CN115967501A - Web service combination generation method based on intelligent contract of block chain - Google Patents
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Translated fromChinese技术领域Technical Field
本发明涉及区块链技术领域，具体涉及到一种基于区块链智能合约的Web服务组合生成方法。The present invention relates to the technical field of blockchain, and in particular to a method for generating a Web service combination based on blockchain smart contracts.
背景技术Background Art
区块链技术最初来源于比特币，2008年11月，中本聪(Satoshi Nakamoto)发表了比特币白皮书，由此标志比特币的诞生。两个月后，比特币从理论转为实践。在接下来的几年里，区块链成为比特币的核心组成部分。Blockchain technology originated from Bitcoin. In November 2008, Satoshi Nakamoto published the Bitcoin white paper, marking the birth of Bitcoin. Two months later, Bitcoin turned from theory to practice. In the following years, blockchain became a core component of Bitcoin.
区块链是一种链式存储结构，基本单位是一个个区块，当交易信息验证通过后便永久存储在区块上。它能够使得各个节点在不需要相互信任的条件下达成交易，解决传统方式中第三方交易机构数据不安全、效率低下、成本较高等问题，在不同领域都有广泛的应用。Blockchain is a chain storage structure, the basic unit is a block, and once the transaction information is verified, it is permanently stored in the block. It enables each node to complete transactions without the need for mutual trust, and solves the problems of insecure data, low efficiency, and high cost of third-party transaction institutions in traditional methods. It has a wide range of applications in different fields.
最近几年来，区块链技术的快速发展给Web服务组合问题带来新的解决方案。区块链技术主要有两点优势：(1)去中心化；(2)不可篡改。相对于传统交易平台，该技术成功摆脱了第三方平台的限制。经过自动调用区块链上部署的智能合约，然后执行Web服务组合算法，最后将服务商与用户达成的协议写入区块链中，并且交易双方不得更改，这有效弥补了传统方法对于可靠性和安全性方面的不足。In recent years, the rapid development of blockchain technology has brought new solutions to the Web service composition problem. Blockchain technology has two main advantages: (1) decentralization; (2) immutability. Compared with traditional trading platforms, this technology has successfully broken away from the limitations of third-party platforms. After automatically calling the smart contract deployed on the blockchain, the Web service composition algorithm is executed, and finally the agreement reached between the service provider and the user is written into the blockchain. The transaction parties cannot change it, which effectively makes up for the shortcomings of traditional methods in terms of reliability and security.
对于Web服务组合算法方面，传统多目标灰狼优化算法中，存在算法后期收敛速度较慢，容易陷入局部最优等问题。因此，本专利对该算法进行改进，有效弥补传统算法不足。As for the Web service composition algorithm, the traditional multi-objective grey wolf optimization algorithm has problems such as slow convergence speed in the late stage of the algorithm and easy to fall into local optimality. Therefore, this patent improves the algorithm to effectively make up for the shortcomings of the traditional algorithm.
发明内容Summary of the invention
该发明的最主要目标是能够确保用户和服务提供商在缺信环境中进行安全交易，摆脱第三方交易平台依赖，自动调用Web服务组合核心算法(改进的多目标灰狼优化算法)，并且根据不同用户需求，在满足用户需求功能的情况下达到最优的服务质量。The main goal of this invention is to ensure that users and service providers can conduct safe transactions in a trustless environment, get rid of dependence on third-party trading platforms, automatically call the core algorithm of Web service combination (improved multi-objective grey wolf optimization algorithm), and achieve the best service quality based on different user needs while meeting user demand functions.
具体提供了一种基于区块链智能合约的Web服务组合生成方法，包括如下步骤，Specifically, a method for generating a Web service combination based on a blockchain smart contract is provided, which includes the following steps:
S1：用户在以太坊上同步部署智能合约；S1: The user deploys the smart contract synchronously on Ethereum;
S2：不同节点的服务提供商利用同步的智能合约发布其所提供的Web服务信息；S2: Service providers at different nodes use synchronized smart contracts to publish the web service information they provide;
S3：用户通过调用智能合约触发Web服务组合核心算法，获得当前适合用户的Web服务组合；S3: The user triggers the core algorithm of Web service combination by calling the smart contract to obtain the current Web service combination suitable for the user;
S4：用户选择服务商提供的服务后，智能合约将所有关于Web服务组合的信息存储在区块链上。S4: After the user selects the services provided by the service provider, the smart contract stores all the information about the combination of web services on the blockchain.
进一步的，所述步骤S2中，服务提供商需提交其签名信息，便于在非对称加密中进行认证身份。Furthermore, in step S2, the service provider needs to submit its signature information to facilitate identity authentication in asymmetric encryption.
进一步的，所述步骤S3中，Web服务组合核心算法选用改进的多目标灰狼优化算法，将收敛因子变为余弦变化，具体为，Furthermore, in step S3, the core algorithm of Web service composition uses an improved multi-objective grey wolf optimization algorithm, and changes the convergence factor into a cosine change, specifically,
其中，MaxIt为最大迭代次数,t为迭代次数。Among them, MaxIt is the maximum number of iterations, and t is the number of iterations.
进一步的，所述步骤S3中，采用Boltzmann选择策略选择第n个灰狼为领导者狼的概率Pn为，Furthermore, in step S3, the probabilityPn of selecting the nth gray wolf as the leader wolf using the Boltzmann selection strategy is,
T＝T0(0.99m-1)T＝T0 (0.99m-1 )
其中，fn代表第n个灰狼个体的适应度值，m为当前的迭代次数，T0为初始温度，T为当前温度，Xn为灰狼的数量.在公式中，第n个灰狼个体的适应度值fn与该个体所在的网络中的灰狼数量Nn成反比，即fn＝1/Nn，而Boltzmann选择策略动态调节领导者狼的选择压力值。Wherefn represents the fitness value of the nth gray wolf individual, m is the current number of iterations,T0 is the initial temperature, T is the current temperature, andXn is the number of gray wolves. In the formula, the fitness valuefn of the nth gray wolf individual is inversely proportional to the numberNn of gray wolves in the network where the individual is located, that is,fn = 1/Nn , and the Boltzmann selection strategy dynamically adjusts the selection pressure value of the leader wolf.
进一步的，所述步骤S3中，采用QoS来选择Web服务组合，不同的Web服务组合之后，作为一个整体，拥有Web服务组合序列，全局QoS值，以及每个QoS指标的权重三个属性，将Web服务组合模型定义为一个三元组S:＝(s,qG,w)，其中s＝(T1,...，Tk)即一个Web服务组合s由k个不同的Web服务组成，T1,...，Tk是Web服务组合序列；qG＝(q1,q2,...qm)代表k个Web服务组合后，全局的QoS值；该Web服务组合的QoS共有m个指标，qi代表第i个属性的值，其中i∈[1,m]；w＝(w1,w2,...,wm)，代表上述m个不同的QoS指标各自的权重，其中w1+w2+...+wm＝1。Furthermore, in the step S3, QoS is used to select a Web service combination. After different Web services are combined, as a whole, they have three attributes: a Web service combination sequence, a global QoS value, and a weight of each QoS indicator. The Web service combination model is defined as a triple S: = (s, qG , w), where s = (T1 ,..., Tk ), that is, a Web service combination s is composed of k different Web services, T1 ,..., Tk is the Web service combination sequence; qG = (q1 , q2 ,...qm ) represents the global QoS value after k Web services are combined; the QoS of the Web service combination has a total of m indicators, qi represents the value of the i-th attribute, where i∈[1,m]; w = (w1 ,w2 ,...,wm ), represents the weights of the above m different QoS indicators, where w1 +w2 +...+wm = 1.
本发明具有以下有益效果：The present invention has the following beneficial effects:
本发明的有益效果：由于大量Web服务有着相近或相同的供能属性，为了给用户提供最优的服务质量，目前解决方案有精确算法和启发式算法等。然而这些解决方案严重依赖第三方交易平台。因此，本发明利用区块链去中性化，不可篡改等特性，为用户和Web服务提供商构建了一个安全可信的交易环境，摆脱了第三方平台限制，更加安全可靠。直接将交易信息写入区块链中，交易双方不得违约，提高Web服务组合可靠性和安全性。并且使用优化的多目标灰狼优化算法解决Web服务组合问题。该算法收敛速度快，不容易陷入局部最优，种群多样性丰富，在寻优精度和求解质量方面都有了显著的提升，有效弥补传统方法的不足。Beneficial effects of the present invention: Since a large number of Web services have similar or identical energy supply properties, in order to provide users with the best service quality, current solutions include precise algorithms and heuristic algorithms. However, these solutions are heavily dependent on third-party trading platforms. Therefore, the present invention utilizes the characteristics of blockchain de-neutralization and non-tamperability to build a safe and reliable trading environment for users and Web service providers, getting rid of the restrictions of third-party platforms and being more secure and reliable. Transaction information is directly written into the blockchain, and both parties to the transaction shall not breach the contract, thereby improving the reliability and security of Web service combination. And an optimized multi-objective gray wolf optimization algorithm is used to solve the Web service combination problem. The algorithm has a fast convergence speed, is not easy to fall into local optimality, has rich population diversity, and has significantly improved the optimization accuracy and solution quality, effectively making up for the shortcomings of traditional methods.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为基于区块链智能合约的Web服务组合方法流程示意图；FIG1 is a flow chart of a Web service composition method based on blockchain smart contracts;
图2为本发明所提供的基于区块链智能合约的示意图；FIG2 is a schematic diagram of a blockchain-based smart contract provided by the present invention;
图3为本发明所提供的Web服务组合智能合约设计流程图。FIG3 is a flowchart of the design of a Web service combination smart contract provided by the present invention.
具体实施方式DETAILED DESCRIPTION
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
本发明提供一种基于区块链智能合约的Web服务组合方法，参考图1所示，具体包括如下步骤：The present invention provides a Web service combination method based on blockchain smart contract, as shown in FIG1 , which specifically includes the following steps:
S1：合约准备阶段，用户将智能合约部署在以太坊上，合约部署成功后，其余以太坊节点同步更新数据；S1: In the contract preparation phase, the user deploys the smart contract on Ethereum. After the contract is successfully deployed, the remaining Ethereum nodes will update the data synchronously;
用户U1首先会把智能合约部署在以太坊节点1上，当矿工拿到合约的执行权利后，会验证用户U1的身份。若验证成功，其余以太坊节点会更新区块链数据。User U1 will first deploy the smart contract on Ethereum node 1. When the miner obtains the execution rights of the contract, it will verify the identity of user U1. If the verification is successful, the remaining Ethereum nodes will update the blockchain data.
S2：合约执行阶段，不同的服务提供商处于不同的节点上，服务提供商利用同步的智能合约发布自己提供的Web服务信息，用户通过调用智能合约触发Web服务组合核心算法(改进的多目标灰狼优化算法)，找到当前最适合用户的Web服务组合；S2: Contract execution stage, different service providers are located on different nodes. Service providers use synchronized smart contracts to publish the Web service information they provide. Users trigger the core algorithm of Web service combination (improved multi-objective grey wolf optimization algorithm) by calling smart contracts to find the most suitable Web service combination for users.
实例中，服务提供商S1,S2和S3分别分布在不同的节点上，他们可以提供不同的Web信息服务，并将这些信息通过智能合约发布出去。这些信息将存储在区块链上。同时，用户可以通过智能合约中特定方法触发Web服务组合核心算法(改进的多目标灰狼优化算法)，从中选出满足用户需求的最优结果。In this example, service providers S1, S2 and S3 are distributed on different nodes. They can provide different Web information services and publish this information through smart contracts. This information will be stored on the blockchain. At the same time, users can trigger the core algorithm of Web service combination (improved multi-objective gray wolf optimization algorithm) through specific methods in smart contracts to select the best result that meets user needs.
本方案中，采用QoS来选择Web服务组合，不同的Web服务组合之后，作为一个整体，拥有Web服务组合序列，全局QoS值，以及每个QoS指标的权重三个属性，将Web服务组合模型定义为一个三元组S:＝(s,qG,w)，其中s＝(T1,...，Tk)即一个Web服务组合s由k个不同的Web服务组成，T1,...，Tk是Web服务组合序列；qG＝(q1,q2,...qm)代表k个Web服务组合后，全局的QoS值；该Web服务组合的QoS共有m个指标，qi代表第i个属性的值，其中i∈[1,m]；w＝(w1,w2,...,wm)，代表上述m个不同的QoS指标各自的权重，其中w1+w2+...+wm＝1。In this scheme, QoS is used to select Web service combinations. After different Web services are combined, as a whole, they have three attributes: Web service combination sequence, global QoS value, and weight of each QoS indicator. The Web service combination model is defined as a triple S: = (s, qG , w), where s = (T1 ,..., Tk ), that is, a Web service combination s is composed of k different Web services, T1 ,..., Tk is the Web service combination sequence; qG = (q1 ,q2 ,...qm ) represents the global QoS value after k Web services are combined; the QoS of this Web service combination has a total of m indicators, qi represents the value of the i-th attribute, where i∈[1,m]; w = (w1 ,w2 ,...,wm ), represents the weights of the above m different QoS indicators, where w1 +w2 +...+wm = 1.
S3：合约结束阶段，用户选择服务商提供的服务后，智能合约将所有关于Web服务组合的信息存储在区块链上。S3: At the end of the contract, after the user selects the services provided by the service provider, the smart contract stores all information about the web service combination on the blockchain.
如图3所示，在最后阶段，用户U1选择Web服务提供商S1与S3提供的服务，此时，他们在协议上达成一致。于是，智能合约调用方法将所有相关信息存储在区块链上。As shown in Figure 3, in the final stage, user U1 selects the services provided by web service providers S1 and S3, at which point they agree on the protocol. Then, the smart contract calls the method to store all relevant information on the blockchain.
根据改进的多目标灰狼优化算法，相比于传统多目标灰狼优化算法，其改进地方在于：According to the improved multi-objective grey wolf optimization algorithm, compared with the traditional multi-objective grey wolf optimization algorithm, its improvements are:
S41：收敛因子调整：传统的多目标灰狼算法线性变化的收敛因子会使得该算法在前期探测能力不足。另一方面，灰狼个体只能在一些集中分布的区域搜索解，使得种群多样性较少，因此算法容易陷入局部最优。改进的多目标灰狼算法将收敛因子变为余弦变化。改进后，收敛因子随着迭代次数的增加进行余弦变化。在算法执行前期，收敛因子的下降速度慢，算法执行后期，收敛因子的下降速度变快，因此，算法拥有较强的探测能力，不易陷入局部最优，同时还能有效避免传统算法后期收敛速度慢的问题。S41: Convergence factor adjustment: The linearly changing convergence factor of the traditional multi-objective gray wolf algorithm will make the algorithm insufficient in early detection capabilities. On the other hand, gray wolf individuals can only search for solutions in some concentrated areas, which makes the population diversity less, so the algorithm is prone to fall into local optimality. The improved multi-objective gray wolf algorithm changes the convergence factor to cosine variation. After the improvement, the convergence factor changes cosine with the increase of the number of iterations. In the early stage of the algorithm execution, the convergence factor decreases slowly, and in the later stage of the algorithm execution, the convergence factor decreases faster. Therefore, the algorithm has a strong detection capability and is not easy to fall into local optimality. At the same time, it can effectively avoid the problem of slow convergence in the later stage of the traditional algorithm.
S42：选择策略调整：在传统灰狼优化算法中，当灰狼个体所在的网格中的灰狼的数量越少时，此网格中的灰狼越可能被选为领导者狼。这种领导狼选举策略容易使得算法过早收敛，从而无法得到多目标优化问题的近似最优解。因此使用Boltzmann选择策略。当执行Boltzmann选择策略时，提高了群体的多样性(即解的多样性)。在算法执行的后期阶段，算法向最优解逼近的速度加快，从而缓解了算法后期收敛速度慢的问题。S42: Selection strategy adjustment: In the traditional gray wolf optimization algorithm, the fewer the number of gray wolves in the grid where the gray wolf individual is located, the more likely the gray wolf in this grid will be selected as the leader wolf. This leader wolf election strategy can easily cause the algorithm to converge too early, and thus fail to obtain an approximate optimal solution to the multi-objective optimization problem. Therefore, the Boltzmann selection strategy is used. When the Boltzmann selection strategy is implemented, the diversity of the group (i.e., the diversity of solutions) is improved. In the later stages of the algorithm execution, the speed at which the algorithm approaches the optimal solution is accelerated, thereby alleviating the problem of slow convergence in the later stages of the algorithm.
改进的多目标灰狼优化算法，其特征在于，所述收敛因子公式为：The improved multi-objective grey wolf optimization algorithm is characterized in that the convergence factor formula is:
其中，MaxIt为最大迭代次数，t为迭代次数。Among them, MaxIt is the maximum number of iterations, and t is the number of iterations.
本发明采用的Boltzmann选择策略选择领导者狼。其中fn代表第n个灰狼个体的适应度值，m为当前的迭代次数，T0为初始温度，T为当前温度，Xn为灰狼的数量.在公式中，第n个灰狼个体的适应度值fn与该个体所在的网络中的灰狼数量Nn成反比，即fn＝1/Nn。The Boltzmann selection strategy adopted by the present invention selects the leader wolf. Whereinfn represents the fitness value of the nth gray wolf individual, m is the current number of iterations,T0 is the initial temperature, T is the current temperature, andXn is the number of gray wolves. In the formula, the fitness valuefn of the nth gray wolf individual is inversely proportional to the number of gray wolvesNn in the network where the individual is located, that is,fn = 1/Nn .
T=T0(0.99m-1)T = T0 (0.99m-1 )
在传统算法中，领导者狼的压力始终保持在一个恒定值。而Boltzmann选择策略可以动态调节领导者狼的选择压力值，避免陷入局部最优解。In traditional algorithms, the pressure of the leader wolf is always kept at a constant value. The Boltzmann selection strategy can dynamically adjust the selection pressure value of the leader wolf to avoid falling into the local optimal solution.
为了验证MBB-MOGWO算法有效性，实验使用基准测试函数进行验证，其中包括UF2,UF5,UF9,ZDT2。并与其余两种多目标优化算法(NSGA-II,MOGWO)进行比较，统计评价参数HV(hypervolume),RGD(reverse generation distance),Spread值，最终结果如下：In order to verify the effectiveness of the MBB-MOGWO algorithm, the experiment uses benchmark functions, including UF2, UF5, UF9, and ZDT2. It is compared with the other two multi-objective optimization algorithms (NSGA-II and MOGWO), and the statistical evaluation parameters HV (hypervolume), RGD (reverse generation distance), and Spread values are statistically evaluated. The final results are as follows:
表1：不同算法评价指标对比Table 1: Comparison of evaluation indicators of different algorithms
由表可以看出，大部分情况下，本发明给出的算法得到指标更优。It can be seen from the table that in most cases, the algorithm provided by the present invention obtains better indicators.
本发明的有益效果：因为大量Web服务有着相近或相同的供能属性，为了给用户提供最优的服务质量，目前解决方案有精确算法和启发式算法等。这些方案严重依赖第三方平台。因此，本发明利用区块链去中性化，不可篡改等特性，为用户和服务提供商构建了一个安全可信的交易环境，摆脱了第三方平台限制，更加安全可靠。直接将交易信息写入区块链中，交易双方不得违约，提高Web服务组合可靠性和安全性。并且使用优化的多目标灰狼优化算法解决Web服务组合问题。该算法收敛速度快，不容易陷入局部最优，种群多样性丰富，在寻优精度和求解质量方面都有了显著的提升，有效弥补传统方法的不足。Beneficial effects of the present invention: Because a large number of Web services have similar or identical energy supply properties, in order to provide users with the best service quality, current solutions include precise algorithms and heuristic algorithms. These solutions rely heavily on third-party platforms. Therefore, the present invention utilizes the characteristics of blockchain de-neutralization and non-tamperability to build a safe and reliable transaction environment for users and service providers, getting rid of the restrictions of third-party platforms and being more secure and reliable. Transaction information is written directly into the blockchain, and both parties to the transaction shall not breach the contract, thereby improving the reliability and security of Web service combination. And an optimized multi-objective gray wolf optimization algorithm is used to solve the Web service combination problem. The algorithm has a fast convergence speed, is not easy to fall into local optimality, has rich population diversity, and has significantly improved the optimization accuracy and solution quality, effectively making up for the shortcomings of traditional methods.
以上仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
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