WO2021109050A1 - 基于粒子群优化的smt焊点质量控制方法及系统 - Google Patents

基于粒子群优化的smt焊点质量控制方法及系统 Download PDF

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WO2021109050A1
WO2021109050A1 PCT/CN2019/123171 CN2019123171W WO2021109050A1 WO 2021109050 A1 WO2021109050 A1 WO 2021109050A1 CN 2019123171 W CN2019123171 W CN 2019123171W WO 2021109050 A1 WO2021109050 A1 WO 2021109050A1
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solder joint
image
parameter
particle
optimization
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PCT/CN2019/123171
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王通永
张龙
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惠州光弘科技股份有限公司
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  • the invention relates to the technical field of swarm intelligence optimization, in particular to a method and system for SMT solder joint quality control based on particle swarm optimization.
  • SMT surface mount manufacturing technology
  • reflow soldering is a key factor that has a key influence on the quality control of the SMT production process.
  • the reflow soldering process of circuit boards using SMT technology is prone to many problems that affect product quality, such as component tubes. If these quality problems are not effectively controlled, such as pin deformation, poor pin wetting, unreasonable pad design, and poor soldering, it will seriously affect the quality of electronic products and cause greater economic losses.
  • the present invention provides a method and system for quality control of SMT solder joints based on particle swarm optimization, which mainly combines particle swarm optimization (Particle Swarm Optimization, PSO) and a comprehensive evaluation method, by acquiring solder joint images, Analyze the characteristic quantity data of the solder joints, and then through the MATLAB simulation PSO optimization method and the comprehensive evaluation method of its optimization results, so as to obtain the optimal SMT solder joint state signal, so as to guide the modulation control unit of the production side to optimize the production quality control.
  • PSO particle Swarm Optimization
  • a quality control method for SMT solder joints based on particle swarm optimization including
  • S2 Construct a POS optimization model based on the characteristic parameters of the solder joints, input the morphological parameters of the solder joints, calculate the current fitness value of each solder joint particle, and obtain the evaluation coefficient of each solder joint particle through the comprehensive evaluation system;
  • S3 Iterate S2 until the next-generation updated solder joint state feature quantity is less than a threshold, terminate the iteration, and output solder joint morphological parameters;
  • the modulation center performs production quality control according to the morphological parameters of the solder joints.
  • the acquisition of the solder joint image includes detecting the solder joint by a sensor, generating a solder joint state signal, and analyzing, filtering, image enhancement and data processing of the solder joint state signal through an image acquisition system to generate the solder joint state characteristic quantity .
  • the solder joint state signal includes a solder joint state parameter and a temperature t
  • the solder joint state parameter includes a length x i , a width y i , an angle parameter ⁇ i , a roundness parameter ⁇ i and a temperature W
  • the solder joint state parameter The point state feature quantity M is: [x i y i ⁇ i ⁇ i ].
  • constructing the POS optimization model includes: suppose the population size is N, the current feature quantity M of the solder joint particle i is [m i1 m i2 ... m im ], and the current temperature T is [w i1 w i2 ... w im ], set the update formula of the solder joint particles in the POS model as:
  • W im and M im respectively represent the temperature of the i-th solder joint particle and the characteristic quantity of the solder joint state
  • P im (t) is the best position parameter experienced by the i-th solder joint particle
  • P jm (t ) Is the best location parameter experienced by the group, with It is an independently distributed random number between [0-1], defined as a random factor
  • c 1 is the individual cognitive acceleration coefficient
  • c 2 is the group cognitive acceleration coefficient.
  • the comprehensive evaluation system provided in the present invention includes: a cross-entropy parameter DM, an edge matching parameter EM, and a noise control degree NM.
  • h(f) is the gray histogram of the image
  • Z is the upper limit of gray
  • t is a preset threshold, which is obtained by comparing the cumulative histograms before and after the solder joint image optimization
  • u1(t ) And u2(t) are the intra-class mean values of the initial solder joint image and the target solder joint respectively
  • CE is the cross entropy
  • DM is the parameter to evaluate the quality of the cross entropy after normalization of the cross entropy. The closer the DM is to 1. The better the solder joint update position.
  • the calculation formula of the edge matching parameter EM is:
  • Y_edge is the binarized edge of the updated solder joint image
  • I_edge is the binarized edge of the original image.
  • the noise control degree NM is:
  • Y(m,n) is the 8-neighborhood pixel that contains the current solder joint pixel Y(i,j)
  • V(i,j) is the target solder joint pixel number
  • Smap is the salt noise mark map
  • Pmap is the pepper Noise marker map
  • N is the total number of pixels in the image
  • k is the normalized control parameter
  • the value range is (0,1). The larger the value of NM, the less the effect of noise on the optimized solder joint image.
  • the present invention also proposes a SMT solder joint quality control system based on particle swarm optimization, including:
  • the information collection unit is used to obtain the solder joint image, analyze and obtain the characteristic quantity of the solder joint state, and generate the characteristic quantity database;
  • Optimization unit used to construct a POS optimization model based on solder joint characteristic parameters, input solder joint morphological parameters, calculate the current fitness value of each solder joint particle, and obtain the evaluation coefficient of each solder joint particle through a comprehensive evaluation system;
  • Modulation unit used to perform production quality control according to the solder joint morphological parameters.
  • the optimization is calculated by MATLAB, and the calculation result is sent to the modulation unit for SMT reflow soldering operation control.
  • the present invention proposes a method and system for SMT solder joint quality control based on particle swarm optimization.
  • SMT solder joints are optimized through a POS optimization model and a comprehensive evaluation system, which effectively solves the product quality problem of SMT reflow soldering, and passes MATLAB
  • the software performs simulation and simulation, which improves the accuracy of optimization, greatly improves production efficiency, and effectively solves the shortcomings of time lag, nonlinearity, and instability of the reflow soldering process.
  • FIG. 1 is a flowchart of SMT solder joint quality control based on particle swarm optimization in an embodiment.
  • [Corrected according to Rule 91 24.12.2019] 2 is a schematic diagram of the structure of an SMT solder joint quality control system based on particle swarm optimization in an embodiment.
  • Figure 1 is a flow chart of SMT solder joint quality control based on particle swarm optimization provided by the present invention, which specifically includes:
  • a quality control method for SMT solder joints based on particle swarm optimization including
  • S2 Construct a POS optimization model based on the characteristic parameters of the solder joints, input the morphological parameters of the solder joints, calculate the current fitness value of each solder joint particle, and obtain the evaluation coefficient of each solder joint particle through the comprehensive evaluation system;
  • S3 Iterate S2 until the next-generation updated solder joint state feature quantity is less than a threshold, terminate the iteration, and output solder joint morphological parameters;
  • the modulation center performs production quality control according to the morphological parameters of the solder joints.
  • the acquisition of the solder joint image includes detecting the solder joint by a sensor, generating a solder joint state signal, and analyzing, filtering, image enhancement and data processing of the solder joint state signal through an image acquisition system to generate the solder joint state characteristic quantity .
  • the solder joint state signal includes a solder joint state parameter and a temperature t
  • the solder joint state parameter includes a length x i , a width y i , an angle parameter ⁇ i , a roundness parameter o i and a temperature W
  • the point state feature quantity M is: [x i y i ⁇ i ⁇ i ].
  • the construction of the POS optimization model includes: suppose the group size is N, and the current feature quantity M of the solder joint particle i is [m i1 m i2 ... m im ], because each solder joint particle changes at a certain temperature trend When changing the state, set the current temperature T to [w i1 w i2 ... w im ], and set the update formula of the solder joint particles in the POS model as:
  • W im and M im respectively represent the temperature of the i-th solder joint particle and the characteristic quantity of the solder joint state
  • P im (t) is the best position parameter experienced by the i-th solder joint particle
  • P jm (t ) Is the best location parameter experienced by the group, with It is an independently distributed random number between [0-1], defined as a random factor
  • c 1 is the individual cognitive acceleration coefficient
  • c 2 is the group cognitive acceleration coefficient.
  • the optimal position in the space is automatically and accurately searched out, that is, the optimal position parameter in the image generated after optimization.
  • the current fitness value of each particle needs to be obtained to evaluate the pros and cons of the particle position, and it is used as an important basis for the update of the particle speed and position.
  • evaluating the position parameters of each solder joint particle is used as a standard for comprehensively evaluating the quality of the optimization results.
  • the comprehensive evaluation system provided in the present invention includes: a cross-entropy parameter DM, an edge matching parameter EM, and a noise control degree NM.
  • h(f) is the gray histogram of the image
  • Z is the upper limit of gray
  • t is a preset threshold, which is obtained by comparing the cumulative histograms before and after the solder joint image optimization
  • u1(t ) And u2(t) are the intra-class mean values of the initial solder joint image and the target solder joint respectively
  • CE is the cross entropy
  • DM is the parameter to evaluate the quality of the cross entropy after normalization of the cross entropy. The closer the DM is to 1. The better the solder joint update position.
  • the calculation formula of the edge matching parameter EM is:
  • Y_edge is the binarized edge of the updated solder joint image
  • I_edge is the binarized edge of the original image.
  • the noise control degree NM is:
  • Y(m,n) is the 8-neighborhood pixel that contains the current solder joint pixel Y(i,j)
  • V(i,j) is the target solder joint pixel number
  • Smap is the salt noise mark map
  • Pmap is the pepper Noise marker map
  • N is the total number of pixels in the image
  • k is the normalized control parameter
  • the value range is (0,1). The larger the NM value, the smaller the effect of noise on the optimized solder joint image.
  • the present invention also proposes a SMT solder joint quality control system based on particle swarm optimization, including:
  • the information collection unit is used to obtain the solder joint image, analyze and obtain the characteristic quantity of the solder joint state, and generate the characteristic quantity database;
  • Optimization unit used to construct a POS optimization model based on solder joint characteristic parameters, input solder joint morphological parameters, calculate the current fitness value of each solder joint particle, and obtain the evaluation coefficient of each solder joint particle through a comprehensive evaluation system;
  • Modulation unit used to perform production quality control according to the solder joint morphological parameters.
  • the optimization is calculated by MATLAB, and the calculation result is sent to the modulation unit for SMT reflow soldering operation control.

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Abstract

基于粒子群优化的SMT焊点质量控制方法及系统，通过获取焊点图像及其特征量数据，根据焊点特征量参数构建POS优化模型，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；进一步通过判断下一代更新后的焊点状态特征量是否小于一误差阈值，终止迭代，输出焊点形态参数；根据优化的结果进行SMT回流焊操作控制。实现了SMT回流焊过程的质量仿真，有效提高产品的质量和可靠性。

Description

基于粒子群优化的SMT焊点质量控制方法及系统 技术领域
本发明涉及群智能优化技术领域，具体为一种基于粒子群优化的SMT焊点质量控制方法及系统。
背景技术
影响电子产品生产质量的因素非常复杂，包括原材料、元器件、环境条件、生产设备精度、生产工艺、防护措施和技能水平等。目前在电子产品的电路板生产中广泛采用表面贴装生产工艺(SMT)，在电子产品领域，SMT技术的诞生，使得电子产品的体积重量有了跨越发展，是应用最广的一种工艺技术，其工艺流程如图1所示。通过将无引脚或短引线表贴元器件安装在PCB的表面，采用回流焊或汽相焊等方法实现元器件的焊接。影响电子产品SMT生产质量的原因较多，其中回流焊是对SMT生产过程质量控制有关键影响的因素，采用SMT工艺的电路板回流焊过程容易出现多种影响产品质量的问题，如元器件管脚变形、管脚润湿不良、焊盘设计不合理、焊接不良等，如果这些质量问题没有进行有效控制，将严重影响电子产品的质量，造成较大的经济损失。
发明内容
本发明为了解决上述技术问题本发明提供一种基于粒子群优化的SMT焊点质量控制方法及系统，主要结合粒子群优化算法(Particle Swarm Optimization，PSO)和综合评价法，通过获取焊点图像，分析焊点的特征量数据，进而通过MATLAB仿真PSO优化方法和对其优化结果进行综合评价法，从而获取最优的SMT焊点状态信号，从而指导生产端的调制控制单元，进行生产质量优化控制，从而提高了SMT回流焊的质量。
一种基于粒子群优化的SMT焊点质量控制方法，包括
S1：获取焊点图像，分析并获取焊点状态特征量，生成特征量数据库；
S2：根据焊点特征量参数构建POS优化模型，输入焊点形态参数，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；
S3：迭代S2直至下一代更新后的焊点状态特征量小于一阈值，终止迭代，输出焊点形态参数；
S4：调制中心根据所述焊点形态参数进行生产质量控制。
其中，所述获取焊点图像，包括通过传感器检测焊点，生成焊点状态信号，及通过图像采集系统对焊点状态信号进行解析、滤波、图像增强及数据处理后，生成焊点状态特征量。
进一步的，所述焊点状态信号包括焊点状态参数和温度t，所述焊点状态参数包括长度x i、宽度y i、角度参数θ i、圆度参数ο i和温度W，所述焊点状态特征量M为：[x i y i θ i ο i]。
作为优选的，构建POS优化模型包括：设群体规模为N，焊点粒子i当前特征量M为[m i1 m i2 ... m im]，当前的温度T为[w i1 w i2 ... w im]，设置在POS模型中焊点粒子的更新公式为：
Figure PCTCN2019123171-appb-000001
M im(t+1)＝M im(t)+W im(t+1)
式中，W im和M im分别表示第i个焊点粒子的温度量与焊点状态特征量，P im(t)为第i个焊点粒子所经历的最佳位置参数，P jm(t)为群体所经历的最佳位置参数，
Figure PCTCN2019123171-appb-000002
和
Figure PCTCN2019123171-appb-000003
为[0-1]之间独立分布的随机数，定义为随机因子，c 1为个体认知加速系数，c 2为群体认知加速系数。
作为优选地，本发明设置的综合评价体系包括：交叉熵参数DM，边缘匹配参数EM以及噪点控制度NM。
其中，所述交叉熵参数DM计算公式为：
Figure PCTCN2019123171-appb-000004
Figure PCTCN2019123171-appb-000005
Figure PCTCN2019123171-appb-000006
式中，是图像灰度值；h(f)是图像的灰度直方图；Z是灰度上限；t是为一预设阈值，通过比较焊点图像优化前后累积直方图得到，u1(t)和u2(t)分别是初始焊点图像和目标焊点的类内均值；CE为交叉熵；DM为将交叉熵归一化处理后得到的评价交叉熵优劣的参数，DM越接近于1，焊点更新位置越优。
所述边缘匹配参数EM计算公式为：
Figure PCTCN2019123171-appb-000007
其中:Y_edge为更新后焊点图像的二值化边缘，I_edge为原图像的二值化边缘。
所述噪点控制度NM为：
Figure PCTCN2019123171-appb-000008
其中，
Figure PCTCN2019123171-appb-000009
Figure PCTCN2019123171-appb-000010
其中:Y(m,n)为包含当前焊点像素Y(i,j)的8-邻域像素，V(i,j)为目标焊点像素数，Smap为盐噪点标记图，Pmap为椒噪点标记图，N为图像总像素数，k为归一化控制参数，取值范围为(0,1)。NM值越大，焊 点优化图像受噪点影响越小。
此外，本发明还提出一种基于粒子群优化的SMT焊点质量控制系统，包括：
信息采集单元，用于获取焊点图像，分析并获取焊点状态特征量，生成特征量数据库；
优化单元：用于根据焊点特征量参数构建POS优化模型，输入焊点形态参数，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；
迭代上述步骤直至下一代更新后的焊点状态特征量小于一阈值，终止迭代，输出焊点形态参数；
调制单元：用于根据所述焊点形态参数进行生产质量控制。
作为优选的，所述优化通过MATLAB进行计算，计算结果发送至所述调制单元，进行SMT回流焊操作控制。
本发明提出了一种基于粒子群优化的SMT焊点质量控制方法及系统，通过POS优化模型和综合评价体系进行了SMT焊点的优化，有效解决了SMT回流焊的产品质量问题，且通过MATLAB软件进行模拟和仿真，提升了优化的精度，极大提高了生产效率，有效解决了回流焊工艺的时滞性、非线性、不稳定性等缺点。
附图说明
图1为一实施例中基于粒子群优化的SMT焊点质量控制流程图。
[根据细则91更正 24.12.2019]　
图2为一实施例中基于粒子群优化的SMT焊点质量控制系统结构示意图。
具体实施方式
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
图1为本发明所提供的基于粒子群优化的SMT焊点质量控制流程图，具体包括：
一种基于粒子群优化的SMT焊点质量控制方法，包括
S1：获取焊点图像，分析并获取焊点状态特征量，生成特征量数据库；
S2：根据焊点特征量参数构建POS优化模型，输入焊点形态参数，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；
S3：迭代S2直至下一代更新后的焊点状态特征量小于一阈值，终止迭代，输出焊点形态参数；
S4：调制中心根据所述焊点形态参数进行生产质量控制。
其中，所述获取焊点图像，包括通过传感器检测焊点，生成焊点状态信号，及通过图像采集系统对焊点状态信号进行解析、滤波、图像增强及数据处理后，生成焊点状态特征量。
进一步的，所述焊点状态信号包括焊点状态参数和温度t，所述焊点状态参数包括长度x i、宽度y i、 角度参数θ i、圆度参数ο i和温度W，所述焊点状态特征量M为：[x i y i θ i ο i]。
作为优选的，构建POS优化模型包括：设群体规模为N，焊点粒子i当前特征量M为[m i1 m i2 ... m im]，由于每一个焊点粒子在以一定的温度变化趋势在改变状态，设置当前的温度T为[w i1 w i2 ... w im]，设置在POS模型中焊点粒子的更新公式为：
Figure PCTCN2019123171-appb-000011
M im(t+1)＝M im(t)+W im(t+1)
式中，W im和M im分别表示第i个焊点粒子的温度量与焊点状态特征量，P im(t)为第i个焊点粒子所经历的最佳位置参数，P jm(t)为群体所经历的最佳位置参数，
Figure PCTCN2019123171-appb-000012
和
Figure PCTCN2019123171-appb-000013
为[0-1]之间独立分布的随机数，定义为随机因子，c 1为个体认知加速系数，c 2为群体认知加速系数。
在焊点粒子的优化过程中，通过对焊点图像的分析及优化，自动准确地搜索出空间中最优位置，即为优化后生成的图像中的最优位置参数。
粒子群算法在更新迭代中，需要得到每个粒子的当前适应值来评价粒子位置的优劣，并作为粒子速度和位置更新的重要依据。在对焊点粒子图像优化时，评价各个焊点粒子的位置参数就是作为综合评价优化结果好坏的标准。
作为优选地，本发明设置的综合评价体系包括：交叉熵参数DM，边缘匹配参数EM以及噪点控制度NM。
其中，所述交叉熵参数DM计算公式为：
Figure PCTCN2019123171-appb-000014
Figure PCTCN2019123171-appb-000015
Figure PCTCN2019123171-appb-000016
式中，是图像灰度值；h(f)是图像的灰度直方图；Z是灰度上限；t是为一预设阈值，通过比较焊点图像优化前后累积直方图得到，u1(t)和u2(t)分别是初始焊点图像和目标焊点的类内均值；CE为交叉熵；DM为将交叉熵归一化处理后得到的评价交叉熵优劣的参数，DM越接近于1，焊点更新位置越优。
所述边缘匹配参数EM计算公式为：
Figure PCTCN2019123171-appb-000017
其中:Y_edge为更新后焊点图像的二值化边缘，I_edge为原图像的二值化边缘。
所述噪点控制度NM为：
Figure PCTCN2019123171-appb-000018
其中，
Figure PCTCN2019123171-appb-000019
Figure PCTCN2019123171-appb-000020
其中:Y(m,n)为包含当前焊点像素Y(i,j)的8-邻域像素，V(i,j)为目标焊点像素数，Smap为盐噪点标记图，Pmap为椒噪点标记图，N为图像总像素数，k为归一化控制参数，取值范围为(0,1)。NM值越大，焊点优化图像受噪点影响越小。
此外，本发明还提出一种基于粒子群优化的SMT焊点质量控制系统，包括：
信息采集单元，用于获取焊点图像，分析并获取焊点状态特征量，生成特征量数据库；
优化单元：用于根据焊点特征量参数构建POS优化模型，输入焊点形态参数，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；
迭代上述步骤直至下一代更新后的焊点状态特征量小于一阈值，终止迭代，输出焊点形态参数；
调制单元：用于根据所述焊点形态参数进行生产质量控制。
作为优选的，所述优化通过MATLAB进行计算，计算结果发送至所述调制单元，进行SMT回流焊操作控制。
虽然对本发明的描述是结合以上具体实施例进行的，但是，熟悉本技术领域的人员能够根据上述的内容进行许多替换、修改和变化、是显而易见的。因此，所有这样的替代、改进和变化都包括在附后的权利要求的精神和范围内。

Claims (10)

  1. 一种基于粒子群优化的SMT焊点质量控制方法，其特征在于，包括
    S1：获取焊点图像，分析并获取焊点状态特征量，生成特征量数据库；
    S2：根据焊点特征量参数构建POS优化模型，输入焊点形态参数，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；
    S3：迭代S2直至下一代更新后的焊点状态特征量小于一阈值，终止迭代，输出焊点形态参数；
    S4：调制中心根据所述焊点形态参数进行生产质量控制。
  2. 根据权利要求1所述的方法，其特征在于：所述获取焊点图像，包括通过传感器检测焊点，生成焊点状态信号，及通过图像采集系统对焊点状态信号进行解析、滤波、图像增强及数据处理后，生成焊点状态特征量。
  3. 根据权利要求2所述的方法，其特征在于：所述焊点状态信号包括焊点状态参数和温度t，所述焊点状态参数包括长度x i、宽度y i、角度参数θ i、圆度参数o i和温度W，所述焊点状态特征量M为：[x i y i θ i o i]。
  4. 根据权利要求1所述的方法，其特征在于：构建POS优化模型包括：设群体规模为N，焊点粒子i当前特征量M为[m i1 m i2 … m im]，当前的温度T为[w i1 w i2 …w im]，设置在POS模型中焊点粒子的更新公式为：
    Figure PCTCN2019123171-appb-100001
    M im(t+1)＝M im(t)+W im(t+1)
    式中，W im和M im分别表示第i个焊点粒子的温度量与焊点状态特征量，P im(t)为第i个焊点粒子所经历的最佳位置参数，P jm(t)为群体所经历的最佳位置参数，
    Figure PCTCN2019123171-appb-100002
    和
    Figure PCTCN2019123171-appb-100003
    为[0-1]之间独立分布的随机数，定义为随机因子，c 1为个体认知加速系数，c 2为群体认知加速系数。
  5. 根据权利要求1所述的方法，其特征在于：综合评价体系包括：交叉熵参数DM，边缘匹配参数EM以及噪点控制度NM。
  6. 根据权利要求5所述的方法，其特征在于：所述交叉熵参数DM计算公式为：
    Figure PCTCN2019123171-appb-100004
    Figure PCTCN2019123171-appb-100005
    Figure PCTCN2019123171-appb-100006
    式中，是图像灰度值；h(f)是图像的灰度直方图；Z是灰度上限；t是为一预设阈值，通过比较焊点图像优化前后累积直方图得到，u1(t)和u2(t)分别是初始焊点图像和目标焊点的类内均值；CE为交叉熵；DM为将交叉熵归一化处理后得到的评价交叉熵优劣的参数。
  7. 根据权利要求5所述的方法，其特征在于：所述边缘匹配参数EM计算公式为：
    Figure PCTCN2019123171-appb-100007
    其中:Y_edge为更新后焊点图像的二值化边缘，I_edge为原图像的二值化边缘。
  8. 根据权利要求5所述的方法，其特征在于：所述噪点控制度NM为：
    Figure PCTCN2019123171-appb-100008
    其中，
    Figure PCTCN2019123171-appb-100009
    Figure PCTCN2019123171-appb-100010
    其中：Y(m,n)为包含当前焊点像素Y(i,j)的8-邻域像素，V(i,j)为目标焊点像素数，Smap为盐噪点标记图，Pmap为椒噪点标记图，N为图像总像素数，k为归一化控制参数，取值范围为(0,1)。
  9. 一种应用权利要求1至8任一项所述方法的基于粒子群优化的SMT焊点质量控制系统，其特征在于：包括
    信息采集单元，用于获取焊点图像，分析并获取焊点状态特征量，生成特征量数据库；
    优化单元：用于根据焊点特征量参数构建POS优化模型，输入焊点形态参数，计算每个焊点粒子的当前适应值，并通过综合评价体系获得各个焊点粒子的评价系数；
    迭代上述步骤直至下一代更新后的焊点状态特征量小于一阈值，终止迭代，输出焊点形态参数；
    调制单元：用于根据所述焊点形态参数进行生产质量控制。
  10. 根据权利要求9所述的方法，其特征在于：所述优化通过MATLAB进行计算，计算结果发送至所述调制单元，进行SMT回流焊操作控制。
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