Anti-seismic fault crossing system and method based on self-adaption

Abstract

The invention discloses an anti-seismic fault crossing system and method based on self-adaption. The system comprises a distributed optical fiber module, a micro electro mechanical system module, a central processing module, a material control module and an execution module. The distributed optical fiber module and the micro-electro-mechanical module monitor temperature, strain and dynamic response data of a building in real time through the optical fiber node and the micro-electro-mechanical node respectively, and transmit the data to the central processing module; the central processing module is responsible for analyzing and predicting data in real time and generating a control instruction; after the material control module receives a control instruction, the control instruction is converted into an electric signal or a magnetic field control signal through the signal processor, the drive circuit and the interface unit, a material assembly in the execution module is driven to deform or change physical characteristics, and the structural rigidity and damping of a building are dynamically adjusted. The rigidity and damping of the building structure are dynamically adjusted, the shock resistance of the building in the fault area is improved, and the safety and stability of the building are improved.

Description

Technical Field

[0001] The present invention relates to the field of earthquake-resistant detection technology, and in particular to an adaptive earthquake-resistant fault crossing system and method thereof. Background Art

[0002] With the acceleration of urbanization, the construction of high-rise buildings and infrastructure in earthquake-prone areas is increasing. However, the damage caused by earthquakes to these buildings and facilities often leads to huge economic losses and casualties. Especially for buildings near earthquake fault zones, the impact of seismic waves is more direct and intense, and seismic design faces greater challenges. In order to improve the safety and stability of buildings during earthquakes, a seismic technology that can monitor and adaptively adjust in real time is needed to cope with the threats posed by earthquakes.

[0003] Traditional earthquake-resistant technologies mainly rely on static design principles, such as increasing structural strength and using seismic isolation devices. Although these methods have improved the earthquake resistance of buildings to a certain extent, they have obvious limitations, such as the inability to dynamically respond to the actual stress conditions of buildings during earthquakes, and the inability to effectively protect the safety of buildings once they are subjected to earthquakes that exceed the design expectations. In addition, traditional technologies lack the ability to monitor the internal status of buildings in real time, and cannot promptly detect and deal with potential safety hazards.

[0004] In recent years, with the development of sensing technology and information processing technology, some new earthquake-resistant technologies have begun to emerge, such as the use of smart materials and advanced control systems to improve the earthquake resistance of buildings. Although these technologies are improvements over traditional methods, they still have some shortcomings, such as high cost, large system complexity, and difficult maintenance. Existing technical solutions can usually only achieve local monitoring and control, and have failed to form a comprehensive and systematic solution. Especially when dealing with complex environments such as earthquake fault crossings, existing technologies are difficult to meet the needs.

[0005] In response to the above problems, this technical solution proposes an adaptive seismic fault crossing system, which combines distributed optical fiber monitoring, micro-electromechanical systems and material technology to achieve all-round, multi-level real-time monitoring and adaptive control of buildings in earthquake fault zones. Summary of the invention

[0006] The present invention is based on an adaptive earthquake-resistant fault crossing system, which includes a distributed optical fiber module, a micro-electromechanical module, a central processing module, a material control module, and an execution module;

[0007] The distributed optical fiber module and micro-electromechanical module are used as front-end sensing units and are connected to the central processing module through wired or wireless communication protocols; the central processing module is connected to the material control module through a built-in communication interface; the material control module drives the execution module to dynamically adjust through transmission instructions;

[0008] The distributed optical fiber module is composed of optical fiber nodes, which monitor the temperature and strain changes of the building in the fault area in real time and transmit the temperature and strain data to the central processing module through the optical fiber link;

[0009] The micro-electromechanical module is composed of micro-electromechanical nodes, which monitor the dynamic response data of the building in the fault zone in real time and transmit the dynamic response data to the central processing module through wireless communication;

[0010] The central processing module includes a computing unit, a storage unit, a communication interface and an output unit; the computing unit receives data from the distributed optical fiber module and the micro-electromechanical module, and performs real-time analysis and prediction through a built-in machine learning algorithm; the storage unit is used to store historical data and analysis results, provide fast access and backup, and support data caching and disconnection reconnection; the communication interface adopts standard wired and wireless communication protocols to achieve data transmission and exchange with the distributed optical fiber module and the micro-electromechanical module; the output unit generates a control instruction according to the analysis result of the computing unit, and sends it to the material control module through a dedicated communication link to drive the execution module;

[0011] The material control module includes a signal processor, a driving circuit and an interface unit; the signal processor receives the control instruction from the central processing module and generates an electric signal or a magnetic field control signal after analysis; the driving circuit drives by amplifying the electric signal or the magnetic field control signal; the interface unit provides a physical interface to ensure the transmission of the electric signal or the magnetic field control signal;

[0012] The execution module is composed of a material component, an actuator drive unit and a state monitoring unit; the material component includes at least shape memory alloy, piezoelectric material and magnetorheological fluid; the actuator drive unit receives the control signal of the material control module to drive the material component to deform or change the physical properties; the state monitoring unit monitors the state changes of the material component in real time and feeds back to the material control module to form a closed-loop control circuit.

[0013] Preferably, the distributed optical fiber module forms a high-density monitoring network through optical fiber nodes and micro-electromechanical nodes of the micro-electromechanical module; the distributed optical fiber module forms an optical fiber network through optical fiber nodes, and uses temperature-sensitive optical fibers and strain-sensitive optical fibers of the optical fiber nodes to monitor temperature and strain changes;

[0014] The micro-electromechanical module forms an induction chain through micro-electromechanical nodes, and each micro-electromechanical node of the induction chain includes at least an accelerometer, a gyroscope and a displacement sensor to monitor the dynamic response data of the building in real time;

[0015] The high-density monitoring network is formed by interlacing the optical fiber network with the induction chain.

[0016] Preferably, the high-density monitoring network has a multi-level grid structure; the first layer of the high-density monitoring network is composed of optical fiber nodes of distributed optical fiber modules, forming an optical fiber network covering the building area; the second layer of the high-density monitoring network is composed of micro-electromechanical nodes of micro-electromechanical modules, forming an induction chain covering dynamic response points in the building area; the optical fiber network and the induction chain are integrated and processed by a central processing module to form a multi-level grid-like high-density monitoring network.

[0017] Preferably, the computing unit of the central processing module is composed of a receiving module, an analog-to-digital conversion module and an algorithm processing module; the receiving module receives and processes real-time data from the distributed optical fiber module and the micro-electromechanical module, and connects the analog-to-digital conversion module through a high-speed data line to ensure efficient reading and conversion of data; the analog-to-digital conversion module converts real-time data and transmits the real-time data to the storage unit and the algorithm processing module; the algorithm processing module analyzes sensor data in real time, extracts key features, generates control instructions and transmits them to the storage unit and the material control module.

[0018] Preferably, the driving circuit of the material control module is composed of a power amplifier, a magnetic field generator, a voltage controller and a current controller; the power amplifier amplifies the electrical signal generated by the signal processor to ensure sufficient power to drive the material components of the execution module; the magnetic field generator generates a magnetic field control signal; the voltage controller and the current controller are used to accurately adjust the output voltage and current respectively;

[0019] The driving circuit is connected to the signal processor via a dedicated interface, receives control instructions and feeds back the current state.

[0020] Preferably, the signal processor of the material control module receives the control instruction from the central processing module through the standard communication interface, parses the received control instruction to generate control parameters, and the parsed control parameters are converted into analog signals through the analog-to-digital conversion module of the central processing module, which are amplified by the power amplifier of the driving circuit to generate electrical signals or magnetic field control signals;

[0021] The electrical signal is adjusted by a voltage controller and a current controller of a driving circuit;

[0022] The magnetic field control signal is generated by a magnetic field generator built into the driving circuit to drive the physical change of the magnetorheological fluid.

[0023] The adaptive seismic fault crossing method includes the following steps:

[0024] S1, a high-density monitoring network is formed by the optical fiber nodes of the distributed optical fiber module and the micro-electromechanical nodes of the micro-electromechanical module, the temperature and strain change data are monitored in real time, and the data is transmitted to the central processing module through wireless communication;

[0025] S2, after the calculation unit of the central processing module receives the monitored temperature and strain change data, it performs real-time analysis and prediction through the algorithm processing module, generates control instructions, and transmits them to the material control module through the output unit;

[0026] S3, the signal processor of the material control module receives these control instructions, generates corresponding electrical signals or magnetic field control signals after analysis, and transmits them to the actuator drive unit of the execution module through the drive circuit for driving;

[0027] S4. The actuator driving unit of the execution module receives the control signal from the material control module, drives the material component to deform or change the physical properties, and feeds back the data to the material control module to form a closed-loop control circuit.

[0028] Preferably, the optical fiber nodes in S1 and the micro-electromechanical nodes of the micro-electromechanical module form a high-density monitoring network to monitor temperature and strain change data in real time, specifically including:

[0029] The optical fiber nodes S1, S2, ..., S n The MEMS nodes M1, M2, ..., M m A high-density monitoring network is formed; the optical fiber node monitors temperature and strain changes in real time through temperature-sensitive optical fiber and strain-sensitive optical fiber. The temperature-sensitive optical fiber formula is: t T =t0+k T (T-T0), where t T is the propagation time of the temperature-sensitive optical fiber, t0 is the propagation time at the reference temperature T0, k T is the temperature sensitivity coefficient, T is the current temperature; the strain sensitive optical fiber formula is: Δφ S =Δφ0+k ∈ ·∈, where Δφ S is the phase change of the strain sensitive fiber, Δφ0 is the initial phase change, k ∈ is the strain sensitivity coefficient, ∈ is the current strain; the MEMS node formula is: S i =k i ·X i +b i , where S i is the response value, k iis the MEMS sensitivity coefficient, X i To measure physical quantity, b i is the MEMS bias value.

[0030] Preferably, after receiving the monitored temperature and strain change data, the computing unit in S2 performs real-time analysis and prediction through the algorithm processing module to generate control instructions, which specifically include:

[0031] Define the received temperature data as T i And the strain data is ∈ i The preprocessed temperature data and strain data are T i ′ and ∈ i ′, calculate the temperature change rate of temperature data and strain data and the strain change rate ∈ i · ′, the probability P of predicting the vibration signal of the building area through machine learning algorithm, Where f is a pre-trained machine learning model that generates control instructions based on the probability P of the vibration signal.

[0032] Preferably, the actuator driving unit in S4 receives a control signal from the material control module, and drives the shape memory alloy, piezoelectric material and magnetorheological fluid of the material component to adjust the structural stiffness and increase the damping of the structure;

[0033] The shape memory alloy controls the deformation amount by changing the temperature value, thereby increasing the rigidity of the building structure in the fault zone;

[0034] The piezoelectric material generates mechanical deformation through an applied electrical signal to adjust the structural stiffness of the building in the fault zone;

[0035] The magnetorheological fluid forms a chain structure through an external magnetic field control signal, thereby increasing the damping property of the building structure in the fault zone.

[0036] Compared with the prior art, the technical solution of this application has the following technical effects:

[0037] The present invention constructs a high-density monitoring network composed of distributed optical fiber modules and micro-electromechanical modules to monitor the temperature and strain changes and dynamic response data of buildings in the earthquake fault zone in real time. This high-density monitoring network can not only cover the entire building area, but also provide high-precision real-time data, which solves the problems of limited monitoring range, low data accuracy and slow response speed of traditional monitoring systems. It can more accurately grasp the actual status of the building in the earthquake, provide reliable data support for subsequent analysis and decision-making, and greatly improve the safety and reliability of the building.

[0038] The present invention integrates a machine learning algorithm into the computing unit of the central processing module for real-time analysis and prediction. It can quickly identify potential risk points and predict the stress conditions of buildings in earthquakes based on the collected temperature, strain and dynamic response data, solving the problem that traditional earthquake-resistant systems cannot respond and predict dynamically, allowing the system to take preventive measures before an earthquake occurs, or make immediate adjustments when an earthquake occurs to reduce building damage. Through early warning and real-time adjustment, the present technical solution significantly improves the survivability and recovery speed of buildings in earthquakes, and reduces the losses caused by earthquakes.

[0039] The present invention realizes dynamic adjustment of the stiffness and damping of the building structure through the coordinated work of the material control module and the execution module. When the central processing module analyzes that a certain part of the building needs to increase stiffness or damping, the material control module will issue a corresponding instruction to cause the shape memory alloy, piezoelectric material, magnetorheological fluid and other intelligent materials in the execution module to deform or change physical properties, solving the problem that traditional earthquake-resistant measures cannot be flexibly adjusted according to actual conditions, so that the building can adaptively adjust its own state when facing earthquake waves of different intensities and frequencies to achieve the best earthquake-resistant effect; in this way, the technical solution significantly enhances the earthquake-resistant performance of the building and reduces the degree of damage to the building structure caused by the earthquake.

[0040] The present invention forms an all-round monitoring system covering the building area through the multi-level grid structure design of the high-density monitoring network. The optical fiber network of the first layer can widely monitor the overall temperature and strain changes of the building, while the micro-electromechanical nodes of the second layer can accurately locate the dynamic response points of the building. This multi-level monitoring structure not only improves the comprehensiveness and accuracy of data collection, but also solves the problem that a single monitoring method is difficult to take into account both breadth and depth. By comprehensively analyzing the data of each layer, the technical solution can understand the performance of the building in an earthquake in more detail, providing a solid foundation for formulating scientific and reasonable earthquake-resistant strategies, thereby further improving the earthquake-resistant safety level of the building.

[0041] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application so that it can be implemented in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings as follows.

[0042] Based on the detailed description of the specific embodiments of the present application in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings without creative work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0044] Figure 1 A schematic diagram of the adaptive seismic fault crossing system of the present invention;

[0045] Figure 2 A schematic diagram of a high-density monitoring network based on an adaptive seismic fault crossing system of the present invention;

[0046] Figure 3 A schematic diagram of a computing unit of a central processing module of the adaptive seismic fault crossing system of the present invention;

[0047] Figure 4 This is a flow chart of the adaptive seismic fault crossing method of the present invention. DETAILED DESCRIPTION

[0048] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, rather than all of the embodiments. In the following description, specific details such as specific configuration and components are provided only to help fully understand the embodiments of the present application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and brevity, the description of known functions and structures is omitted in the embodiment.

[0049] It should be understood that the references to "one embodiment" or "this embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "one embodiment" or "this embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0050] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0051] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" in this article describes another type of association object relationship, indicating that there can be two relationships. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0052] The term "at least one" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0053] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusions.

[0054] Example 1

[0055] This embodiment mainly describes an adaptive seismic fault crossing system. Figure 1 As shown, it includes a distributed optical fiber module, a micro-electromechanical module, a central processing module, a material control module, and an execution module;

[0056] The distributed optical fiber module and micro-electromechanical module are used as front-end sensing units and are connected to the central processing module through wired or wireless communication protocols; the central processing module is connected to the material control module through a built-in communication interface; the material control module drives the execution module to dynamically adjust through transmission instructions;

[0057] The distributed optical fiber module is composed of optical fiber nodes, which monitor the temperature and strain changes of the building in the fault area in real time and transmit the temperature and strain data to the central processing module through the optical fiber link;

[0058] The MEMS module is composed of MEMS nodes, which monitor the dynamic response data of the building in the fault zone in real time and transmit the dynamic response data to the central processing module through wireless communication;

[0059] The central processing module includes a computing unit, a storage unit, a communication interface and an output unit; the computing unit receives data from the distributed optical fiber module and the micro-electromechanical module, and performs real-time analysis and prediction through a built-in machine learning algorithm; the storage unit is used to store historical data and analysis results, provide fast access and backup, and support data caching and disconnection reconnection; the communication interface uses standard wired and wireless communication protocols to achieve data transmission and exchange with the distributed optical fiber module and the micro-electromechanical module; the output unit generates control instructions based on the analysis results of the computing unit, and sends them to the material control module through a dedicated communication link to drive the execution module;

[0060] The material control module includes a signal processor, a driving circuit and an interface unit; the signal processor receives the control instruction from the central processing module and generates an electrical signal or a magnetic field control signal after analysis; the driving circuit drives by amplifying the electrical signal or the magnetic field control signal; the interface unit provides a physical interface to ensure the transmission of the electrical signal or the magnetic field control signal;

[0061] The execution module consists of a material component, an actuator drive unit and a state monitoring unit; the material component includes at least shape memory alloy, piezoelectric material and magnetorheological fluid; the actuator drive unit receives the control signal from the material control module to drive the material component to deform or change its physical properties; the state monitoring unit monitors the state changes of the material component in real time and feeds back to the material control module to form a closed-loop control circuit.

[0062] Furthermore, the distributed optical fiber module forms a high-density monitoring network through the optical fiber node and the micro-electromechanical node of the micro-electromechanical module; the distributed optical fiber module forms an optical fiber network through the optical fiber node, and uses the temperature-sensitive optical fiber and the strain-sensitive optical fiber of the optical fiber node to monitor the temperature and strain changes;

[0063] Temperature-sensitive optical fiber detects temperature changes in a building area in real time by monitoring changes in optical fiber propagation time. There is a linear relationship between its propagation time and temperature, which can accurately reflect temperature fluctuations.

[0064] Strain-sensitive optical fiber detects the strain of building structures in real time by monitoring the changes in the optical fiber phase. There is also a clear mathematical relationship between the phase change and the strain, which can accurately capture the slight deformation of the structure.

[0065] Temperature-sensitive optical fiber and strain-sensitive optical fiber transmit temperature and strain data to the central processing module in real time through optical fiber links, providing basic data for system data analysis and control command generation;

[0066] The MEMS module forms an induction chain through MEMS nodes, and each MEMS node of the induction chain contains at least an accelerometer, a gyroscope and a displacement sensor to monitor the dynamic response data of the building in real time;

[0067] Accelerometers are used to monitor the acceleration changes of buildings during earthquakes in real time and capture the vibration of building structures;

[0068] The gyroscope is responsible for detecting changes in the building's angular velocity, providing information about the building's rotation and tilt;

[0069] Displacement sensors are used to measure the displacement of buildings during earthquakes, reflecting the degree of deformation of building structures. Accelerometers, gyroscopes, and displacement sensors transmit dynamic response data to the central processing module in real time through wireless communication protocols, providing key data support for real-time analysis and prediction of the system.

[0070] The high-density monitoring network is formed by interlacing the optical fiber network with the induction chain.

[0071] Furthermore, the structure of the high-density monitoring network is a multi-level grid structure, such as Figure 2 As shown; the first layer of the high-density monitoring network is composed of optical fiber nodes of distributed optical fiber modules, forming a optical fiber network covering the building area; the second layer of the high-density monitoring network is composed of micro-electromechanical nodes of micro-electromechanical modules, forming an induction chain covering the dynamic response points of the building area; the optical fiber network and the induction chain are integrated and processed by the central processing module to form a multi-level grid-like high-density monitoring network.

[0072] Furthermore, the computing unit of the central processing module is composed of a receiving module, an analog-to-digital conversion module and an algorithm processing module, such as Figure 3 As shown; the receiving module receives and processes the real-time data from the distributed optical fiber module and the micro-electromechanical module, and connects the analog-to-digital conversion module through a high-speed data line to ensure efficient reading and conversion of the data; the analog-to-digital conversion module converts the real-time data and transmits the real-time data to the storage unit and the algorithm processing module; the algorithm processing module analyzes the sensor data in real time, extracts key features, generates control instructions and transmits them to the storage unit and the material control module.

[0073] Furthermore, the driving circuit of the material control module is composed of a power amplifier, a magnetic field generator, a voltage controller and a current controller; the power amplifier amplifies the electrical signal generated by the signal processor to ensure sufficient power to drive the material components of the execution module; the magnetic field generator generates a magnetic field control signal; the voltage controller and the current controller are used to accurately adjust the output voltage and current respectively;

[0074] The drive circuit is connected to the signal processor through a dedicated interface, receives control instructions and feeds back the current status.

[0075] Furthermore, the signal processor of the material control module receives the control instruction from the central processing module through the standard communication interface, parses the received control instruction to generate control parameters, and the parsed control parameters are converted into analog signals through the analog-to-digital conversion module of the central processing module, which are amplified by the power amplifier of the driving circuit to generate electrical signals or magnetic field control signals;

[0076] The electrical signal is regulated by the voltage controller and current controller of the driving circuit;

[0077] The magnetic field control signal is generated by the magnetic field generator built into the driving circuit to drive the physical changes of the magnetorheological fluid.

[0078] This embodiment accurately monitors key parameters such as temperature, strain and dynamic response of a building during an earthquake. It can also analyze and predict the stress conditions of the building in real time through the intelligent algorithm of the central processing module, and respond quickly to adjust the structural stiffness and damping of the building through the material control module and the execution module, thereby effectively reducing earthquake damage to the building and improving the safety and durability of the building.

[0079] Example 2

[0080] This embodiment is based on Embodiment 1 and describes in detail a method for crossing an earthquake-resistant fault based on adaptation. Figure 4 As shown, the following steps are included:

[0081] S1, a high-density monitoring network is formed by the optical fiber nodes of the distributed optical fiber module and the micro-electromechanical nodes of the micro-electromechanical module, the temperature and strain change data are monitored in real time, and the data is transmitted to the central processing module through wireless communication;

[0082] S2, after the calculation unit of the central processing module receives the monitored temperature and strain change data, it performs real-time analysis and prediction through the algorithm processing module, generates control instructions, and transmits them to the material control module through the output unit;

[0083] S3, the signal processor of the material control module receives these control instructions, generates corresponding electrical signals or magnetic field control signals after analysis, and transmits them to the actuator drive unit of the execution module through the drive circuit for driving;

[0084] S4. The actuator driving unit of the execution module receives the control signal from the material control module, drives the material component to deform or change the physical properties, and feeds back the data to the material control module to form a closed-loop control circuit.

[0085] Furthermore, the optical fiber nodes and the MEMS nodes of the MEMS module form a high-density monitoring network to monitor temperature and strain change data in real time, including:

[0086] Fiber Nodes S1, S2, ..., Sn The MEMS nodes M1, M2, ..., M m A high-density monitoring network is formed; the optical fiber node monitors the temperature and strain changes in real time through the temperature-sensitive optical fiber and the strain-sensitive optical fiber. The temperature-sensitive optical fiber formula is: t T =t0+k T (T-T0), where t T is the propagation time of the temperature-sensitive optical fiber, t0 is the propagation time at the reference temperature T0, k T is the temperature sensitivity coefficient, T is the current temperature; the strain sensitive fiber formula is: Δφ S =Δφ0+k ∈ ·∈, where Δφ S is the phase change of the strain sensitive fiber, Δφ0 is the initial phase change, k ∈ is the strain sensitivity coefficient, ∈ is the current strain; the MEMS node formula is: S i =k i ·X i +b i , where S i is the response value, k i is the MEMS sensitivity coefficient, X i To measure physical quantity, b i is the MEMS bias value.

[0087] Furthermore, after receiving the monitored temperature and strain change data, the calculation unit performs real-time analysis and prediction through the algorithm processing module and generates control instructions, including:

[0088] Define the received temperature data as T i And the strain data is ∈ i The preprocessed temperature data and strain data are T i ′ and ∈ i ′, calculate the temperature change rate of temperature data and strain data and the strain change rate ∈ i · ′, the probability P of predicting the vibration signal of the building area through machine learning algorithm, Where f is a pre-trained machine learning model that generates control instructions based on the probability P of the vibration signal.

[0089] Further, the actuator driving unit receives a control signal from the material control module, and drives the shape memory alloy, the piezoelectric material and the magnetorheological fluid of the material component to adjust the structural stiffness and increase the damping of the structure;

[0090] Shape memory alloys control deformation by changing temperature values, increasing the rigidity of building structures in fault zones;

[0091] Piezoelectric materials produce mechanical deformation through applied electrical signals, adjusting the structural stiffness of the building in the fault zone;

[0092] Magnetorheological fluid forms a chain structure through an external magnetic field control signal, increasing the damping of the building structure in the fault zone.

[0093] Furthermore, the temperature, strain and dynamic response data of the building are collected in real time through a high-density monitoring network, and the machine learning algorithm in the central processing module is used to analyze and predict these data in real time; when abnormal temperature changes, strain increases or abnormal dynamic responses are monitored, the central processing module can quickly identify potential risks and generate early warning signals; the early warning signals can be sent to designated monitoring terminals or management systems through output units to remind relevant personnel to take necessary preventive measures. Through this real-time monitoring and intelligent analysis, the present application can provide timely early warnings before risks occur and help technical personnel prepare in advance.

[0094] This embodiment collects the temperature, strain and dynamic response data of the building in real time through a high-density monitoring network, and uses the machine learning algorithm of the central processing module for real-time analysis and prediction, generates precise control instructions, and converts them into electrical signals or magnetic field control signals through the material control module, driving the shape memory alloy, piezoelectric material and magnetorheological fluid and other smart materials in the execution module to deform or change physical properties, thereby dynamically adjusting the structural stiffness and damping of the building. The whole process forms a closed-loop control loop, ensuring that the building can adaptively adjust its own state during an earthquake, effectively reducing the damage to the building caused by the earthquake, and improving the safety and stability of the building.

[0095] The above are only preferred embodiments of the present invention, which do not limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any changes, modifications, replacements, integrations and parameter changes to these embodiments within the spirit and principles of the present invention through conventional substitutions or without departing from the principles and spirit of the present invention fall within the scope of protection of the present invention.

Claims

1. Based on the adaptive seismic fault crossing system, it is characterized by: It includes distributed optical fiber module, micro-electromechanical module, central processing module, material control module and execution module; The distributed optical fiber module and micro-electromechanical module are used as front-end sensing units and are connected to the central processing module through wired or wireless communication protocols; the central processing module is connected to the material control module through a built-in communication interface; the material control module drives the execution module to dynamically adjust through transmission instructions; The distributed optical fiber module is composed of optical fiber nodes, which monitor the temperature and strain changes of the building in the fault area in real time and transmit the temperature and strain data to the central processing module through the optical fiber link; The micro-electromechanical module is composed of micro-electromechanical nodes, which monitor the dynamic response data of the building in the fault zone in real time and transmit the dynamic response data to the central processing module through wireless communication; The central processing module includes a computing unit, a storage unit, a communication interface and an output unit; the computing unit receives data from the distributed optical fiber module and the micro-electromechanical module, and performs real-time analysis and prediction through a built-in machine learning algorithm; the storage unit is used to store historical data and analysis results, provide fast access and backup, and support data caching and disconnection reconnection; the communication interface adopts standard wired and wireless communication protocols to achieve data transmission and exchange with the distributed optical fiber module and the micro-electromechanical module; the output unit generates a control instruction according to the analysis result of the computing unit, and sends it to the material control module through a dedicated communication link to drive the execution module; The material control module includes a signal processor, a driving circuit and an interface unit; the signal processor receives the control instruction from the central processing module and generates an electric signal or a magnetic field control signal after analysis; the driving circuit drives by amplifying the electric signal or the magnetic field control signal; the interface unit provides a physical interface to ensure the transmission of the electric signal or the magnetic field control signal; The execution module is composed of a material component, an actuator drive unit and a state monitoring unit; the material component includes at least shape memory alloy, piezoelectric material and magnetorheological fluid; the actuator drive unit receives the control signal of the material control module to drive the material component to deform or change the physical properties; the state monitoring unit monitors the state changes of the material component in real time and feeds back to the material control module to form a closed-loop control circuit.

2. The adaptive seismic fault crossing system according to claim 1, characterized in that: The distributed optical fiber module forms a high-density monitoring network through optical fiber nodes and micro-electromechanical nodes of the micro-electromechanical module; the distributed optical fiber module forms an optical fiber network through optical fiber nodes, and uses temperature-sensitive optical fibers and strain-sensitive optical fibers of optical fiber nodes to monitor temperature and strain changes; The micro-electromechanical module forms an induction chain through micro-electromechanical nodes, and each micro-electromechanical node of the induction chain includes at least an accelerometer, a gyroscope and a displacement sensor to monitor the dynamic response data of the building in real time; The high-density monitoring network is formed by interlacing the optical fiber network with the induction chain.

3. The adaptive seismic fault crossing system according to claim 1, characterized in that: The high-density monitoring network has a multi-level grid structure. The first level of the high-density monitoring network is composed of optical fiber nodes of distributed optical fiber modules, forming an optical fiber network covering the building area. The second level of the high-density monitoring network is composed of micro-electromechanical nodes of micro-electromechanical modules, forming an induction chain covering dynamic response points in the building area. The optical fiber network and the induction chain are integrated and processed by a central processing module to form a multi-level grid-like high-density monitoring network.

4. The adaptive seismic fault crossing system according to claim 1 or 3, characterized in that: The computing unit of the central processing module is composed of a receiving module, an analog-to-digital conversion module and an algorithm processing module; the receiving module receives and processes real-time data from the distributed optical fiber module and the micro-electromechanical module, and connects the analog-to-digital conversion module through a high-speed data line to ensure efficient reading and conversion of data; the analog-to-digital conversion module converts real-time data and transmits the real-time data to the storage unit and the algorithm processing module; the algorithm processing module analyzes sensor data in real time, extracts key features, generates control instructions and transmits them to the storage unit and the material control module.

5. The adaptive seismic fault crossing system according to claim 1, characterized in that: The driving circuit of the material control module is composed of a power amplifier, a magnetic field generator, a voltage controller and a current controller; the power amplifier amplifies the electrical signal generated by the signal processor to ensure that there is sufficient power to drive the material components of the execution module; the magnetic field generator generates a magnetic field control signal; the voltage controller and the current controller are used to accurately adjust the output voltage and current respectively; The driving circuit is connected to the signal processor via a dedicated interface, receives control instructions and feeds back the current state.

6. The adaptive seismic fault crossing system according to claim 1, characterized in that: The signal processor of the material control module receives the control instruction from the central processing module through the standard communication interface, analyzes the received control instruction to generate control parameters, and the analyzed control parameters are converted into analog signals through the analog-to-digital conversion module of the central processing module, which are amplified by the power amplifier of the driving circuit to generate electrical signals or magnetic field control signals; The electrical signal is adjusted by a voltage controller and a current controller of a driving circuit; The magnetic field control signal is generated by a magnetic field generator built into the driving circuit to drive the physical change of the magnetorheological fluid.

7. The adaptive seismic fault crossing method is characterized by: The following steps are involved: S1, a high-density monitoring network is formed by the optical fiber nodes of the distributed optical fiber module and the micro-electromechanical nodes of the micro-electromechanical module, the temperature and strain change data are monitored in real time, and the data is transmitted to the central processing module through wireless communication; S2, after the calculation unit of the central processing module receives the monitored temperature and strain change data, it performs real-time analysis and prediction through the algorithm processing module, generates control instructions, and transmits them to the material control module through the output unit; S3, the signal processor of the material control module receives these control instructions, generates corresponding electrical signals or magnetic field control signals after analysis, and transmits them to the actuator drive unit of the execution module through the drive circuit for driving; S4. The actuator driving unit of the execution module receives the control signal from the material control module, drives the material component to deform or change the physical properties, and feeds back the data to the material control module to form a closed-loop control circuit.

8. The adaptive seismic fault crossing method according to claim 7, characterized in that: The optical fiber nodes in S1 and the micro-electromechanical nodes of the micro-electromechanical module form a high-density monitoring network to monitor temperature and strain change data in real time, specifically including: The optical fiber nodes S1, S2, ..., S n The MEMS nodes M1, M2, ..., M m A high-density monitoring network is formed; the optical fiber node monitors temperature and strain changes in real time through temperature-sensitive optical fiber and strain-sensitive optical fiber. The temperature-sensitive optical fiber formula is: t T =t0+k T (T-T0), where t T is the propagation time of the temperature-sensitive optical fiber, t0 is the propagation time at the reference temperature T0, k T is the temperature sensitivity coefficient, T is the current temperature; the strain sensitive optical fiber formula is: Δφ S =Δφ0+k ∈ ·∈, where Δφ S is the phase change of the strain sensitive fiber, Δφ0 is the initial phase change, k ∈ is the strain sensitivity coefficient, ∈ is the current strain; the MEMS node formula is: S i =k i ·X i +b i , where S i is the response value, k i is the MEMS sensitivity coefficient, X i To measure physical quantities, b i is the MEMS bias value.

9. The adaptive seismic fault crossing method according to claim 7, characterized in that: After receiving the monitored temperature and strain change data, the computing unit in S2 performs real-time analysis and prediction through the algorithm processing module and generates control instructions, which specifically include: Define the received temperature data as T i And the strain data is ∈ i The preprocessed temperature data and strain data are T i ′ and ∈ i ′, calculate the temperature change rate of temperature data and strain data and strain rate The probability P of the vibration signal in the building area is predicted by machine learning algorithm. Where f is a pre-trained machine learning model that generates control instructions based on the probability P of the vibration signal.

10. The adaptive seismic fault crossing method according to claim 7, characterized in that: The actuator driving unit in S4 receives the control signal from the material control module, and drives the shape memory alloy, piezoelectric material and magnetorheological fluid of the material component to adjust the structural stiffness and increase the damping of the structure; The shape memory alloy controls the deformation amount by changing the temperature value, thereby increasing the rigidity of the building structure in the fault zone; The piezoelectric material generates mechanical deformation through an applied electrical signal to adjust the structural stiffness of the building in the fault zone; The magnetorheological fluid forms a chain structure through an external magnetic field control signal, thereby increasing the damping property of the building structure in the fault zone.

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