Background of the Introduction of AICMP

1.1 Background of AICMP’s Launch

As the first decentralized cryptocurrency, Bitcoin ensures the security of the ledger through the Proof of Work (PoW) consensus algorithm. In the Bitcoin network, miners use specialized hardware (such as ASIC, FPGA, and occasionally GPU) to compete to solve cryptographic puzzles and validate new blocks. With the development of the Bitcoin ecosystem, the mining difficulty continues to rise, and the hash rate keeps growing. Individual miners, in order to obtain more stable income, gradually form mining pools to participate in mining by aggregating computing power.

However, traditional mining pools have exposed many problems in the operation process. In terms of resource allocation, the unified share allocation method used has failed to fully consider the differences in miners’ hardware, power efficiency, and network conditions, resulting in low resource utilization efficiency and serious energy waste. For small miners, due to the weak hardware performance or high electricity costs, they earn meager profits in large mining pools, facing a high entry barrier, which severely hinders the decentralized development of the mining ecosystem. At the same time, the reward calculation mechanism of many mining pools is opaque, lacks real-time adaptability, and is difficult to cope with sudden market price fluctuations and mining difficulty changes, further weakening the trust of participants.

AI-driven Collaborative Mining Pool (AICMP) is designed to address these issues. AICMP utilizes artificial intelligence technology for resource allocation and data-driven decision-making, through innovative designs such as dynamic task allocation, network and market forecasting, fair profit distribution, and reinforcement learning optimization, to improve mining resource utilization efficiency, ensure fair returns for small miners, enhance the adaptability of mining pools to market changes, and provide a new solution for the sustainable development of the Bitcoin mining ecosystem.

1.2 Bitcoin Mining Overview

1.2.1 Bitcoin Protocol Overview

The security model of Bitcoin is based on solving the computationally expensive SHA-256 hash function. The network automatically adjusts the mining difficulty every 2,016 blocks (approximately every 2 weeks) to maintain an average time interval of 10 minutes for generating a new block. When a miner finds a valid block (i.e., a computed hash value that is smaller than the difficulty target), they will receive a block reward (currently 3.125 BTC, halved approximately every four years) as well as all transaction fees included in that block. This incentive mechanism encourages miners to continuously upgrade or expand their hardware to improve mining competitiveness, which has been particularly significant since the birth of Bitcoin.

1.2.2 Evolution and Common Models of Mining Pool

With the increasing difficulty of Bitcoin mining, individual miners have difficulty obtaining stable profits, giving rise to mining pools. Mining pools increase the probability of finding valid blocks by aggregating the computing power of multiple miners, thereby achieving more frequent profit distribution. Currently, there are several popular methods for mining pool reward distribution:

1. Proportional distribution: In a mining round, the reward each miner receives is directly proportional to the number of valid shares they contributed to the mining pool before finding the block. This method is simple and straightforward, but it overlooks the miner’s actual hashing power efficiency, local costs, and hardware constraints.
2. Pay-Per-Share (PPS) Mining: Each valid share has a fixed payment amount, providing miners with predictable income but transferring the risk of income fluctuation to the mining pool operator.
3. Pay-per-last-N-shares (PPLNS): Only the last N valid shares before a block is found are used to determine the reward, reducing the ‘pool-hopping’ behavior of miners switching mining pools frequently to get instant rewards, but it also does not fully consider the miners’ actual situation.

Although these traditional reward models introduce the concepts of trust and fairness, in practical operation, they generally overlook the actual computing power efficiency, local costs, and real-time hardware limitations of miners. At the same time, the lack of an adaptive difficulty adjustment mechanism for each miner leads to low resource utilization efficiency, and insufficient attention to short-term market changes and mining difficulty trends.

1.3 Deficiencies in the design of existing mining pools

1. Inefficient resource utilization: The unified share distribution method does not fully utilize the differences in ASIC models, computing power configurations, and network conditions among different miners. For example, high-performance ASIC miners may be assigned tasks with the same difficulty as low-performance miners, resulting in the underutilization of computing power in high-performance miners and the possibility of low efficiency in low-performance miners due to heavy tasks, causing overall energy waste.
2. Small miners face high barriers to entry: Small-scale mining operations are limited by hardware performance and electricity costs, resulting in minimal profits in traditional mining pools. Large industrial miners dominate the market due to economies of scale, making it difficult for small miners to compete and potentially forcing them to give up mining, which is not conducive to the decentralized development of the Bitcoin network.
3. Opaque reward mechanism: Many mining pools use opaque methods to calculate shares and fees, making it difficult for participants to clearly understand the reward calculation process, which can easily lead to a trust crisis and affect the long-term stable development of the mining pool.
4. Limited Real-time Adaptability: Cryptocurrency market prices fluctuate sharply, and Bitcoin mining difficulty can also change suddenly. Traditional mining pools often struggle to adjust quickly to adapt to these new situations, resulting in unstable miner earnings and affecting the profitability of mining pools.

   2. Core Design and Features of AICMP

2.1 Dynamic Task Allocation

AICMP adopts a task assignment engine driven by artificial intelligence, which customizes share difficulty for each miner based on real-time data. Its key input parameters include:

1. Hash rate: The speed at which miners attempt to solve the problem, reflecting their computing power.
2. Power efficiency: the ratio of hash rate to energy consumption, measuring the energy utilization efficiency of mining hardware.
3. Delay: Refers to the average network round-trip time, affecting the speed of share submission and validation.

By matching the share difficulty with these indicators, AICMP enables high-throughput ASIC miners to handle more complex tasks, while smaller or energy-limited devices undertake relatively lighter workloads. This dynamic task allocation not only improves the utilization efficiency of aggregated hash power, reduces energy waste caused by heavy mining tasks, but also maximizes the effective hash rate of mining pools in the network.

2.2 Network and Market Forecast

The prediction analysis unit of AICMP uses machine learning models, especially time series neural networks (such as RNN, LSTM), to make the following predictions:

1. Upcoming difficulty adjustment: By analyzing historical difficulty data and current network status, predict the next difficulty adjustment of the Bitcoin network, and help mining pools adjust mining strategies in advance.
2. Bitcoin spot price: combining historical price fluctuation patterns and real-time market signals to predict future Bitcoin prices, so that mining pools can optimize profits based on price changes.
3. Optimize transaction fees by predicting the congestion level of transactions in the potential memory pool, and select transactions with higher fees for packaging to increase overall mining pool revenue.

The system can also integrate external data, such as global cryptocurrency market trends, local energy prices, etc., to achieve more accurate modeling. Through this predictive method, AICMP can proactively adjust the share difficulty and energy allocation of the mining pool to maintain profitability and adaptability during price fluctuations or difficulty jumps.

2.3 Fair Distribution of Earnings

AICMP incentivizes small miners to participate in mining through a weighted reward mechanism. Unlike traditional linear reward allocation based strictly on hash rate, the formula for AICMP is as follows:

In this formula, although large miners can still earn more profits due to higher H1, small miners can obtain a larger share of profits compared to a purely linear distribution. This method helps enhance the decentralization of the Bitcoin network, maintain trust between participants, encourage wider participation, and fundamentally support the secure and stable operation of the Bitcoin network.

2.4 Reinforcement Learning Optimization

AICMP uses reinforcement learning (RL) algorithms to continuously optimize the allocation strategy of mining pools. By modeling the operating environment of the mining pool (including miner status, input data, block difficulty, and reward results) as a Markov decision process (MDP), the system trains a policy p to maximize long-term profits. The iterative nature of reinforcement learning makes it particularly suitable for dynamic, sequential decision-making scenarios, and can adapt to constantly changing hardware and market conditions over time.

Three, the technical architecture of AICMP

3.1 AI Orchestrating Layer

The AI orchestration layer is the core hub of AICMP, consisting of four main sub-modules:

1. Data Collection Module: Collects key metrics of miners, such as H, E, L, through secure protocols (such as Stratum V2, WebSockets). Aggregates and normalizes the real-time data collected, and stores it in a time series database. It also continuously monitors the data to detect anomalies or abnormal situations, such as sudden decrease in hash rate.
2. Task Allocation Engine: Applying reinforcement learning strategies to allocate share difficulty, achieving the efficiency goal of the mining pool by solving constrained optimization problems. Task allocation is updated every few seconds to minutes based on the scale and volatility of the mining pool. Direct communication with miners to minimize the latency of share allocation.
3. Prediction Analysis Unit: Based on historical difficulty, price data, and mempool status, train an LSTM-based model to provide short-term predictions for block intervals, network difficulty, and potential transaction fees. Integrated with reinforcement learning agents to enable strategies to consider future possible states.
4. Strategy management and reinforcement learning module: Implement various reinforcement learning algorithms (such as Proximal Policy Optimization (PPO), A2C, DQN) to control resource allocation. Maintain a replay buffer containing $(s, a, r)$ tuples to optimize the policy over time.

3.2 Mining Interface Layer

The Miner Interface layer provides miners with a range of tools and dashboards for:

1. Real-time Performance Visualization: Displays real-time performance of each miner, including submitted shares, accepted shares, and estimated rewards, helping miners understand their mining status.
2. Operation parameter configuration: Miners are allowed to configure operation parameters, such as maximum power usage, temperature thresholds, etc., in order to better manage mining equipment.
3. Exception notice: When there are abnormal situations, such as significantly increased network latency or critical hardware failures, users will be notified promptly to ensure that miners can take timely measures.

A user-friendly interface is crucial for building trust and increasing transparency, especially for miners who may not be familiar with machine learning technology.

3.3 Revenue Distribution Module

When the mining pool successfully mines a block, the block reward and transaction fees will be sent to the mining pool’s coinbase address. The income distribution module is responsible for:

1. Calculate miner’s earnings: Use the weighted formula $\eta$ to calculate the earnings (R) of each miner.
2. Automatic payment: Automatically execute profit payments and ensure that the payment process has an immutable audit trail.
3. Retention fee: Retain a certain proportion of the mining pool fee (delta) to support server infrastructure, AI research, and other operational costs.

3.4 Feedback and Learning Loop

All operational data of AICMP (such as block mining frequency, prediction accuracy, miner performance changes, etc.) will be fed back to the AI orchestration layer. This closed-loop system can continuously optimize the entire process, continuously adjust the share difficulty, adjust the weighted index $\eta$ when necessary, and improve the prediction model for future cycles.

3.5 Security, Trust, and Communication Protocols

AICMP adopts multiple layers of network security measures to prevent attacks:

1. Encryption (TLS/SSL): Protects the share submission process to prevent data interception or tampering.
2. Miner Authentication: Verify the identity of each miner through a unique certificate or encryption key to prevent identity theft and unauthorized use.

3. DDoS protection: Using a distributed architecture, load balancer, and rate limiting mechanism to ensure the normal operation time of the mining pool in a malicious environment.

   4. Basic Information of AICMP Token

4. Market Cap: $2,397,399

5. Fully diluted market cap: $2,397,399
6. Total supply: 932,936,533
7. Maximum Supply: 932,936,533
8. Public Chain: SOL
9. Contract Address: BAEXK4X6B3hkqmEkPuyyZQ5fZUb5iZ6SaJ7a9UDnpump
10. Market Performance of Tokens
    
    Currently, the AICMP token has landed in the Gate.io Innovation Zone,Click to Trade!

Risk Warning: This project may have higher volatility and/or higher risks compared to other tokens. Please do your own research.

Conclusion

AICMP uses artificial intelligence technology for resource allocation and data-driven decision-making. It improves the efficiency of mining resources, ensures reasonable income for small-scale miners, enhances the adaptability of mining pools to market changes, and provides a new solution for the sustainable development of the Bitcoin mining ecosystem through innovative designs such as dynamic task allocation, network and market forecasting, fair income distribution, and reinforcement learning optimization.