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CN-121981821-A - Expert process data-based space core process digital asset uplink right confirmation and transaction method

CN121981821ACN 121981821 ACN121981821 ACN 121981821ACN-121981821-A

Abstract

The invention discloses a method for determining and trading uplink of a digital asset in a space core process based on expert process data, which relates to the technical field of space manufacturing and digital asset, in particular to a method for determining and trading uplink of a digital asset in a space core process based on expert process data, wherein the method collects multi-mode process data of an expert in real time through wearable equipment, generates process fingerprints by utilizing a processing model based on a world model architecture, and judges rare digital asset by calculating unique scores and emotional value scores; the technological fingerprints of rare assets are encrypted in a homomorphic mode and are uplink to generate the NFT, ownership is completely attributed to specialists, transaction benefits are automatically distributed according to a preset proportion, and meanwhile, a hardware fusing protection mechanism for illegal operation is arranged. The invention realizes the safe right-confirming, asset transaction and value sharing of expert process data and effectively promotes the inheritance and innovation of aerospace core technology.

Inventors

  • ZHANG QINGQUAN

Assignees

  • 深圳复现范式科技有限公司

Dates

Publication Date
20260505
Application Date
20251209

Claims (10)

  1. 1. The method for determining and trading the uplink of the digital asset in the aerospace core procedure based on expert process data is characterized by comprising the following steps: (1) Acquiring multi-modal process data of an aerospace core process expert in real time through a wearable device, wherein the wearable device comprises XR glasses and intelligent gloves, and the multi-modal process data comprises electroencephalogram signals, eye movement data, voice data, force data, myoelectricity data, heart rate data and emotion value function tracks; (2) Inputting the multi-mode process data into a processing model to generate a process fingerprint, wherein the processing model is based on a world model architecture and adopts a joint embedded prediction method to generate a latent space representation; (3) Calculating a uniqueness score and a mood value score of the process fingerprint, the uniqueness score calculated based on similarity to a historical process fingerprint database, the mood value score calculated based on a combination of concentration, happiness, and frustration; (4) When the product of the uniqueness score and the emotion value score is larger than a preset threshold, judging as a rare digital asset, and triggering a protection mechanism, wherein original multi-modal process data are permanently reserved in the wearable equipment, and the process fingerprint is uploaded to a blockchain through homomorphic encryption; (5) Generating a digital asset nonvolatile token (NFT) after being up-linked, wherein 100% of ownership belongs to expert individuals, and carrying out transaction on a designated transaction platform; (6) The transaction benefits are automatically distributed according to a preset proportion, wherein 35% of the transaction benefits are distributed to expert individuals, 30% of the transaction benefits enter company funds, and 35% of the transaction benefits are used for a bonus pool; (7) When the illegal operation is detected, triggering a hardware fusing mechanism, destroying a main control chip of the wearable device, and broadcasting an alarm globally.
  2. 2. The method for determining and trading the uplink of the digital asset in the aerospace core process based on expert process data as set forth in claim 1, wherein the NFT of the digital asset is in the form of a visual "art star", and the surface texture is generated by rendering the emotion cost function trace in real time.
  3. 3. The method for determining and trading the digital asset uplink of the aerospace core process based on expert process data according to claim 1, wherein the trading platform automatically reduces the trading fee to 1% when the direct relatives of the expert are registered in the company hosting center.
  4. 4. The method for determining and trading the digital asset uplink of the aerospace core process based on expert process data as set forth in claim 1, wherein the support process inheritance lease mode is that a young technician can rent the process fingerprint in a wearable device for training after paying annual fees, and the lease period is up to 6 months.
  5. 5. The method for determining and trading the uplink of the digital asset in the aerospace core process based on expert process data according to claim 1, wherein when the number of times of trading of the same process fingerprint reaches 1000 times, the method automatically triggers a process seal instrument, and the expert enjoys share reddening of a company for life, wherein the reddening proportion is 1%.
  6. 6. The method for determining and trading the uplink of the digital asset in the aerospace core process based on expert process data as set forth in claim 1, wherein said original multi-modal process data is physically destroyed immediately after the process fingerprint is locally generated, and only the encrypted process fingerprint is retained.
  7. 7. The method for performing digital asset uplink validation and transaction based on expert process data as set forth in claim 1, wherein when a multi-modal process data attempt to cross-border transmission is detected, a hardware fusing mechanism is triggered immediately and a "state-run assets asset disclosure" alarm is broadcast.
  8. 8. The method for determining and trading the uplink of the digital asset in the aerospace core process based on expert process data as set forth in claim 1, wherein the processing model optimizes the compatibility of the state of the latent space by using an energy function for predicting the state of the process world after the action.
  9. 9. The method for determining and trading the uplink of the digital asset in the aerospace core process based on expert process data as set forth in claim 1, wherein the process fingerprint is a 128-dimensional latent space vector, and the generation process is based on a self-supervision joint embedded prediction architecture.
  10. 10. The method for determining and trading the uplink of the digital asset in the aerospace core process based on expert process data according to claim 1, wherein the blockchain is state-run assets blockchain, homomorphic encryption and quantum communication alternative protocols are integrated, and data master right and trade safety are ensured.

Description

Expert process data-based space core process digital asset uplink right confirmation and transaction method Technical Field The invention relates to the technical field of aerospace manufacturing and digital assets, in particular to a method for determining and trading uplink of a digital asset in a aerospace core process based on expert process data. Background In the field of aerospace manufacturing, the process technology of the core process is the most precious strategic asset of countries and enterprises, and is highly dependent on personal experience, intuitive judgment and precise operation skills of senior specialists. These technological knowledge often exists in the brain of an expert in a hidden form, is reflected in subtle hand feeling, in-situ decision making and intuitive response to complex working conditions, is traditionally inherited mainly by means of teaching heart or limited video recording through a teacher and a apprentice, and lacks systematic, standardized and digitized capturing and precipitating means. With the progressive retirement of older technology specialists, the aerospace industry is facing serious technological knowledge faults and risk of loss of skills. The prior art attempts to save the process by standard job program documentation, two-dimensional video recordings, or simple sensor recordings, but these methods suffer from fundamental drawbacks. They generally capture only explicit actions and results and cannot deeply capture multidimensional information such as intrinsic physiological states, cognitive load and mood changes of the expert when performing critical procedures, such as brain electrical activity, eye movement patterns, muscle microcurrents, applied precise forces and accompanying psychological mood trajectories. These multimodal data are precisely the core carriers of the process spirit, the absence of which results in a recorded process flow at the surface, and the inability to reproduce the very state of man-machine integration of the type of expert in dealing with special materials, extreme tolerances or emergency situations. Therefore, how to comprehensively, truly and nondestructively acquire the multi-mode process data of the expert in real working scenes in real time becomes a primary technical problem for breaking through the bottleneck of current knowledge inheritance. After data acquisition, how to perform efficient processing and feature extraction on massive, heterogeneous and high-dimensional multi-mode data to form a digital signature capable of uniquely identifying an expert's unique process style is another major challenge. The existing data analysis model is often designed aiming at a single mode, lacks the capability of carrying out fusion analysis and joint characterization on multi-source heterogeneous time sequence data, and is difficult to extract stable and generalizable process characteristics with distinction from noisy physiological signals. This results in the collected data not being efficiently converted into structured digital assets, which cannot be quantitatively evaluated and compared. In addition, in the digitizing process, the issue of the right and the safety of the process data is particularly prominent. The aerospace core technology relates to national security and core confidentiality, and the traditional data storage and transmission modes have great risks of being copied, tampered or leaked. Centralized databases are easily targeted for attack, and existing blockchain applications often suffer from deficiencies in privacy protection, data hosting and compliance when dealing with such high value, high sensitivity data, lacking solutions that are deeply fused with national security standards. The intellectual property of expert individuals is not fully guaranteed, the created process data value is easily occupied by institutions in a gratuitous way, and the individuals lack direct economic incentives of continuous contribution and knowledge sharing, so that the problems of knowledge sealing and insufficient inheritance dynamics are further aggravated. In terms of value-flow and legacy applications of process data, existing models also appear to be inflexible and inefficient. The training of young technicians relies primarily on traditional post-following learning, is inefficient, and is difficult to access to the core recipe of the top expert. Technological knowledge is an asset, lacking in a public, transparent, trusted trading market, whose value cannot be discovered and honored through market mechanisms. Even with preliminary digital asset attempts, the form is often single, visual and vivid presentation modes are lacking, and the interests and the sense of identity of participants are difficult to excite. More importantly, there is a lack of a complete system of data collection, processing, assessment, validation, transaction, inheritance, and full life cycle security management throughout. Especially on the dat