CN112269818A - Method, system, device and medium for positioning device parameter root cause - Google Patents
Method, system, device and medium for positioning device parameter root cause Download PDFInfo
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Abstract
The invention discloses a method, a system, a device and a medium for positioning equipment parameter root factors, which relate to the technical field of intelligent manufacturing and artificial intelligence, and are characterized in that suspicious equipment is identified by comprehensively adopting a method combining principal component analysis and mean value clustering based on actual performance value data of equipment parameters, and the suspicious parameters of the equipment are positioned by applying a discriminant analysis method; the method overcomes the defect that the existing method cannot effectively analyze the suspicious parameters of the single equipment, greatly reduces the time cost of the traditional analysis method, realizes quick root cause positioning, reduces the burden of manually processing a large amount of historical parameter data, improves the efficiency of positioning the bad root causes and reduces the production cost.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing and artificial intelligence, in particular to a method, a system, a device and a medium for positioning a device parameter root factor.
Background
In the glass production process, the processing equipment (the processing equipment refers to equipment required in the product processing and manufacturing process) can automatically retain the corresponding parameter actual state value in the glass production process, and for a large batch of glass in the same process, the parameter set values of the processing equipment are kept consistent, but the fluctuation ranges of different degrees can cause the output of poor glass. In the prior art, the common effect of multiple devices on the occurrence of the failure is comprehensively analyzed based on regression methods such as XGboost regression and logistic regression on the assumption that a glass sample is sufficiently large and a poor sample is sufficient.
However, the recorded data in the actual production process may have a large amount of missing data, which makes it ineffective to directly apply the conventional machine learning method to identify the fluctuation of the parameters. In addition, the most common defects are that the actual state values of some parameters in the equipment parameter data occur successively, and although the parameter setting values of the glass in different parallel processing equipment are consistent, the actual performances are possibly different, and finally a data wide table of all glass samples cannot be formed, so that the traditional machine learning method only can consider the data collected by a single equipment. The other problem cannot be ignored, and the successive actual state values of the equipment parameters correspond to the same sample label dependent variable, so that the adverse effect caused by the subsequent equipment parameter fluctuation directly interferes with the correlation analysis of the previous equipment parameter fluctuation on the adverse effect, and the effect of identifying the parameter fluctuation by the traditional method is also influenced.
Disclosure of Invention
The invention provides a method, a system, a device and a medium for positioning a root factor of equipment parameters. The method overcomes the defect that the existing method cannot effectively analyze the suspicious parameters of the single equipment, greatly reduces the time cost of the traditional analysis method, realizes quick root cause search, reduces the burden of manually processing a large amount of historical parameter data, improves the positioning efficiency of bad root causes and reduces the production cost.
In order to achieve the above object, an aspect of the present invention provides a method for positioning a parameter root of a device, and a manufacturing apparatus of a product includes a device a1To apparatus AnAnd n is an integer greater than or equal to 2, the method comprising:
for device A1To apparatus AnObtaining a correlation strength value and a correction prediction tag corresponding to each device, sorting all the obtained correlation strength values in a descending order, obtaining correction prediction tag data corresponding to a plurality of devices before sorting, and recording the correction prediction tag data as fifth analysis data;
for the fifth analysis data, sequentially analyzing each device, extracting second analysis data attribute column data and correction prediction tag data of the corresponding device as independent variables and classification dependent variables respectively, analyzing independent variable linear combination of the optimal division classification dependent variables to obtain combination coefficients, recording the combination coefficients as weight coefficients of the device parameters, and sorting the combination coefficients in a descending order according to absolute values of the weight coefficients;
and integrating the combination coefficients by taking the equipment parameters as indexes to form an equipment parameter root factor positioning table, taking the product of the correlation strength value corresponding to each equipment in the equipment parameter root factor positioning table and the absolute value of the weight coefficient as a sequencing field, sequencing in a descending order to obtain a sequencing result, and obtaining the equipment parameter root factor positioning result based on the sequencing result.
For device A1To apparatus AnThe following treatments were respectively carried out:
for device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual values are sorted by taking the product name as an index and taking the parameter name as an attribute name to form first analysis data;
normalizing attribute data in the first analysis data to obtain second analysis data;
taking out data of an attribute column in second analysis data, obtaining principal components in the attribute column data, sorting according to principal component interpretation variances, and selecting candidate principal components from the principal components based on a principal component sorting result and a principal component cumulative interpretation variance ratio;
performing linear transformation on the input second analysis data by using the coefficient matrix of the candidate principal component to obtain score matrix data, and recording the score matrix data as third analysis data;
for the third analysis data, clustering by using a clustering algorithm to form 2 categories, and outputting and recording a clustering label of each product sample to obtain fourth analysis data;
taking out the label data of the product with the final good and bad detection, performing correlation analysis on the fourth analysis data by taking the product name as an index, grouping according to the clustering labels of the product samples, respectively counting the bad proportion, and outputting the maximum bad proportion in the grouping result to obtain a correlation strength value;
and adjusting the clustering label where the maximum bad proportion is located and actually is bad to be 1, and adjusting the rest to be 0 to obtain a corrected and predicted label.
Preferably, the invention is directed to apparatus AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded actual values of the parameters are indexed by using product names, a wide table is formed by sorting the parameter names as attribute names, attribute columns with the loss rate exceeding a preset percentage and attribute columns with the values of the attribute columns being constant values and the standard deviation being 0 are removed, and the retained attribute columns are subjected to loss value interpolation by using the median of the attributes to obtain first analysis data, wherein the method requires that the data have no loss value, if the loss needs to be interpolated, if the loss rate is too high, the interpolation loss value can distort the data misleading analysis result, and in this case, the data need to be removed; in addition, the constant value attribute does not provide any information, and the complexity of the data dimension is eliminated by increasing the null.
Preferably, the method centers the first analytical data according to the attribute mean and then divides the first analytical data by the standard deviation to obtain the second analytical data. The method firstly uses principal component analysis and requires data standardization, so as to prevent the influence of individual attribute dimension on the analysis result.
Preferably, the method uses principal component analysis to obtain principal components in the attribute column data. The original data has high parameter attribute dimensionality, high noise and collinearity, and can interfere with subsequent clustering analysis, so principal components need to be extracted first to obtain main useful information.
The specific steps or modes for obtaining the principal components in the attribute column data by using principal component analysis are as follows:
1. calculating a covariance matrix of the second analysis data;
2. performing characteristic decomposition on the covariance matrix, wherein the eigenvector is a principal component coefficient, and the eigenvalue is recorded as a principal component interpretation variance;
3. and taking the principal components with the cumulative sum ratio higher than 80% in the descending order of the explained variance.
Preferably, the method uses a clustering algorithm to cluster the third analysis data into 2 categories, and a Kmeans clustering algorithm is adopted in the embodiment, but the method is not limited to the Kmeans clustering algorithm, such as spectral clustering and systematic clustering.
Preferably, the method uses linear discriminant analysis to give independent variable linear combination of the optimal division classification dependent variable to obtain the combination coefficient. The method is expected to find independent variables capable of identifying the classification dependent variables and the contribution effect of the independent variables, and the judgment analysis integration meets the requirements.
The method comprises the following steps of obtaining a combination coefficient by using an independent variable linear combination of optimal division classification dependent variables through linear discriminant analysis: setting undetermined coefficients as weights, weighting and summing independent variables, counting intra-group variance and inter-group variance according to two classification groups, and minimizing the intra-group variance divided by the inter-group variance to obtain an optimization problem solution, namely a solution of the undetermined coefficients.
The invention also provides an equipment parameter root factor positioning system, and the product processing equipment comprises equipment A1To apparatus AnAnd n is an integer greater than or equal to 2, the system comprising:
a first to-be-processed analysis data obtaining unit for obtaining analysis data for the device A1To apparatus AnObtaining a correlation strength value and a correction prediction tag corresponding to each device, sorting all the obtained correlation strength values in a descending order, obtaining correction prediction tag data corresponding to a plurality of devices before sorting, and recording the correction prediction tag data as first to-be-processed analysis data;
a second to-be-processed analysis data obtaining unit for obtaining analysis data for the device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual value takes the product name as an index, takes the parameter name as an attribute name, and carries out normalized processing on the attribute data to obtain second to-be-processed analysis data;
the device parameter weight coefficient obtaining and ordering unit is used for sequentially analyzing each device according to the first to-be-processed analysis data, extracting attribute column data and correction prediction tag data in second to-be-processed analysis data corresponding to the device to be respectively used as independent variables and classification dependent variables, analyzing independent variable linear combination of the optimal division classification dependent variables to obtain combination coefficients, recording the combination coefficients as the weight coefficients of the device parameters, and ordering the combination coefficients according to the absolute value descending order of the weight coefficients;
and the device parameter root positioning result obtaining unit is used for integrating the combination coefficients by taking the device parameters as indexes to form a device parameter root positioning table, taking the product of the correlation strength value corresponding to each device in the device parameter root positioning table and the absolute value of the weight coefficient as a sorting field, sorting in a descending order to obtain a sorting result, and obtaining a device parameter root positioning result based on the sorting result.
The invention also provides a device for positioning the device parameter root cause, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the device parameter root cause positioning method when executing the computer program.
The invention also provides a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for root cause localization of device parameters.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
generally speaking, the production record equipment parameter data has the characteristics of large data volume, effective information dispersion, complex flow and the like, and the traditional manual search analysis and troubleshooting mode has low efficiency. The invention can form an automatic searching and matching step, lists all the most suspicious root causes, sorts the root causes in a descending order according to the suspicious degree of the equipment parameters, assists technical personnel to directly check the most suspicious root causes and positions the root causes as many as possible at the highest speed.
The method overcomes the defects of low speed and unsatisfactory effect of the traditional automatic analysis method on the equipment parameter data analysis, not only eliminates the interference of subsequent equipment parameters on the adverse correlation analysis, but also can easily expand and consider the combined action of different equipment parameters by combining the path analysis, and has higher feasibility and reliability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a schematic diagram of a method for locating a root cause of a device parameter according to the present invention;
FIG. 2 is a schematic diagram of the device parameter root cause location system of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflicting with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described and thus the scope of the present invention is not limited by the specific embodiments disclosed below.
It is understood that the terms "a" and "an" should be interpreted as meaning that a number of one element or element is one in one embodiment, while a number of other elements is one in another embodiment, and the terms "a" and "an" should not be interpreted as limiting the number.
Example one
The invention is specifically described below by taking the product as glass as an example, and the product can be other types of products in practical application, and the invention does not limit the specific types of the products.
Referring to fig. 1, the present invention provides an apparatus parameter root cause localization method by comprehensively applying a clustering principal component and a discriminant analysis, which identifies suspicious apparatuses by comprehensively applying a method combining principal component analysis and mean clustering, and locates suspicious parameters of apparatuses by applying a discriminant analysis method, thereby highlighting bad root causes.
The embodiment of the invention provides a method for positioning the root cause of equipment parameters, and product processing equipment comprises equipment A1To apparatus AnAnd n is an integer greater than or equal to 2, the method comprising:
for device A1To apparatus AnObtaining a correlation strength value and a correction prediction tag corresponding to each device, sorting all the obtained correlation strength values in a descending order, obtaining correction prediction tag data corresponding to a plurality of devices before sorting, and recording the correction prediction tag data as first to-be-processed analysis data;
for device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual value takes the product name as an index, takes the parameter name as an attribute name, and carries out normalized processing on the attribute data to obtain second to-be-processed analysis data;
sequentially analyzing each device aiming at first to-be-processed analysis data, extracting attribute column data and correction prediction label data in second to-be-processed analysis data corresponding to the devices to be respectively used as independent variables and classification dependent variables, analyzing independent variable linear combination of the optimal division classification dependent variables to obtain combination coefficients, recording the combination coefficients as weight coefficients of device parameters, and sorting the combination coefficients in a descending order according to absolute values of the weight coefficients;
and integrating the combination coefficients by taking the equipment parameters as indexes to form an equipment parameter root factor positioning table, taking the product of the correlation strength value corresponding to each equipment in the equipment parameter root factor positioning table and the absolute value of the weight coefficient as a sequencing field, sequencing in a descending order to obtain a sequencing result, and obtaining the equipment parameter root factor positioning result based on the sequencing result.
The method comprises the following specific implementation steps:
Step1:
assuming that an analysis processing device A is used, the actual values of the parameters recorded by the device are indexed by glass names, the parameter names are attribute names to be arranged to form a wide table, attribute columns with the deletion rate exceeding 30% and constant attribute columns are removed, the retained attribute columns are subjected to deletion value interpolation by the median of the attributes, and the result table is recorded as an analysis table 1, wherein the percentage of the removed attribute columns can be flexibly adjusted according to actual needs, and the embodiment of the invention is not specifically limited.
Step2:
The analysis table 1 is centered according to the attribute mean and then divided by the standard deviation for attribute normalization, and the result is denoted as analysis 2.
Step3:
And taking out data of the attribute column in the analysis table 2, finding out principal components by using principal component analysis, sorting the principal components in a descending order according to the interpretation variance, and selecting candidate principal components which are close to the front principal components and have the accumulated variance interpretation ratio of more than 80%. And performing linear transformation on the input analysis data by using the screened principal component coefficient matrix to obtain a score matrix, and recording the matrix table data as analysis data 3. The selected percentage of the candidate main components can be flexibly adjusted according to actual needs, and the embodiment of the invention is not specifically limited. The principal component coefficient matrix is a matrix formed by obtaining a characteristic vector column corresponding to a principal component according to a preamble description screening method, and the screening is a post-screening result which is subjected to the conditions that the principal component is analyzed and then is sorted in a descending order according to an explanation variance, and the ratio of the accumulative explanation variance exceeds 80%, so that main information in an original high-dimensional variable is extracted, and noise and co-linear interference are avoided.
Step4:
Taking out the analysis data 3, clustering by using a Kmeans clustering algorithm to form 2 categories, and outputting and recording a clustering label of each glass sample as analysis data 4; in practical application, other clustering algorithms may also be selected for clustering, and the specific clustering method and algorithm are not limited in this embodiment.
Step5:
And taking out the label data of the glass finally detected as good and bad, associating and analyzing the data 4 by taking the name of the glass as an index, grouping according to the clustering labels of the glass samples, respectively counting the bad occupation ratio, outputting the maximum bad occupation ratio in the grouping result, and recording as the association strength value.
Step6:
The clustering label where the maximum bad proportion is located and actually bad is adjusted to be 1, the rest are adjusted to be 0, and the clustering label is recorded as a correction prediction label
Step7:
And (3) executing the operation of the steps 1-6 on each device, outputting the corresponding correlation strength values, sorting the correlation strength values in a descending order, and acquiring corrected predicted label data corresponding to the front devices, wherein the devices which are ranked more front are more suspicious, and the corrected predicted label data are recorded as analysis data 5.
Step8:
And extracting analysis data 5, sequentially analyzing each device, extracting attribute column data and correction prediction tag data of an analysis table 2 of the corresponding device, respectively using the attribute column data and the correction prediction tag data as independent variables and classification dependent variables, giving out independent variable linear combination of the optimal classification dependent variables by using linear discriminant analysis, recording the combination coefficient as the weight coefficient of the device parameters, and sequencing the weight coefficients in a descending order according to absolute values, wherein the parameters which are more front are more suspicious.
Step9:
And integrating the combination coefficients into a table by taking the equipment parameters as indexes, calculating the product of the correlation strength value and the absolute value of the weight coefficient as a sequencing field, and sequencing according to a descending order, wherein the equipment parameters which are more front are more suspicious, and the equipment parameters which are poor in glass are assisted to be positioned.
The method can form an automatic searching and matching step, lists all the most suspicious root causes, sorts the root causes in a descending order according to the suspicious degrees of the equipment parameters, assists technicians to directly check the most suspicious root causes and positions the root causes as many as possible at the highest speed.
The method overcomes the defects of low speed and unsatisfactory effect of the traditional automatic analysis method on the equipment parameter data analysis, not only eliminates the interference of subsequent equipment parameters on the adverse correlation analysis, but also can easily expand and consider the combined action of different equipment parameters by combining the path analysis, and has the advantages of practicability and reliability.
Example two
Please refer toReferring to fig. 2, a second embodiment of the present invention provides an equipment parameter root positioning system, and a product manufacturing apparatus includes an equipment a1To apparatus AnAnd n is an integer greater than or equal to 2, the system comprising:
a first to-be-processed analysis data obtaining unit for obtaining analysis data for the device A1To apparatus AnObtaining a correlation strength value and a correction prediction tag corresponding to each device, sorting all the obtained correlation strength values in a descending order, obtaining correction prediction tag data corresponding to a plurality of devices before sorting, and recording the correction prediction tag data as first to-be-processed analysis data;
a second to-be-processed analysis data obtaining unit for obtaining analysis data for the device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual value takes the product name as an index, takes the parameter name as an attribute name, and carries out normalized processing on the attribute data to obtain second to-be-processed analysis data;
the device parameter weight coefficient obtaining and ordering unit is used for sequentially analyzing each device according to the first to-be-processed analysis data, extracting attribute column data and correction prediction tag data in second to-be-processed analysis data corresponding to the device to be respectively used as independent variables and classification dependent variables, analyzing independent variable linear combination of the optimal division classification dependent variables to obtain combination coefficients, recording the combination coefficients as the weight coefficients of the device parameters, and ordering the combination coefficients according to the absolute value descending order of the weight coefficients;
and the device parameter root positioning result obtaining unit is used for integrating the combination coefficients by taking the device parameters as indexes to form a device parameter root positioning table, taking the product of the correlation strength value corresponding to each device in the device parameter root positioning table and the absolute value of the weight coefficient as a sorting field, sorting in a descending order to obtain a sorting result, and obtaining a device parameter root positioning result based on the sorting result.
EXAMPLE III
The third embodiment of the present invention provides an apparatus for locating a root factor of a device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for locating a root factor of a device when executing the computer program.
The processor may be a Central Processing Unit (CPU), or other general-purpose processor, a digital signal processor (digital signal processor), an Application Specific Integrated Circuit (Application Specific Integrated Circuit), an off-the-shelf programmable gate array (field programmable gate array) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the device parameter root positioning device in the invention by operating or executing the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
Example four
A fourth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for root cause positioning of device parameters are implemented.
The device parameter root cause location means, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of implementing the embodiments of the present invention may also be stored in a computer readable storage medium through a computer program, and when the computer program is executed by a processor, the computer program may implement the steps of the above-described method embodiments. Wherein the computer program comprises computer program code, an object code form, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, a point carrier signal, a telecommunications signal, a software distribution medium, etc. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for locating the root cause of equipment parameter features that the equipment for preparing product includes equipment A1To apparatus AnAnd n is an integer greater than or equal to 2, the method comprising:
for device A1To apparatus AnObtaining a correlation strength value and a correction prediction tag corresponding to each device, sorting all the obtained correlation strength values in a descending order, obtaining correction prediction tag data corresponding to a plurality of devices before sorting, and recording the correction prediction tag data as first to-be-processed analysis data;
for device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual value takes the product name as an index, takes the parameter name as an attribute name, and carries out normalized processing on the attribute data to obtain second to-be-processed analysis data;
sequentially analyzing each device aiming at first to-be-processed analysis data, extracting attribute column data and correction prediction label data in second to-be-processed analysis data corresponding to the devices to be respectively used as independent variables and classification dependent variables, analyzing independent variable linear combination of the optimal division classification dependent variables to obtain combination coefficients, recording the combination coefficients as weight coefficients of device parameters, and sorting the combination coefficients in a descending order according to absolute values of the weight coefficients;
and integrating the combination coefficients by taking the equipment parameters as indexes to form an equipment parameter root factor positioning table, taking the product of the correlation strength value corresponding to each equipment in the equipment parameter root factor positioning table and the absolute value of the weight coefficient as a sequencing field, sequencing in a descending order to obtain a sequencing result, and obtaining the equipment parameter root factor positioning result based on the sequencing result.
2. The device parameter root cause localization method of claim 1, characterized in that for device a1To apparatus AnThe following treatments were respectively carried out:
for device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual values are sorted by taking the product name as an index and taking the parameter name as an attribute name to form first analysis data;
normalizing attribute data in the first analysis data to obtain second analysis data;
taking out data of an attribute column in second analysis data, obtaining principal components in the attribute column data, sorting according to principal component interpretation variances, and selecting candidate principal components from the principal components based on a principal component sorting result and a principal component cumulative interpretation variance ratio;
performing linear transformation on the input second analysis data by using the coefficient matrix of the candidate principal component to obtain score matrix data, and recording the score matrix data as third analysis data;
for the third analysis data, clustering by using a clustering algorithm to form 2 categories, and outputting and recording a clustering label of each product sample to obtain fourth analysis data;
taking out the label data of the product with the final good and bad detection, performing correlation analysis on the fourth analysis data by taking the product name as an index, grouping according to the clustering labels of the product samples, respectively counting the bad occupation ratio, and outputting the maximum bad occupation ratio in the grouping result to obtain a correlation strength value;
and adjusting the clustering label where the maximum bad proportion is located and actually is bad to be 1, and adjusting the rest to be 0 to obtain a corrected and predicted label.
3. The device parameter root cause localization method of claim 2, characterized in that for device aiI is more than or equal to 1 and less than or equal to n, and the equipment AiAnd the recorded actual values of the parameters are indexed by taking the product names as indexes, the parameter names are used as attribute names to arrange and form a wide list, attribute columns with the loss rate exceeding a preset percentage and attribute columns with the values being constant and the standard deviation being 0 are removed, and the retained attribute columns are subjected to loss value interpolation by using median of the attributes to obtain first analysis data.
4. The method of claim 2, wherein the method comprises centering the first analysis data by an attribute mean and dividing the first analysis data by a standard deviation to obtain the second analysis data.
5. The method according to claim 2, wherein the method uses a principal component analysis method to obtain principal components in the attribute column data.
6. The method of claim 2, wherein the method clusters the third analysis data into 2 categories using a Kmeans clustering algorithm.
7. The method of claim 2, wherein the method uses linear discriminant analysis to derive the combination coefficients from linear combinations of the independent variables that give the optimal partition classification dependent variable.
8. A system for locating the root cause of equipment parameter is characterized by that the equipment for preparing product includes equipment A1To is provided withPreparation of AnAnd n is an integer greater than or equal to 2, the system comprising:
a first to-be-processed analysis data obtaining unit for obtaining analysis data for the device A1To apparatus AnObtaining a correlation strength value and a correction prediction tag corresponding to each device, sorting all the obtained correlation strength values in a descending order, obtaining correction prediction tag data corresponding to a plurality of devices before sorting, and recording the correction prediction tag data as first to-be-processed analysis data;
a second to-be-processed analysis data obtaining unit for obtaining analysis data for the device AiI is more than or equal to 1 and less than or equal to n, and the equipment AiThe recorded parameter actual value takes the product name as an index, takes the parameter name as an attribute name, and carries out normalized processing on the attribute data to obtain second to-be-processed analysis data;
the device parameter weight coefficient obtaining and ordering unit is used for sequentially analyzing each device according to the first to-be-processed analysis data, extracting attribute column data and correction prediction tag data in second to-be-processed analysis data corresponding to the device to be respectively used as independent variables and classification dependent variables, analyzing independent variable linear combination of the optimal division classification dependent variables to obtain combination coefficients, recording the combination coefficients as the weight coefficients of the device parameters, and ordering the combination coefficients according to the absolute value descending order of the weight coefficients;
and the device parameter root positioning result obtaining unit is used for integrating the combination coefficients by taking the device parameters as indexes to form a device parameter root positioning table, taking the product of the correlation strength value corresponding to each device in the device parameter root positioning table and the absolute value of the weight coefficient as a sorting field, sorting in a descending order to obtain a sorting result, and obtaining a device parameter root positioning result based on the sorting result.
9. An apparatus for root cause location of device parameters, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the root cause location method of device parameters according to any of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for root cause localization of device parameters according to any one of claims 1 to 7.
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