A deep learning method based on prior knowledge with dual training for solving FPK equation

The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.

Effects of Health Education with Problem-Based Learning Approaches on the Knowledge, Attitude, Practice and Coping Skills of Women with High-Risk Pregnancies in Plateau Areas

Objective: Given the unique cultural background, way of life, and physical environment of the Tibetan Plateau, this study aims to investigate the effects of health education using problem-based learning (PBL) approaches on the knowledge, attitude, practice, and coping skills of women with high-risk pregnancies in this region. Methods: 76 high-risk pregnancy cases were enrolled at Tibet’s Linzhi People’s Hospital between September 2023 and April 2024. 30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education. 46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches. Two groups’ performance on their health knowledge, attitude, practice and coping skills before and after interventions were evaluated, and patient satisfaction were measured at the end of the study. Results: There was no statistical significance (P P P Conclusions: Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge, attitude and practice and healthier coping skills. Also, it can improve patient sanctification.

Analysis on the Changes of Research Hotspots in the Prevention and Treatment of COVID-19 by Traditional Chinese Medicine Based on Knowledge Graph

Objective: To grasp the changing trend of research hotspots of traditional Chinese medicine in the prevention and treatment of COVID-19, and to better play the role of traditional Chinese medicine in the prevention and treatment of COVID-19 and other diseases. Methods: The research literature from 2020 to 2022 was searched in the CNKI database, and CiteSpace software was used for visual analysis. Results: The papers on the prevention and treatment of COVID-19 by traditional Chinese medicine changed from cases, overviews, reports, and efficacy studies to more in-depth mechanism research, theoretical exploration, and social impact analysis, and finally formed a theory-clinical-society Influence-institutional change and other multi-dimensional achievement systems. Conclusion: Analyzing the changing trends of TCM hotspots in the prevention and treatment of COVID-19 can fully understand the important value of TCM, take the coordination of TCM and Western medicine as an important means to deal with public health security incidents, and promote the exploration of the potential efficacy of TCM, so as to enhance the role of TCM in Applications in social stability, emergency security, clinical practice, etc.

Knowledge and practice skills on home-based urinary catheter care among parents of under-five children with urinary catheter

Objectives:The objectives of this study were to assess the knowledge and practice skills on home-based urinary catheter care among parents of under-five children with urinary catheter.Materials and Methods:This cross-sectional study was conducted from June 1,2021,to September 11,2021,in a tertiary hospital in north India.Purposive sampling was used to select 50 participants.Three instruments were employed for data collection after fulfilling sample criteria;for baseline information demographic tool,knowledge questionnaires,and a practice checklist.Data were analyzed using descriptive and inferential statistics.Results:On assessment of 50 participants,the majority of parents aged above 30 years(74%).Most of the participants were male(82%),graduated(38%),and working in the private sector(58%).Similarly,two-thirds of participants were residing in a nuclear family(64%)with a single child 32(64%)and family income<5000 rupees per month(60%).The mean score of knowledge was 1.94±0.81 and that of practice skills was 1.98±0.85 on home-based care.Regression analysis showed that knowledge of parents was significantly associated with qualification(β:1.821,P=0.002).Similarly,association of practice skills of parents with gender(β:1.235,P=0.050)and qualification(β:1.889,P=0.00)was significant.Conclusion:The general findings of our study showed that parents’education and occupation played a significant role in a child’s care.Parental education and catheter care skills positively affect the child and reduce readmission rates.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

Knowledge Graph Analysis of International Chinese Language Textbooks Based on CiteSpace

Drawing upon relevant papers from Chinese core journals and CSSCI source journals in the CNKI China Academic Journals Full-Text Database spanning from 1992 to 2023,this study utilizes CiteSpace as a research tool to visually analyze the knowledge graph structure of research on international Chinese language textbooks in China.The study maps out the publication timeline,authors,institutions,collaborative networks,and keywords pertaining to research on international Chinese language textbooks.The findings indicate that research on international Chinese language textbooks commenced early and continues to maintain a certain level of research interest,yet lacks sufficient research output.Research institutions predominantly reside in universities and publishing groups specializing in language or education,with collaboration between institutions being relatively scarce.High-frequency keywords in recent research on international Chinese language textbooks include“Chinese language textbooks for the Foreigners,”“Chinese language textbooks,”“Teaching Chinese Language for the Foreigners,”“Textbook compilation,”“International Chinese Language Education and Localization,”which reflect a diversified research perspective with interdisciplinary trends.Future research priorities encompass research on localization,customization of textbooks,and evaluation of textbooks which represent forefront directions of research.

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.

Construction of fault diagnosis system for control rod drive mechanism based on knowledge graph and Bayesian inference

Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research object,and a fault diagnosis system was proposed based on knowledge graph.The subject–relation–object triples are defined based on CRDM unstructured data,including design specification,operation and maintenance manual,alarm list,and other forms of expert experience.In this study,we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events.A three-layer robustly optimized bidirectional encoder representation from transformers(RBT3)pre-training approach combined with a text convolutional neural network(TextCNN)was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault query.The RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent simultaneously.Experiments on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity extraction.Additionally,a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator automatically.Furthermore,the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis system.Finally,a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed,making the CRDM fault diagnosis process intuitive and effective.

RotatS:temporal knowledge graph completion based on rotation and scaling in 3D space

As the research of knowledge graph(KG)is deepened and widely used,knowledge graph com-pletion(KGC)has attracted more and more attentions from researchers,especially in scenarios of in-telligent search,social networks and deep question and answer(Q&A).Current research mainly fo-cuses on the completion of static knowledge graphs,and the temporal information in temporal knowl-edge graphs(TKGs)is ignored.However,the temporal information is definitely very helpful for the completion.Note that existing researches on temporal knowledge graph completion are difficult to process temporal information and to integrate entities,relations and time well.In this work,a rotation and scaling(RotatS)model is proposed,which learns rotation and scaling transformations from head entity embedding to tail entity embedding in 3D spaces to capture the information of time and rela-tions in the temporal knowledge graph.The performance of the proposed RotatS model have been evaluated by comparison with several baselines under similar experimental conditions and space com-plexity on four typical knowl good graph completion datasets publicly available online.The study shows that RotatS can achieve good results in terms of prediction accuracy.

Knowledge flow analysis of knowledge co-production-based climate change adaptation for lowland rice farmers in Bulukumba Regency,Indonesia

To increase the resilience of farmers’livelihood systems,detailed knowledge of adaptation strategies for dealing with the impacts of climate change is required.Knowledge co-production approach is an adaptation strategy that is considered appropriate in the context of the increasing frequency of disasters caused by climate change.Previous research of knowledge co-production on climate change adaptation in Indonesia is insufficient,particularly at local level,so we examined the flow of climate change adaptation knowledge in the knowledge co-production process through climate field school(CFS)activities in this study.We interviewed 120 people living in Bulukumba Regency,South Sulawesi Province,Indonesia,involving 12 crowds including male and female farmers participated in CFS and not participated in CFS,local government officials,agriculture extension workers,agricultural traders,farmers’family members and neighbors,etc.In brief,the 12 groups of people mainly include two categories of people,i.e.,people involved in CFS activities and outside CFS.We applied descriptive method and Social network analysis(SNA)to determine how knowledge flow in the community network and which groups of actors are important for knowledge flow.The findings of this study reveal that participants in CFS activities convey the knowledge they acquired formally(i.e.,from TV,radio,government,etc.)and informally(i.e.,from market,friends,relatives,etc.)to other actors,especially to their families and neighbors.The results also show that the acquisition and sharing of knowledge facilitate the flow of climate change adaptation knowledge based on knowledge co-operation.In addition,the findings highlight the key role of actors in the knowledge transfer process,and key actors involved in disseminating information about climate change adaptation.To be specific,among all the actors,family member and neighbor of CFS actor are the most common actors in disseminating climate knowledge information and closest to other actors in the network;agricultural trader and family member of CFS actor collaborate most with other actors in the community network;and farmers participated in CFS,including those heads of farmer groups,agricultural extension workers,and local government officials are more willing to contact with other actors in the network.To facilitate the flow of knowledge on climate change adaptation,CFS activities should be conducted regularly and CFS models that fit the situation of farmers’vulnerability to climate change should be developed.

Multi-Modal Military Event Extraction Based on Knowledge Fusion

Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured text.Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data.Although researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text scenarios.To solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge fusion.Specifically,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained models.This approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and events.For event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger words.This approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater depth.Furthermore,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge fusion.The experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the model.These results validate the effectiveness of our method in extracting event elements from multi-modal data.

Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor malignancy.MRI images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging.Furthermore,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori knowledge.This paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI images.During the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been constructed.Instead of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and Flair.These aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each stage.In the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while downsampling.In the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM outputs.The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge.The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant trend.The existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and capabilities.This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation.First,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss function.It utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high similarity.Then,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side deployment.We propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro perspectives.Through comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault diagnosis.In addition,experiments show that our approach has a smooth training process and good balance.

Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.

Effectiveness of simulation-based learning regarding management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students:A quasi-experimental study

Objective:To assess the effectiveness of simulation-based learning regarding the management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students.Methods:This was a quasi-experimental study conducted among 1152nd-year nursing students.The participants were selected by a simple random sampling technique.The participants were divided into an experimental(n=56)and a comparison group(n=59)by a random table method.Data were analyzed using descriptive and inferential statistics with SPSS version 20.Results:There were significant differences in mean post-test knowledge scores(P=0.03)and mean post-test self-efficacy scores(P=0.001)between the experimental and the comparison groups while the difference in mean post-test clinical decision-making ability scores between the two groups was non-significant(P=0.07).A positive correlation was found between knowledge and clinical decision-making ability in pre-test(P=0.03)and in post-test(P<0.001)and a non-significant correlation was found between pre-test knowledge and self-efficacy score(P=0.52)among the experimental group.Conclusions:Simulation-based learning regarding the management of post-COVID complications is effective among nursing students.Simulation labs should be established in health care settings where simulation training can be provided for updating the knowledge,clinical decision-making ability,and self-efficacy of nursing personnel during program installment and continuous nursing education.

DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph

In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.

Construction and application of knowledge graph for grid dispatch fault handling based on pre-trained model

With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.