191 results about "Smart technology" patented technology

This invention relates to the field of intelligent technology for identifying hidden dangers on reservoir and river / lake embankments, specifically to a method for intelligent identification and risk assessment of embankment hidden dangers based on multimodal perception using a walking robot. The method involves a quadruped robot equipped with multiple sensor modules autonomously inspecting reservoir and river / lake embankment areas to collect multimodal data from the embankment surface. This multimodal data is then transmitted to a data processing center. Based on this data, the types and locations of hidden dangers on the embankment surface are identified. These hidden danger types include cracks, seepage or piping, settlement, and structural deformation. The types and locations of these hidden dangers are quantitatively analyzed to obtain quantitative results. Based on these results, a risk assessment report, a visual map, and an early warning are generated. This invention improves the spatial coverage accuracy and temporal synchronization of data acquisition, and possesses higher sensitivity and complementarity in identifying hidden danger types such as embankment cracks, seepage, and deformation, providing a perceptual foundation for subsequent analysis.

This application discloses a multimodal temporal causal intelligent decision-making method, apparatus, device, and medium for complex scenarios, relating to the field of artificial intelligence technology. The method includes: constructing an integrated intelligent decision-making framework from multimodal data; achieving shared semantic representation of multi-source data through unified encoding; obtaining reliable fusion causal strength through dual-channel causal discovery; constructing and dynamically updating a causal knowledge graph based on this strength; and generating compliant decisions by combining constraint projection and counterfactual verification. The beneficial effects are: realizing dynamic causal knowledge updating and generating constraint-compliant and reliable decisions based on multimodal data; ensuring that decisions strictly comply with physical and business rules; and improving the reliability, robustness, and real-time performance of decisions in complex scenarios.

The invention belongs to the technical field of education artificial intelligence, and particularly relates to an adaptive learning path generation method based on a knowledge graph, which comprises a basic construction stage of constructing a double-layer knowledge graph and acquiring and standardizing multi-source dynamic data, and is used for constructing a course and cross-domain double-layer knowledge system and a data base, a three-dimensional learning condition analysis stage including knowledge dimension, dynamic learning condition dimension and cognitive development dimension; the cross-domain knowledge migration mechanism breaks course barriers, associates current learning with core course knowledge points of the same major, helps students establish a complete professional knowledge network, avoids knowledge fragmentation, embeds cross-domain knowledge points in combination with professional planning and competition requirements, realizes connection of current learning and future application, improves the practical value and migration ability of knowledge, and improves the learning efficiency. The knowledge association weight is dynamically optimized, so that path planning follows domain knowledge logic and fits individual learning feedback, students are assisted in understanding internal association among knowledge, and knowledge construction logicality is enhanced.

This application discloses a task migration method and a mobile terminal, relating to the field of artificial intelligence technology. In response to changes in the screen form of the mobile terminal, task context data is acquired; based on the task context data, task planning information is generated; wherein, the task planning information includes inference information of the current task and at least one of the predicted subsequent executable tasks; thereby, based on the task planning information, a corresponding task management window is output in the changed screen form to form an interactive interface associated with the current task.

The invention relates to the technical field of artificial intelligence, in particular to a display screen integrated bidding document generation method and device, intelligent equipment and a storage medium. The method is applied to the intelligent equipment, and comprises the following steps: obtaining a project demand of a display screen; according to the project demand, constructing a prompt word; inputting the cue words into a large language model, guiding the large language model to perform reasoning based on the project requirements through the cue words, and generating a bidding document draft of the display screen; calling a bidding document template; and generating a target bidding document of the display screen according to the bidding document draft and the bidding document template. By adopting the scheme of the invention, the display screen bidding document can be intelligently generated, and the bidding document generation efficiency and the demand matching degree are improved.

Automated agriculture irrigation control unit using Smart technology

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 application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, device, medium, and program for labor scheduling. The method includes: acquiring labor demand input from the labor client; inputting the labor demand into a pre-trained language model, which outputs a corresponding directed acyclic graph (DAG), wherein the pre-trained language model performs semantic understanding to decompose the labor demand into at least one sub-task, and generates the DAG based on the dependencies between the at least one sub-task; identifying at least one worker's data; and selecting the optimal scheduling path from the DAG based on the worker's data and pushing it to the corresponding worker's end, wherein the worker's end receives and displays the pushed optimal scheduling path and provides a confirmation instruction. This solves the problem that related technologies can only handle standardized, discrete, single-point tasks with structured form input, failing to meet the needs of flexible labor scenarios and resulting in low work efficiency.

The application discloses a financial risk control report generation method, device, equipment, storage medium and product, relates to the technical field of artificial intelligence, and the method comprises the following steps: generating a preference data set through a supervised fine-tuning technology, a preset risk control report corpus and a preset large language model; performing iterative preference training on the preset large language model according to the preference data set; and generating a financial risk control report through a large language model after training and an intelligent agent-based retrieval enhancement generation technology. The application uses the risk control report corpus of a user to generate a preference data set through supervised fine-tuning, a corpus and a large language model, thereby reducing the dependence on a large amount of labeled data; the model is trained through the preference data set, a database query mode is provided to improve the report generation efficiency of the model, thereby solving the problem that a large number of high-quality samples are required for training in the prior art, and only a small number of samples and iteration times are required to align the output of the large language model with the expected behavior of the user.

The application relates to the technical field of artificial intelligence, and discloses a noisy environment adaptive multi-agent medical accompanying diagnosis system and method for old patients, which aims to solve the problems of poor adaptability of the medical accompanying diagnosis technology environment, insufficient information conversion capability and limited system collaboration. The method comprises the following steps: a dual-encoder architecture is fused with voice features and waveform language models, cross attention and knowledge distillation are combined to construct a robust speech recognition module; a plurality of heterogeneous medical knowledge graphs are integrated, and a rule and large model hybrid architecture is used to realize deep analysis and popularization of information; a collaboration framework comprising a routing decision maker and a plurality of special intelligent agents is constructed, a memory and retrieval enhancement generation mechanism is integrated to realize whole-process active assistance; and service interaction is performed through a digital human interface. The system comprises corresponding recognition, fusion and collaboration modules. The application can improve the recognition accuracy in a noisy environment, reduce the cognitive load of old patients, and realize whole-process intelligent guidance and old-age care.

This application discloses an intelligent question-answering method, apparatus, computer device, storage medium, and product, belonging to the field of artificial intelligence technology. It includes: displaying an intelligent question-answering interface; obtaining input question information based on input operations on the intelligent question-answering interface; performing a statement matching query in a structured database based on the question information to obtain a first query result; performing a semantic similarity query in an unstructured knowledge base based on the question information to obtain a second query result, wherein the unstructured knowledge base includes multiple unstructured knowledge items, and the second query result includes the retrieved candidate unstructured knowledge items; determining candidate answer information based on the first and second query results; and performing logical reasoning and natural language generation on the candidate answer information to obtain a first target answer information, wherein the first target answer information is used to describe the candidate answer information using natural language, and the first target answer information contains the logical reasoning process of the candidate answer information.

This application discloses a method, apparatus, device, and medium for item recommendation across multiple user groups, scenarios, and tasks, relating to the field of artificial intelligence technology. The method involves acquiring user-side information, item-side information, and corresponding scene environment information in different scenarios; calculating weight dimensions, shared feature vectors, and environment dimensions to obtain a first dimension, a dense vector, and a second dimension; performing attention mechanism feature cross-calculation to obtain a third dimension; calculating item recommendation scores using the first dimension, dense vector, second dimension, and third dimension to obtain an item recommendation value; determining the target item recommendation value; identifying the target item information corresponding to the item recommendation value; and recommending the target item information to the user. This application can construct a three-dimensional perspective model encompassing multiple scenarios, user groups, and tasks, thereby improving the ability to represent differentiated user groups, meeting diverse user needs, and achieving comprehensive item recommendation across multiple user groups, scenarios, and tasks.

This invention belongs to the field of educational artificial intelligence technology. It discloses a large-scale educational model architecture for personalized learning. Through a "knowledge graph-model joint fine-tuning-knowledge distillation" framework, it addresses problems in educational AI applications such as insufficient knowledge accuracy, high deployment costs, and inadequate personalized support. This invention first constructs an educational knowledge graph, encoding structured knowledge through a relation-type-aware graph neural network. Then, it fuses this knowledge with a large language model through cross-attention, optimizing the model's understanding of educational content through a joint loss function. Finally, it generates lightweight vertical category models through multi-level knowledge distillation. This invention combines user learning profiles and knowledge forgetting patterns to provide learners with personalized content and learning paths. This invention significantly improves the reliability of educational content, effectively reduces model illusions, greatly lowers deployment costs, and achieves accurate identification of knowledge gaps and scientific learning path planning.

The utility model belongs to the electrical distribution equipment field discloses an intelligent safety distribution box, including the box, the electrical assembly of setting in the inside of box and being divided into different function areas and a set of intelligent monitoring protection system, the structure design of electrical assembly of at least partial area of box adopts the bevel installation and recess in the surface of box, the box is equipped with ventilation and heat dissipation system, including the air intake of being located in the lower part of box, the exhaust fan of being located in the top of box and the rainproof top cover with the air outlet structure, and the intelligent monitoring protection system can monitor voltage, current and the like parameter, realizes remote monitoring, fault diagnosis, early warning and a variety of electrical protection function, including overvoltage, overcurrent overload, electric leakage and surge protection, and supports remote opening and closing. The utility model passes through the combination of structure optimization and intelligent technology, has improved the safety, reliability and operation convenience of distribution box significantly.

This application discloses an information processing method, computer equipment, and medium for integrated energy systems, belonging to the field of artificial intelligence technology. The method includes the following steps: responding to the input of a training sample set, inputting the training sample set into a multi-task prediction model to obtain a first predicted value for electrical load, a second predicted value for thermal load, a third predicted value for cooling load, and a fourth predicted value for carbon emissions; wherein the training sample set is constructed based on historical data of electrical load, thermal load, cooling load, carbon emissions, and meteorological data; constructing a carbon balance physical constraint loss term; wherein the carbon balance physical constraint loss term is used to measure the deviation between the fourth predicted value and the estimated value of carbon emissions, the estimated value being obtained through a linear coupling relationship; weighted summing of the carbon balance physical constraint loss term and the data-driven loss term to construct a total loss function, and updating the parameters of the multi-task prediction model through backpropagation. This application improves the accuracy of the prediction results.

The application relates to the technical field of artificial intelligence, in particular to a model optimization method and device based on an intelligent agent. A model optimization instruction is understood by an intention understanding intelligent agent to obtain an intelligent agent scheduling task instruction, a target intelligent agent is called in sequence according to the scheduling sequence of the intelligent agent in the intelligent agent scheduling task instruction, data to be processed is sent to the target intelligent agent to complete output processing until a model parameter adjustment suggestion output by the last intelligent agent in the scheduling sequence is obtained, and parameters in a to-be-optimized model are adjusted based on the model parameter adjustment suggestion. Since the callable intelligent agents include a data query intelligent agent, a code execution intelligent agent, a feature mining intelligent agent, a model evaluation intelligent agent and a model optimization intelligent agent, when the model is optimized, automatic data query, feature analysis, model evaluation and optimization operations can be performed based on the callable intelligent agents, and the efficiency of model optimization is improved.

This disclosure provides a content display method, apparatus, electronic device, and storage medium, relating to the field of artificial intelligence technology, particularly to the fields of large models and deep learning technology. The specific implementation scheme is as follows: In response to a received access request from a target object, the device attribute characteristics, IP scenario characteristics, and behavioral sequence characteristics of the target object are obtained based on the access request; based on the device attribute characteristics, IP scenario characteristics, and behavioral sequence characteristics, a behavioral prediction evaluation value for the target object to generate a target behavior is determined; based on the behavioral prediction evaluation value and multiple preset content delivery strategies, delivery content matching the target object is determined; and the delivery content is returned to the target object's client for rendering and display.

Embodiments of the present application disclose a kind of group adaptive integrated control method, device, electronic equipment and storage medium, involve embodied intelligent technical field, wherein, the method includes: according to target scene characteristic, utilize physical modeling and domain randomization technology to generate diversified environment task set, and pre-processing is carried out to ensure data quality, train reinforcement learning coach based on these tasks, combine decoupling and backtracking policy distillation technology to synthesize training sequence, enhance the generalization and adaptive ability of model, adopt long-time memory model structure design base model, integrate multi-agent observation module to capture time sequence and spatial correlation, dynamic noise injection and asynchronous optimization are carried out in training process, the trained field base model is deployed to target scene, form collaborative network, and set fault-tolerant mechanism to deal with agent offline and other sudden situations, continuously collect actual data to optimize model parameters.The present application effectively solves the problems of high customization, weak migration and high maintenance cost in the prior art.

This application provides a customer satisfaction assessment method, apparatus, device, medium, and product, relating to the field of artificial intelligence technology. The method includes: obtaining user tags for a target user; generating a questionnaire corresponding to the user tags using a first language model; and conducting multiple rounds of interaction with the target user based on the questionnaire to obtain the target user's customer satisfaction result regarding the questionnaire. This enables the generation of personalized questionnaires for different users, and through multiple rounds of interaction, it allows for in-depth analysis of users' actual usage experiences and problems, providing accurate and reliable data support for downstream scenarios.

The application provides an agricultural intelligent question and answer method and device, electronic equipment and storage medium, and relates to the technical field of agricultural intelligence. The application retrieves a space-time knowledge graph with time relationship data and space relationship data through a search condition, obtains a space-time context, supplements the space-time change condition of an answer, enables a large language model to answer in response to the space-time change condition, and improves the space-time accuracy of the answer. The application realizes the generation of differentiated prompt sentences and answers according to regions and user layers, solves the problem of the existing agricultural intelligent answer of 'one face for thousands of people', and can serve professional users and non-professional users at the same time. The application combines space-time reasoning with the powerful generation capability of an LLM, so that the agricultural intelligent question and answer system can not only answer static fact questions, but also has a dynamic analysis and prediction capability based on space-time rules, such as disaster warning and agricultural operation suggestions, and has a more powerful function.

This invention relates to the field of artificial intelligence technology and discloses a heterogeneous terminal interaction method and system based on distributed intelligent agent collaboration. The method includes: a first terminal sensing at least one second terminal within its communication range in real time and exchanging intelligent agent configuration files with the at least one second terminal to determine the state information of the at least one second terminal; wherein the state information includes capability description information, a previous state, and a resource state list; the first terminal determining a collaboration mode between itself and the at least one second terminal based on the state information and the current task to be processed; the first terminal generating task instructions corresponding to the at least one second terminal by calling a lightweight large language model based on the collaboration mode, and issuing them to the at least one second terminal respectively; the at least one second terminal executing the task according to the task instructions. This invention improves the effects of intelligent agent collaboration and dynamic adaptation.

This application relates to the field of artificial intelligence technology, and in particular to a frequency regulation system, method, and storage medium for thermal power units. It includes: an end-side processing device comprising an end-side acquisition layer and an edge computing layer. The end-side acquisition layer acquires the operating data of the thermal power unit and the frequency of the power grid. The edge computing layer calculates the frequency deviation based on the actual frequency and target frequency of the power grid. It deploys a lightweight intelligent agent that outputs a load adjustment amount for the thermal power unit based on at least one of the operating data, frequency, and frequency deviation. This load adjustment amount is then sent to the control system of the thermal power unit. The control system generates a target frequency regulation command based on the load adjustment amount and controls the thermal power unit to perform a frequency regulation action based on the target frequency regulation command. A cloud-based system is used to train the intelligent agent using a training module and distribute the lightweight intelligent agent to the end-side processing device. This solves the problems of difficulty in meeting the low-latency requirements of frequency regulation and poor real-time performance in related technologies.

The application discloses a group creative cooperation method, a computer device and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in response to identifying that a target user is included in all users participating in target group creativity, determining second creative content from a semantic graph based on first creative content input by the target user in a first independent workspace; wherein the semantic graph is constructed by creative content input by all users in corresponding independent workspaces; the target user is a user who needs assistance among all users; and target information related to the second creative content and used for supporting the target user to continue inputting creative content in the first independent workspace is generated. The application can improve the overall efficiency and output quality of group creativity.

The present disclosure relates to the technical field of artificial intelligence, and specifically discloses an intelligent bidding method, system, device and storage medium, wherein the method comprises: analyzing an intelligent agent based on a demand, analyzing a bidding document to obtain bidding demand information corresponding to the bidding document and generating a bidding document directory, each bidding section in the bidding document directory corresponding to one or more bidding demand information; generating a bidding template corresponding to each bidding section based on a bidding generation intelligent agent, and determining the required bidding data content of the bidding template according to an enterprise bidding database; and generating a bidding document corresponding to the bidding document based on the bidding data content, the bidding template and the bidding specification requirements in the bidding document. Through the technical solution provided by the present disclosure, the full-process automatic generation and management of the bidding process can be realized by utilizing the intelligent agent and the enterprise bidding database, and a large amount of manpower and time cost is saved while obtaining a bidding document that can reflect the bidding advantages of the enterprise.

This application provides a method for generating collaborative trajectories of multiple intelligent terminals in ports, relating to the field of artificial intelligence technology. This method achieves safe, efficient, and interpretable trajectory planning for multiple intelligent terminals in ports under complex scenarios by collaboratively generating dynamic obstacle spatiotemporal maps and conditional diffusion models, combined with Pareto front multi-objective optimization and hierarchical game strategies, real-time trajectory planning, multi-objective constraint balancing, and human-machine collaborative decision-making, significantly improving port operation efficiency and safety.