151 results about "Cross field" patented technology

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

The invention discloses an intelligent optimization driving electromagnetic information metasurface multi-dimensional dynamic regulation and control system, solves the problem that the function solidification of an existing metasurface is difficult to dynamically adjust, and belongs to the crossing field of electromagnetic metamaterials and intelligent control technologies. The method comprises the steps that a sensor network collects environment multi-dimensional information of a programmable metasurface unit in real time; the controller processes the environment multi-dimensional information to obtain a multi-dimensional sensing result; the multi-dimensional interaction layer inputs design parameters, the intelligent decision-making layer combines the design parameters and a multi-dimensional perception result to construct a target joint optimization model, reverse design is carried out on the super-surface array coding sequence, and a display array coding pattern and a three-dimensional radiation pattern / two-dimensional radiation pattern are obtained according to the designed super-surface array coding sequence; displaying on the multi-dimensional interaction layer; and the controller performs real-time electromagnetic response reconstruction and 1-bit or 2-bit phase modulation on the programmable metasurface unit through the field programmable gate array according to the designed metasurface array coding sequence.

The invention provides an underwater small target detection method based on YOLOv11 improvement, and belongs to the crossing field of computer vision and marine science research, and the method comprises the steps: carrying out the improvement with YOLOv11 as a reference model, replacing a down-sampling convolution layer in a backbone network with an improved SMDown down-sampling module, so as to enhance the multi-scale feature expression capability, and carrying out the detection of a small target. Fine feature information of the small target is effectively reserved; a UDA attention module is designed to replace a C2PSA module in the YOLOv11 backbone network and is arranged in front of an SPPF module, the module realizes cross-branch information complementation through a bidirectional feature interaction mechanism, and the problem of small target detail loss caused by traditional pooling operation can be effectively solved; a dual-path wavelet enhancement mechanism is introduced into the YOLOv11 feature fusion network, edge details are enhanced for shallow features, frequency domain feature calibration is carried out on deep features, and the feature extraction capacity of the model for small targets is comprehensively improved; and applying the lightweight underwater small target detection model to an actual environment to realize real-time detection of the underwater small target. According to the method, the problems that the traditional method is low in detection precision in a complex underwater environment and is difficult to deploy to edge equipment are solved, and the method has good universality and practicability.

The invention discloses a foundation pit excavation deformation prediction method and device, equipment and a medium, and relates to the crossing field of geotechnical engineering monitoring and artificial intelligence, and the method comprises the steps: collecting multi-source monitoring data of a foundation pit in real time; according to the multi-source monitoring data in a set time period before the current time step, constructing historical engineering construction time sequence data; according to historical engineering construction time sequence data, a deformation prediction model is adopted to predict the deformation value of the foundation pit in the next time step; wherein the deformation prediction model is obtained by adopting a sparrow search algorithm based on adaptive inertia weight to determine initial parameters of a BP neural network, adopting an LM algorithm to carry out local optimization on the initial parameters and adopting a training sample set to train the BP neural network; the training sample set comprises multi-source monitoring data of each time step in a historical set time period. According to the invention, the convergence speed of the BP neural network and the stability of the deformation prediction model are improved, and the deformation prediction precision under complex working conditions is improved.

The invention provides a Chinese quintessence art gene decoding method and system based on cross-modal AI, and belongs to the crossing field of artificial intelligence and digital media art, and the method comprises the steps: S1, constructing a Chinese painting-music-text multi-modal data set; s2, inputting the traditional Chinese painting image into a visual encoder improved based on CLIP-ViT, and outputting a 512-dimensional visual Token sequence through a normalization module, a position encoding module and a Transform encoder; s3, inputting the visual Token sequence and the emotion label into a cross-modal adapter, and directly mapping the visual Token to a music hidden space by adopting a self-attention mechanism to obtain a music embedding vector; and S4, inputting the user parameters into the improved high-frequency fidelity generative adversarial network to generate Chinese traditional music audio conforming to five sound orders. According to the method, intelligent semantic communication between visual art and auditory art is realized.

The invention belongs to the crossing field of artificial intelligence and a power system, and discloses a power grid fault handling plan knowledge retrieval method and a related device. The power grid fault handling plan knowledge retrieval method comprises the following steps: on the basis of an obtained expression, generating a query by utilizing a large language model, and performing retrieval in a constructed graph database to obtain a power grid fault handling plan knowledge retrieval result; when the graph database is constructed, firstly, all themes in a free text of a selected power grid fault field are recognized, and an entity which can best represent the theme in each theme serves as a seed entity; then, entity relation triads in the free text are extracted through a large language model and serve as candidate triads, integration is carried out to obtain a fused knowledge graph, the fused knowledge graph is stored, and a constructed graph database is obtained. The problems that an existing retrieval method is low in query efficiency and poor in accuracy are solved.

The invention relates to a transient electromagnetic and seismic wave multi-mode joint inversion imaging method based on physical information Transform, and belongs to the crossing field of geophysical exploration and artificial intelligence. Comprising the following steps: carrying out anomaly detection, interpolation, filtering and normalization preprocessing on transient electromagnetic and seismic wave original data; extracting features representing electrical property, elasticity and cross physical significance; serializing the spatial data through a gridding and alternating fusion strategy, and constructing an enhanced code fusing absolute and relative positions and physical attributes; a physically constrained encoder-decoder architecture is designed to carry out multi-scale forward modeling-inversion; and quantizing the uncertainty of an inversion result by adopting a Bayesian Monte Carlo method, and generating a confidence map. According to the method, through physical rule driving and multi-modal depth complementary fusion, the fine recognition capability and interpretation reliability of hidden disaster-causing geologic bodies such as underground goaf and collapse columns are effectively improved while the physical consistency of data is kept.

The invention belongs to the crossing field of information and ocean science and technology, and discloses a DOA estimation method for a UUV broadside parallel co-prime array. According to the method, firstly, a virtual extension array is generated by utilizing an array auto-covariance and cross-covariance matrix, then, Toeplitz completion is carried out on'holes' in the virtual array through trace norm regularization and nuclear norm regularization constraints, a complete covariance structure is recovered, and finally, an extension matrix is constructed, and rotation invariance of a signal subspace of the extension matrix is utilized, so that the covariance structure of the virtual array is obtained. The pitch angle and the azimuth angle of the target are jointly solved, automatic angle pairing is achieved, and the effectiveness and the reliability of the method are verified through simulation results. According to the method, aiming at the application challenges that the UUV broadside array space is limited and the underwater environment is complex, all virtual array elements of the broadside parallel co-prime array are fully utilized, the array freedom degree and estimation precision are remarkably improved, the target detection capacity of the UUV is effectively enhanced, and the method has high practical engineering application value.

The invention discloses a medical analysis method and system based on a multi-modal large language model and chain reasoning, and belongs to the crossing field of artificial intelligence and medical image analysis. The method comprises the following steps: firstly, carrying out preprocessing and modal alignment on medical multi-modal data to obtain image visual features and text embedding vectors of the same vector spatial dimension; capturing visual evidence in a structured pathological feature form through a self-excitation mechanism; on the premise of visual evidence, a thinking chain text containing causal logic is generated through evidence anchoring chain type reasoning; and finally, performing vision and text dual consistency verification on the thinking chain text, and outputting an analysis conclusion according to a result, or triggering a negative feedback correction mechanism to regenerate the thinking chain text. According to the method, medical reasoning full-process logic visualization is realized, medical illusion is eliminated, the diagnosis accuracy of complex cases is improved, and the method is suitable for medical scenes such as medical visual questions and answers and image diagnosis report generation.

The invention relates to a design method of a novel crystal structure based on a large language model, and belongs to the crossing field of artificial intelligence and material science. According to the design method, design requirements are recognized through a large language model, a first crystal design constraint is extracted, then an example crystal structure is retrieved from a crystal structure database, a second crystal design constraint is output, and the large language model learns the example structure and generates a first target crystal structure according to the second crystal design constraint. The structure is determined as a second target crystal structure after being checked, then the second target crystal structure is subjected to structural relaxation to obtain a candidate structure set, and the novel crystal structure is obtained through stability screening. According to the design method, dependence on large-scale training can be effectively eliminated, novel crystal structure discovery without additional training is realized, adaptive conversion oriented to diversified novel crystal discovery scenes is realized, a design target is automatically analyzed according to different user intentions and crystal generation task types, and a generation strategy is flexibly adjusted.

The invention relates to the crossing field of mineralogy and geochemistry, in particular to a mineral and element relation analysis method for gold-bearing minerals based on image matching and data fusion and application of the mineral and element relation analysis method. Comprising the steps of ore sample processing, gold-bearing mineral image calibration through AMICS, trace element scanning through fixed-point EPMA and EDS, image-level matching and spatial data fusion and gold-bearing mineral-element relation analysis. According to the method, the AMICS mineral map and the EPMA element information are fused, relation analysis of trace elements in the gold ore and visual reconstruction of a mineral-element corresponding mechanism can be achieved, and effective data and image models are provided for microcosmic metallogenic information requirements in digital mine construction.

The invention discloses an AGV navigation technology based on a normal-temperature quantum sensing chip, and belongs to the crossing field of AGV navigation and quantum sensing technologies. According to the technology, environment magnetic field characteristics or passive magnetic field marking signals are captured through a normal-temperature quantum sensing chip, and high-precision autonomous navigation of the AGV is realized by combining magnetic field map construction, multi-modal data fusion and a dynamic path planning algorithm. The system mainly comprises a normal-temperature quantum sensing module, a magnetic field characteristic processing unit, a path planning system and a positioning correction module, does not need to depend on entity guide media such as a magnetic stripe and a two-dimensional code, and solves the problems of poor flexibility, high maintenance cost and weak interference resistance of traditional fixed path navigation. Experiments show that the positioning precision of the technology can reach the millimeter level, the technology adapts to complex environments such as industrial workshops and warehousing, route adjustment does not need ground modification, the flexibility and stability of AGV navigation are remarkably improved, and the technology is suitable for multi-scene application such as flexible production and intelligent warehousing.

The invention discloses an atmospheric turbulence phase screen generation method based on layered physical constraints, and belongs to the crossing field of optical communication and artificial intelligence. According to the method, a diffusion probability model is combined with layered atmospheric physical constraints, independent physical constraint loss functions are established for a boundary layer, a troposphere and a stratosphere of the atmosphere, and it is ensured that a generated phase screen conforms to turbulent flow statistical characteristics of all layers through power spectrum density constraints and structure function constraints; enhancing feature learning of each layer of phase screen by adopting a physically guided attention mechanism; and designing a time-dependent constraint weight strategy, and adaptively adjusting the physical constraint strength in the diffusion denoising process. The method solves the technical problems that the vertical layering difference is ignored, the physical consistency is insufficient, and the generation quality depends on a large amount of labeled data in the existing method, is particularly suitable for high-fidelity atmospheric turbulence modeling of a satellite laser communication link, and can also be widely applied to the fields of free space optical communication system simulation, adaptive optical system design and the like.

The invention discloses a comprehensive traffic low-altitude global intelligent sensing method based on autonomous evolution, and belongs to the crossing field of intelligent traffic and computer vision. The method comprises the following steps: collecting multi-source sensing data and extracting a structured causal variable; a dynamic causal graph is constructed, and attribution analysis of perception errors is realized through intervention learning; realizing cross-scene model migration and few-sample self-adaption based on the knowledge graph; carrying out adaptive reasoning by adopting an environment-aware dynamic graph neural network; and realizing a causal-driven autonomous evolution closed loop based on multi-source feedback. According to the method, the crossing of the perception system from'perception-optimization 'to'understanding-evolution' in a low-altitude complex traffic environment is realized, the perception precision, the scene adaptability and the system interpretability are remarkably improved, and the manual operation and maintenance cost is reduced.

The invention provides a special article comprehensive monitoring method and device based on multi-modal data, relates to the crossing field of high-end manufacturing and intelligent traffic, and solves the technical problem of poor overall detection effect in the prior art. The method comprises the following steps: collecting multi-source heterogeneous data in a special article transportation process; the multi-source heterogeneous data comprises sound vibration data, multi-dimensional force data and multi-source environment data; the sound vibration data comprises a sound signal and a vibration signal, and the multi-dimensional force data comprises pressure data, tension data and shear force data; the multi-source environment data comprises temperature and humidity data and stress data; performing data fusion and feature extraction on the multi-source heterogeneous data to obtain feature parameters; inputting the characteristic parameters into a multi-physics field coupling monitoring model to obtain an article monitoring result; the article monitoring result is used for representing the transportation state of the special article. The method is used in the comprehensive monitoring process of special articles.

The invention discloses a wheel flash welding optimization system and method based on digital twinning, and belongs to the crossing field of intelligent manufacturing, welding processes and artificial intelligence. The system comprises a material attribute database module, a multi-source data acquisition and preprocessing module, a digital twin module integrating heat-force-microstructure and other multi-physics field models, an AI prediction and multi-objective optimization module, an advanced visualization and interaction module and a closed-loop control module. The method comprises the following steps: initializing and matching material parameters; collecting data in real time to update the twinborn model; running simulation and AI to predict the welding temperature, the structure and the performance; if the prediction does not reach the standard, starting a multi-objective optimization algorithm to generate an optimal process parameter set; and executing optimization parameters through closed-loop control and performing real-time visual feedback. According to the method, full-chain real-time mapping, transparent monitoring and intelligent optimization of technology-structure-performance in the welding process are achieved, the performance stability of joints is remarkably improved, and the efficiency and the product quality are optimized.

The invention belongs to the crossing field of a microwave technology and a sensing technology, and particularly relates to a microwave dielectric parameter measurement sensor based on a micro-flow pipeline embedded in a substrate, and one surface of the substrate is provided with a microstrip transmission line and a resonant structure; the microstrip transmission line is used for exciting and transmitting microwave signals; the resonant structure is used for enhancing electromagnetic field distribution in a certain area of the substrate; a micro-flow pipeline is embedded in the substrate and is used for accurately conveying a substance to be detected to an area where electromagnetic energy is most concentrated; a grounding layer is arranged on one side of the substrate and used for restraining an electromagnetic field, and energy of the electromagnetic field is mainly concentrated in the substrate. The micro-flow pipeline is embedded in a strong electric field area in the substrate, so that a to-be-measured substance directly interacts with an electromagnetic energy concentration area, and dielectric parameter measurement which is compact in structure, high in sensitivity and high in precision is achieved. The sensor provided by the invention has wide application prospects in the fields of biomedical detection, chemical analysis, environmental monitoring, industrial process control and the like.

The invention discloses a Wright staining peripheral blood cell accurate identification system and method based on intelligent microscopic imaging and multi-modal fusion deep learning, belongs to the crossing field of medical examination and artificial intelligence, and is suitable for cell morphology analysis under a 10 * 40-time microscope. The core scheme comprises the following steps: standardized slide preparation and dyeing (the slide pushing angle is 25 + / -2 degrees, the pH is 6.8 + / -0.2, and color sensing dynamic regulation is performed); intelligent imaging (focusing formula and color normalization; the method comprises the following steps of (1) data processing, (2) data processing, (3) data processing, (4) data processing, (5) data processing, (6) data processing, (7) data processing, (8) data processing, (7) data processing, (8) data processing, (8) data processing, (8) data processing, (8) data processing, (8) data processing and (8) data processing.

The invention relates to the crossing field of computer simulation and aerospace propulsion technology, and discloses a numerical simulation method for solid propellant grain cutting treatment, which comprises the following steps: establishing a three-dimensional model of a grain and a cutting tool, and defining a heat-force-damage coupling constitutive relation and boundary conditions; performing multi-physical field monitoring based on the weighted combination of the equivalent plastic strain, the temperature and the damage gradient at each time step; dynamically executing self-adaptive encryption and coarsening of the grid; in combination with dynamic load balancing in parallel computing, the resource utilization rate is increased; and material separation is simulated through a damage fracture criterion, and finally high-precision cutting force, a temperature field, a stress-strain field and a cutting morphology are output. According to the method, the simulation precision and efficiency are improved, and reliable support is provided for process optimization and safety evaluation.

The invention discloses a filtering antenna design method of a naked mole algorithm based on SHAP, and relates to the crossing field of a radio frequency front end of a wireless communication system and a computer algorithm. According to the method, the SHAP value is utilized to quantify the influence of the antenna structure parameters on the antenna performance, the search strategy in the NMRA is dynamically adjusted, and the exploration capability of the algorithm in a high-dimensional space is enhanced. Firstly, the SHAP value of each antenna parameter is initialized, and then the NMRA algorithm stage is entered. In the working stage, SHAP weighted differential evolution is carried out, and in the breeding stage, all breeders update their own states according to a certain probability, and breeders with poor performance are eliminated; self-adaptive variation is carried out according to the probability pm so as to prevent from falling into local optimum; and finally, evaluating the new population, updating the current optimal solution, and outputting the optimal solution after the maximum number of iterations is reached. The method is mainly applied to the fields of short-wave satellite communication, radars and the like, the limitation that a uniform search strategy is adopted for all features in the high-dimensional optimization problem of a traditional NMRA algorithm is overcome, and it is guaranteed that the performance of a filtering antenna is good.

The invention discloses a monomolecular structure design and generation method based on a large language model, and relates to the crossing field of monomolecular electronics and artificial intelligence. In order to solve the problems of low single molecule design efficiency and insufficient data, SingMolT5 is obtained through field fine tuning on the basis of MolT5, and generation from a natural language to a molecular SMILES is realized. According to the method, data are collected from literatures in the single molecule field and an existing disclosed molecule library, a fine tuning data set containing 329 high-quality instructions is constructed, and the fine tuning data set comprises three basic data types including a molecular skeleton, an anchoring group and molecules. The model is initialized by a MolT5 check point, and a cross entropy loss function is used in the fine tuning process; reasoning, generating and using a beam search strategy; the follow-up evaluation indexes comprise BLEU, Levenshtein, MACCS, RDK, Morga and effectiveness. The method provides an innovative technical path for'low experience and data dependent type single molecule design 'in the field of single molecule electronics, and is particularly suitable for aided design of a single molecule device core function unit and a single molecule sensing probe molecule, namely, a target molecular structure can be quickly converted through a natural language, the design threshold of researchers is reduced, and the design efficiency is improved. And a traceable technical basis can be provided for screening and iteration of a monomolecular structure based on a high-quality data set and rigorous evaluation logic.

The invention discloses a bridge concrete apparent damage identification method and system based on texture analysis, and belongs to the crossing field of bridge engineering detection and computer vision technology, and the method comprises the steps: employing an unmanned plane to collect a bridge member surface image, and generating an orthoimage; extracting contrast, correlation and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on a local binary pattern; detecting a corrosion dialysis area based on an HSV color space; fusing the multi-dimensional features, inputting the fused multi-dimensional features into a multi-label classifier, and outputting pixel-level segmentation masks of various damages such as cracks, spalling, exposed reinforcement corrosion and dialysis at the same time; according to the method, the complementary texture features are fused to realize simultaneous identification of multiple types of damages, a pixel-level segmentation result is output, and an evaluation report conforming to engineering specifications is automatically generated.

The invention discloses a controllable sparse migration attack method based on generator generation, and belongs to the crossing field of communication signal processing and artificial intelligence. The method aims at solving the problems that disturbance of existing countermeasure attacks in the field of electromagnetic signals is too concentrated, concealment is insufficient, and disturbance density is difficult to control. The technical scheme comprises the following steps: simultaneously outputting disturbance amplitude and disturbance probability by using a deep generation network; classifying key regions by calculating group frequency spectrum saliency positioning pairs and generating a group mask matrix; generating a binary position matrix through a mask quantization network in combination with the group mask and the disturbance probability; training the generative network by adopting a total loss function comprising adversarial loss and smooth loss; and finally, generating a sparse controllable adversarial sample through the trained network. According to the method, the adversarial sample which is high in concealment, high in mobility and controllable in disturbance proportion can be generated, and an effective means is provided for security assessment of the deep neural network.

The invention belongs to the crossing field of a surface acoustic wave device, a microfluidic technology and a surface enhanced Raman spectrum, and relates to a high-flux SERS substrate preparation device based on acoustic flow control. The device adopted by the method comprises a heating module and an enrichment module. And the heating module controls the temperature change, so that chip breakage caused by overheating is avoided. In the enrichment module, when an alternating electric signal is applied, an interdigital electrode excites the surface of the chip to generate surface acoustic waves, and then vibration of solid particles is triggered, so that target particles move along the surface of the substrate and are enriched. Meanwhile, the device adopts a bionic shark type hydrophobic film, so that the contact angle of liquid drops reaches 140 degrees, internal particles are convenient to gather, and the enrichment speed and the drying efficiency of the particles are remarkably improved. The method is easy and convenient to operate, enrichment and drying treatment is provided for subsequent target particles, and the detection sensitivity of the target particles in Raman spectrum analysis is remarkably improved; and a solution in a liquid sample can be quickly evaporated into solid residues.

The invention discloses a high-precision InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring method combining polarization information and deep learning, and particularly relates to the crossing field of radar remote sensing and deep learning technologies. The method comprises the following steps: acquiring multi-temporal complete polarization or dual polarization SAR original data, generating single-view complex data through radiometric calibration, registration and multi-view processing, and extracting a polarization coherence matrix; constructing a sample set containing polarization features and interferometric phase labels; designing a double-branch deep learning network model composed of a polarization feature extraction branch and a phase optimization branch, and processing a polarization matrix and a phase sequence by adopting a three-dimensional convolution structure and a long and short term memory-convolution hybrid structure; the deep polarization features and the optimized phase information are fused through the trained model, and the deformation quantity is output through regression calculation; and finally, generating a deformation graph under a geodetic coordinate system through geocoding. The polarization scattering characteristic is effectively utilized, and the monitoring precision and reliability in a complex scene are improved.

The present application belongs to the cross field of maritime affairs, ship, shipping, data science, machine learning and artificial intelligence, and particularly relates to a ship lingering AIS trajectory rapid simplification method, which comprises: arranging and processing AIS data of a ship in time sequence to form an AIS trajectory of the ship; cutting the AIS trajectory to obtain a plurality of trajectory segments; performing self-intersection detection on each trajectory segment; constructing a trajectory directed graph for the trajectory segment with intersection points; and using a graph shortest distance solving algorithm to find out a simplified trajectory and output the same. The method provided by the present application can quickly remove and correct trajectory redundancy and disorder caused by lingering, can not change the trajectory form of other normal navigation segments, and can maximize the retention of useful information of the original trajectory; the constructed directed graph is a very sparse graph, and the operation speed is very efficient and fast, without the need of manually fine-tuning parameters, and is suitable for automatically and rapidly processing a large amount of trajectory data.

The present application relates to the cross field of aerial remote sensing imaging and motion control, and specifically provides a multi-mode pod cooperative imaging control method and system, the method steps comprising: integrating a multi-mode photoelectric sensor in a pod to obtain load parameters of the multi-mode photoelectric sensor to calculate various indicators of remote sensing imaging; generating a swing-scan instruction and a push-scan instruction for the pod based on the various indicators; wherein the swing-scan instruction is used for load swing-scan, and the push-scan instruction is used for image motion compensation; the swing-scan instruction and the push-scan instruction are used to cooperatively execute the tasks of swing-scan imaging and image motion compensation based on the same control cycle; under the condition of low-altitude high-speed flight, the present application cooperatively plans the motion of a multi-mode pod and remote sensing imaging, ensures the stable imaging quality of the multi-mode sensor, guarantees the accuracy and reliability of remote sensing data, and improves the remote sensing operation ability and efficiency.

The application provides a portable magnetic resonance imaging-based brain disease remote diagnosis system, and belongs to the cross field of medical imaging and remote medical treatment.The system comprises a portable magnetic resonance imaging device, a remote data transmission and cooperation network and a remote diagnosis and AI auxiliary workstation; wherein the portable magnetic resonance imaging device is arranged in a primary medical institution, is used for performing standardized scanning on a patient's brain disease under a low-field magnetic field, and acquires brain image data of the patient; the remote data transmission and cooperation network is used for connecting the device end and the diagnosis end, and is used for transmitting the brain image data to the remote diagnosis and AI auxiliary workstation; and the remote diagnosis and AI auxiliary workstation is used for performing brain disease analysis based on the brain image data and generating a structured report, and the report is returned to the primary medical institution.The application combines remote medical treatment and on-site magnetic resonance scanning, optimizes medical resources, and improves diagnosis effect.

The invention discloses a permanent magnet-magnetizer magnetic control type electric kinetic energy synchronous output device, relates to the crossing field of permanent magnet power and power generation equipment, and discloses the permanent magnet-magnetizer magnetic control type electric kinetic energy synchronous output device to overcome the defects that an existing device is low in magnetic energy utilization rate and single in function and depends on external energy. The device forms a'starting-regulating-driving-power generating-self-sufficiency 'closed-loop system through the supporting system, the core energy conversion system and other four units. The core unit adopts a neodymium iron boron N52 permanent magnet and a high magnetizer, and the air gap is 0.4-1.5 mm; the intelligent regulation and control system realizes millisecond-level switching of magnetic field polarity through a Hall sensor and a driving module; the power generation coil and the power coil are complementary in magnetic field, and the characteristics of load increasing and torque synchronous increasing are achieved. The magnetic energy conversion efficiency of the device reaches 88%, the service life exceeds 150000 hours, the device is completely separated from external energy after initial starting, and the device can be applied to the fields of industry, aerospace and the like through power expansion.

The invention discloses a sparse electroencephalogram signal source positioning method based on functional magnetic resonance guidance, and relates to the crossing field of electroencephalogram signal processing and neuroimaging technologies. The method mainly comprises three parts of fMRI-guided sparse source space construction, subject specificity forward model construction and multi-target regularization inverse problem solving. The method comprises the following steps: firstly, screening high-activation voxels through BOLD signal intensity of fMRI, and constructing a sparse source space through connected component analysis and representative point selection; then, a subject specific boundary element (BEM) head model is constructed based on the structural MRI, and electrode registration and lead field matrix calculation are completed; and then a multi-target regularization model fusing data fidelity, space compactness and intensity consistency is constructed, optimal estimation of source current intensity is obtained through analysis and solution, and finally a positioning result of brain power activation is output. According to the method, the advantages of high time resolution of the EEG and high spatial resolution of the fMRI are fully played, the limitations of inverse problem morbidity, low spatial resolution and insufficient multi-mode fusion in traditional brain power supply positioning are effectively solved, and brain power supply positioning with high precision, noise resistance and high interpretability is realized; and a new scheme is provided for cognitive neuroscience research, nervous system disease diagnosis and brain-computer interface development.