Adaptive fault-crossing system and method

Abstract

The application is based on an adaptive anti-seismic fault crossing system and method, which comprises a distributed optical fiber module, a micro-electro-mechanical module, a central processing module, a material control module and an execution module; the distributed optical fiber module and the micro-electro-mechanical module respectively monitor the temperature, strain and dynamic response data of the building in real time through optical fiber nodes and micro-electro-mechanical nodes, and transmit the data to the central processing module; the central processing module is responsible for real-time analysis and prediction of the data, and generates control instructions; after receiving the control instructions, the material control module converts the control instructions into electrical signals or magnetic field control signals through a signal processor, a driving circuit and an interface unit, drives the material components in the execution module to deform or change physical properties, and dynamically adjusts the structural stiffness and damping of the building. The application realizes dynamic adjustment of the stiffness and damping of the building structure, increases the seismic resistance of the building in the fault area, and improves the safety and stability of the building.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake-resistant detection, 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, earthquake damage to these buildings and facilities often leads to significant economic losses and casualties. Buildings located near seismic fault zones, in particular, face greater challenges in seismic design due to the more direct and intense impact of seismic waves. To improve the safety and stability of buildings during earthquakes, seismic-resistant technologies that can monitor and adaptively adjust in real time are needed to address the threat posed by earthquakes.

[0003] Traditional earthquake-resistant technologies primarily rely on static design principles, such as increasing structural strength and using seismic isolation devices. While these methods have improved a building's seismic performance to a certain extent, they have significant limitations. For example, they cannot dynamically respond to the actual forces acting on the building during an earthquake, and they are unable to effectively protect the building if it experiences seismic forces exceeding its design expectations. Furthermore, traditional technologies lack the ability to monitor the building's internal conditions in real time, preventing them from promptly identifying and addressing potential safety hazards.

[0004] In recent years, with the advancement of sensing and information processing technologies, new earthquake-resistant technologies have emerged, such as the use of smart materials and advanced control systems to enhance the seismic performance of buildings. While these technologies offer improvements over traditional approaches, they still have drawbacks, such as high cost, system complexity, and maintenance difficulties. Existing solutions typically only provide localized monitoring and control, failing to form comprehensive, systematic solutions. This is particularly true when dealing with complex environments, such as those involving earthquake faults.

[0005] To address the above issues, this technical solution proposes an adaptive seismic fault crossing system. The system combines distributed fiber optic monitoring, micro-electromechanical systems and material technologies 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 seismic 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 serve as front-end sensing units and are connected to the central processing module via wired or wireless communication protocols; the central processing module is connected to the material control module via a built-in communication interface; the material control module drives the execution module to dynamically adjust by transmitting 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 zone 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 using 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 via a dedicated communication link to drive the execution module;

[0011] The material control module includes a signal processor, a drive circuit, and an interface unit; the signal processor receives control instructions from the central processing module and generates an electrical signal or a magnetic field control signal after analysis; the drive 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;

[0012] 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 and drives 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.

[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 the optical fiber nodes, and uses the temperature-sensitive optical fiber and the strain-sensitive optical fiber of the optical fiber nodes to monitor temperature and strain changes;

[0014] The micro-electromechanical module forms an inductive chain through micro-electromechanical nodes, and each micro-electromechanical node of the inductive 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 and the induction chain.

[0016] Preferably, the structure of the high-density monitoring network is 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 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.

[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 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 control instructions from the central processing module through a standard communication interface, parses the received control instructions to generate control parameters, and the parsed control parameters are converted into analog signals by 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 regulated 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 changes 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 to monitor the temperature and strain change data in real time, and transmit the data to the central processing module through wireless communication;

[0025] S2. After receiving the monitored temperature and strain change data, the calculation unit of the central processing module 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 drive 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 and the MEMS nodes M1, M2, ..., M m Form a high-density monitoring network; 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 optical 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 quantities, b i is the MEMS bias value.

[0030] Preferably, after receiving the monitored temperature and strain change data, the calculation unit in S2 performs real-time analysis and prediction through the algorithm processing module and generates control instructions, specifically including:

[0031] Define the received temperature data as T i And the strain data is ∈ i , the pre-processed temperature data and strain data are T i ′ and ∈ i ′, calculate the temperature change rate of temperature data and strain data and strain 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 to drive 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 by changing the temperature value, thereby increasing the structural rigidity of the building in the fault zone;

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

[0035] The magnetorheological fluid forms a chain structure by applying 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 earthquake fault areas in real time. This high-density monitoring network can not only cover the entire building area, but also provide high-precision real-time data, solving 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 buildings in earthquakes, provide reliable data support for subsequent analysis and decision-making, and greatly improve the safety and reliability of buildings.

[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 seismic resistance systems are unable to respond and predict dynamically, enabling the system to take preventive measures before an earthquake occurs, or to make immediate adjustments when an earthquake occurs to reduce building damage. Through early warning and real-time adjustment, this technical solution significantly improves the survivability and recovery speed of buildings in earthquakes, reducing the losses caused by earthquakes.

[0039] The present invention achieves dynamic adjustment of the stiffness and damping of building structures through the collaborative 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 have increased stiffness or damping, the material control module will issue a corresponding instruction, causing the intelligent materials such as shape memory alloys, piezoelectric materials, and magnetorheological fluids in the execution module to deform or change their physical properties. This solves the problem that traditional earthquake-resistant measures cannot be flexibly adjusted according to actual conditions. When faced with earthquake waves of different intensities and frequencies, the building can adaptively adjust its state to achieve the optimal earthquake-resistant effect. In this way, this technical solution significantly enhances the building's seismic performance and reduces the degree of damage to the building structure caused by earthquakes.

[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 first layer of optical fiber network can widely monitor the overall temperature and strain changes of the building, while the second layer of micro-electromechanical nodes 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, this technical solution can more carefully understand the performance of the building in the earthquake, providing a solid foundation for the formulation of scientific and reasonable earthquake resistance strategies, thereby further improving the earthquake 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 embodiment of the present application in conjunction with the accompanying drawings.

[0042] Based on the detailed description of the specific embodiments of the present application in conjunction 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 embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without inventive work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

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

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

[0046] Figure 3 A schematic diagram of a central processing module computing unit 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] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the following description, specific details such as specific configurations 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 has been omitted in the embodiments.

[0049] It should be understood that references throughout this specification to "one embodiment" or "this embodiment" mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present application. Therefore, the appearance of "one embodiment" or "this embodiment" throughout this specification does not necessarily refer to the same embodiment. Furthermore, these particular 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 three relationships can exist. 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 two relationships can exist. 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 subsequent associated objects are in an "or" relationship.

[0052] The term "at least one" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. 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 document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any 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 inclusion.

[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 serve 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 by transmitting instructions.

[0057] The distributed fiber optic module is composed of fiber optic nodes, which monitor the temperature and strain changes of buildings in the fault zone in real time and transmit the temperature and strain data to the central processing module through the fiber optic 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 using a built-in machine learning algorithm. The storage unit is used to store historical data and analysis results, providing fast access and backup, and supports 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 via a dedicated communication link to drive the execution module.

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

[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 and drives 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 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;

[0063] Temperature-sensitive optical fiber detects temperature changes within a building area in real time by monitoring changes in optical fiber propagation time. There is a linear relationship between 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 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 slightest deformation of the structure.

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

[0066] The MEMS module forms an inductive chain through MEMS nodes. Each MEMS node in the inductive 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 the building structure. Accelerometers, gyroscopes, and displacement sensors all transmit dynamic response data to the central processing module in real time via wireless communication protocols, providing key data support for the system's real-time analysis and prediction.

[0070] The high-density monitoring network is formed by interlacing the optical fiber network and 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 in the figure, the first layer of the high-density monitoring network is composed of optical fiber nodes of distributed optical fiber modules, forming a fiber optic 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 real-time data from the distributed optical fiber module and the micro-electromechanical module, and connects to 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 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 control instructions from the central processing module through a standard communication interface, parses the received control instructions to generate control parameters, and the parsed control parameters are converted into analog signals by 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 drive circuit to drive the physical changes of the magnetorheological fluid.

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

[0079] Example 2

[0080] This embodiment is based on the embodiment 1, and describes in detail the adaptive seismic fault crossing method. 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 to monitor the temperature and strain change data in real time, and transmit the data to the central processing module through wireless communication;

[0082] S2. After receiving the monitored temperature and strain change data, the calculation unit of the central processing module 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 drive 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, specifically including:

[0086] Fiber Nodes S1, S2, ..., Sn and 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 fiber formula is: Δφ S =Δφ0+k ∈ ·∈, where Δφ S is the phase change of the strain-sensitive optical 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.

[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, specifically including:

[0088] Define the received temperature data as T i And the strain data is ∈ i , the pre-processed temperature data and strain data are T i ′ and ∈ i ′, calculate the temperature change rate of temperature data and strain data and strain 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] Furthermore, the actuator drive unit 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;

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

[0091] Piezoelectric materials generate mechanical deformation through applied electrical signals, adjusting the structural stiffness of buildings in fault zones;

[0092] Magnetorheological fluid forms a chain structure through the control signal of an external magnetic field, increasing the damping properties 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 detected, the central processing module can quickly identify potential risks and generate early warning signals; the early warning signals can be sent to the designated monitoring terminal or management system through the output unit 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, helping technical personnel to prepare in advance.

[0094] This embodiment uses a high-density monitoring network to collect real-time data on a building's temperature, strain, and dynamic response. The central processing module's machine learning algorithms perform real-time analysis and prediction, generating precise control instructions. These are converted into electrical signals or magnetic field control signals by the material control module. These signals drive the deformation or physical property changes of smart materials such as shape memory alloys, piezoelectric materials, and magnetorheological fluids in the execution module, thereby dynamically adjusting the building's structural stiffness and damping. This entire process forms a closed-loop control circuit, ensuring that the building can adaptively adjust its state during an earthquake, effectively reducing damage to the building and improving its safety and stability.

[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 can be modified and varied in various ways. Any changes, modifications, replacements, integrations and parameter changes to these embodiments through conventional substitutions or that can achieve the same functions 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 serve as front-end sensing units and are connected to the central processing module via wired or wireless communication protocols; the central processing module is connected to the material control module via a built-in communication interface; the material control module drives the execution module to dynamically adjust by transmitting 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 zone 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 using 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 via a dedicated communication link to drive the execution module; The material control module includes a signal processor, a drive circuit, and an interface unit; the signal processor receives control instructions from the central processing module and generates an electrical signal or a magnetic field control signal after analysis; the drive 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; The execution module is composed of a material component, an actuator drive unit, and a state monitoring unit; the material component includes at least a shape memory alloy, a piezoelectric material, and a magnetorheological fluid; the actuator drive unit receives a 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; 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; The micro-electromechanical module forms an inductive chain through micro-electromechanical nodes, and each micro-electromechanical node of the inductive 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 and the induction chain.

2. 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 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 the dynamic response points of 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.

3. The adaptive seismic fault crossing system according to claim 1 or 2, characterized in that: The computing unit of the central processing module consists 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 to the analog-to-digital conversion module via 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.

4. The adaptive seismic fault crossing system according to claim 1, characterized in that: The driving circuit of the material control module consists 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 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.

5. The adaptive seismic fault crossing system according to claim 1, characterized in that: The signal processor of the material control module receives the control instructions from the central processing module through the standard communication interface, analyzes the received control instructions to generate control parameters, converts the analyzed control parameters into analog signals through the analog-to-digital conversion module of the central processing module, and amplifies them by the power amplifier of the driving circuit to generate electrical signals or magnetic field control signals; The electrical signal is regulated 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 changes of the magnetorheological fluid.

6. A self-adaptive seismic fault crossing method, applicable to any one of claims 1-5, characterized in that: 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 to monitor the temperature and strain change data in real time, and transmit the data to the central processing module through wireless communication; S2. After receiving the monitored temperature and strain change data, the calculation unit of the central processing module 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 drive 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.

7. The adaptive seismic fault crossing method according to claim 6, characterized in that: The optical fiber nodes in S1 and the MEMS nodes of the MEMS module form a high-density monitoring network to monitor temperature and strain change data in real time, specifically including: The optical fiber node MEMS nodes with MEMS modules 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: ,in is the propagation time of the temperature-sensitive fiber, is the reference temperature The propagation time under is the temperature sensitivity coefficient, Current temperature; the strain sensitive optical fiber formula is: ,in is the phase change of the strain sensitive fiber, is the initial phase change, is the strain sensitivity coefficient, is the current strain; the MEMS node formula is: ,in is the response value, is the MEMS sensitivity coefficient, To measure physical quantities, is the MEMS bias value.

8. The adaptive seismic fault crossing method according to claim 6, characterized in that: After receiving the monitored temperature and strain change data, the calculation unit in S2 performs real-time analysis and prediction through the algorithm processing module and generates control instructions, specifically including: Define the received temperature data as And the strain data is , the preprocessed temperature data and strain data are and ′, calculate the temperature change rate of temperature data and strain data and strain rate ′, predict the probability of vibration signals in building areas through machine learning algorithms , ,in For pre-trained machine learning models, the probability of vibration signals Generate control instructions.

9. The adaptive seismic fault crossing method according to claim 6, characterized in that: The actuator drive 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 by changing the temperature value, thereby increasing the structural rigidity of the building in the fault zone; The piezoelectric material generates mechanical deformation through an external electrical signal to adjust the structural stiffness of the building in the fault zone; The magnetorheological fluid forms a chain structure by applying an external magnetic field control signal, thereby increasing the damping property of the building structure in the fault zone.

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