WO2023045305A1 - Procédé de positionnement de cause de racine de paramètre de traitement et dispositif associé - Google Patents

Procédé de positionnement de cause de racine de paramètre de traitement et dispositif associé Download PDF

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WO2023045305A1
WO2023045305A1 PCT/CN2022/086785 CN2022086785W WO2023045305A1 WO 2023045305 A1 WO2023045305 A1 WO 2023045305A1 CN 2022086785 W CN2022086785 W CN 2022086785W WO 2023045305 A1 WO2023045305 A1 WO 2023045305A1
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Prior art keywords
parameter
correlation coefficient
subintervals
process parameters
sample
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PCT/CN2022/086785
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English (en)
Chinese (zh)
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方育柯
薛晓明
孙崇敬
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成都数之联科技股份有限公司
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Publication of WO2023045305A1 publication Critical patent/WO2023045305A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31061—Selection of assembly process parameters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to the field of data processing, in particular to a process parameter root cause location method and a related device.
  • one way to determine the root cause is based on the classic Pearson ⁇ Kendall correlation coefficient formula to calculate the correlation between the actual state value of the process parameter of the sample and the label value.
  • the other is to do smooth curve fitting on the parameter time series scatterplot, including exponential smoothing method and smoothing spline interpolation fitting, and calculate the sum of squares of the fitting residuals. If the trend of the scatterplot is smoother, the fitting residuals will be smaller , the stronger the correlation between the fluctuation trend of the parameter and the bad glass, the greater the possibility that the corresponding parameter is the root cause.
  • the purpose of the present invention includes, for example, providing a process parameter root cause location method and a related device, which can take into account the correlation analysis of parameter trend fluctuations and parameter time series trend fluctuations and label fluctuations, and effectively identify suspicious parameters.
  • an embodiment of the present invention provides a method for locating the root cause of a process parameter, the method comprising:
  • the comprehensive index of the process parameter is calculated.
  • the step of dividing the process parameters and sample output time into multiple subintervals to obtain multiple first subintervals and multiple second subintervals includes:
  • the sample production time is divided into a plurality of second subintervals based on the process parameters and the corresponding sample labels.
  • the step of generating the first time data includes:
  • the second median is used as the matching mapping value of the time falling within the range of the second subinterval, and the matching mapping value is used as the transformed first time data.
  • the step of obtaining the correlation coefficient of parameter trend fluctuations includes:
  • the step of performing the second processing on the sample label, the process parameter data and the first time data to obtain the correlation coefficient of parameter time series trend fluctuations includes:
  • the step of calculating the comprehensive index of process parameters based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation includes:
  • TPP max ⁇ P*TP,0 ⁇ 1/2 ;
  • the P1 is the first pearson correlation coefficient
  • P2 is the second pearson correlation coefficient
  • TP1 is the third pearson correlation coefficient
  • TP2 is the fourth pearson correlation coefficient
  • TPP is the comprehensive index of process parameters.
  • the method also includes:
  • the parameter comprehensive index is calculated and sorted. According to the sorting, it is possible to accurately know which types of process parameters are suspicious parameters, and point out the direction for the subsequent correction of the parameters of the process equipment.
  • the method further includes:
  • the embodiment of the present invention also provides a device for locating the root cause of a process parameter, the device comprising:
  • a division module configured to divide the process parameters and the sample output time into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals;
  • a generating module for each of the first subintervals and each of the second subintervals, determine the median of each of the first subintervals, generate first parameter data, and determine the median of each of the second subintervals.
  • the median of the subinterval generating the first time data
  • the first processing module is used to obtain the correlation coefficient of the parameter trend fluctuation after performing the first processing on the sample label and the first parameter data;
  • the second processing module is configured to perform a second processing on the sample label, the process parameter data and the first time data to obtain a correlation coefficient of time series trend fluctuation of parameters;
  • the calculation module is used to calculate the comprehensive index of the process parameter based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation.
  • an embodiment of the present application provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the process parameter root cause location method when executing the computer program .
  • the embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the process parameter root cause location method are implemented.
  • the process parameters and the sample output time are divided into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals.
  • determine The median of each first subinterval generates the first parameter data
  • determines the median of each second subinterval generates the first time data
  • performs the first processing on the sample label and the first parameter data performs the first processing on the sample label and the first parameter data
  • obtains the parameter trend The correlation coefficient of fluctuations, the sample label, the process parameter data and the first time data are processed for the second time to obtain the correlation coefficient of the parameter time series trend fluctuation, based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation, the process is calculated Comprehensive index of parameters.
  • This application takes into account the correlation analysis between parameter trend fluctuations and parameter time series trend fluctuations and label fluctuations, so as to effectively identify suspicious parameters.
  • FIG. 1 is a schematic block diagram of an electronic device provided by an embodiment of the present invention.
  • Fig. 2 is one of the schematic flow charts of a process parameter root cause location method provided by an embodiment of the present invention
  • Fig. 3 is the second schematic flow diagram of a process parameter root cause location method provided by an embodiment of the present invention.
  • Fig. 4 is the third schematic flow diagram of a process parameter root cause location method provided by an embodiment of the present invention.
  • Fig. 5 is the fourth schematic flow diagram of a process parameter root cause location method provided by an embodiment of the present invention.
  • Fig. 6 is a schematic structural diagram of a process parameter root cause locating device provided by an embodiment of the present invention.
  • one way to determine the root cause is based on the classic Pearson ⁇ Kendall correlation coefficient formula to calculate the correlation between the actual state value of the process parameter of the sample and the label value.
  • the other is to do smooth curve fitting on the parameter time series scatterplot, including exponential smoothing method and smoothing spline interpolation fitting, and calculate the sum of squares of the fitting residuals. If the trend of the scatterplot is smoother, the fitting residuals will be smaller , the stronger the correlation between the fluctuation trend of the parameter and the bad glass, the greater the possibility that the corresponding parameter is the root cause.
  • this embodiment provides a process parameter root cause location method and related devices, which can take into account the correlation analysis between parameter trend fluctuations and parameter timing trend fluctuations and label fluctuations, and effectively identify suspicious parameters.
  • the scheme provided by the embodiment is described in detail.
  • the electronic device may be a user terminal.
  • the electronic device may be, but not limited to, a server, a smart phone, a personal computer (PersonalComputer, PC), a tablet computer, a personal digital assistant (Personal Digital Assistant, PDA), Mobile Internet Device (Mobile Internet Device, MID), etc.
  • the electronic device may have a device capable of locating the root cause of the process parameter, for example, a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphic Processing Unit, GPU), etc., so as to execute the process parameter provided in this embodiment Root cause location method.
  • a central processing unit Central Processing Unit, CPU
  • a graphics processing unit Graphic Processing Unit, GPU
  • the electronic device may also be a server capable of communicating with a user terminal.
  • the server can divide the process parameters and sample output time into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals; for each of the first sub-intervals and each of the second sub-intervals respectively Sub-intervals, determining the median of each first sub-interval, generating first parameter data, determining the median of each second sub-interval, and generating first time data; performing a first step on the sample label and the first parameter data After processing, the correlation coefficient of the parameter trend fluctuation is obtained; the second processing is performed on the sample label, the process parameter data and the first time data to obtain the correlation coefficient of the parameter time series trend fluctuation; based on the parameter trend fluctuation The correlation coefficient and the correlation coefficient of the time series trend fluctuation of the parameters are calculated to obtain the comprehensive index of the process parameters.
  • the electronic device 100 includes a process parameter root cause location device 110 , a memory 120 , and a processor 130 .
  • the memory 120, the processor 130, and various components are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.
  • the process parameter root cause location device 110 includes at least one software function module that can be stored in the memory 120 in the form of software or firmware (Firmware) or solidified in the operating system (Operating System, OS) of the service electronic device 100 .
  • the processor 130 is configured to execute executable modules stored in the memory 120 , such as software function modules and computer programs included in the process parameter root cause location device 110 . When the computer-executable instructions in the process parameter root cause locating device 110 are executed by the processor, the process parameter root cause locating method is realized.
  • the memory 120 can be, but not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-OnlyMemory, PROM), can Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), etc.
  • RAM Random Access Memory
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM Erasable Programmable Read-Only Memory
  • EEPROM Electric Erasable Programmable Read-Only Memory
  • the processor 130 may be an integrated circuit chip with signal processing capabilities.
  • the above-mentioned processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, referred to as CPU), a network processor (Network Processor, referred to as NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit ( ASIC), Field Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • CPU Central Processing Unit
  • NP Network Processor
  • DSP digital signal processor
  • ASIC application-specific integrated circuit
  • FPGA Field Programmable Gate Array
  • a general-purpose processor can be a microprocessor, or the processor can be any conventional processor.
  • FIG. 2 is a flow chart of a process parameter root cause location method applied to the electronic device 100 shown in FIG.
  • Step 201 Divide the process parameters and the sample output time into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals.
  • Step 202 For each first subinterval and each second subinterval, determine the median of each first subinterval, generate first parameter data, determine the median of each second subinterval, and generate a first time data.
  • Step 203 After the first processing is performed on the sample label and the first parameter data, the correlation coefficient of the parameter trend fluctuation is obtained.
  • Step 204 Perform a second process on the sample label, the process parameter data and the first time data to obtain the correlation coefficient of the time series trend fluctuation of the parameters.
  • Step 205 Based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation, calculate the comprehensive index of the process parameter.
  • the process parameter is that when the process equipment produces samples, the process equipment has different process parameters based on different samples, wherein the process parameters may include temperature, air pressure, oxygen concentration, and the like.
  • the output time of the sample is that when the process equipment produces the sample, each sample corresponds to a different output time.
  • the process parameters are divided into a plurality of first subintervals, and the sample output time is divided into a plurality of second subintervals.
  • the process parameters are divided into intervals, specifically: the process parameters and the sample labels corresponding to the process parameters are extracted; and the process parameters are segmented into intervals through a preset algorithm to obtain multiple first sub-intervals.
  • the sample label represents the quality of the produced sample. For example, when the sample label is 0, it indicates that the quality of the sample is good; when the sample label is 1, it indicates that the quality of the sample is poor.
  • preset algorithms such as optimal binning algorithm for regression number, chi-square binning, and WOE binning, etc.
  • the embodiment of the present invention adopts the optimal binning algorithm for regression number to perform interval analysis on process parameters. divided.
  • the process parameters using different first sub-intervals can be obtained, and samples of different qualities can be obtained.
  • the quality of the samples obtained by the process parameters corresponding to the other first sub-intervals is poor.
  • the process parameters, corresponding sample labels and corresponding sample output time are obtained; based on the process parameters and corresponding sample labels, the sample output time is divided into are multiple second subintervals.
  • process parameters there is a corresponding relationship among process parameters, sample label and sample output time, that is, a sample corresponds to sample label, sample output time and process parameters.
  • Process parameters and corresponding sample labels and sample output time data check whether the time range of sample output time exceeds the first preset time length, if it exceeds, divide the sample output time into multiple A second sub-interval, if it does not exceed the first preset duration, check whether the time range exceeds the second preset duration, if it exceeds the second preset duration, divide the sample output duration into If the multiple second sub-intervals do not exceed the second preset duration, the sample output time is divided into multiple second sub-intervals in minutes.
  • the median of each first subinterval is determined for each first subinterval and each second subinterval respectively , generate the first parameter data, determine the median of each second subinterval, and generate the first time data.
  • a linear correlation coefficient and a nonlinear correlation coefficient of parameter trend fluctuations are obtained.
  • the second processing is performed on the sample label, the process parameter data and the first time data to obtain the linear correlation coefficient and the nonlinear correlation coefficient of the time series trend fluctuation of the parameters.
  • the comprehensive index of the process parameter is determined, so that the suspicious parameters of the process equipment are determined according to the comprehensive index of the process parameter. Further, the correction direction for the process equipment is determined.
  • a process parameter root cause The positioning method specifically includes the following steps:
  • Step 202-1 For each first subinterval, determine the first median in the first subinterval.
  • Step 202-2 Use the first median as the matching mapping value of the parameter value falling within the first subinterval range, and use the matching mapping value as the transformed first parameter data.
  • Step 202-3 For each second subinterval, determine the second median in the second subinterval.
  • Step 202-4 use the second median as the matching mapping value of the time falling within the second subinterval range, and use the matching mapping value as the transformed first time data.
  • the first time data corresponding to the second subinterval is determined in the same manner as above.
  • a method for locating the root cause of process parameters is provided, which specifically includes the following steps: process parameters that need to be explained It includes multiple types, and the types of process parameters can include temperature process parameters, air pressure process parameters, and oxygen concentration process parameters.
  • process parameters that need to be explained It includes multiple types, and the types of process parameters can include temperature process parameters, air pressure process parameters, and oxygen concentration process parameters.
  • the following steps are specific calculation methods for calculating the correlation coefficient of the parameter trend fluctuation of a certain type of process parameter:
  • Step 203-1 Group the first parameter data into groups.
  • Step 203-2 Determine the group value of each group.
  • Step 203-3 Calculate the first average value of the labels corresponding to each group.
  • Step 203-4 Calculate the first pearson correlation coefficient between the first average value and the group value.
  • Step 203-5 Centralize the group values of the first parameter data group.
  • Step 203-6 Obtain the first absolute value group of the first group of values after the centralization process.
  • Step 203-7 Calculate the second pearson correlation coefficient of the first mean value of the first absolute value group and the label corresponding to each group.
  • the first parameter data is (3,3,3,4,4,4,5,5,5)
  • the grouped data is (3,3,3) , (4,4,4), (5,5,5)
  • determine the group value of each group the group value of group (3,3,3) is 3, the group value of group (4,4,4)
  • the group value is 4, and the group value of group (5,5,5) is 5.
  • Each data in the first parameter data corresponds to a sample label
  • the sample label of the first parameter data is the sample label corresponding to the process parameter data before the first parameter data is processed.
  • the first parameter data is (3,3,3,4,4,4,5,5,5)
  • the corresponding sample label is (1,1,1,0,0,0,1,1,1 )
  • the first average value of the sample label corresponding to the group (3,3,3) is 1
  • the first average value of the sample label corresponding to the group (4,4,4) is 0,
  • the group (5,5,5) The first mean value of the corresponding sample label is 1.
  • Pearson Correlation Coefficient (Pearson Correlation Coefficient) is used to measure whether two data sets are on a line, and it is used to measure the linear relationship between fixed-distance variables.
  • a method for locating the root cause of the process parameter is provided, which specifically includes the following steps:
  • the following steps are specific calculation methods for calculating the correlation coefficient of the parameter trend fluctuation of a certain type of process parameter, wherein the type of process parameter involved in the calculation in step 204 is consistent with that in step 203 .
  • Step 204-1 Sort the first time data, process parameters and sample labels in chronological order to obtain sorted second time data, second process parameters and second sample labels.
  • Step 204-2 Group the second process parameters according to the second time data, and determine the second average value of the process parameters and the third average value of the labels of each group.
  • Step 204-3 Calculate the third pearson correlation coefficient of the second average value and the third average value.
  • Step 204-4 Centralize the second average value of the process parameters of each group.
  • Step 204-5 Take the second absolute value group of the second mean value after the centralization process.
  • Step 204-6 Calculate the fourth pearson correlation coefficient of the second absolute value group and the third mean value.
  • the processing method of determining the correlation coefficient of the time series trend fluctuation of parameters is similar to that of steps 203-1 to 203-7, the difference is that the first time data, process parameters and sample labels need to be sorted in chronological order to obtain the sorted The subsequent second time data, second process parameter and second sample label.
  • the comprehensive index of the process parameter is calculated.
  • the comprehensive index of the process parameter is calculated by the following formula:
  • TPP max ⁇ P*TP,0 ⁇ 1/2 ;
  • P1 is the first pearson correlation coefficient
  • P2 is the second pearson correlation coefficient
  • TP1 is the third pearson correlation coefficient
  • TP2 is the fourth pearson correlation coefficient
  • TPP is the comprehensive index of process parameters.
  • the type parameter comprehensive index of each type of process parameter is calculated separately, and the sizes of the multiple type parameter comprehensive indexes are sorted in descending order. Acquiring the first-ranked parameter comprehensive index; determining that the process parameter corresponding to the acquired parameter comprehensive index is suspicious.
  • the process parameters and the sample output time are divided into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals.
  • determine The median of each first subinterval generates the first parameter data
  • determines the median of each second subinterval generates the first time data
  • performs the first processing on the sample label and the first parameter data performs the first processing on the sample label and the first parameter data
  • obtains the parameter trend The correlation coefficient of fluctuations, the sample label, the process parameter data and the first time data are processed for the second time to obtain the correlation coefficient of the parameter time series trend fluctuation, based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation, the process is calculated Comprehensive index of parameters.
  • This application takes into account the correlation analysis between parameter trend fluctuations and parameter time series trend fluctuations and label fluctuations, so as to effectively identify suspicious parameters.
  • this embodiment also provides a process parameter root cause location device 110 applied to the electronic device 100 shown in FIG. 1 , and the process parameter root cause location device 110 includes:
  • Dividing module 111 is used for dividing process parameter and sample output time into a plurality of sub-intervals respectively, obtains a plurality of first sub-intervals and a plurality of second sub-intervals;
  • the generation module 112 is used to determine the median of each of the first subintervals for each of the first subintervals and each of the second subintervals, generate first parameter data, and determine the median of each of the first subintervals. The median of the two sub-intervals to generate the first time data;
  • the first processing module 113 is configured to obtain the correlation coefficient of the parameter trend fluctuation after performing the first processing on the sample label and the first parameter data;
  • the second processing module 114 is configured to perform a second processing on the sample label, the process parameter data and the first time data to obtain a correlation coefficient of time series trend fluctuation of parameters;
  • the calculation module 115 is configured to calculate and obtain the comprehensive index of the process parameter based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation.
  • the dividing module 111 is specifically configured to:
  • the sample production time is divided into a plurality of second subintervals based on the process parameters and the corresponding sample labels.
  • the generating module 112 is specifically configured to:
  • the second median is used as the matching mapping value of the time falling within the range of the second subinterval, and the matching mapping value is used as the transformed first time data.
  • the first processing module 113 is specifically configured to:
  • the second processing module 114 is specifically configured to:
  • the computing module 115 is specifically configured to:
  • TPP max ⁇ P*TP,0 ⁇ 1/2 ;
  • the P1 is the first pearson correlation coefficient
  • P2 is the second pearson correlation coefficient
  • TP1 is the third pearson correlation coefficient
  • TP2 is the fourth pearson correlation coefficient
  • TPP is the comprehensive index of process parameters.
  • the calculation module 115 is further configured to:
  • the calculation module 115 is further configured to:
  • this application divides the process parameters and sample output time into multiple sub-intervals to obtain multiple first sub-intervals and multiple second sub-intervals, respectively for each first sub-interval and each second sub-interval
  • Two sub-intervals determine the median of each first sub-interval, generate the first parameter data
  • determine the median of each second sub-interval generate the first time data
  • the correlation coefficient of the parameter trend fluctuation is obtained, and the sample label, the process parameter data and the first time data are processed for the second time to obtain the correlation coefficient of the parameter time series trend fluctuation, based on the correlation coefficient of the parameter trend fluctuation and the correlation coefficient of the parameter time series trend fluctuation
  • the coefficient is calculated to obtain the comprehensive index of the process parameters.
  • This application takes into account the correlation analysis between parameter trend fluctuations and parameter time series trend fluctuations and label fluctuations, so as to effectively identify suspicious parameters.
  • the present application also provides an electronic device 100 , and the electronic device 100 includes a processor 130 and a memory 120 .
  • the memory 120 stores computer-executable instructions, and when the computer-executable instructions are executed by the processor 130, the process parameter root cause location method is realized.
  • the embodiment of the present application also provides a computer-readable storage medium.
  • the storage medium stores a computer program.
  • the process parameter root cause location method is implemented.
  • each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.
  • each functional module in each embodiment of the present application may be integrated to form an independent part, each module may exist independently, or two or more modules may be integrated to form an independent part.
  • the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. .

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Abstract

Procédé de positionnement de cause de racine de paramètre de traitement et dispositif associé. Le procédé consiste : à diviser séparément un paramètre de traitement et un temps de sortie d'échantillon en de multiples sous-intervalles pour obtenir une pluralité de premiers sous-intervalles et une pluralité de seconds sous-intervalles (étape 201) ; pour chaque premier sous-intervalle et chaque second sous-intervalle, à déterminer la médiane de chaque premier sous-intervalle pour générer des premières données de paramètre et à déterminer la médiane de chaque second sous-intervalle pour générer des premières données de temps (étape 202) ; après la réalisation d'un premier traitement sur une étiquette d'échantillon et des premières données de paramètre, à obtenir un coefficient de corrélation de fluctuation de tendance de paramètre (étape 203) ; à réaliser un second traitement sur l'étiquette d'échantillon, à traiter des données de paramètre et les premières données de temps pour obtenir un coefficient de corrélation de fluctuation de tendance de série de temps de paramètre (étape 204) ; et sur la base du coefficient de corrélation de fluctuation de tendance de paramètre et du coefficient de corrélation de fluctuation de tendance de série de temps de paramètre, à réaliser un calcul pour obtenir un indice global de paramètre de traitement (étape 205). L'analyse de corrélation entre une fluctuation de tendance de paramètre, une fluctuation de tendance de série de temps de paramètre et une fluctuation d'étiquette est prise en considération, ce qui permet de reconnaître efficacement le paramètre suspect.
PCT/CN2022/086785 2021-09-22 2022-04-14 Procédé de positionnement de cause de racine de paramètre de traitement et dispositif associé WO2023045305A1 (fr)

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