100 results about "Information overload" patented technology

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 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 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 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 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.

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.

The invention relates to the field of security vulnerability early warning, and particularly discloses a software security vulnerability early warning method based on deep learning, which comprises the following steps: starting from a predefined sensitive sink, screening out a code path set directly related to a potential risk by constructing a global function call graph and carrying out call chain reverse tracing; and the analysis redundancy is reduced from the source. Furthermore, according to the scheme, simple sequence modeling is not carried out on the call chain, function nodes in the call chain are converted into deep semantic vectors, and the semantic influence of key nodes is amplified and the interference of irrelevant information is inhibited by dynamically evaluating the time sequence importance of each function node in the call chain and through adaptive weighting. And finally, deep learning is carried out on the key path subjected to information discrimination, and vulnerability classification prediction is carried out, so that the problem of information overload in a long call chain is effectively overcome, and the accuracy of vulnerability early warning and the capture capability of complex semantic association are improved.

The embodiment of the invention discloses a notification information processing method, a vehicle, a storage medium and a program product, and the method comprises the steps: determining the notification priority of each piece of notification information based on the information types corresponding to at least two pieces of notification information; determining a first display condition and a first display parameter corresponding to each piece of first notification information based on the synchronization state of the vehicle corresponding to the at least two pieces of first notification information; and according to the respective notification priorities of the at least two pieces of first notification information, controlling the vehicle to display each piece of first notification information in sequence, and for each piece of first notification information, displaying the piece of first notification information according to the first display parameter under the condition that the vehicle meets the first display condition. According to the embodiment of the invention, while the reachability of the secondary notification information is ensured, the display opportunity and the presentation form are strictly restrained under the safe driving situation, so that the driving interference risk is actively reduced in the information overload environment, and the effective balance between the communication requirement and the driving safety is achieved.

The invention discloses a DPF instrument display method and system based on scene recognition and a storage medium. The DPF instrument display method comprises the steps that firstly, multi-dimensional real-time operation parameters including the overall average vehicle speed, the carbon load percentage and the like are obtained; then calculating a characteristic value representing a vehicle running state by using a long and short characteristic matrix and a moving average window algorithm; inputting the characteristic value into a pre-trained AI scene classification model, and intelligently identifying a currently applicable display scene category which comprises a strong display mode, a medium display mode and a weak display mode; finally, according to the recognition result, the display content, frequency and duration of the numbers, the icons and the characters on the DPF instrument are adjusted in a self-adaptive mode. According to the method, the defects that in the prior art, DPF instrument display is fixed and single, and differential requirements of different users cannot be met are overcome, matching of a display strategy and a real scene of the user is achieved, the risk caused by insufficient information is avoided, interference caused by information overload is eliminated, and user experience and use safety and economical efficiency of a DPF system are improved.

The application relates to the technical field of live content display and interaction optimization, and discloses a live room information display method, system, device and medium, which comprises the following steps: collecting audience data and analyzing audience viewing behavior in real time; automatically matching viewing modes according to the audience viewing behavior and displaying different information levels for different viewing modes; analyzing the audience's barrage and automatically screening the barrage priority; and dynamically displaying live content according to the adjusted information level and barrage priority. Through intelligent dynamic adjustment of the display level of live content and the barrage priority, more personalized and efficient information display is realized. According to the viewing behavior, interaction frequency and device network status of the audience, the system automatically adjusts the information density and display level, avoids information overload or loss, and improves the user experience. Meanwhile, based on the emotional analysis of the barrage, the interaction frequency and the content relevance, high-value barrages are preferentially displayed, and the audience's interaction sense and participation are enhanced.

The invention relates to the technical field of data processing, and provides a data processing method and device, electronic equipment and a readable storage medium. The method comprises the following steps: processing a to-be-predicted sample through a decision tree model to obtain a predicted value corresponding to the to-be-predicted sample; performing decision path extraction processing on the to-be-predicted sample based on the predicted value to obtain decision path data and leaf nodes corresponding to the to-be-predicted sample; performing normalization screening processing on a historical sample set based on the leaf nodes to obtain a similar sample set, the historical sample set being used for constructing a decision tree model; and performing natural language generation processing on the decision path data, the similar sample set, the predicted value and the feature description text corresponding to the to-be-predicted sample to obtain target explanation data corresponding to the predicted value, thereby enhancing the credibility of an output result, ensuring the consistency of the sample and decision logic, improving the correlation of the similar sample, avoiding information overload, and improving the accuracy of the result. And the interpretation focusing performance and effectiveness are improved.

The present disclosure relates to a marking data processing method and device, electronic equipment, storage medium and product. In a first time period, first marking information is received, the first marking information comprising at least one marking data, the marking data being used to prompt a service state of a corresponding service; in a second time period, the first marking information is sent to a marking server, so that the marking server makes a display decision on the marking data in the first marking information, and obtains second marking information to be displayed, the second marking information comprising at least one marking data; in a displayed page currently in the second time period, the marking data in the second marking information is displayed. The present disclosure can avoid interface information overload and disturbance to user experience caused by indiscriminate display of a large amount of marking data.

Embodiments of the present application disclose a task processing method and device, electronic equipment and computer readable storage medium, and relate to the technical field of computers; the method is applied to an extended reality device, the extended reality device comprises a virtual screen and an audio output module; the method comprises: when a first task of a first category and a second task exist simultaneously, identifying a user intention; according to the user intention, determining a task to be converted and an original category task from the first task and the second task; converting the task to be converted into a task of a second category; executing the original category task and executing the task of the second category; wherein the task of the first category is executed by one of the virtual screen and the audio output module, and the task of the second category is executed by the other of the virtual screen and the audio output module. In this way, the audio task and the video task can be converted in the present application, so that the audio channel / video channel information overload can be avoided.

The invention discloses a multi-person picture synthesis method and device, equipment and a medium. The method comprises the following steps: splicing original images at multiple angles to obtain a panoramic image of a current target area; real-time tracking is carried out according to all face position information in the current frame, sound source information is collected synchronously, and a current spokesman in the target area is determined; and generating a multi-person synthetic picture according to the real-time motion picture of the current spokesman and the real-time motion pictures of other people. Multi-angle images are collected for feature point matching and image splicing, a high-quality panoramic image is generated, wide-area visual angle coverage is achieved, a more detailed image is obtained, all face positions are locked, corresponding sound source information is collected, a current spokesman is recognized, a multi-person synthetic picture which highlights the current spokesman and gives consideration to other people is generated, and the multi-person synthetic picture is obtained. The integrity of the panoramic information is guaranteed, meanwhile, the key content is clearly presented, the problems that the visual angle is limited and information is overloaded are solved, and the communication efficiency and watching experience of a teleconference are improved.

The invention relates to a medical alarm management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining patient physiological data, medical equipment state data, clinical workflow information and patient basic information in real time, and generating a multi-dimensional data sequence on a unified time axis; inputting the multi-dimensional data sequence into an adaptive decision engine, dynamically adjusting an alarm triggering condition according to clinical workflow information, and generating an original alarm event when the triggering condition is met; carrying out root analysis on the plurality of original alarm events in a preset time, defining root events of the plurality of original alarm events, marking other original alarm events as derivative alarms, and packaging to form a nested alarm object; and correspondingly sorting the nested alarms according to the clinical emergency degree and the event sequence. According to the medical alarm management method and device, the computer equipment and the storage medium, the problems of alarm fatigue, information overload, unclear source, difficulty in tracing and the like in a traditional system can be fundamentally solved.

The application discloses a hospital logistics scheduling system and method based on a large language model, relates to knowledge question answering, and comprises the following steps: constructing a logistics service data warehouse; reading structured data from the logistics service data warehouse, performing semantic analysis on text data in the structured data by using a large language model, and generating knowledge triples; fusing knowledge in the knowledge triples by calculating vector similarity, and constructing a logistics service knowledge graph according to the fused knowledge triples; receiving a natural language service request of a user, performing intention recognition by using the large language model, and converting the extracted intention into structured query data; searching in the logistics service knowledge graph according to the structured query data, obtaining resource state data from the logistics service data warehouse, and generating a scheduling scheme by a scheduling intelligent agent based on a LangChain framework; and the application effectively solves the problem of low scheduling decision efficiency caused by information overload in hospital logistics equipment fault processing.

The invention relates to the technical field of knowledge graph application, and discloses an automobile brake pad wear resistance detection system based on a knowledge graph, and the system comprises a semantic timestamp fusion and high signal-to-noise ratio graph generation module which is used for binding a semantic tag of a concurrent qualitative event flow with a high-precision timestamp, and generating a semantic timestamp fusion and high signal-to-noise ratio graph; taking the timestamps as anchor points to fuse in-window features of concurrent quantitative sensor data streams, and establishing an anchoring relation to generate a knowledge graph; the method comprises the steps that a knowledge graph is established, a context sensing module, an ontology drive pruning rule base and a dynamic pruning inference engine are established, and the inference engine responds to user query and executes dynamic pruning traversal on the knowledge graph based on user context loading rules to output candidate attribution paths. And dynamic pruning reasoning of context sensing is combined, so that the technical problems of information overload and significant collapse in knowledge graph application are solved.