained model receives the demodulated electrical signal and outputs the structural fault prediction data.

[0099] This optional solution realizes the key conversion from optical signal to structural fault prediction, improving the accuracy of fault prediction. The data-driven prediction model can mine the potential rules in the data and predict structural faults more accurately than traditional empirical methods, providing strong support for timely maintenance measures.

[0100] Optionally, monitoring and early warning processing is performed based on the forecast data, including:

[0101] Monitor and warn the forecast data based on multiple emergency levels to obtain a warning report;

[0102] The warning report is processed by warning push based on the urgency information of the warning report.

[0103] The optional multi-emergency classification is to classify the structural status into different emergency levels based on the comparison results of the predicted data and the preset threshold, so as to take targeted measures. The early warning push is based on the classification results, and the early warning information is promptly delivered to the relevant personnel or systems to ensure timely response.

[0104] In this optional solution, different levels of warning thresholds are set, the forecast data are compared with these thresholds, the warning level is determined based on the comparison results, and a warning report containing information such as the warning level and forecast data is generated. Select an appropriate communication method (such as SMS, email, network push, etc.), set different push priorities and recipients according to the urgency of the warning report, and push the warning report to management personnel, operation and maintenance systems, etc.

[0105] In this alternative solution, refined management of the steel structure status is achieved, enhancing the pertinence and timeliness of early warnings. The classification of different emergency levels and corresponding push methods enable relevant personnel to make rapid decisions based on the actual situation, take appropriate countermeasures, effectively prevent accidents, and ensure the safe operation of the steel structure.

[0106] Next, a steel structure monitoring device based on a distributed optical fiber sensing network provided by an embodiment of the present application will be introduced. A steel structure monitoring device based on a distributed optical fiber sensing network described below and a steel structure monitoring method based on a distributed optical fiber sensing network can be correspondingly referred to each other.

[0107] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a steel structure monitoring device provided by an embodiment of the present application.

[0108] In this embodiment, the device may include:

[0109] A structure recognition module 100, configured to identify structural key points based on the model data of the steel structure to obtain a fiber optic sensor layout plan;

[0110] A sensor deployment module 200, configured to send the fiber optic sensor layout plan to a deployment terminal, so that the deployment terminal performs sensor deployment processing based on the fiber optic sensor layout plan;

[0111] A signal acquisition module 300, configured to convert a physical change quantity into a change quantity of the wavelength of an optical signal by using a fiber grating array through the deployed fiber optic sensors, and send the optical signal;

[0112] A structure fault prediction module 400, configured to perform structure fault prediction processing on the optical signal through a trained time series prediction model to obtain prediction data; wherein, the trained time series prediction model is a prediction model trained based on optical signal training data;

[0113] An early warning processing module 500, configured to perform monitoring and early warning processing based on the prediction data.

[0114] Optionally, the structure recognition module is specifically configured to perform structural mechanics analysis on the model data of the steel structure by using finite element analysis to obtain structural mechanics characteristic data; perform structural vibration mode recognition based on the structural mechanics characteristic data to obtain structural vibration sensitive points and fatigue sensitive regions; based on a multi-objective optimization algorithm, structural vibration sensitive points, and fatigue sensitive regions.

[0115] The present application also provides steel structure monitoring equipment. Please refer to Figure 3 , Figure 3The structural schematic diagram of the steel structure monitoring device provided by the embodiments of this application. The steel structure monitoring device may include:

[0116] A memory for storing computer programs;

[0117] A processor that, when executing the computer program, can implement the steps of any one of the above steel structure monitoring methods based on a distributed optical fiber sensing network.

[0118] As Figure 3 shown, it is the composition structural schematic diagram of the steel structure monitoring device. The steel structure monitoring device may include: a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all complete mutual communication through the communication bus 13.

[0119] In the embodiments of this application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array, or other programmable logic devices, etc.

[0120] The processor 10 may call the program stored in the memory 11. Specifically, the processor 10 may execute the operations in the embodiments of the abnormal IP recognition method.

[0121] The memory 11 is used to store one or more programs. The program may include program codes, and the program codes include computer operation instructions. In the embodiments of this application, the memory 11 stores at least programs for implementing the following functions:

[0122] Based on the model data of the steel structure, identify the structural key points to obtain the optical fiber sensor layout plan;

[0123] Send the optical fiber sensor layout plan to the deployment terminal so that the deployment terminal can perform sensor deployment processing based on the optical fiber sensor layout plan;

[0124] Convert the physical change quantity into the change quantity of the wavelength of the optical signal through the deployed optical fiber sensors using an optical fiber grating array, and send the optical signal;

[0125] Perform structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data; wherein, the trained time series prediction model is a prediction model trained based on the optical signal training data;

[0126] Perform monitoring and early warning processing based on the prediction data.

[0127] In a possible implementation, the memory 11 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function, etc.; the data storage area may store data created during use.

[0128] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage devices.

[0129] The communication interface 12 may be an interface of a communication module for connecting to other devices or systems.

[0130] Of course, it should be noted that Figure 3 the structure shown does not constitute a limitation on the steel structure monitoring device in the embodiments of the present application. In practical applications, the steel structure monitoring device may include more or fewer components than Figure 3 those shown, or combine certain components.

[0131] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above-mentioned steel structure monitoring methods based on a distributed optical fiber sensing network can be implemented.

[0132] The computer-readable storage medium may include: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.

[0133] For the introduction of the computer-readable storage medium provided in the present application, please refer to the above method embodiments, and the present application will not elaborate here.

[0134] The various embodiments in the specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0135] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0136] The steps of the method or algorithm described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0137] The above has introduced in detail a steel structure monitoring method, a steel structure monitoring device, a steel structure monitoring equipment, and a computer-readable storage medium provided by this application. Specific examples are used herein to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and modifications can still be made to this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A steel structure monitoring method based on a distributed optical fiber sensing network, characterized in that, Including: Based on the model data of the steel structure, identify the structural key points to obtain the fiber optic sensor layout scheme; Send the fiber optic sensor layout scheme to the deployment terminal so that the deployment terminal can perform sensor deployment processing based on the fiber optic sensor layout scheme; Through the deployed fiber optic sensors, use the fiber Bragg grating array to convert the physical change quantity into the change quantity of the wavelength of the optical signal, and send the optical signal; Perform structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data; wherein, the trained time series prediction model is a prediction model trained based on the optical signal training data; Perform monitoring and early warning processing based on the prediction data.

2. The steel structure monitoring method according to claim 1, characterized in that Based on the model data of the steel structure, identify the structural key points to obtain the fiber optic sensor layout scheme, including: Use finite element analysis to perform structural mechanics analysis on the model data of the steel structure to obtain structural mechanics characteristic data; Based on the structural mechanics characteristic data, perform structural vibration mode identification to obtain structural vibration sensitive points and fatigue sensitive regions; Based on the multi-objective optimization algorithm, the structural vibration sensitive points, and the fatigue sensitive regions.

3. The steel structure monitoring method according to claim 2, characterized in that, The deployment terminal performs sensor deployment processing based on the fiber optic sensor layout scheme, including: The deployment terminal generates a visual layout guidance interface based on the fiber optic sensor layout scheme; Display the visual layout guidance interface to facilitate sensor deployment processing.

4. The steel structure monitoring method according to claim 3, characterized in that, Through the deployed fiber optic sensors, use the fiber Bragg grating array to convert the physical change quantity into the change quantity of the wavelength of the optical signal, and send the optical signal, including: Through the deployed fiber optic sensors, use the fiber Bragg grating array to convert the physical change quantity into the change quantity of the wavelength of the optical signal; Perform preprocessing on the optical signal to obtain the processed optical signal; Send the processed optical signal.

5. The steel structure monitoring method according to claim 4, wherein, Perform structural fault prediction processing on the optical signal through the trained time series prediction model to obtain prediction data, including: Demodulate the optical signal to obtain the corresponding electrical signal; Perform structural fault prediction processing on the electrical signal through the trained time series prediction model to obtain the prediction data.

6. The steel structure monitoring method according to claim 5, wherein, Perform monitoring and early warning processing based on the prediction data, including: Perform monitoring and early warning processing on the prediction data based on multiple levels of urgency to obtain an early warning report; Perform early warning push processing on the early warning report based on the urgency information of the early warning report.

7. A steel structure monitoring device based on a distributed optical fiber sensing network, characterized in that, Including: A structure identification module, which is used to identify the structural key points based on the model data of the steel structure to obtain the fiber optic sensor layout scheme; A sensor deployment module, which is used to send the fiber optic sensor layout scheme to the deployment terminal so that the deployment terminal can perform sensor deployment processing based on the fiber optic sensor layout scheme; A signal acquisition module, which is used to convert the physical change quantity into the change quantity of the wavelength of the optical signal through the deployed fiber optic sensors using the fiber Bragg grating array, and send the optical signal; A structural fault prediction module, configured to perform structural fault prediction processing on the optical signal through a trained time series prediction model to obtain prediction data; wherein, the trained time series prediction model is a prediction model trained based on optical signal training data; An early warning processing module, configured to perform monitoring and early warning processing based on the prediction data.

8. The steel structure monitoring device according to claim 7, wherein, The structure identification module is specifically configured to perform structural mechanics analysis on the model data of the steel structure by using finite element analysis to obtain structural mechanics characteristic data; perform structural vibration mode identification based on the structural mechanics characteristic data to obtain structural vibration sensitive points and fatigue sensitive regions; Based on a multi-objective optimization algorithm, the structural vibration sensitive points, and the fatigue sensitive regions.

9. A steel structure monitoring device, characterized in that, Including: A memory, configured to store a computer program; A processor, configured to implement the steps of the steel structure monitoring method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the steel structure monitoring method according to any one of claims 1 to 6 are implemented.

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