bard

获取条码序列与生成代码
 

  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MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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