The power system is one of the largest and most complex artificial systems [1, 2]. It involves two-way acquisition, transmission, fusion, interaction and utilisation of information between countless people [3, 4].

The vigorous development of distributed energy, electric vehicles and energy storage has changed the original grid form dominated by unidirectional power flow [5]. The evolution of the energy Internet in which the source, network, load and storage elements of the traditional energy power system are interconnected, shared equally, balanced between supply and demand, and optimised and interactive. The open sharing of data related to power generation. Here and now, substantial data of users is running in isolation, which will be transmitted over the wide area network [6]. How to realise the security of massive power system application data under extensive interconnection will be the first problem to be settled in the current planning and development of the energy Internet.

Identifying the association between data also requires the application of named entity recognition (NER) technology [7, 8, 9, 10, 11, 12, 13, 14, 15].

The organisation and retrieval structure of massive scattered data are extracted from the study mentioned in Reference [16, 17, 18]. In recent years, some studies propose to convert natural language into entities and relationships in knowledge base through information extraction, and construct knowledge triples to form a knowledge graph (KG). A KG is a technique for describing knowledge and the relationship between knowledge with a graph model, which consists of nodes and edges [19, 20, 21, 22]. Nodes can be either entities or abstract concepts. Entities are the most basic elements in a KG, and there are different relationships between different entities [23, 24]. A KG construction method is proposed. A power grid customer service system based on the KG of domain features is constructed [25].

This article suggests the construction and intelligent investigation of the KG of power grid physical data based on the Internet of Things for the power system. First, NLP is used to mine knowledge in the complicated physical text data of the power grid based on the Internet of Things in the power system. At the same time, a hybrid model is propounded for NER, which adopts context knowledge to meliorate the accuracy of extraction in this study. The model has improved analysis efficiency. Finally, a complete technical solution for the construction and intelligent analysis of power grid physical data KG based on the Internet of Things is verified through an example.

The power Internet of Things is the specific demonstration and application. It can comprehend the interrelationship of everything in all aspects of the power system.

Power Internet of Things is the evolutionary development form of the power Internet of Things in the construction of energy Internet. It is a complex large-scale system that deeply integrates various new technologies, showing the characteristics of a collective blend. Through the acquisition and sharing of multi-type data in the whole process of electricity, an extensive recognition inside and outside the entire electricity ecosystem can be realised. The application layer carries internal business and external business, as well as data sharing and basic support.

Data generation: The generation of data includes two forms: one is the data developed by the inherent arrangement of the institution, and the other is data that the institution collects from outside.

Data storage.

Data usage: It refers to the combination of a series of activities that an organisation performs internally on dynamic data.

Data transmission: It refers to the process by which data flow from one entity through the network to another within an organisation.

Data sharing: It refers to the stage in which data pass through the organisation and interacts with external organisations and individuals.

Data destruction: It refers to the process of making data permanently or temporarily unavailable by physical or technical means (Figure 1).

The physical service of the power grid can be partitioned into transmission lines, communication lines, control apparatus, information apparatus and instruments, transportation equipment, houses, buildings, etc. In the mechanism of the evaluation index system of the power grid physical assets, we must keep tabs on the characteristics of the power grid physical assets in furtherance of targeted evaluation and management. The characteristics of the physical service of the power grid mainly are given as follows:

There are various types of assets. The physical service of the power grid involves many industries and professions, such as power transmission, substation, distribution, transportation and communication. Equipment assets for cross-industry need to be subdivided level by level. With the rapid advancement of smart grid construction, new technologies and new products will be widely used, and the types of assets will continue to increase.

The overall scale is huge. The technical scale of the physical service of the power grid is huge, and the geographical distribution of equipment is decentralised.

The update speed is faster. As the basic carrier to support the supply of electricity commodities, the physical service of the power grid is under the background of building a more secure, reliable, environmentally friendly and economical power grid. In view of the continuous improvement of its reliability, intelligence and efficiency requirements, the replacement of power grid physical assets is faster. There are closer linkages between the phases of an asset from commissioning to decommissioning.

A standardised knowledge representation language is implemented. In RDF, knowledge always comes in the form of triples. The subject is an entity, and the predicate is an attribute. An attribute can connect two entities, or connect an entity and its attribute value. If the subject and object of the triple are regarded as the nodes of the graph, the RDF knowledge base can be viewed as a graph model. The Neo4j database can also be shared by multiple departments and users, meeting the needs of interactive infrastructure engineering data between departments.

The information is extracted from the text data of the power grid objects in the power system statistics under the Internet of Things. Triples get stored in a list format. But in the Neo4j database, different parts of the property graph are stored separately in different files, that is, entities and relationships need to be stored in different formats. The process of importing the power grid objects extracted from the information into the Neo4j database to generate a knowledge map library is shown in Figure 2.

First, entity data in text data and table data is read based on Python.

Then, in order to be able to create a relationship, these entity nodes need to be numbered, and the start node and end node of the triplet can be found through the automatically generated ID. Then. potentially duplicated nodes is dealt with so that the same entity has a unique number. The processed entity data can generate relationship files in a standard format of csv.

Finally, the Neo4j import command imports the node and relationship files into the Neo4j database to form a KG based on the power grid objects.

NER aims to recognise entities of a specified category in text.

Entity extraction for tabular data: For the Excel file extraction entity of the equipment inventory class, it is only necessary to estimate the column where the node is placed and store it separately. By numbering, they can correspond to their respective attribute relationships to form a knowledge map.

Knowledge extraction from text data: It is much more difficult to recognise information hidden in text data.

In this study, the CRF model is adopted to optimise the NER model globally for the bidirectional long short-term memory (Bi-LSTM) model (Figure 3).

First, the original data obtained in the text are transformed and encoded into a vector form suitable for computer processing before NLP-related algorithms can be applied. Traditional word mapping has the problem of sparse data. Therefore, this study adopts the skip-gram model to optimise the word vector matrix. The model learns an accurate word vector representation for each word. Given any _{i}_{−n} ⋯_{i}_{i}_{+n}, the model directly uses the word vector _{i}_{t}

Here, _{i}_{i}_{i}

After model training, the optimised word vector matrix ^{*} is achieved, which contains a distributed vector representation of all words in the vocabulary

Then, given a sequence of Chinese characters _{0}, _{1},…, _{T}_{i}_{i}_{1} represents the vector dimension. LSTM is regulated by three gates and one storage memory unit.
_{i}_{i}_{i}_{i}

At the same time, the forward LSTM obtains the hidden layer representation
_{2} represents the number of hidden layer neurons) corresponding to each input text character. In the same way, backward LSTM gets another hidden layer representation
_{0} _{i}_{−1}_{i}

Finally, the decoding of the model uses the Viterbi algorithm, maintaining two sets of variables, _{t}_{t}_{t}_{t}_{t}^{′}) is the state transition probability and _{t}

Knowing the weight vector

The KG and intelligent analysis framework of power grid physical data based on the Internet of Things constructed in this study were pilot tested in a city company. The data are obtained from a new construction of a 110 kV substation. Among them, the unstructured data are the design specification, audit report and equipment test report (Word file) of the project construction. The semi-structured data are the equipment inventory (Excel file) in the completion and acceptance stages of the project. The experimental data obtained after data cleaning and other preprocessing are 4983 Chinese text strings and 32 lists with a length of 187. Information extraction and intelligent analysis are carried out, respectively.

The evaluation standard of intelligent analysis is that natural language is encoded and converted into a word vector by the skip-gram model as the input of the Bi-LSTM-CRF model, and three tasks of Chinese word segmentation, part-of-speech tagging and NER are performed at the same time. Using cross-entropy as the loss function, the operation effect of the model is measured by the accuracy rate, recall rate and F value. Taking Chinese word segmentation as an example, the calculation equation of the indicator is as follows:

Dependency analysis is performed on text strings, and attribute relationships between entities are extracted. The graph-based dependency syntax accuracy evaluation index selects the labelled attachment score (LAS) that considers the type of dependency (represented by LLAS) and the indicator that does not consider the type of dependency unlabelled attachment score (UAS) (represented by UUAS). The equation is as follows:

This study compares the BiLSTM-CRF model with the SVM model. A total of 102,794 words are segmented by the model in this study, and 8,825 words are segmented by the SVM model (Table 1).

Comparison of the model presented in this article and SVM on the training set

P | 96.12 | 85.73 | 97.32 | 81.32 | 94.12 | 77.32 |

R | 97.01 | 82.42 | 94.32 | 80.76 | ||

F | 96.93 | 83.68 | 94.29 | 79.09 |

Bi-LSTM, bidirectional long short-term memory; CRF, conditional random field; NER, named entity recognition.

It can be seen from the results that the performance is significantly better than that of the SVM model, and the reason for the accuracy of NER is slightly lower than that of Chinese word segmentation because the recognition effect of design technical entities needs to be improved.

This study uses the 2o-carreras decoding algorithm to analyse dependencies with traditional first-order decoding (lo). Compared with second-order decoding with descendant information (20-sib), the algorithm has the optimal substructure required by the dynamic programming algorithm, with stronger expressive ability and higher accuracy (Table 2).

Comparison of different decoding methods

UAS | 81.24 | 83.42 | 84.32 |

LAS | 79.91 | 81.01 | 81.52 |

LAS, labelled attachment score; UAS, unlabelled attachment score.

It can be observed that the BiLSTM-CRF intelligent analysis model used in this study can achieve more accurate results than the traditional model in the knowledge map of power grid physical data based on the Internet of Things. After model operation, a total of 429 entity nodes and 461 relational edges between entities are extracted from 5 categories of single project name, installation address, design technology, equipment name and equipment purchase price, forming 566 knowledge triples.

This article suggests the construction and intelligent investigation of a KG of the power grid physical data based on the Internet of Things for the power system. First, NLP is used to mine knowledge in the complicated physical text data of the power grid based on the Internet of Things in the power system. Then, the constructed KG of power grid objects based on the Internet of Things for the power system completes the mining of unstructured text data and semi-structured table data based on the Bi-LSTMCRF intelligent analysis model so that the complex engineering data can be fully analysed and applied. The map can meet the automatic retrieval needs of different users, and as a platform for knowledge sharing, it breaks the barriers of inter-departmental exchange of physical data of the power grid and effectively supports the development of upper-level data applications. The physical data of the power grid based on the Internet of Things are also increasing. Therefore, the construction of the KG also needs to be constantly updated and improved. In the future work, the consistency and normalisation of the input data will be considered, and the automatic update of the physical data of the power grid and the validity of the data will be studied.

#### Comparison of different decoding methods

UAS | 81.24 | 83.42 | 84.32 |

LAS | 79.91 | 81.01 | 81.52 |

#### Comparison of the model presented in this article and SVM on the training set

P | 96.12 | 85.73 | 97.32 | 81.32 | 94.12 | 77.32 |

R | 97.01 | 82.42 | 94.32 | 80.76 | ||

F | 96.93 | 83.68 | 94.29 | 79.09 |

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Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network