Multi-mode driven cross-industry digital twin universal platform architecture and implementation method

Abstract

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

Description

Technical Field

[0001] This invention discloses a multimodal driven cross-industry general platform architecture and implementation method for digital twins, which relates to the fields of digital twins and artificial intelligence technology. Background Technology

[0002] With the cross-application of large-scale models and digital twin technologies in smart cities, smart parks, smart water conservancy, and low-altitude economy, the demand for deep integration of "multimodal data understanding - real-time digital twin simulation - third-party algorithm collaboration" across industries is becoming increasingly prominent. Currently, the industry's application of large-scale models and digital twins is still in the exploratory stage. Existing technologies have significant bottlenecks in cross-modal fusion accuracy, cross-industry adaptability, and heterogeneous system collaboration efficiency. For example, digital twin technologies based on physical or statistical models mainly combine static data to construct digital twins and lack the intelligent scheduling capability of large models. For example, digital twin technologies using large models in single-modal applications have outstanding performance in a single modality, but there are many problems when combined with digital twins. The large model is only used as an independent module for specific tasks and does not form a collaborative closed loop with the digital twin engine. At the same time, the lightweight deployment capability is insufficient. Both the large model and the twin model rely on cloud computing power, and edge nodes cannot bear complex calculations, making it difficult to meet the requirements of low-latency scenarios. Summary of the Invention

[0003] This invention addresses the problems of existing technologies by providing a multimodal driven, cross-industry general digital twin platform architecture and implementation method. It breaks through the bottlenecks of existing technologies in cross-modal collaboration, cross-industry adaptation, and real-time decision-making, and achieves complementary advantages between large-model intelligent reasoning and digital twin physical simulation, providing an efficient and universal intelligent decision-making solution for complex scenarios.

[0004] The specific solution proposed in this invention is as follows:

[0005] This invention provides an implementation method for a multimodal-driven, cross-industry digital twin general platform architecture. It establishes a multimodal-driven, cross-industry digital twin general platform and manages the platform through a layered architecture. The platform includes a data access layer, a multimodal fusion layer, a large model scheduling layer, a twin engine layer, and an interactive application layer.

[0006] Deploy multi-source heterogeneous data acquisition components at the data access layer to access text, image, and time-series data.

[0007] A Transformer-GNN cross-modal encoder is used in the multimodal fusion layer to transform unstructured data into a unified feature space;

[0008] At the large model scheduling layer, the multimodal large model hub analyzes feature vectors based on industry knowledge graphs, dynamically matches algorithms, and generates initial decision strategies. It also supports secure collaborative training of third-party algorithms by third-party algorithm scheduling engines.

[0009] At the twin engine layer, a lightweight digital twin engine is deployed: a parameterized template library is established, pre-defined three templates for geospatial, equipment assets, and business processes; and the twin model is deployed to edge nodes using a knowledge distillation method.

[0010] At the interactive application layer, BIM / GIS models are lightweightly loaded using low-code tools, and scene construction is completed through drag-and-drop components; the state of the virtual twin is rendered in real time through a 3D cockpit, natural language commands are parsed, and corresponding operations are performed.

[0011] When the platform is applied, data preparation and access are performed: multimodal data is collected, data is cleaned, and a 1024-dimensional unified feature vector is generated by inputting it into a cross-modal encoder; initial parameters of the industry knowledge graph and template library are loaded; cross-industry scenarios are constructed: the optimal template is matched based on the meta-learning algorithm, a digital twin framework is generated, third-party algorithms and data interfaces are automatically adapted, and model initialization is completed; multimodal strategies are generated: the industry adaptation algorithm is called based on the multimodal large model hub to generate preliminary decision strategies; twin simulation and strategy verification are performed, and the verified strategy is sent to the physical device, while the twin monitors the execution status in real time.

[0012] Furthermore, in the implementation method of the multimodal-driven cross-industry digital twin general platform architecture, the input cross-modal encoder generates a 1024-dimensional unified feature vector, including:

[0013] The text is parsed, and the semantic features of the file are extracted using the BERT model.

[0014] Spatial modeling: PointNet++ is used to process the BIM / GIS model, generating 256-dimensional spatial feature vectors to represent physical attributes, including terrain elevation and building density.

[0015] Temporal encoding is performed: Sensor data is processed using LSTM to generate a 256-dimensional dynamic feature vector.

[0016] The three types of features are concatenated into a 1024-dimensional unified feature vector, and then combined with the industry knowledge graph to perform semantic-level association between files, spaces, and devices.

[0017] Furthermore, the implementation method of the multimodal driven cross-industry digital twin general platform architecture forms a two-way feedback mechanism of large model strategy generation, digital twin simulation verification, and physical device execution. Real-time interaction of virtual and real data is achieved through the OPCUA and MQTT protocols. In the uplink, sensor data drives the twin's state update at a frequency of 100Hz; in the downlink, the twin simulation results control the physical device, with a control latency of ≤200ms.

[0018] When the platform detects device anomalies or policy deviations, it automatically triggers large model re-inference, combining federated learning and reinforcement learning to dynamically allocate cloud-edge computing power.

[0019] Furthermore, in the implementation method of the multimodal driven cross-industry digital twin general platform architecture, when constructing cross-industry scenarios, the MAML meta-learning algorithm is introduced to automatically generate initial parameters for new industry scenarios based on historical data from 100+ projects.

[0020] This invention also provides a multimodal-driven, cross-industry, general-purpose digital twin platform architecture. The platform employs a layered architecture management system, comprising a data access layer, a multimodal fusion layer, a large model scheduling layer, a digital twin engine layer, and an interactive application layer.

[0021] Deploy multi-source heterogeneous data acquisition components at the data access layer to access text, image, and time-series data.

[0022] A Transformer-GNN cross-modal encoder is used in the multimodal fusion layer to transform unstructured data into a unified feature space;

[0023] At the large model scheduling layer, the multimodal large model hub analyzes feature vectors based on industry knowledge graphs, dynamically matches algorithms, and generates initial decision strategies. It also supports secure collaborative training of third-party algorithms by third-party algorithm scheduling engines.

[0024] At the twin engine layer, a lightweight digital twin engine is deployed: a parameterized template library is established, pre-defined three templates for geospatial, equipment assets, and business processes; and the twin model is deployed to edge nodes using a knowledge distillation method.

[0025] At the interactive application layer, BIM / GIS models are lightweightly loaded using low-code tools, and scene construction is completed through drag-and-drop components; the state of the virtual twin is rendered in real time through a 3D cockpit, natural language commands are parsed, and corresponding operations are performed.

[0026] When the platform is applied, data preparation and access are performed: multimodal data is collected, data is cleaned, and a 1024-dimensional unified feature vector is generated by inputting it into a cross-modal encoder; initial parameters of the industry knowledge graph and template library are loaded; cross-industry scenarios are constructed: the optimal template is matched based on the meta-learning algorithm, a digital twin framework is generated, third-party algorithms and data interfaces are automatically adapted, and model initialization is completed; multimodal strategies are generated: the industry adaptation algorithm is called based on the multimodal large model hub to generate preliminary decision strategies; twin simulation and strategy verification are performed, and the verified strategy is sent to the physical device, while the twin monitors the execution status in real time.

[0027] Furthermore, in the aforementioned multimodal-driven cross-industry digital twin general platform architecture, the platform input cross-modal encoder generates a 1024-dimensional unified feature vector, including:

[0028] The text is parsed, and the semantic features of the file are extracted using the BERT model.

[0029] Spatial modeling: PointNet++ is used to process the BIM / GIS model, generating 256-dimensional spatial feature vectors to represent physical attributes, including terrain elevation and building density.

[0030] Temporal encoding is performed: Sensor data is processed using LSTM to generate a 256-dimensional dynamic feature vector.

[0031] The three types of features are concatenated into a 1024-dimensional unified feature vector, and then combined with the industry knowledge graph to perform semantic-level association between files, spaces, and devices.

[0032] Furthermore, in the aforementioned multimodal-driven cross-industry digital twin general platform architecture, the platform forms a bidirectional feedback mechanism of large model strategy generation, digital twin simulation verification, and physical device execution. It uses the OPCUA and MQTT protocols for real-time interaction of virtual and real data. Specifically, in the uplink, sensor data drives the twin's state update at a frequency of 100Hz; in the downlink, the twin simulation results control the physical device, with a control latency of ≤200ms.

[0033] When the platform detects device anomalies or policy deviations, it automatically triggers large model re-inference, combining federated learning and reinforcement learning to dynamically allocate cloud-edge computing power.

[0034] Furthermore, in the aforementioned multimodal driven cross-industry digital twin general platform architecture, when the platform constructs cross-industry scenarios, the MAML meta-learning algorithm is introduced to automatically generate initial parameters for new industry scenarios based on historical data from 100+ projects.

[0035] The advantages of the method of the present invention are:

[0036] 1. Significantly improved accuracy of multimodal fusion: For the first time, semantic-level association between policy text, spatial model, and real-time data is achieved, the completeness of decision-making basis is improved by 70%, the decision error rate in complex scenarios is reduced from 20% to below 5%, the cross-modal feature unified representation technology supports real-time fusion of 10+ data types, and the data utilization dimensions are increased from 3 categories in traditional solutions to more than 8 categories, covering more than 90% of industry decision-making scenarios.

[0037] 2. Industry-leading cross-industry adaptation efficiency: The parameterized template library and meta-learning algorithm shorten the deployment cycle of new scenarios from 2 weeks to 3 days, increase the technology reuse rate from less than 20% to 80%, reduce R&D costs by 60%, support rapid switching in 5 major fields such as smart cities and low-altitude economy, and the platform architecture does not need to be reconstructed, breaking the siloed model of the traditional vertical platform of "one system for one industry".

[0038] 3. Breakthrough in Real-Time Decision-Making and Collaboration Efficiency: The two-way feedback mechanism reduces emergency response latency from 5 minutes to within 10 seconds, and edge node inference latency is ≤50ms, meeting the millisecond-level response requirements for scenarios such as real-time urban flooding early warning. The federated learning framework enables secure collaboration of third-party algorithms, reducing the risk of data privacy leakage by 90% and improving algorithm integration efficiency by 50%.

[0039] 4. Lightweight Deployment and User Experience Innovation: Model distillation technology reduces the computing power consumption of large models and twin engines by 80%, supports independent operation on edge nodes (power consumption ≤10W), and is suitable for lightweight devices such as drones and water conservancy monitoring terminals. Low-code tools and natural language interaction interfaces improve the operating efficiency of non-professional users by 80%, and lower the threshold for scenario construction from "professional development" to "self-configuration by business personnel".

[0040] 5. Enhanced Comprehensive Decision Support Capabilities: Integrating multimodal reasoning and twin simulation results, the system provides decision-makers with comprehensive reports covering policy compliance analysis, spatial layout optimization, and equipment operation monitoring, reducing decision-making time from hours to minutes. In real-world testing within smart park scenarios, energy optimization strategies reduced overall energy consumption by 15%; in low-altitude economic scenarios, drone swarm mission completion efficiency improved by 25%, and the incidence of flight conflicts decreased by 60%. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall architecture of the platform of this invention.

[0042] Figure 2 This is a flowchart illustrating the two-way feedback mechanism. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0044] Example 1

[0045] This invention provides an implementation method for a multimodal-driven, cross-industry digital twin general platform architecture. It establishes a multimodal-driven, cross-industry digital twin general platform and manages the platform through a layered architecture. The platform includes a data access layer, a multimodal fusion layer, a large model scheduling layer, a twin engine layer, and an interactive application layer.

[0046] Deploy multi-source heterogeneous data acquisition components at the data access layer to access text, image, and time-series data.

[0047] It supports data access for 10+ types of data, including text (files / PDFs), images (BIM / GIS / OBJ models), and time series data. It integrates a universal data gateway, compatible with 60+ industrial protocols such as Modbus, MQTT, and RESTAPI, enabling plug-and-play functionality for hydrological sensors (radar level gauges), low-altitude drones (DJI M300RTK), and park IoT devices (Siemens PLCs), improving data access efficiency by 50%.

[0048] The Transformer-GNN cross-modal encoder is used in the multimodal fusion layer to transform unstructured data into a unified feature space.

[0049] For example, text parsing: using the BERT model, a 12-layer Transformer, and a 768-dimensional semantic vector to extract the semantic features of policy documents, such as the airspace boundary constraints corresponding to "low-altitude no-fly zone";

[0050] Spatial modeling: PointNet++ is used to process BIM / GIS models and generate 256-dimensional spatial feature vectors to represent physical attributes such as terrain elevation and building density;

[0051] Temporal coding: Sensor data such as water level / energy consumption are processed by LSTM to generate a 256-dimensional dynamic feature vector.

[0052] The three types of features are concatenated into a 1024-dimensional unified feature vector. Combined with the industry knowledge graph, semantic-level association between "file-space-device" is achieved. For example, "red rainstorm warning" is automatically associated with the threshold parameter of drainage pump, with a mapping delay of ≤2 seconds.

[0053] At the large model scheduling layer, the multimodal large model hub analyzes feature vectors based on industry knowledge graphs and dynamically matches algorithms such as automatically calling flood evolution models in smart water conservancy scenarios and calling UAV path planning algorithms in low-altitude economic scenarios to generate initial decision strategies. It also supports secure collaborative training of third-party algorithms by third-party algorithm scheduling engines. For example, when UAV obstacle avoidance algorithms are accessed, training data is protected by homomorphic encryption, shortening the access cycle from 2 weeks to 2 hours and reducing the multi-algorithm parallel conflict rate by 70%.

[0054] At the twin engine layer, a lightweight digital twin engine is deployed: a parameterized template library is established, with predefined geospatial space and grid accuracy error ≤0.5 meters; three major templates: device assets, protocol adaptation rate ≥95%; and business processes; the twin model is deployed to edge nodes using a knowledge distillation method with a compression ratio ≥1:10, with inference latency ≤50ms and computing power consumption reduced by 80%.

[0055] At the interactive application layer, BIM / GIS models are lightweightly loaded using low-code tools, and scene construction is completed through drag-and-drop components; the state of the virtual twin is rendered in real time through a 3D cockpit, natural language commands are parsed, and corresponding operations are performed.

[0056] When the platform is used, data preparation and access are performed: multimodal data is collected, data is cleaned, a 1024-dimensional unified feature vector is generated by inputting a cross-modal encoder, and initial parameters of the industry knowledge graph and template library are loaded.

[0057] Building cross-industry scenarios: Based on meta-learning algorithms, optimal templates are matched to generate digital twin frameworks. This involves introducing the MAML meta-learning algorithm, which automatically generates initial parameters for new industry scenarios based on historical data from over 100 projects.

[0058] Automatically adapts to third-party algorithms and data interfaces to complete model initialization; generates multimodal strategies: based on the central multimodal model, it calls industry-adaptive algorithms to generate preliminary decision strategies;

[0059] A digital twin simulation and strategy verification are performed. Verified strategies are then deployed to physical devices, while the twin monitors the execution status in real time. A two-way feedback mechanism is established, encompassing large-scale model strategy generation, digital twin simulation verification, and physical device execution. Real-time interaction of virtual and physical data is achieved via OPCUA and MQTT protocols. Uplink: sensor data drives twin state updates at a 100Hz frequency. Downlink: the twin simulation results control the physical devices, with a control latency ≤200ms.

[0060] When the platform detects device anomalies or policy deviations, it automatically triggers large model re-inference, combining federated learning and reinforcement learning to dynamically allocate cloud-edge computing power.

[0061] Example 2

[0062] This invention also provides a multimodal-driven, cross-industry, general-purpose digital twin platform architecture. The platform employs a layered architecture management system, comprising a data access layer, a multimodal fusion layer, a large model scheduling layer, a digital twin engine layer, and an interactive application layer.

[0063] Deploy multi-source heterogeneous data acquisition components at the data access layer to access text, image, and time-series data.

[0064] A Transformer-GNN cross-modal encoder is used in the multimodal fusion layer to transform unstructured data into a unified feature space;

[0065] At the large model scheduling layer, the multimodal large model hub analyzes feature vectors based on industry knowledge graphs, dynamically matches algorithms, and generates initial decision strategies. It also supports secure collaborative training of third-party algorithms by third-party algorithm scheduling engines.

[0066] At the twin engine layer, a lightweight digital twin engine is deployed: a parameterized template library is established, pre-defined three templates for geospatial, equipment assets, and business processes; and the twin model is deployed to edge nodes using a knowledge distillation method.

[0067] At the interactive application layer, BIM / GIS models are lightweightly loaded using low-code tools, and scene construction is completed through drag-and-drop components; the state of the virtual twin is rendered in real time through a 3D cockpit, natural language commands are parsed, and corresponding operations are performed.

[0068] When the platform is applied, data preparation and access are performed: multimodal data is collected, data is cleaned, and a 1024-dimensional unified feature vector is generated by inputting it into a cross-modal encoder; initial parameters of the industry knowledge graph and template library are loaded; cross-industry scenarios are constructed: the optimal template is matched based on the meta-learning algorithm, a digital twin framework is generated, third-party algorithms and data interfaces are automatically adapted, and model initialization is completed; multimodal strategies are generated: the industry adaptation algorithm is called based on the multimodal large model hub to generate preliminary decision strategies; twin simulation and strategy verification are performed, and the verified strategy is sent to the physical device, while the twin monitors the execution status in real time.

[0069] The information interaction and execution process within the aforementioned platform are based on the same concept as the method embodiments of the present invention, and the specific details can be found in the descriptions in the method embodiments of the present invention, and will not be repeated here.

[0070] Similarly, the advantages of the platform architecture of this invention are:

[0071] 1. Significantly improved accuracy of multimodal fusion: For the first time, semantic-level association between policy text, spatial model, and real-time data is achieved, the completeness of decision-making basis is improved by 70%, the decision error rate in complex scenarios is reduced from 20% to below 5%, the cross-modal feature unified representation technology supports real-time fusion of 10+ data types, and the data utilization dimensions are increased from 3 categories in traditional solutions to more than 8 categories, covering more than 90% of industry decision-making scenarios.

[0072] 2. Industry-leading cross-industry adaptation efficiency: The parameterized template library and meta-learning algorithm shorten the deployment cycle of new scenarios from 2 weeks to 3 days, increase the technology reuse rate from less than 20% to 80%, reduce R&D costs by 60%, support rapid switching in 5 major fields such as smart cities and low-altitude economy, and the platform architecture does not need to be reconstructed, breaking the siloed model of the traditional vertical platform of "one system for one industry".

[0073] 3. Breakthrough in Real-Time Decision-Making and Collaboration Efficiency: The two-way feedback mechanism reduces emergency response latency from 5 minutes to within 10 seconds, and edge node inference latency is ≤50ms, meeting the millisecond-level response requirements for scenarios such as real-time urban flooding early warning. The federated learning framework enables secure collaboration of third-party algorithms, reducing the risk of data privacy leakage by 90% and improving algorithm integration efficiency by 50%.

[0074] 4. Lightweight Deployment and User Experience Innovation: Model distillation technology reduces the computing power consumption of large models and twin engines by 80%, supports independent operation on edge nodes (power consumption ≤10W), and is suitable for lightweight devices such as drones and water conservancy monitoring terminals. Low-code tools and natural language interaction interfaces improve the operating efficiency of non-professional users by 80%, and lower the threshold for scenario construction from "professional development" to "self-configuration by business personnel".

[0075] 5. Enhanced Comprehensive Decision Support Capabilities: Integrating multimodal reasoning and twin simulation results, the system provides decision-makers with comprehensive reports covering policy compliance analysis, spatial layout optimization, and equipment operation monitoring, reducing decision-making time from hours to minutes. In real-world testing within smart park scenarios, energy optimization strategies reduced overall energy consumption by 15%; in low-altitude economic scenarios, drone swarm mission completion efficiency improved by 25%, and the incidence of flight conflicts decreased by 60%.

[0076] It should be noted that not all steps and modules in the above processes and platform structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0077] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. An implementation method for a multimodal-driven, cross-industry, general-purpose digital twin platform architecture, characterized by: A multimodal-driven, cross-industry digital twin general platform is established, and the platform is managed with a layered architecture, including a data access layer, a multimodal fusion layer, a large model scheduling layer, a twin engine layer, and an interactive application layer. Deploy multi-source heterogeneous data acquisition components at the data access layer to access text, image, and time-series data. A Transformer-GNN cross-modal encoder is used in the multimodal fusion layer to transform unstructured data into a unified feature space; At the large model scheduling layer, the initial decision strategy is generated by analyzing feature vectors based on industry knowledge graphs and dynamically matching algorithms through the multimodal large model hub. It also supports secure collaborative training of third-party algorithms by a third-party algorithm scheduling engine. At the twin engine layer, deploy a lightweight digital twin engine: establish a parameterized template library: predefined three templates for geospatial, equipment assets and business processes; The twin model is deployed to edge nodes using a knowledge distillation method; At the interactive application layer, BIM / GIS models are lightweightly loaded using low-code tools, and scene construction is completed through drag-and-drop components; the state of the virtual twin is rendered in real time through a 3D cockpit, natural language commands are parsed, and corresponding operations are performed. When the platform is applied, data preparation and access are performed: multimodal data is collected, data is cleaned, and a 1024-dimensional unified feature vector is generated by inputting it into a cross-modal encoder; initial parameters of the industry knowledge graph and template library are loaded; cross-industry scenarios are constructed: the optimal template is matched based on the meta-learning algorithm, a digital twin framework is generated, third-party algorithms and data interfaces are automatically adapted, and model initialization is completed; multimodal strategies are generated: the industry adaptation algorithm is called based on the multimodal large model centrality to generate preliminary decision strategies; Perform twin simulation and strategy verification. Validated strategies are then sent to physical devices, while the twin monitors the execution status in real time.

2. The implementation method of the multimodal driven cross-industry digital twin general platform architecture according to claim 1, characterized in that: The input cross-modal encoder generates a 1024-dimensional uniform feature vector, including: The text is parsed, and the semantic features of the file are extracted using the BERT model. Spatial modeling: PointNet++ is used to process the BIM / GIS model, generating 256-dimensional spatial feature vectors to represent physical attributes, including terrain elevation and building density. Temporal encoding is performed: Sensor data is processed using LSTM to generate a 256-dimensional dynamic feature vector. The three types of features are concatenated into a 1024-dimensional unified feature vector, and then combined with the industry knowledge graph to perform semantic-level association between files, spaces, and devices.

3. The implementation method of the multimodal driven cross-industry digital twin general platform architecture according to claim 1, characterized in that: A two-way feedback mechanism is established, encompassing large-scale model strategy generation, digital twin simulation verification, and physical device execution. Real-time interaction of virtual and physical data is achieved via OPCUA and MQTT protocols. Uplink: Sensor data drives the twin's state updates at a 100Hz frequency. Downlink: The twin simulation results control the physical device, with a control latency ≤200ms. When the platform detects device anomalies or policy deviations, it automatically triggers large model re-inference, combining federated learning and reinforcement learning to dynamically allocate cloud-edge computing power.

4. The implementation method of the multimodal driven cross-industry digital twin general platform architecture according to claim 1, characterized in that: When building cross-industry scenarios, the MAML meta-learning algorithm is introduced to automatically generate initial parameters for new industry scenarios based on data from 100+ historical projects.

5. A multimodal-driven, cross-industry general-purpose digital twin platform architecture, characterized by: The platform employs a layered architecture for management, comprising a data access layer, a multimodal fusion layer, a large model scheduling layer, a twin engine layer, and an interactive application layer. Deploy multi-source heterogeneous data acquisition components at the data access layer to access text, image, and time-series data. A Transformer-GNN cross-modal encoder is used in the multimodal fusion layer to transform unstructured data into a unified feature space; At the large model scheduling layer, the initial decision strategy is generated by analyzing feature vectors based on industry knowledge graphs and dynamically matching algorithms through the multimodal large model hub. It also supports secure collaborative training of third-party algorithms by a third-party algorithm scheduling engine. At the twin engine layer, deploy a lightweight digital twin engine: establish a parameterized template library: predefined three templates for geospatial, equipment assets and business processes; The twin model is deployed to edge nodes using a knowledge distillation method; At the interactive application layer, BIM / GIS models are lightweightly loaded using low-code tools, and scene construction is completed through drag-and-drop components; the state of the virtual twin is rendered in real time through a 3D cockpit, natural language commands are parsed, and corresponding operations are performed. When the platform is applied, data preparation and access are performed: multimodal data is collected, data is cleaned, and a 1024-dimensional unified feature vector is generated by inputting it into a cross-modal encoder; initial parameters of the industry knowledge graph and template library are loaded; cross-industry scenarios are constructed: the optimal template is matched based on the meta-learning algorithm, a digital twin framework is generated, third-party algorithms and data interfaces are automatically adapted, and model initialization is completed; multimodal strategies are generated: the industry adaptation algorithm is called based on the multimodal large model centrality to generate preliminary decision strategies; Perform twin simulation and strategy verification. Validated strategies are then sent to physical devices, while the twin monitors the execution status in real time.

6. The multimodal driven cross-industry digital twin general platform architecture according to claim 5, characterized in that: The platform inputs a cross-modal encoder to generate a 1024-dimensional unified feature vector, including: The text is parsed, and the semantic features of the file are extracted using the BERT model. Spatial modeling: PointNet++ is used to process the BIM / GIS model, generating 256-dimensional spatial feature vectors to represent physical attributes, including terrain elevation and building density. Temporal encoding is performed: Sensor data is processed using LSTM to generate a 256-dimensional dynamic feature vector. The three types of features are concatenated into a 1024-dimensional unified feature vector, and then combined with the industry knowledge graph to perform semantic-level association between files, spaces, and devices.

7. The multimodal driven cross-industry digital twin general platform architecture according to claim 5, characterized in that: The platform establishes a two-way feedback mechanism: large-scale model strategy generation, digital twin simulation verification, and physical device execution. It uses the OPCUA and MQTT protocols for real-time interaction of virtual and physical data. In the uplink, sensor data drives the twin's state updates at a 100Hz frequency. In the downlink, the twin simulation results control the physical device, with a control latency of ≤200ms. When the platform detects device anomalies or policy deviations, it automatically triggers large model re-inference, combining federated learning and reinforcement learning to dynamically allocate cloud-edge computing power.

8. The multimodal driven cross-industry digital twin general platform architecture according to claim 1, characterized in that: When building cross-industry scenarios, the platform introduces the MAML meta-learning algorithm to automatically generate initial parameters for new industry scenarios based on data from 100+ historical projects.

Images