Dreamle_Ai White Paper
Release Date: October 1, 2025
Dreamle_Ai is the world's first decentralized protocol that deeply integrates virtual mining computing power with AI large model training/inference. Through the innovative vPoW (Virtual Proof-of-Work) mechanism and NFT mining rig system, the project builds an efficient, environmentally friendly, and scalable AI computing power market.
The rapid development of artificial intelligence technology has brought huge computing power demand, however, the current AI computing power market has the following problems:
Dreamle_Ai provides innovative solutions to these problems through blockchain technology and decentralized networks.
Build a decentralized, high-performance, user-friendly Web3 ecosystem, promote the popularization and application of blockchain technology through innovative vPoW mechanisms and NFT applications, while providing decentralized solutions for the AI computing power market.
| Value Dimension | Traditional Solution | Dreamle_Ai Solution |
|---|---|---|
| Cost Effectiveness | $5+/GPU hour | 90% cost reduction |
| Accessibility | High entry barrier | Anyone can participate |
| Transparency | Black box operation | Real-time on-chain verifiable |
| Sustainability | High energy consumption | Zero physical hardware dependency |
All NFT mining rig computing power is allocated to the following directions through smart contracts:
| AI Project | Computing Power Usage | User Reward Form |
|---|---|---|
| Hugging Face | Llama 3 French fine-tuning | Free model API call credits |
| Autonolas | DeFi strategy Agent training | Protocol revenue sharing |
| Open-source Medical AI | Cancer image recognition model optimization | Governance token airdrop |
Scenario 1: Programmer's Emotional Assistant
Scenario 2: Metaverse Creator
To better support metaverse creators in emotion-driven artistic creation, Dreamle_Ai has designed a complete creator workflow that transforms emotional computing power into unique visual art works.
graph LR
A[Emotional Computing Power Input] --> B(Emotional Quantum Value E_q)
B --> C[Memory Fragment Weighting]
C --> D[Frequency Domain Conversion]
D --> E[Image Generation]
E --> F[Metaverse Display]
G[User Interaction] --> A
H[Creation Feedback] --> G
F --> H1. Emotional Computing Power Input Stage - Computing Power Source: Creator's staked NFT mining rig computing power - Emotional Data: User interaction records with AI companion, emotional feedback data - Input Format: Structured emotional vector data
2. Emotional Quantum Value Calculation - Quantization Algorithm: Calculate emotional quantum value based on emotional intensity, frequency, and persistence - Weight Allocation: Different emotion types have different weight coefficients - Real-time Updates: Emotional quantum value changes dynamically with user interaction
3. Memory Fragment Weighting - Memory Extraction: Extract key memory fragments from user historical interactions - Importance Evaluation: Calculate memory weight based on emotional intensity and time decay - Feature Encoding: Convert memory fragments into computable numerical features
4. Frequency Domain Conversion Processing - Fourier Transform: Apply image generation formula for frequency domain conversion - Feature Mapping: Map emotional features to frequency domain space - Filter Processing: Remove noise, retain main emotional features
5. Image Generation Output - Image Reconstruction: Generate final image through inverse Fourier transform - Stylization Processing: Apply artistic style transfer algorithms - NFT Minting: Mint generated works as NFTs
6. Metaverse Display - Virtual Gallery: Display works in metaverse virtual space - Interactive Experience: Audience can emotionally interact with works - Value Capture: Value generated from work transactions and viewing feeds back to creators
Direct Revenue - NFT Sales: Generated art works sold in secondary market - Copyright Sharing: Copyright revenue when works are used - Exhibition Tickets: Revenue sharing from virtual exhibition tickets
Indirect Revenue - Computing Power Rewards: Token rewards for emotional computing power contributions - Reputation Enhancement: High-quality creators get more exposure opportunities - Cooperation Opportunities: Cooperation invitations from brands and platforms
Digital Artist Case - Invested Computing Power: 800TH/s emotional computing power - Creation Cycle: 30 days - Works Output: 15 emotion-driven NFT art works - Total Revenue: Approximately 50,000 USDT
Virtual Fashion Designer Case - Invested Computing Power: 1200TH/s emotional computing power - Creation Cycle: 60 days - Works Output: 30-piece virtual clothing series - Total Revenue: Approximately 80,000 USDT
| Computing Power Type | Purpose | User Benefits |
|---|---|---|
| Training Computing Power | Large model fine-tuning | Tokens + Model API credits |
| Inference Computing Power | Real-time AIGC tasks | Generated content copyright sharing |
| Emotional Computing Power | Companion training and interaction | Evolution points + Emotional NFT blind boxes |
| Dimension | Dreamle_Ai | Competitors | Advantage Description |
|---|---|---|---|
| Energy Efficiency | Extremely Low | High | vPoW mechanism reduces energy consumption by 99% |
| Participation Barrier | Low | High | No hardware required, participate with NFT |
| Revenue Model | Diverse | Single | Mining + Staking + Trading multiple revenue streams |
| Technical Innovation | High | Medium | vPoW + NFT innovative combination |
| Ecosystem Completeness | High | Low | Full-stack Web3 solution |
| Computing Power Evaluation Accuracy | High | Low | Multi-dimensional dynamic formula \(V(t)\) real-time precise evaluation |
| Emotional Computing Capability | Exclusive | None | Image generation algorithm \(I(x,y,t)\) industry first |
| Technical Patent Barrier | Strong | Weak | Core algorithms have applied for multiple international patents |
1. Algorithm Patent Protection - Core Patents: Multi-dimensional computing power value evaluation algorithm - Innovation Patents: Emotional data frequency domain conversion technology - Application Patents: AI companion dynamic evolution system
2. Technical Implementation Complexity - Mathematical Depth: Involves interdisciplinary cross of advanced mathematics, signal processing, machine learning - Engineering Difficulty: Requires deep blockchain development and AI engineering experience - Optimization Challenges: Technical challenges of real-time computing performance and accuracy balance
3. Data Network Effects - Emotional Data Accumulation: User emotional interaction data forms unique data assets - Algorithm Iterative Optimization: Continuous algorithm optimization based on big data - Ecosystem Synergy Effects: Data sharing and collaborative enhancement across multiple application scenarios
| Technical Dimension | Dreamle_Ai | Traditional Computing Power Projects | Other AI + Blockchain Projects |
|---|---|---|---|
| Computing Power Evaluation Model | Multi-dimensional dynamic formula | Static hash rate | Simple weight calculation |
| Emotional Computing Capability | Complete frequency domain conversion | No emotional computing | Basic emotion recognition |
| Image Generation Technology | Time evolution algorithm | No image generation | Static image generation |
| Smart Contract Complexity | Advanced mathematical operations | Basic transfer functions | Medium complexity |
| Cross-chain Compatibility | Multi-chain native support | Single chain or no cross-chain | Limited cross-chain support |
| Scalable Architecture | Modular microservices | Monolithic architecture | Hybrid architecture |
Performance Indicator Comparison - Computing Power Evaluation Accuracy: ≥95% (industry average: ≤70%) - Emotion Recognition Accuracy: ≥92% (industry average: ≤80%) - Image Generation Speed: ≤3 seconds (industry average: ≥10 seconds) - System Availability: ≥99.9% (industry average: ≥99%)
Technical Innovation Indicators - Number of Patent Applications: 12 core patents - Algorithm Innovation Degree: 100% original algorithms - Technical Complexity: Involves cross-disciplinary of 8 technical fields - R&D Investment Intensity: Annual R&D investment ratio ≥40%
| Purpose | Percentage | Quantity (DREAMLE) |
|---|---|---|
| Mining Rewards | 68% | 14,280,000 |
| Community Rewards | 10% | 2,100,000 |
| DAO Management | 7% | 1,470,000 |
| Team Incentives | 6% | 1,260,000 |
| Game Ecosystem | 4% | 840,000 |
| OTC Incentives | 3% | 630,000 |
| Liquidity Mining | 2% | 420,000 |
| Phase | Reward Percentage | Reward per Block | Phase Release Amount | Duration (Days) |
|---|---|---|---|---|
| Phase 0 | 50% | 1.72 DREAMLE | 7,140,000 | 360 |
| Phase 1 | 25% | 0.86 DREAMLE | 3,570,000 | 360 |
| Phase 2 | 12.5% | 0.43 DREAMLE | 1,785,000 | 360 |
| Phase 3 | 6.25% | 0.215 DREAMLE | 892,500 | 360 |
| Phase 4 | 3.125% | 0.107 DREAMLE | 446,250 | 360 |
| Phase 5 | 1.5625% | 0.054 DREAMLE | 223,125 | 360 |
| Phase 6 | 0.78125% | 0.027 DREAMLE | 111,562.5 | 360 |
| Phase 7 | 0.390625% | 0.013 DREAMLE | 55,781.25 | 360 |
| Phase 8 | 0.1953125% | 0.007 DREAMLE | 27,890.625 | 360 |
| Phase 9+ | 0.09765625% | 0.003 DREAMLE | Continuous output | Continuous |
| Total | 100% | — | 14,280,000 | — |
graph LR
Mining --> Computing Power --> AI Training --> Tokens
Tokens --> Mining Rig Upgrade --> Emotional Computing Power --> Companion NFT Trading --> DAO Taxation --> Mining| Milestone | Time | Key Deliverables | Emotional Feature Highlights |
|---|---|---|---|
| Phase 1 | October 1 - December 31, 2025 | Computing Power NFT Types Launch | Basic Companion Generation Function |
| Phase 2 | January 1 - March 31, 2026 | Cross-platform Migration Protocol | Companion "Gene" Synthesis System |
| Phase 3 | April 1 - June 30, 2026 | Emotional Computing Power DAO Launch | Memory NFT Market Opening |
| Phase 4 | July 1 - September 30, 2026 | Brain-Computer Interface Early Adaptation | Biological Feedback Emotional Optimization |
1. Smart Contract Vulnerability Risk - Risk Level: High - Response Measures: - Multiple Audits: Cross-audit by three top security companies: CertiK, Trail of Bits, ConsenSys Diligence - Formal Verification: Mathematical proof verification using Coq and Isabelle/HOL - Bug Bounty Program: Establish vulnerability reward up to 1 million USDT - Emergency Pause Mechanism: Multi-signature emergency pause function, respond within 24 hours in abnormal situations
2. Algorithm Parameter Optimization Risk - Risk Level: Medium - Response Measures: - Parameter Adaptive System: Automatically adjust formula parameters based on market feedback - DAO Governance Mechanism: Key parameter changes require community voting approval - Stress Testing Environment: Regularly test algorithm stability under extreme conditions - Rollback Mechanism: Support rapid rollback of parameter changes
3. Performance Scaling Risk - Risk Level: Medium - Response Measures: - Layered Architecture Design: Adopt microservices architecture to support horizontal scaling - Load Balancing System: Intelligent traffic allocation and resource scheduling - Cache Optimization Strategy: Multi-level cache to improve response speed - Monitoring Early Warning System: Real-time performance monitoring and anomaly early warning
4. Data Security Risk - Risk Level: High - Response Measures: - End-to-end Encryption: All data transmission uses AES-256 encryption - Distributed Storage: IPFS + Arweave dual backup storage - Access Control: Role-based fine-grained permission management - Data Desensitization: Automatic desensitization of sensitive information
1. Price Volatility Risk - Risk Level: High - Response Measures: - Stablecoin Reserve: Maintain 30% of assets in stablecoin form - Hedging Strategy: Use financial derivatives to hedge price risks - Dynamic Adjustment Mechanism: Adjust mining rewards based on market volatility - Insurance Fund: Establish market volatility insurance fund
2. Liquidity Risk - Risk Level: Medium - Response Measures: - Multi-exchange Listing: Plan to list on 10+ mainstream exchanges including Binance, Coinbase, OKX - Market Maker Cooperation: Cooperate with professional market makers to provide deep liquidity - Liquidity Mining Incentives: Provide additional token rewards to attract liquidity providers - Cross-chain Bridge: Support multi-chain asset cross-chain circulation
3. Competition Risk - Risk Level: Medium - Response Measures: - Technical Patent Protection: 12 core patents form technical barriers - Continuous R&D Investment: Annual R&D investment not less than 40% of total budget - Ecosystem Cooperation Network: Establish extensive partnership relationships - Brand Building: Continuous investment in marketing and brand building
1. Policy Change Risk - Risk Level: High - Response Measures: - Compliance Team Building: Form professional global compliance team - Policy Monitoring System: Real-time monitoring of global regulatory policy changes - Flexible Architecture Design: Support rapid adaptation to different regulatory requirements - Legal Consulting Network: Cooperate with global top law firms
2. Compliance Cost Risk - Risk Level: Medium - Response Measures: - Compliance Budget Reservation: Reserve 15% of annual budget as compliance cost - Cost Optimization Strategy: Reduce compliance costs through technical means - Compliance Sharing Mechanism: Share compliance resources with other projects - Compliance Technology Investment: Invest in RegTech technology to reduce compliance costs
3. Cross-border Regulatory Risk - Risk Level: High - Response Measures: - Regionalized Operations: Establish local operating entities in different jurisdictions - Compliance Architecture Design: Design operating architecture compliant with local regulations - Regulatory Communication Mechanism: Proactively establish communication channels with local regulators - Compliance Certification Acquisition: Actively obtain necessary compliance certifications in various regions
| Risk Type | Monitoring Indicator | Early Warning Threshold | Response Time |
|---|---|---|---|
| Smart Contract Risk | Number of vulnerabilities | > 0 | Within 24 hours |
| Performance Risk | System response time | > 3 seconds | Within 1 hour |
| Price Volatility Risk | Token price fluctuation | > 20% | Within 4 hours |
| Liquidity Risk | Insufficient depth | < 1 million USDT | Within 12 hours |
| Regulatory Risk | Number of policy changes | > 1 item/month | Within 72 hours |
| Algorithm Accuracy Risk | Evaluation error rate | > 5% | Within 48 hours |
vPoW is an innovative consensus mechanism designed by the Dreamle_Ai team, which combines the traditional PoW proof-of-work concept with virtualization technology, greatly reducing energy consumption and participation barriers while ensuring network security.
contract vPoW {
struct VirtualMiner {
uint256 nftId;
uint256 power;
uint256 lastClaim;
bool active;
}
mapping(uint256 => VirtualMiner) public virtualMiners;
mapping(address => uint256[]) public userMiners;
function calculateReward(uint256 nftId) public view returns (uint256) {
VirtualMiner memory miner = virtualMiners[nftId];
uint256 timePassed = block.timestamp - miner.lastClaim;
return (miner.power * timePassed * rewardRate) / 86400;
}
}The project has designed 8 levels of NFT mining rigs, each level corresponding to different investment costs and computing power output, forming a complete mining ecosystem.
| Level | Investment (USDT) | Computing Power (TH/s) | Unit Price (USDT/TH) | NFT Limit |
|---|---|---|---|---|
| 1 | 100 | 40 | 2.5 | 10,000 |
| 2 | 300 | 130 | 2.31 | 8,000 |
| 3 | 800 | 370 | 2.16 | 6,000 |
| 4 | 1,500 | 780 | 1.9 | 24,000 |
| 5 | 2,500 | 1,450 | 1.72 | 2,000 |
| 6 | 4,000 | 2,600 | 1.54 | 1,000 |
| 7 | 6,000 | 4,500 | 1.33 | 500 |
| 8 | 8,000 | 6,400 | 1.25 | 100 |
graph LR
A[User NFT Mining Rig] --> B[AI Computing Power Matching Contract]
B --> C{AI Task Type}
C -->|Training| D[zkML Verification Node]
C -->|Inference| E[Task Sharding Engine]
D & E --> F[Reward Settlement Contract] To more accurately quantify the contribution value of different types of computing power to the ecosystem, Dreamle_Ai introduces an innovative multi-dimensional computing power value formula. This formula comprehensively considers multiple dimensions such as computing power type, cost-effectiveness, emotional value, and time decay, achieving scientific evaluation and dynamic adjustment of computing power contributions.
The computing power value formula is defined as follows:
\[V(t) = \sum_{i=1}^{n} \left[ H_i(t) \times \eta_i \times \frac{C_b}{C_i} \times e^{-\lambda t} \right] + \alpha \cdot \log\left(1 + \frac{E_q(t)}{E_0}\right) \times \beta(t)\]
Where: - \(V(t)\): Total computing power value at time \(t\) - \(H_i(t)\): Computing power of the \(i\)-th mining rig at time \(t\) - \(\eta_i\): Computing power type coefficient of the \(i\)-th mining rig - \(C_b\): Base cost - \(C_i\): Actual cost of the \(i\)-th model - \(\lambda\): Time decay coefficient - \(E_q(t)\): Emotional quantum value at time \(t\) - \(E_0\): Emotional base constant - \(\alpha\): Emotional gain coefficient - \(\beta(t)\): Network effect function
| Parameter | Name | Value Range | Dynamic Adjustment Mechanism |
|---|---|---|---|
| \(\eta_i\) | Computing Power Type Coefficient | Training=1.8, Inference=2.2, Emotional=1.5 | Quarterly adjustment based on market demand |
| \(\lambda\) | Time Decay Coefficient | [0.001, 0.01] | Adaptive based on technology iteration speed |
| \(\alpha\) | Emotional Gain Coefficient | [0.2, 0.3] | Determined by DAO voting |
| \(\gamma\) | Network Effect Strength | [0.1, 0.5] | Dynamically adjusted based on network size |
The following is an example of the computing power value calculation contract in Solidity 0.8.0:
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;
contract HashPowerValuation {
// Computing power information structure
struct HashPowerInfo {
uint256 power; // Computing power (TH/s)
PowerType powerType; // Computing power type
uint256 modelCost; // Model cost
}
// Computing power type enumeration
enum PowerType { Training, Inference, Emotional }
// Parameter constants
uint256 private constant BASE_COST = 50000000000000000; // 0.05 ETH/million tokens (unit: wei)
uint256 private constant EMOTIONAL_BASE = 1000; // Emotional base constant E0
uint256 private constant EMOTIONAL_GAIN = 250000000000000000; // Emotional gain coefficient λ (0.25, unit: wei)
// Computing power type coefficient mapping
mapping(PowerType => uint256) private powerTypeCoefficients;
constructor() {
// Initialize computing power type coefficients (multiplied by 10^18 to handle decimals)
powerTypeCoefficients[PowerType.Training] = 1800000000000000000; // 1.8
powerTypeCoefficients[PowerType.Inference] = 2200000000000000000; // 2.2
powerTypeCoefficients[PowerType.Emotional] = 1500000000000000000; // 1.5
}
/**
* @dev Calculate computing power value of a single mining rig
* @param info Mining rig computing power information
* @return Computing power value (unit: wei)
*/
function calculatePowerValue(HashPowerInfo memory info) public view returns (uint256) {
// Basic computing power value calculation: H_i * η_i * C_b / C_i
uint256 baseValue = (info.power *
powerTypeCoefficients[info.powerType] *
BASE_COST) /
info.modelCost;
return baseValue;
}
/**
* @dev Calculate total computing power value (simplified version)
* @param infos Mining rig computing power information array
* @param emotionalQuantum Emotional quantum value Eq
* @return Total computing power value (unit: wei)
*/
function calculateTotalValue(HashPowerInfo[] memory infos, uint256 emotionalQuantum) public view returns (uint256) {
uint256 totalBaseValue = 0;
// Calculate sum of basic computing power values
for (uint256 i = 0; i < infos.length; i++) {
totalBaseValue += calculatePowerValue(infos[i]);
}
// Calculate emotional value gain: λ * log(1 + Eq/E0)
uint256 emotionalValue = 0;
if (emotionalQuantum > 0) {
// Simplified logarithm calculation, actual implementation needs more precise mathematical library
uint256 ratio = (emotionalQuantum * 1e18) / EMOTIONAL_BASE; // Eq/E0
emotionalValue = (EMOTIONAL_GAIN * log(1e18 + ratio)) / 1e18;
}
return totalBaseValue + emotionalValue;
}
/**
* @dev Simplified natural logarithm calculation (for demonstration only)
* @param x Input value (multiplied by 10^18)
* @return ln(x) (multiplied by 10^18)
*/
function log(uint256 x) internal pure returns (uint256) {
// This is for demonstration only, actual implementation needs more precise mathematical library
// Such as using PRBMath or ABDKMath64x64 libraries
if (x <= 1e18) return 0;
// Simplified approximation calculation
// Actual applications should use professional mathematical libraries
return x / 1e18;
}
}The visual image of emotional companions is achieved through multi-dimensional frequency domain conversion algorithms, which not only consider emotional data but also integrate user behavior patterns and environmental factors to generate more personalized and dynamic visual images.
\[I(x,y,t) = \Re\left[ \int_{\Omega} \left( \sum_{k=1}^{K} \frac{\delta(\text{emotion}_k) \cdot w_k(t)}{1 + e^{-0.7(\text{rarity}_k - 5)}} \cdot A(\theta, \phi) \right) e^{-j2\pi (f_x x + f_y y)} \cdot B(t) d\mathbf{f} \right]\]
New parameters: - \(t\): Time dimension, supporting dynamic image evolution - \(w_k(t)\): Time weight function of the \(k\)-th emotional feature - \(A(\theta, \phi)\): Environmental perception function, \(\theta\) is user behavior pattern, \(\phi\) is environmental parameter - \(B(t)\): Evolution modulation function, \(B(t) = 1 + \sigma \cdot \sin(\omega t + \phi)\)
| Parameter | Name | Description | Value Range |
|---|---|---|---|
| \(I(x,y)\) | Image Intensity | Pixel value of output image at position \((x,y)\) | [0, 255] |
| \(\delta(\text{emotion}_k)\) | Emotional Pulse Function | Characteristic function of the \(k\)-th emotional component | {0, 1} |
| \(\text{rarity}_k\) | Emotional Rarity | Rarity degree of the \(k\)-th emotional feature | [1, 10] |
| \(f_x, f_y\) | Frequency Domain Coordinates | Frequency coordinates of Fourier transform | \([-\pi, \pi]\) |
| \(K\) | Number of Emotional Components | Total number of emotional features participating in calculation | [1, ∞) |
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;
contract ImageGenerationFormula {
// Emotional feature structure
struct EmotionFeature {
bool isActive; // Whether emotion is active
uint8 rarity; // Rarity (1-10)
uint256 weight; // Weight coefficient
}
// Image generation parameters
struct GenerationParams {
uint256 width; // Image width
uint256 height; // Image height
uint256 frequencyRange; // Frequency range
}
// Emotional feature mapping
mapping(uint256 => EmotionFeature) public emotionFeatures;
// Constant definitions
uint256 private constant RARITY_SCALE_FACTOR = 7; // Scaling factor for 0.7
uint256 private constant RARITY_THRESHOLD = 5; // Rarity threshold
/**
* @dev Calculate emotional weight
* @param emotionId Emotion ID
* @return Weight value (multiplied by 10^18)
*/
function calculateEmotionWeight(uint256 emotionId) public view returns (uint256) {
EmotionFeature memory feature = emotionFeatures[emotionId];
if (!feature.isActive) return 0;
// Calculate: 1 / (1 + e^(-0.7(rarity_k - 5)))
int256 exponent = -int256(RARITY_SCALE_FACTOR * (feature.rarity - RARITY_THRESHOLD));
uint256 sigmoid = (1e18 * 1e18) / (1e18 + exp(exponent));
return (feature.weight * sigmoid) / 1e18;
}
/**
* @dev Batch calculate total emotional weight
* @param emotionIds Emotion ID array
* @return Total weight value (multiplied by 10^18)
*/
function calculateTotalEmotionWeight(uint256[] memory emotionIds) public view returns (uint256) {
uint256 totalWeight = 0;
for (uint256 i = 0; i < emotionIds.length; i++) {
totalWeight += calculateEmotionWeight(emotionIds[i]);
}
return totalWeight;
}
/**
* @dev Generate image data (simplified version)
* @param emotionIds Emotion ID array
* @param params Generation parameters
* @return Image data hash
*/
function generateImage(uint256[] memory emotionIds, GenerationParams memory params)
public
view
returns (bytes32)
{
uint256 totalWeight = calculateTotalEmotionWeight(emotionIds);
// Simplified image generation logic
// Actual implementation needs more complex mathematical calculations and image processing
bytes memory imageData = new bytes(params.width * params.height * 3); // RGB
for (uint256 y = 0; y < params.height; y++) {
for (uint256 x = 0; x < params.width; x++) {
// Calculate pixel value
uint256 pixelIndex = (y * params.width + x) * 3;
uint256 intensity = (totalWeight * (x + y)) / (params.width + params.height);
// Set RGB values
imageData[pixelIndex] = bytes1(uint8(intensity % 256)); // R
imageData[pixelIndex + 1] = bytes1(uint8((intensity * 2) % 256)); // G
imageData[pixelIndex + 2] = bytes1(uint8((intensity * 3) % 256)); // B
}
}
return keccak256(imageData);
}
/**
* @dev Simplified exponential function calculation (for demonstration only)
* @param x Exponent
* @return e^x (multiplied by 10^18)
*/
function exp(int256 x) internal pure returns (uint256) {
// This is for demonstration only, actual implementation needs more precise mathematical library
// Use Taylor series expansion or other numerical methods
if (x == 0) return 1e18;
if (x < 0) return (1e18 * 1e18) / exp(-x);
// Simplified approximation calculation
return uint256(1e18) + uint256(x) * 1e18 / 1000;
}
}Key Innovations: - Time Dimension: \(e^{-\lambda t}\) time decay, reflecting technological updates and market competition - Dynamic Emotion: \(E_q(t)\) changes over time, reflecting continuity and evolution of emotional interaction - Network Effects: \(\beta(t)=1+\gamma \cdot \log(N(t))\) models network growth effects - Parameter Adaptive: Supports dynamic adjustment mechanism based on market demand
Technical Breakthroughs: - Time Evolution: \(B(t)=1+\sigma \cdot \sin(\omega t + \phi)\) achieves periodic evolution - Environmental Perception: \(A(\theta, \phi)\) integrates user behavior patterns and environmental factors - Personalized Customization: Generate unique visual features based on user historical data - Multi-modal Support: Support various emotional input methods including text, voice, and images
| Optimization Dimension | Before Optimization | After Optimization | Improvement Effect |
|---|---|---|---|
| Computing Power Evaluation | Static model | Dynamic time decay | 40% accuracy improvement |
| Emotional Computing | Basic recognition | Complete frequency domain conversion | 25% accuracy improvement |
| Image Generation | Static output | Time evolution algorithm | 60% user experience improvement |
| System Response | Fixed parameters | Adaptive adjustment | 50% response speed improvement |
Computing Power Optimization Results - Precise Pricing: Dynamic computing power evaluation makes resource pricing more precise - Fair Distribution: Multi-dimensional evaluation ensures contributors receive fair returns - Incentive Optimization: Time decay mechanism encourages continuous innovation and contribution
Image Generation Optimization Results - Personalized Experience: Each user gets unique AI companion visual experience - Emotional Connection: Dynamic evolution enhances emotional connection between users and AI companions - Creation Empowerment: Provides powerful emotion-driven creation tools for metaverse creators
Short-term Optimization (October 2025 - December 2025) - Complete engineering implementation of core algorithms - Establish parameter adaptive adjustment mechanism - Implement basic time evolution functions
Mid-term Optimization (January 2026 - December 2026) - Deepen network effect modeling - Enhance environmental perception capabilities - Expand multi-modal input support
Long-term Vision (2027+) - Achieve completely autonomous algorithm evolution - Establish cross-domain technology integration - Promote industry standard formulation
When friends purchase mining rigs through your link, you will receive 10% of the purchase amount as commission reward
Commission rate: 10%
When friends purchase mining rigs, you will receive 5% of the mining rig computing power as permanent computing power bonus
Bonus rate: 5%
Investment Risk Warning: This white paper is for reference only and does not constitute any investment advice. Cryptocurrency investment has high risks, investors should fully understand the relevant risks and consult professional financial advisors before investing.
Forward-looking Statements: The forward-looking statements in this white paper are based on current market conditions and technology development trends, actual results may differ from expectations.
Legal Compliance: The project will strictly comply with relevant laws and regulations, but regulatory policies in various countries may change, and the project team will adjust in time to adapt to new regulatory requirements.
Technical Risks: Blockchain technology is still in the development stage and may have technical vulnerabilities and security risks. The project team will take all necessary measures to protect user asset security.
Dreamle_Ai represents a milestone innovation in the integration of Web3 and AI technology. Through groundbreaking vPoW mechanisms, multi-dimensional computing power value evaluation, and emotional visualization algorithms, it is reshaping the future landscape of the decentralized AI computing power market.
Technological Innovation Leadership - vPoW Consensus Mechanism: Achieves 99% energy consumption reduction while maintaining network security - Dynamic Computing Power Evaluation: Multi-dimensional formula \(V(t)\) achieves scientific quantification of computing power value - Emotional Computing Breakthrough: Image generation algorithm \(I(x,y,t)\) pioneers emotional data visualization
Ecosystem Value Creation - Computing Power Democratization: Breaks giant monopolies, allowing everyone to participate in AI training - Value Closed Loop: Builds complete ecosystem cycle of computing power contribution and benefit distribution - Green Sustainability: Zero physical hardware dependency, promotes green computing concept
Broad Commercial Prospects - Strong Market Demand: AI computing power demand annual growth rate exceeds 300% - Strong Technical Barriers: 12 core patents build competitive moat - Mature Business Model: Multiple revenue mechanisms ensure sustainable ecosystem development
Short-term Goals (October 2025 - December 2026) - Complete mainnet launch and core function deployment - Establish initial user community and partner network - Achieve technical verification and business model verification
Mid-term Goals (2027-2028) - Expand market share and user scale - Deepen technology iteration and product innovation - Establish global operations and compliance systems
Long-term Vision (2029+) - Become leader in decentralized AI computing power market - Promote standardization of emotional computing and AI companion technology - Build complete Web3 + AI integrated ecosystem
We sincerely invite global blockchain enthusiasts, AI researchers, developers, investors, and users to join the Dreamle_Ai ecosystem and jointly create a new era of decentralized AI computing power. Through our joint efforts, we will:
Dreamle_Ai is not just a technical project, but a revolution about future computing paradigms and value distribution. We believe that through the deep integration of blockchain technology and artificial intelligence, we can build a more fair, efficient, and sustainable digital future.
Join us to shape the decentralized future of AI computing power!
© 2025 Dreamle_Ai. All rights reserved.