57 results about "Knowledge-based systems" patented technology

The invention discloses a meteorological operation and maintenance intelligent question-answering method based on multi-mode GraphRAG. The method comprises the following steps: S1, a meteorological operation and maintenance intelligent question-answering system divides input data into unstructured data and structured data and processes the unstructured data and the structured data respectively; s2, constructing a knowledge base system composed of a vector database, a relational database and a graph database; s3, analyzing the user query text and dividing question types; s4, selecting a corresponding retrieval strategy according to the question type and starting a processing flow, and finally processing a retrieval result through a fusion and rearrangement module to obtain a knowledge evidence set; and S5, the meteorological operation and maintenance intelligent question-answering system organizes the knowledge evidence set and the user questions into structured cues and submits the structured cues to the large language model for processing to generate structured answers. According to the method, optimization is carried out according to characteristics of the meteorological operation and maintenance field, professional terms and field specific knowledge can be effectively processed, and high-quality field professional services are provided.

A knowledge base system based on a large language model includes a processor configured to construct and enable a specific domain function module to perform a function calling mechanism according to the large language model to generate a function corresponding to one of an industrial process monitoring system information and a nature language query information, and establish a knowledge base. The processor is configured to construct and enable a retrieval augmented generation module to perform an embedding transfer mechanism to generate a plurality of embedding vectors, and retrieve embedding vectors and establish a vector store. The processor is configured to construct and enable a knowledge and information management module to manage the knowledge base and the vector store.

The invention discloses a planetary gear dynamic characteristic prediction method. Comprising the following steps: carrying out a planetary gear dynamic transmission test on a real planetary gear test bed, carrying out dynamic modeling on a healthy planetary gear, describing the vibration of a planetary gear system by adopting a fixed coordinate system fixed on a gear ring, providing a dynamic characteristic type, responding to signal analysis, and carrying out dynamic transmission. Comparing and judging the result obtained by analyzing the response signal obtained by the test with the result obtained by analyzing the response signal, and establishing a planetary gear dynamic fault knowledge base system, so as to form digital-analog combination in the field of planetary gear transmission, which is beneficial for identifying the dynamic characteristics of planetary gear transmission. The prediction efficiency of the dynamic characteristics of the planet gear and the prediction precision in the field of fault identification are improved, and the accuracy of dynamic characteristic identification is improved through continuous test and model interaction. And a planetary gear transmission dynamic characteristic knowledge base system is established to realize rapid and accurate analysis of vibration signals captured in tests and projects.

The invention discloses a multi-modal knowledge base system construction method oriented to a large language model, which comprises the following steps: S1, creating a knowledge base instance, including setting a knowledge base name, description and visibility parameters; s2, adding a multi-modal document to the knowledge base, wherein the multi-modal document comprises a text, a PDF (Portable Document Format), an image and a table format; s3, configuring large language model parameters including a model identifier, an API key and a basic API address; s4, realizing a knowledge-based question and answer function, and enhancing a generation technology by combining a standardized API (Application Program Interface) with a retrieval; s5, realizing a dynamic scoring function of knowledge entries, and performing multi-dimensional scoring on the entries through a large language model; and S6, managing knowledge base authority, distinguishing a personal knowledge base mode and a shared knowledge base mode, and setting document visibility. According to the method, a multi-modal analysis engine is adopted, unified storage of multi-modal data is supported, lightweight document indexing is adopted, automatic scoring is performed through LLM, manual auditing dependency is reduced, and meanwhile high-quality knowledge sharing is ensured in combination with permission control.

The invention relates to the technical field of agricultural travel services, and provides a dialect intelligent customer service and culture knowledge base system, the system comprises a perception layer, an analysis layer, a knowledge layer and an interaction layer four-dimensional architecture, the perception layer collects and preprocesses dialect voice, noise reduction is performed through spectral subtraction, and Mel frequency cepstrum coefficient features are extracted; the analysis layer identifies dialects based on a fine tuning Wav2Vec2.0 model, converts the dialects into mandarin through a Transform architecture, and completes intention identification and slot filling by using a BERT related model; the knowledge layer constructs a local culture knowledge graph containing non-abandoned, folk and other entities, and supports dynamic updating; and the interaction layer is combined with multiple rounds of dialogue management to generate multiform responses, and can be connected with an external service system. The system also optimizes a feedback module iterative model and knowledge. The system can cover more than ten dialects, realizes dialect interaction, culture interpretation and service conversion closed loop, and assists rural culture revitalizing and rural cultural travel service upgrading.

The invention discloses a task book demand standardization method based on retrieval enhancement generation, and the method comprises the following steps: S1, gathering historical demand documents and standardized records, completing cleaning, term unification and structured storage, and forming a maintainable and searchable domain knowledge base; s2, segmenting the document into entries, carrying out keyword labeling and vectorization representation and indexing, and establishing a complete knowledge base system; s3, generating a high-quality reference set through pre-retrieval enhancement, semantic matching retrieval and post-retrieval enhancement; s4, based on the multi-layer template, driving the large model to output a structured demand confirmation document draft; and S5, providing interactive auxiliary revision containing user feedback and a double termination mechanism, and providing powerful intelligent support for finally forming a demand document. According to the method, the defects of element recognition, knowledge reuse and consistency guarantee in the prior art can be overcome, and a feasible engineering path is provided for large-scale and cross-scene demand standardization.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is carried out diabetic eye disease diagnosis reasoning, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

The invention discloses a student portrait analysis and intervention measure design method and system based on generative artificial intelligence, and belongs to the crossing field of artificial intelligence and education technologies. The method comprises the following steps: obtaining a prediction result and feature contribution analysis of an interpretable machine learning model; constructing a hierarchical knowledge base system; the LoRA fine tuning technology is adopted to carry out domain adaptation on the large language model, and output consistency and professionality are ensured; establishing a student evaluation standard system of dynamic weights, and adaptively adjusting the evaluation weight of each dimension according to the grade of the student; designing a structured prompt template, and generating a special prompt for different analysis tasks; personalized student portraits are generated based on knowledge base retrieval and large model reasoning, and qualitative description and quantitative scoring are included; and generating targeted intervention measures in combination with the private experience knowledge base. According to the method, organic combination of quantitative analysis and qualitative description is realized, the generated student portrait is more comprehensive and accurate, and intervention suggestions are more professional and operable.

According to the turbine fault diagnosis system, a sensing acquisition unit is provided with a multi-type sensor array on a turbine shell, and vibration signals, temperature distribution and pressure fluctuation data are acquired in real time; the data processing unit performs abnormal value elimination and missing value compensation on the original signal, and outputs standardized time sequence data; a feature analysis unit extracts time-frequency domain mixed features from the standardized time sequence data to generate a feature vector set; and the decision output unit receives the feature vector set, and outputs a fault type and confidence through a pre-trained hybrid diagnosis model. The three core problems in traditional steam turbine fault diagnosis are effectively solved: firstly, the limitation of single signal analysis is broken through, and collaborative diagnosis of vibration, temperature and pressure parameters is realized; secondly, a self-adaptive feature extraction mechanism is adopted, so that the diagnosis reliability in a variable working condition environment is remarkably improved; thirdly, a dynamically updated knowledge base system is constructed, and autonomous accumulation and evolution of diagnosis experience are achieved.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is diagnosed and reasoned to diabetic eye disease, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

The application discloses an intelligent prompting method and system, a computer device and a storage medium. The method comprises the following steps: receiving a user-defined professional label and a knowledge bias label, and configuring a data collection path; collecting and analyzing multi-dimensional behavior data of the user on a personal computer in real time to generate structured knowledge items; starting a cleaning process during a computer idle period or at a fixed time every day, using a large model to judge the novelty and relevance of the knowledge, and storing the knowledge items determined by the judgment in a knowledge base; in the knowledge base, automatically obtaining vertical field frontier knowledge from the network based on the user label and generating an abstract through a large model, and periodically deleting low-frequency obsolete knowledge; capturing user input content in real time, and combining historical behavior data and the knowledge base to perform RAG retrieval to provide intelligent prompts. The embodiments of the application aim to solve the problems of the existing knowledge base system, such as static, insufficient user adaptability and low interaction efficiency.

The application discloses a kind of knowledge base management method, device, computer equipment and storage medium, the knowledge base management method includes steps: quality detection is carried out to knowledge data in knowledge database, obtains quality detection result;According to quality detection result, determine the target knowledge data needing to update;Get reference knowledge data, and calculate the knowledge difference degree between reference knowledge data and target knowledge data;According to the corresponding relationship of knowledge difference degree and data update strategy, determine target data update strategy, update target knowledge data.This method realizes the precision and automation of knowledge update by the dual mechanism of quality detection and difference degree analysis, effectively improves the reliability and efficiency of knowledge base management, is suitable for dynamic maintenance and intelligent optimization of knowledge base system in financial technology, medical health and other business fields, can provide effective knowledge support for decision support and intelligent service in complex scenarios.

The invention discloses a retrieval processing method and device for building an intelligent knowledge base based on AI and a terminal, and relates to the technical field of artificial intelligence. Selecting a knowledge base based on a large language model and a retrieval enhancement generation technology; inputting the document data subjected to format unification and classification into the knowledge base, creating intelligent agents of different posts, and constructing an intelligent knowledge base system; when a questioning question is received, performing agent intention recognition on the questioning question, and calling a corresponding post agent to search a knowledge base according to the recognized questioning intention; when the corresponding post agent does not find the answer in the knowledge base system, obtaining a reply answer, outputting the reply answer, and automatically writing the reply answer into the knowledge base for updating; and when the question answer is found, directly outputting the question answer. According to the method, rapid and accurate retrieval of enterprise internal knowledge can be realized, and missing knowledge contents can be automatically supplemented, so that the problems that employees are difficult to search systems, time is consumed and satisfactory answers cannot be obtained are solved.

This invention discloses a knowledge base system and method for establishing failure effects of aircraft maintenance activities for typical aircraft systems, belonging to the field of failure mode analysis (FMEA) technology for aircraft maintenance activities. It aims to solve problems such as traditional failure effect analysis methods not fully considering the associated effects of maintenance objects and the fact that maintenance activities are mostly stored in text format, which is not conducive to computer storage and reuse. The method includes: using FMEA to conduct failure effect analysis on typical aircraft systems and components; decomposing key operating state parameters for typical systems and components; constructing a knowledge base for the failure effects of maintenance objects; classifying maintenance activity steps and determining the maintenance objects and corresponding operating state parameters according to category correspondences; and associating failure modes and effects through operating state parameters. This improves the efficiency and accuracy of finding failure effects in maintenance activities, providing important data references for failure mode severity analysis and maintenance procedure improvement.

The invention discloses a medicine research and development industry knowledge base system and method based on artificial intelligence, and belongs to the technical field of medicine information processing and artificial intelligence. The system comprises a data acquisition module, a data preprocessing module, a knowledge extraction module, a knowledge integration module, a knowledge reasoning module, a knowledge updating module and an intelligent center layer. The data acquisition module acquires multi-modal medicine research and development data, the preprocessing module performs cleaning, standardization and feature alignment, the knowledge extraction module adopts a deep learning model based on Transformer to extract entity relationships, the knowledge integration module checks conflicts through a chemical rule knowledge base and constructs a knowledge graph, and the knowledge graph is used for establishing a knowledge database. The knowledge reasoning module performs association prediction by using a graph neural network and a time sequence model, the knowledge updating module realizes parameter optimization and incremental updating based on human feedback reinforcement learning, and the intelligent central layer overall plans all the modules to work cooperatively based on a large language model arrangement framework. According to the method, multi-source heterogeneous data can be integrated, domain semantics can be deeply understood, a knowledge structure is dynamically evolved, intelligent decision support is provided, and the efficiency and accuracy of medicine research and development knowledge management and application are remarkably improved.

The invention discloses an enterprise knowledge base construction method based on SaaS private deployment, and particularly relates to the technical field of enterprise knowledge base construction. The method comprises the following steps: firstly, deploying a knowledge base system in a private server or a private cloud environment, and establishing a basic data structure of users, roles, permissions and resources; constructing a permission graph structure of multiple types of nodes and edge relationships thereof based on the entities, introducing a role sensitivity coefficient and a role permission overlapping degree as node features, performing representation learning on the nodes by adopting a graph neural network model, and training the model in a self-supervision mode to generate a user access risk score; when the score is higher than a preset threshold value, triggering an access path analysis and strategy optimization suggestion generation mechanism, dynamically adjusting permission configuration, and synchronously writing an update result into a graph structure to realize closed-loop identification and optimization of permission risks; the method has the advantages of being high in visualization, accurate in recognition, fast in response and the like, and the intellectualization and safety of enterprise authority management are remarkably improved.

The present disclosure relates to the technical field of enterprise collaborative office management, in particular to a data collaboration method and device based on a unified state diagram, which generates a task skeleton corresponding to a document directory structure by analyzing structured directory information of a to-be-processed document, and constructs a unified state diagram containing a content anchor point identifier, a task state identifier, a permission state identifier and a knowledge context field based on the task skeleton, so that the directory position, the task processing state, the permission control state and the related knowledge context in the document can be associated and expressed in the same state node, thereby making the collaborative processing process no longer simply rely on manual synchronization or loose coupling interface association between a document system, a task system or a knowledge base system, and improving the consistency, linkage and traceability between multiple types of collaborative states.

This invention belongs to the field of osteoarthritis management technology, specifically relating to an artificial intelligence-based osteoarthritis management system and a computer storage medium. This invention provides an artificial intelligence-based osteoarthritis management system. This system integrates an LLM-DL hybrid architecture, a multi-specialty module collaboration framework, and a RAG knowledge base system, covering the entire clinical process from initial patient diagnosis to treatment plan development. This system can comprehensively and accurately assess the patient's disease status, providing personalized disease progression predictions. It not only predicts 2-year and 4-year functional outcomes and radiological progression but also identifies patient-specific risk factors, providing a basis for precise intervention. The overall system design significantly improves clinical work efficiency and predictive accuracy, reduces reliance on specialist physicians and high-end equipment, enabling high-quality KOA management to be implemented even in resource-limited areas, thereby effectively reducing overall healthcare resource demands.

The invention belongs to the technical field of application filling, and particularly relates to a college entrance examination application filling auxiliary system which comprises a user input module, a plug-in calling module, a knowledge base system, a data construction and analysis module, a semantic interaction configuration module and an answer synthesis and feedback module. According to the system, a college entrance examination voluntary reporting AI Agent is constructed on the basis of a Baidu cloud sail platform App Builder, a self-defined knowledge base and multiple college entrance examination plug-in tools are fused, and a voluntary auxiliary platform with knowledge driving, semantic question answering and intelligent recommendation is constructed in combination with large model reasoning ability; the platform is devoted to structurally integrate complicated policy requirements and college information, the understanding cost of examinees and parents is reduced, the scientificity and efficiency of volunteer filling are improved, meanwhile, the platform has good expansibility, and the platform can adapt to other new college entrance examination provinces throughout the country.

The application relates to the technical field of the cross of artificial intelligence large models, computer vision, natural language processing and intelligent education, in particular to a knowledge base system based on an AI large model, which comprises a content access layer, an AI analysis processing layer, a knowledge base management layer and a multi-terminal adaptation layer which are sequentially connected, and is also provided with a feedback closed-loop optimization module and a value concept and compliance scanning sub-module; a hierarchical disassembly algorithm driven by a VLM is used to realize minute-level automatic disassembly of a children's book in the order of a book, a page, a page area, picture elements and associated scripts; a double-mode age-appropriate evaluation system of texts and images which is fused with vocabulary complexity, logical depth, image emotion and value concept compliance is constructed, unified knowledge graph metadata standards are established to realize multi-terminal differentiated output, and knowledge base incremental learning is completed by relying on terminal interaction data. The application shortens the children's book disassembly time from several days to several minutes, reduces the artificial cost, and realizes content full-process automatic processing and dynamic optimization.

The invention discloses a knowledge base system for bidding regulations and cases, and relates to the technical field of information processing. Comprising a user terminal, an intelligent retrieval module, an intelligent review module, a personalized recommendation module, a bidding and tendering regulation and case knowledge base and a dynamic updating module. The user terminal is used for a user to log in the system and providing retrieval, review and recommendation operation interfaces for the user; the intelligent retrieval module is used for retrieving cases and bidding and tendering laws and regulations; the intelligent review module is used for reviewing the bidding book; the personalized recommendation module recommends related cases and bidding and tendering laws and regulations in a personalized manner according to the user roles, the belonging industries and browsing records; the bidding and tendering regulation and case knowledge base stores bidding and tendering regulations and classical cases related to the bidding and tendering regulations, and constructs a bidding and tendering regulation and classical case knowledge graph; and the dynamic updating module is used for updating the bidding and tendering regulation and case knowledge base. According to the method, deep association and accurate retrieval of laws and regulations and cases can be realized, and the learning efficiency of a user is improved.

The invention relates to a multi-source heterogeneous document knowledge base construction method and system based on self-adaptive normalization, and belongs to the field of knowledge engineering.The method comprises the steps that original document data of different modals from multiple heterogeneous data sources is collected; performing preprocessing and modal feature extraction on the original document data to obtain modal feature vectors corresponding to the original document data; dynamically mapping the modal feature vectors of different modalities to a unified shared semantic vector space to generate a unified document vector; local reasoning is carried out on the modal feature vectors based on modal exclusive AI agents; recognizing document sets with similar semantics, conflicts or duplicates; performing online aggregation and duplicate removal operation on the document set; constructing a knowledge graph based on a cross-modal semantic association result; and automatically triggering an active learning process to update the model parameters of the AI agent in real time. According to the application, a knowledge base system with self-adaption, real-time performance and autonomous learning ability is constructed.

We provide the system. [Solution] In an interactive knowledge base system that allows employees to search for company information using generating AI, A means of receiving questions entered by employees, A means of sending a query to a generating AI based on an input question and generating an appropriate response, A method for cleaning up the responses obtained from the generated AI and sending them back to the employees, Methods for importing and using internal company policies, procedures, service information, and other data into the system, A system that includes this.

The application relates to a multi-source heterogeneous document knowledge base construction method and system based on adaptive normalization, and belongs to the field of knowledge engineering. The method comprises the following steps: collecting original document data of different modes from multiple heterogeneous data sources; pre-processing and mode feature extraction are performed on the original document data to obtain mode feature vectors corresponding to the original document data; the mode feature vectors of different modes are dynamically mapped to a unified shared semantic vector space to generate unified document vectors; local reasoning is respectively performed on the mode feature vectors based on mode-specific AI agents; a document set with similar semantics, conflicts or repetitions is identified; online aggregation and deduplication operations are performed on the document set; a knowledge graph is constructed based on cross-modal semantic association results; and a model parameter of the AI agent is automatically triggered to update in real time. The application constructs a knowledge base system with adaptive, real-time and self-learning capabilities.

The application provides a consumable intelligent auditing and monitoring knowledge base system, comprising: a file library, a rule library, an admission auditing rule engine, a medical order consumable intelligent auditing engine and a pre-discharge auditing engine; the file library is used for storing relevant files in the medical field; the rule library is used for storing various predetermined rules; the admission auditing rule engine is used for auditing hospitalization behaviors according to hospitalization-related rules in the rule library; the medical order consumable intelligent auditing engine is used for auditing medical orders / prescriptions issued by doctors according to consumable regulations in the file library; and the pre-discharge auditing engine is used for auditing treatment details of pre-discharge patients according to medical insurance policies in the file library. The technical scheme of the application adopts the admission auditing rule engine, the medical order consumable intelligent auditing engine and the pre-discharge auditing engine to replace manual auditing, thereby improving auditing efficiency.

The invention relates to the field of green building design, and discloses a multi-agent cooperation method for green building design thematic management and intelligent review, and the method comprises the steps: obtaining a review report document of green building design, and the review report document comprises problem description; extracting field labels and influence range indexes of problem description based on the review report document to obtain a problem attribute classification set; matching the question attribute classification set based on a preset knowledge base system to obtain the attribute classification of the question; querying an associated responsibility subject library based on the attribute classification to determine a target responsibility subject; and pushing the question description to a terminal device of the target responsibility subject. By means of the technical scheme, intelligent upgrading of the green building design review process is achieved.

The invention discloses an enterprise content knowledge base management method based on an AI technology. Comprising the following steps that S1, an administrator logs in a knowledge base in the background; s2, uploading data information by an administrator; s3, the system supports multi-file uploading; s4, setting an extraction priority of the file; s5, extracting a failure triggering message by the file; s6, recording the abnormity of the extracted data; s7, uploading file record query; according to the intelligent file analysis and extraction knowledge base system developed on the basis of PHP, Vue.js, Python and a third-party platform API, the intelligent file analysis and extraction knowledge base system is innovated and optimized in multiple aspects such as automatic content extraction, intelligent recommendation and comprehensive data processing, so that the working efficiency and the user experience are greatly improved; and the data management, the security, the operation cost and the like are remarkably improved.

The invention belongs to the technical field of ancient building digitization, and provides an ancient building digitization intelligent knowledge base system, which comprises a restoration color deviation decomposition module for acquiring pigment purity / impurity data by using a spectrograph and calculating deviation and color value correction to obtain color restoration structure data; the formula adjustment dynamic calibration module is used for establishing a basic model by fitting the deviation-formula relation coefficient, and calibrating by combining structural data to obtain an exclusive formula adjustment amount; the multivariable dynamic optimization module is used for establishing a mixed color value prediction model and a multi-objective optimization function, and solving an optimal formula proportion through a genetic algorithm; the field verification and calibration module is used for verifying the optimal formula and reversely calibrating the basic model; and the knowledge base iteration multiplexing module integrates a data storage library, corrects a model coefficient for a long time, and reuses a formula of new batches through cosine similarity, so that the system improves the repairing precision and efficiency, and ensures the color rendition consistency.

The invention discloses a knowledge base system construction method and device, equipment, a storage medium and a program product. The method comprises the steps of deploying a language chain framework based on a large language model; preprocessing the collected multi-modal data to obtain picture data and text data; determining a target placeholder of the picture data according to the picture data, and determining a target text paragraph related to the picture data according to the picture data; adding the target placeholder as a target superscript into the target text paragraph to obtain plain text data; and constructing a multi-modal knowledge base system based on plain text data and a language chain framework. According to the embodiment of the invention, the multi-modal data can be converted into the single-modal data in the plain text format. The image is reserved in a text form, and data processing and analysis are simplified; and the processing of the multi-modal data is more integrated and efficient. The technical effects of improving the processing speed and reducing the complexity and error rate possibly occurring when multiple data types are processed are achieved.