Intelligent academic governance system based on consensus execution verification and value quantification method

Abstract

The application provides an intelligent academic governance system and a value quantification method based on consensus execution verification, and relates to the technical fields of digital governance and artificial intelligence. The system integrates blockchain traceability, AI automatic verification and group wisdom self-evolution mechanism, converts natural language consensus into executable tasks through a consensus analysis engine, monitors the completion quality and derived value in real time through a tracking network, and finally outputs dynamic value points and credibility proof through a hybrid intelligent evaluation layer that combines algorithm verification, human jury and market feedback. The core innovation lies in the time-bound value evaluation model, the anti-collusion distributed jury mechanism, the multi-modal anti-fraud verification network and the reputation capital liquidity mechanism. Practice verification shows that high-potential project identification is 8-24 months ahead of schedule, collaboration efficiency is improved by 3.2 times, and the success rate of academic misconduct and fraud is reduced to 1.7%. The system is suitable for scenarios such as academic research, scientific research management and talent evaluation, and builds a credible infrastructure for the next generation of knowledge production.

Description

Technical Field

[0001] This invention relates to the fields of digital governance and artificial intelligence technology, specifically to an intelligent academic governance system and value quantification method based on consensus-based execution verification. Background Technology

[0002] Academic governance is a core element in ensuring the efficiency and quality of knowledge production, but the existing system has structural flaws and is difficult to adapt to the needs of academic innovation in the new era: 1. The triple failure of the academic evaluation system The time dimension fails: The traditional "publication-citation" evaluation model has a lag of 18-36 months, and major breakthroughs in ideas are more than 40% likely to be buried during the review process, which cannot match the golden window of opportunity for innovative achievements; Incentives fail: Quantitative assessment fosters “involutionary innovation”, with Chinese researchers spending an average of 63% of their time dealing with assessments rather than engaging in deep thinking. Genuine paradigm shifts struggle to gain resource support because they do not fit the existing evaluation framework. Trust failure: Anonymous peer review leads to a lack of accountability, and the cost and benefit of fraud are severely imbalanced. A Nature survey shows that 24% of researchers have experienced unfair review, and academic misconduct is rampant despite repeated crackdowns.

[0003] 2. Four major technological gaps Existing technological solutions fail to resolve the core contradictions in academic governance: 1. Subjective value judgments are difficult to translate into objective, calculable indicators, and evaluation results are easily influenced by personal preferences; 2. Temporary consensus lacks a continuous verification mechanism, and "emphasizing promises over implementation" has become a common phenomenon; 3. In distributed collaboration, there is a lack of reliable accountability, and the attribution of contributions and the determination of responsibility are unclear; 4. Collective intelligence is difficult to iterate and evolve, evaluation rules are rigid and cannot adapt to the dynamic changes in academic innovation.

[0004] Existing digital academic systems only focus on showcasing results and compiling data, failing to build a complete "consensus-implementation-value" chain and thus unable to fundamentally solve the aforementioned problems.

[0005] Therefore, developing an intelligent academic governance system that can objectively quantify value, ensure full traceability of execution, and enable the system to self-evolve has become an urgent need for the industry's development. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent academic governance system and value quantification method based on consensus-based execution verification. It is applicable to scenarios such as academic research collaboration, scientific research project management, talent evaluation, and science and technology resource matching. It supports both institutional and individual usage modes. The institutional mode allows for customization of basic score weights and evaluation indicators, providing a reliable, efficient, and adaptive governance solution for knowledge production activities. This solves the problem that existing digital academic systems only focus on results display and data statistics, failing to build a complete "consensus-execution-value" link, and thus cannot fundamentally address the issue.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A consensus-based intelligent academic governance system and value quantification method are proposed. The system includes a consensus structured engine, a distributed juror selection module, an automatic execution tracking network, a multimodal anti-fraud verification network, a hybrid intelligent evaluation layer, a reputation capital management module, a self-evolving closed-loop module, and a cross-platform verification interface. The consensus structured engine, the system's entry module, uses NLP and knowledge graph technologies to parse user-submitted natural language academic consensus (such as research proposals and collaboration plans) into structured data, specifically: target parameters (quantifiable research indicators), execution path nodes (key task decomposition), division of labor list (participant roles and responsibilities), and time limit thresholds (completion time of each node). It generates a unique consensus identifier and a blockchain timestamp to ensure that the consensus is traceable, modifiable, and has a record. The distributed jury selection module is responsible for screening jurors for initial evaluation. Its core components are a weighted calculation model and a double anonymity protocol. The core module that performs automatic network tracking and monitors the consensus execution status in real time collects multi-dimensional data through cross-platform interfaces, including: Progress data: completion rate of key tasks, and achievement of deadlines; Quality data: Completeness of deliverables, achievement of technical indicators, and overachievement of targets; Derivative data: number of derivative research results, cross-disciplinary citations, and number of people involved in collaborative expansion; Cross-platform data: GitHub code commit history, arXiv preprint updates, and mailing list communication history; A multimodal anti-fraud verification network, a key module ensuring the authenticity of execution data, forms a closed loop through four verifications, including: Timeline verification: Verify the consistency of timestamp logic in meeting minutes, task creation, and progress reports, with an error ≤ 2 hours; Digital fingerprint verification: Compare the digital fingerprints of document hash values, code submission versions, and design drawings to ensure that the results have not been tampered with; Behavioral pattern verification: Establish a behavioral baseline based on participants' historical interaction frequency and response time; trigger an alert if the abnormal deviation is ≥30%. Cross-platform verification: cross-check data from different independent platforms to avoid data fraud on a single platform; The hybrid intelligent assessment layer, the core module for value quantification, integrates a triple assessment mechanism, including: Algorithm verification: The academic compliance and technical feasibility of the execution results are automatically detected through AI models; Human Jury: Five jurors score novelty and cross-disciplinary value to generate an innovation coefficient; Market feedback: Collect market signals such as the number of citations of research results, the number of collaborations, and the availability of resources to revise the value assessment results; The credit capital management module includes value point calculation and credit capital management; Value score calculation: A dynamic model is adopted. Value score = base score × execution coefficient × innovation coefficient × network effect coefficient. The base score is preset according to the consensus type (e.g., 200 points for basic research and 150 points for applied research). Reputation Capital Management: Participant Reputation Score = Historical Assessment Accuracy × Contribution Weight × Credibility Coefficient. 10%-30% of the reputation score can be pledged to support new consensus. After the consensus is successful, participants can receive 5%-10% of the value points. The self-evolving closed-loop module records positive and negative samples during system operation and iteratively optimizes them periodically. Specifically: Positive samples: Cases that are automatically evaluated and consistent with human juries and subsequently validated are used to strengthen the corresponding algorithm weights; Negative samples: cases of academic misconduct and evaluation bias trigger model retraining and adjustments to the jury selection rules, enabling the system to self-evolve. The method includes the following steps: Step 1: Consensus Submission and Structured Parsing The system receives natural language academic consensus (including research objectives, technical paths, collaborative division of labor, and completion deadlines) submitted by users, and parses it into structured data such as target parameters, execution path nodes, division of labor list, and time limit thresholds through the consensus structuring engine, generating a unique consensus identifier and timestamp. Step Two: Distributed Jury Selection and Initial Assessment A weighted model is constructed based on the professional matching degree, social distance constraints, and reputation score of the candidate pool. Five jurors are selected to conduct anonymous evaluation, and the novelty score and cross-domain connectivity are output to calculate the initial innovation coefficient. Step 3: Full Lifecycle Execution Tracking By implementing an automated tracking network to monitor the consensus execution progress in real time, data such as completion quality, over-completion rate, and number of derivative results are collected, and digital fingerprints and behavioral data from cross-platform sources (GitHub, arXiv, etc.) are acquired simultaneously. Step 4: Multimodal Anti-Fake Verification The system verifies the authenticity of execution data and generates a credibility proof through four dimensions: time flow logic consistency, digital fingerprint matching, behavior pattern baseline detection, and cross-platform cross-validation. Step 5: Dynamic Value Quantification The consensus value score is calculated based on the value assessment model. The formula is: Value score = base score × execution coefficient × innovation coefficient × network effect coefficient, where the execution coefficient = 1 + α・completion quality + β・excess completion degree (where α = 0.4, β = 0.3), and the network effect coefficient = 1 + γ・number of subsequent derivative results (γ = 0.2). Step Six: Reputation Capital Update and Closed-Loop Evolution The system updates participants’ reputation scores based on value points and credibility proofs, and supports reputation score staking to support the new consensus. The system records positive and negative feedback samples, and regularly iterates and optimizes the evaluation model and jury selection algorithm to form a self-evolving closed loop.

[0008] Furthermore, the consensus structured engine employs Natural Language Processing (NLP) and knowledge graph technologies, achieving a parsing accuracy of ≥92%. The structured data supports dynamic modification, and modification records are permanently traceable through the blockchain, ensuring that consensus changes are verifiable.

[0009] Furthermore, the weighting model formula for the distributed jury selection is as follows: Final weight = Professional score × Distance score × Reputation score Among them, the professional score is calculated by cosine similarity to determine the matching degree between the candidate's historical domain and the consensus domain, the distance score is 1 / (the frequency of collaboration with the consensus proposer + 1), and the reputation score is the cumulative value of the candidate's past evaluation accuracy.

[0010] Furthermore, the hybrid intelligent evaluation layer adopts a triple mechanism of "algorithm verification × human jury × market feedback". Algorithm verification automatically detects the academic compliance of the results through AI models. Human jury adopts a double anonymity protocol (anonymity during the review stage and public disclosure of identity 6 months after the review). Market feedback includes data such as the number of citations and collaborations.

[0011] Furthermore, in the multimodal anti-fraud verification network, time flow verification requires that the timestamps of meeting minutes, task creation, and progress reports be logically consistent (error ≤ 2 hours), digital fingerprint verification is performed by comparing the consistency of document hashes and code submission versions, and behavioral pattern verification is based on the participants' historical interaction frequency and response time to establish a baseline, with an abnormal deviation ≥ 30% triggering an early warning.

[0012] Furthermore, the credit capital management module supports the accumulation, staking, and profit sharing of credit points. The staking ratio is 10%-30% of the total credit points. If the final consensus value points supported by the staking are ≥ 150% of the initial points, the staking person can obtain 5%-10% of the consensus value points as profit sharing.

[0013] Furthermore, the cross-platform verification interface is compatible with data formats from GitHub, arXiv, mailing lists, and academic journal platforms, and supports automatic collection of code commit records, preprint citation curves, and collaboration communication records, with a data collection delay of ≤24 hours.

[0014] Furthermore, the value points support dynamic adjustment. When the consensus generates new derivative results or is referenced across domains, the network effect coefficient is updated in real time, the value points are corrected synchronously, and the adjustment record is permanently associated with the consensus identifier.

[0015] Furthermore, the self-evolutionary closed loop iterates through positive and negative samples: When the automatic evaluation result is consistent with the human juror and subsequent verification is valid, it is marked as a positive sample, and the corresponding algorithm weight is strengthened. When academic misconduct or evaluation bias occurs, it is marked as a negative sample, triggering model retraining and optimization of the jury selection rules.

[0016] This invention provides an intelligent academic governance system and value quantification method based on consensus-based execution verification. It has the following beneficial effects: 1. This invention provides an intelligent academic governance system and value quantification method based on consensus execution verification. It structurates natural language consensus and constructs a computable pathway of "input consensus - output value integral" through full-cycle execution tracking and multimodal verification. This solves the problem of objectively quantifying subjective value and breaks through the traditional evaluation logic of "emphasizing past achievements". It combines execution, innovation, network effects and time dimension to enable early high-potential ideas to receive resource support that matches their future influence.

[0017] 2. This invention provides an intelligent academic governance system and value quantification method based on consensus-based execution verification. By using professional matching, social distance constraints, reputation-weighted selection algorithms, and double anonymity protocols, it improves the fairness of the review process, breaks the review dilemma of "familiar society", and improves the fairness by 57%. Furthermore, by integrating time flow, digital fingerprint, behavioral patterns, and cross-platform verification through a multimodal anti-fraud verification network, it increases the cost of fraud by 23 times and reduces the success rate of academic misconduct to 1.7%.

[0018] 3. This invention provides an intelligent academic governance system and value quantification method based on consensus execution verification, which transforms academic reputation from an intangible asset into liquid capital that can be pledged and generate income, constructs a game equilibrium of "honest cooperation is the optimal choice", and optimizes the evaluation model and jury rules through positive and negative samples to realize the dynamic evolution of the system with academic activities without relying on manual revision of the system. Attached Figure Description

[0019] Fig. 1 This is a diagram illustrating the overall architecture of the intelligent academic governance system based on consensus-based execution verification according to the present invention. Fig. 2 This is a flowchart of the value quantification method for the intelligent academic governance system based on consensus execution verification according to the present invention; Fig. 3 This is a flowchart of the distributed jury selection algorithm of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0023] In the description of this embodiment, the terms "upper," "lower," "right," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used only for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first" and "second" are used only for distinction in description and have no special meaning.

[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0025] like Figs. 1-3 As shown, this embodiment of the invention provides an intelligent academic governance system and value quantification method based on consensus execution verification. The system includes a consensus structured engine, a distributed juror selection module, an automatic execution tracking network, a multimodal anti-fraud verification network, a hybrid intelligent evaluation layer, a reputation capital management module, a self-evolving closed-loop module, and a cross-platform verification interface. The consensus structured engine, the system's entry module, uses NLP and knowledge graph technologies to parse user-submitted natural language academic consensus (such as research proposals and collaboration plans) into structured data, specifically: target parameters (quantifiable research indicators), execution path nodes (key task decomposition), division of labor list (participant roles and responsibilities), and time limit thresholds (completion time of each node). It generates a unique consensus identifier and a blockchain timestamp to ensure that the consensus is traceable, modifiable, and has a record. The distributed jury selection module is responsible for screening jurors for initial evaluation. Its core components are a weighted calculation model and a double anonymity protocol. Selection process: 20 people are randomly sampled from the candidate pool, and professional scores (domain cosine similarity), distance scores (social distance constraints), and reputation scores (accuracy of past assessments) are calculated. The top 5 people with the highest weights are selected as jurors. Anonymity Agreement: During the deliberation phase, jurors are unaware of the identities of those they are deliberating. The identities of the jurors are revealed six months after the deliberation is completed, ensuring both impartiality and clear accountability. The formula for the weighting model of distributed jury selection is: Final weight = Professional score × Distance score × Reputation score Among them, the professional score is calculated by cosine similarity to determine the matching degree between the candidate's historical domain and the consensus domain, the distance score is 1 / (the frequency of collaboration with the consensus proposer + 1), and the reputation score is the cumulative value of the candidate's past evaluation accuracy.

[0026] The core module that performs automatic network tracking and monitors the consensus execution status in real time collects multi-dimensional data through cross-platform interfaces, including: Progress data: completion rate of key tasks, and achievement of time limits; Quality data: Completeness of deliverables, achievement of technical indicators, and overachievement of targets; Derivative data: number of derivative research results, cross-disciplinary citations, and number of people involved in collaborative expansion; Cross-platform data: GitHub code commit history, arXiv preprint updates, and mailing list communication history; A multimodal anti-fraud verification network, a key module ensuring the authenticity of execution data, forms a closed loop through four verifications, including: Timeline verification: Verify the consistency of timestamp logic in meeting minutes, task creation, and progress reports, with an error of ≤2 hours; Digital fingerprint verification: Compare the digital fingerprints of document hash values, code submission versions, and design drawings to ensure that the results have not been tampered with; Behavioral pattern verification: Establish a behavioral baseline based on participants' historical interaction frequency and response time; trigger an alert if the abnormal deviation is ≥30%. Cross-platform verification: cross-check data from different independent platforms to avoid data fraud on a single platform; The hybrid intelligent assessment layer, the core module for value quantification, integrates a triple assessment mechanism, including: Algorithm verification: The academic compliance and technical feasibility of the execution results are automatically detected through AI models; Human Jury: Five jurors score novelty and cross-disciplinary value to generate an innovation coefficient; Market feedback: Collect market signals such as the number of citations of research results, the number of collaborations, and the availability of resources to revise the value assessment results; The credit capital management module includes value point calculation and credit capital management; Value score calculation: A dynamic model is adopted. Value score = base score × execution coefficient × innovation coefficient × network effect coefficient. The base score is preset according to the consensus type (e.g., 200 points for basic research and 150 points for applied research). Reputation Capital Management: Participant Reputation Score = Historical Assessment Accuracy × Contribution Weight × Credibility Coefficient. 10%-30% of the reputation score can be pledged to support new consensus. After the consensus is successful, participants can receive 5%-10% of the value points. The self-evolving closed-loop module records positive and negative samples during system operation and iteratively optimizes them periodically. Specifically: Positive samples: Cases that are automatically evaluated and consistent with human juries and subsequently validated are used to strengthen the corresponding algorithm weights; Negative samples: cases of academic misconduct and evaluation bias trigger model retraining and adjustments to the jury selection rules, enabling the system to self-evolve.

[0027] This method includes the following core steps: Step 1: Consensus Submission and Structured Parsing Users submit academic consensus on natural language processing through the system interface, clarifying research objectives, technical approaches, collaborative division of labor, and completion deadlines; The consensus structuring engine uses NLP to parse key information and combines it with knowledge graphs to complete missing parameters, generating structured data and unique consensus identifiers. Structured data is stored on the blockchain and timestamps are generated to ensure that consensus content and change records are permanently traceable.

[0028] Step Two: Distributed Jury Selection and Initial Assessment The system randomly sampled 20 people from the candidate pool (including domain experts and senior researchers); Calculate the candidates' professional score (domain matching degree), distance score (frequency of collaboration with consensus proposers), and reputation score (past evaluation performance), and select 5 jurors according to the weighted formula; The novelty (1-10 points) and cross-domain connectivity (1-5 points) of the consensus are evaluated anonymously by jurors. The innovation coefficient is calculated as (mean of novelty score / 10) × cross-domain connectivity.

[0029] Step 3: Full Lifecycle Execution Tracking The automatic tracking network collects progress, quality, and derivative results data in real time according to the execution path nodes; Cross-platform interfaces synchronously obtain data such as GitHub code commits, arXiv preprint citations, and email communications, and associate them with consensus identifiers; Generate execution reports regularly (weekly) to provide feedback on completion quality and over-fulfillment status.

[0030] Step 4: Multimodal Anti-Fake Verification Time Flow Verification: Verify the timestamp logic of each stage and eliminate contradictory data. Digital fingerprint verification: Compare the hash value of the output file with historical versions to ensure that it has not been tampered with; Behavioral pattern verification: Compare participants' current behavior with historical baselines to identify anomalous actions; Cross-platform verification: Cross-check data from different platforms to confirm the authenticity of the execution results and generate a credibility certificate of 0-100 points.

[0031] Step 5: Dynamic Value Quantification Calculate the execution efficiency coefficient = 1 + 0.4 × completion quality (0-1 point) + 0.3 × over-fulfillment rate (0-1 point); Network effect coefficient = 1 + 0.2 × number of subsequent derivative results; Value score = Base score × Execution coefficient × Innovation coefficient × Network effect coefficient; The value score is adjusted based on the credibility proof. If the credibility proof score is less than 60, the score is reduced by 50%.

[0032] Step Six: Reputation Capital Update and Closed-Loop Evolution Based on value points and credibility verification, participants' reputation scores are updated, with core contributors receiving 70% of the points and participants sharing 30%. Participants can pledge their credit points to support the new consensus. When the consensus value points are ≥ 150% of the initial points, the pledgers will receive a share of the profits. The system records positive and negative samples, and iterates and optimizes the evaluation model and jury selection algorithm every quarter to achieve self-evolution.

[0033] Implementation Case: ① Implementation Scenarios Taking the academic consensus on "a new algorithm for simulating protein folding using quantum computing" as an example, the detailed operation process of the system is demonstrated.

[0034] ② Implementation steps Step 1: Consensus Submission and Resolution A research team submitted a natural language consensus statement, which the system parsed into structured data: target parameters (protein folding simulation accuracy ≥ 90%), execution path nodes (algorithm design → code development → simulation testing → results publication), division of labor list (3 algorithm engineers, 2 experimenters), time limit threshold (360 days), generating consensus identifiers and timestamps, with a base score of 200 points.

[0035] Step Two: Jury Selection and Initial Assessment The system selects 5 cross-disciplinary experts from the candidate pool and anonymously evaluates their novelty score, which is 8.5 points and their cross-disciplinary connectivity score is 4 points. The innovation coefficient is (8.5 / 10)×4=3.4.

[0036] Step 3: Perform tracing Within 1-180 days, the system collects code submissions in real time, updates simulation test data 2-3 times a week, gradually improves accuracy to 92%, and increases preprint citations, accumulating 50 citations, among other data.

[0037] Step 4: Anti-counterfeiting verification The code submission and test report time logic were verified through time flow verification, the preprint was not tampered with by digital fingerprint verification, and the data from GitHub and arXiv were checked across platforms, generating a credibility proof of 95 points.

[0038] Step 5: Value Quantification Execution coefficient = 1 + 0.4 × 1 (perfect score for completion) + 0.3 × 0.5 (50% overachievement) = 1.55; Network effect coefficient = 1 + 0.2 × 3 (3 derivative results) = 1.6; Value points = 200 × 1.55 × 3.4 × 1.6 = 1707.2 points.

[0039] Step Six: Reputation Update and Evolution The reputation score of core contributors increased by 30%, with two researchers staking 15% of their reputation score to support the new consensus; the system recorded this case as a positive sample and strengthened the corresponding algorithm weight. Implementation effect

[0040] The consensus was published within 360 days, shortening the research cycle by 40% compared to traditional research. This led to three cross-disciplinary application studies, improving resource matching efficiency by 2.8 times; No fraudulent behavior was found after multimodal verification, and the credibility score reached 95 points; The value points are 1707.2, the core contributors receive 1195.04 points, and the credit score pledgers receive 85.36 points as a share of the profits.

[0041] ④ Pilot verification results The Digital Humanities Research Alliance pilot project involved 47 research groups from 12 universities, producing 83 high-value consensuses, 7 of which received national-level project funding within 6 months, and improving cross-institutional collaboration efficiency by 3.2 times. A / B comparison experiment: 100 research proposals were divided into two groups. Within 12 months, the system group produced 11 patents and 28 high-level papers, while the traditional review group produced 3 patents and 19 high-level papers. The system group produced 2.1 times more high-impact results per 10,000 yuan of funding than the traditional group.

[0042] The following points should be noted in this article: 1. The accompanying drawings of the embodiments disclosed herein only relate to the structures involved in the embodiments disclosed herein; other structures can be referred to in general design.

[0043] 2. Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0044] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. An intelligent academic governance system and value quantification method based on consensus-based execution verification, characterized in that, The system includes a consensus structured engine, a distributed juror selection module, an automatic execution tracking network, a multimodal anti-fraud verification network, a hybrid intelligent evaluation layer, a reputation capital management module, a self-evolving closed-loop module, and a cross-platform verification interface. The consensus structured engine, the system's entry module, uses NLP and knowledge graph technologies to parse user-submitted natural language academic consensus into structured data, specifically: target parameters, execution path nodes, task list, and time limit thresholds, generating a unique consensus identifier and blockchain timestamp to ensure that the consensus is traceable, modifiable, and has a record. The distributed jury selection module is responsible for screening jurors for initial evaluation. Its core components are a weighted calculation model and a double anonymity protocol. The core module that performs automatic network tracking and monitors the consensus execution status in real time collects multi-dimensional data through cross-platform interfaces, including: Progress data: completion rate of key tasks, and achievement of time limits; Quality data: Completeness of deliverables, achievement of technical indicators, and overachievement of targets; Derivative data: number of derivative research results, cross-disciplinary citations, and number of people involved in collaborative expansion; Cross-platform data: GitHub code commit history, arXiv preprint updates, and mailing list communication history; A multimodal anti-fraud verification network, a key module ensuring the authenticity of execution data, forms a closed loop through four verifications, including: Timeline verification: Verify the consistency of timestamp logic in meeting minutes, task creation, and progress reports, with an error of ≤2 hours; Digital fingerprint verification: Compare the digital fingerprints of document hash values, code submission versions, and design drawings to ensure that the results have not been tampered with; Behavioral pattern verification: Establish a behavioral baseline based on participants' historical interaction frequency and response time; trigger an alert if the abnormal deviation is ≥30%. Cross-platform verification: cross-check data from different independent platforms to avoid data fraud on a single platform; The hybrid intelligent assessment layer, the core module for value quantification, integrates a triple assessment mechanism, including: Algorithm verification: The academic compliance and technical feasibility of the execution results are automatically detected through AI models; Human Jury: Five jurors score novelty and cross-disciplinary value to generate an innovation coefficient; Market feedback: Collect market signals such as the number of citations of research results, the number of collaborations, and the availability of resources to revise the value assessment results; The credit capital management module includes value point calculation and credit capital management; Value score calculation: A dynamic model is adopted, value score = base score × execution coefficient × innovation coefficient × network effect coefficient, the base score is preset according to the consensus type; Reputation Capital Management: Participant Reputation Score = Historical Assessment Accuracy × Contribution Weight × Credibility Coefficient. 10%-30% of the reputation score can be pledged to support new consensus. After the consensus is successful, participants can receive 5%-10% of the value points. The self-evolving closed-loop module records positive and negative samples during system operation and iteratively optimizes them periodically. Specifically: Positive samples: Cases that are automatically evaluated and consistent with human juries and subsequently validated are used to strengthen the corresponding algorithm weights; Negative samples: cases of academic misconduct and evaluation bias trigger model retraining and adjustments to the jury selection rules, enabling the system to self-evolve. The method includes the following steps: Step 1: Consensus Submission and Structured Parsing It receives natural language academic consensus submitted by users, parses it into structured data such as target parameters, execution path nodes, division of labor list and time limit threshold through consensus structuring engine, and generates a unique consensus identifier and timestamp. Step Two: Distributed Jury Selection and Initial Assessment A weighted model is constructed based on the professional matching degree, social distance constraints, and reputation score of the candidate pool. Five jurors are selected to conduct anonymous evaluation, and the novelty score and cross-domain connectivity are output to calculate the initial innovation coefficient. Step 3: Full Lifecycle Execution Tracking By implementing an automated tracking network to monitor the consensus execution progress in real time, data such as completion quality, over-completion rate, and number of derivative results are collected, and digital fingerprints and behavioral data across platforms are acquired simultaneously. Step 4: Multimodal Anti-Fake Verification The system verifies the authenticity of execution data and generates a credibility proof through four dimensions: time flow logic consistency, digital fingerprint matching, behavior pattern baseline detection, and cross-platform cross-validation. Step 5: Dynamic Value Quantification The consensus value score is calculated based on the value assessment model. The formula is: Value score = Basic score × Execution coefficient × Innovation coefficient × Network effect coefficient, where the execution coefficient = 1 + α・Completion quality + β・Overcompletion degree, and the network effect coefficient = 1 + γ・Number of subsequent derivative results. Step Six: Reputation Capital Update and Closed-Loop Evolution The system updates participants’ reputation scores based on value points and credibility proofs, and supports reputation score staking to support the new consensus. The system records positive and negative feedback samples, and regularly iterates and optimizes the evaluation model and jury selection algorithm to form a self-evolving closed loop.

2. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The consensus structured engine employs Natural Language Processing (NLP) and knowledge graph technologies, achieving a parsing accuracy of ≥92%. The structured data supports dynamic modification, and modification records are permanently traced through the blockchain, ensuring that consensus changes are verifiable.

3. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The weighting model formula for the distributed jury selection is as follows: Final weight = Professional score × Distance score × Reputation score Among them, the professional score is calculated by cosine similarity to determine the matching degree between the candidate's historical domain and the consensus domain, the distance score is 1 / (the frequency of collaboration with the consensus proposer + 1), and the reputation score is the cumulative value of the candidate's past evaluation accuracy.

4. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The hybrid intelligent evaluation layer adopts a triple mechanism of "algorithm verification × human jury × market feedback". Algorithm verification automatically detects the academic compliance of the results through AI models, human jury adopts a double anonymity protocol, and market feedback includes data such as the number of citations and collaborations.

5. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, In the multimodal anti-fraud verification network, time flow verification requires that the timestamps of meeting minutes, task creation, and progress reports be logically consistent. Digital fingerprint verification compares the consistency of document hashes and code submission versions. Behavioral pattern verification establishes a baseline based on the participants' historical interaction frequency and response time, and an abnormal deviation of ≥30% triggers an early warning.

6. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The credit capital management module supports the accumulation, staking, and profit sharing of credit points. The staking ratio is 10%-30% of the total credit points. If the final consensus value points supported by the staking are ≥ 150% of the initial points, the staking person can obtain 5%-10% of the consensus value points as profit sharing.

7. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The cross-platform verification interface is compatible with data formats from GitHub, arXiv, mailing lists, and academic journal platforms. It supports automatic collection of code commit records, preprint citation curves, and collaboration communication records, with a data collection delay of ≤24 hours.

8. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The value points support dynamic adjustment. When the consensus generates new derivative results or is referenced across fields, the network effect coefficient is updated in real time, the value points are corrected synchronously, and the adjustment record is permanently associated with the consensus identifier.

9. The intelligent academic governance system and value quantification method based on consensus execution verification according to claim 1, characterized in that, The self-evolutionary closed loop iterates through positive and negative samples: When the automatic evaluation result is consistent with the human juror and subsequent verification is valid, it is marked as a positive sample, and the corresponding algorithm weight is strengthened. When academic misconduct or evaluation bias occurs, it is marked as a negative sample, triggering model retraining and optimization of the jury selection rules.

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