4917 results about "Aerospace" patented technology

An aero-engine augmented reality virtual-real fusion method based on depth prior scene reconstruction comprises the following steps: firstly, collecting data of an aero-engine look-around RGB image and a depth image, calculating a camera frame pose by using an SFM algorithm and obtaining a sparse point cloud, and optimizing the camera pose by using an ICP algorithm and the point cloud converted from the depth image to obtain an aligned prior dense point cloud; gaussian points are initialized based on the priori dense point cloud and the sparse point cloud, and random generation of 3D Gaussian is limited by using the boundary of the dense point cloud as geometric constraint; through the three-dimensional reconstruction of the adjacent multi-view enhancement strategy, the spatial and visual consistency is improved, and an aero-engine model is reconstructed; then understanding the reconstructed aero-engine model based on semantic segmentation, identifying and marking core components, and finally performing virtual-real fusion visualization on the reconstructed aero-engine through augmented reality to guide training, assembling and maintenance operations. According to the invention, rapid high-quality three-dimensional reconstruction and augmented reality virtual-real fusion visualization of the aero-engine are realized.

The invention relates to the technical field of calibration test, and particularly discloses a strapdown inertial navigation automatic calibration test method of a temperature control biaxial rate position turntable. Comprising the following steps: S1, multi-dimensional error modeling and layered calibration architecture: constructing a comprehensive error model containing a sensor scale factor error, an installation non-orthogonal error, a zero offset error, a cross axis coupling error and a temperature related error; s2, a wide temperature range self-adaptive compensation mechanism: carrying out heat preservation at a preset temperature point and executing turntable positioning and rate movement; s3, performing automatic data processing and parameter estimation; and S4, performing real-time error correction and dynamic verification: performing real-time error correction based on the estimated parameters, and evaluating navigation performance indexes such as position, speed and attitude precision. Through technical innovation, high-precision, automatic and wide-temperature-range calibration of the strapdown inertial navigation system is realized, and the method is suitable for aerospace, intelligent equipment and other scenes with strict navigation precision requirements, and has remarkable engineering application value and market competitiveness.

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

The invention discloses a laser cutting method for cutting complex shapes, and particularly relates to the field of laser cutting. A three-dimensional curved surface model of a to-be-cut workpiece is obtained, and a surface normal tensor field is constructed; extracting a driving point set based on the curvature derivative and generating a sliding path network; constructing a laser power density mapping model in combination with the thermal diffusion response model and the thermophysical parameters of the material; thermal feedback images are collected in the cutting process, an energy offset function is constructed, laser focus displacement and the scanning rate are dynamically adjusted through fuzzy control, and closed-loop control over power, paths and thermal feedback is achieved; according to the method, self-adaptive adjustment of laser power distribution, curved surface adaptation of a cutting path and thermal field real-time compensation in the machining process can be achieved under a complex geometric structure, the cutting precision, stability and energy efficiency are effectively improved, and the method is suitable for laser machining application of high-requirement complex structures such as aerospace, mold manufacturing and medical instruments.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

The invention provides an unmanned aerial vehicle inspection accurate positioning method and device integrating vision and satellite navigation, and relates to the technical field of aerospace information. According to the method, satellite signal quality features reflecting aerospace information reliability and multi-dimensional features such as visual geometric textures reflecting ground environment features are jointly extracted, and the environment state is deeply perceived; based on real-time dynamic generation of a fusion strategy adaptive to the current environment, a data weight and an algorithm model are intelligently adjusted, and the limitation of a traditional fixed parameter fusion mode is broken through; a specific inspection stage and a target object where the unmanned aerial vehicle is located are understood through visual identification, and a corresponding priori task constraint model is loaded, so that the positioning process closely meets the precision and safety requirements of actual operation. And finally, fusion calculation is carried out under the common guidance of a dynamic strategy and task constraints, so that the problem of inaccurate positioning of the unmanned aerial vehicle in a complex environment is solved, and the environmental adaptability, robustness and task guidance accuracy of a positioning system are improved.

The invention discloses an airspace engine 3D printing thermal stress distribution control method which comprises the following steps: S1, importing a three-dimensional CAD model of an airspace engine structure, and generating a printing layer set; s2, constructing a three-dimensional node graph structure of a printing layer; s3, inputting the thermodynamic prediction input tensor into the heterogeneous attention map thermodynamic network to obtain a node-level thermal stress tensor map; s4, analyzing the node-level thermal stress tensor diagram, and extracting an optimized region; s5, calling an improved chameleon search algorithm in the optimization region; s6, optimal printing parameters of the current printing layer are determined; s7, the 3D printing device is controlled based on the optimized printing parameters, and real-time thermal inductance data are collected; and S8, mapping the thermal inductance feedback set of the printing layer to the corresponding node, and repeatedly executing the steps S2 to S7 until the whole airspace engine structure is printed. According to the method, the heterogeneous graph network and the improved chameleon search algorithm are fused, and precise control over the printing thermal stress of the airspace engine is achieved.

The invention discloses an additive manufacturing intelligent repairing and surface optimizing method for a complex special-shaped curved surface, and particularly relates to the technical field of additive manufacturing. A three-dimensional digital model is obtained, and a complex special-shaped curved surface defect area is recognized; extracting a local morphology parameter set; constructing a repair path generation model based on a graph neural network, and performing collaborative optimization of forming precision, residual stress and deposition stability on the path through a multi-objective optimization algorithm; additive manufacturing equipment is controlled to perform material deposition according to the optimized path; carrying out online error detection by adopting a structured light system, judging repair quality through a fuzzy decision tree model, and triggering path reconstruction if the repair quality exceeds a threshold value; after repairing is completed, surface micro texture factors are extracted, and functional texture regulation and control are conducted through the laser micromachining technology; according to the method, high-precision intelligent repair of the complex curved surface area and collaborative improvement of surface function performance are achieved, and the method is suitable for the fields of aerospace, energy, high-end equipment manufacturing and the like.

The invention belongs to the technical field of fastener machining equipment, and discloses bolt tail integrated machining equipment which is characterized in that a material conveying rail comprises a first rail and a second rail, a material containing gap is formed between the first rail and the second rail, a workpiece moves in the material containing gap, the material conveying rail is provided with a station rail section, and the station rail section is connected with the first rail and the second rail. The station rail section and the machining station are correspondingly arranged, and in the station rail section, the position, relative to the horizontal plane, of the first rail is higher than the position, relative to the horizontal plane, of the second rail; when the workpiece moves to a machining station, the movable positioning tool acts to clamp the workpiece between the first positioning groove and the second positioning groove, so that the machining mechanism machines the workpiece; the action direction of the jacking mechanism is parallel to the rail surface of the conveying rail, and the machining axis of the machining mechanism is perpendicular to the action direction of the jacking mechanism. Aiming at tail machining of aerospace fasteners, the machining precision can be guaranteed, the process time can be shortened in a single process or a continuous process, and batch production is achieved in an assembly line mode.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention discloses a mechanical property testing method for an inflatable wing, and belongs to the technical field of aerospace testing. The method comprises the following steps: S1, wing installation: clamping an inflatable wing on a test board, and enabling a chord plane of the inflatable wing to be perpendicular to a loading direction; s2, applying a load: driving a loading mechanism to apply a vertical load to the inflatable wing through an external universal tester; s3, data acquisition: synchronously acquiring a deflection curve and stress distribution data by using a displacement measurement unit and a strain detection unit; and S4, failure judgment: when monitoring that the shape of the deflection curve suddenly changes or the strain data exceeds a threshold value, judging that the inflatable wing is subjected to buckling failure, stopping loading and recording the maximum load. The test bench serves as an expansion platform of a universal tester, can be adapted to a large-size inflatable wing, does not need special equipment, reduces the cost, realizes cooperative monitoring of displacement and strain data, realizes closed-loop control of inflation air pressure-deformation-load, and remarkably improves the test precision and the data reliability.

The invention belongs to the technical field of optical fiber sensing, and discloses a high-temperature-resistant and high-pressure-resistant multi-parameter integrated sensor and system based on optical fiber dislocation welding, and the sensor comprises a packaging main body, and the center of the packaging main body is provided with a center hole and a cavity which are used for accommodating a transmission optical fiber. The tail end of the transmission optical fiber is sequentially connected with a temperature detection device and a pressure / sound detection device which are positioned in the cavity; the temperature detection device comprises a fiber bragg grating; the pressure / sound detection device comprises a single-mode fiber, a multimode fiber, a first hollow capillary fiber and a second hollow capillary fiber which are connected in sequence; the axes of the first hollow capillary optical fiber and the second hollow capillary optical fiber are connected in a staggered manner to form an open structure. The pressure / sound-temperature multi-parameter composite in-situ synchronous measurement device realizes composite in-situ synchronous measurement of multiple parameters of pressure / sound-temperature, has the advantages of high temperature resistance, small size and high test precision, and has wide application prospects in the fields of aerospace, petroleum metallurgy, environmental monitoring and the like.

The invention relates to the field of carbon fiber composite material mechanics, in particular to a fine evaluation method for the strength of a small-size large or full-opening CFRP (carbon fiber reinforced plastic) shell, which comprises the following steps: establishing a microscopic damage model containing material anisotropy, defining damage variables and evolution equations, uniformly quantifying multi-scale damage behaviors such as fiber fracture, matrix cracking and layered expansion, and evaluating the strength of the small-size large or full-opening CFRP shell. The problem that a traditional single-scale model cannot represent the multiphase coupling effect is solved; through cross-scale transmission of damage variables, whole-process prediction from microscopic damage initiation to macroscopic failure is realized, and the precision of strength evaluation of the complex opening structure is significantly improved. The method realizes refinement, reliability and engineering practicability of strength evaluation of the small-size large-opening or full-opening CFRP shell, is particularly suitable for the fields of aerospace and the like with extremely high requirements on structural weight and safety, and has remarkable technical leading and application value.

The invention discloses a double-domain artifact correction method based on multi-energy-level data and physical prior fusion, and the method achieves the efficient and precise removal of dispersive metal artifacts through the construction of a complete technical scheme of the combination of multi-energy-level data collection, double-domain cooperative correction and physical model constraint. The system has the beneficial effects that the system covers multiple fields of medical treatment, industry, security and protection, aerospace and the like, and has extremely high universality and adaptability. On the basis of a multi-energy-level slow switching scanning protocol, full-angle scanning of at least two energy levels is completed by dynamically adjusting radiation source parameters, and obtained complete multi-energy-level projection data is converted into a high-dimensional tensor through a channel dimension splicing image fusion method. The constructed multi-channel virtual image completely retains attenuation characteristics and structure information of a target object under each energy, provides a more comprehensive input source with discrimination for a deep learning network, builds a data basis for accurate correction in different fields fundamentally, and adapts to various imaging scenes containing metal targets.

The invention discloses a method for predicting the elastic property of a fiber reinforced composite material, and aims to solve the problems of low precision, low efficiency and difficulty in processing multi-scale correlation of a traditional method. The method comprises the following steps: firstly, generating a representative volume unit by using a random algorithm, and constructing a high-quality training data set by combining Sobol sequence sampling and an SMOGN data enhancement technology; secondly, designing and training a deep neural network embedded with residual blocks and physical constraints, and improving the generalization ability of the model through Bayesian optimization adaptive parameter adjustment; and finally, establishing a plurality of macrostructure models, and realizing end-to-end prediction of the multi-scale elastic performance. The method realizes breakthrough in prediction precision and calculation efficiency: the time consumed by single analysis is shortened from several hours to a minute-second level, and the average prediction error is lower than 5%. The technology can be widely applied to the fields of aerospace, new energy automobiles, wind power and the like, and intelligent support is provided for design and manufacturing of high-performance composite materials.

The invention discloses a closed-loop self-adjusting method for online defect identification and real-time feedback in the additive manufacturing process of a titanium alloy component based on intelligent sensing, belongs to the field of laser additive manufacturing, and solves the key problems of insufficient defect identification capability, quality control lag and the like in the current additive manufacturing process. The method is based on a multi-mode sensor architecture, integrates an infrared thermal imager, a molten pool camera and a spectrum sensor, and synchronously monitors a molten pool thermal field, radiation characteristics and surface topography. Traditional image processing and signal analysis are adopted to extract defect features such as air holes and cracks, and calibration is implemented in combination with off-line detection such as industrial CT. The recognition result is analyzed in real time through the central control platform, causes are automatically diagnosed based on defect types and distribution characteristics, parameters such as laser power and scanning paths are dynamically adjusted by matching a preset control strategy, and defect suppression and online repair are achieved. And a closed-loop self-adaptive dynamic adjustment mechanism improves the forming quality and reliability. The method is suitable for aerospace and other scenes with extremely high requirements for the quality of large complex titanium alloy components.

The invention relates to the technical field of ignition systems of gas turbines, and discloses a special ignition system for a gas turbine, which comprises a monitoring control layer, an energy distribution layer and a combustion optimization layer. The monitoring control layer monitors parameters such as combustion chamber pressure fluctuation in real time through multiple types of sensors and executes phase synchronization and injection control. The energy distribution layer is provided with a multi-stage energy release strategy and a self-adaptive time sequence matching mechanism, a stable ignition energy field is constructed, and physical and digital space linkage regulation and control are achieved through a multi-modal energy interface; and the combustion optimization layer establishes a multi-dimensional mapping combustion state control model based on a dynamic combustion algorithm and real-time data, and adopts a collaborative optimization algorithm to calibrate an ignition time sequence, regulate and control a fuel mixing ratio and maintain flame stability. According to the system, through multi-dimensional energy fusion, model parameter dynamic correction and intelligent algorithm application, the accuracy, stability and multi-working-condition adaptability of the gas turbine ignition process are remarkably improved, and the system is suitable for gas turbine efficient combustion control in the fields of aerospace, electric power and the like.

The invention discloses a steering engine accurate control method and system based on an optimization control algorithm, and the system comprises a data collection module which collects key working parameters of a steering engine in real time through a plurality of sensors, and carries out the data processing, thereby forming a state vector; the self-adaptive filtering module is used for carrying out self-adaptive filtering fusion processing on the data acquired by the sensor and dynamically estimating the attitude angle and the corresponding angular velocity of the aircraft; the sliding mode control module constructs a sliding mode surface and a control rate of a steering engine system according to the dynamic error of the system, calculates control input of each axis of the aircraft according to the attitude angle real-time state error, and generates a sliding mode control signal; the driving execution module is used for converting the control signal into a voltage or PWM signal and inputting the voltage or PWM signal into a steering engine, so as to change thrust and realize adjustment and control of a posture or a position; and the real-time feedback module monitors the current state of the steering engine in real time to realize dynamic correction and real-time optimization. The adaptive filtering and sliding mode control method provided by the invention is higher in control precision, quicker in system response and higher in adaptability to complex environments, and can effectively improve the application performance and stability of a steering engine execution system in the fields of industrial automation, aerospace, intelligent manufacturing and the like.

The positioning error of a feeding system obviously influences the machining precision, especially under the high-speed and high-load working condition. An existing positioning error compensation system often faces the problems of high calculation cost and complex sensor configuration and is difficult to be widely applied to the industry. Therefore, a simplified model needs to be established and an economical and efficient sensor integration and real-time compensation scheme needs to be developed urgently. Therefore, the invention discloses a dynamic positioning error compensation system of a numerical control machine tool feed driving system, and provides an error suppression system fusing a mechanism driving positioning error prediction model and a real-time compensation strategy. The positioning error prediction model is constructed as a hyperbolic cosine function of temperature and nut position, and the residual error-based dynamic compensation model can effectively process unmodeled errors and system dynamic changes. The dynamic positioning error compensation system shows excellent adaptability and precision under the dynamic working condition, and an extensible practical solution is provided for positioning error suppression in the high-precision manufacturing fields of aerospace, automobiles, electronics and the like.

The invention provides an intelligent adjusting clamping device and method for composite material resistance welding. The intelligent adjusting clamping device mainly comprises a control system, a resistance welding machine, a current sensor, an automatic clamping device, an image recognition system and an infrared thermometer. According to the device, the type of a composite material is automatically recognized through an image recognition system, and optimized welding parameters are called according to material characteristics; the automatic clamping device is provided with a telescopic and rotatable clamping arm, a pressure sensor is integrated in the clamping arm, accurate clamping can be achieved, and the clamping pressure can be monitored in real time; the infrared thermometer and the current sensor are used for monitoring the temperature and the current in the welding process, the control system achieves closed-loop adjustment of welding parameters based on sensor data, and therefore the welding quality is ensured. The device and method can remarkably improve the welding stability, efficiency and consistency of the composite material, and are widely suitable for high-performance composite material connection in the fields of aerospace, automobile manufacturing and the like.

The invention discloses an aerospace equipment data-real fusion performance test method and device based on reinforcement learning, and the device comprises a digital twin model building module which is used for building an aerospace equipment high-fidelity mechanism model fusing multi-physical field features, a lightweight digital twinborn body adaptive to reinforcement learning training is generated through a model order reduction technology; the digital twinborn model correction module is used for carrying out consistency calibration of multi-dimensional features and key physical parameter correction on the reduced-order model based on object reference data; the performance test module based on reinforcement learning actively explores a coupling scene of an extreme working condition and multiple faults in an interaction environment by constructing an attacker agent, and autonomously explores a performance boundary and a limit envelope of equipment; and the number-real fusion verification module is used for carrying out a hardware-in-the-loop point compensation test by taking the performance limit obtained by the digital test as guidance so as to assist in decision making and optimization of a test scheme. According to the method, the coverage, the safety and the test efficiency of the limit performance test of the aerospace equipment are improved.

The invention discloses a digital pre-distortion method, system and device and a storage medium, and relates to the technical field of aerospace communication. The method comprises the following steps: acquiring a first input signal and a first actual output signal of the power amplifier, and acquiring a thermal state characteristic; constructing a three-dimensional dynamic baseline; superposing the detection sequence into the first input signal to obtain a second input signal, obtaining a corresponding second actual output signal, and predicting an ideal output signal of the second input signal; extracting a harmonic component generated after the detection sequence passes through a power amplifier, and determining a distortion early warning moment; recording a third input signal after the distortion early warning moment; constructing a virtual test signal according to the third input signal, and predicting to obtain a reference output signal; and comparing the difference between the distortion output signal and the reference output to obtain a distortion feature vector, constructing a dynamic increment compensation function, and correcting the signal processing flow of the power amplifier based on the dynamic increment compensation function. And the accuracy of digital pre-distortion is improved.

The invention discloses a complex aerospace product man-machine cooperation assembly-oriented body-equipped agent packaging method and device. The method comprises the following steps: a sensing layer realizes multi-modal intention and environment sensing by using a large language model; the reasoning layer analyzes a structured assembly plan sequence through an assembly scene map; and the execution layer converts the target coordinate into a rotation angle to generate an action code. According to the method, the assembly plan sequence is generated according to the sensing result and is automatically converted into the robot execution code through the combination of the body agent packaging and the large language model, so that the self-adaptive capability of the man-machine cooperative assembly process is improved; therefore, the problems of high system integration difficulty, insufficient task adaptability and high-efficiency cooperation difficulty in man-machine cooperation assembly of complex aerospace products are solved.

The invention relates to the technical field of aerospace management, in particular to an edge-cloud collaborative integrated management architecture, which comprises edge computing node equipment, a cloud platform server cluster and a networking sensor network, through a side cloud cooperation mechanism and the unified management platform, centralized management and efficient scheduling of operation and maintenance of the low-altitude aircraft are realized, management links and operation processes are reduced, the management efficiency is improved, and the unified management system can dynamically allocate resources according to operation requirements and real-time states of the low-altitude aircraft, so that the management efficiency of the low-altitude aircraft is improved. The system has the advantages that high-efficiency utilization of resources is guaranteed, waste and bottleneck of the resources are avoided, the real-time monitoring and fault early warning system can find potential faults in advance, occurrence and expansion of the faults are reduced, the maintenance cost is reduced, the downtime of the aircraft is shortened, the flight safety and the operation reliability are improved through the autonomous 4D flight path generation technology and the real-time monitoring system, and the service life of the aircraft is prolonged. And a powerful guarantee is provided for wide application of low-altitude aircrafts.

The invention relates to the technical field of landslide geological disaster hidden danger point identification, in particular to a landslide hidden danger point identification method based on an air-space remote sensing fusion neural network, which comprises the following steps: acquiring satellite SAR data, a digital elevation model, a vector boundary map and a precision orbit ephemeris of a research area; carrying out interference processing on the InSAR data, and carrying out deformation rate inversion, terrain residual error correction and atmospheric delay phase modeling and correction; preprocessing an original high-resolution remote sensing image, interpreting a high-resolution image, and outputting spatial distribution of suspected landslide hidden danger points in a research area; planning an aerial survey route of the unmanned aerial vehicle, and performing distortion correction, feature matching, dense point cloud generation and orthoimage generation processing; performing multi-dimensional feature analysis, and performing comprehensive interpretation in combination with an expert knowledge base; according to the method, the key effect of the multi-source remote sensing fusion technology in landslide hidden danger monitoring is displayed from data acquisition to hidden danger recognition.

The invention discloses a preparation method of a nano lanthanum hexaboride reinforced titanium-based composite material, which comprises the following steps: performing low-energy ball milling and high-energy ball milling on a pure titanium metal powder and / or titanium alloy powder base material and a nano lanthanum hexaboride powder reinforced phase to obtain composite powder; sintering in a rapid hot-pressing sintering furnace, and cooling to obtain a LaB6 / Ti-based sintered blank; and carrying out heat-preservation hot rolling in a vacuum heat treatment furnace. The invention further discloses the nanometer lanthanum hexaboride reinforced titanium-based composite material and application. The nanometer lanthanum hexaboride reinforced titanium-based composite material has excellent strength and ductility, the tensile strength reaches 1035 MPa, the percentage elongation after fracture is 5%-25%, and the strength is improved by 60% or above compared with that of pure titanium; the preparation method is short in process, high in efficiency, low in production cost, high in universality and suitable for various titanium or titanium alloy base materials, and the composite material has good application prospects in the fields of radar, aviation, aerospace, automobiles, electronic industry and biomedicine.

The invention belongs to the technical field of aerospace engineering, and discloses a flexible radiator which comprises a radiation assembly, the radiation assembly comprises a plurality of radiation plates which are rotationally connected in sequence, each radiation plate comprises a liquid cooling pipe set and a radiation film arranged on the liquid cooling pipe set, and cooling liquid circulates in the liquid cooling pipe set. The material density is low, the structural mass is effectively reduced, a plurality of radiant panels are rotationally connected in sequence, the radiant panels are in a folded state during emission, then a radiator with enough radiation area can be emitted under the condition that the size of a spacecraft is limited, and the heat dissipation requirement of high-power electronic equipment is met.

The invention belongs to the technical field of aero-engine maintenance, and discloses an aero-engine intelligent fault diagnosis and maintenance system based on a domain knowledge graph and a large language model. The system comprises four core modules, wherein a data processing module adopts an AemCasRel model to extract a fault entity and relation triple from maintenance data; the knowledge graph management module stores the triple into a Neo4j database, constructs a structured knowledge graph and provides a management interface; the knowledge visualization module realizes visual display and interactive retrieval of the atlas based on D3. Js; the intelligent question and answer module depends on LangChain and a stellar fire big model and combines a multi-hop path reasoning technology to generate an interpretable diagnosis report. According to the system, the whole process integration of fault knowledge from extraction and management to intelligent diagnosis is realized, the precision, efficiency and intelligent level of maintenance diagnosis are remarkably improved, and the system has a wide application prospect in the fields of aviation, aerospace, machinery and the like.

The invention provides an embedded structure energy storage integrated composite material and a preparation method thereof, and belongs to the technical field of composite materials and electric energy storage. A sandwich structure is adopted, an energy storage unit is embedded in an insulation honeycomb / grid core material, and high-strength fiber composite material panels are arranged on the upper side and the lower side of the insulation honeycomb / grid core material. In the aspect of a material system, the panel has both mechanical strength and electrical insulation property; in structural design, gradient thickness and multi-cavity layout are introduced to improve the multi-direction load bearing capacity. Automatic switching, energy management and fault diagnosis among the energy storage units are realized by a flexible circuit and an intelligent chip which are internally integrated; meanwhile, a sensing network is embedded to monitor structural strain, vibration and battery state in real time, and a structural health monitoring function is provided. The obtained composite material has the characteristics of high structural strength, high energy storage efficiency and high safety intelligent degree, can be widely applied to the fields of aerospace, automobiles and the like, realizes the integrated design of the structure and energy storage, and achieves the purposes of reducing weight and improving functions.

The invention discloses a path planning method and system for curved surface integrated conformal jet printing, and belongs to the field of additive manufacturing. According to the method, the geometric data of the non-deployable curved surface are obtained and converted into the discretized grid model, then the plane pattern is designed and mapped to the three-dimensional curved surface, the mapped pattern is locally optimized, and the complete printing area model is generated. Path planning is divided into initial jet printing path generation and five-axis linkage motion path planning, conformal characteristics are achieved through an adaptive interpolation algorithm and NURBS curve optimization, five-axis motion parameters are calculated based on an inverse kinematics algorithm, and the positioning relation between a jet printing head and a curved surface is kept through the RTCP technology. The method solves the problems of precision and path planning efficiency in complex curved surface manufacturing, and is suitable for manufacturing heterogeneous complex curved surface functional structures in the fields of aerospace, medical instruments and the like.