323 results about "Canonicalization" patented technology

The invention belongs to the technical field of knowledge retrieval, and discloses a multi-modal large model-based dynamic vector knowledge base construction and retrieval method, which comprises the following steps of: obtaining a multi-source heterogeneous modal data set, and carrying out preprocessing and modal standardization processing on the multi-source heterogeneous modal data set to obtain a standardized multi-modal data set; performing feature extraction and semantic vector representation generation by using the pre-trained multi-modal large model, and constructing a multi-modal knowledge vector set; semantic association analysis and hierarchical clustering are carried out on the multi-modal knowledge vector set, and a structured vector knowledge base is constructed; performing semantic similarity calculation and relation modeling on the vector knowledge base to form a vector relation network; intention analysis and vector representation are performed based on mixed modal query information input by a user, and efficient similarity retrieval is realized in combination with a vector relation network; dynamic optimization is carried out through user feedback, personalized retrieval result adjustment is achieved, and the problem of limitation of a traditional retrieval system during multi-modal data processing is effectively solved.

The invention discloses an industrial innovation knowledge graph dynamic construction method based on a large language model, and belongs to the technical field of data processing. According to the method, multi-source heterogeneous data are integrated, cleaning, format conversion and standardization processing are performed, entities, relationships and attributes are automatically extracted by adopting a large model driven zero sample extraction technology, a high-precision triple library is constructed by combining entity embedding alignment and context anaphora resolution, and a knowledge graph is generated based on a graph database. According to the method, incremental data are captured in real time through a dynamic sensing layer, graph dynamic updating is achieved through an atomization updating mechanism, and data timeliness is guaranteed in combination with timestamps and multi-source verification. According to the method, the knowledge graph construction efficiency and coverage rate are improved, the defects that a traditional method is high in manual dependency degree, long-tail knowledge is missing, updating lags and the like are effectively relieved, and accurate support is provided for technical innovation decision making.

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The present invention provides a privacy-enhanced structured data simulation and generation method and system. The method comprises: step 1, a data conversion phase: performing normalization preprocessing on data; step 2, a probabilistic graphical model construction phase: on the basis of a Bayesian form, constructing the posterior distribution for variational inference from the data having undergone normalization preprocessing in step 1, obtaining an association relationship between the features of structured data by using a Stein variational gradient descent method, and when introducing differential privacy noise, using a Monte Carlo estimation algorithm to automatically obtain the amount of noise that needs to be added for each update step; and step 3: a data generation phase: using the association relationship obtained in step 2 as a metric set to generate more accurate simulation data than real data. The beneficial effect of the present invention is that: the method of the present invention avoids gradient clipping when applying DP-SGD, thereby avoiding the selection of clipping parameters and reducing the adverse effects of gradient clipping on an inference process.

Various embodiments relate to normalizing raw data by mapping the raw data to a computer-readable tag. A computer-readable tag may be an identifier that at least partially represents a category (e.g., a department) and / or the raw data itself. In response to receiving the raw data, some embodiments perform the mapping by, for example, performing natural language processing (NLP) on each particular department's raw data to associate natural language words in the raw data to its corresponding computer-readable tag and then populating, at a data structure that includes the computer-readable tag, an entry with data (representing the raw data) in a standardized format. In this way, regardless of whether different sets of raw data come from disparate sources that have diverse formats, protocols, or structures relative to each other, the normalized data and standardized form makes the data compatible.

The invention discloses a large model scheduling multi-agent power grid fault coping strategy knowledge graph extension method and system, and the method comprises the steps: carrying out the modeling of task distribution as a mixed integer programming problem, and achieving the solving through a relaxation-correction algorithm; extracting and normalizing a core task of knowledge, and outputting structured data; summarizing the obtained text segments, and prompting a large language model to retain key entities, relationships and domain-specific terms; using an LLM-based named entity recognition technology, combining with prompt and dictionary / ontology filtering in the power dispatching field, recognizing related entities in a text, and normalizing the related entities into a standard form in a knowledge graph; detecting logic contradictions between the newly extracted triples and existing relationships in the knowledge graph, and classifying and solving the contradictions by utilizing debate prompts based on LLM (Logistics Library Model); summarizing the plurality of verification signals, and calculating the global confidence, definition and correlation score of each triple;

A data collection unit (11) collects information related to an object and sets the information as collected data (101). A data normalization unit (12) adds, as an input type, the type of data to the collected data (101) collected by the data collection unit (11), and thereby generates training data (102). A training unit (13) trains a machine learning model (103), which is a large-scale language model, on the basis of the training data (102) generated by the data normalization unit (12). By using the trained machine learning model (103) and a portion of visualization data that includes configuration information, state information, and evaluation information of the object and information indicating relationships of the object with other related objects, an inference unit (14) infers the information in the visualization data other than said portion. A correction unit (15) corrects the information in the visualization data inferred by the inference unit (14), on the basis of the collected data (101).

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

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 data processing method for industrial equipment for industrial control Internet of Things, which comprises the following steps: acquiring industrial equipment sensor data, and carrying out standardization processing to obtain a standardized matrix; initializing a sparse dictionary learning model, setting an initial dictionary structure and a sparse coefficient, and executing preliminary sparse decomposition; constructing a flower pollination algorithm search space, optimizing a layering number, a regular weight and a switching threshold, and updating dictionary parameters; calculating an optimal sparse coefficient matrix, and performing sparse reconstruction processing; and carrying out data denoising, compression and feature extraction, and outputting a final processing result. According to the method, the flower pollination optimization algorithm and the sparse representation dictionary learning model are fused, so that efficient denoising, compression and abnormal feature extraction processing of the industrial equipment data are realized.

The invention discloses a two-stage electric power vertical field large model fine tuning method based on supervised fine tuning and reinforcement learning, and relates to the field of artificial intelligence large model fine tuning training in the electric power field. Performing supervision and fine tuning on the language model according to the field knowledge guide prompt words; constructing a reinforcement learning objective function by using an importance sampling ratio and a cutting mechanism; and constructing an output reward model and a standardized dominant function, and performing fine tuning on the adjusted language model in the initial stage in combination with a reinforcement learning objective function to obtain a fine-tuned power vertical field language model. According to the method, an electric power professional data set is constructed, the advantages of supervision fine tuning and reinforcement learning are combined, a technical path of staged optimization is formed, a standardized output format of electric power problem reasoning is designed, and the interpretability and engineering practicability of a model reasoning result are ensured.

The invention discloses a data cleaning method and system based on a large language model, and the method comprises the steps: collecting and sorting a to-be-cleaned data set, formatting the to-be-cleaned data set into a text format suitable for model processing, and obtaining to-be-cleaned text data; performing text error correction and standardization processing on the to-be-cleaned text data based on the large language model, performing exception processing on the to-be-cleaned text data based on the large language model, and performing entity recognition and standardization processing on the to-be-cleaned text data based on the large language model; performing semantic consistency check on the to-be-cleaned text data based on the large language model, performing missing data generation on the to-be-cleaned text data based on the large language model, and performing repeated data processing on the to-be-cleaned text data based on the large language model; and recording all problems, implemented processing measures, generated results and various parameters in the process of cleaning the to-be-cleaned text data based on the large language model, and generating a structured report based on the record.

The invention relates to a knowledge graph construction method, device and equipment based on multi-source data. The method comprises the following steps: acquiring original data from different data sources; preprocessing the original data to obtain target text data; wherein the preprocessing comprises format conversion, text cleaning and normalization and / or sentence segmentation and segmentation; performing knowledge extraction on the target text data through a pre-optimized large language model to obtain original structured data including an original entity, an original relationship and an original attribute; post-processing the original structured data to obtain target structured data including a target entity, a target relationship and a target attribute; wherein the post-processing comprises format analysis, entity standardization and ambiguity elimination, and relation and attribute verification; and updating nodes and edges of the current knowledge graph according to the target structured data. The method can adapt to multi-source heterogeneous data, and the accuracy and consistency of the knowledge graph are improved.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention provides an intelligent software development auxiliary system and a use method, and belongs to the technical field of computer software development. The system comprises a project demand analysis module which is used for obtaining and analyzing project demand data and generating feature vectors of demands; the code style optimization module is used for analyzing semantics and styles of the original codes, analyzing and normalizing the original code structures, and generating code structures and style specifications meeting project standards; the intelligent code completion module is used for analyzing context semantics of the codes after style optimization, generating code completion suggestions conforming to project standards in combination with project demand data, and outputting structured completion code snippets; the technology stack intelligent recommendation module automatically recommends an optimal technology stack for the project according to the project demand data and the optimized code structure; and the development behavior risk detection module monitors programming behaviors of developers in real time, captures abnormal operations, marks potential risks and generates operation optimization suggestions.

The invention relates to a geology vertical field large language model construction method and system fused with a knowledge graph, and the method comprises the steps: obtaining multi-source geology data, and carrying out the standardization processing, and obtaining a structured geological corpus; constructing a first knowledge graph and a vector index database supporting semantic retrieval based on the structured geological corpus; extracting first path information and constructing a first cue word, and performing fine tuning training on a pre-trained large language model according to the first cue word; and receiving an original text input by a user, and performing normalization processing to obtain first prompt information. And constructing a second cue word in the vector index database and the first knowledge graph based on the first cue information, and inputting the second cue word into a large language model subjected to fine tuning training to obtain a geoscience domain question and answer with a controlled structure. Compared with the prior art, the technical problem of semantic deviation and uncontrollability of question and answer generation content can be solved.

The invention relates to the technical field of OA examination and approval, in particular to a multi-modal file intelligent examination and approval method and system based on a large language model, and the method comprises the steps: configuring exclusive examination and approval templates for different examination and approval roles, and generating a role exclusive intelligent examination model; the method comprises the following steps: receiving a to-be-approved multi-modal file, analyzing file content, and outputting structured JSON (JavaScript Object Notation) data; inputting the structured JSON data and a configured approval template into a corresponding intelligent review model, performing step-by-step reasoning through a thinking chain prompt strategy, and outputting a structured review intermediate result; encoding the review intermediate result into a semantic vector, and retrieving Top-K similar historical approval cases from a knowledge base module based on a vector database to generate a reference suggestion set; fusing the review intermediate result with the reference suggestion set to generate a review result; based on the review result and the feedback of the approver, the standardized approval opinions are generated, and the accuracy and consistency of review are improved.

The invention belongs to the technical field of natural language processing and geographic information systems, and particularly relates to a region address standardization method based on knowledge graph enhanced retrieval. Cleaning and preprocessing the original address text input by the user; utilizing a fine-tuned large language model to identify a geographic entity and performing standardized expansion on variant expression to generate a query candidate set; entity linking and context retrieval are carried out based on the knowledge graph, and attributes, hierarchy and spatial topology information of associated entities are obtained; in combination with the original input and the map context, an enhanced retrieval query text is generated through large language model reconstruction; vectorized semantic retrieval is carried out through the fine-tuned embedding model, and a preliminary candidate address set is obtained; carrying out multi-dimensional refined sorting by adopting a resorting model; and generating a structured standard address by using a large language model, and outputting the structured standard address after multi-level verification. According to the method, the problems of ambiguity resolution, alias recognition and context understanding in address processing are solved, and the accuracy and robustness of address standardization are improved.

The invention relates to the technical field of electrical digital data processing, in particular to an intelligent contract vulnerability detection method based on multi-modal selection state space fusion, and aims to solve the technical problem of low vulnerability detection accuracy in the prior art. The method comprises the following steps: firstly, fusing global and local features of a contract graph to obtain graph modal embedding features; performing feature alignment on the text context information of the intermediate representation text and the byte code text to obtain text modal embedding features; extracting spatial layout features from the color image and the grayscale image, and splicing the spatial layout features to obtain visual modal embedding features; thirdly, performing multiple rounds of feature cross processing on the graph modal embedding features, the text modal embedding features and the visual modal embedding features based on a selection state space to realize intra-modal and inter-modal information interaction, and then performing fusion to obtain multi-modal fusion features; and finally, performing feature normalization and regularization processing on the multi-modal fusion features, and performing classification processing to obtain a vulnerability detection result.

The invention provides a multi-modal data pairing method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: obtaining a video multi-frame sequence and a target text, and respectively extracting an overlapped frame group set and a standardized text sequence; performing spatio-temporal feature extraction and text dependency relationship coding to obtain a video time sequence vector sequence and a text vector sequence; executing cross-modal alignment search, and constructing a monotonic matching path set; calculating a semantic and action entity relationship consistency score of the paired elements on the path to obtain a comprehensive score; and determining an alignment relationship between the video and the text based on the optimal path. According to the method, accurate matching of the video and the text is realized, and the cross-modal retrieval efficiency is improved.

A prompt for a generative artificial intelligence (GenAI) model is received which includes unicode. Unicode fonts in the prompt are identified and then translated into a plaintext representation. Further, unicode characters in the prompt are identified which each have an associated unicode tag. It is determined, based on the associated unicode tags, whether at least a portion of the unicode characters are valid. When at least a portion of the unicode characters are determined to be valid, the unicode characters in the prompt are converted into a plaintext representation. The prompt with the translated fonts and the converted unicode fonts are passed into the GenAI model. When at least a portion of the unicode characters are not determined to be valid, the unicode characters are removed from the prompt. This prompt with the translated unicode fonts, after the unicode characters are removed, is input into the GenAI model.

The invention relates to the technical field of chemical safety intelligent analysis, and discloses a chemical safety production safety intelligent analysis system based on a large language model, and the system comprises a semantic data construction module which is used for obtaining multi-source data and building a standardized database, a semantic anchor point library and a causal fragment set; the mechanism constraint modeling module is used for generating a mechanism constraint library and mapping the mechanism constraint library with the device unit; the semantic compiling constraint module is used for compiling the natural language intention into a formalized constraint set; the interlocking generation matching module is used for generating a semantic interlocking table and an action list; the check amplitude limiting decision module is used for determining a reserved action list, an amplitude limiting parameter and an execution sequence; the simulation verification and evaluation module is used for carrying out full-track simulation and outputting a feasibility result; and the deviation revision backfilling module is used for comparing the actual working condition with the simulation track and generating a revision proposal. According to the method, self-adaptive closed-loop optimization of semantic knowledge unified modeling and safety analysis in the chemical safety production process is realized.

The invention discloses an index optimization and compression storage system and method for a large-scale literature set, and the method comprises the following steps: S1, collecting and preprocessing literature data, and generating a standardized text data set; s2, carrying out keyword semantic vector coding, and constructing a keyword semantic vector matrix; s3, constructing an initial Gaussian mixture model to obtain a clustering center, a covariance matrix and a weight; s4, introducing a sea elephant optimization algorithm to optimize clustering parameters, and outputting an optimal clustering result; s5, constructing a semantic clustering structure, and generating an index tree structure; s6, performing bitmap compression and inverted coding, and constructing an index table supporting Boolean logic; and S7, dynamically accessing the newly added literature, and completing incremental updating of the index structure. The method is used for improving the index construction efficiency and the storage compression rate of a large-scale literature set, and efficient and semantic literature retrieval service capable of being incrementally updated is achieved.

The invention discloses a full-variable optimized B-spline geometric distance fitting method, belongs to the field of computer-aided geometric design, and is suitable for solving the problem of high-precision modeling of complex curves in industrial design and precision manufacturing scenes. According to the method, variables are initialized for a set of given data points; constructing a target function of geometric distance fitting, solving update quantities of control points and nodes through a Gauss-Newton algorithm, and normalizing node vectors; approaching projection points of the data points by using first-order approximation to update parameters; and checking a convergence index of alternate iteration to obtain a final B-spline curve. Compared with the prior art, the method has the advantages that the fitting precision is remarkably improved, the problem that in the prior art, a dependent variable coupling relation is complex and global optimization is difficult is solved, and particularly when data points containing sharp features are processed, a B-spline curve with heavy nodes can be automatically generated, geometric features can be better described, and fitting errors are reduced.

The invention provides a file positioning management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, files are collected from multiple sources and subjected to standardization processing, an element set is generated in combination with multi-modal analysis of texts, images, audios, videos and tables, cross-modal alignment is achieved through a semantic representation model, hierarchical indexes of semantics, keywords and relations are constructed, and a unique traceability identifier is generated; in the query stage, intention recognition and joint retrieval are carried out, a result subjected to permission verification and traceability information labeling is output, online optimization and incremental reconstruction are executed based on user feedback, and comprehensiveness, accuracy, traceability and self-adaptive optimization of file positioning are achieved.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention relates to the technical field of knowledge maps, in particular to a steel manufacturing knowledge map construction method based on multi-source heterogeneous data, which comprises the following steps: converting message data into a standardized semantic triple through a semantic mapping rule, and complementing missing attributes by using physical and chemical component balance logic to form a standardized entity; meanwhile, a semantic processor is used for extracting unstructured report text logic, cross-modal coupling is carried out on the unstructured report text logic and SEM image features, and a failure mechanism enhanced entity is generated. Then, full-process dynamic parameters are collected and regularized into equal-interval time sequence parameters; and finally, taking the normalized entity as an index feature and the mechanism enhanced entity as an association constraint, executing semantic fusion by utilizing a topological feature mapping engine, and constructing a dynamic weight graph structure by combining with a dynamic probability distribution correction operator iteration parameter weight. According to the method, efficient association and semantic organization of manufacturing whole-process multi-modal data are realized, and the retrieval precision and knowledge discovery capability of steel manufacturing data are greatly improved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention discloses a book data processing and intelligent service system based on artificial intelligence, and relates to the technical field of artificial intelligence and digital library crossing, and the technical scheme is characterized in that a multi-format document intelligent conversion and regular cleaning mechanism is adopted, a multi-level metadata extraction algorithm is combined, and a structured knowledge base is accurately constructed; vector semantic retrieval, BM25 keyword retrieval and a cross encoder reordering model are creatively fused, and high-precision context retrieval is achieved through dynamic weight configuration and locality sensitive hash de-duplication. A parallel data processing architecture supporting GPU acceleration is constructed based on a Chroma vector database, the knowledge response capability of a local large model is enhanced in combination with an RAG technology, and verifiable standardized content is generated. The system is compatible with a Linux / Windows platform and containerized deployment, has hundred million-level data throughput efficiency, can be widely applied to the fields of intelligent libraries, knowledge questions and answers and personalized recommendation, and remarkably improves the retrieval accuracy of book data and the intellectualization level of knowledge services.

A system may receive, from a client device, a query requesting quality information of a target device. The system may access a set of data records associated with the target device, pre-process the set of data records for extracting raw data associated with the target device from the set of data records, and convert the pre-processed data to normalized data using a first large language model (LLM). The system may apply a second LLM to the normalized data for generating an output result, which includes the requested quality information of the target device. Applying the second LLM may include: retrieving contextual information related to the target device; generating a prompt to the second LLM, the prompt comprising at least the normalized data, the retrieved contextual information, and the query requesting quality information of the target device; and providing the generated prompt to the second LLM to receive the output result.