21results about How to "Rich dimension" patented technology

This invention relates to the field of non-contact biosignal processing technology, providing a method, device, electronic device, and storage medium for determining psychological state assessment parameters. The method includes: inputting millimeter-wave radar signals into a psychological state assessment model to obtain psychological state assessment parameters; the psychological state assessment model includes a physiological feature extraction module, a micromotor feature extraction module, a multimodal feature fusion module, and a psychological state inference module. This invention reconstructs electrocardiogram waveforms through the physiological feature extraction module to obtain detailed physiological features, extracts micromotor features through the micromotor feature extraction module, and integrates features reflecting internal physiological changes and external behavioral manifestations through the multimodal feature fusion module. This forms a more comprehensive and multidimensional fused feature set for comprehensive analysis by the psychological state inference module, greatly improving the comprehensiveness and accuracy of psychological state assessment in unconstrained daily environments.

The invention relates to the technical field of multi-modal corpus construction, in particular to a multi-modal corpus construction method and system. Comprising a multi-source heterogeneous data acquisition module, a multi-modal preprocessing and analysis module, a cross-modal automatic alignment and association module, a translation feature labeling and management module, a structured storage and index module, an AI intelligent retrieval and teaching application module, a semantic optimization multi-modal adaptation module and a user display terminal. The multi-source heterogeneous data acquisition module comprises a data source interface unit and a metadata extraction unit; a multi-modal corpus system deeply integrated with a translation theory is constructed to directly serve the core demand of a translation subject, and a triple screen array mode is adopted to cooperate with display, so that display content is reasonably distributed, a user can check and operate in multiple dimensions, the use experience and the working efficiency are greatly improved, and the user experience and the working efficiency are improved. And various requirements of translation teaching and the like are met.

This invention discloses a method and system for processing personnel information for business administration based on big data, relating to the field of personnel information data processing technology. The method includes the following steps: acquiring personnel information; preprocessing the personnel information to obtain preprocessed text information; processing the preprocessed text information through an embedding layer based on a CNN-BERT model to obtain a word vector matrix; processing the word vector matrix sequentially through a CNN convolutional layer, pooling layer, and fully connected layer to obtain structured data; using an improved ant colony optimization algorithm to perform path optimization for multi-condition combined queries on the structured data to obtain the globally optimal retrieval path; retrieving personnel information that satisfies the multi-condition combined query from the structured data based on the globally optimal retrieval path; and adaptively updating the global pheromone of the improved ant colony optimization algorithm after each round of multi-ant colony collaborative search to accurately locate the target personnel information.

The invention belongs to the technical field of optical communication, and particularly discloses an underwater encryption communication method and device for multi-dimensional optical parameter fusion, and the method comprises the steps: mapping to-be-transmitted real information (real codes) and false information (virtual codes) to a plurality of physical parameters of an optical carrier at a transmitting end; and a brand-new and high-concealment composite signal is constructed through fusion of the two signals. And the composite signal is decoded at a receiving end, so that real information is obtained. The invention aims to solve the problems that the existing underwater laser communication is easy to intercept, the anti-interference capability is poor and the safety is insufficient.

The invention provides a three-mode rotating support operating under an electric arc wind tunnel condition and a using method thereof. The three-mode rotating support comprises a rotating disc, a rotating shaft, an electric rotating disc, an electric push-pull rod, a model support, a push-pull sliding ring and a supporting rod. The outer ring of the rotating disk is provided with three uniformly distributed mounting sheets, and each mounting sheet is provided with a model support through a rotating shaft. The electric rotating disc drives the rotating shaft and the rotating disc to rotate integrally, and sequential switching of the three models at the arc wind tunnel nozzle outlet checking station is achieved. The electric push-pull rod drives the push-pull slip ring to move axially and converts linear motion into pitching rotation of the model support through the supporting rod, so that the attack angle of the model is adjusted in real time during examination. According to the invention, continuous examination of three models can be completed in one vacuum-pumping period, dynamic change of the angle of attack in the test process is supported, and the efficiency and the data acquisition capability of the arc wind tunnel test are greatly improved.

The application discloses a method for generating a test sample, comprising: receiving a generation request for generating a test sample, the generation request comprising a test sample generation strategy for a target application under test, the target application under test having a downstream application; in response to receiving the generation request, obtaining a to-be-varied sample of the target application under test, the to-be-varied sample comprising historical request data for the target application under test and historical response data responded by the downstream application to the response data of the target application under test, the response data of the target application under test being response data of the target application under test to the historical request data; and generating the test sample according to the test sample generation strategy and the to-be-varied sample for testing the target application under test.

The invention provides a resource pushing method and device, equipment, a medium and a program product, and is applied to the technical field of artificial intelligence and the technical field of financial science and technology. The method comprises the following steps: acquiring a user behavior data stream related to a user, wherein user behavior tensors corresponding to a plurality of specified moments in the user behavior data stream represent interaction behaviors executed by the user at the plurality of specified moments; according to the difference between at least two adjacent user behavior tensors, performing incremental tensor decomposition on the specified user behavior tensor to obtain time factor data and behavior feature data related to the specified moment; performing interaction behavior evolution analysis on the user based on the time factor data corresponding to the plurality of specified moments to obtain behavior evolution data representing a user interaction behavior change trend; and determining an interaction intention of the user based on the behavior evolution data and the behavior feature data, and pushing a target resource matched with the interaction intention to the user.

The application discloses a workshop production quality online detection system based on machine vision and AI, and particularly relates to the field of machine vision detection, and comprises the following steps: through a multi-modal data acquisition and preprocessing module, physical signals, process parameters and actual quality values of products are synchronously acquired, and after data cleaning, semantic alignment and standardization processing, structured data blocks of associated product information are generated; then, through a feature extraction and mixed vector generation module, physical information features and multi-modal data driven features are extracted and fused into a mixed vector, a network is trained to output a comprehensive quality index and a corresponding time sequence; finally, through a quality field energy coupling decision module, a quality toughness coefficient and a process fluctuation conduction coefficient are calculated, three types of energy fields, i.e., compliance, consistency and risk, are defined, a comprehensive decision value is obtained through coupling, four-level quality decisions, i.e., high-quality, qualified, to-be-checked and rejected, are output according to the value and field characteristics, production circulation is guided, and the accuracy and efficiency of quality detection are effectively improved.

The application relates to the technical field of industrial safety production supervision and intelligent identification, and discloses a multi-modal multi-verification access control linkage method for industrial safety, which comprises the following steps: continuously acquiring a visible light video stream and an infrared light video stream of a to-be-passed region, extracting a to-be-processed image frame pair, inputting the to-be-processed image frame pair into a pre-trained multi-task neural network model, and outputting an identity verification result, a living body verification result and a wearing compliance verification result; only when the three verification results are all compliant and the wearing compliance result meets a preset work type-wearing mapping relationship, the door body is controlled to be opened; and within a time window from the opening of the door body to the closing of the door body, the actual number of passing people is counted based on background difference operation, and if the actual number of passing people is greater than the number of people passing through the verification, an abnormal alarm signal is generated. Through multi-modal data fusion and a multi-task parallel reasoning architecture, in combination with a work type-wearing differentiated judgment and a trailing detection mechanism, the application realizes high-precision, anti-fraud and anti-tailing safety access control in an industrial scene.

The invention discloses a VLA-based low-speed park automatic driving method, and relates to the technical field of automatic driving, and the method comprises the steps: collecting a plurality of park environment data, generating a panoramic park environment perception tensor, executing semantic element recognition and dynamic subject motion trend pre-judgment, and generating a park environment semantic analysis topology with a dynamic motion trend; performing advance obstacle avoidance path planning and stationary path optimization on the park environment semantic analysis topology with the dynamic motion trend and the preset driving path, and generating a park automatic driving control instruction set with dynamic safety redundancy; and executing the instruction set and collecting vehicle driving state feedback data to generate a multi-mode-path planning collaborative optimization parameter tensor, and continuously adjusting automatic driving execution logic. Therefore, automatic driving can continuously adapt to scene changes in long-term operation, and the operation stability and robustness are improved.

The invention provides a transformer substation factory boundary noise prediction method and system. The method comprises the steps of constructing a multi-dimensional monitoring network, synchronously collecting and fusing multi-source heterogeneous data, obtaining a historical panoramic data set, inputting the historical panoramic data set to a noise source recognition model, outputting noise equipment and position information, and determining the contribution of the noise equipment to a monitoring point; performing correlation analysis on the contribution of each noise device and a historical panoramic data set, and training a noise feature model based on an operation condition for each noise device; and inputting the running condition data of each noise device collected in real time into the trained corresponding noise feature model, predicting the noise contribution of each device in the target time period, and synthesizing the noise level change trend. Intelligent monitoring of noise standard exceeding trend early warning is realized by automatically identifying and positioning a noise source and correlating and analyzing the operation state of equipment, and compared with a manual inspection mode, the method has the advantages of more comprehensive dimension, more timely timeliness and higher efficiency, and prediction and early warning can be carried out.

This invention belongs to the field of data processing technology, specifically relating to a consumer data report generation system based on machine learning. This system aims to solve technical challenges such as insufficient depth in feature mining of large-scale heterogeneous consumer data and homogenization of report generation. The system includes: a multi-source heterogeneous data adaptive ingestion module, a nonlinear feature automated calculation engine, a heterogeneous behavioral semantic association graph construction unit, an incremental machine learning inference center, and a multi-dimensional logical adaptive report generation platform. Through standardized mapping, nonlinear feature extraction, and semantic graph modeling, combined with incremental model inference, the system can automatically match narrative logic according to needs and generate structured, visual reports. This invention achieves deep perception and dynamic real-time processing of massive amounts of consumer data, improving report production efficiency and business insight, and providing an efficient and adaptive solution for consumer decision-making.

The invention provides a water quality detection device and method based on friction nanometer power generation. The method is used for solving the problem that heavy metal ion type identification and concentration quantification are difficult to consider in an existing friction electric signal-based liquid detection method. Comprising a multi-electrode triboelectric sensor, and the multi-electrode triboelectric sensor comprises a substrate, a metal electrode and a triboelectric layer. More than two metal electrodes are arranged between the base plate and the triboelectric layer, the plurality of metal electrodes are sequentially arranged at equal intervals along the length direction of the base plate, during water quality detection, liquid drops are guided to slide along the length direction of the base plate, and the liquid drops sequentially pass through the plurality of metal electrode areas in the sliding process, so that the water quality is detected. And a time sequence-related friction electric signal is generated at each metal electrode. According to the invention, rapid and accurate detection of the type and concentration of heavy metal ions is realized, and the device is simple, portable, low in cost, self-powered, capable of realizing accurate concentration quantification in a wide concentration range, and extremely low in detection limit.

The invention discloses an ocean data classification method and device based on modal transformation, equipment, a medium and a product, and relates to the field of data processing, and the method comprises the steps: obtaining multi-modal time sequence data; feature extraction is carried out on the multi-modal time sequence data to obtain multi-modal feature data, and the multi-modal feature data comprises geometric modal data, statistical modal data and time-frequency modal data; determining a target feature vector sequence according to the multi-modal feature data; and inputting the target feature vector sequence into a preset classifier, and determining a classification result. The technical problem that in the prior art, multi-dimensional, heterogeneous and high-time-sequence-correlation ocean data cannot be effectively classified is solved.

The application discloses a multi-modal emotion recognition and intelligent guidance device for English learning, comprising a learning terminal, a somatosensory data collector and a central processor. The learning terminal is used for presenting learning content and recording user learning interaction logs. The somatosensory data collector is used for collecting user video stream data and stress distribution data. The central processor comprises a multi-modal feature extraction module, a risk assessment module, a guidance decision module and a content generation module. The device extracts negative interaction behavior features from the logs, extracts posture withdrawal features from the video stream and extracts stress deviation features from the stress data. The user's learned helplessness risk level is obtained through a psychological risk assessment model, a target guidance strategy is matched according to the risk level, and guidance content for cognitive reconstruction is generated and embedded in the learning content. The device can more accurately identify deep psychological risks and provide targeted intervention.

This invention discloses a knowledge graph-based method for root cause localization and diagnosis of IT operations and maintenance faults, belonging to the field of IT operations and maintenance technology. The invention includes constructing a knowledge graph of the target IT system; collecting multi-dimensional operational data of each entity within a fault time window when a fault alarm is detected; constructing a spatiotemporal causal graph of fault propagation, including inter-node propagation delays, based on the topology of the knowledge graph and the multi-dimensional operational data; calculating the causal effect score of each candidate root cause node through spatiotemporal decoupling causal reasoning; and sorting the candidate root cause nodes according to their causal effect scores to determine the root cause node list. This invention, by constructing a spatiotemporal causal graph of fault propagation and combining it with a spatiotemporal decoupling causal reasoning algorithm based on intervention distribution and iterative residual analysis, quantifies the causal effect of each node's anomaly on the overall system fault state on the knowledge graph, effectively decoupling the temporal sequence from the spatial causal logic.

The application provides a roadbed transition section edge compaction detection method and system, and relates to the technical field of roadbed compaction detection.The method comprises the following steps: setting a detection point layout rule based on a compaction correlation scale and a sensor monitoring scale; after locating a compaction blind area in the roadbed transition section, carrying out grid compaction sensor array layout; performing hierarchical collection of multi-modal compaction data to obtain a fusion compaction parameter array; after the edge compaction detection cloud receives the fusion compaction parameter array, tracing back a cross-layer time sequence correlation array mapping calculation distributed compaction degree deviation node; solving a supplementary compaction control sequence to perform targeted correction; and performing a supplementary compaction control closed-loop iteration according to re-measured data.The application solves the technical problem that the prior art only relies on limited detection points for sampling inspection, cannot comprehensively cover the local compaction blind area and uneven area of the roadbed transition section, causes incomplete detection, and thus affects the safety and service life of the road.

This invention provides a post-mortem analysis method for large hydropower plants participating in the spot market, relating to the field of hydropower station management technology. Addressing the lack of an analytical system for large hydropower stations' operational effectiveness after participating in power trading, this invention achieves effective post-mortem analysis of production plans and trading strategies through three parts: data collection, data analysis, and results presentation. Data collection involves daily collection and standardized storage of transaction data, settlement data, forecast data, and hydropower station operation data. Data analysis uses the mean absolute error to measure the degree of deviation of indicators and combines the TOPSIS algorithm to identify key influencing factors, conducting analyses of revenue composition, forecast deviation, production efficiency, rule adaptability, and changing trends. Results presentation uses visual formats such as pie charts and line graphs to present revenue share, deviation indicators, assessment risks, and trend changes. This invention helps managers formulate cost-effective production plans, reduce power generation costs, and improve economic efficiency.

The invention relates to the technical field of medical image processing, and discloses a multi-modal image fusion nasolacrimal duct obstructive disease diagnosis and grading system which comprises the following steps: acquiring head CT data, performing space standardization, and generating osseous channel and soft tissue channel input tensors based on double-window parameters; a double-flow attention interaction network is constructed, a spatial attention mask is generated by using a bony feature extraction branch, and weighted fusion is performed on the pathological features of the soft tissue through a topology guide attention unit; a semi-supervised model training module is adopted, a consistency constraint strategy is executed on unlabeled samples, and network parameters are updated by minimizing a composite loss function; and finally outputting disease property classification and blocking degree grading results. According to the method, the bony anatomical structure and the soft tissue pathological information can be effectively combined, the model performance is improved by using the unlabeled data, and high-precision automatic diagnosis and grading of nasolacrimal duct blockage are realized.

The application discloses a high-speed neutral plane source particle beam density and velocity measurement method, and belongs to the technical field of extremely low orbit particle beam measurement. The measurement method comprises the following steps: selecting multiple measurement points in a measurement plane, controlling a flow field calibration three-axis displacement table to drive an atmospheric detection device to reach each measurement point in sequence, collecting energy spectrum data and count data in a radial direction and a normal direction at each measurement point respectively; calculating a velocity projection component based on an energy spectrum peak value, calculating a number flux and obtaining a number density based on count, integral time and effective receiving area; introducing a spatial distribution prior assumption model, taking velocity vectors and number densities of the measurement points as constraints, adopting a weighted least square method to fit model parameters, and inversely reconstructing two-dimensional velocity distribution and two-dimensional number density distribution. The application realizes single-probe multi-measurement-point multi-direction scanning measurement, combines double projection constraints and prior model inversion, obtains high-precision continuous flow field calibration results with low complexity, and is suitable for extremely low orbit space environment simulation.