High-interpretability cross-modal extensible artificial intelligence system

Abstract

The invention provides a high-interpretability cross-modal extensible artificial intelligence system which comprises a high-interpretability encoder, the high-interpretability encoder is connected with a multi-modal input interface, and the high-interpretability encoder maps a multi-modal parameter input by the multi-modal input interface into a layered traceable tensor; the hierarchical constraint inference engine is connected with the high interpretability encoder, and the hierarchical constraint inference engine is used for cross-scale dynamic constraint propagation and attention coupling between modes; the extensible intelligent interface is connected with the hierarchical constraint inference engine, and the extensible intelligent interface is used for supporting plug-and-play extension of a new domain module; and the bidirectional projection output module is connected with the extensible intelligent interface. The reasoning path is traceable; the cross-modal precision is improved, the modules in the new field are plug-and-play through the extensible intelligent interface, the adaptation time is long, and the extension efficiency is high.

Description

Technical Field

[0001] The present invention relates to the intersection of artificial intelligence and cross-modal computing, and in particular to a highly explainable cross-modal and scalable artificial intelligence system. Background Art

[0002] A highly explainable cross-modal and scalable artificial intelligence system refers to an artificial intelligence system that can process multi-modal data (such as text, images, audio, etc.) and has high explainability and scalability.

[0003] The highly explainable cross-modal scalable artificial intelligence systems in the existing technology have the following limitations: Black box is not explainable: traditional deep learning models lack traceability of the reasoning process and cannot meet the requirements of decision-making transparency in fields such as medicine and engineering; modal fragmentation: multimodal data such as text, images, and physical parameters are processed independently, and lack a unified representation framework; insufficient scalability: models need to be retrained for new fields, and the migration cost is high; disconnected from physical laws: language models cannot effectively embed scientific constraints such as energy conservation and quantization conditions, and cannot establish effective physical law constraints. Summary of the invention

[0004] In order to solve the technical problems of untraceability of reasoning process, modal split, insufficient scalability and disconnection from physical laws, the present invention provides a highly explainable cross-modal scalable artificial intelligence system.

[0005] The present invention solves the above technical problems through the following technical solutions: The present invention provides a highly explainable cross-modal and scalable artificial intelligence system, comprising: A highly interpretable encoder, wherein the highly interpretable encoder is connected to a multimodal input interface, and the highly interpretable encoder maps the multimodal parameters input by the multimodal input interface into a hierarchical traceable tensor; A hierarchical constraint reasoning engine, the hierarchical constraint reasoning engine is connected to the highly interpretable encoder, the hierarchical constraint reasoning engine is used for cross-scale dynamic constraint propagation and inter-modal attention coupling; An extensible intelligent interface, the extensible intelligent interface is connected to the hierarchical constraint reasoning engine, and the extensible intelligent interface is used to support plug-and-play expansion of new domain modules; A bidirectional projection output module is connected to the extensible intelligent interface.

[0006] Preferably, the multimodal parameters are text, images, and physical parameters, and the physical parameters include physical data detected by sensors.

[0007] Preferably, the highly interpretable encoder is provided with a semantic channel binding module to fix the physical quantity to a predetermined position of the tensor.

[0008] Preferably, the highly interpretable encoder is provided with an orthogonal constraint encoding algorithm that satisfies .

[0009] Preferably, the hierarchical reasoning engine is based on the stability control of the Lyapunov function; ensuring: .

[0010] Preferably, the hierarchical constraint reasoning engine has a cross-level attention coupling mechanism, and the calculation formula is: .

[0011] Preferably, the highly interpretable encoder inputs natural language to generate hierarchical interpretable tensors , each layer corresponds to a different physical scale.

[0012] Preferably, the hierarchical constraint reasoning engine is provided with a reasoning path visualization tool to display the contribution of each level in real time and support counterfactual reasoning demonstration.

[0013] Preferably, the scalable intelligent interface has a transfer learning acceleration protocol.

[0014] Preferably, the bidirectional projection output module is used to generate traceable natural language and physical parameter outputs.

[0015] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0016] The positive and progressive effects of the present invention are: The highly explainable cross-modal scalable artificial intelligence system proposed above has three core characteristics: high explainability, cross-modal fusion and scalable intelligence. Its explainability is improved and the reasoning path is traceable; further, the cross-modal accuracy is improved; further, the new domain module is plug-and-play through the scalable intelligent interface, with adaption time and high expansion efficiency; at the same time, it has high physical compliance and low deviation rate from conservation laws. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a system framework diagram of the present invention.

[0018] Figure 2 This is a schematic diagram of semantic channel binding of the present invention.

[0019] Figure 3 Schematic diagram of the hierarchical contribution visualization interface of the present invention.

[0020] Figure 4 This is a cross-modal fusion flow chart of the present invention.

[0021] Figure 5 This is a diagram of Formula 1 of the present invention.

[0022] Figure 6 This is a diagram of Formula 2 of the present invention.

[0023] Figure 7 This is a diagram of Formula 3 of the present invention.

[0024] Figure 8 This is a diagram of Formula 4 of the present invention.

[0025] Fig. 9 This is a diagram of Formula 5 of the present invention.

[0026] Description of Reference Numerals 1. Multimodal input interface, 2. Highly interpretable encoder, 3. Hierarchical constraint reasoning engine, 4. Scalable intelligent interface, 5. Bidirectional projection output module. DETAILED DESCRIPTION

[0027] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples.

[0028] like Figure 1-9 As shown, a highly explainable cross-modal scalable artificial intelligence system includes: A highly interpretable encoder 2, wherein the highly interpretable encoder 2 is connected to the multimodal input interface 1, and the highly interpretable encoder 2 maps the multimodal parameters input by the multimodal input interface 1 into a hierarchical traceable tensor; A hierarchical constraint reasoning engine 3, wherein the hierarchical constraint reasoning engine 3 is connected to the highly interpretable encoder 2, and the hierarchical constraint reasoning engine 3 is used for cross-scale dynamic constraint propagation and inter-modal attention coupling; An extensible intelligent interface 4, the extensible intelligent interface 4 is connected to the hierarchical constraint reasoning engine 3, and the extensible intelligent interface 4 is used to support plug-and-play expansion of new domain modules; A bidirectional projection output module 5 is connected to the extensible intelligent interface 4 .

[0029] The multimodal parameters are text, image, and physical parameters, and the physical parameters include physical data detected by a sensor.

[0030] The highly interpretable encoder 2 is provided with a semantic channel binding module to fix the physical quantity to a predetermined position of the tensor.

[0031] The highly interpretable encoder 2 is provided with an orthogonal constraint encoding algorithm that satisfies .

[0032] The hierarchical reasoning engine is based on the stability control of the Lyapunov function to ensure: .

[0033] The hierarchical constraint reasoning engine 3 has a cross-level attention coupling mechanism, and the calculation formula is: .

[0034] The high interpretable encoder 2 inputs natural language to generate hierarchical interpretable tensors , each layer corresponds to a different physical scale.

[0035] The hierarchical constraint reasoning engine 3 is provided with a reasoning path visualization tool to display the contribution of each level in real time and support counterfactual reasoning demonstration.

[0036] The scalable intelligent interface 4 has a transfer learning acceleration protocol.

[0037] The bidirectional projection output module 5 is used to generate traceable natural language and physical parameter outputs.

[0038] A highly interpretable cross-modal reasoning method includes the following steps: a) Highly interpretable encoding of multimodal data; b) Hierarchical dynamic constraint propagation and cross-modal fusion; c) Scalable knowledge injection and transfer learning; d) Generate traceable natural language and physical parameter output.

[0039] This system achieves three major innovations through a multimodal semantic engine, a hierarchical reasoning architecture, and a dynamic constraint mechanism. It integrates natural language understanding, physical law modeling, and multimodal data reasoning, and is suitable for complex scenarios such as scientific prediction, industrial design, and medical decision-making that require a combination of semantic understanding and domain knowledge.

[0040] Specifically, it has three core characteristics: high explainability, cross-modal fusion and scalable intelligence.

[0041] Figure 1 In the diagram, data flows from left to right; modules are connected by high-speed buses to support real-time constraint propagation.

[0042] Highly interpretable implementation technology (1) Semantic-mathematical bidirectional mapping engine Input natural language generation hierarchical interpretable tensor , each layer corresponds to a different physical scale (micro / meso / macro) Key technologies: Semantic channel binding: fix physical quantities (such as temperature, stress) to predetermined positions in tensors (such as Figure 2As shown, physical quantities are bound by fixed positions) Orthogonality Constraint Encoding: Ensuring (2) Reasoning Path Visualization Tool Real-time display of contribution of each level (e.g. Figure 3 ) Support counterfactual reasoning demonstration (such as automatic warning when ignoring the second law of thermodynamics) Cross-modal fusion architecture Unified tensor representation space: supports unified encoding of multi-modal inputs such as text, images, and physical parameters (e.g. Figure 4 ), whose formula is Formula 1, such as Figure 5 shown.

[0043] Inter-modal correlation matrix: Its formula is formula 2, such as Figure 6 shown.

[0044] Cross-modal attention mechanism: Dynamically calculate the weight of inter-modal information: Its formula is Formula 3, as Figure 7 shown.

[0045] Scalable intelligent implementation method When modular knowledge is injected into new domain expansion, only domain knowledge modules need to be added.

[0046] Transfer learning acceleration protocol; parameter initialization based on tensor similarity: its formula is formula 4, such as Figure 8 shown.

[0047] Dynamic expansion interface; supports adding new modal encoders online , satisfying the compatibility condition: its formula is Formula 5, such as Fig. 9 shown.

[0048] Example 1: Medical Diagnostic System enter: Text: "persistent low-grade fever, splenomegaly, elevated ferritin" Image: CT scan showing enlarged lymph nodes Test data: CD4+ / CD8+=0.7 Coding process: Generate 3rd-order diagnostic tensor Level 1: Immune indicator channel (bound to CD4+ / CD8+ ratio) Level 2: Image feature channel (extracting lymph node texture features) Level 3: Biochemical Constraint Pathway (Ferritin Metabolism Equation) Reasoning process: Dynamic constraint propagation activation: Immune conservation constraint: ∑Tcell = constant Anatomical constraints: spleen volume growth rate ≤ 5% / month Cross-level attention calculation: Image → Immunity Layer Weight: 0.68 Biochemistry → Image layer weight: 0.52 Output: Diagnosis: "Adult Still's disease (94% probability)" Explainable Reports: Key reasoning nodes: 1. Immune imbalance (CD4+ / CD8+<1) → Activate autoimmune detection 2. Imaging feature matching degree 92% → Exclude the possibility of tumor 3. Abnormal ferritin metabolism → Meet the Still disease pathological model Example 2: Industrial material design enter: Text requirement: "High toughness aviation aluminum alloy" Physical parameters: target strength ≥ 600MPa, density ≤ 2.8g / cm³ Image data: SEM image of existing material fracture Coding and Reasoning: Generate 3rd-order material tensor Level 1: Atomic Bonding Energy Channel Level 2: Grain boundary sliding characteristics Level 3: Macroscopic Mechanical Constraints Dynamic injection constraints: Hall-Petch equation: Density-Intensity Pareto Front Output: Recommended composition: Al-5.6%Zn-2.3%Mg-1.1%Cu Predicted performance: strength 623MPa, density 2.76g / cm³ Explanable paths: Reasoning path: Zn / Mg improves solid solution strengthening → strength +38% -> Cu refines grains (d=2.1μm) → toughness +22% -> composition optimization reduces density to the target range The present invention is not limited to the above-mentioned embodiments. Any changes in shape or structure are within the protection scope of the present invention. The protection scope of the present invention is defined by the attached claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and essence of the present invention, but these changes and modifications are within the protection scope of the present invention.

Claims

1. A highly interpretable cross-modal and scalable artificial intelligence system, characterized in that: include: A highly interpretable encoder (2), the highly interpretable encoder (2) being connected to the multimodal input interface (1), and the highly interpretable encoder (2) mapping the multimodal parameters inputted by the multimodal input interface (1) into a hierarchical traceable tensor; A hierarchical constraint reasoning engine (3), the hierarchical constraint reasoning engine (3) being connected to the highly interpretable encoder (2), the hierarchical constraint reasoning engine (3) being used for cross-scale dynamic constraint propagation and inter-modal attention coupling; An extensible intelligent interface (4), the extensible intelligent interface (4) being connected to the hierarchical constraint reasoning engine (3), the extensible intelligent interface (4) being used to support plug-and-play expansion of new domain modules; A bidirectional projection output module (5), wherein the bidirectional projection output module (5) is connected to the expandable intelligent interface (4).

2. A highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The multimodal parameters are text, image, and physical parameters, and the physical parameters include physical data detected by a sensor.

3. A highly explainable cross-modal scalable artificial intelligence system as claimed in claim 2, characterized in that: The highly interpretable encoder (2) is provided with a semantic channel binding module to fix the physical quantity to a predetermined position of the tensor.

4. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The highly interpretable encoder (2) is provided with an orthogonal constraint encoding algorithm that satisfies .

5. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The hierarchical reasoning engine is based on the stability control of the Lyapunov function; make sure: 。 6. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The hierarchical constraint reasoning engine (3) has a cross-level attention coupling mechanism, and the calculation formula is: 。 7. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The highly interpretable encoder (2) generates a hierarchical interpretable tensor by inputting a natural language , each layer corresponds to a different physical scale.

8. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The hierarchical constraint reasoning engine (3) is provided with a reasoning path visualization tool to display the contribution of each level in real time and support counterfactual reasoning demonstration.

9. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The scalable intelligent interface (4) has a transfer learning acceleration protocol.

10. The highly explainable cross-modal scalable artificial intelligence system according to claim 1, characterized in that: The bidirectional projection output module (5) is used to generate traceable natural language and physical parameter output.

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