1947results about "Other databases browsing/visualisation" patented technology

The embodiment of the invention provides a document generation method and device based on artificial intelligence, and the method comprises the steps: extracting multi-modal data features of a historical multi-modal file through natural language processing and a computer vision algorithm, and constructing a corresponding knowledge graph according to the multi-modal data features, converting the multi-modal data subjected to feature extraction into high-dimensional vectors by using a vectorization algorithm, and storing the high-dimensional vectors to form a vector library; splitting a current document generation task into a plurality of sub-tasks by adopting a task planning algorithm, and executing the sub-tasks at the same time to obtain a first document; performing iterative optimization of document content according to the document first draft, the cue word and the language model to obtain an optimized first draft, acquiring related information from the vector library by using an attention mechanism-based retrieval algorithm, and fusing the related information and the optimized first draft through a text generation algorithm to obtain a final document; according to the invention, the accuracy and intelligence in the document generation process can be effectively improved.

The invention provides an emergency event visualization cooperation method and terminal equipment, and the method comprises the steps: obtaining a historical monitoring data set of an emergency event in a target region, carrying out the preprocessing, and generating a standardized event feature set; training a time sequence prediction model according to the dynamic environment parameters to obtain an event evolution prediction model; and constructing a geographic region matching model based on the static geographic parameters, and generating a visual layer template associated with the target region. And collecting a current environment data flow of the target area in real time, performing classification through the event evolution prediction model to obtain an event classification result, and calling a corresponding visual layer template from the geographic area matching model according to the event classification result. And fusing the diffusion path, the intensity level and the called visual layer template in the event classification result to generate a dynamic visual instruction set, and rendering and outputting an emergency event evolution situation map through an interactive interface. According to the invention, the timeliness of emergency response and decision reliability can be improved.

The invention provides an operational research course knowledge graph construction method based on multi-source data fusion. The method comprises the following steps: firstly, discussing logical association of courses, majors and students, collecting data such as textbooks, exercises and teaching programs by taking operational research knowledge points as a core and utilizing technologies such as OCR (Optical Character Recognition) and crawlers, and carrying out preprocessing and manual labeling; then, an improved deep learning model is adopted for entity recognition and relation extraction, BERT + BiLSTM + CRF is adopted for entity recognition, and a dynamic context pooling enhancement model is fused to improve the capture ability of a complex knowledge boundary; bERT + BiLSTM is adopted for relation extraction, a multi-head attention mechanism is combined, and hidden logical relation mining is enhanced. And finally, constructing a multi-level knowledge network which takes knowledge points as nodes and logic relations as edges, and embedding the multi-level knowledge network into a Neo4j graph database for visualization. The map can optimize a teaching path, provides personalized learning recommendation, and is widely applied to the fields of wisdom education, knowledge retrieval and the like.

The invention relates to the technical field of data processing, in particular to a whole-process data monitoring and visual management method and system for an air separation process, and the method comprises the steps: collecting key parameter data in the air separation process, and input data vectors and output data vectors among equipment; constructing a parameter correlation model to analyze the input data vector and the output data vector to obtain data deviation; if the data deviation exceeds a dynamic threshold value, triggering a multi-level alarm mechanism, and generating a block chain log record; generating a hash value for the key parameter data, and marking an abnormal link; a bimodal anomaly detection model is constructed to analyze an abnormal link, an equipment performance attenuation coefficient and an inter-equipment anomaly influence relation coefficient are obtained, and a comprehensive anomaly score is generated through a fusion technology; when the comprehensive abnormal score is greater than a preset threshold value, constructing a fault tree model to reversely trace an abnormal data link for the abnormal link, and positioning a fault source; and full-process data monitoring and management are realized through a visual interface.

The invention relates to the technical field of building operation and maintenance management, and discloses a whole-life-cycle intelligent operation and maintenance management method and system driven by a building information model. The method comprises the steps of obtaining and preprocessing original data of a building information model to obtain standardized structure data; a preliminary operation and maintenance feature model is constructed based on the space-time diagram convolutional network, and operation and maintenance decision features are generated through meta-learning framework dynamic parameter optimization; an intelligent operation and maintenance strategy set is generated in combination with a multi-objective optimization algorithm, and feasibility is verified through a dynamic time warping algorithm and iterative updating is carried out; and generating a dynamic operation and maintenance management scheme to drive equipment operation and maintenance and update a model state. The invention also relates to construction of a digital twin model based on Internet of Things data, anomaly detection and the like. The method effectively integrates the building operation and maintenance data, realizes multi-target intelligent decision, improves the feasibility of the operation and maintenance strategy, adapts to environmental changes in real time, detects abnormities, improves the building operation and maintenance efficiency, guarantees the safe and stable operation of the building, and is suitable for the whole-life-cycle operation and maintenance management of various buildings.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing an aggregated feed interface that includes or presents aggregate summaries for thread data across multiple data feeds of a user account. In some embodiments, the disclosed systems generate an aggregate summary by extracting digital communications and other thread data from a variety of data feeds across multiple computer applications and by utilizing a summary generation model to generate an aggregate summary that combines and condenses the thread data from the different data feeds. For example, the disclosed systems determine topics associated with thread data from various data feeds and generates different aggregate summaries for different topics. The disclosed systems can further rank aggregate summaries and / or other digital communications to present within the aggregated feed interface according to an account-specific ranking algorithm resulting a customized per-account presentation of the aggregated feed interface.

The invention provides a gas turbine exhaust gas temperature prediction and fault early warning system and method, and relates to the technical field of gas turbine state monitoring and fault diagnosis. The system comprises a data acquisition and preprocessing module used for acquiring and preprocessing data; the dynamic graph construction module is used for constructing graph structure data; the graph neural network module is used for extracting spatio-temporal features through learning; the improved auto-encoder module is used for realizing feature reconstruction by learning normal working condition data and then outputting reconstruction errors to the fault early warning module; the temperature prediction module is used for predicting a future exhaust temperature value; the fault early warning module is used for performing fault judgment and decision; and the result display and output module is used for summarizing analysis results. According to the system, the prediction precision of the exhaust temperature is remarkably improved, the fault detection and early warning capabilities are enhanced, the system has better feature representation capability and adaptability to complex working conditions, and close integration and mutual enhancement of prediction and early warning are realized.

The invention relates to the technical field of financial data analysis, in particular to a financial statement multi-dimensional incidence relation visual display method which comprises the following steps: extracting subject codes, amount values and business dimension labels, and generating a standardized financial data set with timestamps; generating an initial edge set based on a preset subject association rule base, endowing each edge with a weight value reflecting the sensitivity of a service scene through a dynamic weight calculation module, and forming a multi-dimensional association network with a weight label; identifying a cross-level association path through a path traversal algorithm, and generating a recessive relationship set including a path intensity index; and dynamically and visually displaying. According to the method, the fusion expression capability of the complex financial flow is enhanced, and a clear data basic support is provided for subsequent multi-dimensional modeling and analysis.

The invention relates to the technical field of industrial data analysis, in particular to an electronic information data visualization intelligent processing method which comprises the following steps: collecting data of a temperature sensor, a pressure sensor and a position sensor of a factory, and performing data cleaning and format standardization to obtain standardized sensor data. According to the method, data from a temperature sensor, a pressure sensor and a position sensor are collected in real time, data cleaning and format standardization are implemented, accurate fusion of data from different sources is achieved, the problems of heterogeneous and insufficient collaboration of multi-source data are solved, and comprehensiveness and consistency of data analysis are improved; intelligent definition and detection of an event mode are implemented based on fusion data, complex association of temperature anomaly and pressure mutation is captured in real time, potential association between data is deeply mined, the source of anomaly occurrence is quickly positioned, and the accuracy and reliability of data analysis are improved.

The invention relates to the technical field of risk management, and discloses an asset and risk management-oriented dynamic risk visual management method, which comprises the following steps of: automatically scanning to obtain asset information in a network, and classifying assets; based on the asset information, determining a connection relationship between assets, and constructing a network topology structure diagram; based on a vulnerability database, vulnerabilities existing in the assets are analyzed, an attack graph is generated, and a preset attack path is determined; analyzing a preset attack path to determine an attack path in combination with the network flow data and an attack technique; evaluating the security defense system in the attack path by using a simulated attack technology, and determining the defense efficiency; based on augmented reality and virtual reality technologies, vulnerabilities, attack paths and defense efficiency of each asset are dynamically displayed on a network topology structure diagram. The invention provides an efficient, intuitive and dynamic risk visualization management method by combining various technologies, and the asset management and risk response capabilities can be remarkably improved.

The invention discloses a port container transportation full-process simulation and space planning quantification system and method under random event deduction. The system comprises a data acquisition module, a data processing module, a process simulation module, an efficiency quantification index generation module and an efficiency quantification module. The data acquisition module is used for acquiring basic data in port operation; the data processing module is used for processing the basic data; the process simulation module is used for constructing port container transportation process simulation covering the whole process of ship navigation, ship berthing, quay crane loading and unloading, storage yard storage and container truck outward transportation based on the processed basic data, and obtaining a simulation result; the efficiency quantitative index generation module is used for obtaining an efficiency quantitative index according to the container transportation characteristics and the port operation experience; and the efficiency quantification module is used for quantifying the simulation result by utilizing the efficiency quantification index. According to the invention, the current operation efficiency level of the port can be measured comprehensively and objectively.

The invention discloses a structured data self-learning method based on a graph neural network, and the method comprises the following steps: S1, analyzing structured data, extracting entity fields and relation fields, and constructing a structure candidate graph; s2, generating a node embedding feature matrix, and initializing and recording the adjacency relation of candidate edges; s3, constructing a graph neural network model, inputting node features and an adjacent matrix, and defining a task loss function; s4, evaluating the gradient contribution degree of edge connection by adopting a gradient sensitive sparse adjacency self-learning algorithm, and updating the graph structure representation; s5, introducing an embedded interpretability gradient backtracking mechanism, correcting an edge connection relation and enhancing interpretability; s6, training the graph neural network by using the corrected structure, and updating the node embedding and graph structure; and S7, outputting a final graph structure and an interpretability index, and generating a graph modeling visualization result. According to the method, efficient modeling and explanatory analysis of structured data are realized through a dynamic graph structure learning and gradient backtracking mechanism.

The invention discloses an intelligent precise irrigation system based on low-altitude remote sensing data driving. The intelligent precise irrigation system comprises a low-altitude unmanned aerial vehicle, a ground sensor network module, a meteorological data integration module, a space-time self-evolution feature map network module, a space-time adaptive attention network module, a context multi-arm tiger machine algorithm module and an automatic irrigation control module. The low-altitude unmanned aerial vehicle is used for acquiring remote sensing data of crops; the ground sensor network module is used for collecting real-time environment information; the meteorological data integration module is used for multi-modal data integration; the space-time self-evolution characteristic graph network module is used for generating a moisture demand distribution graph; the space-time adaptive attention network is used for optimizing an irrigation strategy; the context dobby machine algorithm module is used for dynamically selecting an optimal irrigation strategy; and the automatic irrigation control module is used for executing the optimal irrigation strategy and implementing differential intelligent irrigation management. According to the invention, precise and intelligent irrigation is realized by using the space-time self-evolution feature map network and the context multi-arm tiger machine algorithm.

The invention discloses a three-dimensional energy carbon data visualization terminal and a use method thereof. The visualization terminal comprises a data acquisition module, a data processing module, a visualization display module, an interaction control module and a communication transmission module. The data acquisition module acquires a voltage signal, a current signal and a carbon emission signal of the park power equipment in real time; the data processing module performs energy-carbon coupling analysis on the original data to generate an analysis result; the visual display module dynamically displays the energy and carbon monitoring data, the electric power parameter waveform change trend, the incidence relation between energy consumption and carbon emission and abnormal event information; the interaction control module receives a user instruction and adjusts display content; and the communication transmission module performs data interaction with a remote management platform. Through multi-module cooperation, real-time acquisition, coupling analysis and three-dimensional dynamic visualization of energy and carbon data are realized, comprehensive energy efficiency and carbon efficiency management support is provided for an industrial park, and the decision efficiency of energy optimization and carbon emission regulation is improved.

The invention relates to the technical field of data processing, in particular to an intelligent machine account report fusion method and system based on a large model, and the method comprises the following steps: collecting and preprocessing multi-modal data; dynamic Prompt construction and large model analysis are carried out; generating and recommending a unified standing book; recording and visualizing the blood relationship; performing auditing management and data tracing; the method has the beneficial effects that cross-report semantic similar fields are automatically combined through large model driven semantic analysis and multi-dimensional similarity calculation, the manual checking and rule configuration time is shortened, and the data processing efficiency is remarkably improved.

The invention relates to the technical field of teaching management, discloses an intelligent teaching system based on a knowledge graph and virtual simulation and a construction method, and aims to effectively solve the difficulty in integration and management of teaching resources and provide personalized teaching experience. The system comprises a knowledge graph construction module which is used for integrating and standardizing teaching resources and constructing a knowledge graph containing knowledge points and association relationships thereof; the virtual simulation teaching module is used for displaying knowledge points in a virtual simulation form by constructing a knowledge presentation model and an interactive learning model, and evaluating learning conditions of students; the intelligent teaching control module is used for constructing a teaching strategy model by adopting a reinforcement learning algorithm and dynamically adjusting a teaching instruction according to the learning progress and the interaction effect of the students; and the execution presentation module is responsible for receiving and executing the teaching instruction and presenting the teaching content in the virtual teaching environment. The system improves the pertinence and effectiveness of teaching, and stimulates the learning interest of students.

The present invention relates to a knowledge graph construction method and system for complex relational business databases, including concept-level graph modeling, data-level graph modeling, and graph migration and integration. By accessing a business database or importing a model file, the database table structure and its constraint relationship of the business database are automatically analyzed, and a tree-like classification candidate concept list and candidate relationship list are formed according to certain rules, and a concept-level knowledge graph is quickly constructed. The classification and clustering algorithms are used to realize multi-layer classification and grouping of data records, and a data-level knowledge graph is quickly constructed. The constructed business knowledge graph is migrated and integrated, and it supports importing it into a graph database for storage and retrieval. The problem that the existing knowledge graph construction method fails to effectively consider the relevant characteristics of complex relational business databases is solved, the operation complexity is greatly reduced, and the efficiency of knowledge graph construction for complex relational business databases is improved.

The invention relates to the technical field of big data and data security, in particular to a cloud-edge collaboration and AI fusion data classification and grading method and system and a storage medium, cloud computing and edge computing are combined, the AI technology is used for data classification and grading, and rapid processing and efficient analysis of data are achieved; for structured data and unstructured data, different recognition and processing methods are adopted, including regular expression, keyword matching, dictionary matching, neural network, natural language processing and other algorithm models; particularly, a neural network and a natural language processing technology are combined with an unsupervised learning method and a semi-supervised learning method to realize an automatic classification and grading method of unstructured data in stages, rapid data processing and response can be carried out on an edge layer through a cloud edge collaboration mechanism, and the efficiency is improved. And deep analysis and model training are carried out by using the powerful computing power of the cloud layer.

The invention relates to the field of data visualization, in particular to a visual scheduling method and system for a scientific and technological achievement evaluation system. The method comprises the following steps: identifying system access behavior data of a user; performing attention hotspot field prediction on system access behavior data of the user, performing deep personalized demand evolution, and constructing a personalized scientific and technological achievement demand graph; real-time data flow matching retrieval is carried out according to the personalized scientific and technological achievement demand graph, semantic feature analysis is carried out, and deep semantic features of each text item and visual semantic features of each image and table item are extracted; and performing dynamic clustering processing according to the deep semantic features of each text item and the visual semantic features of each image and table item, and performing multi-dimensional scientific and technological achievement item aggregation to construct a scientific and technological achievement display library. According to the invention, comprehensive, dynamic, accurate and personalized scientific and technological achievement evaluation visualization is realized.

The invention relates to the field of data visualization, in particular to a data visualization method and system for an intelligent automobile data recorder of an electric vehicle. The method comprises the following steps: acquiring a real-time video stream of a driving scene based on an intelligent automobile data recorder, performing motion visual jitter elimination compensation and multi-level scene target feature perception, and constructing a multi-level scene target information sequence; performing space-time connection rendering according to the multi-level scene target information sequence, and performing scene evolution smoothing processing so as to construct a dynamic driving scene topology rendering model; vehicle running state parameters are obtained, multi-parameter correlation analysis and vehicle transient response analysis are carried out, and a real-time vehicle state evaluation report is constructed; and performing multi-target constraint calculation based on the dynamic driving scene topology rendering model, and performing optimal vehicle speed prediction to obtain the optimal vehicle speed. According to the invention, through multi-dimensional driving visual rendering, good real-time feedback and behavior early warning are provided for the user.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to the technical field of supply chain management, in particular to a digital visual management system and method applied to a building material supply chain. The method comprises the following steps: acquiring real-time position data, material attribute data and time sequence node data; performing data preprocessing and integration on the real-time position data, the material attribute data and the time sequence node data to obtain standard building material supply chain data; obtaining a construction drawing; basic building materials of the standard building material supply chain data are extracted based on the construction drawing, wherein the basic building materials comprise concrete, reinforcing steel bars and building blocks; building pouring abnormity monitoring is carried out based on the concrete, the steel bars and the building blocks, and pouring abnormity monitoring data is generated. Through multi-source data integration, ternary coupling analysis, scheduling optimization and three-dimensional dynamic visualization, the problems of information island, scheduling conflict and low management efficiency of the building material supply chain are solved, and intelligent, efficient and transparent management of the supply chain is realized.

The invention relates to an optimization method and device for large-scale graph data visual rendering and a storage medium, and the method comprises the steps: obtaining to-be-rendered graph data, constructing an R-tree spatial index according to the to-be-rendered graph data, and constructing a self-adaptive fragmentation model based on an R-tree data structure; monitoring interaction data of a user in real time, performing viewport prediction according to the interaction data, and determining a to-be-loaded sub-graph according to a prediction result and the adaptive fragmentation model; the method comprises the following steps: acquiring system data of client equipment, analyzing the performance of the client equipment according to the system data, and determining a rendering strategy according to a performance analysis result; and performing off-screen rendering on the to-be-loaded sub-graph according to the rendering strategy, and displaying an off-screen rendering result on the client device after detecting that the user completes the corresponding operation. According to the method, the rendering granularity is dynamically adjusted through the adaptive fragmentation model, and the rendering quality and performance consumption are balanced; the user interaction intention is pre-judged through a viewport prediction algorithm, and window smooth switching is achieved in combination with off-screen rendering.

In an embodiment, a method comprises: generating a user interface configured to allow selecting a product from a plurality of products that are customizable and displaying the user interface on a display device of a user computer; receiving, via the user interface, a selection of a particular product, from the plurality of products, that require customization; in response to receiving the selection of the particular product, automatically generating a plurality of customization options available for customizing the particular product; receiving one or more triggers of a plurality of triggers, generated based on customization options selected from the plurality of customization options; automatically generating one or more corresponding customization attribute values for the particular product; automatically generating one or more digital representations of the particular product, and displaying the digital representations of the particular product on the display device of the user computer.

The invention relates to the technical field of Internet of Things perception, in particular to an intelligent storage digital visual management system and method based on the Internet of Things. The method comprises the following steps: acquiring intelligent storage structure data; constructing a storage three-dimensional model according to the intelligent storage structure data; carrying out material carrying simulation based on the storage three-dimensional model to obtain material carrying data; identifying a high-frequency congestion area based on the material handling data; carrying out cargo placement analysis according to the high-frequency congestion area to obtain cargo placement data; identifying a cargo spatial form based on the cargo placement data to obtain cargo spatial form data; carrying out cargo gravity center shift detection according to the cargo spatial form data to obtain cargo gravity center shift data; and according to the cargo gravity center shift data, acquiring a cargo unique label to obtain cargo label data. The goods stacking safety rate and the goods shelf structure operation stability rate in intelligent storage are improved based on the Internet of Things sensing technology.

The invention discloses a precise urban near-surface fine wind field and pollutant concentration simulation system. The system comprises a data acquisition module, a wind field simulation module, a pollutant diffusion model module and a visualization module. The data acquisition module acquires comprehensive and accurate meteorological and geographical information through various sensors, GIS integration and data transmission processing. The wind field simulation module is based on a fluid mechanics theory, adopts a numerical model and an algorithm, considers influences such as an urban heat island effect, and realizes high-precision simulation of a near-surface wind field. The pollutant diffusion model simulates pollutant diffusion by applying a mathematical model and a proper calculation method in combination with wind field and meteorological data and the like. The visualization module converts simulation results and generates various visual graphs such as map views and time series diagrams, and is further provided with a user interaction interface and an analysis and prediction tool. The system can accurately simulate urban near-surface wind field and pollutant concentration, and provides scientific basis and powerful support for urban planning, environment management and decision making.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The present invention provides a quantitative evaluation method for quasi-real-time and refined supervision of soil and water conservation, and relates to the field of soil and water conservation technology. The quantitative evaluation method for quasi-real-time and refined supervision of soil and water conservation comprises the following steps: S1. Data acquisition; S2. Data preprocessing; S3. Feature extraction; S4. Model construction; S5. Quantitative evaluation; S6. Result visualization; S7. Supervision decision. By utilizing remote sensing technology and automated data processing procedures, the soil and water conservation conditions in a large area can be quickly acquired and analyzed to achieve quasi-real-time monitoring. For possible risks of soil and water loss, timely warnings can be issued and corresponding measures can be taken to reduce losses. Data acquisition, preprocessing, feature extraction, and model application are all automated, which greatly improves work efficiency. Due to its high efficiency, this method is suitable for soil and water conservation supervision in large areas.

The invention discloses a park carbon emission intelligent monitoring and dynamic accounting system based on a big data platform, and relates to the technical field of carbon emission management. Comprising a data acquisition module, a data integration module, a data calculation module, a carbon evaluation module, a carbon asset management module, an automatic report generation module, a visual display module, a real-time monitoring and reminding module, a data verification and auditing module and an updating and improving module. The data calculation module is used for calculating and analyzing the carbon emission according to the integrated carbon emission data to obtain rating calculation data and asset management calculation data; the data verification and auditing module is used for obtaining auditing result data according to the carbon emission report and the monitoring reminding data; and the updating and improving module is used for providing improved feedback data according to the audit result data. The system provided by the invention not only improves the efficiency and accuracy of park carbon emission management, but also provides powerful technical support for achieving the goal of carbon emission reduction through real-time monitoring and dynamic accounting.

The invention provides a multi-modal data management system and method, equipment and a medium, and the system comprises a multi-modal data quality index creation module which is used for creating at least one multi-modal data quality index for multi-modal data; the multi-modal data quality detection task creation module is used for creating a multi-modal data quality detection task for the multi-modal data according to the at least one multi-modal data quality index and distributing the multi-modal data quality detection task to the multi-modal data detection module; the multi-modal data detection module is used for executing a multi-modal data quality detection task so as to carry out quality detection on the multi-modal data; and the multi-modal data detection result generation module is used for generating and displaying a multi-modal data quality detection report according to the execution result of the multi-modal data quality detection task, and the quality and management efficiency of the multi-modal data can be remarkably improved.