857results about How to "Accurate understanding" patented technology

The invention provides an unstructured text data calculation method and system, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the multi-level processing of input unstructured text data, dynamically adjusting the word segmentation granularity according to the word frequency distribution, constructing a part-of-speech co-occurrence matrix in combination with context semantic information, and extracting entity information. And fusing the part-of-speech co-occurrence matrix and the entity information to generate a hierarchical semantic tag sequence. Feature extraction units with different convolution kernel sizes are adopted to extract feature representation, and cosine similarity between different semantic hierarchies is calculated to establish an association weight matrix. A semantic enhancement vector is constructed based on entity information, and adversarial training is carried out to obtain a multi-modal semantic feature matrix. And calculating the semantic similarity between the fusion feature vectors for clustering, setting difficulty weight sorting according to the complexity, consistency and ambiguity of class clusters, then inputting the class clusters into a classifier, and iteratively optimizing a classification result to obtain a calculation result.

The invention provides a plasticizing product recommendation method and system based on user demands, and relates to the technical field of plasticizing product recommendation, and the method comprises the steps: S1, constructing a plasticizing industry knowledge graph; s2, acquiring user historical data and current user requirements, including historical search data, historical browsing data, historical interaction data and historical transaction data; s3, preprocessing the historical data of the user, analyzing the preprocessed historical data of the user, and extracting historical demand features; s4, performing keyword extraction on the current user demand by utilizing the semantic understanding model, and generating an optimized query condition in combination with the chemical and plastic industry knowledge graph; s5, searching in a plasticizing product database by utilizing the optimized query condition to obtain a first list; and S6, carrying out personalized sorting on the first list by utilizing the chemical and plastic industry knowledge graph, generating a recommendation list from the first k lists, and presenting the recommendation list to the user. According to the invention, the relevance and accuracy of recommendation can be improved, so that the user experience is improved.

The invention discloses a park sweeper positioning and environment mapping method based on multi-sensor fusion. The method comprises the following steps: S1, preprocessing multi-modal data acquired by a plurality of sensors; s2, extracting and fusing semantic features and geometric features in the multi-modal data preprocessed by the plurality of sensors; s3, performing geometric feature extraction and recognition on the point cloud data by using a VoxelNet point cloud processing algorithm; s4, performing pose estimation and track plotting by fusing multi-sensor data; s5, identifying places where the motor sweeper repeatedly passes through through loopback detection, and correcting accumulated errors; and S6, constructing a three-dimensional environment map by fusing the laser radar, the visual features and the IMU data, performing global optimization by applying a map optimization technology, correcting errors, improving map precision, and updating the map in real time. According to the invention, the autonomous navigation capability of the sweeper is enhanced, the sweeping efficiency and safety are improved, and the sweeper is suitable for various complex and dynamic park environments.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention provides a large language model intelligent interaction method and system with memory management capability in the technical field of artificial intelligence. The method comprises the following steps: S1, creating a work memory module, a long-term memory module, a memory fusion module and an intelligent interaction module; s2, acquiring external knowledge and integrating the external knowledge into a long-term memory module; s3, the intelligent interaction module obtains an intention sequence based on a user query statement and sends the intention sequence to a work memory module; s4, the memory fusion module retrieves context information from the long-term memory module based on the user query statement and sends the context information to the working memory module; s5, the work memory module constructs a work memory sending intelligent interaction module based on the intention sequence, the context information and the system instruction; s6, the intelligent interaction module outputs a query result based on the working memory; s7, a memory fusion module extracts semantic memory and plot memory from user query statements and query results and stores the semantic memory and plot memory in a long-term memory module. The method has the advantage that the man-machine interaction performance is greatly improved.

The invention discloses a customer relationship intelligent management and analysis system based on CRM, and relates to the technical field of intelligent analysis, a business logic layer comprises a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit, the customer information management unit is used for extracting basic attributes, consumption habits and preference characteristics of customers from customer data by using a data analysis technology so as to construct a comprehensive and three-dimensional customer portrait; and the customer information management unit further divides the constructed customer portraits into subdivided groups with common demands, preferences and behavior patterns through in-depth market research. According to the method, the wide customer groups are divided into the subdivided groups with common requirements, preferences and behavior patterns, so that an enterprise can more accurately understand each user group, a more targeted marketing strategy is formulated, resource allocation can be optimized to a certain extent, and the market response speed is increased.

The invention relates to the technical field of tin smelting, and discloses an AI intelligent tin smelting control system and method, and the system comprises a data collection and preprocessing module, an intelligent decision and control module, and a real-time monitoring and early warning module. The method comprises the following steps: collecting process data in a tin smelting production process in real time, and cleaning the collected process data; extracting and selecting characteristics of which the influence on the smelting quality and efficiency exceeds a preset threshold value, and carrying out standardization processing on the data; receiving the standardized process data, training the model, processing the data monitored in real time, and outputting the adjusted process parameters; various key parameters and data in the production process are displayed in real time, the real-time data are analyzed, abnormal conditions are detected, early warning is carried out on faults, and response measures are automatically triggered. Intelligent and automatic tin smelting is achieved, and the efficiency, quality and safety of the smelting process can be effectively improved.

The invention relates to the technical field of question answering systems. The question answering system comprises a language understanding generation module, a multi-agent coordination module, a knowledge graph retrieval module, an emotion analysis adaptation module, a virtual reality interaction module, a time travel simulation module, a cross-language processing module and a future prediction reasoning module. The language understanding generation module is used for semantic analysis and response generation; the multi-agent coordination module is used for agent distribution and agent interaction; according to the multi-agent question answering system based on the large language model, through deep semantic analysis of the large language model, the intention and key information of a user question can be accurately understood, and according to the type and field of the question, the most suitable agent can be dynamically allocated for answering; necessary communication and cooperation can be carried out among a plurality of agents, information circulation and answer consistency are ensured, a cross-domain knowledge graph is constructed, and rich knowledge support is provided for answer of complex questions.

The invention provides an intelligent mineral resource exploration and evaluation system, and relates to the technical field of data processing, and the system comprises a data preprocessing module which is used for preprocessing data to obtain preprocessed data; the stress analysis module is used for analyzing the rock physical property and the geological structure stress state of the exploration area according to the preprocessed data so as to obtain the relevance between the rock physical property and the geological structure stress state; calculating a dynamic adjustment coefficient according to the relevance between the rock physical property and the geological structure stress state; the resource reserve estimation module is used for estimating mineral resource reserves according to the dynamic adjustment coefficient; and the resource evaluation module is used for evaluating the mineral resource reserves to obtain a final evaluation result. According to the method, accurate analysis of the rock physical property and the geological structure stress state is realized, so that the accuracy and the reliability of exploration and evaluation of mineral resources are greatly improved.

The invention discloses a contract evaluation method, system and device based on a large model and a medium, and the method comprises the steps: obtaining a target contract, carrying out the data preprocessing, obtaining the preprocessed contract data, and inputting the preprocessed contract data into a preset contract large language model; the preset contract large language model stores a vector knowledge base including coastal bulk cargo transportation knowledge, legal clauses and industry regulations; in a preset contract big language model, performing contract classification and contract summary extraction on the current preprocessed contract data by using a first model agent to obtain contract summary information; performing contract risk assessment on the current preprocessed contract data to obtain risk assessment information and / or risk avoidance suggestion information; performing benefit measurement and calculation on the current preprocessed contract by using a third model agent to obtain benefit measurement and calculation information; according to the method, the examination and evaluation of the large model in the coastal bulk cargo transportation contract are realized, and the purposes of saving the labor cost and improving the business turnover rate are achieved.

The invention discloses a holographic display digital human speech recognition enhancement method based on multi-modal interactive learning, and the method comprises the following steps: S1, collecting and preprocessing data, and generating a multi-modal feature matrix; s2, inputting the multi-modal feature matrix into a self-adaptive multi-modal interaction kernel to generate multi-modal interaction features; s3, inputting the multi-modal interaction features into an emotion convolution block to generate semantic-emotion mapping features; s4, inputting the semantic-emotion mapping features into a cross attention generator, and outputting multi-modal fusion features; s5, inputting the multi-modal fusion features into a multi-modal fusion-decomposition two-way network, and generating multi-modal features after decoupling optimization; s6, inputting the multi-modal features into a holographic-speech symbiosis optimization module to generate speech recognition and holographic display data; and S7, performing parameter optimization by applying self-supervised learning and comparative learning strategies to realize self-adaptive adjustment. According to the invention, the enhancement of holographic display digital human speech recognition is realized by using a multi-mode interactive learning method.

A user behavior analysis system based on cloud side-end cooperation of a large language model is used for carrying out information collection, cloud side-end cooperation and user behavior analysis on mobile phones and computer equipment, and comprises an information collection module, a cloud side-end cooperation module and a user behavior analysis module. According to the invention, information acquisition is carried out on electronic data of mobile phone equipment, information privacy collaboration is carried out through the cloud side end module, and then user behavior analysis is carried out based on a large model, so that the defects of low collaboration efficiency, high user behavior identification false alarm rate and the like of an existing user behavior analysis system are overcome; the evidence obtaining efficiency and the user behavior analysis effect are effectively improved by introducing a cloud side-end cooperation and large language model, and the cloud side-end cooperation behavior analysis system which is high in efficiency, high in precision and light in weight is achieved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a video analysis processing system and method based on a multi-modal large model, and the method comprises the steps: enabling the multi-modal control large model to receive a task which is input by a user and needs to be completed through natural language description, carrying out the semantic analysis of the input of the user, extracting task information, and carrying out the analysis of the task information; a task related to video understanding is decomposed into sub-tasks and the sub-tasks are sent to a video understanding module; the video understanding module receives a video or a video clip required by a task, executes a subtask to analyze the video, and generates embedding and description of video content to be input into a multi-modal control large model; when the multi-modal control large model receives a query request of a user, a query task of the user is analyzed into executable subtasks and mapped to a video understanding task, and a response result based on the query request is generated based on embedding and description of video content generated by the video understanding large model. Therefore, effective fusion and mutual enhancement of cross-modal information are realized, and the video understanding precision is improved.

The invention provides a potential diffusion model-based building planning image generation method and system. The method comprises the steps of data preprocessing, potential diffusion model structure improvement, input condition optimization, model training, image generation and the like. The relevance between text description and architectural styles is enhanced by preprocessing data sets of architectural design images and urban planning images and introducing channel crossing and space crossing attention mechanisms. Meanwhile, an architectural style embedding vector, an architectural description text embedding vector and a geometric information embedding vector are combined as additional input conditions to ensure that the generated image contains complete information. In a model training process, a multi-scale strategy and depth separable convolution are adopted, so that the recognition capability and training efficiency of the model to a large scene are improved. The system comprises a data preprocessing module, a model optimization module, a model training module, an image generation module and the like, and can generate a high-resolution building image conforming to design specifications. The method and system can be widely applied to the fields of building design and urban planning, and design efficiency and accuracy are improved.

The invention belongs to the technical field of computer vision, provides a real-time semantic perception system running on equipment, performs real-time instance segmentation and semantic mapping on a working environment, and provides a perception method based on lightweight real-time instance segmentation and semantic mapping. In the first part, firstly, a color image containing color and texture information is used for object classification and bounding box regression; cutting the original depth image into a depth image with an accurate geometric edge, and inputting the depth image into the truncation network to generate a mask of the instance; a second part: (1) calculating the attitude and size of a target object in the view of the camera through principal component analysis; (2) obtaining the category of the object in the view obtained by segmentation; (3) creating a point cloud of the depth image projection of the target object in a map; and then cross-frame tracking and fusion are carried out by using data association to realize the purpose of sensing. The method is mainly applied to automatic sensing occasions.

The invention provides a context understanding-based software operation prompt automatic optimization method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the multi-dimensional context feature data of user software operation, carrying out the feature coding through a multi-modal feature fusion network, modeling the time sequence dependence through a bidirectional long-short-term memory network, and carrying out the feature coding through the bidirectional long-short-term memory network. And a context semantic representation vector is generated based on a multi-head attention mechanism, and a prompt generation strategy is continuously optimized in combination with reinforcement learning. According to the method, personalized customization of software operation prompts can be realized, the accuracy and timeliness of prompt contents are improved, and the software use experience of a user is enhanced.

The invention discloses an operation site safety management and control method and system based on a multi-modal large model, and the method comprises the steps: collecting site violation data, and carrying out the preprocessing, and obtaining multi-modal data; constructing a multi-modal large model; carrying out training and optimization on the multi-modal large model by utilizing the preprocessed multi-modal data; and using the optimized multi-modal large model to realize operation site safety management and control. According to the invention, a visible light image, a safety violation knowledge base and other modal data are fused, and a comprehensive safety monitoring and management platform is constructed by using a large model and a big data processing technology, so that real-time monitoring, early warning and intelligent response of potential safety hazards of a complex operation site are realized, and the safety management level of the operation site is improved.

The invention discloses an interaction method, device and equipment based on a memory system, a storage medium and a vehicle. The method comprises the following steps: acquiring an input query statement; target memory associated with the query statement is determined through the language big model, the target memory comprises at least one of long-term memory, short-term memory and memory summary, and the memory summary is content obtained by the language big model for the long-term memory and the short-term memory summary; fusing the target memory and the query statement to obtain a cue word; and inputting the cue word into the trained language large model to obtain a reply statement output by the language large model. According to the embodiment of the invention, the accuracy of the reply statement output by the language large model can be improved.

The invention discloses a scene understanding information generation method and device, equipment and a medium, and the method comprises the steps: inputting an image into a scene classification model, and obtaining a scene type corresponding to the image outputted by the scene classification model; identifying a target object in the image by using a target detection model to obtain target object information, the target object information including a target type and position information, and the target type including an object and a person; identifying target attribute information of personnel in the image based on a personnel attribute identification model; taking the scene type, the target object information and the target attribute information as context information, and determining model input information based on the context information and a user input text; and inputting the model input information into the natural language processing model to enable the natural language processing model to generate scene understanding information corresponding to the image based on the prompt of the context information. Therefore, the scene perception capability is improved, and more accurate scene understanding information is generated, so that the user experience is improved.

The invention discloses an intelligent interactive Agent system based on Langchain and an implementation method, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps of 1, establishing an intelligent interactive Agent system by using an interface and an integrated tool provided by a Langchain framework, 2, managing calling of various tool modules and services through a central scheduling module, and dynamically registering and unbinding the tool modules by using the interface provided by the Langchain framework, and 3, processing user input by using a large language model through a process management module, and establishing an intelligent interactive Agent system. 4, a series of tool modules are integrated through the integrated tool management module, the tool modules comprise a text search tool, a weather query tool and a video information acquisition tool, each tool module works independently or is in linkage with other tool modules, and the tool modules are connected with the integrated tool management module. And 5, managing a user interaction interface through a front-end management module, supporting a user to query and control in a natural language, displaying a processing progress and a processing result in real time, and providing feedback and help information.

The invention discloses a semantic recognition and task scheduling method and system for digital employees, and relates to the field of artificial intelligence. The method comprises the following steps: preprocessing a natural language text input by a user to generate a preprocessed text; performing semantic analysis on the preprocessed text by adopting a semantic analysis model, and extracting text key information; performing intention recognition on the text key information by using a deep learning model to generate an intention recognition result; according to the intention recognition result, mapping the user request to a specific business process based on a preset business rule base, and generating a business classification result; and according to a service classification result, dynamically allocating resources required by task execution by adopting a task scheduling algorithm, and adjusting a task scheduling strategy in real time according to monitoring data. According to the scheme, through accurate semantic recognition, intelligent business process mapping and dynamic task scheduling, digital employees can efficiently understand user intentions and quickly and accurately complete task execution, and the intelligent level and the response capability are improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering system and method based on self-adaptive feedback circulation, and the method comprises the steps: obtaining historical dialogue content in a set time period from a database, and constructing an initial user portrait; the current question of the user is received, and the association degree between the current question and the last round of dialogue is determined in combination with the historical dialogue; performing sentiment analysis on the current questions, and identifying user sentiment tendency and preference; generating a previous round of dialogue satisfaction evaluation in combination with the correlation degree; the intention of the user who asks questions at present is recognized, the intention of the user is classified according to the recognition result, a plurality of intention groups are formed, and each intention group comprises one or more intentions; generating a corresponding initial answer according to each user intention; according to an emotion analysis result, performing error correction on the initial answer in combination with the initial user portrait and the satisfaction evaluation, generating a final answer, and returning the final answer to the user; and updating the historical dialogue content in the database in real time.

The invention is suitable for the field of intelligent customer service, and discloses an optimization method, system and device of an intelligent customer service system and a medium. Performing natural language processing on the dialogue content; according to the personalized characteristics of the user, adjusting the word segmentation spacing and the boundary of the semantic unit, and generating an optimized word segmentation and semantic analysis result; an emotion analysis model is applied to the optimized word segmentation and semantic analysis result, the emotion state of the user is recognized, and an emotion recognition result is obtained; identifying the question type of the user by adopting a classification model; and generating a reply suggestion in combination with the emotional state and the question type of the user. By identifying the personalized characteristics of the user and adjusting the word segmentation spacing and the semantic unit boundary of the dialogue content, not only can the coherence of expressing brief user semantic understanding be improved, but also the accuracy of redundant expression user semantic analysis can be enhanced, so that a more accurate word segmentation and semantic analysis result is generated.

The invention provides an open vocabulary 3D segmentation method based on a three-dimensional Gaussian sputtering technology, and the method comprises the steps: S1, obtaining a multi-view 2D image set, and carrying out the initialization three-dimensional reconstruction; s2, segmenting the multiple views by using SAM to obtain a 2D mask; s3, associating masks of the same instance by using a video tracking segmentation model (SAM2); s4, extracting open vocabulary features of each mask by using a visual language model (CLIP); s5, performing average pooling and dimension reduction on the CLIP feature of the mask of each instance to form a representative open vocabulary feature of the instance; s7, training the model, and supervising the embedding of a low-dimensional embedding feature # imgabs0 #; and S8, comparing the CLIP feature of the query text with the semantic feature of the Gaussian body to obtain the Gaussian body with high correlation, and generating a mask of the query object through rasterization rendering. According to the method, only a multi-view 2D image is needed, and the three-dimensional object and the scene are segmented by combining the three-dimensional Gaussian sputtering technology and the open vocabulary technology, so that the problems of difficulty in 3D data acquisition and high cost are solved.

The invention relates to the technical field of synchronous positioning and mapping, in particular to a three-dimensional mapping method and system. Extracting feature points of the obtained target map image by using an ORB-SLAM system, and performing three-dimensional visual reconstruction based on the feature points to generate an initial sparse point cloud map; fusing the sparse point cloud data of the target map image acquired by the plurality of sensors to obtain dense point cloud data, and processing the obtained dense point cloud data by using a point cloud segmentation algorithm and a target detection algorithm to obtain target information in the dense point cloud data; and performing spatial segmentation and organization on the dense point cloud data according to the target information in the dense point cloud data by using a tree data structure algorithm to obtain a point cloud map with a hierarchical structure. According to the method, the map data is accurate, and the management of the map data can be efficiently realized.

The invention discloses a long text analysis method and device, a storage medium and a terminal. Identifying at least one title in the to-be-analyzed long text, and generating directory information of the to-be-analyzed long text based on each title; dividing the long text to be analyzed into a plurality of text paragraphs according to the titles and the input length requirement of the vector model; determining a target superior directory corresponding to each text paragraph from the directory information, and respectively integrating each text paragraph and the target superior directory corresponding to each text paragraph; and inputting the integrated text paragraphs into a vector model to obtain vector features output by the vector model for the text paragraphs. Due to the fact that the whole document is divided into the text paragraphs through the title of the long text, the text paragraphs are divided more reasonably. And continuously integrating the long text information according to the context information contained in the superior directory, so that the semantic information of the text paragraphs is richer, and the model can more accurately understand and process the text paragraphs on this basis.

The invention discloses an advertisement compliance knowledge question-answering method, device and equipment for foreign enterprises, and the method comprises the steps: obtaining advertisement compliance data of a global data source, carrying out the document structuring and knowledge extraction of the advertisement compliance data, and constructing an advertisement compliance knowledge graph; converting text data in the advertisement compliance knowledge graph into vectors to obtain vector data; semantic analysis is conducted on a query question input by a user, construction is conducted in combination with the vector data, prompt words are obtained, the prompt words are input into a preset large language model, and target answers are obtained; and outputting the target answer based on the advertisement compliance knowledge graph, and visually displaying the target answer in a corresponding answer query graph. And comprehensive and efficient compliance information query and answer services can be provided, so that compliance review requirements of foreign enterprises on advertisements in a multi-language and cross-regional environment can be met.

The invention discloses a method and system for independent application of multi-modal data after fusion and archiving, and relates to the technical field of archive management. The technical key points are as follows: establishing a service management platform, including a user group and an archive group, managing the user group and the archive group, defining an archive as a node by adopting a chain network structure, starting archiving management, generating an archiving package, and performing independent application between nodes, so that a user can quickly retrieve and position the archive; effective interaction between data is realized to a certain extent; state data of an archiving packet is processed through a target decision network, an archiving score is calculated, a corresponding archiving interval is matched according to the archiving score, different calling levels are correspondingly set, different users are endowed with corresponding permissions for management, and the whole system judges the management quality by designing evaluation factors, so that the management efficiency is improved. And the management efficiency is further improved.