With the development of the digital economy and digital technology, more and more scholars pay attention to data asset research [1–4]. The discussion on grid data capitalisation still focuses on the theoretical level, including the discussion and definition of related concepts, the value expression method of data assets, the conceptual framework, mechanism and challenges of data capitalisation [5, 6]. In the related research on the value expression of data assets, the possible impact of a mining data value on enterprises is mainly discussed [7, 8].
A knowledge graph is a graph knowledge base that can organise scattered knowledge very well [9, 10]. The nodes of the knowledge graph in a certain domain represent the entities in the domain, and the lines represent the connections between entities in the domain, organising scattered domain knowledge . Since knowledge graphs support semantic search, they are mainly divided into open domain and vertical domain knowledge graphs . With the in-depth application of knowledge graphs in specific fields, many knowledge bases or knowledge graph platforms have emerged in the field of power grids.
In recent years, the Internet of Things has been widely used in industrial production optimisation, management improvement, service improvement, energy conservation and emission reduction [13, 14]. The application prospect of the Industrial Internet of Things is very broad. The Internet of Things builds smart factories with adaptability and resource efficiency by improving the computerization, digitisation and intelligence of the manufacturing industry [15, 16]. The series of policy documents for the ‘Internet + Advanced Manufacturing’ to develop the Industrial Internet propose to vigorously develop intelligent manufacturing and accelerate the in-depth integration of information and manufacturing technologies [17, 18]. At present, China’s industrial data acquisition technology and application are still in their infancy, and there are serious problems such as insufficient sensor deployment, a limited amount of collected data, low accuracy, and low efficiency [19, 20]. In addition, practical problems such as the wide variety of industrial communication protocols, poor compatibility and interoperability are prominent. It cannot support real-time industrial data collection and real-time analysis, intelligent optimisation and scientific decision-making and other business requirements [21, 22]. The following are the problems that arise in the Industrial Internet of Things: The networking rate of industrial equipment is low. Industrial communication protocol standards are numerous and incompatible with each other. Industrial data collection has potential data security risks. It involves a large amount of important industrial data and user privacy information. There are certain data security risks during transmission and storage, and there is also the risk of hackers stealing data and attacking enterprise production systems.
The networking rate of industrial equipment is low.
Industrial communication protocol standards are numerous and incompatible with each other.
Industrial data collection has potential data security risks. It involves a large amount of important industrial data and user privacy information. There are certain data security risks during transmission and storage, and there is also the risk of hackers stealing data and attacking enterprise production systems.
The power grid technical standard system planning is the smart grid standard planning, which plays a basic supporting role in the construction of smart grids . With the changes in internal and external situations, the original standard system has been unable to meet the needs of smart grid standardisation in the new era, and the smart grid technical standard system has been revised .
The global energy structure and pattern have undergone profound changes. The large-scale development and wide application of clean energy have become the mainstream of world energy development.
Electricity marketisation has brought new changes. The electricity market reform characterised by the approval of transmission and distribution prices and the liberalisation of the electricity sales market is gradually deepening.
Common supporting technologies improve the level of power grid intelligence. The wide application of common technologies such as cloud-based technology, superconducting materials, and power meteorology enables smart grids to be used in large-scale power grid security early warning and active distribution network fault reconstruction.
There have been major adjustments to achieve some strategic goals in the implementation of smart power grids. The technical structure, business model and management method of the strong smart grid will also undergo great changes.
The reform of standardisation work is deepened. Comprehensive construction mode actively plays the supporting role of standardised services, and carries out the construction of national technology standard innovation base (smart grid).
In summary, this paper focuses on the research of the industrial Internet of Things and power grid technology based on the knowledge graph and the data asset relationship model. First, the existing literature in this paper is sorted out to define the concept and characteristics of data assets. Then, a training model based on the knowledge graph and data asset relationship model is proposed. At the same time, the key technologies of the Industrial Internet of Things and the theoretical model framework of power grid technology are proposed, including device access, protocol conversion, edge data processing, connectivity, the first entry of data and the rapid growth of data volume. Finally, experiments are carried out to verify the framework proposed in this paper. Experiments show that the research on industrial Internet of Things and power grid technology based on the knowledge graph and data asset relationship model is of great significance for enterprises to exert data value and promote the development of the digital economy.
This paper first defines the concept and characteristics of data assets. Then, it introduces the way in which the value of data assets is reflected in the existing research and the related discussions on the path of data assetisation.
Definition of the concept and characteristics of data assets: Clarifying the concept of data assets is an important basis for enterprises to realise data assetisation, which is helpful for enterprises to sort out and take stock of data assets.
The way to reflect the value of data assets: The decision support system based on data analysis is the main way for data to exert its value. Reliable data evidence, rather than intuition and experience, is more accurate in forecasting, and a data-driven approach can significantly improve business performance and competitiveness.
Theoretical and practical exploration of data assetisation: It has become a general consensus that data has become a key asset of an enterprise. Lack of corresponding regulations and standards for data assetisation, such as data ownership definition, privacy and data security protection, and data asset value evaluation standards prevail. Part of it is difficult to effectively develop and utilise relevant data.
In this paper, a knowledge graph model is constructed based on CNN and BiLSTM network. Based on BiLSTM and CNN, CRF is introduced, and the specific structure is shown in Figure 1.
First, the input needs to extract the data asset text input sequence. To calculate the convolution equation:
The contextual semantic content of the word window convolution by the CNN model can better represent the local features of the text sequence.
Then, long short-term memory (LSTM) is a special kind of recurrent neural network (RNN), which consists of one cell unit with three gates. The cell unit is the core computing power and records the current computing state. Forget gates, input gates, and output gates regulate the flow of information to and from memory cells. The forget gate clears the memory cells of useless information. The input gate selects the input information of the current memory cell. The output gate determines the final output of the information so that the storage unit can efficiently store the semantic information of a longer sequence. The calculation process is as follows:
At the same time,
Among them, the hidden layer
Finally, CRF can obtain an optimal prediction sequence through the relationship between adjacent labels. It can make up for the shortcomings of BiLSTM. Calculated as follows:
Industrial data collection is the use of ubiquitous sensing technology to efficiently collect and aggregate information on multi-source equipment, heterogeneous systems, operating environment, human and other elements in real time and in the cloud. The scope of industrial data collection in this paper mainly refers to the data collection of industrial field equipment and the data collection of intelligent products/equipment outside the factory, as shown in Figure 2.
The Industrial Internet of Things belongs to the Internet of Things technology in the industrial field, and its core essence is to establish a comprehensive interconnection between people and various elements of the industrial system. Through data flow and analysis, an intelligent transformation of industrial systems is formed. It can integrate various types of acquisition and sensing devices, control and execution devices with sensing and control capabilities, as well as wireless transmission networks, big data analysis platforms and other technologies into all aspects of the industrial production process to improve production efficiency, reduce production costs, and finally realise the transformation and upgrading of traditional industries to intelligent production processes. Therefore, industrial Internet of Things technology research is an interdisciplinary project involving sensing technology, data interaction technology and information processing technology.
The Industrial Internet of Things needs to solve the technical problem of ubiquitous perception. Industrial systems involve a huge number of devices and the generalisation of information, which requires a high number of sensors and easy maintenance. Sensors with low prices, small sizes, free long-term maintenance, and high intelligence will become the cornerstone of industrial IoT applications. Sensing elements should be miniaturised to reduce resource consumption. The sensing device should be intelligent, with self-calibration, self-diagnosis, self-learning, self-adaptive and self-decision-making capabilities. At the same time, it has the energy capture ability to meet long-term power supply and maintenance requirements.
The Industrial Internet of Things needs to solve the technical problem of data interaction. The data generated by on-site generalised perception needs to be transmitted to regional edge computing nodes and fusion collection stations or ground data centrer for processing to generate corresponding value. Perception data must have an identification mark, which can be identified. A standard interaction protocol is established so that the generalised perception data can be effectively received. For traditional sensing devices in use, corresponding protocol gateways need to be developed to facilitate compatibility.
The Industrial Internet of Things needs to solve the technical problem of data transmission. The network is the main component of the Industrial Internet of Things, through which data can be transferred between different levels of the system. At present, the overall technology of wired broadband networks is relatively mature, and it is mainly used in backbone transmission and data centres. However, for industrial production sites, due to a large number of sensors, to reduce wiring costs and facilitate maintenance, it is necessary to build a wireless sensor network suitable for industrial sites for achieving latency and low power consumption. At the same time, to meet the dynamic expansion of sensors, industrial wireless sensor networks are required to have ad-hoc network capabilities.
The Industrial Internet of Things needs to solve the technical problems of effective processing and sharing of data. The amount of data in industrial systems is relatively large and is called giant systems. It is imperative to develop the knowledge to effectively analyze, process and store relevant data, and extract instructive results for industrial production through fusion and mining analysis. At the same time, the application of industrial data is an ecology, and it is necessary to ensure that the collected data can be applied by all links and objects of use, to realise the organic sharing of data.
Power grid technology is the technical foundation supporting the overall technology of each main business. The power grid technology direction is mainly divided into eight technical fields: power chips, new electrical materials, superconducting power, artificial intelligence, power meteorology, power storage, power quality, and power electronic devices.
Power chips can be embedded in all kinds of primary and secondary power equipment and devices, and are widely used in all aspects of smart grids. They are smart grid systems and equipment.
With the rapid development of new electrical material technology, it is necessary to develop electrical insulating materials, electrical magnetic materials, electrical conductor materials, environmental protection materials, new energy storage materials and intelligent sensing materials.
Superconducting power standards mainly focus on the preparation, construction, testing, online monitoring and system protection of superconducting power core components including superconducting cables, superconducting current limiters, and superconducting transformers.
Artificial intelligence has broad application prospects in the fields of smart grid construction, operation control, maintenance and equipment management. Standardisation work needs to be carried out in power data labelling, power intelligent perception, power intelligent cognition, power intelligent behaviour, artificial intelligence platform and artificial intelligence application maturity evaluation.
Global climate change leads to changes in the spatial and temporal distribution characteristics of wind and solar resources and a significant increase in extreme weather events. Standards for basic observation data of power meteorology, numerical weather forecast and analysis of power meteorology, forecast and early warning of power meteorological disasters, and production and application of power meteorology are formulated.
As the proportion of clean energy increases, local consumption, plug-and-play, and flexible mobility of electric energy storage will become a development trend. Standardisation work on electrochemical energy storage, physical energy storage, hydrogen energy storage planning and design, equipment manufacturing, grid connection and testing are carried out.
In the fields of DC power transmission, new power grid connection, railway power supply, industrial smelting and other fields, the large-scale use of power electronic devices leads to serious power quality problems such as voltage flicker, harmonic distortion and reactive power shock in the power grid. There is a need to formulate and improve the basic standards for power quality, monitor and evaluate, and employ technical standards for power quality control.
The core of new energy grid connection lies in flexible AC and DC transmission, and microgrid operation control. To guide and regulate the development of power electronic devices, it is necessary to carry out standardisation work on silicon power devices and new power devices.
Industrial Internet of Things and power grid technology data collection access different devices, systems and products through various communication means, collect large-scale, deep-level industrial data, protocol conversion and edge processing of heterogeneous data, and build the data foundation of the industrial Internet platform. The technical architecture of industrial data acquisition includes three layers of equipment access, protocol conversion, and edge data processing, connecting equipment or smart products downwards, and connecting with the industrial Internet platform/industrial application system upwards.
Equipment access: Through various wired and wireless communication technologies such as industrial Ethernet, industrial optical fibre network, industrial bus, 3G/4G, NB-IoT, etc., access to various industrial field equipment, such as smart products/equipment, through the collection of power grids technical data.
Protocol conversion: Protocol analysis and conversion, middleware and other technologies are compatible with various industrial communication protocols such as ModBus, CAN, Profinet, etc., to realise data format conversion and unification. HTTP, MQTT and other methods are used to transmit the collected data to the cloud data application analysis system or data aggregation platform.
Edge data processing: Based on technical support such as high-performance computing, real-time operating systems, and edge analysis algorithms, data preprocessing is performed on the network edge side close to the device or data source. Storage and intelligent analysis applications improve operational response sensitivity, eliminate network congestion, and form a synergy with cloud data analysis.
Connectivity: Connectivity is the foundation of industrial data collection with a variety of connected physical objects and a variety of application scenarios. Industrial data acquisition has rich connection functions, such as various network interfaces, network protocols, network topology, network deployment and configuration, network management and maintenance.
The first entrance of data: As a bridge from the physical world to the digital world, industrial data acquisition is the first entrance of data, with a large amount of real-time and complete data. As the first entrance to data, industrial data collection also faces challenges such as real-time data, certainty and diversity.
Rapid growth of data volume: As industrial systems extend from physical space to information space and from the visible world to the invisible world, the scope of industrial data collection continues to expand. The amount of data generated, collected and processed by industrial equipment, including equipment status parameters, operating conditions, loads, and operating environment, is growing explosively, far greater than the data generated by computers and humans in enterprises with the development of smart manufacturing and IoT technology.
After the text data is preprocessed, it is vectorised and then input into the industrial Internet of Things and power grid technology framework model based on the knowledge graph and data asset relationship model for the next step. In this paper, the cleaned power grid corpus data is used for word vector training. The corpus data size is about 5.3 GB, and the Word2Vec tool is used for large-scale text training to convert the text into a low-density vector space. This article selects the Word2Vec tool Skip-gram model to train word vectors. The Skip-gram model takes all the words in the corpus as the central word and predicts the lexical information of its context through the conditional probability distribution of the correspondence between the central word and the context.
In this paper, the cross entropy is used as the loss function, and 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:
In this paper, CNN, CNN-CRF, LSTM, LSTM-CRF, BiLSTM, BiLSTM + CRF and CNN + BiLSTM + CRF are used for performance analysis. The experimental results are shown in Table 1 and Figure 3. Comparing the experimental results of the CNN model and the LSTM model, it can be seen that LSTM is better than CNN in extracting entities from this dataset. Comparing the experimental results of the CNN and LSTM models and the CNN-CRF and LSTM-CRF models, it can be seen that the F value is improved after the CRF module is added. This is because CRF can make full use of the association of adjacent entity labels. Comparing the experimental results of the LSTM-CRF model and the BiLSTM-CRF model, it can be seen that BiLSTM performs better than LSTM because LSTM can only use the above information and cannot use the following information. From the experimental results of the CNN-BiLSTM-CRF model, it can be seen that the F value has been significantly improved because CNN can deeply extract the semantic information of entity texts and fully characterise polysemy.
Model effect comparison (/%)
|BiLSTM + CRF||85.87||85.45||85.09|
|CNN + BiLSTM + CRF||93.63||91.46||92.41|
BiLSTM, bidirectional long short-term memory; CNN, convolutional neural network; LSTM, long short-term memory
This paper focuses on the research of industrial Internet of Things and power grid technology based on the knowledge graph and data asset relationship model. The key technology of the Industrial Internet of Things and the theoretical model framework of power grid technology proposed in this paper can be widely used in the remote monitoring and intelligent maintenance application scenarios of various industrial equipment or intelligent products of process type or discrete type. Through the collection and analysis of the operation data, such as the location, status, sensor, alarm and performance of the equipment/smart products, the functions of remote monitoring of equipment, fault alarm, preventive maintenance and performance optimisation analysis are realised, and the service efficiency and effectiveness are improved. However, the scheme proposed in this paper still has defects. If user information is not considered, the accuracy of entity extraction is not high. In future work, we will experiment with different models for in-depth research.
Model effect comparison (/%)
|BiLSTM + CRF||85.87||85.45||85.09|
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Function Model Design of Tennis Mobile Teaching Assistant System Based on Ordinary Differential Equations Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis High Simulation Reconstruction of Crowd Animation Based on Optical Flow Constraint Equation Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation 3D Animation Simulation of Computer Fractal and Fractal Technology Combined with Diamond-Square Algorithm Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference The Summation of Series Based on the Laplace Transformation Method in Mathematics Teaching Optimal Solution of the Fractional Differential Equation to Solve the Bending Performance Test of Corroded Reinforced Concrete Beams under Prestressed Fatigue Load Animation VR scene mosaic modeling based on generalized Laplacian equation Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure Optimal Model Combination of Cross-border E-commerce Platform Operation Based on Fractional Differential Equations The influence of accounting computer information processing technology on enterprise internal control under panel data simultaneous equation Research on Stability of Time-delay Force Feedback Teleoperation System Based on Scattering Matrix BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation Construction of comprehensive evaluation index system of water-saving irrigation project integrating penman Montei the quation Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression Analysis of technical statistical indexes of college tennis players under the win-lose regression function equation Automatic extraction and discrimination of vocal main melody based on quadratic wave equation Analysis of wireless English multimedia communication based on spatial state model equation Optimization of Linear Algebra Core Function Framework on Multicore Processors Application of hybrid kernel function in economic benefit analysis and evaluation of enterprises Research on classification of e-commerce customers based on BP neural network The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Cross-border E-commerce Controlling Return Rate 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Fractional Differential Equations in Electronic Information Models BIM Engineering Management Oriented to Curve Equation Model Leakage control of urban water supply network and mathematical analysis and location of leakage points based on machine learning Analysis of higher education management strategy based on entropy and dissipative structure theory Prediction of corporate financial distress based on digital signal processing and multiple regression analysis Mathematical Method to Construct the Linear Programming of Football Training Multimedia sensor image detection based on constrained underdetermined equation The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Application of Numerical Computation of Partial Differential Equations in Interactive Design of Virtual Reality Media Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Knowledge Analysis of Charged Particle Motion in Uniform Electromagnetic Field Based on Maxwell Equation Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Term structure of economic management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Study on the influence of adolescent smoking on physical training vital capacity in eastern coastal areas Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G) on the set of maximal subgroups of p-groups Study on transmission characteristics in three kinds of deformed finlines based on edge-based finite element method Research on loyalty prediction of e-commerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A sentiment analysis method based on bidirectional long short-term memory networks A study of local smoothness-informed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color 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