596 results about "Fiber network" patented technology

The invention relates to the technical field of communication, and discloses an optical fiber performance automatic test method, system and device based on optical power detection. The method comprises the following steps: carrying out intelligent scanning detection on an optical fiber network, generating optical fiber resource mapping data, obtaining an OTDR measurement configuration table through dynamic analysis, executing optical pulse injection and signal acquisition to obtain a time domain response characteristic curve, obtaining an optical fiber performance parameter set through wavelet denoising and peak value identification processing, and obtaining the optical fiber performance parameter set. And performing fault analysis by using deep learning and expert rules to obtain diagnosis decision data, and finally generating an optical fiber state evaluation report through digital twin modeling. According to the invention, remote automatic testing, intelligent fault diagnosis and visual monitoring management of the optical fiber network can be realized, and the operation and maintenance efficiency and reliability of the optical fiber communication network are improved.

The invention relates to the technical field of network service testing, in particular to a testing method and system based on optical fiber characteristics. The method comprises the following steps: acquiring multi-dimensional optical fiber characteristic data of an optical fiber link through optical fiber characteristic acquisition equipment; performing characteristic data hierarchical integration on the multi-dimensional optical fiber characteristic data to obtain an optical link characteristic vector group; performing temperature-error code wavelet transform association according to the optical link feature vector group to obtain a temperature-error code wavelet feature set; constructing an association prediction matrix according to the temperature-error code wavelet feature set; performing a service delay exception test according to the association prediction matrix to obtain a delay tail exception event set; and performing micro-bend three-dimensional feature recognition according to the delayed tail abnormal event set to obtain micro-bend mode features. According to the invention, the operation and maintenance efficiency, reliability and toughness of the optical fiber network of the data center are improved through full-link test analysis from optical fiber physical characteristics to storage service delay.

The invention relates to a preparation method and application of a composite leakage-proof agent while drilling, and belongs to the technical field of petroleum drilling engineering. The composite leakage-proof agent while drilling is prepared from the following components in percentage by mass: 20-45% of nano composite particles, 15-35% of a multi-stage fiber reinforced network material, 10-20% of an intelligent response blocking agent, 5-10% of a high-temperature-resistant additive, 1-5% of a dispersing agent and the balance of deionized water, wherein the sum of the contents of all the components is 100%. According to the composite leak-proof agent while drilling provided by the invention, the temperature-sensitive resin with a core-shell structure is introduced, so that a three-dimensional synergistic mechanism of nano-composite particle filling-multistage fiber network bridging-intelligent response curing is realized, and efficient and environment-friendly plugging of an ultrahigh-temperature and high-pressure stratum is realized. The success rate of one-time plugging is increased from 45% to 92%, the average operation time is shortened by 60%, and an efficient solution is provided for deep and ultra-deep drilling.

The invention relates to the technical field of electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.

The invention discloses a distributed computing task non-perception migration method and system under optical fiber network interruption. The distributed computing task non-perception migration method comprises the following steps: constructing a distributed task migration system and an output system architecture; collecting network state indexes, constructing a network topological graph, and analyzing the state of an optical fiber link for fault prediction; obtaining a calculation task operation state, establishing a sub-task mapping relation, constructing a directed dependency graph, and generating a state snapshot; analyzing resource requirements, reserving standby resources, and deploying a distributed cache system; analyzing a fault influence range, extracting an influenced calculation sub-graph, and selecting a migration target node; in a target node preloading environment, reconstructing an execution context, and redirecting a communication path; and setting a data change capture mechanism, synchronizing incremental data and executing consistency verification. According to the method, non-perception migration of the computing tasks is realized, task continuity and data consistency are guaranteed, and the reliability of the distributed computing system in an optical fiber network fault scene is improved.

The invention provides a network fault node automatic detection and roundabout path planning method, which comprises the following steps of: acquiring optical power and bit error rate of an optical fiber link, and recording abnormal time point and position information; obtaining a strong electromagnetic interference influence weight map; generating a deterioration trend curve; obtaining a health degree score of each optical fiber segment, and generating a health degree distribution map; probability distribution of fault nodes is determined, whether the nodes are fault points is judged according to the probability distribution, and a fault node position set is generated; obtaining a roundabout path planning scheme; and calculating a global network health state perception score to obtain an optimized global network health state diagram. The invention further discloses a corresponding storage medium. According to the invention, the reliability and stability of the optical fiber network can be effectively improved, and the efficiency and accuracy of fault detection and processing can be improved.

The invention provides an optical network communication method and a communication device, the method is applied to an optical fiber network, the optical fiber network comprises a master device and at least one slave device, and the at least one slave device comprises a first slave device. Wherein in the initialization phase, after receiving the authentication encryption capability of the slave device, the master device can indicate a determined authentication encryption mode (e.g., a first authentication encryption mode) to the slave device, so that the authentication encryption capability of the slave device is enhanced in a subsequent process (e.g., a roaming parameter configuration process). The master device does not need to configure security-related parameters such as an authentication encryption mode for the slave device, so that the configuration efficiency is improved, and the configuration overhead is saved.

The invention discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

The invention discloses an edge calculation optimization and compression algorithm based on distributed optical fiber sensing data, and the algorithm achieves the full-section continuous monitoring of an embankment through the layering burying of a distributed optical fiber network along the axis of the embankment, and eliminates the blind area of a conventional point sensor. Strain data and temperature data are analyzed by using Brillouin scattering and Raman scattering, time sequence characteristics are analyzed through an LSTM neural network, and piping and crack hidden dangers are identified. External environment parameters such as water level and rainfall are fused to construct a multiple regression model, and the seepage risk level is evaluated. Through combination of BIM three-dimensional modeling and a seepage simulation algorithm, hidden dangers are accurately positioned, and spatial distribution of the hidden dangers is visualized. Based on edge calculation optimization, a data compression technology is adopted to reduce transmission requirements, a self-repairing material injection and high-pressure plugging scheme is triggered through a grading early warning system, closed-loop management from monitoring to repairing is formed, the real-time performance and accuracy of hidden danger recognition are improved, and efficient and reliable monitoring support is provided for dike engineering.

The invention discloses a multi-operator optical fiber network fault diagnosis method and device, equipment and a medium, and relates to the technical field of deep neural networks, and the method comprises the steps: constructing a three-dimensional digital twin model, and carrying out the parameter correction of the three-dimensional digital twin model; acquiring open source library data and operation and maintenance data of a plurality of operators to generate a cross-operator network asset database; respectively deploying the corrected three-dimensional digital twin model to each operator so that the operator can calculate corresponding model training parameters; acquiring all model training parameters, training the graph neural network model based on the model training parameters, and judging whether the trained graph neural network model is converged or not; if convergence occurs, inputting the cross-operator network asset database into the trained graph neural network model to predict an optical fiber network fault point; the fault report and the repair scheme are generated based on the optical fiber network fault points, the accuracy of multi-operator optical fiber network fault diagnosis is improved, and the cross-operator cooperation efficiency and the user experience feeling are improved.

The invention is suitable for the technical field of toll station management, and provides an expressway toll station remote seat guarding and special situation vehicle handling system, which comprises a front-end sensing layer, a rear-end sensing layer and a control layer, the lane equipment comprises a photoelectric sensor, an infrared height limit alarm, a license plate recognition camera, an ETC antenna, an acousto-optic warning ball and a self-service equipment sensor; the network transmission layer is connected with lane equipment, a sentry box cabinet, a machine room core switch and a monitoring center through an optical fiber network; the remote control layer is composed of a remote seat center, and the remote seat center can carry out video monitoring, visual intercom and remote control; and the abnormity alarm layer is used for carrying out early warning on vehicle detention, vehicle height exceeding, ETC passing faults, toll evasion vehicles and handrail abnormity. The vehicle state can be sensed in real time, data are gathered to a remote seat center through a network transmission layer, an information island is broken, and special conditions can be automatically processed.

The invention discloses an underground gas storage gas monitoring method and system based on optical fiber spectrum analysis. The method comprises the following steps: S1, optical fiber network deployment: arranging a corrosion-resistant optical fiber sensor network in a gas storage; s2, spectral data acquisition: transmitting light with a specific wavelength to a gas chamber at the tail end of an optical fiber through a Fourier transform infrared spectrometer, and acquiring a transmitted spectrum after interaction of gas and light; s3, spectrum decoupling and concentration calculation: based on a peak pattern fitting algorithm of the Lambert-Beer law, decoupling the infrared spectrum characteristics of the mixed gas, matching a standard spectrum database, and calculating the concentration of each gas component; s4, constructing a three-dimensional gas model: according to the position of the optical fiber sensor and the gas concentration data, establishing a gas migration model to predict a leakage position and a movement direction; according to the method, the trend, the component distribution, the concentration change and the potential leakage risk of the gas in the storage are effectively evaluated, and technical support is provided for the safety, the effectiveness and the economical efficiency of operation of the gas storage.

The invention discloses a zirconium-based metal organic framework (Zr-MOF) composite semi-solid electrolyte material prepared based on an electrostatic spinning technology and application of the zirconium-based metal organic framework (Zr-MOF) composite semi-solid electrolyte material. The material is composed of a three-dimensional polymer fiber network, surface-coated Zr-MOF nanoparticles and an organic liquid electrolyte solution, a polymer matrix provides mechanical support, and Zr-MOF micropores and a liquid electrolyte form a multistage ion transmission channel. The preparation method comprises the processes of electrostatic spinning forming, in-situ Zr-MOF synthesis and gradient pressure electrolyte infiltration. The ionic conductivity of the material at 25 DEG C reaches 1.2 * 10 S / cm, the lithium ion transference number is 0.65, the capacity retention ratio after 500 cycles is greater than or equal to 90%, and the material is suitable for high-energy-density lithium ion batteries and flexible energy storage devices through acupuncture safety tests.

The invention discloses an optical fiber network port resource checking method and system based on big data, and the method comprises the steps: collecting the real-time data of a port, obtaining a preliminary data set, extracting key features in the preliminary data set, obtaining the abnormal change of the operation state of the port through the key features, and checking the abnormal change of the operation state of the port. Therefore, abnormal ports are identified and communication quality evaluation is carried out. The contribution degree of rate change to communication quality abnormity is further judged, a high-risk port set is obtained, a data visualization technology is applied to generate a resource checking report, the future bottleneck probability is dynamically updated, and a basis is provided for port resource optimization. According to the method and the system, the abnormal port in the optical fiber communication network can be effectively identified, the potential bottleneck can be effectively predicted, and data support is provided for port resource allocation, so that the network operation efficiency is improved, the port resources are dynamically allocated, and the communication quality is improved.

In some embodiments, a fiber optic cable may be monitored while reducing false positives from disturbances in trusted areas. In some embodiments, a system includes a light source, a first remote termination unit in a trusted area, and a second remote termination unit in another trusted area. The light source emits light through the fiber optic cable, which passes through the first remote termination unit, an untrusted area, and then the second remote termination unit. The remote termination units reflect portions of the light back towards the light source. The system uses a prediction model to detect disturbances in the untrusted area based on the reflected light data.

The invention discloses a power distribution communication network fault positioning method and system based on deep learning, and the method comprises the steps: carrying out the fitting processing of obtained time series data of an optical fiber link, and obtaining an actual power attenuation curve; calculating an absolute difference value between the actual power attenuation curve and the theoretical power attenuation curve to obtain standardized difference data; inputting the standardized difference data into a nonlinear attenuation model to obtain nonlinear change characteristic data; inputting the topological characteristic data and the nonlinear change characteristic data of the power distribution network into a fault identification model to obtain a fault probability value of each optical fiber link; comparing the fault probability value with a preset fault probability threshold value to obtain a potential fault link, and performing feature extraction and abrupt change node identification on the potential fault link to obtain attenuation abrupt change node data; and processing the attenuation abrupt change node data, and determining fault point data based on the obtained stress value data. According to the method provided by the invention, the loss fault in the optical fiber network can be quickly positioned.

Methods and systems are described herein for monitoring fault events at a fiber optical network. In particular, a system may receive, from components of an optical network, corresponding component data structures comprising optical measurements. The system may extract, from a component data structure, a set of component metrics for light transmission signals being transmitted or received via fiber optic transmission lines at a corresponding component and input the component data structure into a first machine learning model to obtain an indication of an occurrence of an event at one or more components. The system may generate a prompt for input into a second machine learning model configured to identify corrective actions for addressing any events within optical networks to obtain one or more corrective actions for addressing the occurrence of the event.

The invention discloses an optical network unit (ONU) and a registration method thereof, and the registration method comprises the following steps: in a standby state, judging whether network parameter setting information broadcasted by an optical line terminal (OLT) is received or not; in response to judging that the network parameter setting information is received, entering a serial number state, and starting timing to generate timing time; in the serial number state, receiving at least one piece of serial number requirement information from the OLT; in the serial number state, when the timing time does not exceed the exploration time and when the serial number requirement information is received each time, a serial number response message is sent out, whether the identification code distribution information is received or not is judged, and the exploration time is not smaller than the time interval of the ONU receiving the serial number requirement information for at least two times; and when the timing time does not exceed the exploration time, responding to the judgment that the identification code distribution information is received, and entering a distance measurement state.

The invention provides a carbon nanotube modified high-thermal-conductivity and high-strength carbon fiber network body with gradient pore and hierarchical pore characteristics and a preparation method and application thereof, and belongs to the technical field of structure and function integrated composite materials. The preparation method comprises the following steps: blowing carbon fibers and hot-melt polymer fibers into a net through gas to obtain a two-dimensional fiber network body; laying the two-dimensional fiber network body according to the surface density gradient of'low-high-low ', and introducing fibers in the thickness direction for fixing to obtain a three-dimensional carbon fiber network body; sequentially carrying out carbon phase densification and graphitization treatment on the three-dimensional carbon fiber network body to obtain a high-thermal-conductivity three-dimensional carbon fiber network body; and dispersing the high-thermal-conductivity three-dimensional carbon fiber network body into the composite dispersion liquid, and sequentially performing freezing, vacuum drying and magnetic field-assisted induced carbonization to obtain the high-thermal-conductivity three-dimensional carbon fiber composite material. By reasonably designing the pore structure, the mechanical and thermal performance of the carbon fiber network body is improved, and the micro-area heat exchange efficiency of the phase change energy storage composite material is optimized.

The invention discloses an ultra-flexible stretchable dimming film and a preparation method thereof, and relates to the technical field of dimming films, and the ultra-flexible stretchable dimming film comprises an upper transparent electrode layer, a dimming function layer and a lower transparent electrode layer which are sequentially stacked from top to bottom; the preparation method comprises the following steps: 1) preparing a main matrix material / ultraviolet curing resin mixture; 2) assembling the dimming film; and 3) ultraviolet light curing. According to the invention, through surface phosphonic acid functionalization treatment, covalent bond anchoring of cerium oxide nanoparticles is realized on a thermoplastic polyurethane fiber network; by constructing a full-thermoplastic polyurethane material system, the breakthrough of interfacial compatibility between a fiber reinforced phase and a polymer matrix is realized, so that the film has high tensile strain capacity and excellent fatigue resistance on the premise of keeping high transparency.

Systems and methods for detecting faulty optical fibers. A method, according to one implementation, includes collecting data associated with an optical fiber network, wherein the data includes optical fiber performance data, and data associated with one or more environmental factors; performing a first linear regression analysis on the data; performing a second linear regression analysis on results of the first linear regression analysis; and determining one or more issues relating to the optical fiber network based on results of the second linear regression analysis.

The invention provides a lane toll collection system, which relates to the technical field of traffic toll collection and comprises a global sensing layer, an edge intelligent layer, a cloud intelligent layer and a lane execution layer. The global sensing layer collects data based on the lane execution layer through a multi-modal sensing matrix, and uploads the data to the edge intelligent layer after the data is preprocessed by an edge computing node; the edge intelligent layer completes vehicle identification and rate calculation based on a distributed AI engine, and issues a decision instruction to the lane execution layer; wherein the cloud intelligent layer provides global data storage, model training and cross-domain cooperation functions, and communicates with the edge intelligent layer in real time through a 5G / optical fiber network; the multi-modal sensing matrix collects data based on the lane execution layer, the data are preprocessed by the edge computing node and then uploaded to the edge intelligent layer, the edge intelligent layer completes vehicle identification and rate calculation and issues a decision instruction to the lane execution layer, the architecture realizes rapid processing and efficient decision of the data, and the overall efficiency of the toll collection system is improved.

The invention relates to the technical field of optical fiber communication and sensing, and discloses an optical fiber coding and positioning integrated sensing device based on frequency division multiplexing and an application method, and the optical fiber coding and positioning integrated sensing device comprises a light source module, a coupler module, a frequency division multiplexing module, a sensing optical fiber network, a signal demodulation module, a positioning sensing module and a main control unit. According to the optical fiber coding and positioning integrated sensing device based on frequency division multiplexing and the application method thereof, a light source module generates broadband continuous optical signals, a coupler module realizes optical signal coupling and separation, a frequency division multiplexing module integrates sub optical signals, and an optical fiber sensing unit of a sensing optical fiber network senses and encodes external physical quantities. The positioning sensing module realizes accurate positioning, the signal demodulation module separates sub optical signals and extracts information, the main control unit operates in a coordinated manner, processes and analyzes data and outputs a result, all the modules cooperate to form an efficient and stable monitoring system, the requirements for multi-physical-quantity synchronous monitoring and accurate position positioning in a large-scale and long-distance scene are met, and the system performance is improved.

The invention relates to the technical field of optical fiber detection, in particular to an optical fiber breakage detection method and system. By combining the multi-wavelength optical pulse and the machine learning model, the environmental noise and the real breakpoint signal are distinguished by using the wavelength correlation coefficient, the misjudgment rate caused by interference such as temperature change and connector looseness is effectively reduced, and meanwhile, the bandwidth occupation is avoided by adopting the time division multiplexing design of the low-power detection pulse and the service data signal; a dynamic threshold adjustment mechanism is introduced to adaptively correct a judgment threshold according to real-time environmental parameters, common-mode interference is filtered in a complex topology network in combination with a redundant path comparison algorithm, high-precision breakpoint positioning is realized through fusion of a regression sub-model and optical fiber refractive index real-time calibration, and finally, the detection instantaneity and the positioning precision are improved while the detection accuracy is improved. The self-adaptive operation and maintenance requirements of low-cost deployment and a complex network are considered, and the troubleshooting efficiency of the optical fiber network and the reliability of the system are remarkably improved.

This application relates to a communication method and apparatus. The method includes: A first SMF network element receives first information from a PCF network element, where the first information includes an identifier of a first dedicated data network to which a terminal device subscribes; the first SMF network element sends a first request including the identifier of the first dedicated data network to a second SMF network element that supports the first dedicated data network; the first SMF network element receives, from the second SMF network element, a first response including second tunnel information of a second UPF network element, where the second UPF network element is a UPF network element selected based on the first dedicated data network; and the first SMF network element sends second information to a first UPF network element, where the second information includes the second tunnel information and a first traffic steering rule.

The invention provides an optical network communication method and a communication device, the method is applied to an optical fiber network, the optical fiber network comprises a master device and at least one slave device, and the at least one slave device comprises a first slave device. Wherein in the initialization phase, after receiving the authentication encryption capability of the slave device, the master device can indicate a determined authentication encryption mode (e.g., a first authentication encryption mode) to the slave device, so that the authentication encryption capability of the slave device is enhanced in a subsequent process (e.g., a roaming parameter configuration process). The master device does not need to configure security-related parameters such as an authentication encryption mode for the slave device, so that the configuration efficiency is improved, and the configuration overhead is saved.

The invention discloses an optical fiber network intelligent regular checking and fault closed-loop processing system, method and device, and belongs to the technical field of optical fiber communication network intelligent operation and maintenance. The method comprises the following steps: collecting and fusing multi-source heterogeneous data such as resource management, fault network management, distributed sensing and geographic information; a multi-modal model based on a convolutional neural network and a graph neural network is utilized to analyze fusion data, meter-scale accurate positioning of faults is achieved, and a regular inspection plan is dynamically optimized by adopting a long and short-term memory network model; and based on an analysis decision result, a fault list, a mode list and a maintenance list are automatically created, circulated and associated, and a whole-process closed loop from diagnosis list sending to repair verification is realized. According to the scheme, the problems of data dispersion, single analysis dimension and lack of closed loop in the process in the prior art are solved, and the accuracy, efficiency and automation level of optical fiber network operation and maintenance are remarkably improved.

The invention relates to an intelligent dynamic negative pressure and phototherapy integrated burn wound treatment device, and belongs to the technical field of medical instruments, the device comprises a contact layer, a conduction layer and a honeycomb support layer, the contact layer drains exudate, the conduction layer is integrated with an optical fiber network and a self-adaptive deformation mechanism, and the honeycomb support layer is connected with an intelligent negative pressure chamber and a gradient sterilization system. The pressure of the negative pressure chamber is dynamically adjusted through a corrugated air bag and a rhombic telescopic unit, and the photo-thermal assembly irradiates the wound surface in a time-sharing mode and maintains the constant temperature. And the sterilization system adopts ultraviolet pulse and a multi-stage filter screen to cooperatively purify the percolate. The intelligent regulation and control module optimizes treatment parameters in real time through a multi-mode sensing network, and full-closed-loop cooperation of negative pressure, phototherapy and sterilization is achieved. According to the device, the limitation of traditional treatment is broken through through integrated design, the wound repairing quality and the anti-infection capacity are remarkably improved, meanwhile, the medical care burden is reduced through modular expansion and a non-invasive monitoring function, and an efficient and safe intelligent solution is provided for burn management.

The invention discloses a detection system for water level data and geological data of a weakly cemented coal seam, and belongs to the technical field of mine safety monitoring. Comprising a hardware layer and an algorithm layer, the hardware layer constructs a water surface-water body-water bottom three-in-one dynamic monitoring system, and the algorithm layer fuses multi-source data through a physical information neural network to realize global water bottom DEM inversion and dynamic early warning. And the physical information neural network inverts the global underwater DEM in a data driving item and physical driving item joint optimization mode by performing space-time alignment on the sonar point cloud, the unmanned aerial vehicle water level data and the optical fiber strain gradient and then performing weighted fusion. Sonar, optical fiber and water level data are fused through a PINN model, the crack recognition capacity is improved, and the side-scan sonar can detect cracks with the width larger than or equal to 2 cm; second-level data acquisition is achieved through the optical fiber sensor, the water inrush early warning response time is shortened, the response speed is increased, and the sonar sounding error is still controlled within 5 cm. The system robustness is increased, and the double-ring optical fiber network reduces the single-point fault influence range.

The invention relates to a pressure and temperature multifunctional sensor for energy collection and a manufacturing method thereof, the pressure and temperature multifunctional sensor adopts a double-layer composite structure: an upper wire coil layer is composed of a liquid metal coil embedded in a PDMS substrate, silver paint and a wire connected with the silver paint, and a lower magnetic fiber network layer is formed by weaving magnetic liquid crystal elastomer LCE composite fibers; the two are structurally integrated through a bonding layer; a coil conductive loop in the upper wire coil layer is formed on the surface of the PDMS substrate through a direct-writing printing technology, and packaging protection is completed through secondary pouring of PDMS; the LCE composite fiber takes an LCE hollow fiber tube as an external carrier, magnetorheological fluid is filled in the LCE composite fiber to serve as a magnetic medium, and two ends of the LCE composite fiber are sealed and packaged by adopting a compound of neodymium iron boron magnetic particles and PDMS (Polydimethylsiloxane). The sensor provided by the invention not only can realize pressure and temperature sensing, but also can be used as an energy collection device, provides a new solution for sustainable development of wearable equipment and sensors, and has a wide application prospect.