934 results about "Data graph" patented technology

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.

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 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.

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 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 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 tunnel lining crack identification method and system based on panoramic space image splicing, and relates to the technical field of tunnel visual identification. The method comprises the following steps: acquiring a tunnel lining image array, and performing local splicing on tunnel lining images by using a splicing model based on cylindrical surface projection to obtain a local spliced image; fusing and splicing the local spliced images by using the global image space model to obtain a panoramic spliced data graph; and abnormal region detection is carried out on the panoramic splicing data graph according to the spatial local difference, and a tunnel lining crack identification result is obtained. According to the method, the lining panoramic space model based on image two-stage fusion is used for splicing, so that the visual coherence and naturalness of a global spliced image are improved.

The invention discloses an AI-based enterprise special asset diversified value-added service method and system, and the method comprises the steps: receiving multi-source heterogeneous data of enterprise special assets, and obtaining an enterprise special asset data graph containing asset attributes, market environments and historical disposal records; inputting the enterprise special asset data atlas into a pre-trained AI-based deep value evaluation model, and generating an asset value evaluation report including basic values, potential value-added paths and risk factors; according to the asset value evaluation report, utilizing a multi-target optimization algorithm to generate a plurality of preliminary value-added scheme sets for different value-added targets in parallel; and inputting the initial value-added scheme set into an intelligent matching engine, carrying out scheme feasibility verification and suitability screening, and outputting an optimal customized value-added service scheme package. By utilizing the embodiment of the invention, the accuracy of asset value analysis and the potential mining efficiency can be improved, and dynamic optimization and risk controllability of an asset value-added path are realized.

The invention belongs to the technical field of image processing, and discloses a remote digital image analysis cooperation system. The virtual diagnosis module is used for collecting a continuous pathological section sequence and carrying out space registration to obtain a registration section; the model building module is used for performing three-dimensional voxel reconstruction on the registration slices to obtain a three-dimensional voxel reconstruction model, and performing semantic enhancement to obtain a three-dimensional pathological voxel model; the collaborative interaction module is used for generating virtual avatars of G experts based on the VR technology, labeling the three-dimensional pathological voxel model and obtaining space anchor point labeling data of the experts; the graph driving module is used for constructing a structured semantic tree according to the space anchor point annotation data, detecting annotation conflicts and obtaining an annotation conflict detection result; the conflict resolution module is used for performing conflict resolution on the marking conflict detection result to obtain a consensus suggestion; the collaborative efficiency and conclusion reliability of remote diagnosis are improved, and a systematic solution is provided for precise diagnosis of complex pathological cases.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention provides a retrieval enhancement generation method and system for multi-modal semantic alignment in the technical field of computer vision and natural language processing. The method comprises the steps that S1, a large amount of text data, image data and 3D model data are acquired to construct a multi-modal vector knowledge base; s2, acquiring an input query image and a natural language question, and performing joint semantic coding on the query image and the natural language question to obtain a multi-modal query feature vector; s3, evaluating complexity, dynamically adjusting retrieval parameters based on the complexity, and retrieving Top-K candidate knowledge from the multi-modal vector knowledge base through an approximate nearest neighbor search method based on the retrieval parameters and the multi-modal query feature vectors; and S4, in combination with multi-dimensional similarity and token-level interaction alignment, screening each candidate knowledge, and outputting a retrieval result. The method has the advantages that the reasoning precision, the answer credibility and the response efficiency in a complex question and answer scene are greatly improved.

The invention provides an image data and pattern spot management system and method and electronic equipment, and relates to the technical field of geographic information data processing, and the image data and pattern spot management system comprises an image data indexing module which is used for carrying out three-dimensional indexing on a remote sensing image to obtain a space-time-data source association chain of the remote sensing image, the remote sensing image contains pattern spot data with an identifier; the pattern spot complete cycle construction module is used for performing multi-temporal analysis on the pattern spot data to obtain an analysis result, and constructing a pattern spot change life cycle map of the pattern spot data in combination with the association chain; and the tracing module is used for positioning the target pattern spot when the target pattern spot needs to be traced to obtain a change node of the target pattern spot and a space-time-data source index corresponding to the change node, and searching in the association chain according to the change node and the space-time-data source index to obtain a traceable data chain. According to the invention, the storage and management of the geographic image data and the pattern spot data are more systematized and refined.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle detection tracking device, method and equipment, which comprises a data sensing unit, a calculation unit, a mechanical and power supply unit, a communication and storage unit and a countering execution unit, and is characterized in that the data sensing unit is used for acquiring image data and servo turntable rotation angle data; the calculation unit is used for deploying a lightweight neural network model and a multi-algorithm fusion process, processing sensor data and realizing unmanned aerial vehicle target detection and tracking logic control; the mechanical and power supply unit is used for providing physical support, servo motion capability and energy supply for the device; the communication and storage unit is used for real-time data transmission and storage and encryption archiving of historical data and image videos; and the countering execution unit is used for implementing signal interference or physical interception on the detected and tracked target. Therefore, the problems of easy target loss, low small target identification precision, poor complex environment adaptability, insufficient countering means and the like of a traditional device are solved.

The invention discloses a dynamic form generation and cross-database adaptation method based on metadata driving, and belongs to the technical field of data architectures. The method comprises the steps that an original metadata attribute graph is established; on the basis of the original metadata attribute graph and user roles and equipment parameters for calling the forms, generating an optimal equipment form interaction data graph corresponding to each user permission; verifying an interaction path between the forms and a cross-database demand of a target database type to carry out cross-database mapping adaptation analysis, and generating an optimal form data adaptation database of the corresponding equipment of each user permission; and performing rule compiling based on multi-source heterogeneous data input by a user in real time and execution logic in the original metadata attribute graph, marking an incremental propagation path in the optimal form interaction data graph of the corresponding equipment of each user permission, and updating the optimal form data adaptation database of the corresponding equipment of each user permission. The method has the advantages that the data interaction efficiency is improved, and the interaction round-trip times and redundant data transmission are reduced.

The invention relates to the technical field of intelligent medical monitoring, in particular to a personalized health monitoring and early warning system for elderly people living alone fused with multi-dimensional data, and the system comprises a data collection and preprocessing module which collects multi-dimensional physiological parameter signals of a user and converts the multi-dimensional physiological parameter signals into multi-dimensional medical features; the health prediction module performs health category prediction on the multi-dimensional medical features of the current time node based on a pre-trained deep learning model, generates a data graph under each time node, and stores the data graph and a corresponding health category prediction result in a specified directory; the early warning module analyzes whether the multi-dimensional physiological parameter signals under each time node are abnormal or not in a mode of combining a hard threshold value and a soft threshold value, and if yes, an alarm is given out; and the abnormity output module calls and outputs the data maps of the preset duration before and after the abnormity alarm time node and the corresponding health categories. According to the invention, personalized monitoring and risk early warning of the health state of the elderly can be realized more accurately from multiple dimensions.

The invention discloses a graphic rendering system and method based on OpenGL SC and a related product. The system comprises a rendering interface module used for providing a high-order graphic rendering function interface for an application program to receive graphic modeling data; the data conversion module is used for converting the graphic modeling data into intermediate drawing data conforming to the OpenGL SC standard; the graphic processing module is used for executing graphic calculation and rendering logic based on the intermediate drawing data and generating a rendering instruction; and the graph drawing module is used for calling the underlying OpenGL SC function library to execute the rendering instruction so as to complete graph rendering. According to the graphic rendering system, the underlying graphic pipeline is packaged inside, the development threshold and complexity are reduced through the high-order graphic interface, and the development efficiency and reliability are improved. All the modules are standardized and coordinated to realize complete process packaging from data to rendering, so that optimization and maintenance are integrated inside, and the maintainability, expandability and stability of the system are enhanced.

A method for generating insights into operational data using a language model includes receiving a user query; receiving summarized operational data, wherein summarized operational data is generated by: receiving operational data; generating an operational data graph, wherein the operational data graph comprises a plurality of nodes and edges; generating a plurality of vectors describing relationships between the plurality of nodes and edges; and applying a data model to the plurality of vectors to generate a natural language description of the plurality of vectors; generating, based on the user query and the summarized operational data, a prompt for querying a first large language model; transmitting the prompt to the first large language model; receiving a natural language response to the prompt; and generating, based on the natural language response and one or more properties of the operational data graph, one or more visualizations corresponding to the natural language response.

The invention discloses a light energy power station fault prediction system based on deep learning. The system comprises a data acquisition module used for reading equipment operation data from a sensor; the data preprocessing module is used for denoising, interpolating and standardizing the equipment data; the graph convolutional network construction module is used for constructing an equipment data graph structure and extracting features; the Lemap dimension reduction module is used for mapping the high-dimensional equipment features to a low-dimensional space; the time sequence modeling module is used for constructing a time sequence prediction model based on the low-dimensional features; the hyper-parameter optimization module is used for optimizing hyper-parameters of the time sequence model; the model verification module is used for evaluating the precision and response time of the fault prediction model; the model deployment module is used for deploying the prediction model to a monitoring system; the fault prediction and early warning module is used for monitoring in real time and generating fault early warning; and the continuous optimization module is used for regularly optimizing and retraining the fault prediction model. The method achieves the high efficiency of fault prediction of the light energy power station, remarkably improves the prediction precision and the reliability of equipment operation, and is widely suitable for equipment monitoring and early warning.

The invention provides an AGV task scheduling method and system based on a graph neural network, and the method comprises the following steps: 1, receiving a task request, distributing a task to an AGV according to the task request, and continuously collecting the data of the AGV; 2, cleaning and standardizing the collected data; 3, calling in a graph neural network model, inputting the cleaned and standardized data into the graph neural network model, and performing real-time prediction and AGV task scheduling and execution adjustment through the model; 4, the AGV executes the task and updates the data in real time, and the graph neural network model continues to conduct task scheduling and execution adjustment according to the data updated in real time till the task request is completed. According to the invention, cooperative work of a plurality of AGVs can be realized, and conflicts are avoided, so that the overall working efficiency is improved, and scheduling management of cooperative tasks of the plurality of AGVs is optimized.

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention provides a medical emergency processing method, system and device based on big data and a medium, and belongs to the technical field of medical information.The method comprises the steps that a medical emergency event is responded, and structured event description data is generated; dynamically collecting and cleaning data, and constructing a data graph; performing illness condition criticality grading on the wounded, predicting illness condition development trend by combining a medical image analysis result of the wounded, and outputting an injury condition grading report; based on an improved VRPTW model and a genetic algorithm model, generating a scheduling scheme from ambulance initial assignment to path planning to medical resource pre-allocation; the resource execution state is tracked in real time, when the response time exceeds the set amplitude of the expected response time, the reinforcement learning model is started to dynamically adjust the scheduling scheme based on the data graph, and an effect evaluation report is generated. According to the invention, through multi-mode signal receiving, data integration, intelligent injury assessment, resource scheduling optimization and dynamic adjustment, rapid, accurate and efficient medical emergency response is realized.

The invention discloses an ear-nose-throat endoscope wireless image transmission system based on NFC near field triggering, relates to the field of medical apparatuses and instruments, and solves the problem that an existing wireless image transmission system is poor in image acquisition and transmission efficiency. The data acquisition module is used for performing cavity cross-sectional area analysis according to an image intercepted by an endoscope to obtain target endoscopic video analysis data, screening disease matching patients according to patient symptoms and obtaining corresponding endoscopic video analysis data to obtain endoscopic patient initial matching data; the data analysis module is used for analyzing the cross sectional area of the endoscopic video stream and the endoscopic depth of a lens to obtain endoscopic cavity similarity and performing type division on the endoscopic video stream of the matched patient according to the endoscopic cavity similarity to obtain video stream matching data; and the image transmission module is used for carrying out endoscope near-field triggering setting and endoscopic image wireless transmission on the target endoscopic patient according to the video stream matching data. According to the system and the method, the collection efficiency and the transmission efficiency of the endoscopic image can be improved.

Systems and techniques for environment mapping are described. In some examples, a system receives image data and depth data captured using at least one sensor. The image data and the depth data both include respective representations of an environment. The system processes the image data using semantic segmentation to identify segments of the environment that represent different types of objects in the environment in the image data. The system combines the depth data with the semantic segmentation to generate a voxel-based three-dimensional map of the environment.

The invention relates to a public opinion information intelligent processing method and system based on multi-modal data fusion. The method comprises the steps of obtaining real-time text data, image data, video data and social attribute data; performing keyword rule matching and deep learning model analysis on the text data in parallel, comparing confidence coefficient difference values of rule output and model output, and determining a text analysis result; performing cross-modal fusion on the multi-source feature vectors, and dynamically distributing feature weights according to the data quality of each modal to generate weighted fusion features; calculating a social influence value based on the user authority, the propagation path depth and the time attenuation coefficient in the social attribute data, and identifying a key community cluster; further carrying out weighted matching on a pre-constructed domain strategy matrix through multi-modal evidence; and adjusting a rule weight and a model parameter according to the target domain configuration result and the weighted fusion feature. According to the method, the public opinion field can be accurately identified, the processing scheme is intelligently matched, and a visual and understandable decision basis is provided.

The invention relates to the technical field of intelligent tourism management, and discloses a tourist behavior dynamic modeling method based on space-time big data, which comprises the steps of collecting and preprocessing multi-source positioning data, inputting the multi-source positioning data into a multi-source positioning fusion engine, and combining statistical optimization and deep learning model fusion to obtain a high-precision positioning model. Track reconstruction and time sequence aggregation are carried out on continuous position points of tourists based on a high-precision positioning model, a structured activity data graph is generated, activity data of the tourists at all positions are obtained, activity preference characteristics are extracted through an association rule mining and clustering algorithm, and a Markov chain and a spatio-temporal evolution model are combined to predict a tourist flow trend. And forming a flow prediction result, and generating a visual dynamic decision support by using the prediction result. According to the invention, visual and dynamic decision support is provided, the crowd congestion is relieved, and the operation safety of the scenic area is guaranteed.

The invention discloses a synchronous positioning and mapping method and system based on multi-sensor fusion, a storage medium and electronic equipment. The method comprises the following steps: acquiring IMU data, an image and point cloud data measured by a laser radar; performing pre-integration on the IMU data to obtain real-time pose estimation, and performing laser probability updating and visual probability updating based on the real-time pose estimation to obtain an updated local map; a laser point cloud matching residual error, a visual reprojection residual error, an IMU pre-integration residual error and a marginalization residual error are constructed, a residual error equation is constructed based on the laser point cloud matching residual error, the visual reprojection residual error, the IMU pre-integration residual error and the marginalization residual error, a sliding window is adopted for nonlinear optimization, and robot pose estimation is obtained; and visual loopback detection, laser loopback detection and pose global optimization are carried out. According to the method, the calculation rate, the positioning precision and the reliability can be improved in a complex or feature degradation scene.

The invention relates to the technical field of network advertisement processing, and discloses a method for intelligently identifying and automatically closing website advertisements based on a multi-modal large model. The method comprises the following steps: acquiring text data, image data and interactive behavior data of a website page in real time, and performing format standardization processing on each modal data to obtain a multi-modal original data set; extracting text features, image features and behavior features of the advertisement candidate area according to the data set, and establishing a feature association index; performing cross-modal semantic fusion on the feature association index through a pre-trained multi-modal large model to generate an advertisement recognition probability value; comparing the advertisement recognition probability value with a preset threshold value, judging whether the advertisement candidate area is a target advertisement or not, and generating a recognition result; and triggering a page element operation instruction based on the identification result, executing an automatic closing operation on the target advertisement area, and recording an operation log. According to the method, interference of advertisements on user browsing is effectively reduced, and network use experience is improved.

The invention provides a data analysis large screen generation method and device and a data analysis system.The method comprises the steps that an input data analysis requirement text is obtained, and the data analysis requirement text is a text describing a data analysis requirement; the data analysis demand text, the chart type and the data field are matched, a target chart type and a target data field are obtained, the target chart type is the chart type meeting the data analysis demand, and the target data field is the name of data needed by meeting the data analysis demand; matching a chart template according to the target chart type to obtain a target template; acquiring data corresponding to the target data field and filling the data into the target template to obtain a data chart; and generating a data analysis large screen according to the data chart. The problem of low efficiency of data analysis in the prior art is solved.

The invention relates to the technical field of industrial intelligent control, and discloses a multi-model collaborative optimization industrial MES intelligent control system. The system comprises a data analysis module, a knowledge enhancement module, a collaborative decision engine module, a decision fusion module and a control execution module. And the data analysis module executes tree structure-based data link analysis on the MES multi-source heterogeneous data, and constructs a production data tree structure with a multi-layer father-son relationship. And a knowledge enhancement module injects a domain knowledge template into each node of the structure to form a knowledge-enhanced production data graph structure. And the collaborative decision engine distributes the graph structure to a plurality of heterogeneous artificial intelligence models for parallel analysis through the unified service gateway. And the decision fusion module performs fusion processing based on confidence weighting on the output of each model to generate a comprehensive control decision instruction, and finally the control execution module drives the MES to complete production adjustment, equipment optimization and material scheduling. According to the system, the accuracy, the real-time performance and the interpretability of industrial intelligent decision making are improved.