bard

 

  public string GetBarCodeFordata(string prefix, int shopNum, int iLogoID)
        {
            string strsql = "";
            string sBarCodes = "";
            int jcount = 1;
            int iResult = 0;
            int iResult1 = 0;
            int iMaxLogoNum = 0;
            string sMaxLogoNum = "";
            int iLogoNum = 0;
            int iMaxNum = 0;

            for (int m = 0; m < iLogoID; m++)
            {
                sMaxLogoNum += "9";
            }
            iMaxLogoNum = int.Parse(sMaxLogoNum);
            if (shopNum > iMaxLogoNum)
            {
                return "";
            }
            for (int i = 0; i < shopNum; i++)
            {
                //未使用的条码序号次序
                for (int j = jcount; j <= iMaxLogoNum; j++)
                {
                    try
                    {
                        strsql = "select count(*) from [BarCode] where [preFix]='{0}' and [LoID]={1} and  [LoNum]={2} ";
                        strsql = string.Format(strsql, prefix, iLogoID, j);
                        iResult = int.Parse(SQLHelperSQL.GetSingle(strsql).ToString());
                    }
                    catch { }
                    if (iResult == 0)
                    {
                        if (sBarCodes == "")
                        {
                            sBarCodes = jcount.ToString();
                        }
                        else
                        {
                            sBarCodes += "," + jcount.ToString();
                        }
                        jcount = j;
                        jcount++;
                        break;
                    }
                    jcount++;
                }
                //Recode
                if (jcount == iMaxLogoNum)
                {
                    if (shopNum > sBarCodes.TrimEnd(',').Split(',').Length)
                    {   //申请数量小于生成的代码
                        for (int l = sBarCodes.TrimEnd(',').Split(',').Length + 1; l < shopNum; l++)
                        {//最后剩余的代码
                            for (int k = 0; k <= iMaxLogoNum; k++)
                            {
                                try
                                {
                                    strsql = "select count(*) from [BarCode] where preFix='{0}' and LoID={1} and  LoNum={2} and isnull(Isvalid,0)=0";
                                    strsql = string.Format(strsql, prefix, iLogoID, k);
                                    iResult = int.Parse(SQLHelper.GetSingle(strsql).ToString());
                                }
                                catch { }
                                if (iResult == 0)
                                {
                                    if (sBarCodes == "")
                                    {
                                        sBarCodes = jcount.ToString();
                                    }
                                    else
                                    {
                                        sBarCodes += "," + jcount.ToString();
                                    }
                                    break;
                                }
                            }
                        }
                    }
                }
            }
            return sBarCodes;
        }
        #endregion


        string sLogoNum = class.GetBarCodeData(prefixNumber, 1, 6);
                                                int iLogoNum = int.Parse(sLogoNum);
                                                int GetLength = 6 - sLogoNum.Length;
                                                for (int m = 0; m < GetLength; m++)
                                                {
                                                    sLogoNum = "0" + sLogoNum;
                                                }

                                                string BarNO = prefixNumber + sLogoNum;

 

内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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