Big data intelligent analysis system based on data element circulation

Abstract

The invention belongs to the technical field of data element circulation, and discloses a big data intelligent analysis system based on data element circulation, a quantum neural decision unit is constructed through a data element quantum neural consciousness modeling module, and the unit is implanted into a quantum neural network. 12 types of parameters such as credit rating and scene risk entropy of a receiver are captured in real time by utilizing quantum bit state transition, and quantum superposition state decision of a circulation path is realized in combination with a value preference function matrix; the integrity of synchronously generated metadata labels is guaranteed through quantum error correction coding, the circulation state is fed back in real time through quantum entanglement, data are rehearsed for 100,000 times per second for circulation through digital copies in cooperation with digital twinborn mapping and Monte Carlo simulation of the universe sandbox collaborative deduction module, strategy return and risks are rehearsed, and the circulation state of the universe sandbox collaborative deduction module is guaranteed. Manual intervention is reduced, static rule hysteresis is avoided, circulation efficiency is remarkably improved, data is promoted to be converted from passive transmission to active game, and the dynamic value of the data is released.

Description

Technical Field

[0001] This invention belongs to the field of data element circulation technology, specifically a big data intelligent analysis system based on data element circulation. Background Technology

[0002] Data element circulation and big data intelligent analysis are core areas in the digital economy era. Their core goal is to break down data silos and maximize data value. With the rapid development of digital technology, data has become a key production factor and plays an important role in cross-institutional collaboration in multiple fields such as finance, healthcare, and government.

[0003] The existing data circulation system suffers from the following technical problems: data is treated as a passively transmitted object; traditional neural network decision-making is limited by the deterministic output of classical bits, making it unable to handle uncertainties in circulation scenarios, such as sudden changes in the creditworthiness of the recipient; analytical methods are limited to capturing explicit correlations, using methods such as Pearson correlation coefficients, which are difficult to capture asymmetric implicit causality, such as the nonlinear causality between "drug use frequency" and "recovery cycle" in medical data; ownership management relies on static rules, using smart contracts to preset allocation ratios, which cannot respond in real time to the contribution of algorithm optimization, such as the redistribution of ownership when a third party improves the value of data through model fine-tuning; ethical assessments only meet the legal bottom line, using compliance checklist verification, and lack real-time monitoring of risks such as group discrimination and value depletion.

[0004] A search revealed that invention patent CN202410475942.9 discloses a method and related apparatus for data asset circulation. This method constructs a transaction consortium chain using blockchain, utilizes smart contracts to achieve hierarchical data ownership confirmation and access control, and relies on preset rules for ownership adjustment and revenue distribution. However, this scheme lacks dynamic game theory capabilities and cannot respond to dynamic contributions such as algorithm optimization by the recipient; it can only establish explicit relationships and struggles to resolve cross-domain implicit causality; it does not incorporate quantum technology to ensure the security of ownership identification, and its adaptability in complex scenarios is limited. Summary of the Invention

[0005] The purpose of this invention is to provide a big data intelligent analysis system based on the flow of data elements, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a big data intelligent analysis system based on data element circulation, the system comprising:

[0007] Data Element Quantum Neural Consciousness Modeling Module: Implanted with a "quantum neural decision-making unit" constructed from a quantum neural network, it captures information such as the recipient's credit and scenario risk in real time, autonomously selects the flow path based on the "value preference function matrix", generates metadata tags, and realizes the transformation of data from passive transmission to active game.

[0008] Cross-domain multi-scale causal entropy analysis module: Inheriting the autonomous decision-making requirements of the previous module, it uses the 'multi-scale causal entropy decomposition' algorithm to output the causal intensity coefficient (0-10) as the adjustment weight of the value preference function matrix F (when the causal intensity is >7, the corresponding path priority score increases by 20%), providing quantitative logical support for data-driven autonomous decision-making. Its analysis results provide key simulation parameters for the metaverse sandbox collaborative module.

[0009] The adjustment formula for the value preference function matrix F in the cross-domain multi-scale causal entropy analysis module is as follows:

[0010] F′ ij =F ij ×(1+H c / 10)

[0011] Where F′ ij For the elements of the corrected matrix, F ij H is the original element. c For the total causal entropy, when H c An additional 0.2 bias correction is applied when the value is >7.

[0012] In the value preference function matrix F, the element values ​​for the medical data row and the HIPAA compliance column are determined as follows: when the recipient's HIPAA compliance score is ≥90, the corresponding weight is 0.8; when it is 80-89, the weight is 0.5; and when it is <80, the weight is 0.2. The element values ​​for the financial data row and the anti-fraud rating column follow the same pattern.

[0013] Quantum fingerprint dynamic ownership game module: After receiving the causal analysis results, a dynamic ownership ecosystem is constructed using quantum fingerprint technology, generating quantum state identifiers associated with ownership shares. The ownership ratio is adjusted according to contribution through zero-knowledge proof game and is protected by quantum nonlocality.

[0014] Metaverse Sandbox Collaborative Deduction Module: Based on the ownership framework, circulation scenarios are mapped to the metaverse sandbox, data is circulated in digital avatars, the strategy returns and risks are simulated, mutation factors are introduced to simulate extreme scenarios, and the optimization results provide scenario basis for ethical assessment, transforming real trial and error into virtual pre-simulation;

[0015] Three-dimensional ethical constraint intelligent agent module: verifies sandbox simulation results, constructs a three-dimensional ethical assessment model, quantifies damage, monitors discrimination, assesses consumption, uses empathy learning to understand ethical intuition, freezes circulation and generates repair solutions when disputes arise, provides feedback on value measurement logic, and elevates compliance to the ethical level.

[0016] Active Value Metabolism Network Module: Combining ethical assessment, value generation is viewed as a metabolic process. Contributions are recorded using blockchain to form a metabolic chain. When a threshold is reached, a transition is triggered, and "value DNA" is allocated to trace the trajectory, reflecting value changes in real time.

[0017] The neural symbol sandbox verification module verifies value trajectories by combining large language models and symbolic logic to build closed loops, deduce causal paths, seek evidence, and verify universality. Its output optimization parameters (such as the value preference matrix correction coefficient of the quantum neural decision-making unit) are fed back to the data element quantum neural consciousness modeling module in real time through an encrypted channel, realizing full-link dynamic optimization of 'decision parameters - causal analysis - ownership adjustment - ethical verification - value measurement - parameter iteration', solving the one-sidedness problem of single-module decision-making, and forming a system-level efficiency far exceeding the superposition of independent technologies.

[0018] Preferably, the data element quantum neural consciousness modeling module includes:

[0019] (1) Quantum Neural Decision Unit Construction: This module breaks through the assumption of "data has no subjectivity" and implants decision units composed of quantum neural networks into data elements. It integrates multi-dimensional environmental perception interfaces and captures 12 types of parameters such as the credit rating of the receiver and the scene risk entropy value in real time through quantum bit state transitions. Based on the value preference function matrix, it completes the quantum superposition state decision of the circulation path. For example, medical data dynamically matches the qualifications of the receiving institution through the HIPAA compliance verification interface.

[0020] Expression of the selection mechanism of quantum neural decision-making units:

[0021]

[0022] In the formula: V is a 1×N dimensional value preference vector, V i The priority score (0-100 points) of the i-th circulation path is represented by N, where N is the total number of possible paths.

[0023] F is a 3×12 order value preference function matrix, with rows corresponding to data types (medical / financial / government) and columns corresponding to environmental parameter dimensions;

[0024] θ is a 1×5 order receiver credit rating vector, which includes HIPAA compliance score (θ1), data security level (θ2), historical cooperation score (θ3), qualification certification level (θ4), and privacy technology maturity (θ5).

[0025] σ is a 2×3 order scenario risk entropy tensor, and each row corresponds to a static risk (σ). 11 -σ 13 ) and dynamic risk (σ) 21 -σ 23 ), listing the corresponding policy risks, technological risks, and market risks;

[0026] |ψ> is a 4-dimensional quantum superposition state decision basis vector, |ψ>=α|00>+β|01>+γ|10>+δ|11>, where α / β / γ / δ are probability amplitudes, satisfying the normalization condition;

[0027] Formula source: Combination of quantum decision theory (quantum superposition state) and multi-attribute decision analysis (value function matrix);

[0028] (2) Metadata tag generation mechanism: The decision-making unit synchronously generates metadata tags containing data feature vectors and circulation preference parameters. Quantum error correction coding is used to ensure the integrity of the tags. The tags are embedded in the quantum fingerprint layer of data elements. Real-time feedback of data circulation status is achieved through quantum entanglement state association, promoting the transformation of data from passive transmission mode to active game mode.

[0029] Preferably, the cross-domain multi-scale causal entropy analysis module includes:

[0030] (1) Multi-scale causal decomposition algorithm: To meet the logical support requirements of data-driven autonomous decision-making, a multi-scale causal entropy decomposition algorithm is adopted. At the micro level, the implicit causal relationship between data particles is captured by a quantum entangled state simulator. At the meso level, a causal entropy weight network is constructed to quantify the cross-domain causal strength coefficient. At the macro level, cellular automata are used to simulate the emergence process of causal chains, so as to realize the full-dimensional analysis from data particles to system-level causality.

[0031] Multiscale causal entropy decomposition formula:

[0032] H c =H micro +H meso +H macro

[0033] Where: H c The total causal entropy (bit) ranges from 0 to 10, with higher values ​​indicating more complex causal relationships.

[0034] H micro For microscopic causal entropy, Where p i Let be the quantum entanglement causal probability of the i-th pair of data particles (such as user behavior-consumption data);

[0035] H meso For meso-level causal entropy, w j The weight of the j-th cross-domain causal chain (calculated based on the PageRank algorithm);

[0036] H macro H represents the macroscopic causal entropy, calculated from the state transition probabilities of the cellular automata. macro =-∑ s∈S P(s)log2P(s), where S is the set of all possible macroscopic states of the system;

[0037] Formula source: An extension of the Shannon entropy formula in information theory, combined with quantum entanglement theory and complex systems analysis;

[0038] (2) Cross-domain causal mechanism output: The algorithm transforms unstructured data into causal tensors through feature mapping, extracts cross-domain common causal features through convolutional neural networks, and generates a visual map containing causal strength and evolution path. This result serves as the core input parameter of the metaverse sandbox collaboration module, providing technical support at the causal logic level for the pre-rendering of data flow strategies and improving the interpretability of decisions.

[0039] Preferably, the quantum fingerprint dynamic ownership game module includes:

[0040] (1) Quantum ownership identification system: Based on quantum fingerprint technology, a dynamic ownership ecosystem is constructed. Each data derivative node generates a unique quantum state identifier through a quantum random number generator. The quantum no-cloning principle is used to ensure that the identifier cannot be forged in a quantum computing environment (traditional SHA-256 hash identifiers are easily cracked by quantum algorithms). By associating the ownership share vectors of the original data and the derived data through entangled states, the real-time correlation is improved compared with the traditional chain-based ownership confirmation (response delay ≤10ms).

[0041] (2) Zero-knowledge proof game engine: When data flows between N parties, the module starts the zero-knowledge proof ownership game engine. Based on the contribution tensor algorithm, it calculates the ownership ratio of each party in the data provision, algorithm optimization, and scenario application in real time. The principle of quantum nonlocality ensures the immutability of the adjustment process and provides underlying ownership definition technology for the compliant flow of data elements.

[0042] The expression for calculating the dynamic ownership share tensor is as follows:

[0043] S = T·C

[0044] In the formula: S is a 1×K order weight share vector, S k Let ∑ represent the ownership percentage of the k-th entity, satisfying ∑ k =1 n S k =100;

[0045] T is a K×3 order contribution tensor, T k1 Contribute to the data (based on data volume × quality weight), T k 2 represents the contribution to algorithm optimization (based on the improvement in model accuracy), T k3 Contribute to application scenarios (based on value conversion efficiency);

[0046] C is a 3×1 order scene weight vector, C1 = 0.4 (data layer), C2 = 0.3 (algorithm layer), C3 = 0.3 (application layer), which can be dynamically adjusted according to the scene;

[0047] Formula source: Cooperative game model in game theory, combined with tensor analysis to process multi-dimensional contribution evaluation.

[0048] Preferably, the metaverse sandbox collaborative deduction module includes:

[0049] (1) Digital Twin Mapping System: Based on the ownership framework, a cosmic collaborative space is built. The physical circulation scenario is transformed into a three-dimensional model in the metaverse sandbox through the digital twin mapping engine. Data is loaded with quantum encryption protocol in the form of digital clones. 100,000 risk-free trial circulations are completed in the sandbox. The value returns and risk costs of different strategies are simulated based on Monte Carlo simulation.

[0050] Monte Carlo value return expected expression:

[0051]

[0052] In the formula: E(R) is the expected value of return (ten thousand yuan), and is the average of N simulations;

[0053] γ is the time discount factor; γ = 0.9 for short-term circulation scenarios and γ = 0.6 for long-term scenarios.

[0054] r is the instantaneous reward at step t in the k-th simulation. t = Transaction profits - compliance costs - risk reserves;

[0055] T is the time step of a single simulation (1 step = 1 hour), and its value ranges from 1 to 720 (30 days).

[0056] N represents the number of simulations, with a default value of 10. 5 The number of high-risk scenarios has increased to 10. 6 Second-rate;

[0057] Formula source: Monte Carlo policy evaluation method in reinforcement learning, used to quantify the long-term value of a policy;

[0058] (2) Mutation factor simulation mechanism: The sandbox has a built-in data mutation factor generator, which simulates extreme scenario parameters through Gaussian distribution sampling, such as data leakage attacks and compliance policy changes; combined with the reinforcement learning Actor-Critic algorithm, the circulation strategy is iteratively optimized to generate an evaluation report containing strategy scores and risk probabilities, providing scenario-based input data for ethical verification.

[0059] Preferably, the three-dimensional ethical constraint intelligent agent module includes:

[0060] (1) Ethical assessment model architecture: A three-dimensional assessment model is constructed by integrating affective computing and ethical philosophy. The harm threshold evaluator quantifies the value of rights damage through a neural network trained by federated learning. The fairness index monitor uses Wasserstein distance to detect the risk of group discrimination. The sustainability evaluator calculates the data value consumption rate based on the entropy reduction principle, forming a multi-dimensional ethical assessment matrix.

[0061] The Wasserstein distance expression for the fairness index is:

[0062]

[0063] In the formula: p is the distribution of the reference group data, such as the credit score distribution of male users, and the probability density function is p(x);

[0064] q represents the data distribution of the target group, such as the credit score distribution of female users, with the probability density function q(y).

[0065] Let y be the set of all joint distributions γ(x,y) whose marginal distributions are p and q.

[0066] ||xy|| represents the L2 Euclidean distance. d represents the data dimension;

[0067] W(p,q) ranges from 0 to 10, and values ​​> 5 are considered to indicate a significant risk to fairness.

[0068] Formula source: Wasserstein distance in optimal transport theory, used to measure the difference between distributions;

[0069] (2) Dynamic ethical decision-making mechanism: The intelligent agent analyzes human ethical intuition data through empathy learning neural network. When an ethical dispute risk is detected, a dynamic circulation freeze command is triggered. At the same time, the rule reasoning engine is called to generate a repair plan. The plan is written into the blockchain after being digitally signed by the ethics committee. Its evaluation result serves as the ethical constraint parameter of the active value network module, realizing the upgrade from compliance to the ethical level.

[0070] Preferably, the bioactive metabolic network module includes:

[0071] (1) Construction of value metabolism chain: Combining the results of ethical evaluation, the generation of data value is regarded as a metabolic process of a living organism. The value contribution recorder of the blockchain stores the data of each value interaction in real time... Compared with traditional static value evaluation methods (such as the discounted cash flow method which relies on fixed periodic accounting), this module realizes the dynamic leap of value form through Hopf bifurcation theory (such as the real-time transformation from original data value to algorithm-derived value), which solves the defect of traditional methods that cannot capture the nonlinear evolution of value;

[0072] Hopf bifurcation value transition condition:

[0073]

[0074] In the formula: x is the rate of value accumulation (units / day), x>0 indicates value growth;

[0075] y represents the rate of value consumption (unit / day), and y>0 indicates value decay;

[0076] μ is the bifurcation parameter. Value transition is triggered when μ>0;

[0077] x 2 +y 2 This is a nonlinear damping term to prevent the system from becoming unstable due to excessively rapid value growth.

[0078] Formula source: Hopf bifurcation model in dynamical systems theory, used to describe the nonlinear evolution of value metabolism;

[0079] (2) Value DNA Traceability System: Assign a unique value DNA identifier to each data element, and use the base pairing principle to record the value evolution trajectory throughout the entire life cycle; realize the second-level traceability of the value trajectory through a distributed hash table, and visualize the value flow path by combining graph neural networks.

[0080] Preferably, the neural symbol sandbox verification module includes:

[0081] (1) Closed-loop verification architecture: Construct a closed-loop system of "hypothesis-verification-correction". When the analysis conclusion is generated, the symbolic logic reasoning engine derives 12 possible causal paths, calls the large language model to search for supporting evidence in the academic literature database, forms a confidence score of the evidence chain, and selects high-confidence conclusions by threshold judgment.

[0082] The expression for the chain of evidence confidence scoring model is as follows:

[0083]

[0084] In the formula: C is the confidence score (0-1), and C≥0.8 indicates a highly credible conclusion;

[0085] w i Let i be the weight of the i-th piece of evidence.

[0086] e i For the strength of evidence, e is extracted from the literature database by a large language model. i = 0.7 × Impact Factor + 0.3 × Standardized Citations;

[0087] K represents the number of pieces of evidence, with a default of 5-10 pieces, and key conclusions increased to 10-20 pieces.

[0088] Formula source: Dempster-Shafer theory in evidence theory, combined with symbolic logic and machine learning for credibility assessment;

[0089] (2) Sandbox universality verification: Input candidate conclusions into the metaverse sandbox for multi-scenario verification. Simulate different application environments by changing initial parameters and record the accuracy fluctuation curve of the conclusions. After the verification results are aggregated by federated learning, they are fed back to the quantum neural consciousness modeling module of data elements to optimize the parameter matrix of the quantum neural decision-making unit, realize the closed-loop iterative optimization of the whole system, and solve the AI ​​black box problem.

[0090] Federated learning model aggregation formula:

[0091]

[0092] In the formula: θ global This is the global model parameter vector, with the same dimensions as the local model.

[0093] θ m The local model parameters for the m-th node include the weight matrix (1024x512) and bias vector (512×1) of the neural symbolic model.

[0094] M represents the number of federated nodes. In the medical field, M = 5-10 (hospitals), and in the financial field, M = 10-20 (banks).

[0095] Homomorphic encryption is required before aggregation. Ensure parameter privacy;

[0096] Formula source: Federated averaging algorithm in distributed machine learning, which aggregates model updates while ensuring data privacy.

[0097] The beneficial effects of this invention are as follows:

[0098] 1. This invention constructs a quantum neural decision-making unit through a data element quantum neural consciousness modeling module. This unit is embedded with a quantum neural network and utilizes quantum bit state transitions to capture 12 types of parameters in real time, such as the receiver's credit rating and scenario risk entropy value. Combined with a value preference function matrix, it realizes quantum superposition state decision-making for the circulation path. For example, medical data is dynamically matched with compliant institutions through the HIPAA compliance verification interface. The synchronously generated metadata tags are ensured to be complete through quantum error correction encoding and the circulation status is fed back in real time through quantum entanglement. With the digital twin mapping and Monte Carlo simulation of the metaverse sandbox collaborative deduction module, data is pre-circulated 100,000 times / second as a digital clone, pre-simulating strategy rewards and risks, reducing human intervention and avoiding the lag of static rules, significantly improving circulation efficiency, promoting the transformation of data from passive transmission to active game, and releasing its dynamic value.

[0099] 2. This invention constructs a dynamic ownership ecosystem based on quantum fingerprint technology through a quantum fingerprint dynamic ownership game module. Each data derivative node generates a unique quantum state identifier, and the ownership shares of the original and derived data are associated through entangled states. The zero-knowledge proof game engine adjusts the ownership ratio in real time according to the contribution tensor algorithm. Quantum nonlocality ensures immutability, improving dynamic adaptability compared to traditional static ownership confirmation. The cross-domain multi-scale causal entropy analysis module uses a multi-scale causal entropy decomposition algorithm to mine deep causal relationships in cross-domain data from the micro (quantum entangled state simulator to capture implicit correlations), meso (causal entropy weight network quantization strength), and macro (cellular automata to simulate causal chain emergence), such as the nonlinear causal chain of financial and medical data, breaking down data silos.

[0100] 3. This invention integrates emotional computing and ethical philosophy through a three-dimensional ethical constraint intelligent agent module, constructing a three-dimensional model containing a harm threshold evaluator, a fairness index monitor (Wasserstein distance detection of discrimination), and a sustainability evaluator (entropy reduction principle to calculate value consumption). Through empathic learning, it analyzes human ethical intuition, freezes circulation and generates repair solutions when disputes arise. The active value metabolism network module views value generation as a metabolic process similar to that of a living organism. The blockchain records interactions to form a metabolic chain. According to Hopf bifurcation theory, when metabolic entropy reaches a threshold, it triggers a quantum leap in value, allocating value DNA to trace the entire life cycle trajectory. The combination of these two modules integrates ethical constraints into value measurement, balances compliance and value release, and constructs a virtuous cycle of ethics and value. Attached Figure Description

[0101] Figure 1 This is a flowchart of the big data intelligent analysis system based on data element circulation of the present invention. Detailed Implementation

[0102] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0103] like Figure 1 As shown, this embodiment of the invention provides a big data intelligent analysis system based on data element circulation. The system includes:

[0104] Data Element Quantum Neural Consciousness Modeling Module: Implanted with a "quantum neural decision-making unit" constructed from a quantum neural network, it captures information such as the recipient's credit and scenario risk in real time, autonomously selects the flow path based on the "value preference function matrix", generates metadata tags, and realizes the transformation of data from passive transmission to active game.

[0105] Cross-domain multi-scale causal entropy analysis module: Building upon the autonomous decision-making requirements of the previous module, it uses the "multi-scale causal entropy decomposition" algorithm to mine the deep causal mechanisms of cross-domain data from the micro, meso, and macro levels, providing logical support for data-driven autonomous decision-making. Its analysis results provide key simulation parameters for the metaverse sandbox collaborative module.

[0106] The core parameter output by this module to the Metaverse Sandbox Collaboration Module is the causal intensity coefficient matrix (3×N order, N is the number of circulating scenarios). The parameter format is [Scene ID, causal intensity value (0-10), evolution path vector]. When the causal intensity value is greater than 7, the high-priority pre-play of the sandbox is triggered. The triggering condition is transmitted in real time through the encrypted API interface.

[0107] Quantum fingerprint dynamic ownership game module: After receiving the causal analysis results, a dynamic ownership ecosystem is constructed using quantum fingerprint technology, generating quantum state identifiers associated with ownership shares. The ownership ratio is adjusted according to contribution through zero-knowledge proof game and is protected by quantum nonlocality.

[0108] Metaverse Sandbox Collaborative Deduction Module: Based on the ownership framework, circulation scenarios are mapped to the metaverse sandbox, data is circulated in digital avatars, the strategy returns and risks are simulated, mutation factors are introduced to simulate extreme scenarios, and the optimization results provide scenario basis for ethical assessment, transforming real trial and error into virtual pre-simulation;

[0109] Three-dimensional ethical constraint intelligent agent module: verifies sandbox simulation results, constructs a three-dimensional ethical assessment model, quantifies damage, monitors discrimination, assesses consumption, uses empathy learning to understand ethical intuition, freezes circulation and generates repair solutions when disputes arise, provides feedback on value measurement logic, and elevates compliance to the ethical level.

[0110] Active Value Metabolism Network Module: Combining ethical assessment, value generation is viewed as a metabolic process. Contributions are recorded using blockchain to form a metabolic chain. When a threshold is reached, a transition is triggered, and "value DNA" is allocated to trace the trajectory, reflecting value changes in real time.

[0111] The neural symbol sandbox verification module verifies value trajectories, combines large language models and symbolic logic to build closed loops, deduce causal paths, seek evidence, verify universality, solve the AI ​​black box problem, and optimize decision-making by providing data elements in the quantum neural consciousness modeling module to form a "perception-decision-action-verification" process.

[0112] Before the federated learning model is aggregated, the local model parameters of each node (including the 1024×512 weight matrix and the 512×1 bias vector) are encrypted using the ECC encryption algorithm (curve secp256k1). The encrypted parameters are transmitted to the aggregation node through the P2P network. After decryption, the aggregation node executes the federated averaging algorithm. After the aggregation is completed, the global parameters are re-encrypted and fed back to each node. The key for the entire process is updated once every 24 hours through the key negotiation protocol.

[0113] Taking medical data circulation as an example, the entire process steps are as follows:

[0114] Step 1: The data element quantum neural consciousness modeling module obtains the qualifications of the receiving hospital through the HIPAA compliance verification interface. The quantum neural decision unit inputs 8 qubit parameters (including patient privacy level, hospital compliance score, etc.) and generates quantum superposition state decision results of 3 candidate flow paths based on the value preference function matrix.

[0115] Step 2: The cross-domain multi-scale causal entropy analysis module analyzes the microscopic quantum entanglement causal probability of 'medication records' and 'rehabilitation data' in medical data, calculates the total causal entropy Hc = 8.2, and outputs it to the metaverse sandbox;

[0116] Step 3: The metaverse sandbox loads the causal entropy parameter and simulates 10 in the form of digital clones. 5 In the second circulation, a 'data leakage' variation factor (Gaussian distribution with mean 0.3 and variance 0.1) was introduced, and the expected value return E[R] was calculated to be 125,000 yuan.

[0117] Step 4: The three-dimensional ethical constraint intelligent agent module detected that the harm threshold of the risk of patient privacy leakage in the simulation was 6.2 (threshold > 5), triggered the circulation freeze command, and called the rule reasoning engine to generate a 'de-sensitization processing + permission level' repair plan;

[0118] Step 5: After the repair plan is verified by the neural symbol sandbox (evidence chain confidence C = 0.85), the value preference matrix of the quantum neural decision unit is updated through federated learning to complete the closed-loop optimization.

[0119] The data element quantum neural consciousness modeling module includes:

[0120] (1) Quantum Neural Decision Unit Construction: This module is a decision unit composed of data elements implanted into a quantum neural network. It adopts a 3-layer quantum convolution structure with 8 quantum bits in the input layer, 16 quantum bits in the hidden layer, and 4 quantum bits in the output layer. It simultaneously represents the uncertainty of 12 types of parameters through the superposition state of quantum bits, i.e., the linear combination of |0> and |1>, which solves the problem that traditional binary decision-making cannot evaluate multi-path risks in parallel. It captures the credit rating of the receiver and the scene risk entropy value in real time through quantum bit state transitions. It completes the quantum superposition state decision of the circulation path based on the value preference function matrix. For example, medical data is dynamically matched with the qualifications of the receiving institution through the HIPAA compliance verification interface.

[0121] The specific gate operation sequence of the three-layer quantum convolution structure of the quantum neural decision unit is as follows: the input layer uses the Hadamard gate to realize the superposition of qubits, the hidden layer constructs quantum entanglement through the CNOT gate, and the output layer uses the measurement gate to collapse to the classical bit result; the quantum error correction coding adopts the surface code with a coding redundancy of 5, and 20 auxiliary bits are embedded in every 100 qubits for error detection and correction.

[0122] Expression of the selection mechanism of quantum neural decision-making units:

[0123]

[0124] In the formula: V is a 1×N dimensional value preference vector, V i The priority score (0-100 points) of the i-th circulation path is represented by N, where N is the total number of possible paths.

[0125] F is a 3×12 order value preference function matrix, with rows corresponding to data types (medical / financial / government) and columns corresponding to environmental parameter dimensions;

[0126] θ is a 1×5 order receiver credit rating vector, which includes HIPAA compliance score (θ1), data security level (θ2), historical cooperation score (θ3), qualification certification level (θ4), and privacy technology maturity (θ5).

[0127] σ is a 2×3 order scenario risk entropy tensor, and each row corresponds to a static risk (σ). 11 -σ 13 ) and dynamic risk (σ) 21 -σ 23 ), listing the corresponding policy risks, technological risks, and market risks;

[0128] |ψ> is a 4-dimensional quantum superposition state decision basis vector, |ψ>=α|00>+β|01>+γ|10>+δ|11>, where α / β / γ / δ are probability amplitudes, satisfying the normalization condition;

[0129] Formula source: Combination of quantum decision theory (quantum superposition state) and multi-attribute decision analysis (value function matrix);

[0130] (2) Metadata tag generation mechanism: The decision-making unit synchronously generates metadata tags containing data feature vectors and circulation preference parameters. Quantum error correction coding is used to ensure the integrity of the tags. The tags are embedded in the quantum fingerprint layer of data elements. Real-time feedback of data circulation status is achieved through quantum entanglement state association, which promotes the transformation of data from passive transmission mode to active game mode.

[0131] When a quantum error correction code detects an error, the auxiliary bit triggers the surface code error correction process: 1. Measure the auxiliary bit to obtain the error syndrome; 2. Locate the error position using the minimum weight matching algorithm; 3. Apply an X or Z gate to the erroneous quantum bit for flipping correction, and trigger a data retransmission mechanism if correction fails.

[0132] The quantum fingerprint layer is embedded by mapping data feature vectors to a 64-bit qubit sequence. Each metadata tag is bound to one auxiliary qubit, forming a Bell entangled state (|Φ) with the quantum state of the data element. + >=(|00>+|11>) / √2), which monitors the state changes of the auxiliary qubits to provide real-time feedback on the data flow status.

[0133] The cross-domain multi-scale causal entropy analysis module includes:

[0134] (1) Multi-scale causal decomposition algorithm: Upon receiving the logical support requirement for data-driven autonomous decision-making, a multi-scale causal entropy decomposition algorithm is adopted. At the micro level, the implicit causal relationship between data particles is captured through a quantum entangled state simulator. The quantum entangled state simulator adopts the IBM Qiskit framework, with parameters set to an entanglement threshold of 0.8 and 1000 sampling times. At the meso level, a causal entropy weight network is constructed to quantify the cross-domain causal strength coefficient. The initial weight of each causal chain is calculated first through the PageRank algorithm, and then L1 regularization is applied with λ = 0.01 to eliminate redundant chains. At the macro level, cellular automata are used to simulate the emergence process of causal chains. The rule of cellular automata is defined as the transition probability of the current cell state is 0.7 when the states of three adjacent cells are "activated", realizing a full-dimensional analysis from data particles to system-level causality.

[0135] Multiscale causal entropy decomposition formula:

[0136] H c =H micro +H meso +H macro

[0137] Where: H cThe total causal entropy (bit) ranges from 0 to 10, with higher values ​​indicating more complex causal relationships.

[0138] H micro For microscopic causal entropy, Where p i Let be the quantum entanglement causal probability of the i-th pair of data particles (such as user behavior-consumption data);

[0139] H meso For meso-level causal entropy, w j The weight of the j-th cross-domain causal chain (calculated based on the PageRank algorithm);

[0140] H macro H represents the macroscopic causal entropy, calculated from the state transition probabilities of the cellular automata. macro =-∑ s∈S P(s)log2P(s), where S is the set of all possible macroscopic states of the system;

[0141] Formula source: An extension of the Shannon entropy formula in information theory, combined with quantum entanglement theory and complex systems analysis;

[0142] (2) Cross-domain causal mechanism output: The algorithm transforms unstructured data into causal tensors through feature mapping, extracts cross-domain common causal features through convolutional neural networks, and generates a visual map containing causal strength and evolution path. This result serves as the core input parameter of the metaverse sandbox collaboration module, providing technical support at the causal logic level for the pre-rendering of data flow strategies and improving the interpretability of decisions.

[0143] The quantum fingerprint dynamic ownership game module includes:

[0144] (1) Quantum ownership identification system: Based on quantum fingerprint technology, a dynamic ownership ecosystem is constructed. Each data derivative node generates a unique quantum state identifier through a quantum random number generator, and the quantum no-cloning principle is used to ensure the anti-counterfeiting of the identifier. The ownership share vector (1×K order, K is the number of participants) of the original data and the derived data is associated through entangled state (entanglement degree ≥ 0.9), realizing the quantum-level binding of ownership relationship and solving the efficiency bottleneck of traditional chain-based ownership confirmation.

[0145] (2) Zero-knowledge proof game engine: The module starts the zero-knowledge proof ownership game engine, which calculates the ownership ratio of each party in the data provision, algorithm optimization, and scenario application stages in real time based on the contribution tensor algorithm. The contribution of data provision is T. k1The quality weights are set as follows: for medical data, the weight is "patient privacy protection level (0-10 points) multiplied by data integrity (0-10 points); for financial data, the weight is "anti-fraud label accuracy (0-100%) multiplied by time freshness (1 for the last 30 days, 0.5 for older data); the algorithm optimization contribution is T..." k2 The model accuracy improvement value ΔAccuracy is quantified by subtracting the original Accuracy from the optimized Accuracy; the immutability of the adjustment process is guaranteed by the principle of quantum nonlocality, providing underlying ownership definition technology for the compliant circulation of data elements;

[0146] The expression for calculating the dynamic ownership share tensor is as follows:

[0147] S = T·C

[0148] In the formula: S is a 1×K order weight share vector, S k Let ∑ represent the ownership percentage of the k-th entity, satisfying ∑ k =1 n S k =100;

[0149] T is a K×3 order contribution tensor, T k1 Contribute to the data (based on data volume × quality weight), T k 2 represents the contribution to algorithm optimization (based on the improvement in model accuracy), T k3 Contribute to application scenarios (based on value conversion efficiency);

[0150] C is a 3×1 order scene weight vector, C1 = 0.4 (data layer), C2 = 0.3 (algorithm layer), C3 = 0.3 (application layer), which can be dynamically adjusted according to the scene;

[0151] Formula source: Cooperative game model in game theory, combined with tensor analysis to process multi-dimensional contribution evaluation.

[0152] The Metaverse Sandbox Collaborative Deduction Module includes:

[0153] (1) Digital Twin Mapping System: Based on the ownership framework, a cosmic collaborative space is built. Through the digital twin mapping engine (using point cloud matching algorithm, geometric error ≤0.1mm), the physical circulation scene is transformed into a three-dimensional model in the metaverse sandbox. The synchronization frequency between the data digital clone and the original data is 1 time / second. The data is loaded with quantum encryption protocol (using BB84 protocol) in the form of digital clone. 100,000 risk-free trial circulations are completed in the sandbox. The value return and risk cost of different strategies are simulated based on Monte Carlo simulation.

[0154] Monte Carlo value return expected expression:

[0155]

[0156] In the formula: E(R) is the expected value of return (ten thousand yuan), and is the average of N simulations;

[0157] γ is the time discount factor; γ = 0.9 for short-term circulation scenarios and γ = 0.6 for long-term scenarios.

[0158] r is the instantaneous reward at step t in the k-th simulation. t = Transaction profits - compliance costs - risk reserves;

[0159] T is the time step of a single simulation (1 step = 1 hour), and its value ranges from 1 to 720 (30 days).

[0160] N represents the number of simulations, with a default value of 10. 5 The number of high-risk scenarios has increased to 10. 6 Second-rate;

[0161] Formula source: Monte Carlo policy evaluation method in reinforcement learning, used to quantify the long-term value of a policy;

[0162] (2) Mutation factor simulation mechanism: The sandbox has a built-in data mutation factor generator, which simulates extreme scenario parameters through Gaussian distribution sampling, such as data leakage attacks and compliance policy changes; combined with the reinforcement learning Actor-Critic algorithm, the circulation strategy is iteratively optimized to generate an evaluation report containing strategy scores and risk probabilities, providing scenario-based input data for ethical verification.

[0163] The three-dimensional ethically constrained intelligent agent module includes:

[0164] (1) Ethical assessment model architecture: A three-dimensional assessment model is constructed by integrating affective computing and ethical philosophy. The harm threshold evaluator quantifies the value of rights damage through a neural network trained by federated learning. The fairness index monitor uses Wasserstein distance to detect the risk of group discrimination. The sustainability evaluator calculates the data value consumption rate based on the entropy reduction principle, forming a multi-dimensional ethical assessment matrix.

[0165] The Wasserstein distance expression for the fairness index is:

[0166]

[0167] In the formula: p is the distribution of the reference group data, such as the credit score distribution of male users, and the probability density function is p(x);

[0168] q represents the data distribution of the target group, such as the credit score distribution of female users, with the probability density function q(y).

[0169] Let y be the set of all joint distributions γ(x,y) whose marginal distributions are p and q.

[0170] ||xy|| represents the L2 Euclidean distance. d represents the data dimension;

[0171] W(p,q) ranges from 0 to 10, and values ​​> 5 are considered to indicate a significant risk to fairness.

[0172] Formula source: Wasserstein distance in optimal transport theory, used to measure the difference between distributions;

[0173] (2) Dynamic ethical decision-making mechanism: The intelligent agent analyzes human ethical intuition data through an empathy learning neural network. This network adopts dual-channel input (textual ethical guidelines: containing 100,000 laws, regulations and industry ethical norms; image emotion data: containing 50,000 facial expression images labeled with emotional tendencies), extracts ethical features through a Transformer encoding layer (6-layer encoder, 512-dimensional hidden layer), and is trained using a cross-entropy loss function (100 iterations, learning rate 0.001). When an ethical dispute risk is detected, a dynamic circulation freeze command is triggered, and a rule reasoning engine is called to generate a repair plan, realizing the upgrade from compliance to the ethical level.

[0174] The training data for the empathy learning neural network includes two types: one is 100,000 text ethical guidelines, covering structured clauses of laws and regulations such as the "Declaration of Medical Ethics" and the "Data Security Law"; the other is 50,000 facial expression images labeled with emotional tendencies (30,000 positive emotions and 20,000 negative emotions), with a uniform image resolution of 256×256 pixels. Emotional features are extracted by ResNet50 and then input into the Transformer encoding layer.

[0175] The logic for generating the repair plan is as follows: prioritize reducing the harm threshold (weight 0.5) → balance the fairness index (weight 0.3) → optimize sustainability (weight 0.2). In case of conflict, the priority matrix predefined by the ethics committee is used.

[0176] The bioactive value metabolic network module includes:

[0177] (1) Construction of value metabolism chain: Combining the results of ethical assessment, the generation of data value is regarded as a metabolic process of a living organism. The value contribution recorder of the blockchain stores the data of each value interaction in real time, forming a metabolism chain that includes value generation, transfer and consumption; the value leap algorithm is designed using Hopf bifurcation theory, and the quantum leap of value form is automatically triggered when the metabolic entropy value reaches the critical threshold.

[0178] The critical threshold for metabolic entropy is set as follows: 8.5 bits for medical data, 7.2 bits for financial data, and 6.8 bits for government data (based on statistics of 3,000 historical value jump events).

[0179] Hopf bifurcation value transition condition:

[0180]

[0181] In the formula: x is the rate of value accumulation (units / day), x>0 indicates value growth;

[0182] y represents the rate of value consumption (unit / day), and y>0 indicates value decay;

[0183] μ is the bifurcation parameter. Value transition is triggered when μ>0;

[0184] x 2 +y 2 This is a nonlinear damping term to prevent the system from becoming unstable due to excessively rapid value growth.

[0185] Formula source: Hopf bifurcation model in dynamical systems theory, used to describe the nonlinear evolution of value metabolism;

[0186] (2) Value DNA Traceability System: Each data element is assigned a unique value DNA identifier, and the value evolution trajectory throughout the entire life cycle is recorded using the base pairing principle. A corresponds to "data generation", which records the generation time and subject; T corresponds to "value transfer", which records the counterparty and timestamp; C corresponds to "value consumption", which records the consumption scenario and amount; and G corresponds to "value leap", which records the morphological parameters before and after the leap. The value trajectory is traced through a distributed hash table. The key value of the distributed hash table is "data ID + timestamp", and the value is the base pair sequence of the corresponding stage. The value flow path is visualized by combining graph neural networks.

[0187] The base pair sequence of value DNA and the blockchain mapping rules: Each base pair (such as AT) corresponds to 16 bytes of data in the blockchain. The first byte of the sequence is the operation type identifier (0x01 = generation, 0x02 = transfer). Subsequent bytes store the timestamp (4 bytes), subject ID (8 bytes), and checksum (3 bytes), and are associated with the blockchain block hash through a Merkle tree.

[0188] The distributed hash table uses the Kademlia protocol. The node ID is a 160-bit random number. The initial number of nodes is 10, and one node is automatically added for every 100,000 new data entries. Data synchronization between nodes uses the UDP protocol, with a synchronization frequency of once every 5 minutes. When a node goes offline, data redundancy backup is automatically triggered to the three nearest nodes.

[0189] When a federated learning node goes offline, an edge backup + timeout retransmission strategy is adopted: before the node goes offline, the local parameters are backed up to two edge nodes. When the aggregation node detects a timeout (>5 minutes) without response, it reads the backup parameters from the edge nodes to participate in the aggregation. After the node comes back online, the global parameters are synchronized.

[0190] The neural symbol sandbox verification module includes:

[0191] (1) Closed-loop verification architecture: Construct a 'hypothesis-verification-correction' closed-loop system. The symbolic logic reasoning engine derives 12 possible causal paths based on the first-order predicate logic rule base (containing 200 causal reasoning rules, such as 'data leakage → risk entropy value increase'). The path screening threshold is set to rule matching degree ≥ 0.8. The large language model is called to retrieve supporting evidence in the academic literature database to form an evidence chain confidence score. High credibility conclusions are selected by threshold judgment.

[0192] The expression for the chain of evidence confidence scoring model is as follows:

[0193]

[0194] In the formula: C is the confidence score (0-1), and C≥0.8 indicates a highly credible conclusion;

[0195] w i Let i be the weight of the i-th piece of evidence.

[0196] e i For the strength of evidence, e is extracted from the literature database by a large language model. i = 0.7 × Impact Factor + 0.3 × Standardized Citations;

[0197] K represents the number of pieces of evidence, with a default of 5-10 pieces, and key conclusions increased to 10-20 pieces.

[0198] Formula source: Dempster-Shafer theory in evidence theory, combined with symbolic logic and machine learning for credibility assessment;

[0199] (2) Sandbox universality verification: The candidate conclusions are input into the metaverse sandbox for multi-scenario verification. Different application environments are simulated by changing the initial parameters, and the accuracy fluctuation curve of the conclusions is recorded. The verification results are aggregated by federated learning (using ECC encrypted transmission protocol, and the key is automatically updated every 24 hours) and fed back to the quantum neural consciousness modeling module of data elements to optimize the parameter matrix of the quantum neural decision-making unit, realize the closed-loop iterative optimization of the whole system, and solve the AI ​​black box problem.

[0200] Federated learning model aggregation formula:

[0201]

[0202] In the formula: θ global This is the global model parameter vector, with the same dimensions as the local model.

[0203] θ m The local model parameters for the m-th node include the weight matrix (1024x512) and bias vector (512×1) of the neural symbolic model.

[0204] M represents the number of federated nodes. In the medical field, M = 5-10 (hospitals), and in the financial field, M = 10-20 (banks).

[0205] Homomorphic encryption is required before aggregation. Ensure parameter privacy;

[0206] Formula source: Federated averaging algorithm in distributed machine learning, which aggregates model updates while ensuring data privacy.

[0207] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0208] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations 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.

Claims

1. A big data intelligent analysis system based on data element circulation, characterized in that: The system includes: Data Element Quantum Neural Consciousness Modeling Module: An embedded quantum neural network-based quantum neural decision-making unit that captures recipient credit information and scenario risk information in real time, autonomously selects circulation paths based on the value preference function matrix, and generates metadata tags; Cross-domain multi-scale causal entropy analysis module: Using a multi-scale causal entropy decomposition algorithm, it explores the deep causal mechanisms of cross-domain data, providing logical support for data-driven autonomous decision-making. Quantum fingerprint dynamic ownership game module: Upon receiving the causal analysis results, it constructs a dynamic ownership ecosystem using quantum fingerprint technology, generates quantum state identifiers to associate ownership shares, and adjusts the ownership ratio based on contribution through zero-knowledge proof game theory. Metaverse Sandbox Collaborative Inference Module: Based on the ownership framework, the circulation scenario is mapped to the metaverse sandbox, data is circulated in digital clones, the strategy returns and risks are simulated, mutation factors are introduced to simulate extreme scenarios, and the optimization results are output. Three-dimensional ethical constraint intelligent agent module: verifies sandbox inference results, constructs a three-dimensional ethical evaluation model, uses empathy learning to understand ethical intuition, freezes circulation and generates repair solutions when disputes arise, and provides feedback on value measurement logic; Active Value Metabolic Network Module: Combining ethical assessment, value generation is viewed as a metabolic process. Contributions are recorded using blockchain to form a metabolic chain. When a threshold is reached, a transition is triggered, and the value DNA is allocated to trace its trajectory. The neural symbol sandbox verification module combines a large language model with symbolic logic to build a closed loop. Candidate conclusions are input into the metaverse sandbox for multi-scenario verification. The verification results are aggregated through federated learning and fed back to the data element quantum neural consciousness modeling module.

2. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The data element quantum neural consciousness modeling module includes: (1) Quantum Neural Decision Unit Construction: This module is a decision unit composed of data elements implanted into a quantum neural network. It captures the credit rating of the receiver and the scene risk entropy value in real time through quantum bit state transitions, and completes the quantum superposition state decision of the circulation path based on the value preference function matrix. (2) Metadata tag generation mechanism: The decision-making unit synchronously generates metadata tags containing data feature vectors and circulation preference parameters. Quantum error correction coding is used to ensure the integrity of the tags. The tags are embedded in the quantum fingerprint layer of the data elements, and the real-time feedback of the data circulation status is realized through quantum entanglement state association.

3. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The cross-domain multi-scale causal entropy analysis module includes: (1) Multi-scale causal decomposition algorithm: Upon receiving the logical support requirement for data-driven autonomous decision-making, a multi-scale causal entropy decomposition algorithm is adopted. At the micro level, the implicit causal relationship between data particles is captured through a quantum entangled state simulator. At the meso level, a causal entropy weight network is constructed to quantify the cross-domain causal strength coefficient. At the macro level, cellular automata are used to simulate the emergence process of causal chains. (2) Cross-domain causal mechanism output: The algorithm transforms unstructured data into causal tensors through feature mapping, extracts cross-domain common causal features through convolutional neural networks, and generates a visual map containing causal strength and evolution path.

4. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The quantum fingerprint dynamic ownership game module includes: (1) Quantum ownership identification system: Based on quantum fingerprint technology, a dynamic ownership ecosystem is constructed. Each data derivative node generates a unique quantum state identifier through a quantum random number generator. The ownership share vectors of the original data and the derived data are associated through entangled states to achieve quantum-level binding of ownership relationships. (2) Zero-knowledge proof game engine: The module starts the zero-knowledge proof ownership game engine, which calculates the ownership ratio of each party in the data provision, algorithm optimization and scenario application stages in real time based on the contribution tensor algorithm, and ensures the immutability of the adjustment process through the principle of quantum nonlocality.

5. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The metaverse sandbox collaborative deduction module includes: (1) Digital Twin Mapping System: Based on the ownership framework, a cosmic collaborative space is built, and the physical circulation scenario is transformed into a three-dimensional model in the meta-universe sandbox. Data is loaded with quantum encryption protocol in the form of digital clones. The value return and risk cost of different strategies are simulated based on Monte Carlo simulation. (2) Mutation factor simulation mechanism: The sandbox has a built-in data mutation factor generator, which simulates extreme scenario parameters by sampling through Gaussian distribution, and iteratively optimizes the circulation strategy by combining the reinforcement learning Actor-Critic algorithm to generate an evaluation report containing strategy score and risk probability.

6. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The three-dimensional ethically constrained intelligent agent module includes: (1) Ethical assessment model architecture: A three-dimensional assessment model is constructed by integrating affective computing and ethical philosophy. The harm threshold evaluator quantifies the value of rights damage through a neural network trained by federated learning. The fairness index monitor uses Wasserstein distance to detect the risk of group discrimination. The sustainability evaluator calculates the data value consumption rate based on the entropy reduction principle. (2) Dynamic ethical decision-making mechanism: The intelligent agent analyzes human ethical intuition data through empathy learning neural network. When it detects the risk of ethical controversy, it triggers a dynamic circulation freeze command and calls the rule reasoning engine to generate a repair plan.

7. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The bioactive value metabolic network module includes: (1) Construction of value metabolism chain: Combining the results of ethical assessment, the generation of data value is regarded as a metabolic process of life-like organisms. Value interaction data forms a metabolism chain that includes value generation, transfer and consumption. The value leap algorithm is designed using Hopf bifurcation theory. When the metabolic entropy value reaches the critical threshold, the quantum leap of value form is automatically triggered. (2) Value DNA Traceability System: Assign a unique value DNA identifier to each data element, use the base pairing principle to record the value evolution trajectory throughout the entire life cycle, and realize the traceability of the value trajectory through a distributed hash table.

8. The big data intelligent analysis system based on data element circulation according to claim 1, characterized in that: The neural symbol sandbox verification module includes: (1) Closed-loop verification architecture: When generating analysis conclusions, the symbolic logic reasoning engine derives 12 possible causal paths, calls the large language model to retrieve supporting evidence in the academic literature database, and forms a confidence score for the evidence chain. (2) Sandbox universality verification: Input candidate conclusions into the metaverse sandbox for multi-scenario verification. By changing the initial parameters, simulate different application environments and record the accuracy fluctuation curve of the conclusions to solve the AI ​​black box problem.

Images