An industrial physical AI-based global hardware self-recovery and dynamic topology reconstruction high-end equipment safety control method and system

Abstract

The application discloses a kind of based on industrial physics AI's global hardware self-healing and dynamic topology reconfiguration high-end equipment safety control method and system, belong to industrial physics AI, high-end equipment intelligent safety management and control field.For the defects of existing physical AI pure data driving, lack of physical constraint, no hardware intervention, no true value calibration, difficult to industrialization landing, the application proposes idle steady-state true value self-calibration mechanism, relies on true value hardware online calibration sensing link, generates equipment exclusive physical identity parameter set, guarantees data traceable right protection.The application fuses hardware health vector H (t), working condition risk vector R (t) Iterative dynamic weight matrix, synchronously outputs software topology reconfiguration, hardware self-healing double-path control instruction;Collaborate nine global self-healing module and three-level hierarchical safety protection, carry incremental self-learning mechanism, fixed physical mechanism parameter, iterative control parameter, rely on full-time sequence data backflow iterative baseline model, form virtual-real fusion industry closed loop.

Description

Technical Field This invention belongs to the fields of intelligent safety management and control of high-end equipment, industrial physical AI, hardware health self-healing, and dynamic topology reconstruction. Specifically, it relates to a method and system for high-end equipment safety control that utilizes full-domain hardware self-healing and dynamic topology reconstruction based on laboratory truth benchmarking, equipment no-load operation parameter benchmarking, dual-vector fusion calculation, dynamic weight matrix iteration, and AI online self-learning evolution capabilities. This invention can be widely applied to various types of high-end equipment, including precision manufacturing equipment, aerospace equipment, wind power equipment, marine equipment, autonomous driving vehicles, optoelectronic detection equipment, fluid power equipment, transmission motion mechanisms, and intelligent industrial production lines, for microscopic degradation perception, physical layer in-situ self-healing, adaptive system architecture control, and full life-cycle process iteration optimization. It falls within the core underlying technology category for the industrialization of industrial physical AI. Background Technology Currently, the health management system for high-end equipment in China still heavily relies on traditional PHM (Prognostics and Health Management) and FDIR (Fault Detection, Isolation and Reconstruction) technologies. The overall technical paradigm is characterized by passive monitoring, post-event alarms, pure software fault tolerance, and static parameter control. It can only achieve post-event handling of explicit faults and cannot meet the new generation of industrial requirements in the era of industrial physical AI, which demand stable control of the physical layer of equipment, traceable data, self-healing degradation, and iterative processes. It has obvious technical shortcomings and is also the core bottleneck for the industrialization of industrial physical AI laboratories. In recent years, physical AI has become a core development direction for the iteration of the artificial intelligence industry. Unlike traditional digital AI, which only achieves data fitting and content generation, the core goal of industrial physical AI is to monitor, understand, predict, and intervene in the real physical world. It relies on equipment operating mechanisms, CAE simulation mechanisms, physical prior constraints, and engineering boundary conditions to achieve virtual-real fusion simulation, serving as the core foundation for the intelligent upgrading of high-end equipment. Currently, a large number of physical AI-related research and patent layouts have emerged in the industry, but existing solutions have significant technical shortcomings: limited by the complexity of the physical world and the inability of hardware iteration speed to match the pace of software iteration, most AI prediction solutions only focus on offline simulation modeling, single-condition state prediction, and digital twin visualization, relying on purely data-driven algorithms, inherently suffering from physical defects such as energy non-conservation, object penetration, extrapolation failure, and overtraining distribution failure. At the same time, existing solutions generally lack standardized laboratory truth calibration benchmarks, lack active intervention capabilities at the hardware physical layer, and lack online self-evolutionary iteration mechanisms, remaining only at the level of virtual simulation and data monitoring, unable to truly implement the steady-state management of the entire life cycle of physical industrial equipment. Based on long-term field testing of various high-end equipment such as precision machine tools, wind power equipment, and aerospace component processing, existing technologies face multiple unavoidable practical problems in actual industrial applications. Traditional equipment management models assume that hardware equipment is in an ideal steady state over a long period of time, and only provide alarms and handling for sudden failures and obvious faults. They cannot identify the micron-level and microsecond-level hidden gradual degradation accumulated during the long-term service of equipment, such as relaxation of mechanical preload, micro-wear of structure, micro-creep, electrical parameter drift, temperature field shift, micro-pressure drop of fluid, and vibration resonance shift. The continuous accumulation of these subtle degradations is the main cause of the decline in processing accuracy, decrease in operational stability, and frequent sudden shutdowns of equipment in the field. Moreover, conventional monitoring schemes lack physical mechanism constraints, resulting in low accuracy in identifying hidden degradations and prominent problems of misjudgment and omission, making them unsuitable for high-precision production management requirements. Meanwhile, mainstream industry control and physical AI solutions are limited to purely software-based remedial measures such as software redundancy switching, data fault tolerance, link backup, and simulation prediction optimization. They cannot correct the wear, offset, and deterioration of the hardware itself in situ, nor can they curb the continuous degradation of equipment performance from a physical perspective. They can only rely on manual periodic shutdown maintenance and post-fault repair, which greatly affects equipment uptime and production continuity. In addition, traditional equipment management and control systems and conventional physical AI models adopt static management logic with fixed parameters, fixed weights, and fixed topologies. Once the model is trained, the parameters are completely fixed, making it impossible to adapt to real-time shifts in equipment status caused by complex working conditions, environmental stress fluctuations, and dynamic load changes. In complex field scenarios such as workshop temperature changes, load fluctuations, and continuous mass production, it is prone to problems such as false alarms, missed detections, and fault tolerance failures, making it difficult to meet the dynamic management and control requirements of unattended, high-precision, and high-reliability equipment. More importantly, existing equipment monitoring technologies and physical AI simulation systems lack a unified laboratory true value benchmark. Drift and acquisition deviations caused by long-term sensor operation cannot be autonomously calibrated, and purely data-driven models lack engineering physical constraints. As a result, equipment operation data and simulation results cannot be used as effective basis for quality judgment, process optimization, and supply chain quality control. It is impossible to accumulate full life cycle degradation and control data, and it cannot guide equipment design, material selection, and assembly process iteration. It lacks the complete implementation capability from simulation theory to physical implementation. Furthermore, the parameters, thresholds, and judgment logic of traditional control systems and general physical AI models are all fixed settings, which cannot be autonomously optimized according to the aging characteristics of equipment, changes in operating conditions, environmental migration, and deterioration patterns. After long-term operation, the adaptability continues to decline, the false alarm rate gradually increases, and the control accuracy continues to decrease, making it difficult to meet the control requirements of long-term stable operation of high-end equipment throughout its entire life cycle. In summary, current AI solutions in the field of industrial physics mostly focus on theoretical simulation or single-dimensional data monitoring, making it difficult to achieve the fusion of physical mechanisms and measured data, and the integrated closed-loop control of hardware and software. They also fail to realize the full-process implementation of microscopic degradation perception, in-situ physical self-healing, and dynamic system topology reconstruction. Therefore, the industry urgently needs a fully closed-loop technical solution based on physical mechanisms and centered on AI self-learning, covering design, operation, parameter tuning, evolution, and reverse iteration. Summary of the Invention The purpose of this invention is to address the practical deficiencies of existing technologies and the lack of industrial application of industrial physical AI, by providing a high-end equipment safety control method and system based on industrial physical AI, featuring full-domain hardware self-healing and dynamic topology reconstruction. This method overcomes the technical limitations of traditional PHM and FDIR (Physical Health Management) systems, which are purely software-based, passive, and statically fixed control mechanisms. It also solves the core pain points of existing physical AI solutions, such as lack of physical constraints, hardware intervention, truth benchmarks, and industrial applicability. This invention combines experience from on-site debugging and maintenance of high-end equipment in multiple scenarios, specifically optimizing the entire process logic of equipment data perception, decision-making and control, and system iteration. It constructs a fully closed-loop industrial physical AI underlying architecture adapted to industrial scenarios, comprising "truth benchmarking quality control + dynamic software topology reconstruction + in-situ hardware self-healing + hierarchical safety protection + AI self-learning evolution + industrial process iteration," effectively solving the practical challenges of intelligent self-healing and autonomous evolution at the physical layer of high-end equipment. Compared to existing industry-standard technical solutions, this invention features comprehensive on-site operational optimization and technological innovation. Its core improvements permeate the entire process of data perception, decision execution, and system evolution, thoroughly resolving long-standing practical measurement challenges in the industry. In the equipment data acquisition and calibration phase, it abandons the industry-standard factory-fixed calibration and uncalibrated pure data-driven mode. Instead, it establishes a dedicated truth-based benchmarking system based on the equipment's no-load steady-state operating conditions. Through standardized truth-based hardware, it completes online self-calibration of the sensor link, fundamentally solving the practical problems of long-term sensor drift, distorted data acquisition, and lack of data traceability and ownership. This provides a reliable physical data foundation for subsequent AI simulations and equipment status determination, completely avoiding the practical defects of pure data models that are divorced from physical laws. In the equipment decision-making and control stage, it breaks through the single control mode of existing technologies that rely solely on pure software fault tolerance or pure hardware redundancy. Addressing the issues of disconnect and inability to coordinate and adapt between on-site software and hardware control, it establishes a dual-vector fusion-driven, two-layer collaborative closed-loop architecture. By quantifying the health status of equipment hardware and real-time operational risk status, it dynamically iterates and generates a control weight matrix, simultaneously issuing two control commands: software topology optimization and hardware physical self-healing. This overcomes the limitations of traditional technologies that can only passively monitor and alarm, enabling proactive identification, proactive intervention, and in-situ repair of equipment degradation problems, fully adapting to the on-site needs of continuous industrial mass production and downtime-free maintenance. In the long-term operation phase of the system, to address the issues of fixed parameters in traditional models and their inability to adapt to equipment aging and changing operating conditions, a full-lifecycle self-learning closed-loop system based on feedback from on-site measured data is established. This system abandons the logic of offline training and fixed-parameter general AI models, relying instead on real-life equipment operation, self-healing regulation, and data on changing operating conditions to complete online incremental iterations. This continuously optimizes the system's judgment thresholds, regulation strategies, and model parameters, enabling the system to autonomously adapt to equipment aging characteristics, changes in the workshop environment, and fluctuations in production conditions. This solves the problem of decreased control accuracy and reduced adaptability after long-term equipment operation. In summary, this invention, drawing on extensive experience in industrial field commissioning and maintenance, differs fundamentally from existing technologies in its technical conception, implementation path, and engineering results. It possesses outstanding substantive features and significant progress, and complies with the provisions of the Patent Law regarding novelty and inventiveness. The technical solution of the present invention will be further described in detail below with reference to specific embodiments. This invention features a unique no-load laboratory truth benchmarking self-calibration mechanism that embeds CAE physical mechanisms, engineering constraints, and standard truth priors into the AI ​​control system. This addresses core practical pain points in the industry, such as the inability to trace and assign ownership of sensor data, and the violation of physical laws by pure data models. Through the dual-dimensional fusion calculation of hardware health vector H(t) and operating condition risk level vector R(t), a quantifiable, reproducible, and engineering-applicable dynamic weight matrix W(t) iterative model is constructed, achieving deep integration of physical laws and AI algorithms. Coupled with nine full-dimensional hardware self-healing modules, physical layer degradation is eradicated. A dual-layer collaborative control architecture enables simultaneous optimization of software and hardware. Furthermore, an AI online incremental self-learning mechanism allows for continuous autonomous iterative optimization of model parameters, discrimination thresholds, control strategies, and feature libraries, completely overcoming the shortcomings of traditional static physical AI that is detached from actual operating conditions. After multiple rounds of on-site debugging and parameter iterative optimization, this solution ultimately forms a next-generation industrial physical AI high-end equipment safety control system that is standardized, scalable, and self-evolving. It can use real equipment operating data to reverse-engineer workshop assembly processes, processing parameters, and maintenance specifications. Firstly, a safety control method for high-end equipment based on industrial physical AI, encompassing full-domain hardware self-healing and dynamic topology reconfiguration. Includes the following steps: S1. System Initialization: Load the classification baseline database, basic weight matrix W0, adjustment range thresholds of nine self-healing modules, and three-level safety classification control standards, categorized by equipment type, service stage, and operating environment. The baseline database covers steady-state operating benchmark parameters and degradation evolution laws for different equipment types, service stages, and operating environments. It has built-in standardized physical constraints and engineering boundary conditions to provide the underlying benchmark for accurate physical AI simulation. S2. Comprehensive Multi-Dimensional Parameter Acquisition: Comprehensive acquisition of microscopic latent degradation parameters, real-time operating condition parameters, and environmental stress parameters of high-end equipment across multiple dimensions, including mechanical, electrical, thermal control, fluid, and precision motion. Covering all dimensions of degradation indicators such as preload, wear, vibration, temperature, fluid pressure, electrical parameters, and transmission phase, it is equipped with high-precision sensing and detection units adapted to various detection scenarios, including force sensors, vibration and displacement sensors, temperature sensing modules, fluid pressure and flow sensors, electrical parameter acquisition units, phase encoders, and other comprehensive sensing hardware. This fully matches the latent degradation detection needs of different types of high-end equipment, achieving comprehensive, accurate, and seamless perception of the equipment's physical state. S3. Truth Value Benchmarking and Sensor Self-Calibration Quality Control Closed Loop: This invention, under the stable reference conditions of no-load testing and no load interference at the equipment's factory, uses legally calibrated industrial-grade high-precision metrology equipment (including high-precision multimeters, laser displacement sensors, high-precision force gauges, optical interferometers, standard signal sources, and other precision metrology instruments adapted to different equipment types) as external standard truth value metrology instruments. The original operating state of the equipment under no-load steady-state conditions without any operational load is used as the machine's exclusive no-load reference truth value, completing channel-by-channel benchmarking calibration of the entire sensor chain. The system iteratively generates sensor correction coefficients and component quality baselines specific to each device, and employs a piecewise linear compensation algorithm to specifically correct zero-point drift, proportional deviation, and temperature drift errors of the sensors under full-temperature operating conditions, avoiding the industry problem of traditional single calibration parameters being unable to adapt to complex temperature fields and having insufficient calibration accuracy. All device-specific baseline data is stored locally in an encrypted manner, effectively mitigating the risks of data transmission distortion and malicious tampering in the cloud. This establishes a reliable quality control closed loop with internal and external sources, encompassing "external standard truth instrument benchmarking + local no-load baseline truth verification—acquisition coefficient correction—data quality control verification—device-specific baseline iterative update." This ensures that the device-collected data possesses complete traceability, ownership verification, and evidentiary attributes, providing a true, accurate, and compliant physical data foundation for subsequent physical AI mechanism deduction and device status determination. Based on the above calibration process, the system generates a set of device identity parameters (D) including sensor correction coefficient matrix, component quality baseline, no-load steady-state operation fingerprint, and model-specific assembly tolerance zone. The Integrated Circuit Identity (IPS), serving as a unique digital identifier for each high-end piece of equipment, permeates the entire lifecycle management process of the equipment, becoming the sole and exclusive benchmark for hardware degradation judgment, self-healing control verification, and assembly quality inspection. If the identity parameters after retesting the assembly of the entire machine exceed the standard assembly tolerance zone for the corresponding model, the system can automatically and accurately mark the location and specific deviation direction of the abnormal parts, and feed the abnormal parameter data back to the baseline database for reverse identification of the cause of the problem, distinguishing between batch matching deviations of parts or deviations in on-site assembly processes. This allows for targeted guidance on revising workshop assembly process specifications, optimizing assembly steps, and iterating on parts processing tolerances, achieving a complete quality control closed loop from equipment factory calibration and assembly quality inspection to process iteration and optimization. S4. Quantitative rating of hardware health: The real-time collected parameters after correction by true value benchmarking are compared and analyzed with the steady-state parameters of the classification baseline database. The hardware health vector H(t) is generated by quantitative calculation. The quantitative rating of the hardware health status of the equipment is completed according to the preset rating standard, accurately determining the degree of hidden degradation of the equipment. The physical mechanism constraints are combined to avoid the misjudgment defects of pure data identification. S5. Dual-vector fusion and dynamic weight matrix iteration: Fusion of real-time operating load, environmental stress level, and task priority data to quantify and generate an operating risk level vector R(t); Through a preset engineering-applicable mapping function f, weighted iterative operation W(t)=f(H(t),R(t)) is performed to update and generate a dynamic weight matrix W(t) in real time; The mapping function f can be implemented based on fuzzy logic, finite state machine, or lightweight neural network. All three implementation methods use the weighted fusion formula f=α·H(t)+β·R(t) as the core, which is only used to dynamically adjust the values ​​of the weight coefficients α and β, integrating physical conservation laws and engineering constraints, and completely abandoning the traditional static control mode with fixed weights and fixed thresholds and the unconstrained pure data AI inference mode. During on-site debugging, this solution underwent multiple rounds of parameter iteration and optimization. Initially, the hardware health weight coefficient α=0.7 and the operating condition risk weight coefficient β=0.3 were configured. After continuous mass production testing and adaptation optimization in the workshop, the optimal parameters α=0.6 and β=0.4 were finally determined. This can accurately adapt to the control logic of prioritizing the deterioration of precision equipment hardware and assisting with operating condition risk. Relying on the dynamic weight matrix W(t), the system topology structure of sensor data fusion weight, bus transmission link, computing power resource allocation, task priority, and power load ratio is adaptively reconstructed to achieve dynamic adaptation of the physical AI system architecture to changes in the operating conditions of physical equipment. S6. Generation of dual-layer software and hardware collaborative control instructions: Based on the real-time updated dynamic weight matrix W(t), two independent control instructions are generated simultaneously, namely software topology reconstruction instructions and hardware self-healing control instructions. This achieves dual-layer synchronous collaborative control of adaptive optimization of software system architecture and in-situ correction of hardware physical parameters, thus achieving the core implementation effect of physical AI: "virtual simulation and physical intervention". S7. Three-Tier Differentiated Safety Management: Combining the physical adjustment range of the nine self-healing modules with the real-time degradation level of the equipment, three-tier differentiated safety management is implemented: fully automatic, imperceptible in-situ self-healing for minor degradation, topology locking and precise fault location for high-risk, over-limit degradation, and emergency fallback protection for sudden extreme faults. This covers the safety management needs of all risk scenarios, ensuring the safety and stability of physical AI management. All nine self-healing modules are subject to unified scheduling and priority arbitration by the collaborative control execution module. The adjustment range and execution sequence of each module are centrally allocated by the dynamic weight matrix W(t), effectively avoiding action conflicts and adjustment interference issues caused by the simultaneous operation of multiple modules. S8. AI Online Incremental Self-Learning and Autonomous Evolution: The system achieves online incremental self-learning based on equipment degradation data, self-healing adjustment samples, operating stress data, fault event data, and true value calibration records collected throughout the entire lifecycle. This step employs a lightweight gradient descent iterative logic customized on-site by engineers, freezing fixed parameters at the underlying physical mechanism layer. Iterative updates are only performed on the dual-vector fusion weights, discrimination thresholds, and self-healing adjustment parameters, strictly adhering to the inherent physical laws and engineering constraints of the equipment to prevent the model's autonomous training from damaging the equipment's physical characteristics. Through this customized self-learning logic, the system can continuously optimize its discrimination thresholds and control strategies, adapting to state shifts caused by equipment aging characteristics, operating condition migration, and environmental changes. This continuous iteration improves control accuracy, self-healing precision, and risk identification accuracy, achieving an evolutionary effect for long-term stable system operation. S9. Full Data Feedback and Industry Closed-Loop Iteration: The micro-deterioration parameters, self-healing adjustment amounts, topology iteration strategies, working condition stress data, maintenance records, and self-learning optimization parameters of this control process are fully fed back to the classification baseline database to complete database updates and algorithm model iteration optimization. The steady-state benchmark of the equipment is continuously converged to achieve self-evolutionary closed-loop control of the entire equipment life cycle. Based on the real-world operating data, the workshop assembly process, processing parameters, and equipment maintenance specifications are optimized in reverse. To implement the above control method, nine functional modules are specifically included: 1. Data Acquisition Module: Used for full-domain acquisition of multi-dimensional microscopic hidden degradation parameters, real-time operating condition parameters and environmental stress parameters of high-end equipment, providing raw data support for physical AI state assessment and control calculation. 2. Truth Calibration and Data Quality Control Module: This module is used to construct a dual-source calibration system under no-load steady-state conditions, combining **external standard truth calibration + internal benchmark truth calibration**. On one hand, it connects to high-precision external calibration hardware calibrated by legal metrology to complete standardized calibration of the entire sensor link. On the other hand, it uses the equipment's native operating state under no-load steady-state conditions as its own internal benchmark truth value to complete sensor link self-calibration, deviation correction, and the establishment of a dedicated quality baseline for the entire machine. This enables accurate traceability of collected data, compliant ownership verification, and error self-correction, providing real, accurate, and verifiable physical prior data for physical AI algorithms. The module incorporates a piecewise linear temperature drift compensation algorithm and a local encrypted storage unit, specifically addressing practical issues such as zero-point drift, proportional deviation, temperature drift, and data transmission and storage distortion / tampering by field sensors across the entire temperature range. Simultaneously, it is responsible for generating and encrypting the storage of a set of exclusive digital identity parameters for each single piece of equipment, including sensor correction coefficient matrix, component quality baseline, no-load steady-state operation fingerprint, and model-specific assembly tolerance zone. This serves as the sole exclusive benchmark for determining the degradation of a single piece of equipment throughout its entire life cycle, verifying self-healing control, checking assembly process quality, and optimizing component tolerances. 3. Hardware Health Assessment Module: It has a built-in multi-scenario baseline database and physical constraint mechanism, which is used to quantify and generate a hardware health vector H(t), complete the fine-grained classification and rating of hardware health status, and ensure the accuracy of physical AI status assessment. 4. Risk Level Determination Module: This module integrates multi-dimensional data such as operating load, environmental stress, and task level to quantify and generate an operating risk level vector R(t), which is then combined with engineering constraints to complete the risk classification determination. 5. Topology Reconstruction Adjustment Module: It has a built-in preset mapping function f and physical constraint rules, which are used to generate a dynamic weight matrix W(t) in real time through W(t)=f(H(t),R(t)), and simultaneously output two independent control signals: software topology reconstruction command and hardware self-healing control command. 6. Collaborative Control Execution Module: It has nine built-in adaptive self-healing adjustment modules, which are used to simultaneously execute software topology architecture reconstruction and hardware physical parameter in-situ self-healing correction, realizing two-layer collaborative closed-loop control of physical AI algorithms from virtual simulation to physical hardware implementation. The collaborative control execution module incorporates a three-level hierarchical management logic: Level 1: When the degree of degradation is within the adjustment range of the self-healing module, perform imperceptible in-situ self-healing; Level 2: When degradation exceeds limits, lock the safe topology and output a precise maintenance plan; Level 3: When a sudden extreme failure is detected, the emergency backup module will perform backup protection. 7. Emergency backup handling module: In response to sudden operating conditions such as excessive degradation, module failure, extreme load impact, and equipment attitude loss, it performs graded isolation, redundancy switching, safe shutdown and task completion backup protection. 8. AI Self-Learning Evolution Module: This module is used to collect operational samples, truth calibration records, self-healing and control data, fault and anomaly data, and operating environment data throughout the entire lifecycle of the equipment. Based on customized lightweight incremental self-learning logic, it autonomously optimizes the degradation feature library, dynamically corrects the discrimination threshold, iteratively updates the fusion weight coefficient, optimizes the topology reconstruction strategy and self-healing adjustment accuracy, and achieves continuous autonomous evolution of the physical AI system's control capabilities, while adhering to the constraints of the equipment's physical mechanism. 9. Data Iteration Module: Used to collect all data on the entire lifecycle of equipment operation, degradation, self-healing, regulation, and maintenance. Combined with the AI ​​self-learning evolution module, it continuously iterates and optimizes the baseline database and core algorithm model, supporting the iterative upgrading of equipment processes and forming an industrial physical AI industrialization empowerment system. Specific composition and hardware execution structure of the nine adaptive and self-healing adjustment modules 1. Dynamic preload fine-tuning module: It has a built-in servo hydraulic drive unit, high-frequency pressure closed-loop feedback sensor and segmented PID control controller. It can collect the preload pressure parameters of the equipment transmission structure in real time. It can solve the problems of preload attenuation, stress relaxation and assembly preload offset that are common in the long-term service of precision transmission pairs, support structures and clamping and locking mechanisms of various high-end equipment. It can complete micro hydraulic pressure compensation and pressure adaptive fine-tuning, accurately compensate for transmission clearance deviation and ensure the steady-state accuracy of the feed structure. 2. Micro-balancing compensation module: It integrates a high-precision vibration displacement acquisition sensor and an adaptive balance adjustment motor, which can accurately capture the slight imbalance deviation of the equipment's rotation and transmission structure. Through dynamic counterweight fine adjustment and position compensation, it can achieve structural micro-balancing correction and suppress low-frequency micro-vibration of the equipment. 3. Adaptive vibration damping balance module: Equipped with an adjustable damping hydraulic valve body and a vibration spectrum analysis unit, it can adaptively adjust the damping coefficient according to the real-time vibration amplitude and frequency data of the equipment, and specifically suppress the resonance and micro-amplitude vibration generated by the high-speed operation of the spindle and the operation of the transmission mechanism, adapting to the vibration reduction requirements of different load conditions. 4. Intelligent thermal control adaptive module: It includes multi-point distributed temperature sensors, variable frequency heat exchange control unit and thermal deformation compensation algorithm. It can collect temperature field data of the whole machine in the whole domain, adaptively adjust the cooling system power and heat exchange rhythm, and compensate for thermal drift and thermal deformation caused by continuous operation of the equipment. 5. Fluid pressure and flow compensation module: Built-in high-precision fluid pressure and flow sensors and proportional control valves, which monitor the pressure and flow fluctuations of various working fluids and hydraulic media in real time, adaptively adjust the valve opening degree, compensate for slight pressure drop and flow deviation of fluids, adapt to the steady-state operation requirements of fluid power, lubrication and cooling, and pneumatic and hydraulic control systems of various high-end equipment, and ensure the long-term stability of the equipment's fluid system. 6. Electrical Parameter In-situ Self-calibration Module: Integrates an industrial-grade high-precision reference voltage source, current acquisition unit, and electrical parameter closed-loop correction unit. It can monitor the temperature drift deviation of servo drive voltage and current in real time, and offset the electrical parameter drift in real time by benchmarking with the reference source, thus completing the in-situ calibration and correction of the servo electrical control system. 7. Transmission Phase Adaptive Correction Module: Equipped with a high-precision encoder and phase closed-loop control unit, it collects the phase deviation of the transmission system in real time, adaptively fine-tunes the running sequence of the servo motor and the transmission coordination phase, and corrects the transmission phase offset error caused by long-term operation. 8. Adaptive control module for air flotation suspension gap: Includes an air pressure sensor, a gap detection unit and an adaptive adjustment module for air valves. It can monitor the deviation of the air flotation structure suspension gap in real time, dynamically adjust the air pressure, and ensure that the suspension structure operates with uniform gap and stable state. 9. Multi-power load dynamic distribution module: Integrates load acquisition sensors and power distribution control unit, which can adaptively distribute power load according to the real-time working load of the equipment and the operating status of each power unit, avoid overload operation of a single unit, and optimize the stability of the overall power output. Nine modules fully cover the adjustable physical parameters of equipment in all dimensions, including mechanics, electrical, thermal control, fluid, and precision motion, enabling comprehensive in-situ correction of hidden hardware degradation and supporting the implementation of physical AI entity intervention capabilities. Detailed Implementation This invention represents a core and practical technology in the field of industrial physics AI. It features a unique closed-loop architecture: "dual-source truth calibration quality control (internal and external), dynamic software reconstruction, in-situ hardware self-healing, AI self-learning evolution, and data iteration for industry empowerment." This architecture specifically addresses the current challenges in industrial physics AI, such as "simulation is feasible, data is unreliable, hardware is unrepairable, systems do not evolve, and processes do not iterate." The core innovations of this invention have undergone multiple rounds of on-site debugging, parameter iteration, and operating condition adaptation optimization. They possess the characteristics of being quantifiable, computable, reproducible, and engineering-applicable. By deeply embedding physical laws and engineering constraints into the AI ​​algorithm, it completely avoids the physical defects of pure data AI, meeting the requirements for sufficiency of patent disclosure. Those skilled in the art can replace the equivalent nonlinear mapping function based on this core logic, all of which fall within the scope of protection of this invention. 1. Laboratory truth benchmarking and comprehensive quality control closed loop The core innovation of this invention, distinguishing it from all traditional technologies and existing physical AI solutions, lies in its no-load truth-based self-calibration mechanism. This mechanism is optimized to adapt to complex industrial environments and address the problem of long-term sensor distortion. Under the baseline conditions of factory commissioning, no-load steady-state operation, and no load interference, an industrial-grade high-precision measuring instrument, legally calibrated and adapted to the corresponding equipment testing scenario, is used as the external standard truth-based measuring instrument. The original no-load steady-state operation of the equipment without any operational load is used as the machine's exclusive no-load reference truth value. This completes the channel-by-channel calibration of the entire machine's sensor acquisition link, iteratively generating a single-device-specific correction coefficient and component quality baseline. This addresses the common industry problems of sensor zero-point drift, temperature drift deviation, proportional offset, and acquisition distortion from the source, overcoming the shortcomings of existing physical AI systems, such as the lack of a unified truth benchmark and low data reliability. This system does not rely on absolute detection of internal material defects for judgment. Instead, it accurately captures relatively steady-state gradual shifts such as μm-level deformation, μV-level voltage drift, nA-level leakage current, ns-level timing jitter, micro-stress attenuation, micro-vibration amplitude fluctuations, and micro-pressure difference changes in fluids. It quantifies latent degradation by gradual trends, achieving high identification accuracy and strong engineering applicability. Through true value benchmarking closed loop, the equipment operation data collected by this invention possesses uniqueness, traceability, and evidentiary value. It can be directly used for component quality assessment, supply chain accountability, assembly process optimization, and overall machine quality control before shipment. This represents a generational upgrade of equipment data from "monitoring data" to "traceable quality control basis," providing a core data foundation for the implementation of industrial physical AI. Simultaneously, all baseline data is encrypted and stored locally, avoiding the risks of data distortion and tampering in the cloud and adapting to the data security management requirements of industrial equipment. 2. Core Algorithm Engineering Implementation System The core algorithm of this invention is a dynamic weight matrix iterative model W(t)=f(H(t),R(t)), which accurately integrates the hardware health status and the working condition risk status in two dimensions, and completes adaptive inference by combining physical engineering constraints. This completely solves the technical shortcomings of traditional static control, such as poor adaptability, misjudgment and omission, as well as the unconstrained inference of general physical AI and weak implementation. The core mapping function f adopts a standardized and implementable weighted fusion logic. The specific implementation method is as follows: First, the hardware health vector H(t) and the operating condition risk level vector R(t) are normalized in the 0-1 interval to eliminate the difference in the dimensions of parameters of different dimensions; the equipment steady-state basic weight matrix W0 is preset, and the basic matrix is ​​corrected in real time by the linear weighted fusion formula f=α·H(t)+β·R(t) to output the real-time dynamic weight matrix W(t); where α is the hardware health weight coefficient and β is the operating condition risk weight coefficient, which can be adaptively configured according to different scenarios such as ships, wind power, precision heavy industry, automotive parts, and aerospace. To fully demonstrate the feasibility of the algorithm, this invention provides multiple sets of on-site debugging iteration parameters and directly verifiable numerical examples: In the initial debugging phase of the solution, the preset weight coefficients α=0.7 and β=0.3 are adapted to the precision machine tool scenario, focusing on hardware health status management. However, the adaptability to the working condition risk under actual high-load continuous processing conditions is insufficient. After multiple batches of mass production testing and iteration optimization in the workshop, the optimal parameters α=0.6 and β=0.4 are finalized, perfectly adapting to the on-site requirements of prioritizing hardware accuracy and auxiliary management of working condition risk in precision equipment. Example of parameter verification: When the hardware health vector H(t)=0.8 (slight equipment degradation, overall good condition) and the working condition risk level vector R(t)=0.3 (low load, low risk steady-state condition) are detected, substituting into the formula yields W(t)=0.6×0.8+0.4×0.3=0.6. Based on this dynamic weight matrix, the system adaptively reduces the weight of degraded sensor data, optimizes the allocation of computing power and bus links, and simultaneously issues fine-tuning commands to drive the dynamic pre-tightening fine-tuning module to complete physical compensation, achieving seamless steady-state repair of the equipment. The algorithm logic is quantifiable, verifiable, and reproducible, fully meeting the State Intellectual Property Office's requirements for sufficiency of disclosure during review. 3. Hardware and software dual-layer collaborative self-healing regulation mechanism This invention features a unique closed-loop control architecture that combines software topology reconstruction with hardware physical self-healing, operating in parallel and at two layers. This architecture is a core differentiating innovation from traditional pure software PHM, FDIR technologies, and existing pure simulation physical AI technologies. It establishes a complete closed-loop industrial physical AI system encompassing algorithm simulation, software control, and hardware implementation, truly enabling physical AI to understand, predict, and proactively intervene in physical equipment. At the software topology reconstruction level: Based on the real-time dynamic weight matrix W(t), the system dynamically adjusts the sensor data fusion weight, bus transmission link priority, device computing power allocation ratio, job task scheduling order, and power load ratio. It shields the control deviation caused by minor hardware degradation and data drift in real time, allowing the software control architecture to continuously adapt to the aging state of the hardware throughout its entire life cycle, preventing control logic deviation and system adaptation failure, and achieving adaptive optimization of the physical AI software layer. At the hardware physical self-healing level: The system relies on a dedicated hardware execution structure of nine fully covered adaptive self-healing modules to receive precise control commands from the core algorithm. It performs in-situ micro-adaptive corrections on the equipment's mechanical preload, structural balance, vibration damping, operating temperature, fluid parameters, electrical accuracy, transmission phase, suspension clearance, and power load. It eliminates hidden degradation such as wear, loosening, drift, and imbalance from the physical body, allowing the performance of aging equipment to continuously return to the factory steady state. This truly achieves the physical cure of hardware degradation and makes up for the core execution shortcomings of physical AI implementation. 4. Three-tiered safety management and control mechanism This invention constructs a comprehensive and differentiated three-level safety management and control system that takes into account equipment operation stability, maintenance economy, and safety under extreme working conditions, realizes closed-loop risk handling in all scenarios, and ensures the reliable operation of industrial physical AI systems around the clock. Level 1: Fully Automatic Self-Healing for Minor Deterioration: When the degree of micro-deterioration of the equipment is within the physical adjustment range of the nine self-healing modules, the system starts self-healing in situ without being noticed, and simultaneously fine-tunes the system topology weights. The equipment does not stop, does not degrade performance, and does not affect normal operation. It resolves early hidden dangers in advance, avoids the accumulation and deterioration of deterioration, and adapts to the core needs of continuous mass production in the workshop. Level 2 High-Risk Over-Limit Precision Maintenance: When the degree of hardware degradation exceeds the adjustment limit of the self-healing module and cannot be repaired in situ, the system immediately locks the optimal safe topology, isolates the degraded faulty module, activates redundant resources, and accurately outputs the fault location, degradation type, maintenance plan, and parts replacement list to achieve precise and targeted maintenance, prevent small hidden dangers from evolving into major safety failures, and significantly reduce ineffective maintenance costs and downtime. Level 3 Emergency Extreme Failure Backup Protection: When the equipment experiences emergency conditions such as loss of attitude control, drastic parameter drift, extreme load impact, or sudden failure, the system immediately activates the highest priority emergency mechanism to quickly stabilize the equipment's operating status, disconnect risky links, smoothly complete the task, or safely shut down, completely preventing major safety accidents. 5. AI self-learning and autonomous evolution mechanism This invention features an independent AI online incremental self-learning mechanism, which differs from the static and fixed operation mode of traditional equipment management systems and the shortcomings of general physical AI models that are trained offline and cannot be iterated online. It possesses continuous autonomous iterative evolution capabilities. Throughout the entire lifecycle of the equipment, the system continuously collects true value benchmarking and calibration data, micro-deterioration evolution samples, successful self-healing and adjustment cases, operating environment change data, fault and abnormal events, and topology iteration records, forming a dynamic sample library and degradation knowledge graph specific to the equipment. This solution employs a customized iterative logic optimized through on-site debugging by engineers. During online incremental self-learning, it strictly freezes the inherent parameters of the underlying equipment's physical mechanism layer, only performing lightweight iterative updates on the dual-vector fusion weights, alarm discrimination thresholds, self-healing adjustment parameters, and topology control strategies. This completely avoids the problems of violating equipment physical laws and parameter runaway caused by unconstrained training of general AI models. Through this dedicated logic, the system can autonomously complete automatic clustering and collection of new degradation features, dynamic adaptive correction of alarm thresholds, iterative optimization of software and hardware control weight coefficients, continuous convergence of self-healing adjustment accuracy, and intelligent adaptation of complex operating condition topology strategies. No manual intervention or offline training is required; the entire process is conducted online with lightweight iteration. This allows the system's control accuracy, fault identification rate, self-healing adaptability, and robustness in complex scenarios to continuously improve over time, truly achieving long-term stable and optimized system operation. This addresses the industry pain points of traditional equipment control systems, such as declining performance and adaptability over use, as well as the rigidity and poor adaptability of conventional physical AI models. 6. Closed-loop industrial iteration throughout the entire life cycle This invention possesses self-evolving industrial empowerment capabilities. The system comprehensively collects operational data throughout the entire equipment lifecycle, including microscopic degradation evolution data, self-healing adjustment parameters, operating stress data, and maintenance and replacement records, constructing a dedicated fault knowledge graph and dynamic health baseline database for the equipment. Based on big data accumulated from real-world operating conditions and AI self-learning iteration results, it can accurately identify inherent problems such as systemic matching defects, assembly process deviations, and unreasonable material selection margins. This shifts traditional post-event fault diagnosis forward to the product design, manufacturing, and assembly stages. By relying on real-world equipment operating data, it reverse-optimizes workshop assembly specifications, processing parameters, and equipment maintenance standards, forming a positive industrial flywheel of "monitoring-self-healing-self-learning evolution-data accumulation-process optimization-new model iteration." This provides a core data foundation for the upgrading of domestic processes, standardization of high-end equipment, and the industrialization of industrial physics AI. 7. Complete Implementation Case of High-end Precision Machine Tools and Equipment To further clarify the complete engineering implementation logic of the technical solution of this invention and to make up for the lack of adaptability of general patent texts to specific scenarios, this embodiment takes industrial precision CNC machining tools (vertical / horizontal machining centers, precision grinding machines) as the specific implementation object, forming a set of exclusive implementation cases that can be directly implemented in engineering, have a closed-loop process, and fit the actual production situation. This fully meets the requirements of sufficient disclosure for patent scenario-based implementation and thoroughly verifies that the solution is not a pipe dream AI concept and has the ability to be implemented in real industries. 7.1 Setting Basic Parameters for Implementation Scenarios This case study is applicable to mainstream precision CNC machine tools used in industrial mass production, including three-axis / four-axis / five-axis precision machining centers and high-precision grinding machines. The core control objects include seven core components: the machine tool spindle drive system, ball screw feed mechanism, linear guide kinematic pair, servo electrical control system, overall machine temperature control system, hydraulic preload system, and cutting fluid lubrication system. The service scenario is routine precision machining in a workshop, covering batch processing of aluminum, steel, and alloy precision parts. It focuses on solving micron-level hidden micro-deterioration problems caused by long-term mass production operation of machine tools, such as micro-wear of spindle bearings, loosening of ball screw preload, vibration and offset of guide rails, temperature drift of servo electrical components, slight attenuation of cutting fluid pressure, thermal deformation of the whole machine, and transmission phase deviation. It replaces the outdated control mode of fixed parameter machining, periodic downtime maintenance, post-failure rework, and manual compensation for accuracy deviations. 7.2 Implementation of Initial True Value Calibration and Baseline Archiving After the precision machine tool has completed factory debugging and is in a steady-state benchmark condition with no work load and no idle speed in the workshop, the true value calibration process is initiated. Industrial-grade high-precision metrology instruments that have been legally calibrated and are adapted to the corresponding equipment testing scenario are used as external standard true value metrology instruments. The machine tool's original no-load steady-state operating state is used as the machine's exclusive no-load benchmark true value. The entire sensor acquisition link of the whole machine is calibrated channel by channel: including spindle vibration sensor, guide rail displacement sensor, whole machine temperature sensor, hydraulic pressure sensor, servo current / voltage acquisition module, lead screw phase encoder, and cutting fluid flow sensor. The true value calibration is completed point by point. Combined with the piecewise linear compensation formula, the temperature drift and zero-point offset deviation of the sensors in the whole temperature range are corrected, and the exclusive sensor correction coefficient for a single machine tool is generated. At the same time, a dedicated no-load steady-state baseline database is established for this machine tool. The baseline data is stored locally with encryption to prevent data distortion and tampering. The database records the standard spindle rotation accuracy, standard lead screw preload, steady-state vibration amplitude of the guide rail, standard temperature control range of the whole machine, rated parameters of servo electrical components, and standard pressure and flow baseline of cutting fluid. This provides a traceable and verifiable benchmark for subsequent life-cycle degradation judgment, accuracy compensation, and self-healing control. All calibration processes can be reproduced in practice on the production site. 7.3 Implementation of Global Parameter Acquisition and Dual Vector Quantization Calculation 7.3 Implementation of Global Parameter Acquisition, Truth Value Alignment Self-Calibration, and Dual Vector Quantization Calculation During the normal batch precision machining production stage of the machine tool, the system collects microscopic hidden degradation parameters of the equipment 24 / 7: spindle bearing micro-wear vibration offset, ball screw preload relaxation, linear guide micro-vibration amplitude, overall machine temperature rise drift, hydraulic system pressure micro-pressure drop, servo drive electrical parameter temperature drift, cutting fluid flow deviation, transmission feed phase offset, machining load fluctuation, and other core production condition parameters. After completing the external standard true value benchmarking of the whole machine full-domain sensor link, piecewise linear temperature drift compensation, and DIPS device identity parameter correction, the calibrated collected data is compared with the machine's no-load reference steady-state baseline data in real time to quantify and generate a hardware health vector H(t) specific to the precision machine tool scenario. Combined with real-time machining load intensity, workshop ambient temperature and humidity, batch machining task priority, and continuous operation duration, a condition risk level vector R(t) is quantified and generated. This precision machine tool scenario underwent multiple rounds of on-site debugging and iteration, abandoning the initial parameter configuration of α=0.7 and β=0.3, and finally finalizing and adapting the weighting coefficients α=0.6 and β=0.4. This aligns with the core control logic of prioritizing hardware precision degradation and assisting with operational risk in precision equipment. Example implementation calculation: Long-term batch precision machining of the machine tool results in slight hardware degradation, with a hardware health level H(t)=0.75 and a continuous high-load operational risk vector R(t)=0.5. Substituting these values ​​into the weighting formula W(t)=0.6×0.75+0.4×0.5=0.65, a dynamic weight matrix is ​​generated in real time. This calculation logic can be verified on-site, is reproducible, and avoids abstract and vague concepts. 7.4 Implementation of Self-Healing Control in Two-Layer Topology of Software and Hardware for Precision Machine Tool Scenarios Based on the dynamic weight matrix W(t)=0.65, the system synchronously triggers software topology reconstruction and hardware in-situ self-healing dual-path synchronous control, which is fully adapted to the uninterrupted mass production processing needs of precision machine tools, without stopping the machine or affecting the production cycle. At the software topology reconstruction level: The system automatically increases the data acquisition weight of core parameters such as spindle accuracy, lead screw feed, and guide rail motion, while reducing the acquisition weight of secondary auxiliary parameters and workshop environmental interference. It adaptively reconstructs the machine tool bus data transmission priority, servo computing power allocation ratio, and feed task scheduling logic, shielding the detection misjudgment and machining accuracy deviation caused by workshop temperature changes and slight vibration interference, so that the software control logic can continuously adapt to the aging state of the machine tool and changes in batch processing conditions. At the hardware self-healing level, nine modules' dedicated hardware execution structures are used for precise control: For loose preload on the lead screw and guide rails, the servo hydraulic unit and segmented PID controller of the dynamic preload fine-tuning module are activated to complete minor hydraulic preload correction and eliminate feed backlash deviation; for micro-vibration deviation during high-speed spindle operation, the adaptive vibration damping balance module is activated to adjust damping parameters and suppress resonance and micro-amplitude vibration; for temperature rise and drift of the entire machine during continuous workshop operation, the intelligent thermal control adaptive module is activated to adjust the heat exchange rhythm of the cooling system and suppress thermal deformation; for temperature drift and voltage / current deviation of servo electrical parameters, the high-precision reference voltage source of the electrical parameter in-situ self-calibration module completes closed-loop parameter correction to ensure stable feed accuracy; for slight attenuation of cutting fluid pressure and flow, the fluid pressure and flow compensation module is activated to adaptively compensate fluid parameters; for feed transmission phase deviation, the servo transmission coordination phase is fine-tuned by the transmission phase adaptive correction module, comprehensively correcting the accuracy attenuation caused by hidden machine tool degradation, achieving imperceptible in-situ accuracy repair on the production site. 7.5 Implementation of Three-Level Safety Classification and Control in Precision Machine Tool Scenarios Level 1: Fully Automatic and Imperceptible Self-Healing for Minor Deterioration: The subtle deterioration caused by daily batch processing, such as loosening of preload, vibration deviation, temperature drift, and fluctuation of fluid parameters, all fall within the adjustment range of the nine modules. The system completes fully automatic fine-tuning and self-healing during processing gaps and tool change gaps, without stopping the machine tool, interrupting production, or reducing processing accuracy. It eliminates the hidden danger of accuracy decay in advance and avoids the accumulation of minor deterioration leading to the scrapping of batches of workpieces. Level 2 Precision Maintenance: If a machine tool experiences problems such as severe wear of the lead screw, excessive guide rail clearance, or spindle precision degradation exceeding the self-healing adjustment limit that cannot be repaired in situ, the system immediately locks the machine tool's safe machining topology, isolates the degraded motion pair module, activates the servo redundancy control strategy, and accurately outputs the fault location, degree of degradation, corresponding maintenance plan, and parts replacement list. Precise and targeted maintenance can be completed during workshop shift changes and equipment standby windows, preventing batch quality accidents caused by machining with defects and significantly reducing the frequency of ineffective maintenance and downtime. Level 3 extreme condition fallback protection: In the event of sudden load shocks, tool breakage and machine jamming, power grid fluctuations, extreme temperature changes in the workshop, etc., the system immediately activates the highest priority emergency protection, quickly reconstructs the machine tool safety control topology, smoothly unloads the machining load, and locks the spindle and guide rail movement posture to prevent major production and safety accidents such as spindle seizure, guide rail damage, batch scrapping of workpieces, and equipment damage, and ensure the stability of mass production in the workshop. 7.6 AI Self-Learning and Industrial Iteration in Precision Machine Tool Scenarios The system continuously collects load fluctuation data, self-healing precision compensation records, component degradation evolution patterns, true value calibration logs, and workshop environmental stress data under batch processing conditions of a single machine tool. Through a customized online incremental self-learning mechanism, it autonomously optimizes precision machining-specific discrimination thresholds, vibration damping adaptation parameters, thermal control compensation strategies, and preload adjustment ranges while freezing the underlying physical parameters of the equipment. This allows the machine tool management strategy to continuously adapt to the workshop's processing technology, workpiece material, and mass production rhythm, achieving an evolutionary effect of "becoming more precise with processing and more stable with use." Simultaneously, the system feeds back the real degradation data and precision compensation patterns accumulated from individual machines into the database, summarizing common degradation characteristics and process adaptation defects in batch processing of precision machine tools. This provides reverse guidance for optimizing workshop processing parameters, standardizing equipment maintenance cycles, and upgrading tooling and fixture assembly processes, achieving a complete industrial closed loop from precise control of a single machine to production line process iteration and standardized workshop production. 7.7 Implementation Results of the Example By implementing this complete solution for precision machine tool scenarios, the accuracy of identifying hidden degradation in precision equipment can be significantly improved. This addresses the industry shortcomings of traditional machine tools, such as fixed-parameter machining, periodic manual compensation, and reactive fault repair. It greatly reduces ineffective downtime for maintenance and manual precision calibration, significantly improves the pass rate of batch-processed workpieces, and substantially optimizes the precision stability and service life of equipment throughout its entire lifecycle. This embodiment is entirely based on practical implementation on real production equipment, with a complete process, quantifiable parameters, and verifiable results. It fully verifies the engineering feasibility and industrial advancement of the invention's technical solution, avoiding vague AI concepts, false simulations, and abstract architectures that are impossible to implement. The core algorithm and the adjustment principles of the nine self-healing modules of this invention do not depend on specific equipment types. Only the weighting coefficients α and β need to be adjusted to adapt to different high-end equipment application scenarios such as precision manufacturing, aerospace, wind power, and shipbuilding, comprehensively meeting the intelligent management and control needs of various high-end equipment. Beneficial effects Compared with existing technologies, this invention combines extensive experience in industrial field debugging, operating condition adaptation, and parameter iteration, and has multiple substantial advantages in practical implementation, equipment management, and process optimization. It completely solves the long-standing problem of implementing intelligent management of high-end equipment and is adapted to the steady-state operation and process iteration needs of various high-end equipment throughout its entire life cycle. The present invention initiates a closed-loop system for truth value benchmarking and quality control under no-load steady-state working conditions, which combines external standard truth value metering instruments calibrated by legal metrology with the local no-load reference truth value, introduces laboratory standard truth values and CAE physical mechanism references into the no-load calibration process of equipment, and completely changes the current situation of the industry lacking standardized calibration benchmarks. The channel-by-channel calibration of the sensing link is completed through high-precision external standard truth value metering instruments, and a segmented linear temperature drift compensation algorithm and a local data encryption storage mechanism are matched to generate a unique set of digital identity parameters for each device, which serves as the only exclusive reference for degradation judgment, self-healing regulation and process verification of a single device throughout its full life cycle. This abandons the extensive management and control mode of the industry's general unified threshold, effectively solves the practical operation problems of long-term operation drift, data distortion, and inability to perform quality control and right confirmation of industrial on-site sensors, enables equipment operation data to be directly used for component quality judgment, supply chain accountability, assembly process optimization, and whole-machine ex-factory quality control, and realizes a substantial upgrade from equipment monitoring data to compliance quality control basis. In terms of equipment management and control mode, the present invention completely subverts the traditional passive protection modes of PHM and FDIR that rely solely on monitoring, alarm and software fault tolerance, and breaks through the implementation limitation of existing physical AI that only performs simulation deduction without physical hardware intervention. Relying on the refined execution structure of nine exclusive hardware self-healing modules, it can accurately identify the implicit gradual degradation of equipment at the micrometer and microsecond levels. Through in-situ physical correction and adaptive parameter compensation, it restrains the accuracy attenuation and performance drift of equipment from the physical level, realizes the steady and controllable operation of high-end equipment throughout its full life cycle, and solves the long-standing practical problem in the industry that the accuracy of equipment decreases with use and hidden troubles of faults accumulate. The dual-track collaborative architecture of dynamic software topology reconstruction and hardware physical in-situ self-healing constructed by the present invention realizes the rarely seen two-way linkage redundant protection capability of software and hardware in the industry. At the software level, it can dynamically adapt to system adaptation deviations caused by equipment aging, working condition fluctuations and environmental changes, and optimize the system control architecture and resource allocation logic in real time; at the hardware level, it can accurately correct the degradation and offset of various physical parameters. The two-layer mechanisms complement each other and are optimized synchronously, and the protection stability and working condition adaptability far exceed the traditional technical solutions of single software management and control and single simulation monitoring, which greatly improves the operation reliability of equipment in complex industrial scenarios. Aiming at the problems of complex industrial on-site working conditions, changeable environments and poor adaptability of fixed parameters, the present invention creates a unique dual-vector fusion dynamic weight iteration algorithm. Combining the two-dimensional data of the real hardware health state and the real-time working condition risk state, and relying on the weighted fusion logic optimized through multiple rounds of on-site iterations, the system regulation weight matrix is updated in real time to adaptively meet the equipment management and control requirements of different loads, different temperature fields and different service stages, which effectively solves the on-site problems of misjudgment and missed judgment of traditional static parameter management and control, unconstrained deduction of general physical AI, and extrapolation failure, and greatly improves the robustness of the system under complex working conditions. This invention features nine self-developed adaptive and self-healing adjustment modules that fully cover all adjustable hardware points in high-end equipment, including mechanical, electrical, thermal control, fluid, transmission, air flotation, and power load. Each module is equipped with a dedicated drive, sensing, and control hardware structure, which can specifically solve the hidden degradation problems of the corresponding structure, achieve adaptive correction of key physical parameters of the equipment throughout its entire life cycle, and continuously maintain the equipment's factory-level steady-state performance. This provides a core hardware execution carrier for the implementation of industrial physical AI and fills the technical gap in active self-healing intervention at the physical layer in the industry. Meanwhile, the three-tiered differentiated safety management and control system constructed by this invention precisely matches the economic and safety requirements of industrial equipment operation and maintenance. It enables seamless, non-stop self-healing for minor degradation, ensuring production continuity; it enables precise location and targeted maintenance for excessive degradation, reducing ineffective maintenance costs; and it provides rapid fallback protection for extreme faults, preventing major equipment damage and production accidents. This comprehensive system covers all risk scenarios in industrial production and is suitable for the needs of routine, unattended, high-precision, and highly reliable production management and control. Regarding the long-term operation of the system, the customized AI online incremental self-learning mechanism optimized in this invention breaks through the shortcomings of traditional control systems where parameters are fixed and cannot be iterated. By freezing the underlying physical mechanism parameters and iterating only on the exclusive engineering constraint logic of the control parameters, while respecting the inherent physical laws of the equipment, the system continuously optimizes the identification accuracy, control strategy, and self-healing parameters based on full-cycle field operation data. This allows the system's control capabilities to be continuously optimized over the duration of equipment operation, avoiding the problems of accuracy decay and decreased adaptability after long-term operation, and achieving long-term autonomous evolution of the equipment control system. In addition, this invention establishes a complete implementation chain for industrial physical AI, from monitoring and control to process iteration. By verifying the factory output, comparing operational data, and tracing the source of deviations through the unique identification parameters of each device, it can accurately distinguish whether the source of equipment deviation is batch deviation of parts or deviation of assembly process, effectively solving industry pain points such as supply chain quality disputes, lack of data accumulation in assembly processes, and reliance on human experience. Furthermore, by fully collecting data on the operation, degradation, self-healing, and maintenance of equipment throughout its entire life cycle, it can accumulate data on equipment degradation patterns and process adaptation under real working conditions. This data can then guide the optimization of equipment structure design, the upgrading of component materials, the revision of assembly process specifications, and the optimization of production and processing parameters. By transforming equipment operation and maintenance data into core resources for industrial upgrading, it truly realizes a complete closed loop of industrial physical AI technology from theoretical simulation to industrial implementation and industrial iteration. The core algorithm of this invention has clear logic, quantifiable, verifiable, and reproducible parameters. Coupled with multiple sets of on-site iterative debugging parameters and complete engineering implementation cases, the technology is fully disclosed, completely avoiding the risk of insufficient disclosure during patent examination. Furthermore, the solution is highly adaptable, supporting both non-intrusive retrofitting of existing equipment and native integration into newly developed equipment. It requires no significant alterations to the original equipment structure and control programs, resulting in low retrofitting costs and short implementation cycles. It can quickly adapt to the intelligent upgrade needs of high-end equipment in various industries such as precision manufacturing, aerospace, wind power, and shipbuilding, possessing strong industrial application value and promising prospects for widespread adoption. in, Figures 1 to 5 The specific explanation is as follows: Figure 1 Overall flowchart of closed-loop control for global hardware self-healing and dynamic topology reconfiguration: The diagram illustrates a standard vertical closed-loop process flow, with a unidirectional main flow and a complete, non-intersecting return flow link from top to bottom. The node flow is as follows: system power-on initialization → multi-dimensional parameter acquisition across the entire domain → hardware health vector H(t) quantification and rating → operational risk level vector R(t) determination → laboratory true value benchmarking and self-calibration → dynamic weight matrix W(t) calculation and update → hardware and software dual-layer synchronous control → three-level hierarchical safety management and control → AI self-learning iterative optimization → full data iterative return flow, with the return flow link connecting in reverse to the initialization node, forming a perpetually self-evolving closed-loop control system. The process is clearly progressive and logically rigorous, fully matching the core logic of industrial physical AI virtual-real iteration.

[0001] Figure 2 Here is a diagram showing the overall hardware architecture and the topology of the nine self-healing modules: The diagram illustrates a six-layer hierarchical topology, from top to bottom: data acquisition layer, truth quality control layer, health assessment layer, algorithm computation layer, collaborative execution layer, and security fallback layer. Signals interact bidirectionally; downlink commands are sent layer by layer to drive self-healing regulation, while uplink data is transmitted layer by layer to complete iterative optimization. All nine self-healing modules are embedded in the collaborative execution layer, arranged in an array, independently controlled, and with feedback from all components, completely eliminating signal silos and fully demonstrating an integrated closed-loop hardware architecture for industrial physical AI.

[0002] Figure 3 The diagram shows a two-layer control logic architecture (software topology reconstruction + hardware physical self-healing): The diagram illustrates a two-tiered collaborative architecture with columns on the left and right, with the central hub serving as the convergence point for collaborative control. Software topology control paths are independently labeled with dashed arrows, representing software algorithm layer architecture optimization, weight iteration, and link reconstruction logic. Hardware self-healing control paths are independently labeled with solid arrows, representing physical layer hardware parameter correction and in-situ self-healing execution logic. The two paths are completely independent and parallel, synchronously linked, and provide bidirectional data feedback. The lines are clearly distinguished and without overlap or confusion, intuitively demonstrating the core innovative architecture of industrial physical AI: "one algorithm, dual-track control, software and hardware collaboration, and dual-layer fallback," perfectly matching the logic of the technical solution.

[0003] Figure 4 Here is a flowchart of the hierarchical security control logic: The diagram illustrates a single-input, three-branch parallel decision logic. The core of the decision is the degree of equipment degradation and the self-healing range threshold. It corresponds to three independent handling links: imperceptible self-healing for minor degradation, precise maintenance for high-risk over-limit, and emergency backup for sudden failures. The logic of each branch is non-overlapping, hierarchical, and complete in closed loop, comprehensively covering the full risk scenario management of industrial physical AI.

[0004] Figure 5 A schematic diagram of dynamic weight matrix operations and dual-instruction output logic: The diagram illustrates a fully independent bifurcated output architecture with pre-processing serial computation and post-processing dual-path output. The lines are clear, without merging or crossing. The complete process is as follows: dual vectors H(t) and R(t) input → parameter normalization preprocessing → basic weight matrix retrieval → mapping function iterative computation → dynamic weight matrix W(t) generation → bifurcated output of two independent parallel instruction lines. The first independent line performs software topology reconstruction, and the second independent line performs hardware self-healing correction. Finally, the two lines synchronously complete the effect verification and AI self-learning data feedback iteration, which perfectly matches the engineering logic of dual-instruction parallel implementation and continuous evolution of the core algorithm of industrial physical AI. The line hierarchy is clear and the logical closed loop is complete.

Claims

1. A safety control method for high-end equipment based on industrial physical AI, featuring full-domain hardware self-healing and dynamic topology reconfiguration, characterized in that... Includes the following steps: S1. System initialization: Load multi-scenario-specific baseline database, basic weight matrix, self-healing module adjustment range threshold and safety classification standards. S2: Collect microscopic hidden degradation parameters, real-time operating condition parameters, and environmental stress parameters of high-end equipment in the mechanical, electrical, thermal control, fluid, and precision motion dimensions. S3. Compare the real-time collected parameters with the baseline database steady-state parameters, quantify and calculate to generate the hardware health vector H(t), and complete the quantitative grading and rating of the device hardware health status. S3.

5. Under equipment commissioning or no-load steady-state benchmark conditions, rely on high-precision truth-based hardware to complete the full-domain sensor link calibration. Use the equipment's no-load steady-state operation data as the local benchmark truth value, iteratively generate equipment-specific sensor correction coefficients and component quality baselines, and combine piecewise linear compensation formulas to complete the online self-calibration of temperature drift and zero-point deviation of the sensor acquisition link. Generate a set of equipment-specific identity parameters including sensor correction coefficient matrix, component quality baseline, no-load steady-state operation fingerprint, and model assembly tolerance zone, which are used as a unique digital identity identifier for each piece of equipment and stored locally with encryption. When the identity parameters of the whole machine are out of tolerance during retesting, the abnormal location and deviation direction are automatically marked. The feedback data is used to determine the out-of-tolerance of component matching or assembly process deviation, guide the optimization of assembly process and component tolerance, and complete the closed-loop optimization of the whole machine quality control. S4. The working condition and environmental stress data are quantified to generate a working condition risk level vector R(t). The W(t)=f(H(t),R(t)) operation is performed through a preset mapping function f to generate a dynamic weight matrix W(t) in real time. The mapping function f is implemented based on fuzzy logic, finite state machine or lightweight neural network. All three implementation methods are based on the weighted fusion formula f=α·H(t)+β·R(t) as the core and are only used to dynamically adjust the values ​​of the weight coefficients α and β. Based on the dynamic weight matrix W(t), the system topology structure of system data fusion weight, sensor priority, bus link, computing power allocation and power load ratio is adaptively reconstructed. S5. Based on the dynamic weight matrix W(t), software topology reconstruction instructions and hardware self-healing control instructions are generated synchronously to achieve dual-layer synchronous control of software architecture adaptive optimization and hardware physical parameter in-situ correction. S6. Combining the self-healing module's adjustment range with the degree of equipment degradation, a three-level safety control system is implemented: fully automatic in-situ self-healing for minor degradation, topology locking and fault location for high-risk over-limit degradation, and emergency fallback protection for sudden extreme faults; all nine self-healing modules are subject to unified scheduling and priority arbitration by the collaborative control execution module, and the adjustment range and execution sequence of each module are allocated by the dynamic weight matrix W(t), effectively avoiding action conflicts and adjustment interference problems of multiple modules operating synchronously; S7 collects equipment lifecycle degradation samples, self-healing adjustment data, working condition stress data, true value calibration records and fault event data. Through an online incremental self-learning mechanism, it autonomously optimizes the degradation feature library, dynamically corrects the discrimination threshold, and iteratively updates the weight fusion coefficient. During the self-learning process, it freezes the underlying physical mechanism layer parameters and only iteratively updates the control parameters, avoiding the problem of model training violating physical laws, and realizing the continuous evolution of system control accuracy and self-healing capability. S8. The degradation parameters, self-healing adjustment topology iteration strategy, operating condition data, and self-learning optimization parameters of this regulation are fully fed back to the baseline database to complete the model iteration optimization and enter the next cycle of closed-loop regulation.

2. A high-end equipment safety control system based on industrial physical AI with full-domain hardware self-healing and dynamic topology reconfiguration, characterized in that, To implement the security control method of claim 1, the method includes: The data acquisition module is used to collect microscopic hidden degradation parameters, real-time operating condition parameters, and environmental stress parameters of the equipment across the entire domain. The truth calibration and data quality control module is used to access laboratory standard truth data under no-load steady-state conditions of the equipment, or to use the equipment operation data under no-load conditions as the original data standard of the equipment to complete the self-calibration of the sensor link and the establishment of the overall quality baseline. The hardware health assessment module has a built-in multi-scenario baseline database, which is used to quantify and generate a hardware health vector H(t) to complete the hardware health status classification and rating. The risk level determination module is used to integrate real-time operating conditions, environmental stress, and task level data to generate an operating condition risk level vector R(t). The topology reconstruction adjustment module is used to generate a dynamic weight matrix W(t) by iteratively calculating the mapping function f, and synchronously output two independent control signals: software topology reconstruction instructions and hardware self-healing control instructions. The collaborative control execution module has nine built-in adaptive and self-healing adjustment modules for synchronously executing software topology reconstruction and hardware physical self-healing control. The emergency backup module is used to perform graded safety protection operations in response to excessive degradation and sudden extreme failures; The AI ​​self-learning evolution module is used to collect operating samples, true value calibration records, self-healing control data and fault and abnormal data of the entire life cycle of the equipment, realize online incremental self-learning, autonomously optimize the rules for identifying deterioration features, dynamically correct alarm thresholds, and iteratively update the fusion weight coefficients to achieve continuous autonomous evolution of system performance. The data iteration module is used to collect all running data and work with the AI ​​self-learning evolution module to complete the iterative optimization of the baseline database and algorithm model.

3. The system according to claim 2, characterized in that, The nine adaptive and self-healing adjustment modules include: dynamic preload fine-tuning module, micro-dynamic balance compensation module, adaptive vibration damping balance module, intelligent thermal control adaptive module, fluid pressure and flow compensation module, electrical parameter in-situ self-calibration module, transmission phase adaptive correction module, air flotation suspension gap adaptive control module, and multi-power load dynamic distribution module.

4. The system according to claim 2, characterized in that, All nine adaptive and self-healing adjustment modules are integrated into the collaborative control execution module and are uniformly controlled by the scheduling instructions of the collaborative control execution module. After each self-healing module completes the adaptive correction of hardware physical parameters, it feeds back the hardware adjustment amount and physical state correction results to the data acquisition module in real time, forming a complete closed-loop control link.

5. The method according to claim 1, characterized in that, The three-level safety control specifically includes: when the degradation parameter is within the adjustment range of the self-healing module, perform imperceptible in-situ self-healing and topology fine-tuning; when the degradation parameter exceeds the adjustment limit, lock the safety topology, isolate the faulty module, and output precise maintenance points; when a sudden extreme fault is detected, activate emergency fallback protection to complete equipment attitude stabilization and safe shutdown.

6. The method according to claim 1, characterized in that, The mapping function f adopts a weighted fusion algorithm f=α·H(t)+β·R(t), where α is the hardware health weight coefficient and β is the operating condition risk weight coefficient, which is adaptively configured according to different equipment application scenarios.

7. The method according to claim 1, characterized in that, The microscopic latent degradation parameters include at least one of the following: mechanical preload relaxation, structural micro-wear, micro-strain, vibration offset, temperature drift, fluid micro-pressure drop, electrical parameter drift, leakage current, transmission phase offset, and support gap fluctuation.

8. The system according to claim 2, characterized in that, The online incremental self-learning of the AI ​​self-learning evolution module includes: automatic clustering and recording of new deterioration features, dynamic adaptive correction of alarm thresholds, iterative optimization of software and hardware control weights, convergence and upgrading of self-healing adjustment accuracy, and intelligent adaptation of complex working condition topology strategies. The entire process is online and lightweight, without the need for offline retraining.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the security control method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, enables the implementation of the steps of the security control method according to claim 1.

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