403 results about "Usability" patented technology

The invention relates to the technical field of computer aided design, in particular to a text-driven CAD modeling method and system based on a diffusion and visual language model.The method comprises the steps that natural language text description is obtained, and CAD semantic features of the natural language text description are extracted; carrying out geometric standardization on the CAD semantic features by adopting a fine-tuning diffusion model, and generating a CAD view image conforming to engineering specifications; carrying out fusion by adopting a fine-tuned visual language model to generate a parameterized CAD construction sequence; a three-mode alignment mechanism is adopted, and the semantic consistency of the CAD semantic features, the CAD view images and the CAD construction sequences is checked; performing verification and post-processing on the CAD construction sequence, and outputting an executable Python code or STEP file; the CAD modeling method disclosed by the invention performs explicit modeling based on flexible modal description, and has the characteristics of high geometric constraint and high usability.

The invention belongs to the technical field of big language model security, and particularly discloses a defense method and system for big language model cue word attack, a terminal and a medium. Comprising the steps of receiving information content input by a user, and generating a plurality of detection input copies based on a preset rule; inputting the copies into mutually independent detection processes in parallel to obtain a plurality of risk scores; constructing a comprehensive risk score based on the risk score, and determining a risk level of the input content according to the comprehensive risk score; when the risk level falls into a defense triggering interval, executing an active defense strategy, and implementing instruction confusion, semantic dilution and structural isolation processing to generate first output content; when the risk level is below a pass threshold, second output content is generated based on the user original input. According to the method, on the premise that normal interaction experience is not affected, fine-grained, controllable and dynamic safety protection can be carried out on multi-type cue word attacks, and the overall safety and usability of a large language model are improved.

The invention discloses a fault diagnosis method and system for a coil cutting all-in-one machine based on a large language model, and relates to the technical field of equipment fault diagnosis, and the system comprises a table data intelligent processing module, a knowledge base construction module, a diagnosis reasoning module and a user interaction module. The table data intelligent processing module is used for carrying out automatic fault mode labeling and description standardization on a cutting and rolling all-in-one machine fault diagnosis case library provided by a user; the domain knowledge graph construction module is used for integrating the standardized table database and automatically constructing a domain knowledge graph; the diagnosis reasoning module is used for performing fault diagnosis reasoning based on a large language model and a knowledge graph; and the user interaction module provides a multi-modal user interaction interface and supports visual display of fault diagnosis. Therefore, by adopting the fault diagnosis method and system for the cutting and winding all-in-one machine based on the large language model, the intelligent level of fault diagnosis can be improved, the knowledge base construction and expansion cost can be reduced, and the usability and maintainability of the system can be enhanced.

The invention discloses a natural language driven mechanical arm control method, device and equipment, a medium and a product, and relates to the technical field of artificial intelligence and robot crossing. The method comprises the steps that after a natural language instruction and multi-mode perception data are obtained, the natural language instruction is decomposed into semantic embedding vectors, large language model hierarchical semantic analysis is adopted to obtain structured task representation, and then the multi-mode perception data are fused to generate environment representation needed by task execution. And then the structured task representation and the environment representation are converted into a task graph, the task graph is converted into a joint control instruction of the target mechanical arm, and finally the joint control instruction is sent to a joint controller to be executed, so that through multi-level semantic understanding, multi-mode perception fusion and self-adaptive execution control, the joint control accuracy is improved. Accurate understanding and efficient execution of the mechanical arm on complex natural language instructions can be achieved, and the task execution precision, adaptability, flexibility, usability and intelligent level of the mechanical arm in a dynamic environment are improved.

The invention discloses a software and hardware combined data interaction method and system based on a Modbus communication component. According to the method and the system, different types of Modbus slave devices are managed through a unified communication configuration file and a point table configuration file, so that compatible access to heterogeneous devices is realized; through an optimized point table analysis mechanism, efficient and self-adaptive data acquisition of discontinuous register addresses is supported, and fine processing and conversion are performed on different data types; a graphical configuration management tool is provided, and the equipment configuration and point table management process is simplified; and a standardized command model is used, so that unified control and data interaction of an upper-layer application on Modbus equipment are facilitated. According to the invention, the flexibility, usability and data interaction efficiency of Modbus communication can be remarkably improved, the system integration and operation and maintenance cost is reduced, and an efficient and reliable Modbus communication solution is provided for the field of industrial automation.

The invention relates to the technical field of monitoring and data acquisition systems, and discloses an SCADA (supervisory control and data acquisition) system based on a large language model, an implementation method and equipment, and the system comprises a user interaction terminal which receives a natural language instruction input by a user and displays a dynamic user interface; the natural language processing module is used for preliminarily analyzing the natural language instruction to obtain a user intention and key information; the large language model is used for deeply analyzing the user intention and the key information and generating a task description or a task execution plan; the MCP arrangement engine is used for mapping the task description or the task execution plan to a predefined function to obtain executable structured data; and the dynamic UI generator is used for generating a dynamic user interface based on the executable structured data and a preset UI instruction. According to the method, the usability and the data analysis capability of the SCADA system are improved through natural language interaction, and meanwhile, the reliability and the safety of the system are ensured by utilizing an MCP and a predefined function mechanism.

The invention provides an automatic bug fixing method based on a large model sensing dynamic program state. The method comprises the following steps of: 1, running verification: verifying existence of vulnerabilities through running, and extracting non-crash constraints; step 2, obtaining an expected state in a repair positioning step, obtaining the expected state based on non-collapse constraint, specifically obtaining the expected state of a collapse position and obtaining the expected state of a potential repair position; 3, a debugging process in the positioning step is repaired, an interface set is provided for the LLM, a program real state is obtained based on program debugging, and static information is obtained based on a tool set; and 4, summarizing debugging information, and generating and verifying patches. According to the method, an actual running state is obtained through program debugging, and an expected state is deduced based on collapse free constraints. By continuously comparing the two states, the nature of the vulnerability can be deeply understood, so that a more accurate and effective repair patch is generated, and the patch repair accuracy, effectiveness and actual availability are remarkably improved.

The invention provides a humanoid robot data set quality evaluation method and a humanoid robot data set quality evaluation system based on multi-dimensional evaluation, and provides a systematized and standardized evaluation process aiming at the quality problems of the existing robot multi-modal data set in the aspects of normalization, integrity, accuracy, consistency, timeliness, accessibility, availability and the like. According to the method, an automatic tool is combined with manual auditing, the structure, metadata, annotation content, data formats, multi-modal alignment and the like of a data set are comprehensively detected, a deep learning algorithm is introduced to achieve detection of pornographic pictures and sensitive words of visual content, and content safety and compliance are effectively improved. The system can flexibly adjust the weight of each evaluation dimension according to different application scenes, automatically generates a quality evaluation report, and supports continuous monitoring and optimization and improvement. Practical application shows that the method can significantly improve the overall quality of the robot data set, and provides reliable data basis and technical support for intelligent training and application of the robot.

The invention provides a robot task planning method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a natural language instruction of a user; determining a task target based on the natural language instruction and scene context information; constructing a semantic map based on the environment data and the visual identification and relation reasoning result; based on the task target and the semantic map, generating an action sequence through a formalized planning engine; and controlling the robot to execute the action sequence. By adopting the method, the usability, accuracy and reliability of robot task planning can be improved.

The invention relates to the technical field of diffusion model AI application, and discloses an AI aided design workflow method and system based on a diffusion model, and the method comprises the steps: receiving multi-modal data; vectorizing the sketch, and extracting key set features; analyzing the text into structured semantics; parameter constraints are converted into mathematical expressions or boundary conditions; mapping the multi-modal data to a unified space; selecting a generation path with the minimum loss function from the candidate paths; gaussian noise is gradually added into the submerged space, and noise distribution is predicted; injecting the parameter constraint into a denoising process to generate a path; and the optimized latent vector is converted into a vector graph through a decoder, and a geometric topological relation is reserved. The system comprises a unified multi-modal data module, a path generation module and a user interaction module. According to the method, the actual availability of a design result is improved; the design period is shortened, the process is simplified, and human resource investment is reduced.

The invention discloses an intelligent interface service generation method and system based on a multi-large-model collaborative architecture, and the multi-mode large model integrates the advantages of a semantic analysis large model, a code generation large model and a knowledge enhancement large model, and can operate cooperatively. Wherein the large semantic analysis model is responsible for converting a natural language input by a user into a structured demand element of an interface, so that the usability of interface generation is improved; the large code generation model is responsible for generating an interface according to the structured demand elements and continuously optimizing the interface according to the running log and performance data of the interface, so that self-inspection and self-correction of the interface are realized; the knowledge enhancement large model is responsible for generating a knowledge graph according to information such as generation, operation and exception handling of the interface, providing reasoning ability based on the knowledge graph, and realizing intelligent recommendation of the interface.

To improve usability for obtaining content to be arranged in a product in an information processing apparatus for generating data of the product such as a poster.SOLUTION: The first prompt determined by the determining means when the receiving means has received a first target impression is different from the second prompt determined by the determining means when the receiving means has received a second target impression that is different from the first target impression. The information processor is provided with a receiving means that receives a user's designation of a target impression, which is an impression that the product is ultimately required to hold, and a determining means that determines a prompt for the generation AI to generate content to be arranged in the product.SELECTED DRAWING: Figure 2

To improve the usability of application programs. [Solution] A program including a first application program for communicating with a communication device, which causes a computer of an information processing device having an operating system to execute: an acquisition step in which the first application program acquires a password for an access point outside the information processing device and outside the communication device, which is managed by a second application program different from the first application program, via the file sharing function of the operating system called by the second application program; and a first transmission step in which the first application program transmits connection information, including the password acquired via the file sharing function of the operating system, to the communication device.

The invention relates to a dialogue type visual chart generation method and system, belongs to the technical field of application intelligence, and solves the problems of low security and poor usability of a BI system based on a large language model in the prior art. The method comprises the following steps: receiving a visual chart generation demand input by a natural language of a user; enterprise database configuration information related to the demand is extracted from a pre-configured refined business knowledge base; using a large language model to generate SQL query statements meeting requirements according to enterprise database configuration information and general query constraint conditions; executing the SQL query statement in the enterprise database to obtain structured data of a query result; and generating the structured data into a visual chart meeting the requirements by using a large language model. According to the method, automatic adaptation of the large language model and the enterprise database is realized through multi-module collaborative optimization, and the user experience is improved while the safety of the enterprise database is ensured.

The invention discloses a GRU-based vehicle-mounted CAN intrusion detection method and system, a storage medium and a computer system. According to the method, an automatic encoder (AE) is introduced to deepen the understanding of a model on input sequence characteristics, a sliding window is used for selecting batch CAN data to be preprocessed to obtain 13-dimensional time sequence data, and a scalar value within the range of [0, 1] is obtained through processing of the encoder, a GRU, a decoder, a full connection layer and a sigmoid activation function and used for classification of abnormal data. The Conv1D is used as a hidden layer, and compared with two-dimensional convolution, the one-dimensional convolution parameter quantity is smaller, and the calculation is simpler and more convenient. An attack message and a normal message can be completely distinguished, the precision and the accuracy rate reach 100%, and the precision in Fuzz detection is 0.9983; compared with the prior art, the method has high accuracy and reliability in the aspect of intrusion behavior detection, can effectively identify most intrusion events, and can keep a relatively low overall error rate, so that good balance between safety and availability is realized.

The invention discloses a middleware-based cloud rendering method and a middleware system, and relates to the technical field of cloud rendering, and the middleware-based cloud rendering method comprises the following steps: creating an application instance, loading the application instance to a cloud rendering platform system, and starting a three-dimensional rendering task; a three-dimensional rendering task is scheduled and allocated through a resource management and scheduling module of the cloud rendering platform system according to the resource use conditions of the load and the server; receiving a three-dimensional rendering task through middleware, and transmitting three-dimensional rendering data of the three-dimensional rendering task between a client and a Web server through a two-way communication module of the cloud rendering platform system to realize real-time communication; and under the condition that the three-dimensional rendering task is finished, waiting for a next rendering task instruction. According to the method, the resource management and operation and maintenance process in the containerized environment can be effectively optimized, and the expandability, stability and usability of the system are improved.

A data analytics system uses a grid-based data structure to improve the usability of LLMs in the analysis of large data sets, to synthesize information for use in other generative AI contexts, and to improve a user's ability to interface with an LLM. A grid-based data structure is a data structure or database that stores the results of column prompts applied to sources. The grid-based data structure may store the results in a relational manner. For example, a grid-based data structure may have rows that correspond to sources (e.g., documents, files, or databases) and columns that correspond to prompts. Each cell of the grid-based data structure stores the output of the column prompt applied to a source using an LLM. Thus, each column prompt may be systematically applied to each source to generate information based on the sources in an organized way.

The embodiment of the invention provides an address matching method and related equipment, and belongs to the technical field of natural language processing. The method comprises the following steps: receiving an original address input by a user; correcting and optimizing the original address through the fine-tuned large language model to obtain standardized output; performing address matching on the standardized output based on the candidate address data through a distributed search engine to obtain an address matching result, the address matching result comprising a plurality of standard addresses and a plurality of standard address codes; and analyzing the address similarity between the original address and the standard address, adding the standard address of which the address similarity is greater than or equal to a completely consistent threshold value into a target address list, and returning the target address list to the user equipment. According to the embodiment of the invention, automatic semantic error correction and search engine matching can be realized, the site selection accuracy, the system usability and the user experience are improved, and the service efficiency and the decision precision are improved.

The invention discloses a sensor calibration parameter management system, method and device, a computer readable storage medium and a computer program product, and the system comprises a calibration parameter loading module which is used for reading and analyzing a sensor calibration parameter file to obtain a sensor calibration parameter; the calibration parameter registration module is used for registering the sensor calibration parameters into a predefined calibration parameter storage structure; and the external service module is used for externally providing sensor calibration parameter services based on a predefined calibration parameter storage structure, and the sensor calibration parameter services comprise at least one of a calibration parameter query service and a modification service. According to the method, the external parameter file of the vehicle end is read at a time and registered into the predefined storage structure, dynamic and efficient management of the external parameter file is achieved, the situation that the same parameter file needs to be loaded to multiple modules or processes is avoided, and development usability and efficiency are improved. In addition, external parameter addition, deletion, modification and checking functions are provided for the outside, so that downstream external parameter use is facilitated.

The invention provides an enterprise-oriented MCP-combined AI service unified management method and system in the technical field of enterprise information technology management and artificial intelligence application. The method comprises the steps that S1, a server registers all models as AI services in a unified mode; s2, the AI gateway obtains an AI task request sent by an employee terminal to perform identity authentication, and matches authority information; step S3, interacting with the server through the load balancing and security gateway, querying AI services of which authority information can be called, and pushing the AI services to a visual interface of an employee terminal; s4, arranging each AI service by the visual interface based on the triggered drag signal and click signal to generate an AI task, and sending the AI task to a load balancing and security gateway for execution; s5, in the AI task execution process, an abnormal event is captured to give an alarm; and S6, recording an execution log in real time. The method has the advantages that the centralization, the safety and the usability of AI service management are greatly improved.

The invention discloses an OCR (Optical Character Recognition) result correction method and device, equipment and a computer storage medium. The correction method comprises the following steps: acquiring a to-be-corrected text after OCR (Optical Character Recognition), wherein the to-be-corrected text comprises one of messy code characters, character confusion errors or grammar errors caused by OCR errors; constructing a semantic correction instruction template, wherein the semantic correction instruction template comprises a task definition, a rule definition and a format constraint target; the to-be-corrected text and the semantic correction instruction template are input into a large language model, a corrected text result is output through the large language model, and the large language model is a pre-training language model. According to the method and the device, messy code characters, character confusion errors or grammar errors caused by OCR recognition errors can be accurately corrected, so that the accuracy and availability of texts are effectively improved, and high-quality basic data are provided for subsequent text processing; the method can adapt to various documents in different formats and multi-language mixed text scenes, the maintenance cost is saved, and meanwhile the correction efficiency is improved.

The invention provides an automatic classification and analysis method and system for network equipment configuration files, and belongs to the technical field of network file analys.The method comprises the steps that an original configuration file is obtained from a heterogeneous data source; converting the obtained original configuration into an analysis-friendly standardized format; analyzing the converted standardized format, generating an analysis file, and performing multi-dimensional quality monitoring on the analysis file; and converting the monitored analysis result into a format available for a downstream system for subsequent data analysis and integration. According to the method, through a multi-layer template extraction mechanism and nested rule definition, the semantic hierarchical structure of the configuration file can be accurately restored, the common problems of structure flattening and semantic loss in a traditional analysis method are avoided, and the accuracy and availability of an analysis result are improved; by means of a template forest architecture and an automatic classification algorithm, equipment configurations of different manufacturers and models are dynamically identified and adapted, the universality and expansibility are improved, and the integration complexity in a multi-manufacturer environment is reduced.

The invention belongs to the technical field of code generation, particularly provides a large model tool calling method and system based on demand awareness, and aims to solve the technical problems that an existing code generation system lacks an active tool calling capability and cannot automatically select a proper tool according to information loss. According to the method, an information demand identification model, a tool mapping knowledge graph and a self-adaptive scheduling strategy are constructed, so that a large language model can actively identify an information gap in the code analysis and generation process, and an external tool is automatically selected and called to obtain required context information. The core of the system comprises four modules, namely a demand awareness engine, a tool selection decision maker, a multi-modal information fusion device and an iterative optimization feedback mechanism. By introducing an information missing detection algorithm based on an attention mechanism and a hierarchical tool calling planning strategy, the system can realize intelligent processing from a single file to a warehouse-level code task, and the accuracy and availability of code generation are remarkably improved.

The invention discloses a code change generation method based on a multi-agent proxy library migration scene, which can accurately locate all code action ranges related to library migration in a file range by performing structured analysis on a source code file and combining source library import information and code feature recognition, and can improve the code change generation efficiency. The problems of omission and misjudgment caused by manual analysis or simple rule matching are avoided, and the integrity and accuracy of migration site recognition are improved. According to the method, the corresponding migration plan is generated by combining key information of the source library and the target library aiming at the identified action range to be migrated, so that the code change process is not limited to simple interface replacement any more, targeted adjustment can be performed according to a specific use scene, and the consistency of migrated codes in terms of semantics and behaviors is ensured. According to the method, through a collaborative code change generation and verification mechanism, the code change related to library migration is automatically generated and evaluated, and the reliability and availability of a generation result are improved while manual intervention is reduced.

The invention discloses a double-engine distributed performance testing method and system. The method comprises a test node service, a Kafka message queue, a data processing service, a data storage service, a registration center and a platform management module, supports JMeter and Locust double test engines, adopts a distributed architecture and containerized deployment, and realizes scheduling of test tasks, real-time data processing, log acquisition and unified management. The method is suitable for enterprise-level performance test scenes, and has high expansibility, usability and real-time performance.

The invention discloses a database script logic conversion system and method, and relates to the technical field of database scripts. The method comprises the steps that the database scene configuration module defines configuration layer keywords, the distributed scheduling module designs script execution keywords, the weak type semantic mapping module establishes a weak type automatic conversion rule base, and the automatic testing module establishes a script performance monitoring model. The problems that the script deployment process is complex, version management is difficult, and normalization is poor are solved, cross-platform and cross-application maintenance unified management is achieved, the deployment configuration mode can be remotely maintained and managed, the mandatory requirements of historical version backtracking and metadata specification are met, and the method can be combined with other script languages to be embedded, executed and used. Complete design from demand analysis to function landing is achieved, and finally a script system integrating multi-environment adaptation, multi-language integration, usability and expansibility is formed.

The invention provides a macro-cell layout evaluation optimization method based on a large language model, and belongs to the field of electronic design automation. According to the method, a large language model is introduced to assist layout generation, a current initial layout is improved in combination with connection weight analysis, on the other hand, an improved result is judged and continuously iterated according to natural language feedback of an experience manual or an expert, and closed-loop optimization is formed; carrying out weight quantization on direct and indirect signal paths among macro units, constructing a logic association strength matrix, and realizing accurate identification and priority ranking of key time sequence paths; the availability score of each candidate layout scheme is calculated through a layout availability scoring module, dynamic measurement is carried out on surrounding standard unit resources, the resource distribution state is evaluated in real time, the position of a macro unit is actively adjusted, and resource congestion or waste is avoided; the method is deeply integrated with an open source EDA tool, the dependence on a commercial tool is reduced, the tool chain deployment and iteration period is shortened, and the transportability and usability of the method are guaranteed.

The present disclosure provides an optimization method, device and medium for distributed labeling of a computation graph, relating to the technical field of computers, and in particular to artificial intelligence, deep learning and distributed training technology. The specific implementation scheme is as follows: obtaining a computation graph of a distributed running model and an optimization paradigm of a target optimization strategy; identifying a corresponding to-be-optimized computation subgraph in the computation graph according to the optimization paradigm; adjusting the to-be-optimized computation subgraph according to the optimization paradigm; and adjusting tensor distributed labeling in the computation graph according to original tensor distributed labeling in the computation graph and operator running logic. The optimization paradigm is used to automatically identify and convert the computation subgraph of the multiple optimization strategies integrated into the distributed networking, ensuring the ease of use of automatic parallelism, reducing the difficulty and cost of using the optimization strategy, enhancing the adaptability and flexibility of automatic parallelism, and improving the efficiency and performance of distributed training.

This application relates to a method, system, device, and medium for fine-tuning a multimodal large language model, belonging to the interdisciplinary field of artificial intelligence and computer vision. The fine-tuning method includes: acquiring original business scene images and preprocessing them to obtain preprocessed images; using annotation tools to annotate bounding box coordinates and define semantic labels for entity targets in the preprocessed images, outputting a structured annotation file; constructing a training sample set including multi-turn text-image dialogue based on the preprocessed images and the structured annotation file; loading the pre-trained multimodal large language model, configuring low-rank matrix factorization parameters, and generating a fine-tuning instruction set; inputting the training sample set into the pre-trained multimodal large language model, performing joint training operations based on the fine-tuning instruction set, and outputting the fine-tuned multimodal large language model. This application improves the recognition accuracy, interaction capabilities, and system usability of visual question answering systems in practical application scenarios.

The application is a federated learning out-of-distribution generalization method based on causal inference, and belongs to the technical field of edge intelligent devices. The method comprises the following steps: constructing a federated learning framework to train a task model, in each round of training, a server sends a global task model parameter and a global prototype library to a client participating in training; the client trains a local model by using a local sample, obtains individualized features and global features of the sample by a decoupling representation learning module, and obtains causal features after removing pseudo-association by a mixed factor removing training module; the client sends updated model parameters and various local prototypes to the server; the server updates the global prototype library and the global model parameter by using a hierarchical update strategy module; the training is repeated until a convergence condition is met, and the obtained global task model is deployed to a new device for use. The application improves the generalization ability of the task model when facing invisible distribution data, and ensures the usability and applicability of the task model.