An electrical mechanical machining system based on multi-modal fusion technology

Abstract

The application discloses an electrical mechanical machining system based on a multi-modal fusion technology, and relates to the technical field of electrical mechanical machining.The technical scheme is as follows: the electrical mechanical machining system comprises an electrical mechanical research and development system, a multi-modal fusion technology system is arranged at the output end of the electrical mechanical research and development system, a cross-modal feature fusion technology is arranged at the output end of the multi-modal fusion technology system, and a blockchain combination and data sharing technology and a multi-source data fusion technology are further arranged at the output end of the multi-modal fusion technology system.The application has the beneficial effects that: the modal barrier problem of heterogeneous data such as images, texts and sensors is solved through a shared encoder, the application of the geometric complexity, precision requirement and machining efficiency in the machining of complex and special-shaped parts of electrical machines is completed, the accuracy is greatly improved, and the core technical indexes of the multi-source heterogeneous data technology cover the technical ability and efficiency of the whole data processing process, thereby realizing multi-dimensional technical support.

Description

Technical Field

[0001] This invention relates to the field of electromechanical processing technology, and more specifically to an electromechanical processing system based on multimodal fusion technology. Background Technology

[0002] Electromechanical processing technology integrates knowledge from multiple fields such as mechanical engineering, electrical automation, and materials science, and is the cornerstone of modern manufacturing.

[0003] Existing electromechanical processing technologies cannot be based on multimodal fusion technology for production. As a result, modal barriers arise from heterogeneous data such as images, text, and sensors. The existence of these problems leads to geometric complexity in the processing of complex and irregular electromechanical parts and also fails to improve production accuracy. Summary of the Invention

[0004] To address this, the present invention provides an electromechanical processing system based on multimodal fusion technology, which solves the modal barrier problem of heterogeneous data such as images, text, and sensors by constructing a shared encoder.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an electromechanical processing system based on multimodal fusion technology, comprising an electromechanical research and development system, wherein the output end of the electromechanical research and development system is equipped with a multimodal fusion technology system, the output end of the multimodal fusion technology system is equipped with cross-modal feature fusion technology, and the output end of the multimodal fusion technology system is further equipped with blockchain integration and data sharing technology and multi-source data fusion technology; The cross-modal feature fusion technology output terminal is equipped with feature alignment and semantic mapping technology and fusion architecture design technology. The cross-modal feature fusion technology output terminal is also connected to optimization and engineering technology, synthetic data generation technology and typical scenario performance improvement technology.

[0006] Preferably, the blockchain combined with data sharing technology output terminal is connected to a layered fusion architecture module and a data security transmission and storage module.

[0007] Preferably, the blockchain combined with data sharing technology output terminal is also connected to a data sharing process, and the data sharing process output terminal is connected to an incentive mechanism design process.

[0008] Preferably, the blockchain combined with data sharing technology output terminal is also connected to a scenario application module and a challenge and optimization direction module.

[0009] Preferably, the output terminal of the multi-source data fusion technology is connected to a technology definition and core technology module, a key technology breakthrough module, and a technology application module.

[0010] Preferably, the feature alignment and semantic mapping techniques include attention mechanism alignment techniques, knowledge graph guided alignment techniques, and public subspace projection techniques.

[0011] Preferably, the layered fusion architecture module includes an edge layer fusion architecture, a blockchain layer fusion architecture, and an application layer fusion architecture.

[0012] Preferably, the data security transmission and storage module includes dual encryption technology and dynamic permission and access control mechanism.

[0013] Preferably, the data security transmission and storage module further includes a behavior trust verification mechanism and a consensus optimization mechanism.

[0014] Preferably, the scenario application module includes a supply chain collaboration module and a predictive maintenance module.

[0015] The beneficial effects of this invention are: By constructing a shared encoder, the modal barrier problem of heterogeneous data such as images, text, and sensors is solved, and the application of geometric complexity, precision requirements, and processing efficiency in the processing of complex and irregular parts of electromechanical machinery is completed, which greatly improves the accuracy. The core technical indicators of multi-source heterogeneous data technology cover the technical capabilities and efficiency of the entire data processing process, and achieve multi-dimensional technical assurance. At the same time, through conflict resolution, data deduplication, outlier cleaning and other technologies, the logical consistency and accuracy of the fusion results are ensured, effectively improving the support capabilities of value density, feature extraction, correlation analysis, and data decision-making. In addition, this system adopts a distributed architecture and elastic architecture design to support the growth of data scale. It can still output valid results stably when the data source is abnormal, reducing the risk of single point of failure. Storage and query optimization, through data partitioning, index optimization, and query, effectively improve the fault tolerance and stability of multi-source hybrid systems. Attached Figure Description

[0016] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0017] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0018] Figure 1 The overall control system diagram provided for this invention; Figure 2 A diagram illustrating the blockchain integration and data sharing technology provided by this invention; Figure 3 A diagram illustrating the multi-source data fusion technology provided by this invention; Figure 4 This invention provides a diagram illustrating the feature alignment and semantic mapping technology. Figure 5 A diagram of the layered fusion architecture module provided by this invention; Figure 6 A diagram of a data security transmission and storage module provided by this invention; Figure 7 This is a diagram illustrating the application scenarios provided by the present invention.

[0019] The diagram shows: 1. Electromechanical R&D System; 2. Multimodal Fusion Technology System; 3. Cross-modal Feature Fusion Technology; 4. Blockchain Integration and Data Sharing Technology; 5. Multi-source Data Fusion Technology; 6. Feature Alignment and Semantic Mapping Technology; 7. Fusion Architecture Design Technology; 8. Optimization and Engineering Technology; 9. Synthetic Data Generation Technology; 10. Typical Scenario Performance Improvement Technology; 11. Layered Fusion Architecture Module; 12. Data Security Transmission and Storage Module; 13. Data Sharing Process; 14. Incentive Mechanism Design Process; 15. Scenario Application Module; 16. Challenges and Optimization Directions Module; 17. Technology Definition and Core Technology Module; 18. Key Technology Breakthrough Module; 19. Technology Application Module; 20. Attention Mechanism Alignment Technology; 21. Knowledge Graph Guided Alignment Technology; 22. Public Subspace Projection Technology; 23. Edge Layer Fusion Architecture; 24. Blockchain Layer Fusion Architecture; 25. Application Layer Fusion Architecture; 26. Dual Encryption Technology; 27. Dynamic Permission and Access Control Mechanism; 28. Behavioral Trust Verification Mechanism; 29. ​​Consensus Optimization Mechanism; 30. Supply Chain Collaboration Module; 31. Predictive Maintenance Module. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] See attached document Figure 1 -Appendix Figure 7 The present invention provides an electromechanical processing system based on multimodal fusion technology, comprising: The electromechanical R&D system 1 has a multimodal fusion technology system 2 at its output end, a cross-modal feature fusion technology 3 at its output end, and a blockchain integration and data sharing technology 4 and a multi-source data fusion technology 5 at its output end. The output end of the cross-modal feature fusion technology 3 is equipped with feature alignment and semantic mapping technology 6 and fusion architecture design technology 7. The output end of the cross-modal feature fusion technology 3 is also connected to optimization and engineering technology 8, synthetic data generation technology 9 and typical scenario performance improvement technology 10. Feature alignment and semantic mapping technology 6 includes attention mechanism alignment technology 20, knowledge graph guided alignment technology 21 and common subspace projection technology 22. What needs to be explained is: Fusion architecture design technology 7: Dynamically adjust contribution weights based on modal reliability, apply a lightweight cascaded architecture method, screen using lightweight single-modal models, initiate multimodal fusion for suspicious areas, balance computational efficiency and accuracy, and meet real-time requirements. Optimization and engineering technology 8: Employ multimodal feature selection and recursive feature elimination algorithms to screen key features, reduce redundancy, integrate process flow modeling, multimodal fusion, industrial application scenarios, heterogeneous data alignment integrity, and synthetic data generation technology 9: Apply models to generate cross-modal mapping images to solve the data scarcity problem. Typical scenario performance improvement technology 10: Through cross-modal feature fusion technology 3, applied to improve technical process performance, in the manufacturing of core components of electrical machinery, the visual dynamic fusion recognition rate increased from 72% to 94%, achieving knowledge graph-guided visual text fusion effect, with an accuracy improvement of 20% and an industrial detection fault location accuracy improvement of 35%. Attention mechanism alignment technology 20 uses a cross-modal attention layer to automatically align heterogeneous features. The attention mechanism associates image features with synchronous screen actions and subtitle text, improving product positioning accuracy. It aligns image features with usage data through visual semantic alignment, visual structure alignment measurement, and model evaluation. Knowledge graph-guided alignment technology 21 introduces a domain knowledge graph to build semantic associations between modalities, realizing the fusion of visual attributes of product manufacturing scene images, including color and shape, with material and usage described in text, according to graph relationships. The result is a 30% improvement in relevance, a 40% improvement in mention rate, and a jump rate increase from 8% to 35%. Entity disambiguation → attribute fusion → relationship completion → quality assessment form an iterative closed loop. Common subspace projection technology 22 maps multimodal data to a unified cross-modal self-supervised semantic space through adversarial training or contrastive learning, solving the modal difference problem. Common subspace projection optimizes the orthogonal projection component norm to improve noise resistance, verifying the algorithm's ability to maintain fitting accuracy in high-noise environments, noise resistance coefficient stability, and stable ratio of high-resolution image spectral and spatial features. The output end of the blockchain-integrated data sharing technology 4 is connected to a layered fusion architecture module 11 and a data security transmission and storage module 12. The output end of the blockchain-integrated data sharing technology 4 is also connected to a data sharing process 13. The output end of the data sharing process 13 is connected to an incentive mechanism design process 14. The output end of the blockchain-integrated data sharing technology 4 is also connected to a scenario application module 15 and a challenge and optimization direction module 16. The output end of the multi-source data fusion technology 5 is connected to a technology definition and core technology module 17, a key technology breakthrough module 18, and a technology application module 19. The layered fusion architecture module 11 includes an edge layer fusion architecture 23, a blockchain layer fusion architecture 24, and an application layer fusion architecture 25. The data security transmission and storage module 12 includes dual encryption technology 26 and a dynamic permission and access control mechanism 27. The data security transmission and storage module 12 also includes a behavior trust verification mechanism 28 and a consensus optimization mechanism 29. The scenario application module 15 includes a supply chain collaboration module 30 and a predictive maintenance module 31. Blockchain combined with data sharing technology 4. Utilizing industrial time-series data and blockchain to achieve secure data sharing throughout the entire process. Through the distributed knowledge transfer algorithm (Fed DKT), the differences in heterogeneous data distribution and its dynamic perception and computing power collaboration are solved, a heterogeneous computing resource pool is constructed, and edge nodes in industrial application scenarios are realized to achieve secure data sharing and integration throughout the entire process, ensuring the authenticity, privacy and collaborative efficiency of the data. Edge layer integration architecture 23. Deploy lightweight blockchain nodes to collect real-time equipment time-series data such as temperature, vibration, and flow, and perform data cleaning and preprocessing. Blockchain layer integration architecture 24. Adopting a consortium blockchain architecture, composed of authorized nodes such as enterprises, suppliers, and regulators, it stores data hash values ​​and key metadata through a distributed ledger. Application layer integration architecture 25. Based on smart contracts, it realizes data access control, transaction settlement and sharing incentives, and supports API interface docking with analysis platforms. Dual encryption technology 26 pairs of time-series data are encrypted at the edge using national cryptographic algorithms to generate ciphertext. Data keys are dynamically allocated through blockchain smart contracts. Dynamic permission and access control mechanism 27 uses application attribute-based encryption technology to improve dynamic decryption of data based on user role attributes, thereby achieving permission control. Behavior trust verification mechanism 28 uses AI models to analyze user operation logs, intercept abnormal requests in real time, and employs privacy protection technology, learning fusion, and other technical means to improve the exchange of encrypted parameters in blockchain coordination, achieving the effect of "usable but invisible" data, obtaining the validity of original data, and completing cross-node data synchronization. Consensus optimization mechanism 29 adopts a fault-tolerant consensus algorithm to ensure second-level data synchronization in industrial real-time scenarios and reduce network load. Data sharing process 13: Collect time-series data from devices to edge encryption computing, then to blockchain storage, then to the data requester, then to submit an application for smart contract verification permission, then by returning the data key and storage address, finally decrypt the data and add analysis applications. Incentive mechanism design process 14: Calculate the usage of data providers into the smart contract points system to stimulate enterprises' willingness to share. The supply chain collaboration module 30 shares the production line sequence data of component suppliers with customers. Blockchain ensures the authenticity and transparency of quality parameters, shortening the delivery cycle by 30%. The predictive maintenance module 31 achieves accurate early warning of faults and data reading through learning modeling and sharing technology. The Technology Definition and Core Technology Module 17 uses mathematical and algorithmic methods to preprocess, extract features, and fuse heterogeneous data from multiple sources. The Key Technology Breakthrough Module 18 employs wavelet decomposition and component analysis dimensionality reduction techniques to improve data quality through preprocessing and feature extraction. It combines long short-term memory to extract spatiotemporal features, innovates fusion algorithms, attention mechanisms, and cross-attention transformers to enhance the complementarity of multimodal data. The Technology Application Module 19 improves accuracy through product identification and information construction, integrates stress sensor data to enhance the real-time performance of fault diagnosis, and reduces maintenance costs. It combines infrared and hyperspectral data to generate 3D models, supporting a multimodal large model framework to promote intelligent application analysis of remote sensing data. It enhances the security of data sharing and traceability through reinforcement learning and blockchain technology. It applies multi-source data fusion technology 5 to move from single information integration to dynamic extrapolation and edge computing optimization, upgrading system association and becoming the core support for digital twins and intelligent decision-making. It further breaks through the research on algorithm adaptability, cross-modal modeling, and ethical boundaries, realizing the deep integration application of the company's electrical machinery equipment and its core components in the deep collaborative architecture of multi-source heterogeneous data fusion.

[0022] The system control flow of this invention is as follows: S1: Fusion Architecture Design Technology 7. Dynamically adjust contribution weights based on modal reliability, apply a lightweight cascade architecture method, screen using lightweight single-modal models, initiate multimodal fusion for suspicious areas, balance computational efficiency and accuracy, meet real-time requirements, optimization and engineering technology 8. Employ multimodal feature selection and recursive feature elimination algorithms to screen key features, reduce redundancy, integrate process flow modeling, multimodal fusion and industrial application scenarios, heterogeneous data alignment integrity, synthetic data generation technology 9. Apply models to generate cross-modal mapping images to solve the data scarcity problem, typical scenario performance improvement technology 10. Through cross-modal feature fusion technology 3, applied to improve technical process performance, in the manufacturing of core components of electrical machinery, the visual dynamic fusion recognition rate increased from 72% to 94%, achieving knowledge graph-guided visual text fusion effect, accuracy improved by 20%, and industrial detection fault location accuracy improved by 35%; S2: Attention Mechanism Alignment Technology 20: Automatically aligns heterogeneous features using a cross-modal attention layer. The attention mechanism associates image features with synchronous screen actions and subtitle text, improving product positioning accuracy. Alignment is achieved through visual semantic alignment, visual structure alignment measurement, and model evaluation of image features and usage data. Knowledge Graph-Guided Alignment Technology 21: Introduces domain knowledge graphs to construct semantic associations between modalities, realizing the fusion of visual attributes of product manufacturing scene images, including color and shape, with material and usage described in text, according to graph relationships. The result relevance is improved by 30%, mention rate is improved by 40%, and jump rate is improved from 8% to 35%. Entity disambiguation → attribute fusion → relationship completion → quality assessment form an iterative closed loop. Common Subspace Projection Technology 22: Maps multimodal data to a unified cross-modal self-supervised semantic space through adversarial training or contrastive learning to solve the modal difference problem. Common subspace projection optimizes the orthogonal projection component norm to improve noise resistance, verifying the algorithm's ability to maintain fitting accuracy in high-noise environments, noise resistance coefficient stability, and stable ratio of high-resolution image spectral and spatial features. S3: Blockchain combined with data sharing technology 4. Utilizing industrial time-series data combined with blockchain to achieve secure sharing of data throughout the entire process. Through distributed knowledge transfer (DLT), the differences in heterogeneous data distribution and their dynamic perception and computing power collaboration are solved. A heterogeneous computing resource pool is built to realize edge nodes in industrial application scenarios, achieving secure sharing and integration of data throughout the entire process, ensuring the authenticity, privacy and collaborative efficiency of data. Edge layer integration architecture 23. Deploy lightweight blockchain nodes to collect real-time equipment time-series data such as temperature, vibration, and flow, and perform data cleaning and preprocessing. Blockchain layer integration architecture 24. Adopting a consortium blockchain architecture, composed of authorized nodes such as enterprises, suppliers, and regulators, it stores data hash values ​​and key metadata through a distributed ledger. Application layer integration architecture 25. Based on smart contracts, it realizes data access control, transaction settlement and sharing incentives, and supports API interface docking with analysis platforms. S4: Dual Encryption Technology 26. Time-series data is encrypted at the edge using national cryptographic algorithms to generate ciphertext. Data keys are dynamically allocated through blockchain smart contracts. Dynamic Permission and Access Control Mechanism 27. By applying attribute-based encryption technology, data is dynamically decrypted based on user role attributes to achieve permission control. Behavior Trust Verification Mechanism 28. By analyzing user operation logs through AI models, abnormal requests are intercepted in real time. Privacy protection technology, learning fusion, and other technical means are used to improve the exchange of encrypted parameters in blockchain coordination, achieving the effect of "usable but invisible" data, obtaining the validity of the original data, and completing cross-node data synchronization. Consensus Optimization Mechanism 29. Fault-tolerant consensus algorithms are adopted to ensure second-level data synchronization in industrial real-time scenarios and reduce network load. S5: Data Sharing Process 13 From collecting time-series data through devices to edge encryption computing, then to on-chain storage, then to the data requester, then to submitting an application for smart contract verification permissions, then by returning the data key and storage address, finally decrypting the data and adding analysis applications, incentive mechanism design process 14 Based on the usage of data providers, it is included in the smart contract points system to stimulate enterprises' willingness to share. S6: Supply chain collaboration module 30 shares the production line sequence data of component suppliers with customers. Blockchain ensures the authenticity and transparency of quality parameters, shortening the delivery cycle by 30%. Predictive maintenance module 31 achieves accurate early warning of faults and data reading through learning modeling and sharing technology. S7: Technology Definition and Core Technology Module 17 uses mathematical and algorithmic methods to preprocess, extract features, and fuse heterogeneous data from multiple sources. Key Technology Breakthrough Module 18 employs wavelet decomposition and component analysis dimensionality reduction techniques to improve data quality through preprocessing and feature extraction. It combines long short-term memory to extract spatiotemporal features, innovates fusion algorithms, attention mechanisms, and cross-attention transformers to enhance the complementarity of multimodal data. Technology Application Module 19 improves accuracy through product identification and information construction, integrates stress sensor data to improve the real-time performance of fault diagnosis, and reduces maintenance costs. It combines infrared and hyperspectral data to generate 3D models, supporting a multimodal large model framework to promote intelligent application analysis of remote sensing data. It enhances the security sharing and traceability of data through reinforcement learning and blockchain technology. It applies multi-source data fusion technology 5 to move from single information integration to dynamic extrapolation and edge computing optimization, upgrading system association and becoming the core support for digital twins and intelligent decision-making. It further breaks through the research on algorithm adaptability, cross-modal modeling, and ethical boundaries, realizing the deep integration application of the company's electrical machinery equipment and its core components in the deep collaborative architecture of multi-source heterogeneous data fusion.

[0023] The above description is merely a preferred embodiment of the present invention. Any person skilled in the art can modify the present invention or modify it into an equivalent technical solution using the technical solutions described above. Therefore, any simple modifications or equivalent substitutions made based on the technical solutions of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. An electromechanical processing system based on multimodal fusion technology, characterized in that: include The electromechanical research and development system (1) is equipped with a multimodal fusion technology system (2) at its output end. The multimodal fusion technology system (2) is equipped with cross-modal feature fusion technology (3) at its output end. The multimodal fusion technology system (2) is also equipped with blockchain integration and data sharing technology (4) and multi-source data fusion technology (5) at its output end. The cross-modal feature fusion technology (3) outputs a feature alignment and semantic mapping technology (6) and a fusion architecture design technology (7). The cross-modal feature fusion technology (3) outputs an optimization and engineering technology (8), a synthetic data generation technology (9), and a typical scenario performance improvement technology (10).

2. The electromechanical processing system based on multimodal fusion technology according to claim 1, characterized in that: The output end of the blockchain combined with data sharing technology (4) is connected to a layered fusion architecture module (11) and a data security transmission and storage module (12).

3. The electromechanical processing system based on multimodal fusion technology according to claim 1, characterized in that: The output end of the blockchain combined with data sharing technology (4) is also connected to a data sharing process (13), and the output end of the data sharing process (13) is connected to an incentive mechanism design process (14).

4. The electromechanical processing system based on multimodal fusion technology according to claim 1, characterized in that: The output end of the blockchain combined with data sharing technology (4) is also connected to a scenario application module (15) and a challenge and optimization direction module (16).

5. The electromechanical processing system based on multimodal fusion technology according to claim 1, characterized in that: The output end of the multi-source data fusion technology (5) is connected to a technology definition and core technology module (17), a key technology breakthrough module (18), and a technology application module (19).

6. The electromechanical processing system based on multimodal fusion technology according to claim 1, characterized in that: The feature alignment and semantic mapping techniques (6) include attention mechanism alignment techniques (20), knowledge graph guided alignment techniques (21), and public subspace projection techniques (22).

7. The electromechanical processing system based on multimodal fusion technology according to claim 2, characterized in that: The layered fusion architecture module (11) includes an edge layer fusion architecture (23), a blockchain layer fusion architecture (24), and an application layer fusion architecture (25).

8. The electromechanical processing system based on multimodal fusion technology according to claim 2, characterized in that: The data security transmission and storage module (12) includes dual encryption technology (26) and dynamic permission and access control mechanism (27).

9. An electromechanical processing system based on multimodal fusion technology according to claim 2, characterized in that: The data security transmission and storage module (12) also includes a behavior trust verification mechanism (28) and a consensus optimization mechanism (29).

10. An electromechanical processing system based on multimodal fusion technology according to claim 4, characterized in that: The scenario application module (15) includes a supply chain collaboration module (30) and a predictive maintenance module (31).

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