137 results about "Information overload" patented technology

The invention discloses a data acquisition method and system for virtual digital platform construction, particularly relates to the field of virtual digital interaction, is used for solving the problems of user interest capture and cognitive fatigue, and aims at accurately capturing long-term interest of a user in scenes such as a virtual exhibition and an online shopping platform and effectively coping with a short-term cognitive fatigue phenomenon. Through a fusion strategy of a time-varying covariance matrix and a graph neural network, multi-modal behavior data of a user is monitored in real time, modal weights are dynamically adjusted, and multi-dimensional features are fused. In combination with time sequence analysis and a clustering algorithm, the system compares a current behavior with a historical baseline, and accurately extracts deviation data. The cognitive fatigue state of the user is evaluated in real time through a deep learning model, high-risk and low-risk feedback signals are generated, and recommendation optimization based on long-line interest is triggered. According to the technical scheme, it is ensured that the pushed content is consistent with the long-term demand of the user, cognitive burden caused by information overload is avoided, recommendation accuracy and user experience are improved, and the loss rate is reduced.

The invention relates to the field of content generation, and particularly discloses an automatic content generation system and method based on user data, which comprises the following steps: firstly, acquiring keyword text data input by a user and a history browsing news manuscript, then performing feature extraction and correlation analysis on the keyword text data and the history browsing news manuscript by using a deep learning technology, and finally, generating a content through a generator. According to the method, the user personalized news manuscript recommendation is generated, so that the requirements and interests of the user are more accurately understood, the problem of information overload is effectively reduced, and the user experience and the information acquisition efficiency are improved.

The invention discloses a precise advertisement putting method based on user behavior pattern mining and clustering analysis, and relates to the technical field of artificial intelligence and big data analysis, and the method comprises the steps: S1, carrying out pattern mining based on time distribution data of historical click behaviors of a user to generate a click time preference model of the user, the method comprises the steps of S1, selecting a click time preference model, S2, inputting the click time preference model into a clustering analysis algorithm to obtain a time period classification result of different user groups, S3, determining an advertisement display priority and a frequency threshold value of each user group according to the time period classification result, and S4, regulating and controlling the advertisement display frequency of the corresponding user group according to the frequency threshold value to reduce the influence of information overload. According to the accurate advertisement putting method based on user behavior pattern mining and clustering analysis, the advertisement display frequency is regulated and controlled according to the time distribution of user click behaviors so as to solve the problem of information overload.

The invention provides an indication information display method and device, a vehicle and a computer readable storage medium, and belongs to the technical field of intelligent cabins, and the method comprises the steps: obtaining multi-mode driving monitoring data, and predicting the driving intention of a driver based on the multi-mode driving monitoring data; obtaining the influence degree of a sensing object in the surrounding environment of the vehicle on the driving task of the vehicle, and carrying out weighted calculation on the influence degree of the sensing object on the driving task of the vehicle to obtain the saliency of the sensing object; and displaying the indication information corresponding to at least part of the perception objects on the front windshield in an AR form based on the saliency of the perception objects. According to the method, the indication information can be rendered based on the predicted driving intention of the driver and the current driving situation, the information most needed by the driver is provided, and then potential safety hazards caused by visual interference to the driver due to information overload can be avoided.

The invention discloses an insulation defect discharge identification method and device, electronic equipment and a storage medium, which are used for solving the technical problem of coexistence of early warning information overload and effective alarm omission caused by the fact that real insulation defect discharge and interference events such as line operation transient state and lightning pulse cannot be effectively distinguished in the prior art. The method comprises the following steps: when insulation defect discharge occurs, synchronously acquiring an electrical sequence signal of each node in a medium-voltage complex network, and generating a frequency-energy spectrogram of each node by adopting the electrical sequence signal; performing spectrogram reconstruction on the frequency-energy spectrogram to obtain a reconstructed frequency-energy spectrogram; extracting insulation defect features from the reconstructed frequency-energy spectrogram; taking the insulation defect characteristics of each node as input, and adopting a random forest algorithm to construct an insulation defect discharge identification model; and when the real-time characteristic data of the medium-voltage complex network are collected, inputting the real-time characteristic data of the medium-voltage complex network into the insulation defect discharge identification model, and carrying out insulation defect discharge identification.

The invention relates to the technical field of ship monitoring, and particularly discloses a ship monitoring method based on a Beidou satellite and AI data, and the method comprises the steps: obtaining the AI data of a ship and surrounding ships through employing an AI receiving and transmitting module, carrying out the encryption of the obtained AI data through employing a data encryption algorithm based on deep learning, and carrying out the encryption of the obtained AI data, the method comprises the following steps: firstly, encrypting an AI data packet to identify and filter invalid or irrelevant ship information, and then sending the encrypted AI data packet to an AI data center for decoding analysis through a Beidou satellite communication array, thereby obtaining AI data such as position, speed, course and the like of a ship. Therefore, the ship monitoring capability of ocean and remote sea areas can be effectively improved, the problems of information overload and low data transmission efficiency are solved through data encryption processing, and meanwhile, the data transmission safety is enhanced.

The invention provides a group chat message processing method and device, equipment and a storage medium, relates to the technical field of computers, in particular to the fields of information flow, artificial intelligence, large models and the like, and can be applied to application scenes of intelligent assistants, virtual assistants, intelligent e-commerce and the like. According to the specific implementation scheme, a to-be-processed message in a chat group is acquired; based on the context information of the to-be-processed message, determining an emotion category of the to-be-processed message and a model field required by a preset message model; matching the emotion category and the model field of the to-be-processed message with a preset message model set of the target object; under the condition that any preset message model in the preset message model set is matched, determining a reminding mode of the to-be-processed message; and performing message reminding on the to-be-processed message based on the reminding mode. In the embodiment of the invention, the important information in the group chat is identified, captured and reminded, so that various troubles brought to the user due to information overload are reduced.

The invention relates to the technical field of computers, and discloses a document collaboration platform based on cloud computing. Real-time semantic complementation, automatic typesetting optimization and content abstract generation are provided through the intelligent editing assistant module, the manual editing burden is remarkably reduced, and the efficiency is improved; a rule engine of an automatic operation module is combined to realize automatic management of document versions, intelligent resolution of conflicts and standardized review, and the problems of communication cost and conflicts in multi-person cooperation are thoroughly solved; the problem of information overload is solved by relying on the deep semantic processing capability, and accurate positioning of core content is realized; and meanwhile, based on the elastic expansion capability of the distributed cloud architecture, the stability and high availability of multi-terminal real-time cooperation are ensured. Finally, a document processing closed loop integrating intelligent assistance, an automatic process and efficient cooperation is formed, and the bottlenecks of a traditional platform in the aspects of editing efficiency, cooperation fluency, information processing and the automation level are comprehensively broken through.

The invention relates to a context redundancy optimization method (CRORALLM) oriented to large language model retrieval enhancement. According to the method, the input context of the large language model is optimized by adopting a strategy of clustering first and then abstracting, so that the redundant influence is reduced, and the content comprehensiveness is ensured while the retrieval accuracy is improved. CRORALLM firstly classifies a candidate document set in order in a clustering mode, so that content themes can be recognized more effectively, and the subsequent abstract generation quality is improved; then, a fine-tuning abstract model is used for analyzing the correlation of the clustering document set according to the problem, and a weighted abstract is generated to serve as a new context: texts with high relevancy with the problem are reserved, texts with low relevancy with the problem are subjected to high-degree abstracting, and texts irrelevant to the problem are filtered. And finally, the contexts are used for large language model retrieval enhancement, so that the problems of information overload and redundant noise are solved, and the model answering accuracy is improved. According to the method, four base models at home and abroad such as Qwen are selected in an experiment, a common data set is adopted, different noise ratios are set for the experiment, and the result shows that the model accuracy of the CRRALLM is obviously improved under the different noise ratios, so that the method can effectively optimize the context in large language retrieval enhancement and improve the accuracy of large language model answering.

The invention relates to the technical field of role recognition, and discloses a large model dialogue control method based on role enhancement, which comprises the following steps: step 1, user role information is defined and configured, the user role information comprises internal roles and external roles, the internal roles comprise at least one type of enterprise internal users, and the external roles comprise at least one type of enterprise internal users; the external role comprises at least one type of enterprise external users; step 2, configuring corresponding task background and authority information for each user role, and determining accessible resources and data types according to the user roles; by accurately identifying the role identity of the user, customized dialogue and information services can be provided according to requirements and task backgrounds of different roles. For internal employees and external clients of an enterprise, the system can respectively provide in-depth technical support and basic product information query, and information overload or permission leakage possibly caused by a unified response is avoided.

The invention provides a script outline extraction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-analyzed script; dividing the script to be analyzed into a plurality of sub-scripts; for each sub-script, analyzing the script content of the sub-script based on a large model to obtain a script probability of the sub-script; and based on the large model, analyzing the script outlines of all the sub-scripts to obtain a script outline corresponding to the to-be-analyzed script. According to the scheme, through the intelligent analysis advantage of a large model, key information is rapidly and accurately extracted from the sub-scripts to form an infarction, the infarction is further summarized into the script outline, compared with manual script reading, the time for obtaining the key architecture information of the script is greatly shortened, the large-size script is disassembled into the sub-scripts to be processed respectively, and the implementation efficiency is greatly improved. The analysis difficulty is reduced, and the problem of information overload caused by processing massive texts at a time is avoided.

The invention provides a task agent generation and task processing method based on a meta agent, and relates to the technical field of autonomous agent application. The method mainly comprises the steps of initialization, task input, task analysis, subtask generation, task agent generation and distribution, task execution and cooperation, and result integration and feedback. According to the method, the meta-agent with task understanding, decomposition and planning capabilities is introduced, so that the task agent is dynamically generated according to task requirements, the tool calling process is optimized, the problem of low model processing efficiency caused by tool information overload in the prior art is effectively solved, and the model processing efficiency is improved. And the response speed, the flexibility and the intelligent level of task processing of the automatic software / hardware system are remarkably improved.

The invention relates to a time sequence enhanced and multi-granularity intention guided adversarial generation recommendation method, which comprises the following steps of: firstly, obtaining an original user commodity interaction sequence and interaction time data, and obtaining a time sequence enhanced sequence through a plurality of data enhancement modes; then, by constructing a plurality of cross entropy loss functions and comparison loss functions, model training is constrained from different angles, and user behavior patterns and potential correlation are fully mined; and finally, performing joint training on the feature coding module, and performing fine adjustment on the prediction module to obtain a recommendation model. The method focuses on solving the problems of data sparseness and insufficient model generalization ability of sequence recommendation (SR) under information overload. Under the background that information technology development causes serious information overload, although the SR is concerned, the SR faces many challenges; a traditional method depends on project prediction task optimization parameters, is easily influenced by data sparsity, and is difficult to capture real intentions of users and correlation between sequences; the method effectively improves the accuracy and reliability of a recommendation system, provides more accurate recommendation services for users, and has significant application value in the field of sequence recommendation.

The invention discloses a rigid registration method and system for visible light and infrared images of an unmanned aerial vehicle, and the method comprises the steps: gradually achieving the high-precision cross-modal image alignment through rough registration and fine registration based on a bimodal feature interaction and causal attention enhancement mechanism; therefore, a stable spatial transformation result can still be obtained under the complex conditions of low signal-to-noise ratio, local shielding and the like. According to the method, high-precision rigid registration of the infrared and visible light images of the unmanned aerial vehicle under the condition of significant modal difference and scale difference can be realized, a registration task is modeled as a long sequence dependence problem by referring to a vision mechanism of step-by-step partition comparison when people observe a large scene, and a CALS module is introduced to gradually focus a local area, so that the registration accuracy is improved. Confusion caused by information overload in traditional global modeling is avoided, and therefore balance is achieved between global scale consistency and local geometric accuracy.

The invention relates to a visualization method of a domain knowledge graph, belongs to the technical field of knowledge graphs, and solves the technical problems of fuzzy display, poor visualization effect and only static display caused by huge quantity, numerous types and deep hierarchical structure of the domain knowledge graph. According to the domain knowledge graph visualization method, a multi-layer distribution layout is constructed by presetting display levels and node parameters, the node size and position are optimized, and overlapping is prevented; calculating an area distribution canvas space by using a heap algorithm; and node folding, local query and cross-dimension relation display are supported, and an anti-bloating mechanism and an interaction function are combined. Clear and multi-layer visual layout is realized, node overlapping is avoided, and readability is improved; flexible interaction enhances user experience, and efficient space utilization adapts to multiple resolutions; anti-bloating and detail display are combined, so that information overload is reduced; and the method is suitable for dynamic knowledge management.

The invention belongs to the technical field of recruitment information publishing, and discloses a recruitment information publishing method and system based on artificial intelligence. The method comprises the following steps: collecting real-time job application multi-dimensional data of a user of a recruitment platform and real-time recruitment multi-dimensional data of a position, and generating a corresponding real-time user portrait and a real-time position portrait by using a pre-trained dynamic portrait generation model; collecting real-time interaction records of the users and the positions, and generating a real-time interaction prediction result by using a pre-trained interaction prediction model according to the real-time interaction records, the real-time user portraits and the real-time position portraits; and generating a real-time recruitment information release strategy by using a pre-trained release strategy generation model according to the real-time interaction prediction result, and performing release optimization on the real-time recruitment multi-dimensional data of the position. The method solves the problems that in the prior art, information is overloaded and inefficient, dynamic adaptability is lacked, multiple targets are difficult to balance, and data value mining is insufficient.

The invention discloses a method for predicting the residual life of a bearing based on ECANTet-TCN and Self-Attention-BiGRU mechanisms. The method mainly comprises two parts, namely a training stage and a testing stage. In the training stage, data detected by different mechanical devices under different working conditions are divided into training set vibration signal data and test set vibration signal data, and then a prediction model is constructed; secondly, carrying out batch standardization processing and feature processing on the vibration signal data of the training set and the test set; inputting the features into the model, and predicting and checking the RUL of the bearing; after processing in a test stage, the training set signals and the test set signals are mapped one by one through a prediction model, and prediction of the service life of the bearing is completed; compared with an existing method, the method is beneficial to enhancing the robustness of the model and improving the accuracy of bearing RUL prediction, meanwhile, a self-attention mechanism is added behind the BiGRU layer, attention on irrelevant information is reduced, dependence on external information is reduced, and the problem of model information overload is solved.

The invention discloses a power grid dispatcher cognitive load dynamic adjustment system and method based on multi-modal biofeedback, and the method comprises the steps: carrying out the real-time quantification of the cognitive load of a power grid dispatcher through fusing the multi-modal biofeedback signals, such as electroencephalogram, eye movement and galvanic skin; and a human-computer interface information presentation mode and a task allocation strategy are dynamically adjusted according to the load level. The system specifically comprises a biological signal acquisition module, a cognitive load calculation engine module, a dynamic adjustment execution module and a closed-loop feedback adjustment module. According to the method, the problems of information overload, poor individual adaptability, high misoperation risk and the like in a traditional power grid dispatching system can be effectively solved, the efficiency and safety of dispatchers in complex fault handling are improved, and the time sensitivity requirement of power control is met.

The invention discloses an environment abnormity alarm method, device and equipment and a storage medium, and relates to the technical field of cloud computing, and the environment abnormity alarm method comprises the steps: building an operation and maintenance knowledge graph for a system environment based on a system configuration management database; generating an abnormal node in the operation and maintenance knowledge graph based on an anomaly detection result of the system environment, associating the abnormal node with an anomaly occurrence node, and updating the operation and maintenance knowledge graph; and traversing the updated operation and maintenance knowledge graph to obtain path information of the abnormal node in the operation and maintenance knowledge graph, and carrying out abnormity alarm according to the path information. Through the above technical scheme, aggregation query of abnormal information according to various node dimensions is realized, and information overload and alarm fatigue caused by frequent alarm are avoided.

The invention provides a big language model decision optimization method and device and electronic equipment, and the method comprises the steps: obtaining an input text, generating each sequence unit through a big language model based on the input text, calculating the correlation degree of each sequence unit and the input text, and obtaining a big language model decision optimization result; the target sequence units with the correlation degree larger than the preset correlation degree threshold value are taken as the basis for generating the reply text, and a large amount of interference information irrelevant to the input text can be screened out. And performing content verification on the generated reply text by calling a preset cross-domain knowledge base, adjusting model parameters of the large language model according to a verification result until the generated reply text conforms to knowledge and facts specified by the preset cross-domain knowledge base, and finally outputting the reply text conforming to the specification. The influence of information overload on the precision of the output reply text is effectively avoided, the knowledge and facts in the professional knowledge base are adopted to verify the output reply text, the output reply text can more comply with objective facts, and the precision is higher.

The invention discloses a commodity matching method fusing a dynamic demand map and budget constraint, and belongs to the technical field of electronic commerce, and the method comprises the steps: collecting multi-modal data and budget data inputted by a user, and outputting an optimal commodity combination through a trained commodity recommendation model; the method specifically comprises the steps that feature extraction is conducted on multi-modal data, and the multi-modal data are spliced and fused into a unified semantic vector; mapping the unified semantic vector into a predefined entity library, and constructing a dynamic demand map; constructing a demand vector, establishing a planning problem taking the weighted sum of the maximum matching degree and the novelty as a target under the budget constraint, and solving to find an optimal commodity combination; and according to the interaction behavior of the user on the optimal commodity combination, adjusting the node weight and the budget constraint, and continuously updating the optimal commodity combination. According to the method, the problems of information overload, weak fuzzy demand analysis, recommendation system stiffness and budget constraint time consumption in an e-commerce scene can be solved, and the purchase efficiency and experience of the user are effectively improved.

This invention addresses key housing issues: constrained affordability, information overload, and limited options. It offers: 1) affordable rent-to-own: transparent auctions set rent based on tenants' creditworthiness, while mandatory savings build equity; 2) streamlined information: a single platform simplifies property comparison and saves time; 3) efficient allocation: auctions optimize property placement based on demand and income, guiding future development; 4) increased usability: underutilized properties enter the market, while investors retain asset exposure; 5) smart savings: mandatory savings overcome cognitive biases and reduce defaults. By overcoming these technical hurdles, this solution aims to improve housing accessibility and affordability.

The invention discloses a flight operation prompting method and system, and relates to the technical field of flight auxiliary decision making. Comprising the following steps: acquiring offline data of an aircraft, taking a flight state of the aircraft at any moment as an initial state, determining a gradient corresponding to the initial state, and presetting a strategy network; based on the initial state, selecting a flight operation from the strategy network, determining the flight state of the aircraft at the next moment according to the adopted action, and obtaining the flight state of each historical moment in the preset time period through multi-round iteration; in each round of iteration, the stochastic gradient of the selected flight operation is determined; training the reinforcement learning model to obtain an auxiliary decision-making model; and inputting the flight state of the aircraft at the current moment into the auxiliary decision-making model to obtain an optimal flight operation, and generating an optimal flight operation prompt. According to the method, redundant information can be removed, pilot information overload can be relieved, a pilot can be assisted in fast decision making, and decision making efficiency and operation accuracy are remarkably improved.

The invention relates to the technical field of vehicle control, in particular to a reminding information display method and device of a vehicle, the vehicle and a storage medium. The method comprises the steps that when new reminding information is received, the type, the signal source and the priority of the new reminding information and the type, the signal source and the priority of current reminding information are acquired; and based on the type, the signal source and the priority of the new reminding information and at least one of the type, the signal source and the priority of the current reminding information, controlling the vehicle foreground to display the reminding information according to a preset priority display rule and a preset display duration rule. Therefore, the problems that in the prior art, the increasing alarm requirements of configuration functions cannot be well met, when multiple pieces of alarm information exist at the same time, the multiple pieces of alarm information cannot be reasonably displayed according to the priority of the alarm information, and consequently a user cannot obtain important reminding information in time are solved; information overload and interference on attention of a user are avoided, and driving safety and comfort are improved.

The application discloses a kind of live content auditing method and system based on AI, belong to network live content supervision technical field.The method extracts the multi-dimensional characteristics of anchor by cognitive consistency analysis model, calculates the deviation degree of feature correlation matrix to detect cognitive dissonance;Adopt optical flow tracking and LSTM network analysis microexpression infers implicit intent;Monitoring semantic trajectory drift identifies sensitive content;Construct audience group dynamic graph to analyze emotional transmission intensity;Through bayesian network, from audience abnormal reaction, deduce illegal behavior;Evaluate cognitive load to identify information overload cover;Calculate node centrality to implement transmission blocking;Fusion multi-source information outputs rule determination.System includes cognitive feature analysis, microexpression identification, semantic trajectory monitoring, emotional transmission analysis, reverse reasoning, cognitive load calculation, transmission blocking and decision output eight modules, work cooperatively through event-driven mechanism.The application breaks through the surface limit of traditional audit, can identify cognitive fraud, psychological manipulation and other hidden violations, realizes active defense.

The invention discloses a multi-source hotel optimal matching method and device based on dynamic weight, equipment and a medium, and relates to the technical field of hotel service, and the method comprises the steps: receiving a query request of a user for a target hotel room type, obtaining a plurality of corresponding inventory items from a plurality of supplier interfaces, carrying out the extraction and standardization processing of a plurality of attributes of each inventory item, and obtaining a plurality of attributes of each inventory item; obtaining a standardized numerical value of each attribute; obtaining and analyzing context information of a current decision, and constructing a context vector; based on the context vector, through a preset dynamic weight calculation model, forming a dynamic weight vector; and carrying out weighted calculation by utilizing the standardized numerical value of each attribute of the inventory items and the dynamic weight vector to obtain a comprehensive score, and selecting the inventory item with the highest comprehensive score as an optimal matching result to be output. According to the method, the limitation of information overload and static sorting is effectively solved, self-adaptive recommendation based on multi-dimensional attributes and real-time scenes is realized, and the user experience and the platform decision-making efficiency are remarkably improved.

The invention discloses a hospital logistics scheduling system and method based on a large language model, and relates to knowledge questions and answers. The method comprises the following steps: constructing a logistics service data warehouse; reading the structured data from the logistics service data warehouse, and performing semantic analysis on text data in the structured data by using a large language model to generate a knowledge triple; performing knowledge fusion on entities and relationships in the knowledge triads by calculating vector similarity, and constructing a logistics service knowledge graph according to the fused knowledge triads; receiving a natural language service request of a user, performing intention recognition by using a large language model, and converting the extracted intention into structured query data; searching in a logistics service knowledge graph according to the structured query data, acquiring resource state data from a logistics service data warehouse, and generating a scheduling scheme through a scheduling agent based on a LangChain framework; the problem of low scheduling decision-making efficiency caused by information overload in hospital logistics equipment fault processing is effectively solved.

The invention discloses a forest region disaster risk assessment method based on machine learning, and relates to the technical field of forest region disaster risk assessment, and the method comprises the steps: S1, building a forest region disaster evolution trajectory database, carrying out the modeling of historical disaster events according to a time sequence, analyzing the similarity between the current forest region environment characteristics and historical disaster trajectories, and obtaining a forest region disaster evolution trajectory database; deducing disaster types and focus points of attention, and clustering disaster features to form a risk category set; according to the forest disaster risk assessment method based on machine learning, the disaster risk condition can be displayed with higher information density and a clearer structure, so that the risk identification speed is improved, the understanding burden caused by information overload is reduced, and the accuracy, interpretability and response efficiency of forest disaster risk assessment are remarkably enhanced on the whole.

To enable modern people and persons who worry about too much information to easily acquire suitable meal information and have healthy eating habits.SOLUTION: A system includes: means for providing suitable food information for solving information overloading; means for analyzing the life pattern and preference of a user; and means for providing information in a timely manner for the user.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of intelligent transportation, and particularly relates to a dynamic environment sensing and auxiliary positioning method based on V2X communication and an application system thereof. The method comprises the following steps: defining a multi-dimensional value vector, and carrying out dynamic value quantification and screening on V2X information based on a driving task and environment uncertainty to generate effective V2X information; performing depth fusion and interactive intention prediction based on a unified space-time reference on the effective V2X information and the vehicle-mounted sensor data, and generating a predictive interactive environment model for obtaining a dynamic environment perception result; and in a GNSS weak signal or no signal environment, effective V2X information and vehicle-mounted sensor data of the vehicle are optimized and fused based on a factor graph, continuous auxiliary positioning is realized, and a global optimization pose is output. According to the method, the problems of space-time dislocation of sensing information, lack of prediction capability, unreliable positioning when the GNSS fails, overload of communication information and the like in the existing V2X technology are solved.