24results about How to "Increase information density" patented technology

The invention discloses a data compression method and device and a data decompression method and device.In the compression method, data to be compressed are processed according to a Huffman coding mode to obtain a first data table, then the first data table is standardized to obtain a second data table, and the second data table is compressed according to the Huffman coding mode. Obtaining a hierarchical table according to the second data table, obtaining serial numbers of all leaf nodes through a formula on the basis of the hierarchical table, outputting codes corresponding to the characters according to the serial numbers and depth of the leaf nodes, and compressing the data according to the codes; in the decompression method, decompression can be carried out only by acquiring the coding table or the weight table generated in the compression process of the data to be decompressed; and the Hoffman tree does not need to be traversed in the compression and decompression processes, so that the compression and decompression efficiency of the data can be improved.

The invention discloses a universal interpretable sequence recommendation method and system based on a multi-task self-prompt technology, and belongs to the technical field of sequence recommendation. A user interaction sequence is obtained, and a sequence recommendation module and a self-prompt generator are pre-trained based on the user interaction sequence; constructing a short input sequence based on the latest interaction of the user interaction sequence, inputting the short input sequence into a pre-trained sequence recommendation module to obtain an embedded representation of the short input sequence, inputting the embedded representation into a pre-trained self-prompt generator, extracting user features and generating personalized prompts of the user features; and on the basis of personalized prompts of user characteristics, alternately performing prompt fine adjustment on the pre-trained sequence recommendation module and the pre-trained explanation generation module, predicting a user recommendation result and generating natural language explanation. A flexible and efficient multi-task interpretable sequential recommendation framework ARTS is provided, accurate personalized recommendation is provided for recommendation results, and high-quality natural language interpretation is generated.

The invention provides a two-dimensional resistive sensor array suitable for measurement in a high-temperature environment, and belongs to the technical field of sensors, the resistive sensor array comprises a substrate, an array circuit and an insulation protection layer, the array circuit comprises a front sensor array and a back terminal circuit, the front sensor array comprises a transverse wire, a longitudinal wire, a sensor unit and a node insulating layer, the sensor unit is a dual-electrode sensor, electrodes at the two ends of the sensor unit are connected with the transverse wire and the longitudinal wire respectively, the node insulating layer is used for insulating the crossed part of the transverse wire and the longitudinal wire, and the resistance type sensor array is a thick film or thin film circuit; the number of external connection ports can be greatly reduced while a large number of two-dimensional distribution physical parameter fields are measured, measurement under various high-temperature, high-pressure and high-vibration severe environments can be achieved through the manufacturing material and the packaging technology, and the influence caused by invasion of a measured object is reduced to the maximum extent.

The present invention discloses an obstacle detection method, device, electronic device and storage medium. The method comprises obtaining a current point cloud to be detected corresponding to a scene in front of a vehicle, wherein the current point cloud to be detected is obtained by projecting a historical frame point cloud corresponding to the scene in front of the vehicle onto a current frame point cloud; constructing a first number of features for each point cloud in the current point cloud to be detected, and obtaining a processed current point cloud to be detected; inputting the processed current point cloud to be detected into a pre-trained obstacle detection model, and obtaining an obstacle detection result, wherein the obstacle detection result includes a static obstacle detection result and a dynamic obstacle detection result. The technical solution of the present invention effectively improves the information density of a single-frame point cloud, thereby improving the obstacle detection effect.

According to the training method for the table understanding ability of the large language model, effective information of the table can be compressed, meanwhile, row and column information capable of improving the effect is reserved, the effect can be improved at the same time, the effect improvement is embodied in the fact that the same table content is expressed in the form, the number of required tokens is generally small, and the method is easy to implement. The method has the advantages that the number of tokens participating in training and reasoning is smaller, computing power is utilized more efficiently, three major capabilities, namely table reading, numerical reasoning and data analysis, of table understanding are considered, the generation probability of correct answers is further increased, and generation of wrong answers caused by the probability is reduced. According to the method, the accuracy of table understanding is improved, and the information density after token conversion is effectively represented, optimized and improved through the table data, so that the efficiency of table data training and reasoning is improved.

The embodiment of the invention provides a user annotation information processing method and device, electronic equipment and a storage medium, and belongs to the technical field of information processing.The method comprises the steps that a user drawing view is customized on a document reading page of the electronic equipment; touch events of pressing, sliding and lifting on the user drawing view by the user are monitored, and coordinates and timestamp information of touch points are recorded and stored; displaying drawing content in a user drawing view according to the coordinate and timestamp information of the touch point and the page size of the electronic equipment, namely calculating the display scale of a document page according to the aspect ratio of the electronic equipment, and converting the coordinate of the touch point of the user into the coordinate in the current document page according to the display scale; and constructing line segments for all the touch points subjected to coordinate conversion, and displaying the constructed line segments in a user drawing view by using a linear interpolation algorithm. According to the scheme, the accuracy and flexibility of document labeling can be improved, and the labeling experience of a user is improved.

This invention relates to a gait recognition method and system based on multi-branch collaborative learning and spatiotemporal representation enhancement, comprising: acquiring gait contour map sequences and constructing them into a dataset; constructing a gait recognition model, including a dynamic temporal adaptive module, an enhanced 3D feature extraction network branch module, a global spatiotemporal feature extraction network branch module, a multi-scale spatiotemporal feature extraction network branch module, and a multi-layer adaptive fusion module; the enhanced 3D feature extraction network branch module includes a multi-layer enhanced 3D feature extraction network, the global spatiotemporal feature extraction network branch module includes a multi-layer global spatiotemporal feature extraction network, and the multi-scale spatiotemporal feature extraction network branch module includes a multi-layer multi-scale spatiotemporal feature extraction network; jointly training the gait recognition model using improved triplet loss and cross-entropy loss to obtain a trained gait recognition model; and using the trained gait recognition model to extract identity features to complete gait recognition.

The application provides a distributed offshore wind turbine control method based on a consistency algorithm, comprising: taking the distributed offshore wind turbine as a distributed intelligent agent, and obtaining total active power shortage of a plurality of intelligent agents; establishing an initial first adjacency matrix according to a communication relationship between adjacent intelligent agents, and adjusting the first adjacency matrix to obtain a second adjacency matrix; modifying an initial first Laplacian matrix according to the second adjacency matrix to obtain a second Laplacian matrix; establishing a target function taking maximization of a wind energy utilization coefficient of each distributed offshore wind turbine as an objective, and obtaining an output power increment allocated to each distributed offshore wind turbine according to the second Laplacian matrix, so that the distributed offshore wind turbine outputs corresponding output power; and the application can improve power quality and improve power control accuracy of the distributed offshore wind turbine.

The invention discloses a fuel cell fault diagnosis method and device based on a sensor signal and a medium, belongs to the technical field of fuel cells, and is used for solving the technical problem that the diagnosis accuracy is difficult to improve due to the fact that existing fuel cell fault diagnosis is limited to information of the sensor signal in a two-dimensional space. The method comprises: acquiring a sensor signal of a fuel cell; performing time-frequency transformation on the sensor signal to obtain a time-frequency spectrum image; feature points of the time-frequency spectrum image are obtained through feature extraction; the method comprises the following steps: acquiring a time-frequency spectrum image, copying the time-frequency spectrum image to obtain a first time-frequency spectrum image and a second time-frequency spectrum image, placing the first time-frequency spectrum image on a first plane, placing the second time-frequency spectrum image on a second plane, connecting mutually matched feature points to obtain a feature landscape, and determining the fault type of the fuel cell based on the feature landscape. According to the method, the limitation of observing the state information of the fuel cell from a low-dimensional angle is overcome, and the fault diagnosis accuracy of the fuel cell is improved.

The invention provides a video key frame extraction method and device, equipment and a storage medium, and the method comprises the steps: obtaining an original frame sequence of an input video; performing content analysis on the original frame sequence, and identifying whether a user-defined attention target exists or not; if the concerned target is not recognized, selecting a key frame from the original frame sequence according to a preset extraction strategy; and if the attention target is identified, constructing a candidate frame set based on the frames containing the attention target and calculating visual difference measurement among the frames in the candidate frame set, and selecting a key frame from the candidate frame set based on the visual difference measurement and outputting the key frame. By adopting the method, the key frame extraction efficiency, pertinence and information density can be improved, and the accuracy of subsequent video analysis is further improved.

This invention discloses a method for encoding structured light patterns, a structured light projection module, and a three-dimensional measurement system, belonging to the field of optical measurement. Addressing the problem of fixed and inaccurate structured light encoding units in existing methods, this invention provides a method for encoding structured light patterns. The method divides the structured light pattern into multiple types of grid units, forming a grid encoding group with a predetermined number of consecutively arranged grid units. The grid encoding group is encoded based on the type of each grid unit, ensuring that each grid encoding group has a unique and non-repeating code. The code of the grid encoding group maps to its spatial position within the structured light pattern. Decoding includes acquiring the structured light pattern, determining the type of each grid unit, and matching the grid encoding group with the encoding set to obtain its position. This invention achieves flexible, high-density, and highly occlusion-resistant absolute position encoding by uniquely and non-repeating grid encoding groups composed of multiple consecutive grid units, making it suitable for high-precision three-dimensional measurement.

The present application relates to the technical field of micro-hemispherical resonator gyroscope design and manufacturing, and particularly relates to a physical feature construction method for manufacturing parameter prediction of a micro-hemispherical resonator, comprising the following steps: determining a manufacturing parameter to be predicted, and obtaining a basic design parameter set of the micro-hemispherical resonator required by the manufacturing parameter; defining the basic design parameter set as an input parameter, obtaining a corresponding manufacturing parameter as an output parameter, constructing a first physical feature representing mold thermal asymmetry, and constructing a second physical feature representing glass material deformation strain energy density; combining the first physical feature and the second physical feature to form an enhanced feature set for manufacturing parameter prediction, taking the enhanced feature set as an input and taking a manufacturing parameter as an output, and training a prediction model by using a deep learning method to quantify the contribution of each input feature to the final prediction result, so as to realize physical interpretability of model decision, and the present application aims to solve the technical problem that the prior art lacks a theory and method system of a prediction feature with clear physical meaning.

The present specification provides an alarm processing method and device, the method comprises: acquiring a query text, and inputting the query text into a first large language model; generating identification information of a reference product based on the query text by the first large language model; searching in a real-time database based on the identification information of the reference product to obtain real-time related information of the reference product from the real-time database; wherein the real-time database is used to store the corresponding relationship between the identification information and the real-time related information of the product; based on the query text, the identification information and the real-time related information of the reference product, the identification information of the target product related to the query text is determined.

The invention discloses a fuel cell fault diagnosis method and device based on a voltage signal and a medium, belongs to the technical field of fuel cells, and aims to solve the problems that in an existing fuel cell fault diagnosis method based on an EIS, interference signals participate in impedance calculation, high information redundancy is caused, the calculation time is increased, and the fault diagnosis efficiency is high. And the fault diagnosis speed of the fuel cell is reduced. The method comprises the following steps: acquiring a voltage response signal after an interference signal is injected into a fuel cell; and inputting the voltage response signal into the classifier model, and determining the fault type of the fuel cell. According to the method, the speed and efficiency of determining the fault type of the fuel cell are improved.

The application discloses a low-voltage area distribution network fault diagnosis method based on light storage equipment trusted access, and belongs to the technical field of power grid fault diagnosis. The steps comprise the following: in a trusted computing environment constructed by a security chip, a protocol deep analysis is performed on a data packet of a light storage equipment to obtain trusted standardized data; a minimum feature subset required for fault diagnosis is dynamically selected based on a maximum correlation minimum redundancy algorithm, and encrypted transmission is collected to obtain efficient sensing data; the efficient sensing data is fused with historical fault cases to construct an edge fault diagnosis knowledge unit; when a fault occurs, the edge fault diagnosis knowledge unit is used to perform parallel execution of fault type rapid identification and power supply recovery potential pre-evaluation. The application adopts protocol deep analysis and a trusted computing environment, can ensure the integrity and security of non-standard access data, and dynamically optimizes the dimension of measurement data through a maximum correlation minimum redundancy algorithm, thereby improving the real-time performance and efficiency of fault type rapid identification and power supply recovery potential pre-evaluation.

The application relates to a video description large model training method and device and electronic equipment. The method comprises: performing spatiotemporal feature analysis on a sample video to calculate the video complexity thereof and determining the optimal length of the text description corresponding to the sample video; performing entity detection and action recognition on the sample video to construct a video element ground truth library of the sample video; inputting the sample video into a video description large model to generate at least one candidate video description text; based on the optimal length of the text description and the video element ground truth library, calculating the video description score, the adaptive length score and the fine-grained alignment score of the candidate video description text, and obtaining the comprehensive score of the candidate video description text; and taking the comprehensive score as a reinforcement learning reward signal and iteratively updating the parameters of the video description large model by using a reinforcement learning strategy optimization algorithm. The application solves the technical problems of low information density, redundancy and lack of complexity adaptation of an existing video description model.

The invention relates to the technical field of text summarization, and discloses an improved TextRank text summarization method based on key phrase selection, and the method comprises the steps: firstly, carrying out the preprocessing of an original text, and converting the original text into an independent sentence set; obtaining a syntactic structure tree, selecting non-terminal clause nodes, and constructing a key phrase set; performing feature vectorization on the key phrases by using a SimBERT pre-training model, calculating semantic similarity and constructing a text semantic network graph; meanwhile, auxiliary weights such as sentence positions, title similarity and keywords are calculated, and a final sentence weight matrix is generated through linear weighted fusion; and finally, obtaining a global score, carrying out iterative screening and redundancy elimination processing on the candidate phrases, and outputting an abstract after reordering according to the original text logic. According to the method, syntactic structure fine-grained screening, deep semantic interactive calculation and a dynamic redundancy elimination mechanism are combined, the problems of insufficient semantic mining and result redundancy of a traditional abstract method are solved, and the abstract quality is remarkably improved.

The present invention belongs to the technical field of online loan fraud prediction, and discloses an online loan fraud prediction method based on multi-stage data representation, comprising the following steps: step S101: select available fields according to original online loan application data to construct an original information network, then divide the nodes into entities and attributes, and construct a hierarchical information network consisting of an entity layer and an attribute layer; step S102: extract an attribute network from the attribute layer, attach a pre-trained word vector to each attribute to reflect the semantic similarity between the attributes, and use network representation learning to integrate the attribute similarity information and attribute co-occurrence association obtained from prior and external semantic knowledge into the embedding vector corresponding to the attribute; step S103, entity representation; step S104, fraud prediction. The present invention enhances the density of information association in online loan application transactions, improves the detection capability of online loan fraud prediction models, and provides better protection for the financial security of users and enterprises.

The invention relates to a method for detecting the quality of a synthetic thread, in which at least one thread is extruded with a spinning device, the extruded thread is wound in a winding device into a thread reel, production and / or product parameters relating to the thread are recorded in the form of a time series before and / or during the winding of the thread into the thread reel, in order to detect the quality, the time series is analyzed by means of a data processing program for machine learning and / or for statistical time series analysis, and wherein at least one quality signal relating to the extruded and / or wound thread and / or relating to the extrusion and / or winding of the thread is generated on the basis of the result of the analysis of the data processing program.

This invention relates to a stress monitoring and early warning system for suspension clamps in high-voltage power lines, belonging to the field of intelligent monitoring of high-voltage lines. The system consists of RFID sensing tags installed on the suspension clamps and an inspection RFID reader. The RFID sensing tag integrates a force-sensitive overload memory unit, a tag chip, and an antenna. Magnetostrictive alloy microwires within the unit are connected to the force transmission path. When the tensile force exceeds a threshold, an irreversible martensitic phase transformation occurs, resulting in a permanent step decrease in magnetic permeability. The tag chip detects this change via a magnetic sensing circuit, hardware-locking and rewriting the overload flag in the non-volatile storage area. The reader reads the flag non-contactly to determine the overload history and calculates the real-time tensile force through the antenna resonant frequency deviation. The reader can be equipped with a drone or handheld terminal to automatically record overload information and generate work orders. The backend server integrates the data to achieve lifespan prediction and batch replacement planning. This system is passive, maintenance-free, and tamper-proof, combining overload memory and stress detection to improve the safety and intelligence level of line operation and maintenance.

This invention relates to the field of large language model application technology and heuristic optimization algorithms, specifically a method for optimizing toolsets and calling toolchains based on native APIs of large language models. The core steps include: Step 1, Sample Dataset Construction: Extracting multiple sets of "task-conclusion" pairs formed by human analysis from historical reports to construct a sample dataset D; Step 2, Toolset Refinement: Structuring the native APIs of the data platform to refine them into an initial toolset with higher information density; Step 3, Toolset Fine-tuning: Iteratively optimizing the refined toolset using a greedy algorithm to obtain a fine-tuned toolset T adapted for large language model calls. opt Step 4: Using Monte Carlo tree search toolchain invocation; Monte Carlo tree search technology is used to improve the efficiency and stability of toolchain invocation, ensuring high information density, personalized optimization and strong robustness.

The invention provides a container cloud environment security threat visual alarm method and system. The method comprises the steps that S1, environment information and original data are collected; s2, converting the environment information and the original data into time sequence data; s3, marking the time sequence data, and analyzing the time sequence data to obtain threat intelligence; and S4, visualizing the original data and the threat intelligence, and giving an alarm. The current environment and architecture are automatically judged, modules related to the current system are selectively started, information sources are rich, and the information density is large. The data is marked in a time sequence format, so that original characteristics of the data can be reserved to the maximum extent, and a data context call chain can be better reserved.

The invention discloses a three-valued circulation logic circuit based on a threshold type memristor and application of the three-valued circulation logic circuit, and belongs to the technical field of image encryption. Comprising two direct-current power supplies, an STI circuit, an operational amplifier and a voltage division circuit, the voltage division circuit comprises a VTEAM memristor; wherein one direct-current power supply supplies power to the main circuit, the other direct-current power supply supplies working voltage to the operational amplifier, input voltage is converted by the STI circuit and then input to the operational amplifier for voltage stabilization, output voltage of the operational amplifier is compared with threshold voltage of the VTEAM memristor, corresponding resistance is generated for voltage division, and therefore three kinds of voltage are output. And three-valued logic can be mapped. The memristor has remarkable feasibility in the design of a three-valued logic circuit, has the advantages of high performance, low power consumption, high integration level and the like, and provides a valuable implementation path for storage and calculation integration and security encryption application in the future.

The invention belongs to the technical field of biological medicine, particularly relates to a drug targeted therapy effect verification system based on a transgenic mouse model, and discloses a drug therapeutic effect verification system integrating genetic engineering modeling, multi-modal sensing and AI data fusion. According to the system, transgenic mice modified by humanized immunization and controllable pathogenic genes are combined with an implantable multifunctional biosensor array, so that in-situ dynamic monitoring on drug concentration, target response and microenvironment change is realized, and physiological and behavior multi-dimensional data are synchronously acquired. Space-time alignment and fusion analysis are carried out through a deep learning engine, a quantitative evaluation matrix containing pharmacodynamics and off-target risks is generated, and closed-loop feedback regulation drug administration is supported. The problems that a traditional model is poor in predictability and static and single in detection means are solved, and the new drug research and development efficiency and clinical transformation accuracy are remarkably improved.