CN-122020355-A - Method and system for constructing personal digital consciousness mirror image and realizing cross-generation intelligent inheritance

CN122020355ACN 122020355 ACN122020355 ACN 122020355ACN-122020355-A

Abstract

The invention provides a method and a system for constructing a personal digital consciousness mirror image and realizing cross-generation intelligent inheritance, which solve the problems of cross-generation knowledge inheritance and the like, and comprise the following steps of S1, multi-mode data acquisition and double-track recording; S2, constructing a personalized digital behavior cognitive model, S3, packaging the model and accessing the model conditionally, and S4, activating and interacting the multifunctional mode. The invention has the advantages of cross-generation knowledge inheritance, good digital community treatment effect and the like.

Inventors

MEI HAIQING
Mei Chenguanyan
Mei Chenguanxu
CHEN XUANFENG
Mei Chenguaner

Assignees

安徽海宣健康科技有限公司

Dates

Publication Date: 20260512
Application Date: 20251222
Priority Date: 20251106

Claims (10)

1. A method for constructing a personal digital conscious mirror image and implementing cross-generation intelligent inheritance, comprising the steps of: S1, multi-mode data acquisition and double-track recording, wherein multi-mode data in a life cycle of a user are acquired based on a preset multi-dimensional mental state evaluation framework, and main and auxiliary double-track recording is carried out on active reserved data; S2, constructing a personalized digital behavior cognitive model, processing and training the multi-mode data by using a computable algorithm framework, generating a personalized behavior decision model capable of simulating decision logic and behavior tendency of a user, and constructing a parameterized decision case knowledge graph; S3, model encapsulation and conditional access, namely encapsulating the trained model and related data into an independently-callable digital avatar asset package, and configuring hierarchical access rights for the digital avatar asset package; And S4, activating and interacting the multifunctional mode, and activating multiple functional interfaces of the model by an authorized user or a inheritor when preset conditions are met, so as to realize interaction of decision simulation, auxiliary deduction and case inquiry.
2. A method for creating a personal digital conscious mirror and implementing cross-generation intelligent inheritance in accordance with claim 1, wherein said step S1 comprises the steps of: s11, defining core cognitive dimension parameters based on a preset multidimensional mental state evaluation framework, and constructing a computable algorithm framework; s12, collecting multi-mode data generated by a user in the life cycle of the user, wherein the multi-mode data comprises explicit behavior and decision data and personal historical statement and situation feedback data actively provided by the user; And S13, for the active reserved data, performing double-track processing, namely completely storing the active reserved data in an original format as a main track, simultaneously performing content analysis and mapping the active reserved data to the evaluation frame, and generating parameter coordinate data as an auxiliary track.
3. A method for creating a personal digital conscious mirror and implementing cross-generation intelligent inheritance in accordance with claim 1, wherein said step S2 comprises the steps of: S21, storing the acquired multi-mode data in an associated mode to form a personal digital consciousness data set, and automatically generating a structured consciousness mirror image element data index; s22, processing and training the data set by using the computable algorithm framework, and performing personalized fine adjustment through a language model to generate a personalized behavior decision model; S23, constructing a case library associated with the cognitive dimension parameters, and carrying out parameterization indexing on the decision case with positive results verified in the user history or the decision scheme with correct results verified to be failed and verified by the multiple disks to form a structured decision case knowledge graph.
4. The method for constructing a personal digital conscious mirror and implementing cross-generation intelligent inheritance according to claim 1, wherein the functional modes in the step S4 include: A history backtracking mode, which is to restore the original decision process and state of a user in a specific history decision scene according to the stored original data main track and decision data; The interactive mode is simulated, namely the personalized behavior decision model is called, and the thinking process and the decision path are reproduced according to the historical data of the user; and (3) a decision assistance mode, namely calling a deduction interface of the model, and generating a proposal scheme which accords with the historical decision preference and thinking mode of the new problem faced by the user.
5. The method for constructing a personal digital conscious mirror and implementing cross-generation intelligent inheritance according to claim 4, wherein the functional modes in the step S4 include: a case library mode, namely inquiring the decision case knowledge graph, and providing a validated positive decision case similar to the current situation as a reference; And presetting an information triggering mode, namely delivering the data to the appointed object as it is when the specific condition preset by the user for actively preserving the data is met.
6. The method for constructing a personal digital conscious mirror image and implementing cross-generation intelligent inheritance of claim 1, the method is characterized in that the following substitution inheritance and activation mechanism is adopted: S41, packaging the personalized behavior decision model, the decision case knowledge graph and the related digital rights and interests together and legally defining the personalized behavior decision model, the decision case knowledge graph and the related digital rights and interests as inheritable digital avatar asset packages; S42, when the inheritance condition is triggered, the system automatically executes a digital asset transfer contract to transfer the management authority of the asset pack to a designated inheritor.
7. The method for constructing a personal digital conscious mirror and implementing cross-generation intelligent inheritance according to claim 6, wherein the method adopts the following authority activation mechanism: S43, after the inheritance person passes identity authentication, a history backtracking mode and a case library mode can be activated; S44, the inheritor further activates the simulation interaction mode and the decision auxiliary mode, and the acquired interaction authority is more complete as the matching degree is higher through decision feature matching degree algorithm verification based on comparison of the self behavior data and the creator model.
8. A system for constructing a personal digital conscious mirror and implementing cross-generation intelligent inheritance using the method of any of the preceding claims 1-7, characterized by comprising a data input layer, said data input layer being connected to an output and application layer through a core processing layer, said output and application layer having underlying support.
9. A method of generating an adaptive digital community rule constructed based on the method of any one of claims 1-7, comprising the steps of: S51, acquiring a personalized behavior decision model of at least one core user in a community; S52, analyzing commonality behavior criteria and decision preferences internalized by one or more models; S53, automatically or semi-automatically generating a set of digital community rule draft based on criteria and preferences, wherein the digital community rule draft comprises at least one or more of digital identity rights and interests allocation rules, digital asset contract execution rules, community contribution measurement and rewarding rules and community dispute mediation rules; s54, when a rule related to resource allocation is generated, a dynamic balance optimization algorithm based on a balance thought in the preset framework is called to simulate and optimize a draft so as to prevent excessive concentration of resources; and S55, deploying the finally determined rule in the distributed system in the form of a machine-readable and executable intelligent contract.
10. A digital community rule's intergenerational collaborative governance system for implementing the continuous evolution of rules of claim 9, comprising: the memory is used for storing a personalized behavior decision model of the initiating user and an initial digital community rule derived from the personalized behavior decision model; The rule dynamic adjustment engine is configured to call a deduction interface for creating a user model when a rule revision proposal is initiated by a later user community, evaluate feasibility and influence of the proposal based on built-in decision logic, and generate an evaluation report; The community consensus module is configured to assist contemporary community members to agree through a predefined voting mechanism based on the assessment report and complete iterative updating of rule intelligence contracts deployed on the distributed system.

Description

Method and system for constructing personal digital consciousness mirror image and realizing cross-generation intelligent inheritance Technical Field The invention belongs to the technical field of artificial intelligence, and particularly relates to a method and a system for constructing a personal digital consciousness mirror image and realizing cross-generation intelligent inheritance. Background In the prior art, digital heritage management is mostly focused on transfer and inheritance of static resources such as financial assets, digital files and the like. In the field of artificial intelligence, there are interactive applications such as chat robots, but the nature is a generic model trained based on public data, or advisors that aim to provide optimization suggestions to users. The method has the core defects that the true mind and life processes of a specific individual cannot be recorded and reproduced truly, stereoscopically and without judgment, the original interaction information is not ensured, an effective indexing mechanism is not available, the formed digital file is difficult to effectively access and understand, and the user cannot be supported to actively and conditionally reserve specific information, such as accurate inheritance of privacy and secret words. In order to solve the above problems, the present application provides a method and system for implementing holographic data recording, intelligent index generation and diversified directional inheritance to construct a personal digital consciousness mirror image and implementing cross-generation intelligent inheritance. Disclosure of Invention The invention aims to solve the problems and provides a method and a system which are reasonable in design and capable of effectively realizing cross-generation intelligent inheritance. Another object of the present invention is to provide a method and system for implementing digital community management in view of the above problems. In order to achieve the purpose, the invention adopts the following technical scheme that the method for constructing the personal digital consciousness mirror image and realizing the cross-generation intelligent inheritance comprises the following steps: S1, multi-mode data acquisition and double-track recording, wherein multi-mode data in a life cycle of a user are acquired based on a preset multi-dimensional mental state evaluation framework, and main and auxiliary double-track recording is carried out on active reserved data; S2, constructing a personalized digital behavior cognitive model, processing and training the multi-mode data by using a computable algorithm framework, generating a personalized behavior decision model capable of simulating decision logic and behavior tendency of a user, and constructing a parameterized decision case knowledge graph; S3, model encapsulation and conditional access, namely encapsulating the trained model and related data into an independently-callable digital avatar asset package, and configuring hierarchical access rights for the digital avatar asset package; And S4, activating and interacting the multifunctional mode, and activating multiple functional interfaces of the model by an authorized user or a inheritor when preset conditions are met, so as to realize interaction of decision simulation, auxiliary deduction and case inquiry. In the above method for constructing a personal digital conscious mirror image and implementing cross-generation intelligent inheritance, step S1 includes the following steps: s11, defining core cognitive dimension parameters based on a preset multidimensional mental state evaluation framework, and constructing a computable algorithm framework; s12, collecting multi-mode data generated by a user in the life cycle of the user, wherein the multi-mode data comprises explicit behavior and decision data and personal historical statement and situation feedback data actively provided by the user; And S13, for the active reserved data, performing double-track processing, namely completely storing the active reserved data in an original format as a main track, simultaneously performing content analysis and mapping the active reserved data to the evaluation frame, and generating parameter coordinate data as an auxiliary track. In the above method for constructing a personal digital conscious mirror image and implementing cross-generation intelligent inheritance, step S2 includes the following steps: S21, storing the acquired multi-mode data in an associated mode to form a personal digital consciousness data set, and automatically generating a structured consciousness mirror image element data index; s22, processing and training the data set by using the computable algorithm framework, and performing personalized fine adjustment through a language model to generate a personalized behavior decision model; S23, constructing a case library associated with the cognitive dimension parameters, and carrying