51 results about "Scene labeling" patented technology

The application discloses a body intelligent agent interaction system and a control method, and relates to the technical field of artificial intelligence. The method comprises the following steps: a terminal sensor module is configured to collect physical signals of environment or user interaction, and generate standardized event data; a reflection processing module comprises a lightweight decision unit, which is used for receiving scene labels and user feedback information to self-optimize protected core behavior logic, and deep evolution is performed after a growth authorization signal is received; a narrative processing module is used for processing complex interaction cognition, generating scene labels and sending the scene labels to the reflection processing module, sending an inhibition instruction and a growth authorization signal to the reflection processing module, and performing context interpretation; wherein, the reflection processing module and the narrative processing module communicate through a preset interface, and the narrative processing module has no right to directly modify the core behavior logic; the system guarantees the explanation right and adjustment right of the narrative layer to the reflection layer, and prevents tampering of the core instinct at runtime through strict permission isolation.

This invention relates to a multi-mode intelligent switching method for human-machine collaborative outbound calling scenarios, belonging to the field of artificial intelligence technology. The method includes: acquiring voice semantic information during the outbound calling interaction process, determining the human-machine interaction adaptability, maintaining an intelligent outbound calling mode and triggering a switching command for a manual outbound calling mode; classifying outbound calling tasks by scenario tags, matching corresponding interaction processing strategies, dynamically monitoring interaction fluency and problem complexity, adjusting human-machine processing weights, and switching outbound calling modes; acquiring historical interaction data and real-time interaction information, establishing a human-machine collaborative switching judgment model, generating human-machine mode judgment results, and autonomously switching between intelligent and manual outbound calling modes; dividing outbound calling interaction stages, setting switching conditions, verifying the interaction response effect in real time, and switching outbound calling modes based on the verification results. This invention can achieve stable, intelligent, and adaptive switching of outbound calling modes, improving the efficiency and interaction effect of human-machine collaborative outbound calling.

This invention provides a method for automatic cross-system invocation of AI marketing based on the MCP protocol, comprising the following steps: receiving marketing task instructions generated by the AI ​​marketing system; parsing the marketing task instructions, extracting task elements and labeling them with scene tags; matching available tools from the MCP registered tool library based on the scene tags and task type; performing permission verification and security verification; invoking the available tools according to the standardized format of the MCP protocol by calling the execution engine; collecting the execution results of each tool and generating an execution report in a unified format; and feeding the execution report back to the AI ​​marketing system. This invention provides a method for automatic cross-system invocation of AI marketing based on the MCP protocol, which has a high degree of standardization and reduces integration costs: it extends marketing-specific standards on the basis of the standard MCP protocol, unifies the task description format, communication protocol, and data format, and solves the API fragmentation problem.

This application discloses a display parameter adjustment method, an electronic device, and a computer-readable storage medium, comprising: acquiring display scene-related metadata through an application and framework layer and reporting it to a system service layer; the system service layer aggregating the metadata to generate standard scene tags; the display driver layer performing scene prediction based on the standard scene tags, and querying a preset mapping table according to the prediction results to determine the target drive voltage parameters and target refresh rate, thereby generating corresponding collaborative control instructions, and sending the collaborative control instructions to the power management chip and the display processing unit respectively, thereby realizing the collaborative adjustment of drive voltage and refresh rate, while reducing the relevant power consumption of the display module.

The application relates to a peripheral feedback effect control method and system, a storage medium and an electronic device, and relates to the technical field of peripheral control. The method comprises the following steps: determining whether a target scene tag exists in a target game of a current target user according to actual game state data; if the target scene tag exists, determining first light parameters and first vibration parameters of an actual peripheral according to the target scene tag; optimizing the first light parameters and the first vibration parameters according to actual power to obtain final light parameters and final vibration parameters, and controlling the feedback effect of the actual peripheral according to the final light parameters and the final vibration parameters; and if the target scene tag does not exist, determining second feedback parameters of the actual peripheral according to user game state data, and controlling the feedback effect of the actual peripheral according to the second feedback parameters. The application has the effect of improving the adaptation of the peripheral feedback effect to users.

This invention relates to a method, device, and medium for constructing a minority language image-text dataset for internet data, comprising: acquiring an HTML webpage file; extracting a multimodal document containing minority language image-text data from the file; filtering image-text related pairs based on replaceable text corresponding to images in the multimodal document; inputting the plain images (excluding replaceable text) and the text information after removing the images, along with custom prompts, from the multimodal document into a first multimodal large model to generate corresponding image description information and image-text question-and-answer information; combining the plain images and image description information into image-text description pairs; and combining the plain images and image-text question-and-answer information into visual question-and-answer pairs; inputting the multimodal document, image-text related pairs, image-text description pairs, and visual question-and-answer pairs into a second multimodal large model to output image scene labels; and combining the above information to obtain a minority language image-text dataset. Compared with existing technologies, this invention can achieve the construction of high-quality, diverse, and standardized minority language image-text datasets.

The application relates to the technical field of artificial intelligence, and discloses a method, device and system for product selection scheme generation and a storage medium. The method comprises the following steps: performing semantic analysis on current product demand information, and extracting a current demand vector, wherein the demand vector comprises fields corresponding to one or more element information in a scene label, a technical parameter list, a constraint condition key-value pair and a regional adaptation requirement; based on a large language model, a current demand vector is disassembled according to a thinking chain guide mechanism, and a corresponding current product selection scheme component list is obtained, wherein the component list comprises one or more of function information, technical parameter information and adaptation standard information of each component; current product information matched with the current product selection scheme component list is searched from a set product database, and a corresponding structured current product selection scheme is generated. In this way, a complete scheme covering "architecture-product-guarantee" is generated, which is suitable for the demand of Chinese industry cooperation in overseas market.

This invention relates to the field of water treatment equipment condition monitoring technology, and in particular to a full life-cycle operation and maintenance system for membrane separation water treatment equipment. The system acquires influent environmental data to generate scene labels and configures tolerance coefficients. A baseline array is constructed based on the scene labels and the initial transmembrane pressure difference. Real-time operating data is matched to determine the target scene label. The difference between the real-time pressure difference and the initial pressure difference is calculated to output deviation characteristics. When a threshold is exceeded, a cleaning command is issued, and an attenuation index is calculated based on the pressure difference after cleaning to update the baseline array. The permeate flux within adjacent cleaning intervals is accumulated to generate a periodic load. The attenuation index is normalized using the periodic load and tolerance coefficient to output the aging equivalent. Each aging equivalent is accumulated over time to generate a cumulative attenuation value. When the cumulative attenuation value exceeds a preset critical value, an equipment replacement command is triggered. This improves the long-term accuracy of membrane module condition monitoring and effectively prevents equipment from exceeding its service life.

The application discloses a bronchoscope supply-demand dynamic matching method based on multi-source data fusion, comprising: collecting multi-source heterogeneous data and performing fusion processing to obtain a device feature matrix, a demand feature matrix and an environment feature vector; determining a score calculation strategy according to a comparison result of a historical matching data accumulation amount and a preset threshold, determining a device availability score and a demand urgency score based on the device feature matrix and the demand feature matrix according to the score calculation strategy; constructing a device queue and a demand queue, attaching an initial scene label to each demand and performing identification, and outputting a standardized scene type; outputting an optimal matching scheme through a reinforcement learning intelligent agent based on the device queue, the demand queue, the standardized scene type and the environment feature vector; performing intelligent scheduling based on the optimal matching scheme, updating the queue based on external real-time events, and if both queues are non-empty at the same time, obtaining a new optimal matching scheme, otherwise, keeping a waiting state. The application can improve matching efficiency and accuracy.

This invention discloses an image processing method, apparatus, vehicle, and medium. The method includes: acquiring images from multiple cameras and current computing power performance indicators; determining a panoramic image containing multi-granularity semantic annotations based on the multi-camera images, current computing power performance indicators, and a lightweight AI model, wherein the multi-granularity semantic annotations include global scene labels and local region masks; and processing the panoramic image based on the global scene labels and local region masks to obtain an enhanced final output image. By dynamically allocating resources using the current computing power performance indicators and accurately perceiving the scene in the image based on a lightweight AI model, a panoramic image containing multi-granularity semantic annotations is formed. The panoramic image is processed using a matching algorithm, enabling dynamic selection of the enhancement algorithm and differentiated processing of local problem areas. The final output image is clearer, more detailed, and reduces the risk of accidents.

The application relates to a rail transit passing control method and a read-write industrial control all-in-one machine adopting the method, and the method comprises the following steps: collecting passenger biological characteristics or electronic certificates through a multi-modal recognition device, and associating environment and passenger flow data. When verification is performed, dynamic adaptation is performed: the biological characteristics are matched according to scene parameter adjustment; the electronic certificate is checked for state, space-time compliance and cross-verification with the bound biological characteristics. Finally, through a three-level weight algorithm of a characteristic layer, an evidence layer and a decision layer, verification results, scene labels, passenger flow density and passenger passing posture data are fused to generate a comprehensive judgment value to control the opening and closing of a gate, and a scene-based prompt is output when the verification fails. The application has the following effects: the passing verification is upgraded from single static verification to multi-dimensional dynamic intelligent decision-making which fuses identity, behavior, environment and state, and under the premise of ensuring safety, the passing efficiency and passenger convenience are systematically improved.

The application relates to the technical field of artificial intelligence, in particular to an intelligent retrieval method and system based on a big data model. The intelligent retrieval method based on the big data model comprises the following steps: acquiring user query text features and context features, splicing the features after standardization processing to form total text features; based on the total text features, identifying business scenario labels corresponding to user queries after semantic analysis, wherein the business scenario labels comprise question and answer labels, attribution labels and analysis labels; acquiring corresponding retrieval weights through the business scenario labels, adopting a multi-path recall strategy in combination with the retrieval weights, acquiring retrieval results from an RAG external hanging knowledge base based on the total text features, inputting prompt words and the business scenario labels into a preset large language model, and outputting question and answer results. The application has the advantages of reducing use cost, improving data use safety and retrieval accuracy.

The present application relates to the technical field of natural language processing, and provides a translation method and device, electronic equipment and a storage medium, wherein the method comprises: determining to-be-translated data and a scene label corresponding to the to-be-translated data; performing dependency syntax analysis on the to-be-translated data to obtain syntax tree data representing the grammatical dependency relationship between words in the to-be-translated data; and translating the to-be-translated data based on the scene label and the syntax tree data to obtain target translation data corresponding to the to-be-translated data, which not only effectively solves the problems of part-of-speech misjudgment of polysemous words and structural confusion in translation from an agglutinative language to a fusional language, but also takes into account the grammatical accuracy of the language structure and the business professionalism of the specific field expression, greatly improving the translation accuracy and the final text quality of machine translation in the vertical field and complex conversational scenarios.

The present disclosure provides a presentation generation method and device, equipment and storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of natural language processing, deep learning, large language model, generative model and the like. Specifically, it comprises the following steps: according to the scene label and the requirement feature, a target narrative model is matched in a narrative model library; according to the narrative structure of the target narrative model, a presentation outline is generated; and according to the requirement feature, the scene label, the target narrative model and the presentation outline, a target presentation is generated by using a large model.

The present disclosure provides an agent-based query rewriting model training method and a search method, and relates to the technical field of artificial intelligence such as deep learning, agents, intelligent search, and intent understanding. The agent-based query rewriting model training process comprises: processing sample scene labels by using a first scene understanding layer of a teacher rewriting model to generate sample intent information and sample parameter constraint information; processing sample initial query words, sample intent information, and sample parameter constraint information by using a first query word rewriting layer of the teacher rewriting model to generate sample rewriting results of the sample initial query words; training a second scene understanding layer of a student rewriting model, which is smaller in size than the first scene understanding layer, by using sample scene labels, sample intent information, and sample parameter constraint information; and training a second query word rewriting layer of the student rewriting model, which is smaller in size than the first query word rewriting layer, by using sample initial query words, sample intent information, sample parameter constraint information, and sample rewriting results to obtain a target rewriting model.

This application discloses a scene label recognition method, apparatus, device, and storage medium applied in the field of data processing technology. The method acquires scene data to be recognized, extracts a first feature from the scene data, calculates the similarity between the first feature and each matching feature belonging to a first number of scene dimensions in a feature library, determines a second number of second features from the multiple matching features based on the calculated similarity, and determines the preset label of the second feature as the scene label of the scene data to be recognized. In another implementation, the method acquires scene data to be recognized, inputs the scene data into a classification model, obtains the classification result output by the classification model, and determines the scene label of the scene data based on the classification result. Using a feature library or a classification model trained with multi-label training data, more multi-dimensional and richer scene labels can be determined for the scene data to be recognized.

This invention discloses an intelligent switching method for a broadcast master / backup system based on AI multimodal perception, comprising the following steps: Step 1, constructing an AI multimodal state assessment module to achieve real-time assessment and early warning of the master node through multimodal acquisition, LSTM evaluation, and fault prediction; Step 2, constructing a high-precision lossless switching module to achieve μ-level synchronization and seamless switching based on clock synchronization, a custom protocol, and dual buffering; Step 3, constructing a dynamic threshold adaptation module to adjust thresholds based on scene tags and historical data to solve the fixed threshold adaptation problem; Step 4, constructing a fault self-healing closed-loop module to achieve closed-loop self-healing after the master node is repaired, reducing manual intervention. This invention improves the stability, reliability, and intelligence level of the broadcast system while reducing manual intervention.

The present application provides a method for realizing intelligent recommendation and visualization of the number of control boreholes in geological exploration, comprising: calling / building a geological exploration resource library; building a knowledge graph: based on different exploration specifications and engineering requirements, establishing the correlation between the term dictionary value, specification clause ID, engineering scene label and geological condition factor; extracting the specification clause that adapts to the current engineering type and geological condition from the knowledge graph; automatically recommending the value range of the number of control boreholes in the term interval through a self-optimizing recommendation algorithm; dynamically adjusting the value range of the number of control boreholes through a rule engine combined with the engineering type, geological complexity and exploration stage, so as to obtain the optimal value range of the number of control boreholes; forming an operational processing system for realizing intelligent recommendation and visualization of the number of control boreholes in geological exploration. The present application can effectively form an intelligent system for improving the efficiency and accuracy of the visualization of borehole arrangement in geotechnical engineering geological exploration.

The application relates to the technical field of personalized intelligent decision-making, in particular to a mobile terminal rich-situation perception and personalized intelligent agent decision-making method, which comprises the following steps: constructing a three-state monitoring mechanism based on a minimum perceptible energy threshold, wherein the three-state monitoring mechanism comprises a dormant state, a short-time monitoring state and a long-time monitoring state; in the short-time monitoring state, a light-weight Gaussian mixture model is used to perform coarse-granularity classification on a sampled audio frame, a binary label representing human voice or environmental sound is outputted, and a continuous audio stream is converted into a structured acoustic event; when the structured acoustic event is a non-silence event, a multi-sensor context is collected and inputted into a multi-modal context model to obtain a scene label; and the scene label, the structured acoustic event and an audio segment are jointly inputted into a personalized intelligent agent decision-making model to perform situation-level reasoning and generate a concise and executable situational guide. The method realizes a closed-loop processing of low-power continuous monitoring, deep situation understanding and personalized guidance.

The application discloses an asynchronous language input mode closed-loop control method and system based on evolution state machine feedback. The method comprises: continuously maintaining a multi-dimensional evolution state matrix representing a target voice input process; collecting multi-source trigger signals containing voice features, behavior interaction and scene labels; calculating mode switching gain based on the matching degree of the evolution state matrix and the multi-source trigger signals, and driving the system to dynamically migrate between multiple asynchronous audio input modes when the gain meets the standard. The method performs smooth compensation of the audio stream gain during the migration process, and synchronously updates the audio processing path and the output strategy. The system reversely corrects the evolution state matrix according to the feedback features fed back by the second terminal in real time, forms a closed-loop scheduling of the target language auditory load output frequency, complexity and cooperation ratio. The application realizes long-term consistency and logical coherence of the audio input environment under the condition of non-text starting and low interaction intervention, and reserves a synchronous alignment interface for cross-modal semantic mapping.

The application discloses a dynamic updating and retrieving method of vehicle-mounted knowledge base question and answer pairs, which receives an updating request through an incremental updating interface, and only writes changed question and answer pair data into the knowledge base after verification, thereby avoiding data transmission redundancy and service interruption problems of full updating, and improving updating efficiency and system availability. After receiving a user query, semantic vectorization retrieval is performed by using a vehicle-mounted field pre-training model, thereby solving the defects of traditional keyword matching in synonym matching and context understanding, and improving the accuracy and relevance of retrieval. By acquiring real-time vehicle state data and matching and calculating scene labels of candidate answers, state matching degree and semantic similarity are fused and sorted, so that the finally output answer is more suitable for the current driving scene, and the safety and scene practicability of interaction are improved. The application organically combines incremental updating, semantic retrieval and state perception sorting, and forms an efficient, accurate and scene-based solution for the vehicle-mounted vertical field.

The application discloses a notebook multi-scene adaptive power consumption regulation method and system, relates to the technical field of notebook computer power consumption intelligent management, and comprises the following steps: collecting and processing multi-dimensional running information such as the load of a processor, the memory usage, the peripheral connection state and the user activity type in real time, and processing the multi-dimensional running information into a standardized state feature vector; inputting the feature vector into a pre-trained real-time scene recognition model; the model outputs a multi-level scene label combination composed of a primary scene label and a secondary detailed activity label; the system queries a preset power consumption strategy library according to the combined label, acquires and executes a corresponding initial regulation strategy, and the strategy content covers a processor frequency benchmark, core scheduling preference, a screen refresh rate range and background process resource limitation. The method realizes dynamic and accurate adaptive regulation of power consumption by intelligently identifying specific use scenes and matching fine strategies, and effectively improves the energy efficiency and user experience of the device under different use situations.

This application relates to the field of vehicle infotainment system (VIS) management technology, specifically a method and system for proactive VIS response in low-speed vehicle scenarios. This application uses existing scene recognition models to identify scenes and further filters target scene labels by combining lane-level positioning information and pedal signals. The target scene labels and the original image are input into a segmentation model to extract low-speed root cause targets that are strongly correlated with the target scene labels. Based on the low-speed root cause targets, proactive response rules are extracted from a rule base, and the VIS proactive response is executed. This application combines scene recognition and low-speed root cause target extraction to configure multiple proactive rules, enriching the proactive response function of the VIS in low-speed scenarios and improving the intelligence and convenience of the VIS.

The application discloses an engineering safety hidden danger image recognition method and system based on multi-model fusion, comprising: acquiring an original image of an engineering site, recognizing a scene type and outputting a scene label, loading a target detection model and a visual language reasoning model from a preset model library according to the scene label; determining whether a detection item corresponding to the scene label belongs to a known category target or an open category target according to a preset scene-detection item configuration table, outputting a boundary box coordinate through the target detection model for the known category target indicated by the scene label, and cutting a target subgraph from the original image, inputting the target subgraph and a preset hidden danger prompt word template into the visual language reasoning model; inputting the original image and the hidden danger prompt word template into the visual language reasoning model for the open category target indicated by the scene label, and obtaining a hidden danger reasoning result; and fusing the boundary box coordinate and the hidden danger reasoning result, and outputting hidden danger data including a target identifier, a coordinate, a hidden danger type and a risk level.

The application discloses an audio and video processing system and method supporting AI intelligent repair technology, and relates to the technical field of audio and video data processing.The application comprises the following steps: scene recognition based on the playing source type, user interaction mode, audio and video code rate, audio sampling rate and network transmission delay to generate scene labels of live broadcast, movie watching and singing; according to the scene labels, the upper limit of the delay, the resolution target, the detail restoration level, the audio fidelity index and the beat alignment threshold are determined to form a performance constraint vector. The application aims at the differentiated needs of live broadcast, movie watching and singing, and constructs a closed-loop mechanism of scene recognition, performance constraint setting, repair link arrangement, cloud and terminal task allocation, real-time adaptive adjustment and parameter iterative optimization, realizes low delay, high quality, high sound quality and power self-adaption, and improves the system stability and continuous optimization ability through versioning and phased release.

This invention provides an adaptive pedestrian gesture understanding method for autonomous driving, including acquiring raw visual data; extracting initial features from the raw visual data and performing visual encoding; self-supervised decoupling; cross-scene consistency constraints; inferring gesture intent; and model training. This invention proposes an adaptive pedestrian gesture understanding method for autonomous driving, using scene labels as weakly supervised signals to learn a scene-independent semantic subspace and a scene-related style / intent subspace, achieving adaptive gesture understanding in different scenarios.

The application discloses a UAV nest cooperative operation method and system based on personalized federated learning, and belongs to the technical field of edge computing and cloud computing cooperation. The method comprises the following steps: collecting original operation and maintenance data of a plurality of UAV nests, wherein the original operation and maintenance data comprises at least one sensor reading and a corresponding scene label; performing scene clustering processing on the original operation and maintenance data to generate a scene clustering cluster; calculating the feature weight of each operation and maintenance feature; weighting and fusing the client data in the scene clustering cluster by using the feature weight to generate weighted training data, and training a scene-specific basic parameter on the edge server side; uploading the scene-specific basic parameter to a cloud server for federated aggregation to generate global model parameters; distributing the global model parameters to the client, and performing personalized updating on the global model parameters based on local data on the client to generate personalized parameters; and performing a UAV nest operation and maintenance prediction task based on the personalized parameters.