Game optimization system and method on greedy chain based on dynamic evaluation and intelligent prediction

Abstract

The invention provides a greedy on-chain game optimization system and method based on dynamic evaluation and intelligent prediction. The system comprises a dynamic network evaluation module which monitors and completes evaluation work of network stability and on-chain game difficulty in a block chain network operation state in real time; the mining behavior prediction module is used for predicting a generation trend of a future block and an on-chain game behavior mode of a competitor; the block mining and hiding strategy module is used for determining whether a mined block is hidden or not; the self-adaptive publishing mechanism module is used for dynamically adjusting a block publishing strategy according to network changes and competitor behaviors; and the multi-block management and optimization module is used for carrying out priority ranking and combination optimization on the plurality of backup blocks. According to the method, a Markov reward process is constructed, an on-chain game between honest miners and greedy enemies is analyzed, and relative additional rewards which can be obtained by the greedy enemies compared with the honest miners under different system parameter settings are calculated.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a greedy chain game optimization system and method based on dynamic evaluation and intelligent prediction. Background Art

[0002] In today's technology field, blockchain technology is booming at an unprecedented speed, profoundly changing the operating mode of many industries. Among them, blockchain networks based on proof of work, such as Bitcoin-NG, have gradually exposed a series of serious problems in the process of development.

[0003] Among them, with the popularization of blockchain technology, more and more participants are pouring into the field of on-chain gaming, resulting in increasingly fierce competition for computing power. This has greatly increased the difficulty for individual miners to obtain on-chain gaming benefits, and the benefits have shown a clear downward trend. Under such fierce competition, the traditional honest on-chain gaming strategy can no longer meet the miners' urgent pursuit of higher benefits. At the same time, the existing incentive analysis has obvious limitations. It only covers the limited on-chain gaming attack strategy space of the adversary, and lacks comprehensive consideration of the complex situations that may arise in the network. For example, there are whale transactions with huge transaction value in the network. Such transactions have a potential significant impact on the on-chain gaming benefits and network ecology, but they have not been included in the scope of current incentive analysis. Faced with these difficulties, miners urgently need an innovative on-chain gaming strategy to break through the status quo and achieve a significant increase in benefits while ensuring network stability and fairness.

[0004] Through searching patent documents, it is found that the invention patent with publication number CN107819756B discloses a method for improving the benefits of on-chain gaming. This method analyzes the two situations of miners with the same and different computing power based on the PoW consensus algorithm and the dilemma model, and obtains the miners' on-chain gaming benefit matrix; constructs a model without ZD strategy, a single ZD strategy, and a double ZD strategy, and calculates the system on-chain gaming benefits when the computing power is the same and different based on the benefit matrix and the Markov transfer matrix. However, this method lacks modules such as dynamic evaluation and intelligent prediction, and cannot monitor the network status in real time or predict on-chain gaming behavior. It is weak in the flexibility of responding to network changes and improving benefits.

[0005] In summary, in response to the above-mentioned problems of the prior art, studying a greedy chain game optimization system and method based on dynamic evaluation and intelligent prediction has become a key task that needs to be solved urgently. Summary of the invention

[0006] In view of the defects in the prior art, the purpose of the present invention is to provide a greedy chain game optimization system and method based on dynamic evaluation and intelligent prediction.

[0007] According to the present invention, a greedy chain game optimization system based on dynamic evaluation and intelligent prediction is provided, comprising:

[0008] Dynamic network evaluation module: used to monitor and complete the evaluation of network stability and on-chain game difficulty under the operation status of the blockchain network in real time to obtain the network status;

[0009] Mining behavior prediction module: used to use deep learning algorithms to predict future block generation trends and competitor on-chain gaming behavior patterns and obtain prediction results;

[0010] Block mining and hiding strategy module: decides whether to hide mined blocks based on network status and prediction results, and manages multiple backup blocks;

[0011] Adaptive release mechanism module: used to dynamically adjust block release strategies based on network changes and competitor behavior;

[0012] Multi-block management and optimization module: used to prioritize and combine multiple backup blocks to maximize the probability of continuously mining multiple blocks.

[0013] Preferably, the greedy chain game optimization system also includes a security and compliance assurance module: used to ensure that the implementation of the strategy complies with the protocol rules and legal and regulatory requirements of the blockchain network.

[0014] The present invention also provides a greedy chain game optimization method based on dynamic evaluation and intelligent prediction, comprising the following steps:

[0015] Step S1, building a system architecture for realizing the greedy chain game optimization function based on dynamic evaluation and intelligent prediction, the system architecture includes multiple modules;

[0016] Step S2, for each module of the system architecture, write a corresponding algorithm program to implement the functions and strategy logic of each module, and obtain a system architecture with an algorithm program;

[0017] Step S3, deploying the system architecture with the algorithm program into the simulated blockchain environment, conducting preliminary tests to verify the effectiveness and stability of the algorithm program, and obtaining an optimized system architecture;

[0018] Step S4, deploy the optimization strategy to the actual Bitcoin-NG blockchain network, optimize the actual greedy on-chain game behavior, build a Markov reward process and conduct long-term monitoring and tuning, and collect the operating status information of each module in real time;

[0019] Step S5, based on the operation status information, network feedback and on-chain game benefits, continuously optimize and adjust the strategy parameters to adapt to network changes and improve benefits.

[0020] Preferably, step S2 includes the following sub-steps:

[0021] Step S2.1, research and design a dynamic network evaluation module to monitor and evaluate the blockchain network status in real time;

[0022] Step S2.2, develop a mining behavior prediction module to learn and predict on-chain gaming behaviors;

[0023] Step S2.3, formulate a block mining and hiding strategy module to decide the release and hiding of blocks;

[0024] Step S2.4, construct an adaptive release mechanism module to dynamically adjust the block release strategy;

[0025] Step S2.5, design a multi-block management and optimization module to achieve priority sorting and combination optimization of backup blocks.

[0026] Preferably, step S2.1 includes the following sub-steps:

[0027] Step S2.1.1, determine monitoring indicators: Study and determine key network status indicators.

[0028] Step S2.1.2, data collection: Develop a data collection mechanism to collect the required indicator data from the blockchain network in real time.

[0029] Step S2.1.3, data processing: design data processing algorithms to clean, denoise and preprocess the collected data.

[0030] Step S2.1.4, network status evaluation: Based on the processed data, the network stability is evaluated using a deep learning algorithm.

[0031] Preferably, step S2.2 includes the following sub-steps:

[0032] Step 2.2.1, data preparation: collect historical on-chain gaming behavior data, including block release time, transaction volume, and computing power distribution;

[0033] Step 2.2.2, model construction: Use deep learning algorithms to build a prediction model;

[0034] Step 2.2.3, model training: use historical data to train the prediction model and optimize the model parameters;

[0035] Step 2.2.4, behavior prediction: Use the trained model to predict future block generation trends and competitors’ on-chain game behavior patterns.

[0036] Preferably, step S2.3 includes the following sub-steps:

[0037] Step 2.3.1, strategy design: design block mining and hiding strategies based on network status and prediction results;

[0038] Step 2.3.2, strategy implementation: After a block is mined, decide whether to publish it immediately or hide it for a period of time before publishing it according to the strategy;

[0039] Step 2.3.3, backup block management: manage multiple backup blocks, including priority sorting and resource allocation.

[0040] Preferably, step S5 includes the following sub-steps:

[0041] Step S5.1, analyzing the operation status data to identify problems and improvement points;

[0042] Step S5.2, adjusting policy parameters to adapt to network changes and improve benefits;

[0043] Step S5.3, test the adjusted strategy to ensure system stability and revenue improvement;

[0044] Step S5.4, continuously monitor the effectiveness of the strategy and perform necessary iterative optimization.

[0045] Preferably, step S5.1 includes the following sub-steps:

[0046] Step S5.1.1, data aggregation: Aggregate operation status data from different nodes and blockchain networks;

[0047] Step S5.1.2, trend analysis: Analyze data trends to identify any unusual patterns or signs of performance degradation;

[0048] Step S5.1.3, problem diagnosis: diagnose the specific causes that may cause performance problems;

[0049] Step S5.1.4, improvement point identification: determine possible improvement points.

[0050] Preferably, step S5.2 includes the following sub-steps:

[0051] Step S5.2.1, parameter sensitivity analysis: perform parameter sensitivity analysis;

[0052] Step S5.2.2, parameter adjustment plan: the parameter adjustment plan includes the adjustment range and expected target;

[0053] Step S5.2.3, parameter implementation: modify the strategy parameters one by one according to the adjustment plan and record the changes.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. This paper proposes a new on-chain game strategy - Greedy-Mine, and constructs a Markov Reward Process (MRP) to analyze the on-chain game between honest miners and greedy adversaries, and calculates the relative additional rewards that greedy adversaries can obtain compared to honest miners under different system parameter settings.

[0056] 2. The present invention significantly improves the on-chain gaming benefits. Through sophisticated strategy control and optimization methods, miners can obtain higher on-chain gaming benefits under the same conditions. It also enhances network stability. The use of dynamic evaluation and intelligent prediction mechanisms helps miners to respond to network changes in a timely manner and reduce network fluctuations and fork risks caused by greedy on-chain gaming. At the same time, it improves the efficiency of on-chain gaming. Thanks to the multi-block management and optimization mechanism, miners can more effectively utilize the mined block resources and improve the efficiency of on-chain gaming. At the same time, it reduces operating costs. By reducing on-chain gaming interruptions and waste of computing power caused by frequent network fluctuations and fork risks, the present invention helps miners reduce operating costs, improve profitability and market competitiveness. It also enhances the autonomy of miners. The adaptive release mechanism gives miners higher autonomy and flexibility, allowing them to flexibly adjust on-chain gaming strategies according to the real-time network environment and competitor behavior, and realize personalized and intelligent on-chain gaming management.

[0057] 3. The present invention innovatively proposes a highly refined greedy on-chain game optimization method for the Bitcoin-NG blockchain system that adopts the Proof of Work (PoW) mechanism. The method integrates dynamic network evaluation, mining behavior prediction, block mining and hiding strategies, and adaptive publishing mechanisms. It aims to solve the problems of limited profit improvement, difficulty in adapting to the rapidly changing network environment, and possible network fluctuations and fork risks in existing blockchain game strategies, significantly improve the benefits of miners in the on-chain game process, while maintaining the stability and security of the blockchain network. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0059] Figure 1 This is an architecture diagram of a greedy on-chain game optimization system based on dynamic evaluation and intelligent prediction in an embodiment of the present invention;

[0060] Figure 2 A transaction fee allocation diagram in an embodiment of the present invention;

[0061] Figure 3Initialize the greedy on-chain game algorithm in the embodiment of the present invention;

[0062] Figure 4 This is a flow chart of the greedy mining pool on-chain game algorithm in an embodiment of the present invention;

[0063] Figure 5 This is a flow chart of the honest mining pool on-chain game algorithm in an embodiment of the present invention;

[0064] Figure 6 It is a diagram of the game state transfer process on the greedy chain in an embodiment of the present invention;

[0065] Figure 7 It is the relative benefits when the greedy mining pool adopts different on-chain game strategies under different system parameters in the embodiment of the present invention;

[0066] Figure 8 It is the minimum computing power that the greedy mining pool can obtain excess returns under different system parameters in the embodiment of the present invention;

[0067] Fig. 9 It is the excess profit and winning condition of the greedy mining pool in the embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0069] 1. Greedy-Mine Algorithm Process

[0070] For simplicity and generality, it is assumed that the on-chain gaming power is divided into two categories: one is the minority on-chain gaming pool that follows the greedy on-chain gaming strategy, and the other is the majority on-chain gaming pool that follows the honest on-chain gaming strategy. The key intuition of the greedy on-chain gaming strategy is that the greedy mining pool has an incentive to compete with the honest pool for the longest legal chain, which will weaken the on-chain gaming computing power of the honest mining pool on the non-longest legal chain. As the effective computing resources in the network decrease, greedy miners have a greater chance of winning in the fork competition. Therefore, they are motivated to adopt a greedy on-chain gaming strategy to obtain excess returns.

[0071] Honest miners follow the honest strategy given in Algorithm 3. Figure 5The execution flow of Algorithm 3 (honest game strategy) is given: When the honest mining pool finds a main block, it will continue to use the honest strategy for on-chain game. If the whale transaction has not been packaged, it will generate a main block containing the whale transaction and set $state_{tx}=1$. If the whale transaction has been packaged, the honest mining pool strategy is determined by the system state. If the honest branch length is longer than the greedy branch, the honest mining pool will publish a main block on the honest branch and increase the honest branch length by one. If the honest branch length is equal to the adversary branch length, the result of the fork competition depends on the choice of the honest mining pool. Specifically, if the honest mining pool chooses the honest branch, the honest branch length is increased by one. Otherwise, the adversary branch length is increased by one. If the adversary branch length is longer than the honest branch length, the competition ends and the adversary mining pool obtains all whale transaction fee rewards.

[0072] Each honest miner observes the state structure of the blockchain, selects the longest legitimate main chain and then conducts on-chain games. Once a new main block is discovered, it will be broadcast to other participants in the network. In addition, it needs to update the state of the local blockchain and then continue to mine the next main block.

[0073] In contrast, the greedy mining pool deviates from the honest protocol and strategically plays on-chain on its own private branch, trying to obtain the entire whale transaction fee reward, following the greedy strategy given in Algorithm 2. Figure 4 The execution process of Algorithm 2 (Greedy Game Strategy) is given: When the greedy mining pool finds a main block and the whale transaction has not been packaged, it will release the main block containing the whale transaction, $state_{tx}$ becomes 1, and the adversary branch length increases by one. If the whale transaction has been packaged by the greedy mining pool, and the length of the adversary branch is not shorter than the honest branch, it will release the main block and increase the length of the greedy branch by one. If the whale transaction has been packaged by the honest mining pool, and the length of the honest branch is longer than the adversary branch, the greedy mining pool will release the main block and increase the length of the greedy branch by one.

[0074] Once the microblock containing the whale transaction is published, the greedy mining pool will immediately engage in chain gaming behind the microblock containing the whale transaction, resulting in a fork competition. In this case, the greedy mining pool and the honest miner who first receives the greedy block will contribute to the greedy branch. Since the default transaction confirmation in Bitcoin-NG is about six blocks, when the private branch where the greedy mining pool is located lags behind the public branch by more than six blocks, the greedy mining pool will have to give up the attack. When the greedy mining pool finds a new main block, it will selectively publish it. Its publishing strategy and purpose is to make the greedy branch the longest legal chain, resulting in the greedy mining pool receiving all transaction fee rewards in each cycle. Usually, once the whale transaction appears in the pending transaction pool, the greedy mining pool will try to generate the main block and the microblock containing the whale transaction. In this case, the adversary can obtain disproportionate rewards through the greedy chain gaming strategy.

[0075] Initialization: With the above intuition, the present invention proposes a greedy on-chain game attack, which is driven by the on-chain game events of the greedy mining pool or the honest mining pool. Among them, the greedy mining pool strategy is determined by the specific state of the whale transaction. tx Divided into three categories: state tx =0 means the whale transaction has not been packaged; state tx =1 means that the whale transaction is packaged by the greedy mining pool; state tx =2 means that the whale transaction is packaged by the honest mining pool. The initialization of the greedy chain game is described by Algorithm 1.

[0076] Figure 3 The execution process of Algorithm 3 (Greedy Game Initialization) is given: Greedy attacks are driven by on-chain game events of greedy mining pools or honest mining pools. Among them, the greedy mining pool strategy is determined by the specific state of whale transactions. This chapter divides the transaction state $state_{tx}$ into three categories: $state_{tx}=0$ means that the whale transaction has not been packaged; $state_{tx}=1$ means that the whale transaction is packaged by the greedy mining pool; $state_{tx}=2$ means that the whale transaction is packaged by the honest mining pool.

[0077] Greedy mining pool strategy: When the greedy mining pool finds a main block and the whale transaction has not been packaged, it will publish the main block containing the whale transaction, state tx becomes 1, and increases the length of the adversary branch by one. If the whale transaction has been packaged by the greedy mining pool, and the length of the adversary branch is not shorter than the honest branch, he will release the main block and increase the length of the greedy branch by one. If the whale transaction has been packaged by the honest mining pool, and the length of the honest branch is longer than the adversary branch, the greedy mining pool will release the main block and increase the length of the greedy branch by one. The specific strategy of the greedy mining pool is described by Algorithm 2.

[0078] Honest mining pool strategy: When an honest mining pool finds a main block, it will continue to use the honest strategy on-chain game. If the whale transaction has not been packaged, it will generate a main block containing the whale transaction and set the state tx =1. If the whale transaction has been packaged, the honest mining pool strategy is determined by the system state. If the honest branch length is longer than the greedy branch, the honest mining pool will publish a main block on the honest branch and increase the honest branch length by one. If the honest branch length is equal to the adversary branch length, the result of the fork competition depends on the choice of the honest mining pool. Specifically, if the honest mining pool chooses the honest branch, the honest branch length increases by one. Otherwise, the adversary branch length increases by one. If the adversary branch length is longer than the honest branch length, the competition ends and the adversary mining pool obtains all whale transaction fee rewards. The specific strategy of the honest mining pool is described by Algorithm 3.

[0079] 2. State Transfer

[0080] The present invention constructs a state probability transfer model based on the greedy chain game strategy and the honest chain game strategy. Figure 6 The state probability transfer process is shown. We define the proportion of computing power resources owned by the adversary as a to the total computing power of the entire network system; when all nodes of the system disagree on the longest legal chain, the proportion of honest miners playing on-chain games on malicious branches is λ, and the proportion of playing on-chain games on honest branches is 1-λ.

[0081] Status s means that the whale transaction has not been packaged. Status h 0 Indicates that the whale transaction was packed by greedy miners and was not linked to any new main block afterwards. 1 is a terminal state, indicating that the whale transaction is packaged by the greedy adversary and the main block linked later is also generated by the greedy miner. At this time, all the whale transaction fees are obtained by the greedy adversary. 0k (k≥0) indicates that the whale transaction is packaged by the greedy adversary and then only links to honest blocks, and the length of these honest block branches is k+1. State h 10 Indicates that the whale transaction is packaged by the greedy adversary and the length of the branch where the honest miner is linked later is equal to the length of the branch where the greedy miner is located. State h 1k (k≥1) indicates that the whale transaction is packaged by the greedy adversary and the length of the branch of the honest block linked later is k longer than the branch of the greedy block. 0 Indicates that the whale transaction is packaged by honest miners and is not linked to any new main block afterwards. 1 Indicates that the whale transaction is packaged by honest miners and only one honest block is linked afterwards. 0k(k≥0) indicates that whale transactions are generated by honest miners and then linked to honest blocks. The length of these honest blockchains is k+2. 2 It means that at a certain moment, a whale transaction on the honest branch is packaged and generated by an honest miner and then linked to an honest block. The greedy miner attempts to launch a greedy attack, publishes a greedy main block after the main block before the whale transaction, and packages and generates a micro block containing the whale transaction, thus forming a competitive state. State a 1k (k≥0) means in state a 2 On the basis, a new honest block is generated after the honest branch and the length of the honest branch is k+1 longer than the greedy branch. 2k (k≥0) means in state a 2 On this basis, new blocks are generated after the honest branch and the greedy branch, and the difference between the honest branch length and the greedy branch length is k.

[0082] 3. Reward Analysis

[0083] Bitcoin-NG divides blocks into two categories: main blocks and micro blocks. Main blocks are responsible for leader election and ensure the security of the consensus protocol. Micro blocks are responsible for packaging transactions and are used to increase transaction throughput. In order to incentivize miners to play honest on-chain games and ensure the security of the system, the leader of each cycle will receive two types of rewards: a minting reward for generating main blocks and a partial transaction fee reward for generating micro blocks. Transaction fee rewards should be distributed to the two adjacent leaders before and after the current cycle. Specifically, 40% of these transaction fees are earned by the leader of the current cycle and 60% are earned by the subsequent leaders, see Figure 1 .

[0084] Honest Mining Pool Rewards:

[0085] The adversary generates two main blocks in succession, and packs the whale transaction into a micro block after the first main block. In this state, the adversary obtains a reward α 2 100%.

[0086] The adversary first generates a main block and packages the whale transaction to generate a micro block. The honest miners generate a new main block after the micro block. In this state, the adversary obtains a reward of α(1-a)·r leader .

[0087] Honest miners first generate a main block and package whale transactions to generate micro blocks. The adversary generates a new main block after the micro block. In this state, the adversary obtains a profit of (1-α)α(100%-r leader ).

[0088] Based on the above three honest on-chain game behaviors, we calculate the expected benefits that a miner with computing power α can obtain:

[0089]

[0090] Greedy Pool Rewards:

[0091] Status 0 Transfer to terminal state h 1 , at this time the greedy adversary can gain benefits

[0092] Status 10 Transfer to terminal state h 1 , at this time the greedy adversary can gain benefits

[0093] Status α 2 Transfer to terminal state h 1 , at this time the greedy adversary can gain benefits

[0094] Status α 20 Transfer to terminal state h 1 , at this time the greedy adversary can gain benefits

[0095] Based on the above analysis of the termination state of the greedy on-chain game, we calculate the expected profit that a greedy adversary with computing power α can obtain:

[0096]

[0097]

[0098] Embodiment 1:

[0099] Figure 1 This is an architecture diagram of a greedy on-chain game optimization system based on dynamic evaluation and intelligent prediction in an embodiment of the present invention.

[0100] like Figure 1 As shown, this embodiment provides a Greedy-Mine optimization system based on dynamic evaluation and intelligent prediction, including:

[0101] Dynamic network evaluation module: used to monitor in real time and complete the evaluation of network stability and on-chain game difficulty under the operation status of the blockchain network to obtain the network status.

[0102] Specifically, the dynamic network evaluation module monitors the operating status of the blockchain network in real time. The operating status includes key indicators such as block generation rate, network latency, transaction fees, and competitor computing power distribution. It uses big data analysis technology to deeply mine and analyze the operating status to evaluate the stability of the current network environment and the difficulty of on-chain games to obtain the network status.

[0103] Mining behavior prediction module: used to use deep learning algorithms to predict future block generation trends and competitors' on-chain gaming behavior patterns to obtain prediction results.

[0104] Specifically, the mining behavior prediction module uses a neural network algorithm to learn and train the historical data of the blockchain network, build a prediction model, and use the prediction model to predict future block generation trends, competitors' on-chain game behavior patterns, and potential network volatility risks to obtain prediction results.

[0105] Block mining and hiding strategy module: decides whether to hide mined blocks based on network status and prediction results, and manages multiple backup blocks.

[0106] Specifically, the block mining and hiding strategy module formulates a block mining and hiding strategy based on the network status obtained by the dynamic network evaluation module and the prediction results of the mining behavior prediction module. After mining a block, it decides whether to publish the block immediately or hide the block for a period of time before publishing it based on the network status and prediction results.

[0107] Adaptive release mechanism module: used to dynamically adjust block release strategies based on network changes and competitor behavior.

[0108] Specifically, the adaptive release mechanism module designs an adaptive block release mechanism to dynamically adjust the block release strategy according to the network status and the behavior of competitors; when it is predicted that the network status is stable and the competitor is weak, the block release strategy is changed to maximize the benefits; when potential network fluctuations or fork risks are detected, the block release is reduced or delayed to avoid adverse effects.

[0109] Multi-block management and optimization module: used to prioritize and combine multiple backup blocks to maximize the probability of continuously mining multiple blocks.

[0110] Specifically, the multi-block management and optimization module prioritizes and optimizes multiple reserve blocks to maximize the probability of mining multiple blocks in succession and the overall revenue. The multi-block management and optimization module implements an efficient multi-block management mechanism, allowing miners to simultaneously manage and maintain multiple mined but unreleased blocks.

[0111] Security and compliance assurance module: used to ensure that the implementation of policies complies with the protocol rules and legal and regulatory requirements of the blockchain network.

[0112] Specifically, the security and compliance assurance module introduces a security audit mechanism to regularly check and evaluate the implementation process of the greedy chain game strategy to prevent potential security risks and violations. The security and compliance assurance module ensures that the implementation of all strategies complies with the protocol rules and legal and regulatory requirements of the blockchain network.

[0113] The various modules work together to maximize the miners' on-chain gaming benefits while maintaining the stability of the blockchain network.

[0114] Embodiment 2:

[0115] This embodiment provides a greedy-mine optimization method based on dynamic evaluation and intelligent prediction, and a greedy-mine optimization system based on dynamic evaluation and intelligent prediction in the above embodiment 1, including the following steps:

[0116] Step S1, constructing a system architecture for realizing the greedy on-chain game optimization function based on dynamic evaluation and intelligent prediction. The system architecture includes multiple modules.

[0117] Figure 2 is a transaction fee allocation diagram in an embodiment of the present invention, Figure 3 Initialize the greedy chain game algorithm in the embodiment of the present invention.

[0118] like Figure 2 , 3 As shown, the system architecture includes building the transaction allocation ratio and initializing the greedy on-chain game algorithm.

[0119] Step S2, for each module of the system architecture, write a corresponding algorithm program to implement the functions and policy logic of each module, and obtain a system architecture with an algorithm program.

[0120] Specifically, step S2 includes the following sub-steps:

[0121] Step S2.1, research and design a dynamic network evaluation module to monitor and evaluate the blockchain network status in real time.

[0122] In this embodiment, step S2.1 includes the following sub-steps:

[0123] Step S2.1.1, determine monitoring indicators: Research and determine key network status indicators, such as block generation rate, network latency, transaction fees, and competitor computing power distribution.

[0124] Step S2.1.2, data collection: Develop a data collection mechanism to collect the required indicator data from the blockchain network in real time.

[0125] Step S2.1.3, data processing: design data processing algorithms to clean, denoise and preprocess the collected data.

[0126] Step S2.1.4, network status evaluation: Based on the processed data, the network stability is evaluated using a deep learning algorithm.

[0127] Step S2.2, develop a mining behavior prediction module to learn and predict on-chain gaming behaviors.

[0128] In this embodiment, step S2.2 includes the following sub-steps:

[0129] Step 2.2.1, data preparation: Collect historical on-chain gaming behavior data, including block release time, transaction volume, computing power distribution, etc.

[0130] Step 2.2.2, model construction: Use deep learning algorithms, such as long short-term memory network (LSTM) or convolutional neural network (CNN), to build a prediction model.

[0131] Step 2.2.3, model training: Use historical data to train the prediction model and optimize the model parameters.

[0132] Step 2.2.4, behavior prediction: Use the trained model to predict future block generation trends and competitors’ on-chain game behavior patterns.

[0133] Step S2.3, formulate a block mining and hiding strategy module to decide the release and hiding of blocks.

[0134] In this embodiment, step S2.3 includes the following sub-steps:

[0135] Step 2.3.1, strategy design: design block mining and hiding strategies based on network status and prediction results.

[0136] Step 2.3.2, strategy implementation: After a block is mined, decide whether to publish it immediately or hide it for a period of time before publishing it based on the strategy.

[0137] Step 2.3.3, backup block management: manage multiple backup blocks, including priority sorting and resource allocation.

[0138] Step S2.4, construct an adaptive publishing mechanism module to dynamically adjust the block publishing strategy.

[0139] In this embodiment, step S2.4 includes the following sub-steps:

[0140] Step 2.4.1, strategy framework design: Design an adaptive block release mechanism to dynamically adjust the strategy based on network status and competitor behavior.

[0141] Step 2.4.2, strategy adjustment: When it is predicted that the network status is stable and the competitor is weak, adjust the strategy to maximize the profit; when potential network fluctuations or fork risks are detected, reduce block release or delay release to avoid adverse effects.

[0142] Step S2.5, design a multi-block management and optimization module to achieve priority sorting and combination optimization of backup blocks.

[0143] In this embodiment, step S2.5 includes the following sub-steps:

[0144] Step S2.5.1: Multi-block management: Design a multi-block management mechanism to achieve maintenance and management of multiple backup blocks.

[0145] Step S2.5.2: Prioritization: Prioritize the backup blocks according to the strategy to ensure the probability of mining multiple blocks continuously.

[0146] Step S2.5.3: Combination optimization: Perform combination optimization on the backup blocks to maximize the overall benefit.

[0147] Step S3, deploy the system architecture with the algorithm program into a simulated blockchain environment, conduct preliminary tests to verify the effectiveness and stability of the algorithm program, and obtain an optimized system architecture.

[0148] Step S4, deploy the optimized system architecture to the actual Bitcoin-NG blockchain network, optimize the actual greedy on-chain game behavior, build a Markov reward process and conduct long-term monitoring and tuning, and collect the operating status information of each module in real time.

[0149] Figure 4 This is a flow chart of the greedy mining pool on-chain game algorithm in an embodiment of the present invention; Figure 5 The figure is a flow chart of the honest mining pool on-chain game algorithm in an embodiment of the present invention.

[0150] like Figure 4 , 5 As shown, in step S4, by constructing the Markov reward process, the greedy on-chain game algorithm flow chart and the honest mining pool on-chain game algorithm flow chart can be obtained.

[0151] Step S5: Based on the operation status information, network feedback and on-chain game benefits, continuously optimize and adjust the strategy parameters to adapt to network changes and improve benefits.

[0152] Specifically, step S5 includes the following sub-steps:

[0153] Step S5.1, analyze the operation status data and identify problems and improvement points.

[0154] In this embodiment, step S5.1 includes the following sub-steps:

[0155] Step S5.1.1, data aggregation: Aggregate operation status data from different nodes and blockchain networks.

[0156] Step S5.1.2, Trend Analysis: Analyze data trends to identify any unusual patterns or signs of performance degradation.

[0157] Step S5.1.3, problem diagnosis: Diagnose the specific causes that may cause performance problems, such as network delays, computing power fluctuations, etc.

[0158] Step S5.1.4, Identification of improvement points: Identify possible improvement points, such as algorithm efficiency, policy logic, or resource allocation.

[0159] Step S5.2, adjust the policy parameters to adapt to network changes and improve benefits.

[0160] In this embodiment, step S5.2 includes the following sub-steps:

[0161] Step S5.2.1, parameter sensitivity analysis: Perform parameter sensitivity analysis to determine which parameters have a significant impact on the strategy performance.

[0162] Step S5.2.2, parameter adjustment plan: formulate a parameter adjustment plan, including the adjustment range and expected goals.

[0163] Step S5.2.3, parameter implementation: modify the strategy parameters one by one according to the adjustment plan and record the changes.

[0164] Step S5.3, test the adjusted strategy to ensure system stability and profit improvement.

[0165] In this embodiment, step S5.3 includes the following sub-steps:

[0166] Step S5.3.1, Test Plan: Develop a detailed test plan including test scenarios, test cases, and expected results.

[0167] Step S5.3.2, performance test: perform performance test to evaluate the system stability and response speed after the strategy adjustment.

[0168] Step S5.3.3, profit analysis: Analyze the profit after the strategy adjustment and compare it with the profit before the adjustment.

[0169] Step S5.4, continuously monitor the effectiveness of the strategy and perform necessary iterative optimization.

[0170] In this embodiment, step S5.4 includes the following sub-steps:

[0171] Step S5.4.1: Real-time monitoring: Continuously monitor the effectiveness of the strategy to ensure it is working as expected.

[0172] Step S5.4.2: Evaluate effectiveness: Regularly evaluate the effectiveness of the strategy and identify any new issues or opportunities for improvement.

[0173] Step S5.4.3: Iterative optimization: Perform iterative optimization of the strategy based on the monitoring and evaluation results.

[0174] Embodiment 3:

[0175] This paper implements a Monte Carlo simulator to verify the accuracy of the theoretical analysis of greedy on-chain games in Bitcoin-NG. Specifically, a blockchain system containing 1,000 miners with the same computing resources is simulated, where the greedy mining pool controls at most 500 miners. The model is run for more than 10 8 In 20 different rounds, the relative benefits of greedy miners are obtained in 20 different scenarios. The greedy mining pool follows the on-chain game strategy defined in Algorithm 2, and the honest mining pool follows the on-chain game strategy defined in Algorithm 3.

[0176] Next, under different system parameter settings, the profit of the adversary adopting the greedy on-chain game strategy and the minimum computing power threshold for obtaining disproportionate rewards are evaluated. Figure 7 The results show the income of the adversary under different on-chain game computing power and different propagation factor parameters compared with the honest on-chain game protocol in Bitcoin-NG. The results show that the greedy mining pool with higher on-chain game power will obtain higher returns by adopting the greedy on-chain game strategy, which is consistent with the theoretical analysis. In addition, the simulation results also show that the Bitcoin-NG honest on-chain game is not incentive compatible even when the honest mining pool has a dominant computing power. We set the propagation factor to four cases (γ=0, 0.25, 0.5, 1). When γ=1, the minimum on-chain game computing power corresponding to the greedy mining pool's excess revenue is α=0.18. Once the greedy mining pool has more than 0.181 proportion of on-chain game power, the greedy on-chain game is always a better on-chain game strategy than the honest on-chain game. Figure 8It shows that when greedy on-chain games are used, the adversary can obtain the minimum on-chain gaming rights with disproportionate rewards under different propagation factor parameters γ compared with honest on-chain games. The solid line in the figure indicates that the benefits of greedy on-chain games and honest on-chain games are equal. The right side of the solid line indicates that greedy on-chain games are the better strategy, while the left side indicates that honest on-chain games are the better strategy. The areas of the two regions are calculated separately, where the winning area of ​​greedy on-chain games is larger than the winning area of ​​honest on-chain games, which shows that in most cases, the returns of greedy on-chain games are always higher than those of honest on-chain games.

[0177] When the propagation factor γ is larger, honest miners are more likely to contribute to the greedy branch. Compared with the honest on-chain game in Bitcoin-NG, the minimum on-chain game power for the adversary to obtain additional income by adopting the greedy on-chain game strategy is 0.181, which shows that the Bitcoin-NG on-chain game incentives are incompatible.

[0178] For ease of description, the relative extra reward (RER) is defined to show the benefits of the greedy mining pool:

[0179]

[0180] where τ 1 and τ 2 Represents different on-chain game strategies, Indicates that the adversary adopts the on-chain game strategy τ 1 The corresponding income.

[0181] The present invention Fig. 9 The relative extra reward and winning conditions of the adversary are shown in . More specifically, the right side of the solid line is the winning condition of the adversary when adopting the greedy on-chain game. When α and γ are large, the adversary who adopts the greedy on-chain game can obtain a higher relative extra reward. The reason is that in the fork competition, the more miners who choose to play on-chain games on the greedy branch, the higher the probability of the adversary winning. At the same time, compared with the honest on-chain game, miners with larger on-chain game rights have more advantages when adopting greedy on-chain games. Therefore, miners with relatively large on-chain game rights have an incentive to launch greedy on-chain games. The experimental results show that regardless of the propagation factor parameters, adversaries with larger on-chain game rights have an incentive to adopt a greedy on-chain game strategy. In addition, as γ increases, the adversary can obtain more relative extra rewards compared with the honest on-chain game, which is consistent with the theoretical analysis results.

[0182] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0183] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A greedy on-chain game optimization system based on dynamic evaluation and intelligent prediction, characterized in that: include: Dynamic network evaluation module: used to monitor and complete the evaluation of network stability and on-chain game difficulty under the operation status of the blockchain network in real time to obtain the network status; Mining behavior prediction module: used to use deep learning algorithms to predict future block generation trends and competitor on-chain gaming behavior patterns and obtain prediction results; Block mining and hiding strategy module: decides whether to hide mined blocks according to the network status and the prediction results, and manages multiple backup blocks; Adaptive release mechanism module: used to dynamically adjust block release strategies based on network changes and competitor behavior; Multi-block management and optimization module: used to prioritize and combine multiple backup blocks to maximize the probability of continuously mining multiple blocks.

2. According to claim 1, a greedy chain game optimization system based on dynamic evaluation and intelligent prediction is characterized in that: The greedy chain game optimization system also includes a security and compliance assurance module to ensure that the implementation of the strategy complies with the protocol rules and legal and regulatory requirements of the blockchain network.

3. A greedy chain game optimization method based on dynamic evaluation and intelligent prediction, based on a greedy chain game optimization system based on dynamic evaluation and intelligent prediction as described in any one of claims 1 to 2, characterized in that: The steps include: Step S1, constructing a system architecture for realizing a greedy chain game optimization function based on dynamic evaluation and intelligent prediction, wherein the system architecture includes multiple modules; Step S2, for each module of the system architecture, write a corresponding algorithm program to implement the functions and strategy logic of each module, and obtain a system architecture with an algorithm program; Step S3, deploying the system architecture with the algorithm program into a simulated blockchain environment, performing preliminary tests to verify the effectiveness and stability of the algorithm program, and obtaining an optimized system architecture; Step S4, deploy the optimization strategy to the actual Bitcoin-NG blockchain network, optimize the actual greedy on-chain game behavior, build a Markov reward process and conduct long-term monitoring and tuning, and collect the operating status information of each module in real time; Step S5, based on the operation status information, according to network feedback and on-chain game revenue, continuously optimize and adjust strategy parameters to adapt to network changes and improve revenue.

4. According to claim 3, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S2 includes the following sub-steps: Step S2.1, research and design a dynamic network evaluation module to monitor and evaluate the blockchain network status in real time; Step S2.2, develop a mining behavior prediction module to learn and predict on-chain gaming behaviors; Step S2.3, formulate a block mining and hiding strategy module to decide the release and hiding of blocks; Step S2.4, construct an adaptive release mechanism module to dynamically adjust the block release strategy; Step S2.5, design a multi-block management and optimization module to achieve priority sorting and combination optimization of backup blocks.

5. According to claim 4, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S2.1 includes the following sub-steps: Step S2.1.1, determine monitoring indicators: research and determine key network status indicators; Step S2.1.2, data collection: Develop a data collection mechanism to collect the required indicator data from the blockchain network in real time; Step S2.1.3, data processing: design data processing algorithms to clean, denoise and preprocess the collected data; Step S2.1.4, network status evaluation: Based on the processed data, the network stability is evaluated using a deep learning algorithm.

6. According to claim 4, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S2.2 includes the following sub-steps: Step 2.2.1, data preparation: collect historical on-chain gaming behavior data, including block release time, transaction volume, and computing power distribution; Step 2.2.2, model construction: Use deep learning algorithms to build a prediction model; Step 2.2.3, model training: use historical data to train the prediction model and optimize the model parameters; Step 2.2.4, behavior prediction: Use the trained model to predict future block generation trends and competitors’ on-chain game behavior patterns.

7. According to claim 4, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S2.3 includes the following sub-steps: Step 2.3.1, strategy design: design block mining and hiding strategies based on network status and prediction results; Step 2.3.2, strategy implementation: After a block is mined, decide whether to publish it immediately or hide it for a period of time before publishing it according to the strategy; Step 2.3.3, backup block management: manage multiple backup blocks, including priority sorting and resource allocation.

8. According to claim 3, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S5 comprises the following sub-steps: Step S5.1, analyzing the operation status data to identify problems and improvement points; Step S5.2, adjusting policy parameters to adapt to network changes and improve benefits; Step S5.3, test the adjusted strategy to ensure system stability and revenue improvement; Step S5.4, continuously monitor the effectiveness of the strategy and perform necessary iterative optimization.

9. According to claim 8, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S5.1 includes the following sub-steps: Step S5.1.1, data aggregation: Aggregate operation status data from different nodes and blockchain networks; Step S5.1.2, trend analysis: Analyze data trends to identify any unusual patterns or signs of performance degradation; Step S5.1.3, problem diagnosis: diagnose the specific causes that may cause performance problems; Step S5.1.4, improvement point identification: determine possible improvement points.

10. According to claim 8, a greedy chain game optimization method based on dynamic evaluation and intelligent prediction is characterized in that: The step S5.2 includes the following sub-steps: Step S5.2.1, parameter sensitivity analysis: perform parameter sensitivity analysis; Step S5.2.2, parameter adjustment plan: the parameter adjustment plan includes the adjustment range and expected target; Step S5.2.3, parameter implementation: modify the strategy parameters one by one according to the adjustment plan and record the changes.

Images