43 results about "Scenario" patented technology

The invention relates to the technical field of agent construction, in particular to an agent construction method based on a large language model, and the method comprises the steps: S1, configuring a scenarized execution specification which comprises the steps: receiving constraint information of an execution component in a scene according to a preset scene; converting the constraint information into a structured cue language which can be understood by a large language model, and generating a standard verification rule base which can be analyzed by an execution component; and storing the structured prompt words and the standard verification rule base. According to the method, by configuring the scenarized execution specification, the constraint information of the execution component is converted into a structured cue which can be understood by a large language model and a specification verification rule base which can be analyzed by the execution component, and the problems of non-explicit constraint such as inconsistent parameter naming and non-standardized data type are solved.

[Problem] To support automation of a test. [Solution] This information processing system is characterized by including: a scenario generation unit for generating a scenario of a test by giving a first prompt instructing generation of the scenario to a large-scale language model on the basis of a test case describing an outline of the test; and a script generation unit for generating a script by giving a second prompt to the large-scale language model, the second prompt instructing generation of the script that causes a computer to execute the test on the basis of the scenario.

The invention discloses a scenario generation method and system based on a large language model, and relates to the technical field of computers. By designing the scenario generation system comprising a scenario generation subsystem, a scenario setting subsystem, a scenario deduction subsystem and a scenario management subsystem, based on a large language model, a process of quickly generating a simulation scenario in a manner of retrieval enhancement generation or combat scenario text conversion is realized; moreover, the system has the functions of scenario editing, scenario deduction, scenario management and the like, achieves a workflow closed loop of intelligent generation, editing management, deduction inspection and modular storage, and improves the scenario generation efficiency and the intelligent level.

The invention discloses a simulation scene generation method and device based on a large language model, and the method comprises the steps: S1, constructing a fine tuning model based on a preset first large language model and a second large language model; s2, acquiring a user scene description text; s3, processing the user scene description text by utilizing the fine tuning model to obtain an MSDL simulation text set; and S4, performing simulation evaluation processing on the MSDL simulation text set to obtain an optimal evaluation value and an optimal MSDL text. According to the method, deep reasoning can be carried out on a high-level natural language intention in combination with domain knowledge, and a standardized and executable optimal simulation scene and a corresponding evaluation value are automatically generated.

The invention relates to a computer-implemented method for generating test scenarios for testing a technical system, comprising the steps: a. providing a search space (10) (S1); wherein the search space (10) is defined by an operating design area (20), ODD; wherein the operating design area (20) comprises a plurality of elements (22); wherein the technical system is operated in the operating design area (20); b. generating at least one first test scenario based on the search space (10) (S2); wherein the at least one first test scenario comprises at least one first element of the plurality of elements of the operating design area (20), ODD; wherein the at least one first test scenario examines at least one corresponding dimension (12) of the search space (10); c. generating at least one associated first test result by executing the at least one first test scenario (S3); d.Evaluating the at least one associated first test result (S4); e. Generating at least one further test scenario based on the search space (10) taking the evaluation into account (S5); f. Repeating steps c. to d. with the at least one further test scenario and e. until the search space (10) is exhausted (S6); and g. Providing the at least one first test scenario, the at least one further test scenario and / or the corresponding test results (S7). Furthermore, the invention relates to a technical system and a corresponding computer program product.

This application provides an AR application method and system based on CATIA software modeling. The method utilizes the secondary development environment of Navisworks software to convert the CATProduct file (CATIA's unique output format) into an NWC file. Then, Navisworks' secondary development is used to extract the geometric, material, and attribute data from the NWC file model, outputting the analytical results, which are then loaded into the AR application. This application uses a low-cost and lossless analytical method to convert CATIA data into a data type recognizable by AR applications, helping AEC-related enterprises reduce costs and increase efficiency, expand AR application scenarios, and improve user satisfaction with model AR applications.

This invention provides an artificial intelligence-based software refactoring method. The method includes: acquiring the current code data and user data of the software to be refactored; determining a target refactoring strategy based on the current code data and user data; generating refactoring code data corresponding to the target refactoring strategy based on a large language model; and refactoring the software based on the refactoring code data to obtain the target software. This invention analyzes code data and user data, considering both the code problems of the software to be refactored and user business expectations, to generate a target refactoring strategy that balances technical optimization and business needs. This avoids the problem of the separation between technical optimization and business goals in traditional methods, ensuring that the refactored code accurately matches the user's actual business scenario while optimizing technical indicators, reducing the cost of secondary modifications due to misunderstandings of requirements, and thus improving the accuracy and efficiency of software refactoring.

A system and method for validating expectations based on a set of specifications is provided. The invention comprises receiving, a set of specifications, a set of expectations, and a set of requests. Further a set of scenarios are generated based on the set of specifications. The set of expectations are compared with the set of scenarios. A set of valid and invalid expectations from the set of expectations are identified based on the comparison and stored. The received requests are services based on the set of scenarios and the set of valid expectations. While servicing, the set of specifications are monitored to identify any changes and the set of scenarios are dynamically updated. The set of expectations are compared again with the updated scenarios, to identify updated set of valid expectations. Further set of new requests are serviced based on updated set of valid expectations and updated scenarios.

This application provides a method and system for intelligent interaction state tracking and reasoning applied to multi-turn dialogues, relating to the field of natural language processing technology. First, it retrieves the initial interaction state of the multi-turn dialogue, covering the user's initial needs, the system's initial response location, and dialogue scenario-related content. Then, it captures the state evolution triggering conditions for each round of interaction. Next, it constructs a bidirectional correlation evolution model based on the initial interaction state and triggering conditions to characterize the correlation and influence between preceding and subsequent interaction states. The current interaction state is derived through this model, integrating the state evolution results of all rounds and the bidirectional correlation influence. Finally, a dynamic guidance strategy is generated based on the current interaction state, adapting to subsequent interaction trends and adjusting the preceding correlation logic in reverse. This invention improves the intelligence and coherence of multi-turn dialogues.

This application discloses a security protection method, device, storage medium, and electronic device. The method includes: analyzing the source code of the target program by combining the business semantics of the target program, the kernel semantics of system calls, and the scenario semantics of program execution; generating a system call whitelist; forming a three-dimensional semantic constraint rule set based on the linkage relationship between business semantics, kernel semantics, and scenario semantics; binding the protection strategy to the running process of the target program to intercept system calls initiated by the process that are not on the system call whitelist; collecting the runtime business semantics, kernel semantics, and scenario semantics of system calls within the system call whitelist to generate a system call sequence; determining deviation items based on the system call sequence and the three-dimensional semantic constraint rule set; dynamically quantifying and evaluating the deviation items to obtain a risk level; and implementing corresponding permission control or blocking operations on the process according to the risk level to achieve runtime security protection for the target program.

The invention relates to the field of system architecture design, analysis and tradeoff, in particular to a scene-driven model-based system architecture tradeoff method and device. The method comprises the following steps: step 1, constructing a scene evaluation table; 2, establishing a system efficiency tree; 3, carrying out system architecture modeling based on the model; and 4, carrying out model-based system architecture multi-quality characteristic analysis, and identifying sensitive points and risk points. Modifying system architecture elements based on the sensitive points to generate a new architecture, and repeating the steps 3-4; and 5, performing quantitative evaluation and visual analysis on the alternative system architecture. According to the method, based on user requirements and case scenes, the interactive influence between field characteristics of the system and multi-quality characteristics of the system is considered, quantitative evaluation and visual analysis are combined, the confidence coefficient of system alternative architecture tradeoff is improved, the traditional influence on architecture tradeoff based on engineering experience, non-technical factors and the like is reduced, and the subjectivity of an evaluation result is reduced.

[Problem] To allow a user to easily understand information about an RPA scenario. [Solution] When a check / comment instruction (instruction for natural language output) for a scenario Sm (automation scenario) is received from a user (step S201), an improvement scenario Smr output from an LLM 12 according to the check / comment instruction is displayed on an improvement scenario display part 85, and a text (natural language output) indicating an improvement point of the improvement scenario Smr is displayed on an improvement point explanation display part 84. Therefore, the user can easily understand the information about the scenario Sm of the RPA by confirming the displayed text (natural language output).

This invention discloses a cross-platform model transfer method combining CATIA and MIDAS. The method generates a skeleton model containing node, element, cross-section, and material information based on an engineering design scheme in CATIA. It then uses an EKL script to automatically extract model data and store it in a standardized skeleton information table (node ​​information includes coordinates and spacing; element / cross-section / material information is converted to MIDAS-compatible MCT format strings). A Python script then reads the table data to generate an MCT file, which is finally imported into MIDAS to complete model construction and structural analysis. For scenarios involving multiple scheme comparisons, unique labels are added to the skeleton models of different design schemes. After unifying boundary conditions and load cases, the optimal scheme is quickly selected based on the calculation and analysis results.

This invention provides an LLM-driven natural language AI-powered automatic operation and maintenance (O&M) execution method and apparatus. The method includes acquiring fuzzy instructions, preprocessing the fuzzy instructions, inputting the preprocessed instructions into an LLM model to generate an O&M script, and automatically validating the O&M script to determine its execution risk level. If the execution risk level is low, an API protocol is selected based on the target O&M architecture type. The O&M script is converted into O&M instructions according to the API protocol type, and the O&M instructions are sent to the target O&M architecture, receiving the execution result returned by the target O&M architecture. The automatically generated O&M script incorporates a validation mechanism, performing multiple checks on the script's syntax, logic, permissions, and high-risk commands, thereby reducing O&M failure rates and fault response delays. This method, while improving O&M security and reducing O&M complexity, supports O&M operations across various architectures and achieves cross-scenario adaptation.

The invention relates to the technical field of artificial intelligence, and can be applied to software code reconstruction scenes in financial and medical scenes. The invention provides a software reconstruction method, system and device and a storage medium, and the method comprises the steps: carrying out the context analysis of a target code entity through a planning agent, and generating a structured reconstruction plan based on an analysis result; generating a reconstructed code by the generation agent according to the reconstruction plan; the compilation agent performs compilation verification on the reconstructed code, and when compilation fails, compilation error information is fed back to the generation agent for iterative correction; and the test agent performs function test on the reconstructed code after successful compiling, and feeds back test failure information to the generation agent for iterative correction when the test fails. The method can be applied to financial / medical software code reconstruction scenes, and the success rate of software reconstruction is improved through multi-agent cooperation and double feedback closed loops of compiling and testing.

The invention discloses a dialogue generation method, device and equipment and a computer readable storage medium, and is applied to the technical field of computers, and the method comprises the steps: extracting a quintuple from the context of a current dialogue, and the quintuple comprises a subject, a relationship, an object, a timestamp and a context; the relationship can be dynamically adjusted; based on the logic consistency constraint, performing dynamic planning on the quintuple by using a progressive thinking chain extension algorithm to obtain a thinking chain; and inputting the thinking chain and the context of the current dialogue into the generative model to obtain an output dialogue. The context and the timestamp in the quintuple can accurately identify the scene of the current dialogue, and meanwhile, the weight of the relationship in the dialogue can be dynamically adjusted along with the content, so that the method can adapt to different field scenes and multiple rounds of dialogues, and the adaptivity and the flexibility for various professional scenes are improved; moreover, the thinking chain is generated based on the quintuple and the progressive thinking chain extension algorithm, the specific logic mode of the scene can be captured, and the accuracy and logic consistency of dialogue generation are enhanced.

The embodiments of the present disclosure disclose a large language model application method, device, equipment, medium and product, which comprises: obtaining a real-time input task and a preset large language model; determining a scoring result corresponding to the real-time input task based on a candidate action set of the real-time input task; if the scoring result meets a preset threshold condition, determining a target adjustment parameter corresponding to the real-time input task in an adjustment parameter set based on the task type of the real-time input task; determining a target model parameter based on the target adjustment parameter and the original parameter of the preset large language model; taking the target model parameter as the model parameter of the preset large language model to process the real-time input task by using the preset large language model to determine the processing result of the real-time input task. The technical scheme realizes low-cost, high-precision model capability directional reinforcement and self-adaptive optimization on the premise of keeping the original parameters of the preset large language model unchanged, and realizes efficient identification in multiple task scenarios.

A system, method, and device for storing a scenario version as a base version for a hypercube are provided. The method includes (i) obtaining a request to store a scenario version of a hypercube as a base version; (ii) obtaining a set of transactions corresponding to hypercube updates for the scenario version, and (iii) writing the set of transactions to the base version.

Embodiments of the present application relate to a method, a computing device and a storage medium for large language model-oriented multi-tool invocation. The method comprises: receiving an input request of a user, parsing via a predetermined large language model to obtain an original intention of the user; based on the original intention of the user, determining a candidate tool list from a pre-registered tool set via the predetermined large language model, determining at least one target tool from the candidate tool list, generating a tool invocation scheme about the original intention of the user via the predetermined large language model, the tool invocation scheme being associated with the at least one target tool; based on the tool invocation scheme, running the at least one target tool in a predetermined system environment; inputting a running result of the at least one target tool into the predetermined large language model, and determining response information about the input request. Thus, the business scenarios of a large number of heterogeneous APIs can be adapted, the risk of large model context overflow is reduced, and the accuracy and stability of the output result are improved.

The application discloses a sched_ext scheduling mechanism method based on a Shenwei platform, which comprises the following steps: analyzing the architecture and operation principle of a sched_ext programmable scheduling framework to clarify the core mechanism of dynamic loading and replacement of scheduling strategies; analyzing the technical features of the existing scheduling mechanism of the Shenwei platform, combining the advantages of the sched_ext in strategy flexibility and expansibility, and determining the feasibility of the fusion of the two; carrying out kernel adaptation and interface implementation of the sched_ext on the Shenwei platform, completing the deployment and function support of the programmable scheduling framework on the target architecture; and evaluating the performance of the scheduling mechanism under typical load and extreme scenarios through systematic testing and multi-level function verification. The application provides programmable capability of a user-mode scheduler which can be customized, and improves the resource utilization rate and task execution efficiency of the Shenwei platform in complex computing scenarios.

The application discloses a kind of problem solving system and method based on multi-agent cooperation, the system is aimed at the low reliability, inefficiency, lack of error detection mechanism and other defects existing in complex scientific problem reasoning of existing large language model, proposes "decomposition-verification-conquest" architecture, includes four core agents of planner, verifier, aggregator, evaluator.Planner agent dynamically selects problem processing strategy, verifier agent executes multi-level scientific rationality verification, aggregator agent realizes scheme merging driven by dependency relationship, evaluator agent judges problem simplification degree.Experiments show that the accuracy of the system on AIME, GPQA-Diamond and other data sets is significantly improved, has high reliability, explainability and adaptability, and can be widely used in high-risk scientific reasoning scenarios.

The invention discloses an application scene-driven high-quality data set construction process, and belongs to the technical field of data engineering. The process is formed by sequentially connecting seven core modules of an application scene development module, and full-process closed-loop management from scene definition to data set output is achieved. According to the method, the problems of indefinite target, unstable quality, disjunction with a business scene and the like in the traditional data set construction process are effectively solved, and the practicability, accuracy and engineering application value of the data set are remarkably improved.

The invention discloses a Shenwei platform-based schemart scheduling mechanism method, which comprises the following steps of: analyzing the architecture and operation principle of a schemart programmable scheduling framework so as to define a core mechanism of a dynamic loading and replacement scheduling strategy of the schemart programmable scheduling framework; the technical characteristics of an existing scheduling mechanism of the SW platform are analyzed, and the feasibility of fusion of the SW platform and the SW platform is determined by combining the advantages of schemart in the aspects of strategy flexibility and expansibility; kernel adaptation and interface implementation of schemart on the SW platform are carried out, and deployment and function support of the programmable scheduling framework on the target architecture are completed; and through systematic testing and multi-level function verification, the performance of the scheduling mechanism in typical load and extreme scenes is evaluated. According to the method, the programmable capability of the user state scheduler capable of being customized is provided, and the resource utilization rate and the task execution efficiency of the SW platform in a complex calculation scene are improved.

The invention discloses an SMV code automatic generation method and device, equipment and a medium, and relates to the technical field of software engineering. The method comprises the following steps: receiving a natural language demand text and extracting a standard fragment from the natural language demand text; on the basis of the standard fragments, prompt information used for guiding the large language model to output is constructed, and SMV candidate codes are generated according to the prompt information through the large language model; and sequentially carrying out primary structure compliance check and secondary formal verification on the SMV candidate code, structuring the error information into a repair instruction to carry out iterative repair on the SMV candidate code, and outputting a target SMV code until the SMV candidate code meets a preset stop condition. By means of the technical scheme, the natural language description requirement is converted into the SMV code capable of being recognized by the nuXmv tool, the manual correction cost and the technical threshold of formalized modeling are greatly reduced, and the usability and practicability of an automatic tool in an actual scene are improved.

This invention discloses a unified intermediate representation data model and construction method for hardware design, belonging to the field of electronic design automation. The model includes: a unified representation base class; structural relationship graph representation and temporal behavior representation, describing the structural composition and runtime behavior of the design, respectively; formal semantic consistency constraints to ensure semantic consistency between structure and behavior; and a semantic association architecture to establish bidirectional semantic mapping between various scenario representations such as HDL code, schematics, and waveforms and the unified representation base class. The construction method based on this model includes an incremental synchronization process, achieving automatic synchronization after any representation modification through change capture, cross-representation mapping indexing, and change propagation rules. This invention also provides an AI-assisted design method based on this model, improving AI interaction efficiency and the automation level of design verification through multi-dimensional context construction, syntax stripping, incremental interaction protocols, and automatic closed-loop verification.

Described herein are systems and methods for providing wager recommendations and wager comparisons based on a player prompt by preparing the input context of a large language model. A system can maintain an indexed data structure comprising wager scenarios for live events, each associated with a respective odds value. The system can receive, from a client device during a session identifying a first wager scenario, a prompt requesting a comparison to the first wager scenario. An input context can be generated using the prompt, data of the first wager scenario, and second wager scenarios retrieved by searching multiple indexes of the data structure. Using the language model and the input context, the system can generate an output message indicating the comparison between the first and second wager scenarios, and provide the output message to the client device in response to the prompt.

This invention discloses a code incremental injection method, apparatus, electronic device, and storage medium, relating to the field of computer software development technology. The method includes: obtaining a syntax tree representation of target code; receiving a functional incremental description, wherein the functional incremental description defines the behavioral characteristics of the function to be injected; determining the injection position in the syntax tree representation based on the behavioral characteristics; generating a structured code fragment based on the code context corresponding to the injection position and the code template associated with the functional incremental description; integrating the structured code fragment into the injection position to obtain an updated syntax tree representation; and outputting the updated target code based on the updated syntax tree representation. This invention is applicable to any software development scenario requiring automated incremental expansion of existing source code, and can solve the problem that existing technologies cannot automatically determine appropriate positions in the source code and inject adaptable code according to functional requirements while maintaining the integrity of existing handwritten code.

The invention discloses an interpretability-based ability fusion and recovery method after large language model training, and belongs to the technical field of artificial intelligence, and the method comprises the steps: carrying out the interpretability analysis of a plurality of candidate large language models, and forming an ability missing sample library; counting the missing degree of each model on each capability dimension, and generating a capability feature vector; selecting at least one complementary model which is complementary with the basic model in capability and a negative sample model according to a target scene requirement; performing parameter fusion on the basic model, the complementary model and the negative sample model by adopting a geometric interpolation algorithm, wherein the weight of the negative sample model is lower than that of the complementary model; and carrying out standardized evaluation on the fused model, when an evaluation result shows that a certain capability dimension is degraded, positioning a degraded sample according to interpretability analysis, updating a capability feature vector, and carrying out iterative optimization until the comprehensive capability meets the requirement. According to the method, multi-capability directional enhancement and overall performance balance are realized, and capability collapse caused by post-training of a large language model is avoided.

The invention discloses a Verilog code time sequence violation debugging method based on a large language model, and the method comprises the steps: firstly collecting time sequence violation debugging knowledge and a seed Verilog code, and obtaining a repair demonstration pair; then, time sequence violation is subjected to sub-scene classification, a structured time sequence violation knowledge base is constructed, Verilog codes are converted into abstract syntax trees for representation, and a signal time sequence dependency graph is constructed; and then obtaining a code execution track corresponding to a time sequence violation path based on a track analysis algorithm, obtaining violation reason description through a large language model, obtaining a specific violation sub-scene based on a scene judgment algorithm, and retrieving a targeted repair strategy. And finally, pre-defining a plurality of prompt template structures for different types of violations, and performing code debugging through LLM according to the retrieved targeted repair strategy. According to the method, the condition that violation information of a time sequence report is separated from code design is broken, and the debugging efficiency of time sequence violation is improved.