General artificial intelligence system based on EGS structure

Abstract

The application discloses a kind of general artificial intelligence systems based on EGS structure, including basic layer, core modeling layer, core fusion layer, core execution layer, feedback optimization layer and evolution discovery layer, based on sparse optimization and variational method, physical information neural network and model-data double-driven framework improvement.Basic layer realizes EGS labeling and sparse optimization preprocessing;Core modeling layer carries out five-dimensional entity modeling, six-class rule modeling and four-layer constraint modeling;Core fusion layer completes the triple fusion of general intelligence-cross-domain adaptation-causal logic;Core execution layer realizes structured execution and full-link verification;Feedback optimization layer carries out double-time-scale dynamic optimization;Evolution discovery layer realizes sample-independent generalization rule extraction and controllable emergence screening.The system in the application can be extrapolated to unknown fields following the same physical laws, with improved accuracy in few-sample tasks, enabling zero-sample cross-domain reasoning, and can be widely applied in intelligent production, scientific research and innovation.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a general artificial intelligence system based on the EGS architecture. Background Technology

[0002] Artificial General Intelligence (AGI) aims to build systems with intelligence levels equal to or exceeding those of humans, capable of autonomous learning, cross-domain collaboration, causal reasoning, and adaptation to complex and unknown environments. Existing AI technologies are largely confined to specialized intelligence domains, exhibiting technical pain points such as insufficient generalization ability, uninterpretable logic, poor cross-domain adaptability, and weak autonomous evolution capabilities. Traditional AI systems rely on training with data from specific domains, making it difficult to quickly adapt to the needs of new domains; their decision-making processes are often black-box operations, lacking traceable logical chains; system updates require global retraining, resulting in low efficiency in knowledge evolution; and they struggle to achieve deep collaborative execution of tasks across multiple domains.

[0003] Entity Grammar System (EGS), as a formal structural modeling tool, possesses advantages such as strong grammatical regularity, logical traceability, and high modularity, and has been successfully applied in fields such as intelligent agent software production. In existing technologies, Professor Terence Tao has disclosed mathematical optimization algorithms, providing a theoretical basis for improving the data processing efficiency of AI systems; however, these algorithms suffer from feature redundancy issues in handling multimodal data and cross-domain requirements. Professor Bin Dong of Peking University has disclosed a machine learning framework, providing technical support for the autonomous learning and modeling of AI systems; however, this framework lacks flexibility in cross-domain adaptation and dynamic optimization, making it difficult to adapt to the general needs of AGI. Therefore, this paper proposes a general artificial intelligence system based on the EGS structure to address the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a general artificial intelligence system based on the EGS architecture to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A general artificial intelligence system based on the EGS structure includes a basic layer, a core modeling layer, a core fusion layer, a core execution layer, a feedback optimization layer, and an evolutionary discovery layer. Each layer uses the EGS structure as a unified constraint standard, and the entire process is scheduled and controlled through the EGS-AI steward. The core technologies of the system are based on sparse optimization and variational methods, physical information neural networks, and improvements to the model-data dual-drive framework.

[0007] Preferably, the base layer includes a multi-source AGI training data layer with EGS structure annotation, a data preprocessing module, and an AGI basic toolset, wherein:

[0008] The training data layer includes task data, knowledge graphs, and environmental interaction data from different domains. All data are labeled with structural features such as EGS terminal / non-terminal symbols and production rule associations. Additionally, labels for physical conservation quantities (such as energy and momentum) and mathematical symmetry (such as translation invariance and rotation invariance) are added. This labeling method draws inspiration from the PDE constraints in physical information neural networks.

[0009] The data preprocessing module performs feature selection and redundancy elimination based on sparse optimization and variational methods (such as ℓ1 norm minimization), mapping unstructured data (text, images, sensor streams) into EGS-producible symbol-numerical hybrid representations, thus optimizing the data redundancy problem.

[0010] The AGI basic toolset includes cross-domain task adaptation tools, causal reasoning tools, and autonomous evolution tools, providing standardized support for subsequent stages.

[0011] Preferably, the core modeling layer is the core modeling stage of the system, and the entire modeling process is completed by AI tools scheduled by EGS-AI Manager. It includes a five-dimensional AGI entity modeling module, a six-category AGI rule modeling module, a four-layer AGI constraint modeling module, and a front-end dual-verification engine, wherein:

[0012] The five-dimensional AGI entity modeling module, based on the EGS terminal / non-terminal symbol structure, performs five-dimensional modeling of intelligent agent entities, knowledge entities, task entities, etc., with the entity defined as... ,in For entity ID, For entity attribute set, For entity state parameters, For general intelligent feature characters, For cross-domain adaptation features, The physical partial differential equations that entities follow are constrained by the physical information neural network concept, which makes the entity representation physically consistent and conforms to the EGS terminal / non-terminal symbol structure specification. The feature extraction logic is based on sparse optimization and variational method improvement.

[0013] The six AGI rule modeling modules are based on EGS production rules, integrating cross-domain mapping rules, causal reasoning rules, autonomous evolution rules, etc. Formal mathematical rules are introduced into the rule base, binding production rules with logical rules in the theorem prover to ensure the mathematical rigor of reasoning.

[0014] The four-layer AGI constraint modeling module is based on EGS operation rules, constructs an EGS structural constraint, general intelligent constraint, cross-domain adaptation constraint, and security compliance constraint system, and adds a variational constraint layer, which requires the system output to satisfy the minimum condition of a certain energy functional based on the principle of extreme values.

[0015] The front-end dual-verification engine completes EGS structure compliance and general intelligent pre-verification, eliminating invalid elements.

[0016] Preferably, the core fusion layer achieves deep fusion of general intelligent features, cross-domain adaptation, and causal logic, and includes a dual embedding engine, a triple semantic consistency mapping engine, and a preliminary screening engine for the executability of the fusion results, wherein:

[0017] Dual embedding engines complete general intelligent feature embedding Cross-domain adaptation feature embedding The error constraint is controlled by an adaptive threshold derived from sparse optimization theory, satisfying the dual embedding error constraint. , ;

[0018] The triple semantic consistency mapping engine introduces differential geometric isomorphism mapping, which unifies cross-domain knowledge, causal logic, and general intelligent features onto the Riemannian manifold. It requires the mapping to maintain geodesic distance to achieve semantic alignment.

[0019] The executability screening engine for fusion results is based on general intelligent judgment criteria and EGS structural specifications to eliminate invalid fusion features.

[0020] Preferably, the core execution layer implements structured intelligent generation and cross-domain task execution based on EGS inference logic, including an AGI structured execution engine, a causal inference assistance module, a full-link verification engine, and an EGS-AI steward closed-loop scheduling engine, wherein:

[0021] The AGI structured execution engine generates hierarchical execution trees and cross-domain task execution processes, ensuring clear logic and standardized execution.

[0022] The causal reasoning auxiliary module is based on the causal structure learning algorithm, extracts the causal graph from the EGS production trajectory, and uses the counterfactual reasoning axiom to verify the reliability of the causal relationship. During the execution process, it outputs formal proof steps.

[0023] The end-to-end verification engine avoids execution risks through cross-domain adaptation verification, logical consistency verification, security compliance verification, and mathematical consistency verification (such as energy conservation and symmetry breaking constraints);

[0024] The EGS-AI Butler closed-loop scheduling engine enables automated closed-loop scheduling of execution, verification, and feedback.

[0025] Preferably, the feedback optimization layer achieves dynamic system optimization based on multi-source feedback, including a feedback feature extraction engine that extracts feedback features such as cross-domain adaptation effectiveness, logical consistency, task execution performance, generalization error bound, and causal structure stability; a rule / constraint local adjustment engine that dynamically adjusts local rules based on online learning theory; and an AGI model local fine-tuning engine that uses PDE constraint fine-tuning in transfer learning, adjusting parameters only within the subspace allowed by physical laws to avoid catastrophic forgetting. The local rule dynamic adjustment update formula of the rule / constraint local adjustment engine is as follows: , The basic parameters to satisfy EGS hard rules.

[0026] Preferably, the evolutionary discovery layer realizes AGI knowledge evolution and innovation capability mining, including a local knowledge update and cross-domain fusion engine to realize local knowledge updates and cross-technology feature isomorphism mapping; a sample-independent generalization rule extraction engine based on VC dimension theory, designing a structural risk minimization algorithm to extract generalization rules from a small number of samples; a controllable emergence screening engine based on four necessary and sufficient conditions, as well as physical realizability and mathematical provability tests, to screen beneficial emergence; and an innovation direction generation engine inputs beneficial emergence into a generative adversarial network (GAN) variant to generate human-understandable innovation candidate directions, which are then screened by an EGS validator, wherein: the upper bound of VC dimension is... The four necessary and sufficient conditions are: conforming to the EGS structural specification, following the EGS production rules based on the initial elements, not being decomposable into simple combinations of the initial elements, and meeting the requirements of general intelligence and cross-domain adaptation.

[0027] A preferred general artificial intelligence implementation method based on the EGS architecture includes the following steps:

[0028] Step 1: EGS Structure-Oriented AGI-Cross-Domain Adaptation Paradigm Fusion Modeling

[0029] Based on the EGS terminal / non-terminal structure, five-dimensional modeling is performed on all AGI entities, achieving a triple embedding of general intelligence, cross-domain adaptation, and causal logic. Six types of AGI rules are integrated, and the triggering conditions of the rules are clarified. A four-layer constraint system is constructed to ensure compliance with EGS structural specifications and general intelligence requirements. The modeling process combines a physical information neural network and a dual-drive framework to automatically extract PDE constraints from multimodal data and transform them into preconditions in EGS production rules.

[0030] Step 2: Preprocessing for standardization of multi-source AGI requirements

[0031] The system utilizes AI natural language processing tools to analyze unstructured cross-domain requirements, autonomous evolution requirements, and causal explanation requirements, extracting technical features and capability requirements that conform to the EGS terminal / non-terminal symbol specification. A convex optimization parser is employed to semantically analyze fuzzy requirements, transforming them into constrained mathematical programming problems to obtain a feature set conforming to the EGS specification. A dual mapping between requirements and general intelligent capabilities is established, clarifying the key points of cross-domain adaptation, and eliminating invalid requirement features based on general intelligent judgment criteria and the EGS structural specification. Preprocessing efficiency is accelerated using a primal-dual algorithm.

[0032] Step 3: Cross-domain collaboration + structured execution + end-to-end verification

[0033] Based on EGS production reasoning logic, a multi-step recursive closed loop of "cross-domain knowledge mapping → causal relationship modeling → task collaborative execution" is constructed. The overall requirements are decomposed into reducible sub-requirements, and the execution logic of each sub-module is clarified. The general intelligent capabilities and cross-domain tasks are accurately reduced, and the implementation paths of cross-domain knowledge mapping, causal relationship modeling and task collaborative execution are clarified. A hierarchical execution tree is synthesized to execute cross-domain tasks according to the process. The execution process and results are verified in the whole chain, and a verification report is generated.

[0034] Step 4: Multi-source feedback dynamic optimization

[0035] The feedback features of each stage are collected, classified, and labeled to adapt to the EGS structure specification. Based on the EGS production rules and operation rules, the system rules / constraints are locally adjusted, with a focus on optimizing cross-domain adaptation rules and causal inference rules. A dual-timescale update mechanism is adopted, with continuous parameters (neural network weights) adjusted on a fast timescale and discrete rules (EGS production probabilities) adjusted on a slow timescale to fine-tune the AGI model locally, thereby improving the accuracy and cross-domain adaptability of subsequent execution.

[0036] Step 5: AGI Knowledge Evolution + Controlled Emergent Mining

[0037] Update the knowledge of the corresponding subspace to achieve feature isomorphism mapping across technical fields and task types; extract general intelligent rules and cross-domain adaptation logic from a small number of samples, and use the limit lemma to determine whether new knowledge can be regarded as the limit case of old knowledge; based on the four necessary and sufficient conditions for effective emergent entities and the EGS structural specification, add physical realizability and mathematical provability tests to screen beneficial emergents; input the beneficial emergents into the adversarial network variant to generate innovative directions, and then screen and confirm them through the EGS validator.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This invention embeds Physical Information Neural Network (PINN) with PDE constraints to ensure physical consistency in entity representations. Combined with sparse optimization preprocessing to remove noisy features, the evolutionary discovery layer extracts sample-independent generalization rules based on VC dimension theory. The system can extrapolate to unknown domains that follow the same physical laws, improving accuracy in low-sample tasks and achieving zero-sample cross-domain inference, fundamentally solving the generalization bottleneck that relies on large-scale, identically distributed data. Adaptive threshold control of the dual embedding engines ensures embedding accuracy. The system only needs to adjust the EGS subspace parameters to adapt to new domains. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the basic layer structure of the present invention;

[0041] Figure 2 This is a schematic diagram of the core modeling layer structure of the present invention;

[0042] Figure 3 This is a schematic diagram of the core fusion layer structure of the present invention;

[0043] Figure 4 This is a schematic diagram of the core execution layer structure of the present invention;

[0044] Figure 5 This is a schematic diagram of the feedback optimization layer structure of the present invention;

[0045] Figure 6 This is a schematic diagram of the evolution discovery layer structure of the present invention;

[0046] Figure 7 This is a schematic diagram illustrating the implementation steps of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1: Intelligent Production Collaboration System Based on EGS-AGI

[0049] Please see Figures 1-7 In this embodiment, a general-purpose artificial intelligence system based on the EGS architecture is applied to a multi-process collaborative production scenario in a smart factory, enabling cross-domain collaboration and autonomous evolution in equipment scheduling, process optimization, and quality control.

[0050] Hardware environment: Local server cluster in the factory, with edge computing nodes deployed at key workstations for real-time data processing.

[0051] Software environment: Ubuntu 22.04 LTS, Python 3.10, PyTorch 2.1, TensorFlow 2.13, Lean4 theorem prover, custom EGS engine.

[0052] Specific implementation steps:

[0053] I. System Deployment and Infrastructure Construction

[0054] 1. Deploy the EGS-AGI system described in this invention on a server cluster, with the base layer loading multi-source EGS annotation data, including:

[0055] a. Machining process data: Label EGS terminators (such as "lathe", "tool", "speed") and their production relationships (such as "turning operation → workpiece clamping + tool selection + cutting parameters"), and add labels for physical conservation quantities (such as cutting force, power consumption) and mathematical symmetry (such as rotational symmetry).

[0056] b. Assembly process data: Label the assembly entities (such as "robotic arm", "fixture", "vision sensor") and their PDE constraints (such as the dynamic equations of the robotic arm).

[0057] c. Quality inspection process data: The mapping relationship between defect type entities and image features and physical parameters.

[0058] 2. The data preprocessing module performs feature selection on multi-source heterogeneous data (device logs, images, sensor streams) based on sparsity optimization (ℓ1 norm minimization), removes redundancy, and generates EGS-producible symbol-numerical hybrid representations.

[0059] II. Demand Preprocessing

[0060] 1. Receive cross-domain task requirements from the factory production management system: "Improve the overall efficiency of production line A, reduce energy consumption by 15%, and ensure a product qualification rate of ≥98%."

[0061] 2. Use NLP tools to analyze requirements and extract core requirements: "equipment scheduling optimization", "process parameter tuning", and "real-time quality control".

[0062] 3. Employ a convex optimization parser to transform fuzzy requirements into constrained mathematical programming problems, solve for a feature set that conforms to the EGS specification, and clarify the key points of cross-domain adaptation (such as mapping "energy consumption reduction of 15%" to PDE constraints on equipment operating time).

[0063] III. Modeling and Configuration

[0064] 1. Perform five-dimensional modeling for production equipment entities, process knowledge entities, and task entities. For example, model the entity of a "five-axis machining center":

[0065] ,in The partial differential equations of machine tool dynamics (such as the spindle thermal deformation equation) are embedded through a physical information neural network (PINN).

[0066] 2. Integrate six types of AGI rules: equipment scheduling rules (based on EGS production rules "IF high task priority AND equipment idle THEN task allocation"), process optimization rules ("IF workpiece material is titanium alloy THEN cutting speed ≤ 80m / min"), cross-process collaboration rules, etc., and bind key rules with formal mathematical theorems (such as scheduling rules needing to satisfy Lie group homomorphism constraints).

[0067] 3. Construct a four-layer constraint system: EGS structural constraints, general intelligent constraints (such as the minimum energy consumption principle), cross-domain adaptation constraints (such as interface specifications for different processes), and safety and compliance constraints (such as equipment overload protection). Among them, the energy consumption constraint is based on the variational method to derive the minimum value condition of the energy functional.

[0068] IV. Cross-domain collaborative execution

[0069] 1. The system breaks down the overall requirements into sub-tasks: equipment scheduling sub-task, process parameter optimization sub-task, and real-time quality control sub-task.

[0070] 2. Based on EGS production reasoning, construct a recursive closed loop of "cross-domain knowledge mapping → causal relationship modeling → task collaborative execution":

[0071] a. Cross-domain knowledge mapping: Through differential geometric isomorphic mapping, the equipment status (sensor data) is mapped to the process parameter space while maintaining the geodesic distance.

[0072] b. Causal relationship modeling: The causal reasoning module extracts causal graphs from historical data (such as "spindle temperature rises → tool wear accelerates → surface roughness increases") and verifies causal relationships using counterfactual reasoning.

[0073] c. Collaborative task execution: Synthesize a hierarchical execution tree, and perform scheduling, parameter optimization and quality inspection according to priority. Each step is verified by a full-link verification engine to check logical consistency and physical feasibility (such as checking whether the scheduling scheme meets the energy conservation principle).

[0074] V. Feedback Optimization and Evolution

[0075] 1. Real-time collection of feedback features such as production efficiency, energy consumption, and pass rate, and calculation of generalization error bound and causal structure stability.

[0076] 2. Dynamically adjust local rules based on online learning theory: For example, fine-tune the priority weights in equipment scheduling rules based on actual energy consumption data, with the update formula as follows:

[0077]

[0078] in Initial parameters to satisfy EGS hard rules.

[0079] 3. Employ dual time-scale updates: adjust neural network weights at a fast scale (e.g., in equipment failure prediction models), and adjust EGS rule confidence at a slow scale.

[0080] 4. The evolutionary discovery layer extracts new generalization rules (such as "new batch of titanium alloy material → cutting speed needs to be reduced by 5%) from a small number of anomalous samples, and incorporates them into the knowledge base after physical feasibility test (consistent with the material mechanics equations) and mathematical provability test (formally provable within the system).

[0081] 5. The controllable emergence screening engine identifies beneficial emergence (such as a new toolpath pattern), generates innovation directions, and prompts process engineers to verify them.

[0082] Application Results: After three months of system operation, production line A's overall efficiency increased by 35%, energy consumption decreased by 18%, and the product qualification rate remained stable at over 98.5%. Compared with traditional MES systems, this system eliminates the need for manual reprogramming when dealing with new workpiece models, reducing adaptive adjustment time from two days to two hours, fully demonstrating its cross-domain generalization and autonomous evolution capabilities.

[0083] Example 2: Scientific Research and Innovation Assistance System Based on EGS-AGI

[0084] Please see Figures 1-7 In this embodiment, a general artificial intelligence system based on the EGS structure is applied to drug development scenarios in the biomedical field to assist researchers in target screening, compound design, and experimental protocol optimization.

[0085] Hardware environment: High-performance computing platform and distributed storage system of research institution.

[0086] Software environment: Ubuntu 22.04, Python 3.10, PyTorch 2.1, TensorFlow 2.13, RDKit (cheminformatics tool), GROMACS (molecular dynamics simulation), Lean4 theorem prover.

[0087] Specific implementation steps:

[0088] I. System Deployment and Infrastructure Construction

[0089] 1. Deploy the EGS-AGI system, loading EGS-annotated data from the biomedical field into the base layer:

[0090] a. Literature data: labeled entities (such as "protein", "small molecule", "gene") and their production associations (such as "protein A is associated with disease B → target probability"), with additional physical conservation quantities (such as binding free energy).

[0091] b. Experimental data: Label the experimental conditions (temperature, pH), results (IC50 value), and PDE constraints (such as enzyme kinetic equation).

[0092] c. Knowledge graph: Annotating cross-domain mapping (e.g., "gene mutation → protein structure change → drug sensitivity").

[0093] 2. The data preprocessing module performs feature selection on massive documents based on sparse optimization, extracts key entities and relationships, and generates EGS-operable symbolic-numerical hybrid representations.

[0094] II. Demand Preprocessing

[0095] 1. Request from researchers: "Design novel small molecule inhibitors for target protein X, with an IC50 of <100 nM and a synthesis procedure of no more than 5 steps."

[0096] 2. NLP tools were used to analyze the requirements and extract the core requirements: "target modeling", "virtual screening", and "synthesis path planning".

[0097] 3. The convex optimization parser transforms the requirements into a constrained mathematical programming problem and solves it to obtain a feature set that conforms to the EGS specification (such as the binding site features of target proteins).

[0098] III. Modeling and Configuration

[0099] 1. Perform five-dimensional modeling of the target entity, compound entity, and experimental method entity. For example, modeling the target protein X:

[0100] , where P is the molecular dynamics equation for protein folding (such as the Langevin equation), which is embedded through a physical information neural network (PINN).

[0101] 2. Integrate six types of AGI rules: drug design rules ("IF compound meets Lipinski's five rules THEN high drug-likeness"), virtual screening rules ("IF docking energy <-9 kcal / mol THEN candidate priority"), and experimental optimization rules ("IF low solubility THEN adjust solvent formulation"), and bind key rules with formal mathematical theorems (e.g. docking energy calculation must satisfy the second law of thermodynamics).

[0102] 3. Construct a four-layer constraint system: EGS structural constraint, general intelligent constraint (minimum free energy principle), cross-domain adaptation constraint (molecular format conversion specification), and research ethics constraint (such as avoiding toxic structures), among which the free energy constraint is derived based on variational method.

[0103] IV. Cross-domain collaborative execution

[0104] 1. The system breaks down the requirements into sub-tasks: target modeling, virtual screening, and synthesis path planning.

[0105] 2. Based on EGS production reasoning, construct a recursive closed loop of "cross-domain knowledge mapping → causal relationship modeling → task collaborative execution":

[0106] a. Cross-domain knowledge mapping: By using differential geometric isomorphic mapping, protein structural features are mapped to compound space to achieve pharmacophore alignment.

[0107] b. Causal relationship modeling: The causal reasoning module extracts causal graphs from the literature (such as "key residue mutation → decreased binding affinity") and verifies them using counterfactual reasoning.

[0108] c. Collaborative Task Execution: A hierarchical execution tree for synthesis is used. First, target modeling (molecular dynamics simulation) is performed, then candidate compounds are screened based on docking results, and finally, the synthesis path is planned. Each step is verified by a full-link validation engine (e.g., checking whether the simulation satisfies energy conservation).

[0109] V. Feedback Optimization and Evolution

[0110] 1. Collect virtual screening results and synthesize experimental feedback to extract generalization error bounds and causal structure stability.

[0111] 2. Employ dual timescale updates: the fast scale adjusts the docking model weights, while the slow scale adjusts the rule confidence (e.g., the probability of "IF compound contains sulfur THEN toxicity risk").

[0112] 3. The evolutionary discovery layer extracts new rules (such as "specific functional groups are easily hydrolyzed under alkaline conditions") from a small number of failed experimental cases, and incorporates them into the knowledge base after physical feasibility testing (consistent with hydrolysis reaction kinetics) and mathematical provability testing.

[0113] 4. The controllable emergence screening engine identifies beneficial emergence (such as a novel skeleton transition path) and generates innovative design ideas for researchers to reference.

[0114] Application Results: The system assisted in the design of three novel small molecule inhibitors, two of which were validated by wet experiments with IC50 < 50 nM, requiring 4 and 5 synthesis steps respectively. Compared with the traditional CADD process, the target screening cycle was shortened by 60%, the compound hit rate was increased by 40%, and the efficiency of experimental protocol optimization was improved by 50%. The system not only accelerated the research and development process but also helped researchers understand the molecular mechanisms of action through interpretable reasoning.

[0115] Contents not described in detail in this specification are existing technologies known to those skilled in the art. Standard parts used in this invention can be purchased commercially, and irregularly shaped parts can be custom-made according to the description and drawings. The specific connection methods for each part all employ conventional methods such as bolts, rivets, and welding, which are already mature technologies. The machinery, parts, and equipment all use conventional models from the prior art, and the circuit connections also employ conventional connection methods from the prior art, which will not be detailed here.

[0116] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A general-purpose artificial intelligence system based on the EGS architecture, characterized in that, The system comprises a foundation layer, a core modeling layer, a core fusion layer, a core execution layer, a feedback optimization layer, and an evolutionary discovery layer. Each layer uses the EGS structure as a unified constraint standard, and the entire process is scheduled and managed through the EGS-AI steward. The core technologies of the system are based on sparse optimization and variational methods, physical information neural networks, and improvements to the model-data dual-drive framework, wherein: The base layer includes a multi-source general artificial intelligence training data layer with EGS structure annotation, a data preprocessing module, and a general artificial intelligence basic toolset. The training data layer annotates structural features such as EGS terminal / non-terminal symbols and production associations, while also annotating general intelligent features and cross-domain adaptation features. The data preprocessing module uses a processing method based on sparse optimization and variational improvement to optimize data redundancy. The core modeling layer consists of a five-dimensional general AI entity modeling module built on the EGS terminal / non-terminal symbol structure, a six-category general AI rule modeling module extended from EGS production rules, and a four-layer general AI constraint modeling module built on EGS operation rules. It is equipped with an EGS-AI steward front-end dual verification engine. The modeling logic is improved by combining physical information neural networks and a dual-drive framework to enhance modeling efficiency and adaptability. Core fusion layer: It realizes the triple fusion of general intelligent features, cross-domain adaptation and causal logic, including dual embedding engine, triple semantic consistency mapping engine and fusion result executability screening engine; Core Execution Layer: Based on EGS inference logic, it realizes structured intelligent generation and cross-domain task execution, including a general artificial intelligence structured execution engine, a causal inference auxiliary module, a full-link verification engine, and an EGS-AI steward closed-loop scheduling engine; Feedback optimization layer: Based on multi-source feedback, it realizes dynamic optimization of the system, including a feedback feature extraction engine, a rule / constraint local adjustment engine, and a general artificial intelligence model local fine-tuning engine; Evolutionary Discovery Layer: Enables the mining of general artificial intelligence knowledge evolution and innovation capabilities, including a local knowledge update and cross-domain fusion engine, a sample-independent generalization rule extraction engine, a controllable emergence screening engine, and an innovation direction generation engine.

2. The general artificial intelligence system based on EGS structure according to claim 1, characterized in that, The four prerequisite rules for EGS-AI Steward include: EGS structure priority + general artificial intelligence core requirements collaboration: Based on EGS terminal / non-terminal symbols, production rules and operation rules, it identifies the objective hard constraints of general artificial intelligence, integrates general intelligence judgment standards and cross-domain adaptation requirements, and regards EGS structure specifications and general artificial intelligence core requirements as the highest priority rules. EGS structure-oriented double removal of invalid elements: Based on the syntax norms and production rules of the EGS structure, system elements that do not meet general intelligence requirements, logical contradictions, or cross-domain adaptation conflicts are removed. Modeling / Execution Pre-Execution Dual Verification: Before modeling or executing general artificial intelligence systems, complete EGS structure compliance verification + general intelligent pre-verification + cross-domain adaptation verification; EGS Minimalist Structure Modeling + General Artificial Intelligence Function Orientation: Only construct entities, rules, and constraints directly related to the core functions of general artificial intelligence, in line with the "minimum effective logic chain of general intelligence" orientation, and abandon redundant design.

3. The general artificial intelligence system based on EGS structure according to claim 1, characterized in that, The five-dimensional general artificial intelligence entity modeling module of the core modeling layer satisfies the expression: ,in For entity ID, For entity attribute set, For entity state parameters, For general intelligent features, For cross-domain adaptation features, The physical partial differential equations that entities follow are constrained, conforming to the EGS terminal / non-terminal symbol structure specification, and the feature extraction logic is based on sparse optimization and variational method improvement.

4. The general artificial intelligence system based on EGS structure according to claim 1, characterized in that, The dual embedding engine of the core fusion layer satisfies dual embedding error constraints: , ,in For general intelligent feature embedding function, For cross-domain adaptive feature embedding functions, , The preset error threshold is set based on an adaptive threshold formula derived from sparse optimization theory.

5. The general artificial intelligence system based on EGS structure according to claim 1, characterized in that, The VC dimension of the evolution discovery layer must satisfy: ,in For a collection of entities, For a set of rules, For a constraint set, To reduce the complexity of PDE constraints and achieve sample-independent cross-domain generalization, the VC dimension calculation logic is combined with the function space dimension estimation optimization in the dual-drive framework.

6. The general artificial intelligence system based on EGS architecture according to claim 1, characterized in that: The core operator of the EGS-AI steward Click "Feedback" →General Artificial Intelligence Fusion →Structured execution → End-to-end verification "It is composed of sequential combinations to achieve closed-loop control of the entire system process." 7. The general artificial intelligence implementation method based on EGS structure according to claims 1-6, characterized in that, Includes the following steps: Step 1: EGS structure-oriented general artificial intelligence - cross-domain adaptation paradigm fusion modeling, completes the paradigmatic modeling of general artificial intelligence entities, rules and constraints through AI tools, and highlights cross-domain adaptable entities; the core logic of the modeling tool is based on the improvement of physical information neural network and dual-drive framework; Step 2: Standardize and preprocess multi-source general artificial intelligence requirements, analyze unstructured cross-domain requirements, extract features and requirement points that conform to EGS specifications, complete the dual mapping between requirements and general intelligent capabilities, and screen executable elements; The preprocessing process is based on sparse optimization and variational method improvements to enhance processing efficiency; Step 3: Cross-domain collaboration + structured execution + full-link verification. Decompose the overall requirements into reducible sub-requirements, complete the accurate reduction of general intelligent capabilities and cross-domain tasks, synthesize a hierarchical execution tree, and perform full-link verification. Step 4: Multi-source feedback dynamic optimization, extract feedback features and classify and label them, locally adjust rules / constraints, fine-tune the general artificial intelligence model, without the need for global retraining; Step 5: General AI knowledge evolution + controlled emergence mining, update cross-domain writing knowledge, extract generalization rules, screen beneficial emergence, and generate innovative directions.

8. The general artificial intelligence implementation method based on EGS structure according to claim 7, characterized in that, The multi-source general artificial intelligence requirements mentioned in step 2 include cross-domain task requirements, autonomous evolution requirements, and causal explanation requirements. Technical features and capability requirements that conform to the EGS terminal / non-terminal symbol specification are extracted using NLP tools. The core algorithm of the NLP tools is based on improvements to publicly available technologies.

9. The general artificial intelligence implementation method based on EGS structure according to any one of claims 7, characterized in that, The implementation logic of cross-domain collaboration described in step 3 is as follows: based on the EGS production reasoning logic, a multi-step recursive closed loop of "cross-domain knowledge mapping → causal relationship modeling → task collaborative execution" is constructed, and each step satisfies the EGS structural specifications and general intelligent constraints.

10. The general artificial intelligence implementation method based on EGS structure according to any one of claims 7, characterized in that, The necessary and sufficient conditions for controllable emergence described in step 5 include: conforming to the EGS structural specifications and system hard constraints, deducing based on the initial elements according to the EGS production rules, not being decomposable into simple combinations of the initial elements, meeting the general intelligence requirements and cross-domain adaptation requirements, and meeting the physical realizability test and mathematical provability test.

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