CN107506515B - Method and device for constructing calculation model of PCB (printed Circuit Board) adding rate - Google Patents

Method and device for constructing calculation model of PCB (printed Circuit Board) adding rate Download PDF

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CN107506515B
CN107506515B CN201710530954.7A CN201710530954A CN107506515B CN 107506515 B CN107506515 B CN 107506515B CN 201710530954 A CN201710530954 A CN 201710530954A CN 107506515 B CN107506515 B CN 107506515B
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rate
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CN107506515A (en
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宫立军
邱醒亚
张可
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Shenzhen Fastprint Circuit Tech Co Ltd
Yixing Silicon Valley Electronic Technology Co Ltd
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Shenzhen Fastprint Circuit Tech Co Ltd
Yixing Silicon Valley Electronic Technology Co Ltd
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Abstract

The invention relates to a method and a device for constructing a calculation model of a PCB adding investment rate, wherein the method comprises the following steps: extracting related parameter data of the PCB adding rate, and performing data screening on the related parameter data to obtain initial parameter data; analyzing data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data and evaluating the correlation and significance of each initial parameter and the PCB adding rate to obtain overall distribution information and overall change rule information of each initial parameter and overall prediction results of correlation factors and significance factors; performing regression analysis by combining the initial parameter data and the overall prediction result to obtain each final reserved parameter and parameter factors thereof related to the PCB adding and throwing rate; and determining a PCB adding rate calculation model according to each final reserved parameter and each parameter factor. The PCB adding rate calculation model constructed by the invention can be used for predicting the PCB order adding rate, and the efficiency and the accuracy of the PCB order adding rate prediction are improved.

Description

Method and device for constructing calculation model of PCB (printed Circuit Board) adding rate
Technical Field
The invention relates to the technical field of PCBs (printed circuit boards), in particular to a method and a device for constructing a PCB adding throw-in rate calculation model.
Background
The PCB (Printed Circuit Board) industry has the characteristics of many customer sources and strong order randomness of the PCB, and the PCB product has the characteristics of strong individuation, many manufacturing parameters and process characteristics, high precision requirement and the like. Due to the characteristics, scrapping is easy to occur in the production process of the PCB, so that PCB enterprises generally carry out proper multi-feeding, namely additional feeding, when the PCB is fed, so as to make up for the loss caused by scrapping. Meanwhile, the PCB generally cannot be directly processed according to the size of the finished board, and a jointed board form is required, and when the delivery quantity divided by the number of produced jointed boards is not an integer, an upward rounding mode is required, which also results in an increase in the adding rate. These need to be taken into account when predicting PCB order placement rates.
In a traditional mode, the adding and throwing rate of a PCB order is mainly predicted by a production scheduling worker according to the difficulty degree of the PCB manufacture evaluated by experience, and the mode has high subjectivity, easily causes inaccurate prediction and consumes long time for prediction.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for constructing a PCB adding rate calculation model, a computer-readable storage medium, and a computer device, which do not require manual intervention when the constructed adding rate calculation model is applied to prediction of a PCB order adding rate, and can improve efficiency and accuracy of prediction of the PCB order adding rate.
In a first aspect, a method for constructing a PCB adding-throwing-rate calculation model is provided, which includes: extracting related parameter data of a PCB adding rate, and performing data screening on the related parameter data to obtain initial parameter data; analyzing data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, and evaluating the correlation and significance of each initial parameter and the PCB adding rate according to the initial parameter data to obtain an overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises a correlation factor and a significance factor; performing regression analysis by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and a parameter factor of each final retention parameter; and determining a PCB adding rate calculation model according to each final retention parameter and each parameter factor.
With reference to the first aspect, in a possible implementation manner of the first aspect, the step of extracting the parameter data related to the PCB adding rate includes: extracting relevant parameter data of the PCB adding rate from the ERP system through a pre-written query statement, wherein the query statement is written according to a predetermined parameter range.
With reference to the first aspect or some possible implementations of the foregoing, in a possible implementation of the first aspect, the step of performing regression analysis by using the initial parameter data and the overall prediction result to obtain final retention parameters and parameter factors of the final retention parameters related to the PCB adding rate includes: determining the coupling influence of a regression model and each initial parameter according to the initial parameter data and the overall prediction result; determining final retention parameters related to the PCB adding and the parameter factors of the final retention parameters by judging the fitting precision of the regression model and combining the coupling influence of the initial parameters
With reference to the first aspect or some possible implementations described above, in one possible implementation of the first aspect, the final remaining parameters include the number of layers, the plating times of the board, the thickness of the finished board, the minimum copper thickness of the hole wall, the ratio of thickness to diameter of the through hole, the total number of holes, the total number of flow, the delivery quantity, the historical yield, the area of the finished unit, whether copper, nickel and gold are plated, whether hard gold is plated, whether a gold finger is present, whether negative film plating is present, whether thinning copper is present, whether a photoelectric board is present, whether a high-frequency board is present, whether an IPCIII standard is required to be reached, whether a line width spacing is less than 3.5 mils, and the pressing times;
the number of layers, the number of times of plating the board, the finished board thickness, the minimum hole wall copper thickness, the through hole thickness ratio, the total number of holes, the total number of flow paths, the delivery quantity, the historical yield, the finished unit area, whether the copper nickel gold is plated, whether the hard gold is plated, whether the gold finger is provided, whether the negative plate is plated, whether the copper is thinned, whether the photoelectric board is provided, whether the high-frequency board is provided, whether the IPCIII standard is required to be achieved, whether the line width spacing is less than 3.5 milli-inches, and the parameter factors corresponding to the number of pressing are 0.3347, 0.5837, 0.2654, 0.0118, 0.1757, 0.00006001, 0.04282, 0.0002636, 0.05532, 42.07, 2.377, 0.828, 0.6899, 0.886, 0.6842, 0.2591, 0.3622, 0.4906, 0.4961, and 2.6278, respectively.
With reference to the first aspect or some of the foregoing possible implementations, in a possible implementation of the first aspect, the PCB adding projection rate calculation model is:
Rplus＝(((Roundup((Qdelivery*(1-(k1*Nlayers+k2*Tplate+k3*TF-plate+k4*Sct-hw
+k5*Rth-dm+k6*Nholes+k7*Nt-processes-k8*Qdelivery-k9*Yhis+k10*AF-unit
+k11*Wplate1+k12*Wplate2+k13*Wchefs+k14*Wn-plate+k15*Wt-copper
+k16*Wp-panel+k17*Wh-fq-board+k18*WIPCIII+k19*WL-width
+k20*Npress)))/Nspells))*Nspells)-Qdelivery)/Qdelivery
wherein R isplusDenotes the add rate, NlayersNumber of layers, TplateIndicating the number of plate platings, TF-plateShows the thickness of the finished board, Sct-hwDenotes the minimum hole wall copper thickness, Rth-dmRepresents the ratio of the thickness to the diameter of the through hole, NholesDenotes the total number of pores, Nt-processesRepresenting the total flow number, QdeliveryIndicating quantity of delivery, YhisRepresenting the historical yield, AF-unitDenotes the finished unit area, Wplate1Indicates whether the plate is plated with Ni, Au, Wplate2Indicates whether to plate hard gold, WchefsIndicates whether there is a golden finger, Wn-plateIndicating whether or not negative plating is present, Wt-copperIndicates whether there is thinning copper, Wp-panelIndicating whether it is a photovoltaic panel, Wh-fq-boardIndicates whether it is a high frequency plate, WIPCIIIIndicates whether the IPCIII standard is required to be met, WL-widthIndicates whether the linewidth spacing is less than 3.5 mils, NpressIndicates the number of press-fits, NspellsDenotes the number of panels, Roundup denotes rounding-up, k1、k2、k3、k4、k5、k6、k7、k8、k9、k10、k11、k12、k13、k14、k15、k16、k17、k18、k19And k20Is a parameter factor.
In a second aspect, a PCB adding-rate calculation model building apparatus is provided, which includes:
the data extraction unit is used for extracting relevant parameter data of the PCB adding and throwing rate;
the data sorting unit is used for carrying out data screening on the related parameter data to obtain initial parameter data;
the basic analysis unit is used for analyzing the data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, and evaluating the correlation and the significance between each initial parameter and the PCB adding rate according to the initial parameter data to obtain the overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises a correlation factor and a significance factor;
the regression analysis unit is used for carrying out regression analysis by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and a parameter factor of each final retention parameter;
and the creating unit is used for determining a PCB adding and throwing rate calculation model according to each final reserved parameter and each parameter factor.
With reference to the second aspect, in a possible implementation manner of the second aspect, the data extraction unit extracts the relevant parameter data of the PCB adding rate from the ERP system through a pre-written query statement, where the query statement is written according to a predetermined parameter range.
With reference to the second aspect, in a possible implementation manner of the second aspect, the regression analysis unit determines a coupling influence between a regression model and each of the initial parameters according to the initial parameter data and the overall prediction result, and determines each of final retention parameters and a parameter factor of each of the final retention parameters related to the PCB adding rate by determining a fitting accuracy of the regression model and combining the coupling influence of each of the initial parameters.
In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, realizes the steps of the PCB up-conversion calculation model building method as described above.
In a fourth aspect, a computer device is provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the PCB adding projection rate calculation model building method.
According to the scheme of the invention, the relevant parameter data of the PCB adding rate is extracted, the data of the relevant parameter data is screened to obtain initial parameter data, analyzing the data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, evaluating the correlation and significance of each initial parameter and the PCB adding rate to obtain the overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises correlation factors and significance factors, regression analysis is carried out by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and parameter factors of each final retention parameter, and determining a PCB adding rate calculation model according to each final retention parameter and each parameter factor. When the adding rate calculation model constructed by the scheme of the invention is applied to the prediction of the adding rate of the PCB order, the prediction of the adding rate of the PCB order can be realized without manual intervention, and the efficiency and the accuracy of the prediction of the adding rate of the PCB order can be improved.
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FIG. 1 is a schematic flow chart of an implementation of a method for constructing a computation model of PCB adding investment according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a PCB adding computation model building apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The influence factors of the PCB adding and throwing rate mainly include two aspects. One aspect is that the rejection rate is influenced, certain rejection conditions inevitably occur in the PCB processing process, and the reasons for unqualified products and rejection include subjective factors and objective factors. The subjective factors mainly refer to the scrap caused by unreasonable artificial design process, misoperation and the like, and the person can be directly traced. The objective factors mainly cause that the product is inevitably scrapped at a certain ratio in the processing and manufacturing process due to the fact that the product needs more processing parameters, the processing characteristics are complex, the process requirement is high, the equipment processing capacity is limited, the environment is uncertain and the like. Obviously, the more complex the product, the higher the requirement for feature accuracy, and the higher the manufacturing difficulty under the same other conditions, the higher the rejection rate due to objective reasons. The difficulty of manufacture is generally described by the manufacturing parameters (product parameters) of the PCB, and the closer to the capability boundary, the higher the difficulty, and the exceeding of the capability boundary may cause the rapid increase of the rejection rate. Also, the range of values of one or more characteristics processed by a process directly affects the rejection rate of the process. The greater the difficulty of the product is, the higher the rejection rate is, and in order to guarantee delivery, the adding rate is correspondingly improved. On the other hand, due to the influence of the jointed boards, when the delivery quantity divided by the number of the produced jointed boards is not an integer, the delivery quantity of the client needs to be rounded up, and thus the investment is increased due to the phase change.
The traditional method for predicting the adding rate of the PCB order adding rate is mainly characterized in that production scheduling personnel estimates the difficulty degree of PCB manufacturing according to experience to predict, and the method has high subjectivity, is easy to cause inaccurate prediction and consumes long time for prediction. And if the PCB adding rate calculation model is established, the prediction of the PCB order adding rate can be realized by acquiring and substituting parameters, manual intervention is not needed, and compared with the traditional mode of manually checking order parameters and estimating the adding rate according to personal experience, the method can well solve the problems of inaccurate adding rate calculation result, missing product parameters, inconsistent standards and the like, and is important for constructing the PCB adding rate calculation model. The PCB adding investment calculation model construction scheme of the invention is explained by the embodiment.
In one embodiment, a method for constructing a PCB adding investment computation model is provided. As shown in fig. 1, the method for constructing a PCB adding-rate calculation model in the present embodiment includes the following steps:
step S101: extracting related parameter data of a PCB adding rate, and performing data screening on the related parameter data to obtain initial parameter data;
specifically, parameter data related to the PCB up-rate calculation, i.e., the related parameter data, may be first extracted from each PCB history order, where the related parameter data includes parameter values of related parameters related to each PCB up-rate calculation; and primarily screening the related parameter data, excluding data (or called garbage data, mainly comprising incomplete data and error data) which do not meet the quality requirement, and obtaining residual data which are the initial parameter data.
Here, the initial parameter data refers to parameter values of each initial parameter obtained after screening.
Step S102: analyzing data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, and evaluating the correlation and significance of each initial parameter and the PCB adding rate according to the initial parameter data to obtain an overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises a correlation factor and a significance factor;
the correlation factor and the significance factor are characterized by the one-to-one correspondence of the PCB adding and throwing rate and each initial parameter.
Step S103: performing regression analysis by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and a parameter factor of each final retention parameter;
in performing the regression analysis, the dependence of each initial parameter of the PCB loading rate is considered.
When regression analysis is carried out, the PCB adding and throwing rate is used as an explained variable, each initial parameter is used as an explained variable, and the initial parameter data is used as sample data. Starting from sample data, mathematical relations between variables (interpreted variables and interpreted variables) are determined, various statistical tests are performed on the credibility of the data relations, and the variables affecting a specific variable are found out to be significant and not significant. The regression analysis significance was continuously screened out by iterative iterations for the significance-affecting insignificant parameters until all remaining variables were significant. The remaining variables are the final retained parameters, and the corresponding coefficients are the parameter factors.
It should be noted that, the above steps S102 and S103 can be implemented by a common mathematical statistical method, and the specific implementation manner does not limit the scope of the present invention.
Step S104: and determining a PCB adding rate calculation model according to each final retention parameter and each parameter factor.
After the PCB up-rate calculation model is determined, a PCB order up-rate budget may be performed according to the determined PCB up-rate calculation model. Specifically, the PCB order budget process may include: acquiring a PCB order file of a PCB order to be subjected to adding rate prediction; and determining parameter values of all parameters in the PCB adding rate calculation model according to the PCB order file, and substituting the obtained parameter values of all parameters into the PCB adding rate calculation model to calculate the adding rate of the PCB order.
Accordingly, according to the solution of the present embodiment, the related parameter data of the PCB adding rate is extracted, and the related parameter data is subjected to data screening to obtain the initial parameter data, analyzing the data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, evaluating the correlation and significance of each initial parameter and the PCB adding rate to obtain the overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises correlation factors and significance factors, regression analysis is carried out by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and parameter factors of each final retention parameter, and determining a PCB adding rate calculation model according to each final retention parameter and each parameter factor. When the adding rate calculation model constructed by the scheme of the embodiment is applied to the prediction of the adding rate of the PCB order, the prediction of the adding rate of the PCB order can be realized without manual intervention, and the efficiency and the accuracy of the prediction of the adding rate of the PCB order can be improved.
In one embodiment, the step of extracting the parameter data related to the PCB adding rate may include: extracting relevant parameter data of PCB (printed Circuit Board) adding rate from an ERP (Enterprise Resource Planning) system through a pre-written query statement, wherein the query statement is written according to a predetermined parameter range.
Here, the parameter ranges may be determined according to the results of research and analysis in combination with the experience of experts in the industry. The parameter ranges may specifically include process parameters, product parameters, and delivery parameters of the PCB order. The specific type of the query statement can be selected according to actual needs. Preferably, the query statement is an SQL (Structured query language) statement.
The process of screening the relevant parameter data to obtain the initial parameter data may specifically include: the data quality of the data after preliminary sorting is judged through sorting operations such as statistics, mean value solving, distribution characteristic analysis and the like to obtain a judgment result, the data is preliminarily screened according to the judgment result, and 'garbage' data (mainly comprising incomplete data and error data) is excluded to obtain the initial parameter data.
Wherein, the derivation means that the extracted relevant parameter data is derived and displayed by a set template; filling refers to filling and assigning partial missing data according to rules; the transformation means that partial parameters in the related parameter data are subjected to data transformation, for example, "finished product size length" and "finished product size width" are transformed into "finished product unit area". The statistics refers to counting the number of items of parameter data of each parameter; the average value means that the parameter values of the digital parameters are averaged; the distribution characteristic refers to normal distribution analysis of parameter values of the digital parameters. Whether the number of items of each parameter meets the set requirement or not can be judged through statistic operation, whether the average value of the parameter values of each parameter meets the set requirement or not can be judged through averaging operation, and whether the normal distribution result of the parameter values of each parameter meets the set requirement or not can be judged through distribution characteristic analysis. And determining parameter data corresponding to each parameter which does not meet the setting requirement as problem data, namely data with substandard quality, mainly data with data loss and data errors, and screening the data.
For the step S102, the data distribution characteristics of each initial parameter in the initial parameter data may be analyzed according to the initial parameter data, so as to obtain the overall distribution information and the overall change rule information of each initial parameter; and evaluating the correlation and significance of each initial parameter and the PCB adding rate according to the initial parameter data to obtain a correlation factor and a significance factor of each initial parameter. Wherein, the correlation evaluation and the significance evaluation can be carried out by adopting a method of F-test of variance analysis and the like.
The way of measuring whether the correlation between two variables is significant is different between the variables of different metering types, and the embodiment of the invention aims at numerical output, so the following two ways are mainly adopted: (1) the input variables are numerical, the output variables are numerical: and calculating a Pearson simple correlation coefficient, and then, through an observed value of the t-test statistic and a corresponding 1-probability P-value, the larger (closer to 1) the 1-probability P-value is, the larger the overall influence of the input variable on the output variable is, and the significance is realized on the prediction output. (2) The input variables are classified, the output variables are numerical: by statistically calculating the observed value of the F-test statistic in the analysis of variance and the corresponding 1-probability P-value, the larger (closer to 1) the 1-probability P-value is, the larger the total influence of the input variables on the output variables is, and the significance is brought to the prediction output.
In one embodiment, the step of performing regression analysis by combining the initial parameter data and the overall prediction result to obtain each final retention parameter and the parameter factor of each final retention parameter related to the PCB throwing rate includes: determining the coupling influence of a regression model and each initial parameter according to the initial parameter data and the overall prediction result; and determining final retention parameters related to the PCB adding and throwing rate and parameter factors of the final retention parameters by judging the fitting precision of the regression model and combining the coupling influence of the initial parameters. The specific implementation of these processes may be in a common mathematical statistical manner, e.g., may be by residual error R2The fitting accuracy of the regression model is judged. Parameters with the significance influence of the regression analysis on the significance can be screened out in an iterative mode continuously until all the remaining variables are significant, and the remaining variables are the final retention parameters.
The fitting precision influences the reliability and stability of the PCB adding and throwing rate calculation model; the set check levels are different, the obtained parameter factors of each final retention parameter and each final retention parameter are also different, and the variation of the original data also affects each final retention parameter and the parameter factor of each final retention parameter.
In one embodiment, the final retention parameters include the number of layers, plating times, finished board thickness, minimum hole wall copper thickness, through hole thickness-diameter ratio, total hole number, total flow number, delivery number, historical yield, finished unit area, whether copper nickel gold is plated, whether hard gold is electroplated, whether gold fingers are provided, whether negative film electroplating is provided, whether thinning copper is provided, whether photoelectric board is provided, whether high frequency board is provided, whether IPCIII standard is required to be achieved, whether line width spacing is less than 3.5 milli-inches, and pressing times;
the number of layers, the number of times of plating the board, the finished board thickness, the minimum hole wall copper thickness, the through hole thickness ratio, the total number of holes, the total number of flow paths, the delivery quantity, the historical yield, the finished unit area, whether the copper nickel gold is plated, whether the hard gold is plated, whether the gold finger is provided, whether the negative plate is plated, whether the copper is thinned, whether the photoelectric board is provided, whether the high-frequency board is provided, whether the IPCIII standard is required to be achieved, whether the line width spacing is less than 3.5 milli-inches, and the parameter factors corresponding to the number of pressing are 0.3347, 0.5837, 0.2654, 0.0118, 0.1757, 0.00006001, 0.04282, 0.0002636, 0.05532, 42.07, 2.377, 0.828, 0.6899, 0.886, 0.6842, 0.2591, 0.3622, 0.4906, 0.4961, and 2.6278, respectively.
In one embodiment, the PCB plus throw ratio calculation model is:
wherein R isplusDenotes the add rate, NlayersNumber of layers, TplateIndicating the number of plate platings, TF-plateShows the thickness of the finished board, Sct-hwDenotes the minimum hole wall copper thickness, Rth-dmRepresents the ratio of the thickness to the diameter of the through hole, NholesDenotes the total number of pores, Nt-processesRepresenting the total flow number, QdeliveryIndicating quantity of delivery, YhisRepresenting the historical yield, AF-unitDenotes the finished unit area, Wplate1Indicates whether the plate is plated with Ni, Au, Wplate2Indicates whether to plate hard gold, WchefsIndicates whether there is a golden finger, Wn-plateIndicating whether or not negative plating is present, Wt-copperIndicates whether there is thinning copper, Wp-panelIndicating whether it is a photovoltaic panel, Wh-fq-boardIndicates whether it is a high frequency plate, WIPCIIIIndicates whether the IPCIII standard is required to be met, WL-widthIndicates whether the linewidth spacing is less than 3.5 mils, NpressIndicates the number of press-fits, NspellsDenotes the number of panels, Roundup denotes rounding-up, k1、k2、k3、k4、k5、k6、k7、k8、k9、k10、k11、k12、k13、k14、k15、k16、k17、k18、k19And k20Is a parameter factor.
The PCB adding rate calculation model in the embodiment considers the influences of the rejection rate and the jointed board. The PCB order adding rate forecasting is carried out on the basis of the PCB adding rate calculation model of the embodiment, and the accuracy of adding rate forecasting can be obviously improved. When the PCB adding rate calculation model of the embodiment is adopted to predict the PCB order adding rate, when the circuit board product is plated with copper, nickel and gold, Wplate1Is 1, otherwise is 0; when the circuit board product is electroplated with hard gold, Wplate2Is 1, otherwise is 0; when the circuit board product has a golden finger, WchefsIs 1, otherwise is 0; when the circuit board product is electroplated with a negative film, Wn-plateIs 1, otherwise is 0; when the circuit board product has thinned copper, Wt-copperIs 1, otherwise is 0; when the circuit board product is a photoelectric board, Wp-panelIs 1, otherwise is 0; when the circuit board product is a high-frequency board, Wh-fq-boardIs 1, otherwise is 0; when the circuit board product is required to reach the IPCIII standard, WIPCIIIIs 1, otherwise is 0; when the line width spacing of the circuit board product is less than 3.5 milli-inches, WL-widthIs 1, otherwise is 0.
Substituting the values of the parameter factors in the embodiment into the PCB adding projection rate calculation model in the embodiment to obtain:
according to the method for constructing the PCB adding rate calculation model in the embodiment, the invention also provides a device for constructing the PCB adding rate calculation model. Fig. 2 is a schematic diagram illustrating a component structure of a PCB adding-rate calculation model building apparatus according to an embodiment of the present invention. As shown in fig. 2, the PCB adding investment computation model construction apparatus of the present invention includes a data extraction unit 201, a data sorting unit 202, a basic analysis unit 203, a regression analysis unit 204, and a creation unit 205, wherein:
a data extraction unit 201, configured to extract parameter data related to a PCB adding rate;
a data sorting unit 202, configured to perform data screening on the relevant parameter data to obtain initial parameter data;
a basic analysis unit 203, configured to analyze data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, and evaluate a correlation and a significance between each initial parameter and a PCB adding rate according to the initial parameter data to obtain an overall prediction result of each initial parameter, where the overall prediction result includes overall distribution information and overall change rule information, and also includes a correlation factor and a significance factor;
a regression analysis unit 204, configured to perform regression analysis by combining the initial parameter data and the overall prediction result to obtain final retention parameters related to a PCB adding rate and parameter factors of the final retention parameters;
a creating unit 205, configured to determine a PCB adding rate calculation model according to each of the final retention parameters and each of the parameter factors.
In one embodiment, the data extraction unit 201 extracts the relevant parameter data of the PCB adding rate from the ERP system through a pre-written query statement, wherein the query statement is written according to a predetermined parameter range.
In one embodiment, the regression analysis unit 204 determines a coupling effect between a regression model and each of the initial parameters according to the initial parameter data and the overall prediction result, and determines each of final retention parameters and a parameter factor of each of the final retention parameters related to the PCB adding rate by determining a fitting accuracy of the regression model and combining the coupling effect of each of the initial parameters.
In one embodiment, the final retention parameters include the number of layers, plating times, finished board thickness, minimum hole wall copper thickness, through hole thickness-diameter ratio, total hole number, total flow number, delivery number, historical yield, finished unit area, whether copper nickel gold is plated, whether hard gold is electroplated, whether gold fingers are provided, whether negative film electroplating is provided, whether thinning copper is provided, whether photoelectric board is provided, whether high frequency board is provided, whether IPCIII standard is required to be achieved, whether line width spacing is less than 3.5 milli-inches, and pressing times;
the number of layers, the number of times of plating the board, the finished board thickness, the minimum hole wall copper thickness, the through hole thickness ratio, the total number of holes, the total number of flow paths, the delivery quantity, the historical yield, the finished unit area, whether the copper nickel gold is plated, whether the hard gold is plated, whether the gold finger is provided, whether the negative plate is plated, whether the copper is thinned, whether the photoelectric board is provided, whether the high-frequency board is provided, whether the IPCIII standard is required to be achieved, whether the line width spacing is less than 3.5 milli-inches, and the parameter factors corresponding to the number of pressing are 0.3347, 0.5837, 0.2654, 0.0118, 0.1757, 0.00006001, 0.04282, 0.0002636, 0.05532, 42.07, 2.377, 0.828, 0.6899, 0.886, 0.6842, 0.2591, 0.3622, 0.4906, 0.4961, and 2.6278, respectively.
In one embodiment, the PCB plus throw ratio calculation model is:
Rplus＝(((Roundup((Qdelivery*(1-(k1*Nlayers+k2*Tplate+k3*TF-plate+k4*Sct-hw
+k5*Rth-dm+k6*Nholes+k7*Nt-processes-k8*Qdelivery-k9*Yhis+k10*AF-unit
+k11*Wplate1+k12*Wplate2+k13*Wchefs+k14*Wn-plate+k15*Wt-copper
+k16*Wp-panel+k17*Wh-fq-board+k18*WIPCIII+k19*WL-width
+k20*Npress)))/Nspells))*Nspells)-Qdelivery)/Qdelivery
wherein R isplusDenotes the add rate, NlayersNumber of layers, TplateIndicating the number of plate platings, TF-plateShows the thickness of the finished board, Sct-hwDenotes the minimum hole wall copper thickness, Rth-dmRepresents the ratio of the thickness to the diameter of the through hole, NholesDenotes the total number of pores, Nt-processesRepresenting the total flow number, QdeliveryIndicating quantity of delivery, YhisRepresenting the historical yield, AF-unitDenotes the finished unit area, Wplate1Indicates whether the plate is plated with Ni, Au, Wplate2Indicates whether to plate hard gold, WchefsIndicates whether there is a golden finger, Wn-plateIndicating whether or not negative plating is present, Wt-copperIndicates whether there is thinning copper, Wp-panelIndicating whether it is a photovoltaic panel, Wh-fq-boardIndicates whether it is a high frequency plate, WIPCIIIIndicates whether the IPCIII standard is required to be met, WL-widthIndicates whether the linewidth spacing is less than 3.5 mils, NpressIndicates the number of press-fits, NspellsDenotes the number of panels, Roundup denotes rounding-up, k1、k2、k3、k4、k5、k6、k7、k8、k9、k10、k11、k12、k13、k14、k15、k16、k17、k18、k19And k20Is a parameter factor.
Based on the above-mentioned embodiments, an embodiment further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the PCB adding projection rate calculation model building method as described in any one of the above embodiments.
Based on the above-mentioned embodiments, an embodiment further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the steps of the PCB adding projection rate calculation model building method in any one of the above-mentioned embodiments. The computer device may be any terminal device including a mobile phone, a tablet computer, a PDA (Personal Digital Assistant), a POS (Point of Sales), a vehicle-mounted computer, a wearable device, and the like.
It will be understood by those skilled in the art that all or part of the processes in the methods of the embodiments described above may be implemented by a computer program, which is stored in a non-volatile computer readable storage medium, and in the embodiments of the present invention, the program may be stored in the storage medium of a computer system and executed by at least one processor in the computer system to implement the processes of the embodiments including the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (9)

1. A method for constructing a calculation model of a PCB adding investment rate is characterized by comprising the following steps:
extracting related parameter data of a PCB adding rate, and performing data screening on the related parameter data to obtain initial parameter data;
analyzing data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, and evaluating the correlation and significance of each initial parameter and the PCB adding rate according to the initial parameter data to obtain an overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises a correlation factor and a significance factor;
performing regression analysis by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and a parameter factor of each final retention parameter;
determining a PCB adding rate calculation model according to each final reserved parameter and each parameter factor;
the calculation model of the PCB adding throwing rate is as follows:
Rplus＝(((Roundup((Qdelivery*(1-(k1*Nlayers+k2*Tplate+k3*TF-plate+k4*Sct-hw
+k5*Rth-dm+k6*Nholes+k7*Nt-processes-k8*Qdelivery-k9*Yhis+k10*AF-unit
+k11*Wplate1+k12*Wplate2+k13*Wchefs+k14*Wn-plate+k15*Wt-copper
+k16*Wp-panel+k17*Wh-fq-board+k18*WIPCIII+k19*WL-width
+k20*Npress)))/Nspells))*Nspells)-Qdelivery)/Qdelivery
wherein R isplusDenotes the add rate, NlayersNumber of layers, TplateIndicating the number of plate platings, TF-plateShows the thickness of the finished board, Sct-hwDenotes the minimum hole wall copper thickness, Rth-dmRepresents the ratio of the thickness to the diameter of the through hole, NholesDenotes the total number of pores, Nt-processesRepresenting the total flow number, QdeliveryIndicating quantity of delivery, YhisRepresenting the historical yield, AF-unitDenotes the finished unit area, Wplate1Indicates whether the plate is plated with Ni, Au, Wplate2Indicates whether to plate hard gold, WchefsIndicates whether there is a golden finger, Wn-plateIndicating whether or not negative plating is present, Wt-copperIndicates whether there is thinning copper, Wp-panelIndicating whether it is a photovoltaic panel, Wh-fq-boardIndicates whether it is a high frequency plate, WIPCIIIIndicates whether the IPCIII standard is required to be met, WL-widthIndicates whether the linewidth spacing is less than 3.5 mils, NpressIndicates the number of press-fits, NspellsDenotes the number of panels, Roundup denotes rounding-up, k1、k2、k3、k4、k5、k6、k7、k8、k9、k10、k11、k12、k13、k14、k15、k16、k17、k18、k19And k20Is a parameter factor.
2. The method for constructing a computational model of PCB add-on rate as claimed in claim 1, wherein the step of extracting the relevant parameter data of PCB add-on rate comprises:
extracting relevant parameter data of the PCB adding rate from the ERP system through a pre-written query statement, wherein the query statement is written according to a predetermined parameter range.
3. The method as claimed in claim 1 or 2, wherein the step of performing regression analysis by combining the initial parameter data and the overall prediction result to obtain final retention parameters and parameter factors of the final retention parameters related to the PCB up-conversion rate comprises:
determining the coupling influence of a regression model and each initial parameter according to the initial parameter data and the overall prediction result;
and determining final retention parameters related to the PCB adding and throwing rate and parameter factors of the final retention parameters by judging the fitting precision of the regression model and combining the coupling influence of the initial parameters.
4. The PCB adding input rate calculation model construction method according to claim 1, wherein the final retention parameters comprise the number of layers, plating times, finished product thickness, minimum hole wall copper thickness, through hole thickness-diameter ratio, total hole number, total flow number, delivery quantity, historical yield, finished product unit area, whether copper nickel and gold are plated, whether hard gold is plated, whether a gold finger is provided, whether negative film plating is provided, whether thinning copper is provided, whether a photoelectric board is provided, whether a high-frequency board is provided, whether IPCIII standard is required to be achieved, whether line width spacing is less than 3.5 milli-inches and pressing times;
the number of layers, the number of times of plating the board, the finished board thickness, the minimum hole wall copper thickness, the through hole thickness ratio, the total number of holes, the total number of flow paths, the delivery quantity, the historical yield, the finished unit area, whether the copper nickel gold is plated, whether the hard gold is plated, whether the gold finger is provided, whether the negative plate is plated, whether the copper is thinned, whether the photoelectric board is provided, whether the high-frequency board is provided, whether the IPCIII standard is required to be achieved, whether the line width spacing is less than 3.5 milli-inches, and the parameter factors corresponding to the number of pressing are 0.3347, 0.5837, 0.2654, 0.0118, 0.1757, 0.00006001, 0.04282, 0.0002636, 0.05532, 42.07, 2.377, 0.828, 0.6899, 0.886, 0.6842, 0.2591, 0.3622, 0.4906, 0.4961, and 2.6278, respectively.
5. A PCB adds throw ratio calculation model construction equipment, characterized by that, including:
the data extraction unit is used for extracting relevant parameter data of the PCB adding and throwing rate;
the data sorting unit is used for carrying out data screening on the related parameter data to obtain initial parameter data;
the basic analysis unit is used for analyzing the data distribution characteristics of each initial parameter in the initial parameter data according to the initial parameter data, and evaluating the correlation and the significance between each initial parameter and the PCB adding rate according to the initial parameter data to obtain the overall prediction result of each initial parameter, wherein the overall prediction result comprises overall distribution information and overall change rule information, and also comprises a correlation factor and a significance factor;
the regression analysis unit is used for carrying out regression analysis by combining the initial parameter data and the overall prediction result to obtain each final retention parameter related to the PCB adding and throwing rate and a parameter factor of each final retention parameter;
the creating unit is used for determining a PCB adding rate calculation model according to each final reserved parameter and each parameter factor;
the calculation model of the PCB adding throwing rate is as follows:
Rplus＝(((Roundup((Qdelivery*(1-(k1*Nlayers+k2*Tplate+k3*TF-plate+k4*Sct-hw
+k5*Rth-dm+k6*Nholes+k7*Nt-processes-k8*Qdelivery-k9*Yhis+k10*AF-unit
+k11*Wplate1+k12*Wplate2+k13*Wchefs+k14*Wn-plate+k15*Wt-copper
+k16*Wp-panel+k17*Wh-fq-board+k18*WIPCIII+k19*WL-width
+k20*Npress)))/Nspells))*Nspells)-Qdelivery)/Qdelivery
wherein R isplusDenotes the add rate, NlayersNumber of layers, TplateIndicating the number of plate platings, TF-plateShows the thickness of the finished board, Sct-hwDenotes the minimum hole wall copper thickness, Rth-dmRepresents the ratio of the thickness to the diameter of the through hole, NholesDenotes the total number of pores, Nt-processesRepresenting the total flow number, QdeliveryIndicating quantity of delivery, YhisRepresenting the historical yield, AF-unitDenotes the finished unit area, Wplate1Indicates whether the plate is plated with Ni, Au, Wplate2Indicates whether to plate hard gold, WchefsIndicates whether there is a golden finger, Wn-plateIndicating whether or not negative plating is present, Wt-copperIndicates whether there is thinning copper, Wp-panelIndicating whether it is a photovoltaic panel, Wh-fq-boardIndicates whether it is a high frequency plate, WIPCIIIIndicates whether the IPCIII standard is required to be met, WL-widthIndicates whether the linewidth spacing is less than 3.5 mils, NpressIndicates the number of press-fits, NspellsDenotes the number of panels, Roundup denotes rounding-up, k1、k2、k3、k4、k5、k6、k7、k8、k9、k10、k11、k12、k13、k14、k15、k16、k17、k18、k19And k20Is a parameter factor.
6. The PCB additive casting ratio calculation model building device of claim 5, wherein:
the data extraction unit extracts relevant parameter data of the PCB adding rate from the ERP system through a pre-written query statement, wherein the query statement is written according to a pre-determined parameter range.
7. The PCB-adding-ratio calculation model construction device as claimed in claim 5 or 6, wherein:
and the regression analysis unit determines a regression model and the coupling influence of each initial parameter according to the initial parameter data and the overall prediction result, and determines each final retention parameter related to the PCB adding rate and a parameter factor of each final retention parameter by judging the fitting precision of the regression model and combining the coupling influence of each initial parameter.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the PCB up-rating calculation model building method according to one of claims 1 to 4.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the PCB up-conversion calculation model construction method according to one of claims 1 to 4.
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