0) ///<----- ethcall } return ret; } 在C++中，根据调用的参数，编写函数ethcall()，实现逻辑。 //C++ u256 ethcall(vector_ref result, std::string d1, std::string d2) { /* * 实现同态加密逻辑，结果直接写到result中 */ return #include <vector> using namespace std; using namespace dev; using namespace dev::eth; namespace dev { namespace eth { class EthCallDemo : EthCallExecutorvector_ref, string> a, int b, vector_refbstr, string str) override //必须u256，override很重要 { LOG(INFO) << "ethcall " << a + b << " " << bstr.toString() << str; //用vector_ref定义的vector可直接对sol中的array赋值

"jinny" 80000 bool isParallelPrecompiled() override { return true; } // 根据并行接口，从参数中取出互斥对象，返回互斥对象 std::vector getParallelTag(bytesConstRef param) override { （3）编译，重启节点 手动编译节点的方法，参考：这里 编译之后，关 a） uint32_t func = getParamFunc(param); bytesConstRef data = getParamData(param); std::vector results; if (func == name2Selector[DAG_TRANSFER_METHOD_TRS_STR2_UINT]) // 函数是并行接口 本次交易允许最多消耗的gas数量，FISCO BCOS可配置该值 2 2 data vector< byte > 与交易相关的数据，或者是创建合约时的初 始化参数 6 6 chainId u256 记录本次交易所属的链信息/业务信息 - 7 groupId u256 记录本次交易所属的群组 - 8 extraData vector< byte > 预留字段，记录交易信息，内部使用“#”分割 信息 - 9

"jinny" 80000 bool isParallelPrecompiled() override { return true; } // 根据并行接口，从参数中取出互斥对象，返回互斥对象 std::vector getParallelTag(bytesConstRef param) override { // 获取被调用的函数名（func）和参数（data） uint32_t uint32_t func = getParamFunc(param); bytesConstRef data = getParamData(param); std::vector results; if (func == name2Selector[DAG_TRANSFER_METHOD_TRS_STR2_UINT]) // 函数是并行接口 本次交易允许最多消耗的gas数量，FISCO BCOS可配置该值 2 2 data vector< byte > 与交易相关的数据，或者是创建合约时的初 始化参数 6 6 chainId u256 记录本次交易所属的链信息/业务信息 - 7 groupId u256 记录本次交易所属的群组 - 8 extraData vector< byte > 预留字段，记录交易信息，内部使用“#”分割 信息 - 9

"jinny" 80000 bool isParallelPrecompiled() override { return true; } // 根据并行接口，从参数中取出互斥对象，返回互斥对象 std::vector getParallelTag(bytesConstRef param) override { // 获取被调用的函数名（func）和参数（data） uint32_t uint32_t func = getParamFunc(param); bytesConstRef data = getParamData(param); std::vector results; if (func == name2Selector[DAG_TRANSFER_METHOD_TRS_STR2_UINT]) // 函数是并行接口 本次交易允许最多消耗的gas数量，FISCO BCOS可配置该值 2 2 data vector< byte > 与交易相关的数据，或者是创建合约时的初 始化参数 6 6 chainId u256 记录本次交易所属的链信息/业务信息 - 7 groupId u256 记录本次交易所属的群组 - 8 extraData vector< byte > 预留字段，记录交易信息，内部使用“#”分割 信息 - 9

"jinny" 80000 bool isParallelPrecompiled() override { return true; } // 根据并行接口，从参数中取出互斥对象，返回互斥对象 std::vector getParallelTag(bytesConstRef param) override { // 获取被调用的函数名（func）和参数（data） uint32_t uint32_t func = getParamFunc(param); bytesConstRef data = getParamData(param); std::vector results; if (func == name2Selector[DAG_TRANSFER_METHOD_TRS_STR2_UINT]) // 函数是并行接口 本次交易允许最多消耗的gas数量，FISCO BCOS可配置该值 2 2 data vector< byte > 与交易相关的数据，或者是创建合约时的初 始化参数 6 6 chainId u256 记录本次交易所属的链信息/业务信息 - 7 groupId u256 记录本次交易所属的群组 - 8 extraData vector< byte > 预留字段，记录交易信息，内部使用“#”分割 信息 - 9

"jinny" 80000 bool isParallelPrecompiled() override { return true; } // 根据并行接口，从参数中取出互斥对象，返回互斥对象 std::vector getParallelTag(bytesConstRef param) override { // 获取被调用的函数名（func）和参数（data） uint32_t uint32_t func = getParamFunc(param); bytesConstRef data = getParamData(param); std::vector results; if (func == name2Selector[DAG_TRANSFER_METHOD_TRS_STR2_UINT]) // 函数是并行接口 本次交易允许最多消耗的gas数量，FISCO BCOS可配置该值 2 2 data vector< byte > 与交易相关的数据，或者是创建合约时的初 始化参数 6 6 chainId u256 记录本次交易所属的链信息/业务信息 - 7 groupId u256 记录本次交易所属的群组 - 8 extraData vector< byte > 预留字段，记录交易信息，内部使用“#”分割 信息 - 9

multi-property aspect of states is a powerful feature – it allows us to think of a Fabric state as a vector rather than a simple scalar. We then represent facts about whole objects as individual states, which for sophisticated object retrieval. See how MagnetoCorp’s paper 00001 is represented as a state vector that transitions according to different transaction stimuli: A commercial paper state is brought set of distinct Hyperledger Fabric states Notice how each paper in the list is represented by a vector state, with a unique composite key formed by the concatenation of org.papernet.paper, Issuer and

multi-property aspect of states is a powerful feature – it allows us to think of a Fabric state as a vector rather than a simple scalar. We then represent facts about whole objects as individual states, which for sophisticated object retrieval. See how MagnetoCorp’s paper 00001 is represented as a state vector that transitions according to different transaction stimuli: 6.3. Process and Data Design 111 set of distinct Hyperledger Fabric states Notice how each paper in the list is represented by a vector state, with a unique composite key formed by the concate- nation of org.papernet.paper, Issuer and

multi-property aspect of states is a powerful feature – it allows us to think of a Fabric state as a vector rather than a simple scalar. We then represent facts about whole objects as individual states, which for sophisticated object retrieval. See how MagnetoCorp’s paper 00001 is represented as a state vector that transitions according to different transaction stimuli: A commercial paper state is brought set of distinct Hyperledger Fabric states Notice how each paper in the list is represented by a vector state, with a unique composite key formed by the concatenation of org.papernet.paper, Issuer and

multi-property aspect of states is a powerful feature – it allows us to think of a Fabric state as a vector rather than a simple scalar. We then represent facts about whole objects as individual states, which for sophisticated object retrieval. See how MagnetoCorp’s paper 00001 is represented as a state vector that transitions according to different transaction stimuli: 6.3. Process and Data Design 111 set of distinct Hyperledger Fabric states Notice how each paper in the list is represented by a vector state, with a unique composite key formed by the concate- nation of org.papernet.paper, Issuer and