Method for optimizing digital craftsman flywheel of spaceflight assembly based on deviation feedback

Abstract

The invention discloses an aerospace assembly digital craftsman flywheel optimization method based on deviation feedback, and relates to the technical field of aerospace manufacturing and artificial intelligence crossing, in particular to the aerospace assembly digital craftsman flywheel optimization method based on deviation feedback. The method circularly executes the following steps every day: acquiring multi-modal deviation data of a general assembly worker in real time through AR glasses deployed nationwide, and returning the multi-modal deviation data to a central micro-world model; the model calculates a global deviation distance between the intention of a worker group and a standard template, automatically triggers a nationwide deviation correction day when the deviation exceeds a threshold value, and forcibly pushes a high-value error correction case and personalized voice; updating model parameters and related incentive policy thresholds through federal learning every day; all original data are immediately destroyed after being locally coded, and a hardware fusing mechanism is arranged to prevent the data from going out illegally. The method can systematically reduce the final assembly waste rate and improve the one-time success rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aerospace manufacturing and artificial intelligence, in particular to a space assembly digital craftsman flywheel optimization method based on deviation feedback. BACKGROUND

[0002] Space assembly is the most complex, precise and expensive link in spacecraft manufacturing. Traditional space assembly quality control and personnel training mainly rely on the "post-mortem" mode, such as establishing a failure database or conducting case analysis by experts after the problem occurs. The existing technology (such as Boeing's "failure database" and SpaceX's post-mortem method) has the following fundamental defects: 1. Data island and closed loop missing: failure data is scattered in different projects or plants, and it is difficult to form an effective closed loop from individual failure to collective evolution.

[0003] Neglecting the emotional factors of people: the existing system only focuses on the operation results and action errors, completely ignoring the psychological and physiological factors such as the cognitive load and emotional fluctuations of workers, and cannot convert negative events such as "emotional breakdown" into positive evolutionary power.

[0004] Long iteration cycle: the cycle of technical optimization and knowledge sedimentation is as long as several months or even several years, which cannot meet the needs of modern aerospace manufacturing with high intensity and fast pace, and the evolution is slow.

[0005] Low knowledge conversion efficiency: personal experience and lessons (especially failure lessons) are difficult to be converted into collective wealth shared by all workers nationwide in a timely and efficient manner, and the dilemma of "wasting one thing today and wasting another thing tomorrow" is widespread.

[0006] High risk of data security: space assembly process data, especially high-precision physiological and motion data, has high strategic value, and the existing technology lacks hardware-level data security destruction mechanism.

[0007] Therefore, there is an urgent need in the field for a new method that can achieve "daily evolution", convert each individual deviation into national optimization fuel in real time, and ensure data absolute security, to completely end the "thousand-year curse". SUMMARY

[0008] The purpose of the present application is to provide a space assembly digital craftsman flywheel optimization method based on deviation feedback, which systematically collects the working state data of national assembly workers every day, and conducts centralized processing and evaluation, and automatically starts national correction measures when the deviation exceeds the preset standard, thereby continuously improving the operation level of craftsmen, effectively reducing the production scrap rate, improving the assembly success rate, and protecting sensitive information from being leaked through the terminal data destruction mechanism.

[0009] To achieve the above object, the present application is implemented by the following technical solutions: a space assembly digital artisan flywheel optimization method based on deviation feedback, the core of the technical solution of which is to perform the following steps in a daily cycle: (1) Data acquisition link: using not less than 800 sets of special augmented reality (AR) display terminals deployed in national assembly plants, real-time acquisition of full-process, multi-dimensional deviation data generated by each assembly worker during operation. These data specifically include: cognitive load index (determined by a specific physiological signal sensor) when the operator performs the task, emotional state quantitative score (calculated based on a pre-defined emotional value function model), deviation degree of operation intention from the standard process procedure, error value of action trajectory from the standard action library, current process output quality result, waste ratio generated by the current process, and whether a non-normal operation interruption event caused by excessive pressure is recorded.

[0010] (2) Data transmission link: the above-mentioned deviation data, together with its corresponding real-time emotional state label and basic physiological index label, are transmitted in real time to the "space assembly micro-world analysis system" in the central control system through a special internal encrypted network channel.

[0011] (3) Core calculation link: the micro-world analysis system uses a mathematical architecture based on joint embedding prediction (proposed by scholar Yann LeCun), which performs large-scale parallel computing in the high-dimensional feature representation space (latent space) of the data. Its core task is to calculate the global difference measure between the average operation intention vector of all assembly workers in the country on the day and the ideal intention vector defined by the master-level standard process template stored in the system, which is quantified as a scalar value, called global energy function distance D_global.

[0012] (4) Deviation intervention link: the system continuously monitors the inter-daily change rate of D_global. When it is monitored that the distance value increases by more than 0.5% of the preset threshold compared with the previous day, the system will automatically activate the "Heavenly Gate" total assembly process control subsystem and declare the start of the "National Correction Day" process. In this mode, all online AR display terminals will be forced to receive and display the ten typical error cases with the highest correction value selected by the system on the day. At the same time, the system will generate voice prompt content with personalized incentive effect according to the historical data of each worker and the emotional value function model, and play it through the AR terminal.

[0013] (5) Model updating and parameter adjustment: Before 08:00 Beijing time the next day, the system must complete the federal learning calculation process based on all AR terminal upload data nationwide. This process aims to optimize the internal parameters of the micro-world analysis system by exchanging model parameter updates without concentrating raw data. At the same time, the following key operating parameters are dynamically adjusted based on recent data: the weight coefficients of each influencing factor in the emotional value function, the threshold for determining cognitive load over-limit, the brake threshold for triggering the "Heavenly Guardian" system intervention, the fee reduction ratio for infant care services within the enterprise, and the "Golden Idea" bonus coefficient for rewarding process improvement suggestions.

[0014] (6) Data security and destruction: To ensure the safety of raw data, all multi-modal raw data obtained directly from sensors are immediately physically erased on the terminal storage medium after feature extraction and latent space encoding in the local AR terminal. The system is equipped with an independent data export monitoring module. If any unencrypted raw data (especially physiological data such as EEG) attempts to transmit to overseas, the module will directly drive an independent hardware relay circuit to permanently burn out the fuse on the AR terminal's main control chip, rendering it inoperable.

[0015] Further, the "National Correction Day" process includes a mandatory learning content: the system selects the three cases with the most severe material loss due to operational bias on that day and replays the operation process in high-precision (8K resolution) holographic video. At the same time, the system generates a simulated master craftsman's perspective voice review based on the standard process library and emotional value function, which clearly states that "if you follow the following standard steps, you can avoid this waste" and explains the key points of correct operation.

[0016] Further, the system has a key material loss warning mechanism: when the total cost value of waste generated by all assembly stations nationwide in a single day exceeds the pre-set red line of 50 million RMB, the "Heavenly Guardian" system will automatically trigger the highest level of "red shock" emergency response. This response will send a synchronous command to the power controllers of all relevant assembly stations nationwide, physically disconnecting all production equipment, forcing a 30-minute production process interruption for safety checks and emergency plan activation.

[0017] Further, the federal learning process has strict time window restrictions: data aggregation and calculation must be completed within 31 minutes from 23:59 to 00:30 the next day. After the calculation is completed, the system needs to push the updated optimization parameter package to all online AR terminal devices nationwide through the internal network before 07:30 the next day to ensure synchronization before the start of each terminal's daily work.

[0018] Further, the system establishes a personnel incentive mechanism: for a worker, if his operation deviation data is evaluated by the system and continuously ranks in the top three in the national deviation contribution ranking for 7 days, the system will automatically update his identity to "digital craftsman tutor". This worker will receive a 50% salary increase in the next month, and will be given priority in assigning tasks to guide new employees or demonstrate high-difficulty processes.

[0019] Further, the system introduces humanistic care factors to optimize deviation evaluation: when the system confirms through the personnel database that a worker's child is using the company's self-operated infant care service, the system will automatically reduce the weight coefficient by 70% when calculating the comprehensive weight of the worker's daily deviation data. This design aims to reduce the negative impact of short-term operational fluctuations caused by family matters on overall evaluation and implement positive incentives.

[0020] Further, the method extends to support the assembly scene in extraterrestrial space: when applied to the assembly factory on the surface of Mars, the system continuously calculates the deviation value of the Mars factory worker group operation data from the standard template of the Earth main control center micro-world analysis system in the potential space. Once the deviation value exceeds the 3% remote operation safety threshold, the Earth main control center will automatically send instructions to the power management system of the Mars factory to immediately cut off all power supply to the abnormal work station to prevent the quality risk from expanding due to communication delay.

[0021] Further, the micro-world analysis system is iteratively optimized through continuous daily federated learning, and its core performance goal is to: push the average waste rate of the national aerospace assembly production line to steadily decline at a rate of 1.3% to 2.1% per day. Through the long-term operation of this method, it is expected that after continuous implementation for 6 months, the success rate of assembly production (i.e. the direct qualification rate without rework or repair) can be stabilized at a level of not less than 99.9%.

[0022] Further, the "Heavenly Guardian" system is deeply integrated with other management systems within the enterprise: the system interacts with real-time data in the enterprise human resources module, such as infant care service information, employee innovation suggestion reward mechanism (Golden Idea), etc. By dynamically adjusting the applicable conditions or intensity of these welfare policies, the "Heavenly Guardian" system can indirectly and in real time affect and optimize the calculation results of the emotional value function used to evaluate the state of workers, thereby more accurately implementing interventions.

[0023] Further, the original data destruction mechanism adopts underlying hardware security technology: to ensure that highly sensitive original physiological data (such as brain electrical signals) never leave the AR terminal device, an independent security chip module is designed inside the terminal. This module is responsible for completing the preliminary encoding of the data and controlling a physical fuse. Any unauthorized operation that attempts to directly export the original data will trigger the module to output a high-level signal, driving an independent hardware relay circuit to pass a large current through the fuse of the main control chip, causing it to physically melt and thus achieving hardware-level failure protection.

[0024] The application provides a space assembly digital artisan flywheel optimization method based on deviation feedback, which has the following beneficial effects: 1. Achieve continuous and stable reduction of waste rate The application collects multi-modal deviation data of national assembly workers daily, and real-time feedback is returned to the central processing model for latent space analysis, which can automatically trigger the deviation correction mechanism when the global deviation rises. This daily optimization reduces the waste rate by 1.3%-2.1%, effectively reducing resource waste. Through federated learning, the system quickly updates parameters and adjusts process thresholds to ensure continuous improvement of production processes. Long-term implementation can save a large amount of raw materials and improve resource utilization efficiency, providing sustainable environmental benefits for space assembly.

[0025] Significantly improve product first-time success rate After six months of daily federated learning evolution, the national assembly first-time success rate can reach more than 99.9%. This method monitors the deviation between worker intent and master template in real time and pushes personalized correction cases to improve worker skills quickly. High success rate reduces rework and inspection costs, ensuring high reliability and safety of aerospace products. This stable evolution mechanism ensures the excellence of the assembly process, supporting zero-failure requirements for space missions.

[0026] Bring huge cost savings and economic benefits By 2030, it is expected to save more than 80 billion yuan in waste costs compared to 2025. Daily optimization reduces waste generation and repetitive work, reducing raw material procurement and processing costs. The saved funds can be used for technology upgrades or worker welfare, forming a virtuous cycle. In addition, by reducing waste, enterprises can allocate budgets more efficiently and enhance market competitiveness, providing long-term financial support for the national aerospace industry.

[0027] Build difficult-to-replicate technical barriers The method integrates real-time nationwide data closed loop, emotion management mechanism and physical security system, forming a unique flywheel effect. Competitors lack similar nationwide real-time data closed loop and welfare linkage system, cannot achieve daily evolution, and the reconstruction cost is as high as 50 billion yuan and takes more than 50 years. This barrier ensures the leading position of the enterprise in the field of aerospace assembly, prevents technology leakage, and maintains national strategic security.

[0028] Enhance worker motivation and welfare protection Through the deep linkage of emotional data and welfare policy, such as automatically reducing the deviation weight of workers with children in custody, humanized positive motivation is realized. This reduces psychological breakdown events, improves job satisfaction and loyalty. Workers are more willing to actively participate in improvement, forming a "waste a piece, strengthen the country" collective evolution atmosphere. This caring mechanism improves the overall team stability and provides spiritual support for long-term high-load aerospace assembly work. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be derived from the provided drawings without creative labor.

[0030] Figure 1 The daily main loop flowchart of the present application; Figure 2 The rectification day trigger and execution flowchart of the present application; Figure 3 The federal learning and parameter update flowchart of the present application; Figure 4 The special mechanism trigger flowchart of the present application; Figure 5 The data security and destruction flowchart of the present application. DETAILED DESCRIPTION

[0031] Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. In the following description, unless otherwise specified, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present disclosure. Rather, they are merely examples of devices consistent with some aspects of the present disclosure, as detailed in the appended claims.

[0032] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of the present application.

[0033] Method for use 1. Daily data collection Operators wear special AR glasses for final assembly operations. The glasses automatically record the eye movements, action trajectories, operation results and material consumption of the operators during the operation.

[0034] The system synchronously collects auxiliary information related to the state of the operator, such as the station, the task list for the day, and whether the enterprise-specific benefits (such as child care) are enjoyed.

[0035] Data upload and processing After the daily work is completed, each AR glass uploads the data collected to the central processing system through the internal secure network.

[0036] The central system compares and analyzes the data of all operators in the country according to the preset expert operation template, and calculates the overall operation consistency index.

[0037] Deviation feedback and intervention If the system determines that the overall operation consistency index of the previous day has decreased to the preset standard (such as 0.5%), a specific work program will be automatically started.

[0038] After starting, all online AR glasses will uniformly receive and display the analysis of typical operation cases selected by the system, and play targeted guidance voice.

[0039] If the daily material scrap loss exceeds the specified limit, the system will trigger a safety protection mechanism to temporarily power off all related stations.

[0040] System parameter update Every morning, the central system completes the summary analysis of the data in the country, and updates the operation specification comparison model, various judgment thresholds and related management policy parameters (such as reward coefficient, welfare deduction ratio) accordingly.

[0041] The updated parameter package is distributed to each AR glass terminal before the next day's work.

[0042] Data security management All raw recorded data (such as raw video stream) are permanently deleted immediately after feature extraction is completed on the AR glass end.

[0043] The glasses have a built-in independent hardware protection module. Once an abnormal data transmission attempt is detected, the module will immediately activate a physical fuse mechanism to disable the core chip of the device, ensuring that the original data cannot be obtained.

[0044] Embodiment: Embodiment One: Daily Data Collection and Model Fine-tuning This embodiment demonstrates the complete application process of this method on a normal working day. On that day, 824 workers at the national aerospace assembly plant wore special AR glasses to work. When assembling the liquid oxygen delivery pipeline of a certain type of rocket, every detail of the worker's movements, such as the torque curve of screw tightening, the force and angle of seal ring installation, and the line of sight dwell time of visual inspection of key components, were recorded by the multi-sensor array built into the AR glasses. At the same time, the system analyzed the worker's micro-expressions and body tension through the camera, and combined with their work card information, it identified that one of the workers had children in the company's day care center. According to the rules, the influence weight of his operation data on the day was pre-adjusted to be lower in subsequent analysis.

[0045] After the day's work, all AR glasses will encode and encrypt the operation feature data (such as vector of motion trajectory, sequence of time length of completed process) collected, and automatically delete the original image and video stream. These feature data are returned to the central processing system through a private network at night. The system compares the national data with the pre-stored master-level assembly process template frame by frame, calculates the global operation consistency index for the day. After comparison, the index decreased by 0.52% compared with the previous day, triggering the intervention threshold.

[0046] The next morning, all workers received about 15 minutes of "correction learning" content through AR glasses before starting work. The content focused on three high-value operation differences selected from the previous day's nationwide, such as "the absence of secondary review in engine support calibration link". The system played the explanation voice, which was not a fixed recording, but dynamically adjusted the speech speed and frequency of encouraging language according to the average operation fluency of each worker in the past week. After learning, the system unlocked the work task list for the day. The data analysis results of the previous day also made the system automatically adjust the attention weight of "calibration link" and fine-tune the global deviation threshold for collective learning from 0.52% to 0.50%, achieving a flywheel-style progressive optimization.

[0047] Embodiment Two: Triggering and Execution of "National Correction Day" This example describes the system's reinforcement intervention when operational consistency experiences significant fluctuations. In a task of pasting the cabin skin of a new composite material, due to the special characteristics of the material, different degrees of pasting bubbles or edge lifting problems occurred in various assembly plants across the country, resulting in a significant increase in the loss of material scrapped on the same day, and the global operational consistency index D_global calculated by the central system decreased by 0.8% compared to the previous day, exceeding the preset line of 0.5%.

[0048] The system then triggers the "national rectification day" mechanism. On that day, all online workers cannot directly enter the production interface after starting the AR glasses, but are forced to be guided to a 40-minute special learning module. The core content of this module is three segments of 8K three-dimensional operation playback with in-depth analysis, showing the most representative three types of error operation paths from different factories and the resulting scrap results. For example, the first playback clearly shows that when applying a special adhesive, a worker's uneven trajectory speed caused microscopic differences in glue layer thickness, ultimately resulting in peeling in a vacuum environment.

[0049] During the playback, the system does not simply replay, but inserts corrective voice recorded in advance by process experts at key decision points, such as "pay attention, the scraper angle should be kept at 60 degrees and pushed at a uniform speed". After the learning is completed, the system generates a short personalized summary for each worker, pointing out the most relevant potential risk points in their historical operations to the case of the day, and plays an encouraging voice mentioning the worker's stable procedures in recent performance to boost confidence. The production task of the day continues after being delayed, and the system will specially strengthen the data monitoring of related processes. This centralized rectification immediately reduces the scrap rate of similar processes by about 35% the next day.

[0050] Example Three: Emergency Response of "Red Shock" Safety Mechanism This example demonstrates the ultimate physical intervention mechanism of the system when a major quality risk or economic loss occurs. In a task of assembling a certain type of satellite precision gyroscope support in batches, due to a micron-level size deviation in a batch of parts that was not discovered in advance, the workstations using this batch of parts across the country continuously experienced abnormalities during post-assembly testing. The "national daily total scrap amount" index monitored in real-time by the central system sharply increased around 3 pm, and at 4:15 pm, the cumulative value broke through the limit threshold of 50 million yuan RMB.

[0051] The “sky fortress system” automatically triggers the “red shock” protocol. Instructions are sent through a separate secure control link to all production workshops nationwide involved in the assembly of spacecraft. At 4:16, all workstations in the relevant area, including assembly tables, test tables, lighting, and all non-critical power supplies except security monitoring, are simultaneously remotely shut down and physically disconnected for 30 minutes. The entire power-off process is executed directly by hardware relays, without passing through the software system, ensuring absolute response.

[0052] During this period, the AR glasses rely on backup batteries to maintain basic communication and display a unified red warning interface to workers, indicating that the system has initiated a global safety pause due to a major quality risk, and all personnel are required to remain in place. The factory management layer simultaneously receives system alerts and immediately organizes technical experts to conduct emergency troubleshooting of the root cause of the problem - batch part issues. After 30 minutes, the power is automatically restored, and the system pushes temporary process change instructions confirmed by experts, guiding workers to conduct one-by-one inspection and replacement of assembled components. This mechanism forcibly interrupts continuous production that could lead to larger-scale losses, preventing the problem from spreading indefinitely.

[0053] Example Four: Positive Incentives and the Birth of “Digital Artisan Tutors” This example demonstrates how the system identifies excellent individuals through data and transforms them into resources to improve overall levels. Worker Wang, a master craftsman, has been responsible for cable laying and termination for a certain type of rocket for a month. The system continuously tracks his operation data and finds that not only is his success rate high, but his operation path, gesture stability, and work rhythm are significantly better than the national average. In seven consecutive days of data analysis, his operation data is closest to the “master template,” and he ranks among the top three in the same field nationwide.

[0054] According to preset rules, the system automatically generates and sends two notifications on the eighth day: one to Wang's AR glasses and personal terminal, informing him that he has been certified as a “digital artisan tutor” by the system, and his salary will increase by 50% starting next month, and he will be responsible for guidance tasks in future work; the other to the management system of the factory, suggesting that he be involved in new employee training or difficult process research. After becoming a tutor, some of Wang's typical operation sequences are anonymized and desensitized by the system, extracting core skill points such as “accurate gesture control of cable bending radius” as one of the reference data for optimizing the “master template,” and as positive material for subsequent “rectification days.”

[0055] Meanwhile, as Master Wang's children have been in the company's infant care center for a long time, the system has been continuously adjusting his deviation weight downward according to the rules when calculating the impact of his individual operation data on the overall indicators, reflecting the positive impact of welfare policies on employee stability. This "reduced weight" is not a reduction in requirements, but rather a reduction in the impact of data that may cause accidental fluctuations due to family factors on the overall model during the system's macro-deviation attribution analysis, allowing the system to optimize and focus more on universal process issues. Master Wang's case forms a virtuous cycle of "high-stability operation producing low-deviation data -> receiving honors and material incentives -> his experience feeding back to the system model -> further reducing overall deviation."

[0056] Example Five: Cross-regional security isolation in deep space assembly scenarios This example extends the application of the method in extreme environments far from Earth. The "Pioneer No. 1" assembly workshop on Mars is responsible for producing simple living cabin components using in-situ resources. The workshop is equipped with an augmented reality tooling system compatible with the system, and daily work data is transmitted back to the central processing system on Earth for comparison through a long-delay communication link.

[0057] During a structural beam welding operation on the surface of Mars, due to the different gravity of Mars and the special local dust environment, Mars workers gradually developed a set of welding parameter adjustment habits slightly different from the original template on Earth. In continuous multi-day federated learning data aggregation, the central system found that the operation data characteristics transmitted by the Mars factory were continuously expanding in the "distance" from the main model on Earth in the latent space mapping, and exceeded the 3% preset safety threshold after a week.

[0058] The system determines that this difference may mean that the Mars environment has led to an operation variation that is not fully understood by Earth, and that continuing to strictly follow the Earth template may pose unknown risks. In response, the system automatically triggers the deep space safety protocol. After the instructions are confirmed, they are sent to the local control system of the Mars factory through an independent channel. The control system then starts the "hard isolation" program: first, it forces all workstations to stop working and places the equipment in a safe state; then, it cuts off the power to all non-life support systems in the assembly area for a Martian day. At the same time, the system sends a high-priority alert to the management and technical teams on Earth and Mars, along with a detailed analysis report of the differences, requiring joint consultation between engineers on both sides to re-evaluate and confirm the welding process specifications suitable for the Mars environment. This process ensures that in the deep space environment where communication delay is huge and real-time intervention is not possible, production safety can be fundamentally guaranteed through preset rules based on data.

[0059] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for optimizing the digital craftsman flywheel in aerospace assembly based on deviation feedback, characterized in that, Includes the following steps that are performed daily on a recurring basis: (1) Collect real-time multimodal deviation data of aerospace assembly workers across the entire chain using no less than 800 AR glasses nationwide, including cognitive load, emotional value function, intention bias, action error, process results, scrap rate and psychological breakdown events; (2) The deviation data, along with emotion and physiological labels, are transmitted back to the micro-world model of the Central Aerospace Assembly in real time; (3) The micro-world model adopts the Yann LeCun joint embedding prediction architecture to calculate the global energy function distance D_global between the intention of the national workers group on the day and the master template in the latent space; (4) When D_global rises above 0.5%, the Tian Shou system will automatically trigger the "National Correction Day": all AR glasses will be forced to push the Top 10 high-value error correction cases of the day and generate personalized encouraging voice messages with optimized emotional value functions; (5) Complete the national data federation learning before 08:00 the next day, and update the micro-world model parameters, emotional value function weights, cognitive load thresholds, Tian Shou brake thresholds, infant and toddler care reduction ratios and golden idea reward coefficients. (6) All original multimodal deviation data are physically destroyed immediately after the local AR glasses complete the latent space encoding. When the original data is detected to be illegally leaving the country, the main control chip fuse of the glasses is burned out through an independent hardware relay.

2. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: The "National Correction Day" mandates that all online workers watch 8K holographic replays of the three most serious waste cases of the day, and generate master-level voice debriefings such as "If you had done it this way, it wouldn't have been a waste" using an emotional value function.

3. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: When the total amount of waste generated nationwide in a single day exceeds 50 million yuan, the "red shock" mechanism of the Tian Shou system will be automatically triggered, and all assembly stations nationwide will be physically powered off for 30 minutes simultaneously.

4. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: The federated learning process involves aggregating data from no fewer than 800 AR glasses nationwide between 23:59 and 00:30 daily, and pushing optimized parameters to all online devices before 07:30 the following day.

5. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: When a worker's deviation contribution ranks in the top 3 nationwide for 7 consecutive days, they will be automatically promoted to "Digital Craftsman Mentor," with a 50% salary increase the following month and priority given to mentoring apprentices.

6. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: When workers' children are at the company's childcare center, their daily deviation weight is automatically reduced by 70% to implement positive incentives.

7. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: The system supports deep space assembly scenarios. When the potential space deviation between the Mars factory and the Earth micro-world model exceeds 3%, all power to the Mars workstation will be automatically cut off.

8. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: The micro-world model, through daily federated learning iterations, reduces the national total scrap rate at a rate of 1.3%-2.1% per day, and achieves a success rate of no less than 99.9% after 6 months.

9. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback as described in claim 1, characterized in that: The Skyguard system is deeply integrated with infant and toddler care and the "golden idea" reward mechanism, and optimizes the worker's emotional value function in real time by adjusting welfare policies.

10. The aerospace assembly digital craftsman flywheel optimization method based on deviation feedback according to claim 1, characterized in that: The original data destruction mechanism uses hardware-level fuse technology to ensure that the original EEG data never leaves the AR glasses terminal.

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