Multi-dimensional digital twinning modeling method

Abstract

The invention discloses a multi-dimensional digital twinning modeling method. The method comprises the following steps: S1, performing dynamic fusion and real-time modeling on multi-source heterogeneous data in a complex scene; and high-precision multi-dimensional data expression is formed. S2, based on a cross-domain knowledge graph and a semantic reasoning algorithm, establishing semantic association among multi-dimensional data; and the cross-domain data fusion problem is solved. And S3, performing real-time evaluation and early warning on the system state through an anomaly detection and trend prediction technology. The defects of a traditional digital twin system in multi-scene data fusion and predictive analysis are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of multidimensional data processing, and specifically, relates to a modeling method based on multidimensional digital twins. Background Art

[0002] Traditional digital twin technology focuses on static modeling of low-dimensional data, and is mainly used to describe simple physical and temporal information. However, with the rapid development of industry, smart manufacturing and smart cities, digital twin systems are gradually facing new demands such as cross-domain, multi-dimensional, dynamic data integration and real-time prediction.

[0003] The defect of existing technology is that existing digital twin technology is usually constructed separately in a specific field, and it is difficult to effectively integrate information from multiple fields such as physical fields, chemical fields, and biological fields. Summary of the invention

[0004] In view of the problem that the existing digital twin modeling methods are constructed separately in specific fields and cannot realize multi-dimensional digital twin modeling, the present invention provides a multi-dimensional digital twin modeling method.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0006] A multi-dimensional digital twin modeling method, comprising the steps of:

[0007] S1. Dynamically integrate and model multi-source heterogeneous data in complex scenarios in real time to form high-precision multi-dimensional data expressions.

[0008] S2. Based on cross-domain knowledge graphs and semantic reasoning algorithms, establish semantic associations between multi-dimensional data and solve the problem of cross-domain data fusion.

[0009] S3. Real-time assessment and early warning of system status through anomaly detection and trend prediction technology.

[0010] Furthermore, the detailed steps of step S1 include:

[0011] Dynamic fusion: Use a convolutional neural network (CNN) and a recurrent neural network (RNN) architecture to extract and dynamically aggregate multidimensional data features to capture the correlation between different dimensions.

[0012] Multi-level representation construction: Using the adaptive weight mechanism, feature data from different scenarios and fields are modeled in multiple levels according to time, space and semantic dimensions;

[0013] Real-time feedback and update mechanism: The data layer is monitored in real time through a distributed computing architecture to ensure that model parameters at each level are quickly updated when the data stream changes.

[0014] Furthermore, the detailed steps of step S2 include:

[0015] Construction of cross-domain knowledge graph: Knowledge graph expresses complex relationships between different fields through triple structures of entities, attributes and relationships;

[0016] Intelligent semantic reasoning algorithm: Use graph neural network (GNN) to perform semantic reasoning on the knowledge graph, extract implicit associations between data in different fields, and realize cross-domain semantic reasoning;

[0017] Dynamic optimization mechanism: Use reinforcement learning algorithms (such as Q-Learning) to dynamically optimize the inference results, so that the inference layer can continuously adjust the inference path based on feedback, ensuring the accuracy and real-time nature of the inference results.

[0018] Further, the detailed steps of step S3 are:

[0019] Data stream anomaly detection: Deep learning algorithms for anomaly detection monitor abnormal features in data streams in real time to identify potential failures or abnormal behaviors.

[0020] Multi-dimensional fault analysis: Extract potential patterns of data streams through deep learning models and perform multi-dimensional analysis on detected abnormal features;

[0021] Trend prediction and early warning: Combining historical data and current status, long short-term memory network (LSTM) is used to predict future trends and generate early warning information based on the prediction results.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] Through high-quality data input, it provides a basis for semantic reasoning and fault prediction; further explores implicit associations based on modeling data to provide explanatory support for fault diagnosis; through comprehensive analysis of modeling data and reasoning results, it completes the closed loop from diagnosis to optimization. It solves the shortcomings of traditional digital twin systems in multi-scenario data fusion and predictive analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is an overall flow chart of a multidimensional digital twin modeling method in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0026] like Figure 1As shown, this embodiment provides a multi-dimensional digital twin modeling method, including the steps of:

[0027] S1. Dynamically integrate and model multi-source heterogeneous data in complex scenarios in real time to form high-precision multi-dimensional data expressions.

[0028] S2. Based on cross-domain knowledge graphs and semantic reasoning algorithms, establish semantic associations between multi-dimensional data and solve the problem of cross-domain data fusion.

[0029] S3. Real-time assessment and early warning of system status through anomaly detection and trend prediction technology.

[0030] The detailed steps of step S1 include:

[0031] Dynamic fusion: Use a convolutional neural network (CNN) and recurrent neural network (RNN) architecture to process the extraction and dynamic aggregation of multidimensional data features to capture the correlation between different dimensions; CNN is used to extract static spatial information, while RNN is used to model changes in time series to achieve collaborative analysis of spatiotemporal data.

[0032] Multi-level representation construction: Using the adaptive weight mechanism, feature data from different scenarios and fields are modeled in multiple levels according to time, space and semantic dimensions;

[0033] Real-time feedback and update mechanism: The data layer is monitored in real time through a distributed computing architecture to ensure that model parameters at each level are quickly updated when the data stream changes.

[0034] Implementation code:

[0035]

[0036] Code explanation:

[0037] The adaptive_dynamic_fusion function processes the spatial and temporal characteristics of the data stream by combining CNN and RNN networks.

[0038] dynamic_fusion realizes the real-time fusion of multi-dimensional features and adjusts the weights of different features through an adaptive mechanism to adapt to changes in external data.

[0039] Traditional digital twin systems usually use a single or limited hierarchical dimension for data modeling, which is difficult to adapt to complex multi-scenario environments. The present invention proposes a multi-level, multi-dimensional adaptive data modeling method, which realizes real-time dynamic change processing of complex scene data and cross-dimensional multi-level information fusion through a "dynamic fusion layer" and a "multi-level representation layer". Not only can the dynamic adaptation of data be achieved, but the overall information accuracy of the system can also be improved through hierarchical association.

[0040] The detailed steps of step S2 include:

[0041] Construction of cross-domain knowledge graph: The knowledge graph expresses the complex relationships between different fields through the triple structure of entities, attributes and relationships; for data in different fields, a cross-domain knowledge graph is constructed to describe and store the association information between fields.

[0042] Intelligent semantic reasoning algorithm: Use Graph Neural Network (GNN) to perform semantic reasoning on the knowledge graph, extract implicit associations between data in different fields, and realize cross-domain semantic reasoning; this method not only improves the system's semantic understanding ability, but also can make reasonable inferences and judgments when data is scarce or information is incomplete.

[0043] Dynamic optimization mechanism: Use reinforcement learning algorithms (such as Q-Learning) to dynamically optimize the inference results, so that the inference layer can continuously adjust the inference path based on feedback, ensuring the accuracy and real-time nature of the inference results.

[0044] Implementation code:

[0045]

[0046]

[0047] Code explanation:

[0048] The build_knowledge_graph function builds a cross-domain knowledge graph to provide a basis for semantic reasoning.

[0049] GNN_infer uses graph neural networks for reasoning and dynamically adjusts the reasoning path through real-time feedback optimize_model.

[0050] Traditional digital twin systems usually adopt a simple splicing or aggregation method when processing data fusion from different fields, lacking the semantic understanding and intelligent reasoning capabilities of multi-field data. This paper proposes an intelligent reasoning layer based on cross-domain semantic fusion, which realizes semantic association and reasoning between data from different fields by constructing a cross-domain knowledge graph and a semantic reasoning algorithm based on deep learning.

[0051] Detailed steps of step S3:

[0052] Data stream anomaly detection: Use deep learning algorithms for anomaly detection (such as variational autoencoders (VAEs)) to monitor abnormal features in data streams in real time to identify potential failures or abnormal behaviors.

[0053] Multi-dimensional fault analysis: Deep learning models are used to extract potential patterns in data streams and perform multi-dimensional analysis on detected abnormal features, thereby determining the potential causes and impact of faults.

[0054] Trend prediction and early warning: Combining historical data and current status, long short-term memory network (LSTM) is used to predict future trends and generate early warning information based on the prediction results, thereby effectively preventing potential risks.

[0055] Implementation code:

[0056]

[0057] Code explanation:

[0058] VAE_detect_anomalies detects anomalies in a data stream using a variational autoencoder.

[0059] · analyze_faults performs multi-dimensional fault analysis, and LSTM_forecast uses LSTM to predict trends and trigger warnings when necessary.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] Through high-quality data input, it provides a basis for semantic reasoning and fault prediction; further explores implicit associations based on modeling data to provide explanatory support for fault diagnosis; through comprehensive analysis of modeling data and reasoning results, it completes the closed loop from diagnosis to optimization. It solves the shortcomings of traditional digital twin systems in multi-scenario data fusion and predictive analysis.

[0062] The above is a detailed introduction to the multidimensional digital twin modeling method provided by the present application. The description of the specific embodiment is only used to help understand the method and its core idea of ​​the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A multi-dimensional digital twin modeling method, characterized in that: Includes steps: S1. Dynamically integrate and model multi-source heterogeneous data in complex scenarios; S2. Establish semantic associations between multidimensional data based on cross-domain knowledge graphs and semantic reasoning algorithms; S3. Real-time assessment and early warning of system status through anomaly detection and trend prediction technology.

2. A multi-dimensional digital twin modeling method according to claim 1, characterized in that: The detailed steps of step S1 include: Dynamic Fusion: Use a convolutional neural network combined with a recurrent neural network to extract and dynamically aggregate multi-dimensional data features to capture the correlation between different dimensions. Multi-level representation construction: Using the adaptive weight mechanism, feature data from different scenarios and fields are modeled in multiple levels according to time, space and semantic dimensions; Real-time feedback and update mechanism: The data layer is monitored in real time through a distributed computing architecture to ensure that model parameters at each level are quickly updated when the data stream changes.

3. A multi-dimensional digital twin modeling method according to claim 2, characterized in that: The detailed steps of step S2 include: Construction of cross-domain knowledge graph: Knowledge graph expresses complex relationships between different fields through triple structures of entities, attributes and relationships; Intelligent semantic reasoning algorithm: Use graph neural networks to perform semantic reasoning on knowledge graphs, extract implicit associations between data in different fields, and achieve cross-domain semantic reasoning; Dynamic optimization mechanism: The reinforcement learning algorithm is used to dynamically optimize the reasoning results, so that the reasoning layer can continuously adjust the reasoning path based on feedback.

4. The multidimensional digital twin modeling method according to claim 3 is characterized in that: Detailed steps of step S3: Data stream anomaly detection: Use deep learning algorithms for anomaly detection to monitor abnormal features in data streams in real time; Multi-dimensional fault analysis: Extract potential patterns of data streams through deep learning models and perform multi-dimensional analysis on detected abnormal features; Trend prediction and early warning: Combining historical data and current status, long short-term memory networks are used to predict future trends and generate early warning information based on the prediction results.

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