Method and system for constructing personal digital awareness mirror image and realizing cross-generation intelligent inheritance

Abstract

The invention provides a method and a system for constructing a personal digital awareness mirror image and realizing cross-generation intelligent inheritance, and solves the problems of cross-generation knowledge inheritance and the like, and the method comprises the following steps: S1, multi-modal data acquisition and double-track recording; s2, constructing a personalized digital behavior cognitive model; s3, model packaging and conditional access are carried out; and S4, multifunctional mode activation and interaction. The method has the advantages of cross-generation knowledge inheritance, good digital community treatment effect and the like.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically relating to a method and system for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance. Background Technology

[0002] In existing technologies, the management of digital assets largely focuses on the transfer and inheritance of static resources such as financial assets and digital files. While interactive applications like chatbots exist in the field of artificial intelligence, their essence lies in general models trained on public data, or advisors designed to provide users with optimization suggestions. Their core shortcomings are: the inability to truthfully, comprehensively, and impartially record and reproduce the true nature, wisdom, and life journey of a specific individual; a lack of preservation of original interactive information; a lack of effective indexing mechanisms, making the resulting digital archives difficult to access and understand; and an inability to support users' proactive and conditional retention of specific information, such as the precise inheritance of privacy and secret messages.

[0003] To address the aforementioned issues, this application proposes a method and system for constructing a personal digital consciousness mirror and achieving intergenerational wisdom inheritance by realizing holographic data recording, intelligent index generation, and diversified targeted inheritance. Summary of the Invention

[0004] The purpose of this invention is to address the above-mentioned problems by providing a reasonably designed method and system that can effectively realize the intergenerational transmission of wisdom.

[0005] Another objective of this invention is to provide a method and system for realizing digital community governance in response to the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance, comprising the following steps: S1: Multimodal data acquisition and dual-track recording. Based on a preset multidimensional mental state assessment framework, it collects multimodal data during the user's lifecycle and records the actively retained data in a primary and secondary dual-track manner. S2: Construction of a personalized digital behavior cognitive model. The multimodal data is processed and trained using a computable algorithm framework to generate a personalized behavior decision model that can simulate the user's decision-making logic and behavioral tendencies, and a parameterized decision case knowledge graph is constructed. S3: Model encapsulation and conditional access, which encapsulates the trained model and related data into a digital identity asset package that can be called independently, and configures hierarchical access permissions for it; S4: Multifunctional mode activation and interaction. When preset conditions are met, authorized users or successors activate multiple functional interfaces of the model to realize interactive functions such as decision simulation, auxiliary inference, and case query.

[0007] In the above-mentioned method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance, step S1 includes the following steps: S11: Based on a pre-defined multi-dimensional mental state assessment framework, define core cognitive dimension parameters and construct a computable algorithm framework; S12: Collect multimodal data generated by users during their lifecycle, including explicit behavioral and decision-making data, as well as personal historical statements and contextual feedback data proactively provided by users; S13: For actively retained data, a dual-track processing method is implemented: the main track is to save the data in its original format, while the content is analyzed and mapped to the evaluation framework to generate parameter coordinate data as the auxiliary track.

[0008] In the above-mentioned method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance, step S2 includes the following steps: S21: The collected multimodal data is associated and stored to form a personal digital consciousness dataset, and a structured consciousness mirror metadata index is automatically generated; S22: The dataset is processed and trained using the aforementioned computable algorithm framework, and personalized fine-tuning is performed through a language model to generate a personalized behavior decision model; S23: Construct a case library associated with the cognitive dimension parameters, and parameterize the decision cases in the user's history that have been verified as positive or as negative but have been verified as correct after review, to form a structured decision case knowledge graph.

[0009] In the aforementioned method for constructing a personal digital consciousness mirror and achieving intergenerational wisdom inheritance, the functional modes in step S4 include: Historical backtracking mode: Based on the stored original data master track and decision data, it restores the user's original decision-making process and state in a specific historical decision-making scenario; Simulated Interaction Mode: Invokes the personalized behavioral decision-making model to reproduce the user's thought process and decision-making path based on historical user data; Decision Support Mode: This mode calls the model's deduction interface to generate suggested solutions that align with the user's historical decision-making preferences and thinking patterns for the new problems the user is currently facing.

[0010] In the aforementioned method for constructing a personal digital consciousness mirror and achieving intergenerational wisdom inheritance, the functional modes in step S4 include: Case Library Mode: Query the decision case knowledge graph to provide verified positive decision cases similar to the current situation as a reference; Preset information trigger mode: When specific conditions preset by the user for actively retaining data are met, the data is delivered to the specified object as is.

[0011] In the aforementioned method for constructing a personal digital consciousness mirror and achieving intergenerational wisdom transmission, the method employs the following intergenerational transmission and activation mechanism: S41: The personalized behavior decision-making model, decision-making case knowledge graph, and related digital rights are jointly packaged and legally defined as an inheritable digital identity asset package; S42: When the inheritance conditions are triggered, the system automatically executes the digital asset transfer contract and transfers the management rights of the asset package to the designated heir.

[0012] In the aforementioned method for constructing a personal digital consciousness mirror and achieving intergenerational wisdom inheritance, the method employs the following permission activation mechanism: S43: After the heir passes identity verification, the history retrospection mode and case library mode can be activated; S44: The successor further activates the simulation interaction mode and decision support mode, and verifies the decision feature matching degree algorithm based on the comparison between his own behavioral data and the founder's model. The higher the matching degree, the more complete the interaction permissions are obtained.

[0013] A system for constructing a personal digital consciousness mirror and realizing the intergenerational inheritance of wisdom includes a data input layer, which is connected to the output and application layers through a core processing layer, and the output and application layers have underlying support.

[0014] An adaptive digital community rule generation method includes the following steps: S51: Obtain a personalized behavioral decision-making model for at least one core user in the community; S52: Analyze the common behavioral norms and decision preferences internalized by one or more models; S53: Based on criteria and preferences, automatically or semi-automatically generate a draft set of digital community rules; the draft digital community rules include at least one or more combinations of rules for the allocation of digital identity rights, rules for the enforcement of digital asset contracts, rules for measuring and rewarding community contributions, and rules for mediating community disputes; S54: When generating rules involving resource allocation, a dynamic equilibrium optimization algorithm based on the inherent balancing idea of ​​the preset framework is invoked to simulate and optimize the draft to prevent excessive concentration of resources; S55: Deploy the finalized rules in a distributed system as machine-readable, executable smart contracts.

[0015] An intergenerational collaborative governance system for digital community rules, comprising: Storage: Used to store the personalized behavioral decision-making models of the founding users and the initial digital community rules derived from them; The rule dynamic adjustment engine is configured to call the deduction interface of the founding user model when the user community initiates a rule revision proposal, and to evaluate the feasibility and impact of the proposal based on its internalized decision-making logic, and generate an evaluation report. Community consensus module: It is configured to assist contemporary community members in reaching a consensus through a predefined voting mechanism based on the evaluation report, and to complete the iterative update of the rule smart contract deployed on the distributed system.

[0016] Compared with existing technologies, the advantages of this invention are as follows: It enables the intergenerational inheritance of dynamic cognitive abilities, breaking through the traditional static storage model of digital assets and achieving continuous inheritance of user decision-making models, behavioral paradigms, and value judgment systems; it constructs the basic architecture of a digital social system, using a technical solution that generates community self-governance rules based on individual user behavior data, providing complete technical support for the formation of digital communities with self-optimization capabilities, cultural adaptability, and continuous evolution, laying the foundation for the basic framework of a digital social governance system; it establishes a distributed consensus governance mechanism: rule generation originates from user group consensus data, rather than unilateral decisions by centralized institutions, which is more in line with the modern governance concept of technological ethics and equal digital rights; the digital identities and rules generated by the system can be bound to physical products with digital identity identifiers, allowing users to obtain access to the digital community by purchasing or holding such products, thus achieving a closed loop of online and offline traffic; when used in specific offline experience spaces, it provides users with an immersive data collection and mirror interaction experience. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a flowchart of the personal digital consciousness mirror construction method of the present invention; Figure 3 This is a flowchart of the endogenous digital society rule generation and deployment process of the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0019] Example 1 like Figure 1-3 As shown, this embodiment demonstrates a highly faithful transformation from a philosophical model to personalized digital consciousness, specifically including the following steps: S1: Multimodal data acquisition and processing, collecting diverse user data through a dedicated client. It employs proactive narrative to guide users in inputting life stories on topics such as career and emotions; scenario simulation: recording user interactions in specific philosophical contexts, such as Zhuangzi's butterfly dream; and real-world decision mapping: users voluntarily import anonymized data on significant real-world decisions. All data, after anonymization and cleaning, forms a standardized dataset for model training.

[0020] S2: Algorithmic Transformation of Philosophical Models. This involves converting the registered philosophical ideas of *Dao Shu·Wenbo Xing Tu* into a computable algorithmic framework. Core steps include: defining quantifiable dimensions, such as establishing a "Free and Easy Wandering - Human World" coordinate axis to quantify the user's tendency towards spiritual freedom, and establishing a "Qi Wu - Separate" coordinate axis to quantify their tendency towards unified thinking, collectively forming a high-dimensional philosophical state space; and establishing an algorithmic framework, based on the above dimensions, constructing an algorithmic model capable of evaluating and deducing the user's mental state. This algorithmic framework is not only used to construct personal models, but its inherent balancing principles, such as reducing excess and supplementing deficiency, directly serve as the core parameters and optimization objectives of the dynamic equilibrium optimization algorithm, making the generated digital community rules inherently fair and adaptive.

[0021] S3: Mirror training and functional encapsulation.

[0022] Model training: A large language model LLM is used as the base, and user data is used for instruction fine-tuning and reinforcement learning. The goal is to enable the model to internalize the user's decision-making logic and value preferences.

[0023] Functional encapsulation: The trained image encapsulation has three callable functional modes: Image mode, which reproduces the user's own way of thinking and tone; Guidance mode, which provides heuristic guidance to future generations based on its own logic; and Retention mode, which delivers preset last words or information when triggered by specific conditions.

[0024] Example 2 This embodiment realizes the automated derivation from individual consciousness mirroring to community governance rules, including the following steps: S1: Consensus Value Extraction and Rule Draft Generation: The system analyzes the digital consciousness mirror of one or more founders to extract consensus-based value propositions, such as fairness and innovation. Based on the extracted values, the system automatically calls the rule template library to generate rule drafts such as the "Digital Identity Charter," "Digital Asset Co-ownership Charter," and "Community Dispute Mediation Convention."

[0025] S2: Optimization of the Heavenly Equilibrium Algorithm: When generating rules for resource allocation, rewards and punishments, the Heavenly Equilibrium Algorithm is introduced as a core parameter. This algorithm simulates the flow of community resources and dynamically adjusts the rule parameters to achieve a balance effect of reducing surpluses and supplementing deficiencies, thus preventing the excessive concentration of power and resources.

[0026] S3: Rule Deployment on the Chain: The finalized rules are transformed into precise smart contract code; the smart contracts are deployed on a consortium blockchain jointly maintained by multiple physical nodes (such as the Hefei headquarters and Xuanzhou base) to ensure the transparency, immutability and automatic execution of the rules.

[0027] Example 3 This embodiment ensures the orderly evolution of digital society rules during generational transitions, including the following steps: S1: Rule Evolution Engine Workflow: When a rule revision proposal reaches the seconding threshold, the engine starts; it invokes the guidance mode of the founder's digital consciousness mirror to simulate and deduce the proposal; the mirror generates a "Logical Compliance Review Report" to assess the degree of conformity between the proposal and the founder's core values, and publishes it to the community as a decision-making reference.

[0028] S2: Consensus Mechanism Module Workflow: It adopts a reputation-weighted voting mechanism, where members' voting weight is linked to their historical reputation and contributions; the contemporary community votes after referring to the opinions of the founders, and if passed, the module automatically generates and upgrades the smart contract to complete the rule iteration.

[0029] Example 4 This embodiment achieves intergenerational inheritance of digital personality from both legal and technical perspectives, including the following steps: S1: Asset Package Definition and Inheritance Trigger: Define an individual's digital consciousness mirror and its associated digital rights as an inheritable digital personality asset package in law; trigger the digital will smart contract through on-chain evidence, such as a death certificate or a preset time.

[0030] S2: Automatic transfer of permissions and hierarchical activation: The smart contract automatically verifies the identity of the successor and transfers management permissions.

[0031] The successor's permissions are tiered, including: basic permissions, which allow for mirror-mode dialogue with the mirror image at any time; and advanced permissions, which require authentication through a thought similarity algorithm. The higher the matching degree, the more fully the guidance mode can be activated, ensuring that wisdom is passed on to the descendants who can best understand its spirit.

[0032] In summary, the principle of this embodiment lies in: by parameterizing and algorithmizing a structured cognitive behavioral assessment model, a multi-dimensional feature space capable of quantitatively analyzing user decision-making logic and value orientation is constructed. Based on this, a basic artificial intelligence model is personalized-trained using long-term behavioral records and key choice data collected from users, thereby generating a personal cognitive dynamic model that can highly reproduce individual decision-making characteristics and preference patterns, possessing three functional states: simulation, consultation, and archiving. Furthermore, the system automatically aggregates the common value orientations contained in one or more such cognitive models and introduces a dynamic equilibrium algorithm, with maintaining system stability as its core, to iteratively optimize the rule clauses, thereby deriving... A set of self-generating community norms, encompassing identity authentication, asset ownership, and behavioral guidelines, is created and deployed on a distributed network in the form of smart contracts. Ultimately, a long-term governance system consisting of a rule iteration engine and a collaborative decision-making module is used to adaptively evaluate modification suggestions by invoking the cognitive model of the initial creator when rules are updated. This system guides current community members to reach a consensus based on a contribution-weighted voting mechanism, thereby achieving the continuity and self-governance of community rules in intergenerational transmission. At the same time, by defining the cognitive model and its associated assets as inheritable digital asset packages and supplementing them with a permission-based hierarchical activation mechanism based on decision-making pattern matching, a systematic upgrade from data inheritance to behavioral logic inheritance is completed.

[0033] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0034] Although this paper makes extensive use of terms such as multimodal data acquisition and intergenerational knowledge transfer, the possibility of using other terms is not excluded. These terms are used merely to facilitate the description and explanation of the essence of this invention; interpreting them as any additional limitation would contradict the spirit of this invention.

Claims

1. A method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance, characterized in that, Includes the following steps: S1: Multimodal data acquisition and dual-track recording. Based on a preset multidimensional mental state assessment framework, it collects multimodal data during the user's lifecycle and records the actively retained data in a primary and secondary dual-track manner. S2: Construction of a personalized digital behavior cognitive model. The multimodal data is processed and trained using a computable algorithm framework to generate a personalized behavior decision model that can simulate the user's decision-making logic and behavioral tendencies, and a parameterized decision case knowledge graph is constructed. S3: Model encapsulation and conditional access, which encapsulates the trained model and related data into a digital identity asset package that can be called independently, and configures hierarchical access permissions for it; S4: Multifunctional mode activation and interaction. When preset conditions are met, authorized users or successors activate multiple functional interfaces of the model to realize interactive functions such as decision simulation, auxiliary inference, and case query.

2. The method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance according to claim 1, characterized in that, Step S1 includes the following steps: S11: Based on a pre-defined multi-dimensional mental state assessment framework, define core cognitive dimension parameters and construct a computable algorithm framework; S12: Collect multimodal data generated by users during their lifecycle, including explicit behavioral and decision-making data, as well as personal historical statements and contextual feedback data proactively provided by users; S13: For actively retained data, a dual-track processing method is implemented: the main track is to save the data in its original format, while the content is analyzed and mapped to the evaluation framework to generate parameter coordinate data as the auxiliary track.

3. The method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance according to claim 1, characterized in that, Step S2 includes the following steps: S21: The collected multimodal data is associated and stored to form a personal digital consciousness dataset, and a structured consciousness mirror metadata index is automatically generated; S22: The dataset is processed and trained using the aforementioned computable algorithm framework, and personalized fine-tuning is performed through a language model to generate a personalized behavior decision model; S23: Construct a case library associated with the cognitive dimension parameters, and parameterize the decision cases in the user's history that have been verified as positive or as negative but have been verified as correct after review, to form a structured decision case knowledge graph.

4. The method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance according to claim 1, characterized in that, The functional modes in step S4 include: Historical backtracking mode: Based on the stored original data master track and decision data, it restores the user's original decision-making process and state in a specific historical decision-making scenario; Simulated Interaction Mode: Invokes the personalized behavioral decision-making model to reproduce the user's thought process and decision-making path based on historical user data; Decision Support Mode: This mode calls the model's deduction interface to generate suggested solutions that align with the user's historical decision-making preferences and thinking patterns for the new problems the user is currently facing.

5. The method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance according to claim 4, characterized in that, The functional modes in step S4 include: Case Library Mode: Query the decision case knowledge graph to provide verified positive decision cases similar to the current situation as a reference; Preset information trigger mode: When specific conditions preset by the user for actively retaining data are met, the data is delivered to the specified object as is.

6. The method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance according to claim 1, characterized in that, The method described employs the following intergenerational inheritance and activation mechanism: S41: The personalized behavior decision-making model, decision-making case knowledge graph, and related digital rights are jointly packaged and legally defined as an inheritable digital identity asset package; S42: When the inheritance conditions are triggered, the system automatically executes the digital asset transfer contract and transfers the management rights of the asset package to the designated heir.

7. The method for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance according to claim 6, characterized in that, The method described uses the following permission activation mechanism: S43: After the heir passes identity verification, the history retrospection mode and case library mode can be activated; S44: The successor further activates the simulation interaction mode and decision support mode, and verifies the decision feature matching degree algorithm based on the comparison between his own behavioral data and the founder's model. The higher the matching degree, the more complete the interaction permissions are obtained.

8. A system for constructing a personal digital consciousness mirror and realizing intergenerational wisdom inheritance, comprising the method described in any one of claims 1-7, characterized in that, It includes a data input layer, which is connected to the output and application layers through the core processing layer, and the output and application layers have underlying support.

9. An adaptive digital community rule generation method based on the method described in any one of claims 1-7, characterized in that, Includes the following steps: S51: Obtain a personalized behavioral decision-making model for at least one core user in the community; S52: Analyze the common behavioral norms and decision preferences internalized by one or more models; S53: Based on criteria and preferences, automatically or semi-automatically generate a draft set of digital community rules; the draft digital community rules include at least one or more combinations of rules for the allocation of digital identity rights, rules for the enforcement of digital asset contracts, rules for measuring and rewarding community contributions, and rules for mediating community disputes; S54: When generating rules involving resource allocation, a dynamic equilibrium optimization algorithm based on the inherent balancing idea of ​​the preset framework is invoked to simulate and optimize the draft to prevent excessive concentration of resources; S55: Deploy the finalized rules in a distributed system as machine-readable, executable smart contracts.

10. A generational collaborative governance system for digital community rules, used to achieve the continuous evolution of the rules as described in claim 9, characterized in that, include: Storage: Used to store the personalized behavioral decision-making models of the founding users and the initial digital community rules derived from them; The rule dynamic adjustment engine is configured to call the deduction interface of the founding user model when the user community initiates a rule revision proposal, and to evaluate the feasibility and impact of the proposal based on its internalized decision-making logic, and generate an evaluation report. Community consensus module: It is configured to assist contemporary community members in reaching a consensus through a predefined voting mechanism based on the evaluation report, and to complete the iterative update of the rule smart contract deployed on the distributed system.

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