2095 results about "Highly sensitive" patented technology

The invention relates to the technical field of unmanned aerial vehicle-mounted nuclear radiation detection, and provides an unmanned aerial vehicle-mounted nuclear radiation detection method and system based on deep learning, and the method comprises the steps: collecting a gamma energy spectrum signal, and carrying out the digitization of the gamma energy spectrum signal, and obtaining original energy spectrum data; preprocessing the original energy spectrum data to obtain preprocessed energy spectrum data; constructing a nuclide identification model based on a deep learning network, and performing nuclide identification on the preprocessed energy spectrum data to obtain an original nuclide identification result; performing post-processing analysis and refinement on the original nuclide identification result to obtain a nuclide identification result; and fusing the nuclide identification result with the geographic position information to obtain a nuclear radiation pollution distribution diagram, and setting a multi-level early warning mechanism based on nuclide types and intensity. According to the invention, the precision of nuclide identification in a complex environment is improved, and high-sensitivity detection of various nuclides under the condition of low signal-to-noise ratio is realized.

The invention belongs to the technical field of computer data security, and discloses an enterprise data encryption storage system based on strategy optimization. The system is composed of a strategy map construction and optimization module, a sensitivity identification and encryption strategy generation module, an encryption execution and ciphertext structure processing module, a strategy-driven storage scheduling module and an audit recording and feedback optimization module. According to the method, a strategy graph containing multiple types of nodes such as users, data, behaviors, risks and resources is constructed, the edge weight and the node confidence coefficient are dynamically adjusted through the behavior frequency, the access relation and historical audit information, a graph structure oriented to behavior contexts is formed, and on the basis, an encryption strategy label is generated in combination with a fuzzy semantic reasoning mechanism; a traditional static recognition mode based on field rules can be broken through, potential high-sensitivity data and abnormal behavior paths are recognized, and the generation accuracy and the self-adaptive capacity of a data encryption strategy are improved.

The invention provides a high-resolution small-angle measuring device based on laser frequency measurement, which comprises a laser generating device, a reflecting mirror group and a beat frequency measuring module, and is characterized in that a narrow-band interference sheet (3) and a transparent solid medium (4) are fixed on a high-precision horizontal rotating table (5); the fixing positions of the narrow-band interference sheet (3) and the transparent solid medium (4) are axially symmetric and are not parallel to each other by taking the diameter, perpendicular to the light path, of the rotating table as the axis; and when the high-precision horizontal rotating table (5) rotates to cause the frequency change delta f of the measured laser, the variable quantity of the beat frequency is equal to delta f. According to the small-angle measuring device, angle measurement is converted into frequency measurement by means of the high sensitivity of laser frequency to the rotation angle of the transparent solid medium, and the cavity mode frequency and the transmission frequency of the interference piece are synchronously changed by adding the interference piece; the problem that the tunable range of laser frequency is small due to rotation of a transparent solid medium is solved, and continuous measurement of micro angles is achieved.

The invention relates to the technical field of drug concentration detection, and relates to a clinical pharmaceutical blood concentration detection method, which comprises the following steps: capturing drug molecules in a blood sample based on a micro-fluidic chip, enabling a sensor to detect the drug molecules and combine with the surface of the drug molecules, and collecting high-sensitivity blood reaction data. According to the method, main variables in data are identified and utilized by adopting a principal component analysis method, so that the complexity of spectral data is effectively reduced, meanwhile, the most critical information for calculating the drug concentration is reserved, the data processing flow is optimized, the analysis speed and efficiency are improved, and the prediction of the drug concentration is more accurate; according to the method, more reliable data support is provided for clinic, the data is smoothed through the moving average algorithm, random fluctuation caused by sampling errors or external variables is reduced, the overall quality and credibility of the data are improved, errors can be obviously reduced, and therefore doctors are supported to conduct more accurate medicine dosage adjustment and curative effect evaluation.

The invention discloses a data classification management and control method based on artificial intelligence, and relates to the technical field of data classification management and control, and the method comprises the following steps: capturing all data streams transmitted from a sensitive information storage device in real time through a network flow monitoring tool, employing a deep packet detection technology, analyzing a network packet, and obtaining specific transmission data; the method comprises the following steps: preprocessing captured transmission data, and dividing the preprocessed transmission data according to predefined fields; according to the method, a deep packet detection technology is utilized to obtain transmission content and pre-process and divide data, a field collection database is established in combination with an enterprise security policy, similarity analysis is performed through a machine learning model, and the data is divided into high-sensitivity data and low-sensitivity data. Face recognition authentication is carried out before high-sensitivity data transmission, and data which does not pass authentication are encrypted and transmitted by adopting TLS / SSL, so that the security in the transmission process is ensured, financial information leakage is effectively prevented, the financial security of enterprises is ensured, and economic loss and financial crisis are avoided.

The invention provides a data leakage prevention system building method, which comprises the steps of building a data leakage risk prediction model according to principal component characteristics, training the model by adopting a support vector machine algorithm to obtain a data leakage risk prediction model, inputting to-be-predicted multi-source heterogeneous data into the risk prediction model, and obtaining a data leakage risk prediction result. Calculating a leakage risk score of the data combination; a sensitivity threshold value is preset, whether the leakage risk score exceeds the preset sensitivity threshold value or not is judged, if the leakage risk score exceeds the preset sensitivity threshold value, the data combination is marked as highly sensitive data, an early warning mechanism is triggered, and desensitization and encryption are conducted on the data combination according to a preset desensitization rule and an encryption method; and continuously monitoring data updating conditions of different data sources, when newly added data is detected, automatically triggering a data fusion and leakage risk prediction process, obtaining sensitivity characteristic representation of the newly added data, updating a data leakage risk prediction model, and dynamically adjusting the sensitivity degree and protection measures of data combination.

A displacement sensor including an optical whispering gallery mode (WGM) microresonator and a package encasing at least a portion of the WGM microresonator, the package comprising polydimethylsiloxane (PDMS). The WGM microresonator can be configured as a sensor and used in a displacement detection system that can detect displacement with high quality. Artificial intelligence can be implemented in the displacement detection system for improved sensing of different variables and / or pinpointing the location of perturbations.

The invention relates to the technical field of remote sensing landform enhancement, and discloses a remote sensing landform enhancement algorithm, which comprises the following steps of: S101, preprocessing an existing high-resolution DEM (Digital Elevation Model) image and an existing low-resolution DEM image to construct a training data set; step S102, constructing a double-domain multi-scale attention fusion super-resolution network; step S103, training the double-domain multi-scale attention fusion super-resolution network through the training data set; according to the method, the edge information and spatial continuity characteristics of the DEM image are comprehensively considered, the high-frequency and low-frequency information of the DEM image is extracted through Haar wavelet transform by utilizing the characteristic that a wavelet domain is highly sensitive to the high-frequency and low-frequency information, and spatial domain feature information obtained by a parallel branch convolutional layer is combined, so that the high-frequency and low-frequency information of the DEM image is obtained. Through fusion of the two, the network has a feature enhancement function in a shallow layer, and meanwhile, the importance relationship between different depth feature maps is obtained through the multi-scale attention fusion module group, so that the precision of DEM super-resolution reconstruction is improved.

The invention discloses an Alzheimer's disease early screening method based on magnetic resonance imaging, and relates to the technical field of magnetic resonance imaging, and the method comprises the steps: obtaining information identity data and magnetic resonance scanning image data of a to-be-detected person; preprocessing the magnetic resonance scanning image data, and calculating a brain area volume and a DTI-ALPS index according to the preprocessed magnetic resonance scanning image data; constructing a brain network and calculating global network attributes and regional node attributes according to the brain network; substituting the information identity data, the brain area volume, the DTI-ALPS index, the global network attribute and the area node attribute of the to-be-tested person into an Alzheimer's disease screening model to calculate diagnosis parameters; and comparing the diagnosis parameter with the optimal diagnosis threshold value to obtain a screening result. According to the method, the brain colloidal lymphatic network function is evaluated by means of multiple high-sensitivity nerve image indexes, and the early screening capability of the Alzheimer's disease is improved.

The invention relates to the technical field of power system monitoring and fault diagnosis, and particularly discloses a fault diagnosis system of an electrical variable measurement insulator detection device. The system comprises a synchronous acquisition module, a multi-physical field feature extraction module, a self-adaptive fault diagnosis engine and a feedback module. By synchronously acquiring voltage harmonic waves, leakage current and temperature data, extracting feature vectors of coupling electric-thermal influence and performing two-stage collaborative diagnosis by using a dynamic threshold value and a mahalanobis distance, high-sensitivity and self-adaptive accurate identification and early warning of an early latent fault of the insulator are realized. According to the system, by constructing a synchronous data acquisition module and a multi-physical field feature extraction module, two key influence factors, namely an operation voltage harmonic component and an environment temperature, are brought into a diagnosis system in a quantifiable feature form for the first time.

The invention discloses a preparation and measurement method of a polymer-based multi-point force flexible sensor for knee joint pose measurement, and relates to the technical field of polymer-based flexible sensors and soft measurement. The structure of the multi-point force polymer-based flexible sensor shaped like a Chinese character'ao 'is designed for knee joint stress and posture state measurement, and the flexible sensor is prepared and packaged through a silk-screen printing method. A flexible sensor strain input-output knee joint stress data mapping set and a flexible sensor strain input-pose angle output data mapping set required by modeling are completed through a flexible sensor, a thin film force sensor corresponding to multi-point force and an inertial sensor corresponding to pose angle measurement of installed multi-point force polymers. And establishing a neural network soft measurement model based on an MLP algorithm, and carrying out training and verification. And the knee joint pose angle with reliable precision and the maximum stress state of the knee joint are obtained. The sensor has the advantages of being flexible, thin, skin-friendly, high in sensitivity and stable in signal output.

The invention discloses a static database self-adaptive fuzzification desensitization implementation method for highly sensitive data in the power industry. The method comprises the following steps: a data preprocessing module, a sensitivity analysis module, a fuzzification algorithm module, a self-adaptive mechanism module and an effect evaluation and verification module. Compared with the prior art, the method has the advantages that 1, self-adaptive dynamic adjustment is performed: a desensitization strategy is automatically optimized according to data characteristics and use scenes, and privacy protection indexes (such as k-anonymity values) are improved by 30%-50%; 2, multi-dimensional balance: the information loss rate is reduced by 10%-20%, the query accuracy is improved by 5%-10%, and safety and service requirements are considered; 3, system expansibility: a plug-in architecture supports dynamic loading of new algorithms and rules, and a configuration management module realizes strategy real-time adjustment; and 4, compliance guarantee: through a law and regulation rule base and a compliance check algorithm, the compliance is ensured to meet the standard.

The invention discloses a high-sensitivity touch control capacitor chip device and system, and relates to the technical field of touch control. The method comprises the steps that an original capacitance signal matrix is collected through mixed frequency band excitation, environment compensation is conducted on original capacitance signals through a nonlinear compensation mechanism, and an effective capacitance matrix is generated; extracting a time-domain pure touch signal through fractional order time-frequency combined noise suppression and dynamic baseline tracking; the method comprises the following steps: extracting deformation curvature characteristics of a contact area through a time-domain pure touch signal, and constructing a spatial-temporal characteristic tensor; and through self-adaptive convolution kernel generation and a multi-modal attention mechanism, taking the spatial-temporal feature tensor as input, taking the touch behavior classification probability distribution as output, and generating a final touch instruction according to the category corresponding to the maximum value of the probability distribution. Environmental interference is suppressed through multi-frequency excitation and frequency domain decoupling, temperature and humidity coupling compensation is introduced to improve signal stability, multi-dimensional spatial-temporal characteristics are constructed, and the recognition precision and robustness of the capacitive touch control system in a complex environment are remarkably enhanced.

The invention discloses a high-sensitivity metal micro-strain sensor label and a detection method thereof, which are applied to the field of nondestructive testing and the field of structural health monitoring. The problems that an existing tag antenna is low in strain detection sensitivity and difficult in micro-strain detection are solved. The high-sensitivity metal micro-strain RFID sensing tag is attached to the surface of a component, when the tag deforms, the impedance change of the tag is Zsened, and the radar cross section changes accordingly; the reader can remotely monitor antenna parameters according to the change of the RCS0, the RCS1 and the intensity while communicating with the tag, so that the change of the intensity RSSI of an echo signal is deduced, and the deformation of the tag is indirectly known; when the impedance of the tag antenna changes, the resonant frequency of the tag changes accordingly, and the deformation information of the tag can be indirectly obtained by analyzing the deviation of the resonant frequency.

The invention provides an image generation method based on style feature injection, which can efficiently migrate the spatial layout and color texture of a style reference image on the basis of a text image. According to the method provided by the invention, on the basis of a Stable Diffusion text map, the spatial layout and the preliminary style of an image are learned by utilizing the capability of binding features of Dreambooth, and then attention injection is executed to strengthen color features and detail textures. Therefore, the method provided by the invention has high generalization in various styles, does not need tedious parameter adjustment for a single specific style, and is highly sensitive to context features such as composition, texture, color and the like. In addition, the model needs less data, only 3-5 training images are needed, only one attention reference image is needed in the reasoning process, additional training is not needed, and the robustness is very high. Therefore, the style image generation method provided by the invention can adapt to various different styles so as to have a higher degree of freedom in a personalized task.

The invention relates to a high-sensitivity anti-interference satellite navigation signal receiving system and method based on Rydberg atoms, and the system comprises a signal detection unit which is used for receiving a satellite navigation direct signal and outputting an electric signal; the data receiving unit is used for processing the electric signal output by the signal detection unit and outputting a navigation message; the power distribution unit is used for providing power for the signal detection unit and the data receiving unit; the signal detection unit comprises an atomic antenna which comprises four miniature atomic air chambers and is used for receiving satellite navigation direct signals from different directions; the light path system is used for providing detection laser and coupling laser for the miniature atomic gas chamber so as to excite atoms in the miniature atomic gas chamber from a first low-energy-level state to a high-energy-level state; and the patch antenna is arranged on one side of the miniature atomic gas chamber and is used for providing a local oscillation microwave signal for the miniature atomic gas chamber so as to realize superheterodyne high-sensitivity microwave signal receiving.

The subject invention is concerned with systems and methods advantageously applying finite element analysis to establish design rules for a highly sensitive piezo-resistive pressure sensor with an output that is high enough to be detectable by simple and inexpensive circuits and therefore ensure wearability. Four frequently reported micro-feature shapes in micro-patterned piezo-resistive sensors are provided, where the micro-dome and micro-pyramid yield the highest sensitivity. Investigations of different conductivity values of micro-patterned elastomers show that coating the elastomer with a conductive material (e.g., a metallic coating) leads to higher current response when compared to composited conductive elastomers. Advantageous geometric parameters and spatial configurations of micro-pyramid design of piezo-resistive sensors are provided. Results show that an enhanced sensitivity and higher current output can be achieved by a lower spatial density configuration of three micro-features per millimeter length, a smaller feature size of around 100 μm, and a 60-50 degrees pyramid angle.

A method for calculating carbon storage in a mixed forest ecosystem is provided. The method includes: acquiring basic geographic data, meteorological data, eco-physiological parameter, thinning management history data, and validation data; proposing an improved biome-biogeochemical cycles (Biome-BGC) model suitable for simulating carbon storage of a mixed forest ecosystem under management by improving a phenology module, adding a thinning operation management module, and optimizing the eco-physiological parameter, based on an existing Biome-BGC model; simulating, by taking a pine-oak mixed forest as a research object, the carbon storage based on the improved Biome-BGC model; validating the improved model; analyzing sensitivity of the eco-physiological parameter by an extended Fourier amplitude sensitivity test (EFAST) method; and selecting a highly sensitive parameter, and analyzing an effect of the highly sensitive parameter on the carbon storage by a path analysis method. The improved model exhibits good performance in calculating the carbon storage of the mixed forest.

The invention provides a threshold access system supporting threshold decryption and an NTRU type multi-party homomorphic encryption method, and belongs to the technical field of information security and data security. The threshold access system supporting threshold decryption comprises an initialization stage, a key generation stage, a secret share sharing stage, an encryption stage, a decryption stage, a homomorphic calculation stage and a data reconstruction stage, and a new NTRU type multi-party homomorphic encryption method is realized based on the threshold access system. Comprising an initialization stage, a key generation stage, an encryption stage, a decryption stage and a homomorphic calculation stage, so that the method can be more suitable for application scenarios of highly sensitive data processing, the dependence on a trusted third party is very small, the communication overhead and the calculation overhead are very small, the privacy of data is ensured, subdomain lattice attacks can be resisted, and the security of the data is improved. And the efficiency is relatively high.

This invention belongs to the field of biological detection technology, specifically relating to a monoclonal antibody combination for detecting feline NT-proBNP protein and its applications. The monoclonal antibody combination of the present invention comprises monoclonal antibodies 3A6 and 5H5. These two antibodies possess unique heavy and light chain variable region complementarity-determining regions, respectively, and their amino acid sequences have been fully defined (SEQ ID NOs. 1-12). This antibody combination can specifically recognize and efficiently bind to feline NT-proBNP protein, making it suitable for the development of a highly sensitive and specific diagnostic tool for feline cardiomyopathy. By constructing a double-antibody sandwich ELISA assay system, the minimum detection limit can reach 100 pg / mL, and it exhibits excellent specificity and stability.

The invention provides an instance segmentation method and device for early intestinal cancer vessel infringement phenomenon analysis and a readable storage medium, and the method comprises the steps: constructing an end-to-end automatic process: firstly, carrying out the multi-magnification overlapping cutting of a digital pathological image, and carrying out the high-sensitivity instance segmentation through a Mask2form instance segmentation network; combining the overlapped masks by adopting an efficient union lookup algorithm, and recursively complementing incomplete instances in combination with an ROI (Region of Interest) expansion algorithm; hE, CD-31 and D2-40 dyeing full slice level alignment is realized through rigid-non-rigid-micro registration, and a three-mode instance level graph block is cut based on a registration result; and finally, fusing multi-modal features by using a hybrid expert network enhanced by a large pathological model, and dynamically screening expert paths to carry out true and false positive judgment. The invention provides an efficient technical scheme for early accurate diagnosis of intestinal cancer, and is suitable for pathological big data analysis and clinical auxiliary diagnosis.

The invention discloses a cognitive training system and method, electronic equipment and a storage medium, and the system comprises an electroencephalogram signal collection module which uses a high-sensitivity non-invasive electroencephalogram sensor to collect an electroencephalogram signal of a user in real time; the signal processing module is used for preprocessing the collected electroencephalogram signals by using a deep learning algorithm, removing noise and extracting useful features; the training strategy optimization module is used for analyzing the electroencephalogram signals of the user based on a game theory model and formulating a personalized cognitive training strategy; and the feedback control module is used for feeding back the optimized training strategy to the user, and improving the cognitive participation degree and the training effect of the user through multi-sensory stimulation such as visual sense and auditory sense. According to the cognitive training system and method, the electronic equipment and the storage medium, the game theory is applied to the BCI system, and the cognitive training effect can be enhanced through strategy optimization.

The invention discloses a high-sensitivity tactile sensing system based on a magnetic cilia array, a sensor comprises the magnetic cilia array and a sensor array, and the magnetic cilia array is attached to the upper surface of the sensor array; the magnetic cilia array comprises a substrate and cilia structures which are orderly arranged and fixed on the substrate; the cilia structure is made of ecological silicone rubber mixed with magnetic powder, and a plurality of groups of sensors are adopted to form a sensor array to construct a sensing system. By optimizing the magnetization process, uniform distribution of the magnetic particles in each cilium is ensured, and the output stability and sensitivity of the sensor are improved; meanwhile, the multi-sensor array layout is adopted, and high-precision sensing of multi-dimensional tactile information such as pressure, direction and vibration is achieved.

The invention discloses design and application of a colorimetric-catalytic signal enhancement combined graphene oxide nano-enzyme rapid pesticide residue detection platform, and belongs to the technical field of immunochromatography detection. Graphene (GO) is used as a substrate, 30 nm Pt nanoparticles are adsorbed on the surface of the substrate to provide a colorimetric signal, and gaps of Pt NPs are filled with 5 nm AuIr nanoparticles to enhance the catalytic performance of the substrate, so that the colorimetric / catalytic dual-signal enhanced nano-enzyme is prepared. The GO (at) Pt30-AuIr nano-enzyme is synergistically assembled with Pt NPs and AuIr NPs by utilizing the large specific surface area, excellent stability and dispersibility of GO, so that the catalytic performance of the system is remarkably improved. The method has the beneficial effects that a GO-Pt30-AuIr-LFA-based detection system can realize high-sensitivity dual detection on heavy metal cadmium ions (Cd < 2 + >) and pesticide residue imidacloprid (IMI) within 20 minutes, and the detection limit reaches the level of pg / mL. The detection method provided by the research shows significant application potential in the aspect of rapid screening of small molecules in a complex matrix, and a new technical approach is provided for food safety monitoring.

The invention discloses an ultrafast coherent Raman spectrometer based on quantum entanglement photon enhancement and a light path device thereof, which realize ultrafast and high-resolution detection of a molecular vibration mode through integration of a quantum light source assembly, an ultrafast laser assembly, a light path assembly and a nonlinear crystal assembly and a high-sensitivity detection technology. By utilizing the characteristics of quantum entangled photons, the sensitivity and resolution of spectral measurement are improved, the system structure is simplified, and the cost is reduced. The quantum light source assembly adopts a nonlinear optical crystal to perform parametric down-conversion to generate entangled photon pairs. The ultrafast laser assembly generates femtosecond pulses, and the femtosecond pulses and the entangled photon pairs carry out nonlinear interaction. The light path assembly accurately controls the path and polarization state of photons to realize nonlinear Raman scattering of sample molecules. And the high-sensitivity detector is used for detecting and measuring the Raman light signal scattered by the sample. The invention aims to provide a spectral analysis method with high time resolution and high spectral resolution so as to realize ultrafast and high-resolution detection of a molecular vibration mode. According to the spectrograph, by utilizing the characteristics of quantum entangled photons, through entangled photon pairs generated by the nonlinear optical crystal, the sensitivity and resolution of spectral measurement are improved, and the problem of balance between time and spectral resolution in the prior art is solved.

The invention relates to the technical field of cosmetics, and discloses an anti-inflammatory soothing cosmetic composition which comprises the following components in percentage by weight: 0.5%-4% of a barrier repairing component, 0.5%-3% of an immunoregulation component, 0.3%-2.5% of a nerve soothing component, 0.3%-3% of an anti-inflammatory plant compound, 1%-10% of a moisturizing soothing enhancer and the balance of auxiliary materials. According to the components, through a five-dimensional synergistic mechanism of barrier repair, immune tolerance, nerve desensitization, anti-inflammatory itching relieving, moisturizing and stability maintaining, anti-inflammatory relieving and barrier repair are remarkably enhanced; ceramide, rare ginsenoside, phytosterol ester and free fatty acid are introduced to synergistically construct a bionic liposome which is closer to the composition of human cuticle lipid, so that the transdermal absorption and stability are enhanced, and the repair efficiency is remarkably improved; the dipotassium glycyrrhizinate, the tamarix chinensis flower extract, the purslane extract, the dinoflaxanthine and the baicalin cooperate to quickly resist inflammation and relieve itching and strengthen barrier repair, so that the composition is suitable for highly sensitive skin.

The invention discloses a winter wheat LAI inversion method based on scenarized parameter calibration and ensemble learning. The method comprises the following steps: fusing a remote sensing image with auxiliary geographic information such as terrain and soil, and performing typical planting scene division on a research area by adopting a spectral clustering method; then scene optimization is carried out on highly sensitive parameters (such as LAI, Cab, Cm and ALA) in the radiation transfer model PROSAIL based on a Markov chain Monte Carlo (MCMC) algorithm, and parameter posterior distribution more consistent with actual observation is obtained; further utilizing optimization parameters to generate a high-quality and homogenized simulation sample set, and mapping the simulation sample set to a Sentinel-2 waveband to construct a training data set; and finally, performing training by adopting various machine learning models (such as LightGBM, XGBoost, RF and KNN), and constructing a fusion model through a stacking integration strategy to realize high-precision inversion of LAI. Experimental results show that the method has higher fitting precision and stronger generalization ability in a plurality of typical planting scenes.

The invention discloses a digital cultural tourism management system based on multi-source data analysis, and the system comprises a multi-source heterogeneous data collection module which is used for collecting tourist behavior data, environment data, cultural resource data and third-party platform data in real time; the user demand intelligent analysis module is used for constructing a dynamic user portrait through the multi-modal interaction data and identifying dominant and implicit demands; the culture knowledge graph construction module is used for generating a reasonable multi-dimensional knowledge network based on culture resource attributes and historical data; the travel route dynamic generation module is used for generating a personalized touring route in combination with the user portrait, the real-time environment and the resource state; according to the digital cultural tourism management system based on multi-source data analysis disclosed by the invention, the utilization rate of cultural resources is greatly improved, and the decision response speed is increased; the cultural cognition depth of tourists is improved, and the residence time of the tourists is prolonged; the damage rate of high-sensitivity cultural relics is reduced; and the special group culture acquisition efficiency is improved in a breakthrough manner.

The invention provides an orbital angular momentum interferometer based on a double-resonance spiral long-period fiber grating and application, and belongs to the field of optical fiber sensing and optical communication passive devices. According to the invention, the double-resonance spiral long-period fiber grating is used as an OAM mode converter, the wavelength of the laser is secondarily regulated and controlled to match left and right resonance peaks, phase superposition of conjugate OAM beam pairs is realized, and secondary sensitization of the OAM interferometer is realized based on a single mode converter. The OAM interferometer is applied to the field of temperature and strain sensing, high-sensitivity detection of temperature variation and strain variation is achieved by combining a double-phase matrix decoupling algorithm, and the problem of cross sensitivity is effectively solved. The invention provides an all-fiber integrated and environment interference resistant innovative solution for high-sensitivity OAM optical fiber sensing, and is suitable for the fields of industrial monitoring, environment sensing and optical communication.

The invention belongs to the crossing field of medical information technology and privacy calculation, and particularly relates to a medical privacy protection method based on zero-knowledge deep learning, which comprises the following steps: constructing a medical privacy protection system comprising a client and a doctor side, and deploying a zero-knowledge neural network model based on an improved zkReLU activation function at the doctor side; the patient end receives physiological data input by a patient, carries out sensitivity grading on the physiological data, and encrypts the physiological data with different sensitivities by adopting different algorithms to obtain encrypted data; inputting the encrypted data into a zero-knowledge neural network model for forward propagation to obtain a middle-layer zero-knowledge proof; the patient terminal initiates a diagnosis result query request, and the doctor terminal integrates the intermediate layer zero-knowledge proof to generate an aggregate zero-knowledge proof to wait for verification; the verifier verifies the correctness of the aggregated zero-knowledge proof through the verification public key, and confirms that the diagnosis result accords with a preset rule and the irrelevant privacy of the patient is not leaked; local decryption of high-sensitivity data and encryption reasoning of low-sensitivity data are realized, and the leakage risk is eliminated.