2120 results about "Data graph" patented technology

The invention discloses an intelligent operation and maintenance management and alarm system based on a large model agent, and the system comprises a data collection and fusion module which is used for processing original data collected from an IT system to obtain an operation and maintenance data graph, and the original data at least comprise logs, indexes and link tracking data; the anomaly detection module is used for performing analysis based on the preprocessed data and the time sequence characteristics to determine abnormal data; the alarm optimization module is used for analyzing the semantic information corresponding to the abnormal data so as to determine the priority of the corresponding alarm; the root cause analysis module is used for performing fault reasoning on the abnormal data based on a pre-constructed knowledge graph and generating fault interpretation according to data corresponding to the abnormal data; and the automatic operation and maintenance decision module is used for generating an automatic repair strategy according to the abnormal data and the fault interpretation and executing the automatic repair strategy to repair the abnormal data.

The invention discloses an enterprise global data analysis method based on a knowledge graph and a large language model, and the method comprises the following steps: collecting internal and external multi-source data of an enterprise, cleaning, standardizing and mapping, and constructing a unified data graph; after a user inputs a natural language query, a reasoning target and task description are generated in combination with semantic understanding, two reasoning paths are formed through structural reasoning and semantic reasoning respectively, and the two reasoning paths are fused for interactive verification and optimization. Logic conflicts and data missing are detected in real time in the reasoning process, and supplementary knowledge is automatically called for local reasoning correction. Through the corrected reasoning result, the system carries out semantic alignment and correlation analysis on multi-source data, a unified enterprise data analysis view is dynamically generated, a reasoning path and evolution information are recorded in the reasoning and analysis process, and subsequent traceable query is supported. According to the method, the data integration, intelligent reasoning and decision support capabilities of an enterprise in a complex data environment are improved.

The invention relates to the technical field of knowledge graph and virtual reality fusion, in particular to a knowledge association learning method and system based on a knowledge graph and virtual reality, and the method comprises the following steps: obtaining multi-source data including text data, image data, video data and three-dimensional model data; constructing an interdisciplinary knowledge graph based on the multi-source data; mapping the interdisciplinary knowledge graph into a virtual reality environment; generating a personalized learning path based on knowledge representation in a virtual reality environment; interactive knowledge exploration is realized in a virtual reality environment; updating the interdisciplinary knowledge graph based on the user interaction data; the virtual reality environment is synchronously updated to reflect the change of the interdisciplinary knowledge graph, and the understanding ability of a learner to complex concepts is greatly improved by visualizing an abstract knowledge structure in the virtual reality environment; and the learner can more easily find and understand the relation among different discipline knowledge.

The invention discloses a digital human real-time interaction system and method. The interaction system comprises terminal equipment and a cloud server. The terminal device receives voice input by a user and transmits the voice to the cloud server. The cloud server comprises a video acquisition and processing module, an intelligent question and answer and voice generation module, an audio processing module and an image rendering module. A video capture and processing module captures original video data indicative of an object of a digital figure and generates successive image frames. The intelligent question and answer and voice generation module converts the voice into a text; performing semantic analysis on the text, understanding the intention of the user, and generating a corresponding reply text; the reply text is also converted into a reply voice. The audio processing module processes the reply voice and the continuous image frames to generate first video data. And the image rendering module fuses the first video data with the virtual background, and performs real-time processing on the fused video to generate a response video. And the terminal equipment outputs the response video.

Systems and methods for generating output data based on a data graph are provided. An output request for the output data based on the data graph is received. The output request comprises one of a natural language request from a target user or an application request from an application of the target user. The data graph has nodes and edges between the nodes. The nodes represent entities associated with an enterprise organization and the edges represent relationships among the entities. A graph data query is generated with a large language model (LLM) using the output request as a first input to the LLM. The graph data query is performed against the data graph to obtain a graph data output that represents a sub-portion of the data graph. The output data is generated with the LLM using the graph data output as a second input to the LLM.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

Systems, methods and non-transitory computer readable media for image analysis for personal interaction are provided. Audio data may be received. The audio data may include an input from an entity in a natural language. The input may include at least a first part and a second part. Further, image data may be received. The image data may depict a particular movement. The particular movement may be a movement of a particular portion of a particular body. The particular movement and the first part may be concurrent. The particular body may be associated with the entity. Further, a conversational artificial intelligence model may be used to analyze the audio data and the image data to generate a response in the natural language to the input. The response may be based on the input and the particular movement. Further, the generated response may be provided to the entity.

The invention relates to a multi-modal large model robot control method based on meta-learning fine tuning, and the method comprises the steps: carrying out the meta-learning pre-training of an OpenVLA model, and obtaining a pre-trained VLA model; performing task specific fine tuning to obtain a fine-tuned VLA model; and inputting the current visual data, the language cue word, the robot sensor data and the task target data graph into the fine-tuned VLA model, and outputting to obtain a decision control instruction for correspondingly controlling the working state of the robot. Compared with the prior art, the method has the advantages that meta-learning pre-training is carried out on diversified tasks, and universal features and strategies are extracted, so that the generalization ability of the model is improved; in addition, in a specific task, rapid fine tuning is performed through a small number of new samples, so that the model can adapt to new task requirements in a short time; and multi-modal information of vision, character cues and robot control is combined, so that the perception and decision-making capabilities of the model are improved, and the adaptability and operation efficiency of the robot in a complex environment are effectively improved.

The invention discloses an AI generation content detection and review method, device and equipment and a storage medium, and the method comprises the steps: receiving multi-modal input data which comprises text data, image data and video data; calling a large language model to carry out compliance analysis on the text data to obtain an analysis result; when the analysis result is compliance, calling a multi-modal model to identify and detect the image data and the video data, and respectively obtaining an image detection result and a video detection result; and aggregating the analysis result, the image detection result and the video detection result to obtain a content detection result. According to the method, the processing paths are automatically allocated according to the content types, redundant calculation is avoided, hardware consumption is remarkably reduced, the multi-modal detection results are aggregated into structured output, the large-batch content processing efficiency is remarkably improved, calculation resource occupation is greatly reduced, and seamless integration of the detection results and a downstream service system is achieved.

The invention provides a time-space dynamic graph construction method and system for an engineering construction project, and relates to the field of railway tunnel engineering management.The method comprises the steps that construction data entities are extracted, and an entity set is generated; taking entities in the entity set as nodes to be added into the graph, and configuring attributes for each node; adding a relation edge for each node according to a logic structure between the construction data; adding a time label and a space label to each node so as to form a data graph; associating each entity in the data atlas with a time chain and a space chain to form a data atlas attached to the time-space chain; combining a node merging strategy and a graph neural network to optimize a graph structure; and dynamically modeling dimensional changes of entities and relationships in time by means of a time sequence diagram neural network. According to the method, the dynamic change graph of the relationship between the construction data entities is constructed, so that the spatial-temporal information and the entity relationship of each process are accurately expressed and efficiently associated, and the retrieval efficiency and the management level are favorably improved.

The invention provides an agricultural field large language model training method and device and a medium, and the method comprises the steps: collecting agricultural field data book information, and carrying out the preprocessing of the information, and obtaining a first Chinese data set and a first minority language data set, collecting the first Chinese data set and the first minority language data set, generating a first mixed language data set, processing the first mixed language data set by adopting a word segmentation library, performing secondary processing by adopting a preset special character processing method, constructing a word segmentation list, and constructing a WordPiece word piece model to process and divide the word segmentation list; converting each word in the word segmentation list into a word vector by adopting a word embedding algorithm; the method comprises the following steps of: constructing a coding component and a decoder component which are based on a Transform architecture and adopt a self-attention mechanism, generating an agricultural field large language model, and training the agricultural field large language model by taking a word vector as an input to obtain a trained agricultural field large language model. The problem of supporting a large language model of Chinese and minority languages is solved.

Systems, methods, devices, and computer readable storage media described herein provide techniques for generating and / or repairing query language (QL) queries. In an aspect, an embedding is determined based on a request to generate a QL query. The embedding is compared to a layer embedding(s) of a deep data map to determine a similarity between a layer embedding and the embedding satisfies similarity criteria. A prompt is provided to a large language (LLM) to cause the LLM to generate the QL query, the prompt comprising a description of an item associated with the layer embedding. In another aspect, an alert indicating an undefined variable of the QL query is received. A query embedding associated with the QL query is compared to the layer embedding(s) to determine a candidate variable. The candidate variable is substituted in for the undefined variable, and a response comprising the repaired QL query is generated.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention provides a semantic enhancement and dynamic completion method and system for a power market data graph. The method comprises the following steps: acquiring target entity information and external data of a power market data graph by responding to a data query instruction; converting external data into structured semantic tags, then traversing the topological structure, establishing a mapping relationship between the tags and entity attributes, and combining query instruction influence logic to calculate topological integrity indexes and mark missing entities or correlate to form a missing tag set; establishing attribute mapping based on a target entity and external data, generating a semantic vector fusing cross-domain features, and projecting the semantic vector to a topological space according to a missing mark set to form cross-domain feature topological nodes and connections; embedding a missing position to reconstruct a topological structure, and checking logic connectivity; the dynamic conduction chain is analyzed, and a feedback interface containing the semantic relation and the completion structure is generated to respond to query. According to the method, conduction logic closed-loop verification of the missing elements in the power market data graph is realized.

Embodiments of the present invention provide a runner defect intelligent diagnosis and evaluation method based on a knowledge graph, comprising the following steps: collecting basic information including data, pictures, standards, and regulations related to design, manufacturing, testing, acceptance, operation, and maintenance of a runner of a hydro turbine generator set; by means of analysis and screening, reviewing and retaining data having high reliability and practicality; on the basis of the data, pictures, standards, and regulations and a similar case, extracting, from the data, defect diagnosis related service knowledge as well as entities, relations, and attributes, and constructing a defect diagnosis concept graph and a defect diagnosis entity graph; and sorting defect diagnosis knowledge points, performing semantic representation on runner defect diagnosis knowledge, and establishing a diagnosis rule knowledge base. According to the present invention, a repetition semantic matching model is used to perform text and extraction rule matching for different defects so that a relation instance of defect representation features can be obtained, the defect of a runner is evaluated on the basis of the relation instance and an accurate diagnosis result is obtained, so as to guide production.

The invention belongs to the technical field of multi-source heterogeneous data fusion and intelligent decision, and discloses a multi-source heterogeneous information fusion and analysis method for an AI large model and data knitting, which comprises the following steps: accessing multi-source heterogeneous data through data knitting, calibrating time and space by taking a key event as an anchor point, generating time and space alignment data, and synchronously constructing a dynamic metadata network. Generating a metadata graph; constructing a dynamic hypergraph, and calling a modal exclusive coding cluster to generate a multi-modal fusion feature; edge-cloud collaborative reasoning is started, a collaborative reasoning intermediate result is generated, confidence data is output by the general large model to arbitrate cross-domain conflicts, and a final decision packet is generated; triggering two-way feedback closed-loop optimization, driving data source optimization and knowledge completion of a metadata graph by an AI large model, further dynamically scheduling the priority of a data stream, and encrypting and updating large model parameters; and iterating the metadata atlas, and feeding back to a link of constructing a dynamic metadata network and generating a multi-modal fusion feature.

The invention discloses a multi-modal knowledge-based automotive body innovative design system, which comprises a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module and a multi-modal data fusion module, wherein the multi-modal data fusion module is used for receiving text data, picture data, a three-dimensional CAD (Computer Aided Design) model file and an engineering symbol expression from an automotive body design process and is used for carrying out feature extraction and semantic alignment on input data of four modals; generating a unified semantic vector representation; the cross-modal knowledge mining and graph construction module is used for extracting multi-level entities and relationships from the unified semantic vector and constructing a dynamically weighted multi-modal knowledge graph; the large-model-driven multi-hop collaborative reasoning module is used for analyzing the multi-modal design requirement of a user, carrying out multi-hop reasoning on a knowledge graph, and outputting design parameter recommendation and an interpretable reasoning chain. The method aims at breaking through the limitation of an existing design system in the aspects of multi-modal processing and shallow semantic understanding, and deep fusion and intelligent application of multi-source heterogeneous design data are achieved.

Disclosed in the embodiments of the present invention are a smart algorithm-based method and apparatus for constructing a carbon emission data graph, and a device. A specific embodiment of the method comprises: from hierarchical time series carbon emission data corresponding to a target carbon emission node, extracting time series carbon emission data corresponding to a last layer as last-layer time series carbon emission data; determining each layer of time series carbon emission data in the hierarchical time series carbon emission data that differs from the last-layer time series carbon emission data to be a prior-layer time series carbon emission data set; acquiring a carbon emission amount prediction result set corresponding to the prior-layer time series carbon emission data set; on the basis of the last-layer time series carbon emission data and a carbon emission amount prediction model, generating a carbon emission amount prediction result; on the basis of carbon emission amount prediction constraint information, performing correction processing on the carbon emission prediction result to obtain a carbon emission amount prediction output result; and, on the basis of the carbon emission amount prediction output result, constructing a carbon emission amount trend data graph. The present embodiments increase the accuracy of predicted carbon emission amount hierarchical time series.

The invention discloses a commerce and trade circulation supply chain optimization method based on multi-modal data fusion, and the method comprises the following steps: 1, multi-modal data collection: collecting the multi-modal data of the whole supply chain in real time through an Internet of Things device, an enterprise ERP system, a logistics tracking system and a third-party platform, the multi-modal data comprises text data, image data, voice data, time sequence data and spatial data. When the scheme is implemented, multi-source heterogeneous data of the Internet of Things, ERP and the like are integrated, so that the problem of an information island in a traditional supply chain is effectively solved; position coding and a cross-modal attention mechanism are introduced into the multi-modal fusion model, so that the data alignment precision is remarkably improved, for example, when'emergency order adding 'in a voice communication record is associated with a corresponding order image, the weight distribution accuracy is improved; the application of the block chain technology ensures the transparency and traceability of the supply chain decision.

The invention discloses a multi-modal data processing method, a household appliance and a control method and system of the household appliance. The method comprises the following steps: acquiring multi-modal data; wherein the multi-modal data at least comprises at least one of text data, image data and voice data, and sensor data; the sensor data is acquired by a sensor corresponding to the household appliance; and carrying out data fusion on the multi-modal data by utilizing at least two fusion modes to obtain multi-modal fusion data with a unified semantic space. Through the above mode, effective fusion of multi-modal data in the field of household appliances can be realized, data islands can be broken, further, accurate alignment of different modal data in a unified semantic space can be realized, and the understanding ability of a large language model can be effectively improved after the multi-modal fusion data is input into the large language model.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a classroom teaching-oriented student dynamic expression and action recognition evaluation system and method, and the system comprises a video collection module, an image processing module, an expression and action recognition module, an evaluation analysis module, and a feedback output module. The video acquisition module is used for acquiring video data of a classroom site, the image processing module is used for preprocessing the video data, the expression and action recognition module is used for recognizing expressions and actions of students by using a deep learning algorithm, and the evaluation analysis module is used for evaluating learning states of the students. And the feedback output module outputs the evaluation result to a teacher in a chart or report form. By integrating video acquisition, image processing, deep learning recognition, data analysis and other technologies, expressions and actions of students are monitored and analyzed in real time, so that more accurate and timely learning state feedback is provided for teachers, the teachers can adjust the teaching method in time according to the results, and the classroom teaching effect can be effectively improved.

The invention provides a cross-modal knowledge graph construction method and device, and the method comprises the steps: obtaining text data, image data and structured data of the agricultural industry; performing preprocessing and feature extraction on the text data, the image data and the structured data to obtain a text feature vector, an image feature vector and a structured feature vector; embedding and splicing the text feature vector, the image feature vector and the structured feature vector to obtain an overall feature sequence; the overall feature sequence is input into a multi-mode Transform model, and a comprehensive feature vector output by the multi-mode Transform model is obtained; the comprehensive feature vector is input into a Bi-LSTM-CRF model, and an entity and a relation label output by the Bi-LSTM-CRF model are obtained; a knowledge graph is constructed based on the entities and the relation labels, nodes of the knowledge graph are used for representing the entities, and edges of the knowledge graph are used for representing the relation labels; according to the invention, the real-time performance and the accuracy of the knowledge graph can be improved.

The invention relates to the technical field of archive management, and provides an archive resource intelligent classification and association indexing method based on multi-modal ai analysis, which comprises the following steps: preprocessing collected archive data to form a multi-modal data set comprising text data, image data and audio data; extracting feature vectors of the text data, the image data and the audio data, and fusing the feature vectors into a unified archive representation vector through a cross-modal attention fusion mechanism; constructing a hierarchical classification model, and performing intelligent classification on the archives in combination with a domain knowledge graph; the method comprises the following steps: constructing an associated index of an archive from four dimensions of entities, semantics, time and space and events, establishing a feedback mechanism, dynamically updating and optimizing the associated index, and providing multi-modal retrieval and visual display based on a classification system and the associated index. According to the intelligent classification and association indexing method for the archive resources, the problems that in an existing archive classification and indexing method, the multi-modal processing capacity is insufficient, the association mining depth is insufficient, and the efficiency is low are solved.

The invention discloses a multi-modal fusion power grid dispatching knowledge graph generation method and device, and the method comprises the steps: obtaining historical multi-modal data, including text data, image data and audio data, in power grid dispatching, inputting the historical multi-modal data into a pre-trained feature extraction fusion model, and obtaining the fusion features of power grid equipment, a power grid dispatching knowledge graph is constructed based on fusion features of power grid equipment, each node in the power grid dispatching knowledge graph represents a power grid equipment entity and a state feature thereof, and edges among the nodes represent a topology or dispatching operation relationship among the power grid equipment, so that the power grid dispatching knowledge graph is constructed through multi-modal data; through the power grid dispatching knowledge graph, reasoning basis and decision support are provided for power grid dispatching, the problems that the subjectivity is high and a correct and effective regulation and control decision cannot be made by using multi-modal data when a dispatching transaction is manually processed are solved, and a dispatcher can objectively, accurately and quickly make a regulation and control decision by referring to the power grid dispatching knowledge graph.

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 environment monitoring, and discloses a completion process data-based environment monitoring parameter comparison evaluation method, which comprises the following steps of: obtaining completion process data and environment monitoring data, and constructing a multi-dimensional data graph, mapping the completion process data and the environmental monitoring data to generate a standardized data set containing a construction feature vector and an environmental parameter vector; calculating a dynamic weight coefficient through a multi-objective optimization model based on the construction feature vector and the environment parameter vector in combination with the construction progress and the environment sensitivity, and outputting a weight matrix; the intelligent contract automatically triggers an evaluation process, generates a dynamic evaluation report containing an overproof event identifier, a compliance repair suggestion and a digital signature, and links an AR visualization system to be superposed to a BIM model to generate a three-dimensional thermodynamic diagram; and in combination with anomaly detection and a time sequence prediction model, identifying potential risks, generating a dynamic risk early warning map, and finally outputting a predictive evaluation report. According to the invention, the accuracy and real-time performance of environment monitoring evaluation can be improved.

The invention discloses a construction elevator intelligent dynamic scheduling system and method based on multi-modal deep learning, and relates to the technical field of elevator scheduling management, the system comprises a multi-modal data acquisition module, a multi-modal deep learning processing module, a hierarchical decision module, an adaptive learning module, a hybrid reinforcement learning scheduling module, and a conflict prediction and solution module; time sequence data, image data and text requirements are processed at the same time through a multi-mode deep learning architecture, a three-layer neural network decision architecture is adopted to realize full-process intelligent decision from strategy to execution, and a learning strategy and decision weight are automatically adjusted according to a project progress stage in combination with an adaptive learning system. The optimal scheduling scheme can be automatically generated according to information such as material types, quantity and latest completion time submitted by a team, the use efficiency of the construction elevator is effectively improved, resource waste and scheduling conflicts are reduced, and the method is suitable for scenes such as building construction elevator scheduling and a multi-elevator cooperative working environment.

In a method for providing a visual representation of search results, a natural language query may be obtained from a user. The natural language query may be processed to obtain key query terms. Based on the key query terms, a relevance-ranked search on a plurality of documents may be performed to obtain a subset of the plurality of documents relevant to the natural language query. Answers to the natural language query may be extracted from relevant documents among the subset of documents, and graph data may be obtained using document links associated with the relevant documents from which the answers were extracted. The graph data may include entities and relationships identified in the relevant documents. The graph data may be used to generate a graph that may be displayed to the user to enable the user to discover information previously unknown to the user.

The invention discloses a civil aircraft PHM modeling method based on cross-modal coupling, a medium and equipment, and belongs to the technical field of aircrafts. The method comprises the steps of S1, multi-modal data preprocessing: performing feature extraction and time-space / event alignment for three modalities of flight time sequence data, text data and image data, and laying a foundation for subsequent fusion modeling; s2, cross-modal feature fusion and holographic data sample construction: constructing a holographic data sample library covering the whole life cycle through cross-domain fusion and dynamic optimization; and S3, PHM physical-knowledge fusion comprehensive modeling: constructing a double-layer collaborative framework combining physical simulation and knowledge reasoning, and realizing controllable fusion of physical simulation and multi-modal knowledge. The problems that in the prior art, multi-source heterogeneous data alignment is difficult, physical mechanism and data driving fusion is insufficient, and model interpretability is poor can be solved.

The invention discloses a building quality inspection management system, and the system comprises a distributed intelligent sensor network which comprises a multi-type sensor array disposed at a key node of a building structure, a mobile detection terminal, and an unmanned plane inspection unit; the multi-modal data fusion module is used for performing space-time alignment and feature extraction on the sensor data, the image data and the BIM model data; the digital twinborn visual platform comprises a quality evaluation engine, a defect traceability module and a three-dimensional visual interface; the defect detection rate in a dark light environment is obviously improved, full-coverage detection of floors can be realized in a short time, field sensor data and a BIM model are synchronized in real time through a 5G network, and when a quality problem is found, the system analyzes 12 types of influence factors such as materials, processes and environments through a Bayesian network, so that the quality problem is solved. The system can trace back to specific construction teams, equipment numbers and even meteorological data, and can reduce the requirements of personnel through intelligent detection, thereby reducing the working cost.