Data Processing Method, Apparatus, Device, and Storage Medium

Abstract

A data processing method, the method comprising: obtaining sample data of each sample among a plurality of samples generated within a preset time period; the sample data including values of device parameters of the devices passed by the sample at each acquisition time, and the test results of the samples; dividing the sample data into positive samples and negative samples according to the test results of the samples; determining a sample cut-off point for each sample according to the values of the device parameters, to obtain N value groups of target values corresponding to each sample; the target value being a value whose time difference between two adjacent acquisition times among the values of the device parameters is less than a first threshold; determining a relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples, the relevant quantization value being used to characterize the influence degree of the device parameters on defective samples.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, and particularly to a data processing method, apparatus, device, and storage medium. Background Art

[0002] In the manufacturing process of a product, the equipment involved in the production processes through which the production raw materials pass and the equipment parameters corresponding to the equipment will affect the performance of the product, and may cause the performance of the product to fail to meet the standard (also known as being defective). Therefore, for products with unqualified performance, it is necessary to determine the reasons for the unqualified performance of the products from the equipment and the equipment parameters of the equipment. Summary of the Invention

[0003] On the one hand, a data processing method is provided. The method includes: obtaining sample data of each sample in a plurality of samples generated within a preset time period; the sample data includes the values of the equipment parameters of the equipment passed by the sample at each acquisition time and the inspection result of the sample; dividing the sample data into positive samples and negative samples according to the inspection result of the sample; determining a sample cut-off point for each sample according to the values of the equipment parameters, and obtaining N value groups of target values corresponding to each sample; the sample cut-off point of each sample is used to represent the mutation point of the values of the equipment parameters of each sample, the target value is the value with a time difference less than a first threshold between two adjacent acquisition times in the values of the equipment parameters, and N is a positive integer greater than or equal to 1; determining a relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples, and the relevant quantization value is used to represent the influence degree of the equipment parameters on the defective samples, and M is a positive integer less than or equal to N.

[0004] In some embodiments, the determining the relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples includes: determining a first value of the statistical index of the Mth value group in the negative samples and a second value of the statistical index of the Mth value group in the positive samples; the statistical index is used to represent the central tendency or the change trend of the values in the value group; determining the difference between the first value and the second value; and determining the relevant quantization value according to the difference.

[0005] In other embodiments, the determining the difference between the first value and the second value includes: determining the difference between the first value and the second value according to the characteristic parameters in the plurality of first values in the negative samples and the characteristic parameters in the plurality of second values in the positive samples.

[0006] In other embodiments, the characteristic parameters include the value at the target position and / or the overall mean.

[0007] In some other embodiments, determining the difference between the first values and the second values according to the characteristic parameters among the multiple first values in the negative samples and the characteristic parameters among the multiple second values in the positive samples includes: determining a first difference between the value at the target position of the multiple first values in the negative samples and the value at the target position of the multiple second values in the positive samples; determining a second difference between the overall mean of the multiple first values in the negative samples and the overall mean of the multiple second values in the positive samples; and determining the difference between the first values and the second values according to the first difference, the second difference, and a preset weight.

[0008] In some other embodiments, determining the sample cut-off point of each sample according to the numerical value of the device parameter and obtaining N numerical groups of the target numerical value corresponding to each sample includes: determining the sample data of the reference sample according to the numerical value of the device parameter; the reference sample is a sample in the positive samples; determining the signal-to-noise ratio of the reference sample and determining the absolute value of the signal-to-noise ratio as the absolute signal-to-noise ratio; taking the numerical values in the filtered numerical values of the device parameter whose absolute value is greater than the absolute signal-to-noise ratio as the reference sample cut-off point; determining the sample cut-off point of each sample according to the reference ratio and the reference sample cut-off point to obtain N numerical groups of the target numerical value corresponding to each sample; the reference ratio is the ratio of the number of numerical values of the device parameter of the reference sample to the number of numerical values of the device parameter of each sample.

[0009] In some other embodiments, determining the sample cut-off point of each sample according to the reference ratio and the reference sample cut-off point includes: determining the preliminary sample cut-off point of each sample according to the reference ratio and the reference sample cut-off point; obtaining the correlation between the numerical group of the device parameter whose distance from the sample cut-off point is within the preset window size and the numerical group of the device parameter whose distance from the reference sample cut-off point is within the preset window size according to the determined sample cut-off point and the preset window size; and correcting the sample cut-off point of each sample according to the correlation.

[0010] In some other embodiments, determining the sample data of the reference sample according to the numerical value of the device parameter includes: performing a Fourier transform on the numerical value of the device parameter of each sample in the positive samples; taking the minimum number of the transformed numerical values of the device parameter in the positive samples as the truncation number; obtaining multiple truncated numerical groups by taking the first truncation number of numerical values in the numerical value of the device parameter of each sample in the positive samples; the number of numerical values included in each truncated numerical group is the truncation number; obtaining the median of the numerical values at each position in the multiple truncated numerical groups according to the arrangement order of the numerical values in each truncated numerical group to obtain a median sequence; and determining the sample data of the reference sample from the positive samples; the reference sample is the sample in the positive samples with the smallest difference value from the median sequence.

[0011] In some other embodiments, obtaining the sample data of each sample among the multiple samples generated within a preset time period includes: obtaining the sample data of each sample generated within the preset time period; obtaining the number of values included in the target value of the positive sample; determining a value range according to the number of values included in the target value of the positive sample; filtering out the positive samples whose number of values included in the target value of the positive sample in the sample data of each sample generated within the preset time period is outside the value range, so as to obtain the sample data of each sample among the multiple samples generated within the preset time period.

[0012] And / or, obtaining the sample data of each sample produced within a preset time period; determining a clipping length according to the median of the number of values included in the target value of each sample, and clipping the obtained sample data of each sample according to the clipping length, so as to obtain the sample data of each sample among the multiple samples generated within the preset time period.

[0013] In some other embodiments, the above method further includes: sorting the magnitudes of the relevant quantization values and outputting the sorting of the value groups of the device parameters corresponding to the relevant quantization values.

[0014] In some other embodiments, the above method further includes: outputting the information parameters of the value group of the device parameters, where the information parameters include the position of the value group in the device parameters and / or the percentage of the value group in the target value.

[0015] On the other hand, a data processing method is provided, and the method includes: receiving a sample screening condition input by a user in a condition selection interface; obtaining the sample data of each sample among the multiple samples corresponding to the sample screening condition; the sample data includes the values of the device parameters of the devices passed by the samples at each acquisition time and the test results of the samples; dividing the sample data into positive samples and negative samples according to the test results of the samples; determining the sample cut-off point of each sample according to the values of the device parameters to obtain N value groups of the target value corresponding to each sample; the sample cut-off point of each sample is used to represent the mutation point of the values of the device parameters of each sample, the target value is the value whose time difference between two adjacent acquisition times in the values of the device parameters is less than a first threshold, and N is a positive integer greater than or equal to 1; determining a relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples, where the relevant quantization value is used to represent the influence degree of the device parameters on the defective samples, and M is a positive integer less than or equal to N; displaying the relevant quantization value on an analysis result display interface.

[0016] In some embodiments, the method further includes: sorting the magnitudes of the relevant quantization values; the above displaying the relevant quantization value on the analysis result display interface includes: displaying the sorting of the value groups of the device parameters corresponding to the relevant quantization value on the analysis result display interface.

[0017] In some other embodiments, the method further includes: outputting information parameters of a numerical group of device parameters, where the information parameters include the position of the numerical group in the device parameters and / or the percentage of the numerical group in the target value.

[0018] In another aspect, a data processing device is provided, including: an acquisition module, configured to acquire sample data of each sample among a plurality of samples generated within a preset time period; the sample data includes the numerical values of the device parameters of the devices passed by the samples at each acquisition time, and the test results of the samples; a division module, configured to divide the sample data into positive samples and negative samples according to the test results of the samples; a determination module, configured to determine, according to the numerical values of the device parameters, a sample cut-off point of each sample to obtain N numerical groups of the target value corresponding to each sample; the sample cut-off point of each sample is used to characterize the mutation point of the numerical values of the device parameters of each sample, the target value is the numerical value whose time difference between two adjacent acquisition times in the numerical values of the device parameters is less than a first threshold, and N is a positive integer greater than or equal to 1; determining a correlation quantification value according to the difference between the Mth numerical group in the positive samples and the Mth numerical group in the negative samples, where the correlation quantification value is used to characterize the influence degree of the device parameters on the defective samples, and M is a positive integer less than or equal to N.

[0019] In some embodiments, the determination module is specifically configured to: determine a first value of a statistical index of the Mth numerical group in the negative samples and a second value of the statistical index of the Mth numerical group in the positive samples; the statistical index is used to characterize the central tendency or change trend of the numerical values in the numerical group; determine the difference between the first value and the second value; and determine the correlation quantification value according to the difference.

[0020] In some other embodiments, the determination module is specifically configured to: determine the difference between the first value and the second value according to the characteristic parameters in the plurality of first values in the negative samples and the characteristic parameters in the plurality of second values in the positive samples.

[0021] In some other embodiments, the characteristic parameters include the value at the target position and / or the overall mean.

[0022] In some other embodiments, the determination module is specifically configured to: determine a first difference between the values at the target position of the plurality of first values in the negative samples and the values at the target position of the plurality of second values in the positive samples; determine a second difference between the overall means of the plurality of first values in the negative samples and the overall means of the plurality of second values in the positive samples; and determine the difference between the first value and the second value according to the first difference, the second difference, and a preset weight.

[0023] In other embodiments, the determination module is specifically used to: determine the sample data of the reference sample based on the numerical value of the device parameter; the reference sample is a sample in the positive sample; determine the signal-to-noise ratio of the reference sample, and determine the absolute value of the signal-to-noise ratio as the absolute value of the signal-to-noise ratio; use the value of the filtered device parameter whose absolute value is greater than the absolute value of the signal-to-noise ratio as the reference sample cutting point; determine the sample cutting point of each sample based on the reference ratio and the reference sample cutting point, and obtain N numerical value groups of the target numerical value corresponding to each sample; the reference ratio is the ratio of the number of numerical values of the device parameter of the reference sample to the number of numerical values of the device parameter of each sample.

[0024] In other embodiments, the determination module is specifically used to: determine the preliminary sample cutting point of each sample based on the reference ratio and the reference sample cutting point; the acquisition module is also used to: obtain the correlation between the numerical group of device parameters whose distance from the sample cutting point is within the preset window size range and the numerical group of device parameters whose distance from the reference sample cutting point is within the preset window size range based on the determined sample cutting point and the preset window size; the data processing device also includes a correction module for correcting the sample cutting point of each sample based on the correlation.

[0025] In other embodiments, the determination module is also used to: perform Fourier transform on the numerical value of the device parameter of each sample in the positive sample; use the minimum number of numerical values of the device parameter in the transformed positive sample as the truncation number; obtain the first truncation number of numerical values of the device parameter of each sample in the positive sample to obtain multiple truncation value groups; the number of numerical values included in each truncation value group is the truncation number; according to the arrangement order of the numerical values in each truncation value group, obtain the median of the numerical values at each position in the multiple truncation value groups to obtain a median sequence; determine the sample data of the reference sample from the positive sample; the reference sample is the sample in the positive sample with the smallest difference value from the median sequence.

[0026] In other embodiments, the acquisition module is specifically used to: obtain sample data of each sample generated within a preset time period; obtain the number of values included in the target value of the positive sample; determine the numerical range based on the number of values included in the target value of the positive sample; filter the positive samples whose number of values included in the target value of the positive sample in the sample data of each sample generated within the preset time period is outside the numerical range, and obtain sample data of each sample in multiple samples generated within the preset time period.

[0027] And / or, obtain sample data of each sample produced within a preset time period; determine the cutting length based on the median of the number of values included in the target value of each sample, cut the sample data of each sample obtained according to the cutting length, and obtain sample data of each sample in the multiple samples generated within the preset time period.

[0028] In some other embodiments, the data processing device further includes: a sorting module, configured to sort the magnitudes of relevant quantization values; and an output module, configured to output the sorted numerical groups of the device parameters corresponding to the relevant quantization values.

[0029] In some other embodiments, the output module is further configured to: output the information parameters of the numerical groups of the device parameters, where the information parameters include the position of the numerical group in the device parameters and / or the percentage of the numerical group in the target value.

[0030] In another aspect, a data processing device is provided, including: a receiving module, configured to receive the sample screening conditions input by a user in a condition selection interface; an obtaining module, configured to obtain the sample data of each sample corresponding to the sample screening conditions; the sample data includes the values of the device parameters of the devices in the sample path at each acquisition time and the test results of the samples; a partitioning module, configured to partition the sample data into positive samples and negative samples according to the test results of the samples; a determining module, configured to determine the sample cut-off point of each sample according to the values of the device parameters, and obtain N numerical groups of the target values corresponding to each sample; the sample cut-off point of each sample is used to characterize the mutation point of the values of the device parameters of each sample, the target value is the value whose time difference between two adjacent acquisition times in the values of the device parameters is less than a first threshold, and N is a positive integer greater than or equal to 1; determining relevant quantization values according to the difference between the Mth numerical group in the positive samples and the Mth numerical group in the negative samples, where the relevant quantization values are used to characterize the influence degree of the device parameters on the defective samples, and M is a positive integer less than or equal to N; and a display module, configured to display the relevant quantization values on an analysis result display interface.

[0031] In some other embodiments, the data processing device further includes: a sorting module, configured to sort the magnitudes of relevant quantization values; and the display module is specifically configured to: display the sorted numerical groups of the device parameters corresponding to the relevant quantization values on an analysis result display interface.

[0032] In some other embodiments, the display module is further configured to: display the information parameters of the numerical groups of the device parameters on an analysis result display interface, where the information parameters include the position of the numerical group in the device parameters and / or the percentage of the numerical group in the target value.

[0033] In another aspect, an electronic device is provided, including: a processor and a memory for storing executable instructions executable by the processor; wherein, the processor is configured to execute the executable instructions to implement one or more steps in the data processing method provided in any of the above aspects and their embodiments.

[0034] In another aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores computer program instructions, and when the computer program instructions run on a processor, the processor is caused to execute one or more steps in the data processing method described in any of the above embodiments.

[0035] In yet another aspect, a computer program product is provided. The computer program product includes computer program instructions, and when the computer program instructions are executed on a computer, the computer program instructions cause the computer to execute one or more steps in the data processing method described in any of the above embodiments.

[0036] In yet another aspect, a computer program is provided. When the computer program is executed on a computer, the computer program causes the computer to execute one or more steps in the data processing method described in any of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] To more clearly illustrate the technical solutions in the present disclosure, the following briefly introduces the drawings required for use in some embodiments of the present disclosure. Obviously, the drawings in the following description are only the drawings of some embodiments of the present disclosure, and those of ordinary skill in the art can also obtain other drawings based on these drawings. In addition, the drawings in the following description can be regarded as schematic diagrams and do not limit the actual sizes of the products, the actual processes of the methods, the actual timings of the signals, etc. involved in the embodiments of the present disclosure.

[0038] Figure 1 Structural diagram of a data processing system according to some embodiments;

[0039] Figure 2 Structural diagram of an electronic device according to an embodiment;

[0040] Figure 3 Flowchart of a data processing method according to an embodiment;

[0041] Figure 4 Result diagram of determining the cutting point of a reference sample according to some embodiments;

[0042] Figure 5 Flowchart of determining the cutting point of a sample according to some embodiments;

[0043] Figure 6 Flowchart of determining the first difference, the second difference, and the difference value between the first difference and the second difference according to some embodiments;

[0044] Figure 7 Flowchart of another data processing method according to some embodiments;

[0045] Figure 8 Structural diagram of a condition selection interface according to some embodiments;

[0046] Figure 9 Structural diagram of a result variable input interface according to some embodiments;

[0047] Figure 10 Structural diagram of a reason variable input interface according to some embodiments;

[0048] Figure 11 Sample distribution diagram according to some embodiments;

[0049] Figure 12 Structural diagram of displaying relevant quantitative values of a numerical group in an analysis result display interface according to some embodiments;

[0050] Figure 13 Structural diagram of displaying relevant quantitative values of two numerical groups in an analysis result display interface according to some embodiments;

[0051] Figure 14 Structural diagram of a data processing device 80 according to some embodiments;

[0052] Figure 15 Structural diagram of a data processing device 90 according to some embodiments. Detailed implementation manners

[0053] Next, the technical solutions in some embodiments of the present disclosure will be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments provided by the present disclosure, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present disclosure.

[0054] Unless the context otherwise requires, throughout the specification and claims, the term "comprise" and its other forms, such as the third-person singular form "comprises" and the present participle form "comprising", are to be construed in an open, inclusive sense, i.e., "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example", or "some examples", etc. are intended to indicate that the specific features, structures, materials, or characteristics related to the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials, or characteristics may be included in any one or more embodiments or examples in any suitable manner.

[0055] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present disclosure, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0056] When describing some embodiments, the expressions "coupled" and "connected" and their derivatives may be used. For example, when describing some embodiments, the term "connected" may be used to indicate that two or more components have direct physical contact or electrical contact with each other. Another example is that when describing some embodiments, the term "coupled" may be used to indicate that two or more components have direct physical contact or electrical contact. However, the term "coupled" or "communicatively coupled" may also mean that two or more components do not have direct contact with each other, but still cooperate or interact with each other. The embodiments disclosed herein are not necessarily limited to the content herein.

[0057] As used herein, depending on the context, the term "if" is optionally construed to mean "when" or "at the time of" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if it is determined that..." or "if [the stated condition or event] is detected" is optionally construed to mean "when it is determined that..." or "in response to determining..." or "at the time of detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]".

[0058] As used herein, the use of "configured to" or "adapted to" means open and inclusive language that does not exclude devices that are configured to or adapted to perform additional tasks or steps.

[0059] Additionally, the use of "based on" means open and inclusive because a process, step, calculation, or other action "based on" one or more of the stated conditions or values can in practice be based on additional conditions or values beyond those stated.

[0060] As used herein, "about" or "approximately" includes the stated value and an average within an acceptable deviation range of the specific value, where the acceptable deviation range is determined by a person of ordinary skill in the art in view of the measurement being discussed and the errors associated with the measurement of a particular quantity (i.e., the limitations of the measurement system).

[0061] In the related art, in the process of manufacturing a product, the equipment and equipment parameters involved in any production process of the product path will affect the performance of the product, which may lead to unqualified product performance (also known as defective). The inspection stations for detecting product performance are usually after multiple devices, so it is impossible to locate the defective device in time. In the process of locating the defective device, it is necessary to trace each device involved in the production process. After locating the device, the equipment parameters of the device (including information such as temperature, pressure, humidity, and flow rate) need to be obtained. Since there are many equipment parameters of the device, for example: the equipment parameters at the subunit level of the device can reach up to 130 at most. If the device contains 10 subunits, then the device has a total of 13,000 equipment parameters. Confirming each equipment parameter one by one will consume a lot of time.

[0062] Based on this, an embodiment of the present disclosure provides a data processing method, which obtains sample data of each sample in a plurality of samples generated within a preset time period; divides the sample data into positive samples and negative samples according to the inspection results of the samples; determines the sample cut-off point of each sample to obtain N numerical groups (also known as sequence segments) of the target value corresponding to each sample; the sample cut-off point of each sample is used to represent the mutation point of the value of the equipment parameter of each sample, and determines a relevant quantization value according to the difference between the Mth numerical group in the positive samples and the Mth numerical group in the negative samples. The relevant quantization value is used to represent the influence degree of the equipment parameter on the defective samples. N is a positive integer, and M is a positive integer less than or equal to N, so as to improve the detection efficiency and enable the user to make a decision quickly and locate the cause of the defective samples.

[0063] Next, the technical solutions in some embodiments of the present disclosure will be described clearly and completely with reference to the accompanying drawings. Apparently, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided by the present disclosure fall within the protection scope of the present disclosure.

[0064] The data processing method provided by the embodiments of the present disclosure is applicable to a data processing system 10 as shown in Figure 1 Figure 1. The data processing system 10 includes a data processing device 100, a display device 200, and a distributed storage device 300. The data processing device 100 is respectively coupled to the display device 200 and the distributed storage device 300.

[0065] The distributed storage device 300 is configured to store production data generated by multiple devices (or referred to as factory devices). For example, the production data generated by multiple devices includes sample data of multiple devices. For example, the sample data includes the identifiers of the devices passed by multiple samples during the production process, the parameters corresponding to the devices, the inspection results, and the production time, and each sample experiences at least one device during the production process.

[0066] Among them, relatively complete data (such as a database) is stored in the distributed storage device 300. The distributed storage device 300 may include multiple hardware memories, and different hardware memories are distributed at different physical locations (such as in different factories or on different production lines), and information transmission between them is achieved through wireless transmission (such as a network, etc.), so that the data is in a distributed relationship, but logically constitutes a database based on big data technology.

[0067] The raw data of a large number of different devices is stored in the corresponding production manufacturing systems, such as in relational databases (such as Oracle, Mysql, etc.) of systems such as Yield Management System (YMS), Fault Detection & Classification (FDC), and Manufacturing Execution System (MES). And these raw data can be extracted from the original table through data extraction tools (such as Sqoop, kettle, etc.) and transmitted to the distributed storage device 300 (such as Hadoop Distributed File System (HDFS)) to reduce the load on the devices and the production manufacturing system and facilitate subsequent data reading by the data processing device 100.

[0068] The data in the distributed storage device 300 can be stored in the format of Hive tools or Hbase database. For example, according to the Hive tools, the above raw data is first stored in the database; then, data cleaning, data transformation and other preprocessing can be continued in the Hive tools to obtain the sample data data warehouse of the samples. The data warehouse can be connected to the display device 200, the data processing device 100, etc. through different API interfaces to realize data interaction with these devices. The display device 200 displays a selection page, which is used for users to select filtering conditions. The filtering conditions include result variables, cause variables, and filtering conditions (such as product category and time period, etc.). The data processing device 100 performs intelligent mining to conduct bad diagnosis analysis. The analysis results obtained by the data processing device 100 through bad diagnosis analysis are displayed to users on the analysis result display page of the display device 200.

[0069] Among them, since it involves multiple devices in multiple factories, the data volume of the above raw data is very large. For example, the raw data generated by all devices every day may be several hundred G, and the data generated per hour may also be dozens of G.

[0070] In the embodiments of the present disclosure, a relational database can be used to store massive structured data, and distributed computing can be used to calculate massive data. Exemplarily, a big data solution of a distributed file management system (Distributed FileSystem, DFS) is adopted to store and calculate massive structured data.

[0071] The big data technology based on DFS allows multiple inexpensive hardware devices to be used to build a large cluster to process massive data. For example, the Hive tool is a data warehouse tool based on Hadoop and can be used for data extraction, transformation and loading (ETL). The Hive tool defines a simple SQL-like query language and also allows complex analysis work that the default tool cannot complete through custom MapReduce mappers and reducers. The Hive tool does not have a dedicated data storage format and does not build an index for the data. Users can freely organize the tables therein and process the data in the database. It can be seen that the parallel processing of distributed file management can meet the storage and processing requirements of massive data. Users can process simple data through SQL queries, while custom functions can be used to implement complex processing. Therefore, when analyzing the massive data of the factory, it is necessary to extract the data in the factory database to the distributed file system, which will not damage the original data on the one hand and improve the data analysis efficiency on the other hand.

[0072] Exemplarily, the distributed storage device 300 may be a memory, may be multiple memories, or may be a collective term for multiple storage elements. For example, the memory may include: Random Access Memory (RAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDR SRAM), and may also include non-volatile memory, such as disk memory, Flash, etc.

[0073] The data processing device 100 may be any terminal device, server, virtual machine, or server cluster.

[0074] The display device 200 may be a display, or may also be a product including a display, such as a television, a computer (all-in-one or desktop), a computer, a tablet, a mobile phone, an electronic painting screen, etc. Exemplarily, the display device may be any device that displays both moving (e.g., video) and stationary (e.g., still images) and whether text or images. More specifically, it is contemplated that the embodiments may be implemented in or associated with a variety of electronic devices, such as (but not limited to) game consoles, television monitors, flat panel displays, computer monitors, automotive displays (e.g., odometer displays, etc.), navigators, cockpit controllers and / or displays, electronic photos, electronic billboards or signs, projectors, architectural structures, packaging, and aesthetic structures (e.g., a display for an image of a piece of jewelry), etc.

[0075] Exemplarily, the display device 200 described in the text may include one or more displays, including one or more terminals with display functions, so that the data processing device 100 can send the data processed by it (such as influencing parameters) to the display device 200, and the display device 200 then displays it. That is to say, through the interface of the display device 200 (i.e., the user interaction interface), full interaction (control and receiving results) between the user and the data processing system 10 can be achieved.

[0076] It can be understood that the functions of the above data processing device 100, display device 200, and distributed storage device 300 may be integrated in one electronic device or two electronic devices, or may be separately implemented by different devices for the functions of the data processing device 100, display device 200, and distributed storage device 300. The embodiments of the present disclosure do not limit this.

[0077] The functions of the above data processing device 100, display device 200, and distributed storage device 300 can all be performed by, such as Figure 2implemented by the illustrated electronic device 30. Figure 2 The electronic device 30 includes, but is not limited to, a processor 301, a memory 302, an input unit 303, an interface unit 304, and a power supply 305, etc. Optionally, the electronic device 30 includes a display 306.

[0078] The processor 301 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 301 may include one or more processing units; optionally, the processor 301 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 301.

[0079] The memory 302 can be used to store software programs and various data. The memory 302 mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required by at least one functional unit, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. Optionally, the memory 302 can be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0080] The input unit 303 can be a device such as a keyboard or a touch screen.

[0081] The interface unit 304 is an interface for connecting an external device to the electronic device 30. For example, the external device may include a wired or wireless headphone port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, and so on. The interface unit 304 can be used to receive inputs from an external device (such as data information, etc.) and transmit the received inputs to one or more components within the electronic device 30, or can be used to transmit data between the electronic device 30 and an external device.

[0082] A power supply 305 (such as a battery) can be used to power each component. Optionally, the power supply 305 can be logically connected to the processor 301 through a power management system, so as to manage functions such as charging, discharging, and power consumption management through the power management system.

[0083] The display 306 is used to display information input by the user or information provided to the user (such as data processed by the processor 301). The display 306 may include a display panel, and the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. In the case where the electronic device 30 is the display device 200, the electronic device 30 includes the display 306.

[0084] Optionally, the computer instructions in the embodiments of the present disclosure may also be referred to as application program code or a system, and the embodiments of the present disclosure do not make specific limitations thereto.

[0085] It should be noted that Figure 2 The illustrated electronic device is only an example, and it does not limit the electronic devices applicable to the embodiments of the present disclosure. In actual implementation, the electronic device may include more or fewer devices or components than Figure 2 shown.

[0086] As Figure 3 shown is a flowchart of a data processing method provided by an embodiment of the present disclosure. This method can be applied to the Figure 2 illustrated electronic device, Figure 3 The illustrated method may include the following steps:

[0087] S100: The electronic device obtains the sample data of each sample among a plurality of samples generated within a preset time period. The sample data includes the values of the device parameters of the devices passed by the sample at each acquisition time, and the test results of the sample.

[0088] In a possible implementation manner, the electronic device receives the sample data of each sample among a plurality of samples of the same model produced by each device on the sample production line within a preset time period.

[0089] In another possible implementation manner, the electronic device performs data preprocessing through the following steps to obtain the sample data of each sample among a plurality of samples produced within a preset time period:

[0090] Step 1: The electronic device obtains initial sample data. The initial sample data is the sample data of each sample produced within a preset time period.

[0091] Exemplarily, the electronic device obtains batch information related to products of a specific model and / or raw material identification information for producing the products within a preset time period from an Hbase database, and obtains sample data of each sample of the same model produced within the preset time period from a memory or a distributed storage system as initial sample data according to the obtained batch information or identification information.

[0092] It should be noted that the sample in the embodiments of the present disclosure may be a display panel in a display panel production line; of course, the sample in the embodiments of the present disclosure may also be other products. The sample data corresponding to the sample may further include a display panel mother board (glass), and the display panel mother board can be processed into multiple display panels (panels).

[0093] The embodiments of the present disclosure do not limit the representation method of the inspection results of the samples. Exemplarily, the inspection results may be 0 or 1, where 0 represents that the sample belongs to one type, and 1 represents that the sample belongs to another type. In one example, 0 represents that the sample is a good sample, and 1 represents that the sample is a bad sample. Specifically, the bad can be divided into different types according to needs. For example, it can be classified according to the direct impact of the bad on the sample performance, such as bright line bad, dark line bad, hot spot bad, etc.; or, it can also be classified according to the specific cause of the bad, such as signal line short circuit bad, alignment bad, etc.; or, it can also be classified according to the general cause of the bad, such as array process bad, color film process bad, etc.; or, it can also be classified according to the severity of the bad, such as bad causing scrapping, bad causing quality reduction, etc.; or, it can also not distinguish the types of bad, that is, as long as there is any bad in the sample, it is considered to have bad, otherwise it is considered to have no bad. Among them, the variables corresponding to the inspection results of each sample among multiple samples in the embodiments of the present disclosure are the same variable.

[0094] Exemplarily, assume that the sample data corresponding to device parameter 1 of device A in the sample data obtained by the electronic device is shown in Table 1 below:

[0095] Table 1

[0096]

[0097]

[0098] In Table 1, in the first column, 1 is the identifier of Sample 1. In the first row, 49.5456, 49.5823, and 46.9352 are the values of Device Parameter 1 respectively. In the second row, the production time corresponding to the value of Device Parameter 1 being 49.5456 is 00:01:47, the production time corresponding to the value of Device Parameter 1 being 49.5823 is 00:01:48, and the production time corresponding to the value of Device Parameter 1 being 46.9352 is 00:01:49. The rest is similar and will not be elaborated here.

[0099] Step 2: The electronic device performs cropping and / or filtering on the initial sample data to obtain the sample data of each sample among the multiple samples produced within the preset time period.

[0100] The electronic device can perform cropping and / or filtering on the initial sample data in at least one of the following ways to obtain the sample data of each sample among the multiple samples produced within the preset time period:

[0101] Method 1: The electronic device divides the initial sample data into positive samples (also known as good samples) and negative samples (also known as bad samples) according to the inspection results of the samples. For the negative samples in the initial sample data, the electronic device filters out the negative samples whose ratio of the number of numerical values of the device parameter to the number of acquisition times does not meet the preset condition. Exemplarily, the electronic device filters out the negative samples in which 95% of the number of numerical values of the device parameter is greater than the number of acquisition times of the device parameter from the initial sample data. Based on the initial sample data in Table 1, in the sample data with sample identifier 4, the number of numerical values of the device parameter is 5, the number of acquisition times of the device parameter is 4, and 5 * 95% of the number of numerical values of the device parameter is greater than 4. Therefore, the electronic device filters out the sample data with sample identifier 4.

[0102] Method 2: The electronic device divides the initial sample data into positive samples (also known as good samples) and negative samples (also known as bad samples) according to the inspection results of the samples. The electronic device obtains the number of numerical values included in the target numerical value of the positive sample through the following steps; determines the numerical range according to the number of numerical values included in the target numerical value of the positive sample; and filters out the positive samples in which the number of numerical values included in the target numerical value of the positive sample in the sample data of each sample produced within the preset time period is outside the numerical range.

[0103] Step 1: The electronic device obtains the number of numerical values included in the target numerical value of the positive sample. Among them, the target numerical value is the numerical value in the numerical values of the device parameter where the time difference between two adjacent acquisition times is less than the first threshold.

[0104] Based on the example in Table 1, the target values of the sample with sample identification 1 are 49.5456, 49.5823, and 46.9352. The number of target values of the sample with sample identification 1 is 3. Similarly, the number of target values of the sample with sample identification 2 is 5, the number of target values of the sample with sample identification 3 is 7, and the number of target values of the sample with sample identification 4 is 5.

[0105] Step 2: The electronic device determines the value range according to the number of values included in the target value of the positive sample.

[0106] In a possible implementation, the electronic device obtains the median and the interquartile range (IQR) of the number of values of the device parameters, and the electronic device determines the value range according to the median and the interquartile range.

[0107] Based on the example of the sample data filtered by Step 1 and Method 1, the median of the number of values of the device parameter is 5, and the IQR satisfies the formula: IQR = Q3 - Q1, where Q3 is the third quartile and Q1 represents the first quartile. Q3 is obtained as 6, Q1 is obtained as 4, and IQR is obtained as 2. The electronic device determines the sum of the median and 4 times the interquartile range as the upper bound of the value range, and obtains the upper bound of the value range as 13. The electronic device determines the difference between the median and four times the interquartile range as the lower bound of the value range, and obtains the lower bound of the value range as -3.

[0108] Step 3: The electronic device filters out the positive samples in the initial sample data whose number of values included in the target value is outside the determined value range.

[0109] Based on the example where the number of values of device parameter 1 is 3, 5, and 7 respectively. In this example, the number of device parameters is within the value range (-3, 13), so the electronic device does not filter the positive samples.

[0110] Method 3: The electronic device determines the cropping length according to the median of the number of values included in the target value of each sample, and crops the sample data of each obtained sample according to the cropping length.

[0111] In a possible implementation, the electronic device obtains the number of values of the target value of each sample. The electronic device obtains the median of the number of values of the target value, and the electronic device determines the cropping length according to the obtained median and a preset percentage. The electronic device crops the number of values of the target value by the cropping length from the starting acquisition time of the target value backward, or the electronic device crops the number of values of the target value by the cropping length from the ending acquisition time of the target value forward.

[0112] Based on the example in Method 2, the median of the number of values of the target value of Parameter 1 is 5, and the preset percentage is 3%. The cropping length of the target value of Parameter 1 is obtained as 5 * 3% = 0.15. Then, rounding down gives a cropping length of 0. The electronic device does not need to crop the positive samples in the initial sample data.

[0113] S101: The electronic device divides the sample data into positive samples and negative samples according to the test results of the samples.

[0114] Based on the example in Table 1, assume that the test results of the samples with sample identifiers from 1 to 3 are good, and the test result of the sample with sample identifier 4 is bad. Then, among the divided sample data, the positive samples include: the samples with sample identifiers from 1 to 3, and the negative samples include the sample with sample identifier 4.

[0115] S102: The electronic device determines the sample cut-off point for each sample according to the value of the device parameter, and obtains N numerical groups of the target value corresponding to each sample. The sample cut-off point of each sample is used to represent the mutation point of the value of the device parameter of each sample. The target value is the value of the device parameter whose time difference between two adjacent acquisition times is less than the first threshold, and N is a positive integer greater than or equal to 1.

[0116] In a possible implementation, the electronic device determines the sample cut-off point for each sample through the following steps:

[0117] Step 1: The electronic device determines the sample data of the reference sample according to the obtained value of the device parameter. The reference sample is the positive sample among multiple samples.

[0118] The electronic device can perform Fourier transform on the values of the device parameter of each sample in the positive samples; take the minimum number of values of the transformed values of the device parameter in the positive samples as the truncation number; obtain multiple truncated numerical groups by taking the first truncation number of values in the values of the device parameter of each sample in the positive samples; the number of values included in each truncated numerical group is the truncation number; the electronic device obtains the median of the values at each position in the multiple truncated numerical groups according to the arrangement order of the values in each truncated numerical group, and obtains the median sequence; the electronic device determines the sample in the positive samples with the smallest difference value from the median sequence as the reference sample.

[0119] Optionally, if the number of positive samples in the sample data corresponding to the device parameters (hereinafter referred to as the number of samples) is greater than or equal to 200, the electronic device extracts the sample data of 1% of the samples from the sample data corresponding to the positive samples. If 20 < the number of samples < 200, then the sample data of 20 samples is extracted. If the number of samples is less than or equal to 20, then all samples are extracted. The electronic device determines the reference samples from the extracted samples by the above method. In this way, determining the reference samples from the extracted samples can improve the efficiency of data processing.

[0120] Based on the examples of the sample data with sample identifiers 1, 2, and 3 in Table 1, the number of values of the device parameters of the sample with sample identifier 1 is 3, the number of values of the device parameters of the sample with sample identifier 2 is 5, and the number of values of the device parameters of the sample with sample identifier 3 is 7. Among them, 3 is the minimum number of values of the device parameters. The electronic device determines the truncation length to be 3. The truncated value groups of the sample with sample identifier 1 obtained by the electronic device are (49.5456, 49.5823, 46.9352), the truncated value groups of the sample with sample identifier 2 are (47.0249, 47.0248, 47.0248), and the truncated value groups of the sample with sample identifier 3 are (49.5344, 46.8889, 46.8889). Then, the median at the first position obtained by the electronic device is 49.5344, the median at the second position is 47.0248, and the median at the third position is 46.8889. The median sequence obtained by the electronic device is (49.5344, 47.0248, 46.8889). Among the samples with sample identifiers 1, 2, and 3 in the sample data, the sample with sample identifier 3 has the smallest difference from this median sequence. The electronic device determines the sample with sample identifier 3 as the reference sample.

[0121] Step 2: The electronic device determines the signal-to-noise ratio of the reference sample and determines the absolute value of the signal-to-noise ratio as the absolute value of the signal-to-noise ratio.

[0122] Step 3: The electronic device uses the values in the filtered device parameter values whose absolute values are greater than the absolute value of the signal-to-noise ratio as the reference sample cut-off points.

[0123] Exemplarily, assume that the absolute value of the signal-to-noise ratio determined by the electronic device is threshold. The electronic device uses the device parameter values outside the threshold range [-threshold, threshold] in the filtered device parameter values as the reference sample cut-off points. As Figure 4 shown, the numerical points in Curve 1 are the sample data, and the numerical points in Curve 2 are the sample data obtained using a high-pass filter. Figure 4 There are no values exceeding the threshold range. Therefore, Figure 4 the sample data in it is used as a numerical group. Figure 4The abscissa is the serial number corresponding to the acquisition time of the numerical value of the device parameter of the sample data.

[0124] Optionally, the electronic device can adjust the reference sample cut-off point according to the fluctuation range of the numerical value of the device parameter near the determined reference sample cut-off point. Exemplarily, when the difference between the numerical values of the device parameters on both sides of the determined reference sample cut-off point is less than the fluctuation threshold, the reference sample cut-off point is adjusted. The fluctuation threshold is used to help determine the mutation point of the numerical value of the device parameter.

[0125] Step Four: The electronic device determines the sample cut-off point of each sample according to the reference ratio and the reference sample cut-off point. The numerical groups obtained based on the sample cut-off points determined by the same reference sample cut-off point correspond to each other; the reference ratio is the ratio of the number of numerical values of the device parameter of the reference sample to the number of numerical values of the device parameter of each sample.

[0126] In a possible implementation manner, for each sample, the electronic device determines the preliminary sample cut-off point of each sample according to the reference ratio and the reference sample cut-off point; according to the determined sample cut-off point and the preset window size, obtains the correlation between the numerical group of the device parameter whose distance from the sample cut-off point is within the preset window size range and the numerical group of the device parameter whose distance from the reference sample cut-off point is within the preset window size range; and corrects the sample cut-off point of each sample according to the obtained correlation.

[0127] As Figure 5 shown, assume that the reference ratio is 2, and the electronic device selects the 2nn interval before the cut-off point of the sample data as the preset window size range [start, end]; the electronic device traverses each point within the preset window size range [start, end], starting from start, obtains the data segment X with the length of the preset window size range backward, and selects another data segment Y with the length of nn before and after the reference sample, and calculates the correlation between the data segment X and the data segment Y using the Pearson correlation coefficient. Then, the electronic device takes the cut-off point with the highest correlation within the preset window size range [start, end] as the sample cut-off point of the sample.

[0128] It can be understood that the sample cut-off point of a sample divides the target value corresponding to each sample into N value groups. N is a positive integer greater than or equal to 1. When N equals 1, it indicates that it is determined that the sample has no sample cut-off point, and there is no need to divide the target value of the sample. The target value is one value group. In the actual production process, a device may include multiple device recipes (recipe steps). Among them, the device recipe is used to describe the instructions on how the device should process the sample (also known as: the setting of the device parameters for the device to process the sample). In the specific manifestation, a device recipe includes the values of the device parameters and the time corresponding to the values of the device parameters. The time difference between the acquisition times of the values of the same device parameter in a device recipe is less than the first threshold (for example: 1 second).

[0129] Based on the example in Table 1, the cut-off point of the sample with sample identification 2 determined by the electronic device is 47.0013. Then, the electronic device cuts the sample data with sample identification 2 into two value groups. The first value group includes the values 47.0249, 47.0248, and 47.0248. The second value group includes the values 47.0013 and 47.0013.

[0130] It can be understood that the change trends of the values in the obtained same value group tend to be the same. The values in the same value group are stable and there are no mutations. In this way, based on the differences between the positive and negative samples of the value group at the same position of the device parameters, the relevant quantization value of the value group can be used to characterize the degree of influence of the value group on the defective sample.

[0131] S103: The electronic device determines a relevant quantization value based on the difference between the Mth value group in the positive sample and the Mth value group in the negative sample. The relevant quantization value is used to characterize the degree of influence of the device parameters on the defective sample, and M is a positive integer less than or equal to N.

[0132] In a possible implementation manner, the electronic device determines the relevant quantization value through the following steps:

[0133] S103-1: The electronic device determines the first value of the statistical index of the Mth value group in the negative sample. The statistical index is used to characterize the central tendency or change trend of the values in the value group.

[0134] The statistical indexes used to characterize the central tendency of the values in the value group include at least one of the characteristics reflecting the integrity of the value group, such as the maximum value, the minimum value, the mean value, the median value, the standard deviation, the subscript of the minimum value, and the subscript of the maximum value.

[0135] Statistical indicators for characterizing the trend of a numerical group include at least one of slope, range difference, sum of differences in the downward trend (Stat_downtrend), sum of differences in the upward trend (Stat_uptrend), sum of positive values (Positive_sum), maximum value in the sum of upward intervals (Positive_max), starting subscript of the maximum interval in the sum of consecutive upward intervals (Positive_maxstart), ending subscript of the maximum interval in the sum of consecutive upward intervals (Positive_maxend), sum of negative values (Negative_sum), maximum value in the sum of downward intervals (Negative_max), starting subscript of the maximum interval in the sum of consecutive downward intervals (Negative_maxstart Index), ending subscript of the maximum interval in the sum of consecutive downward intervals (Negative_maxend index), or sum of absolute values (L1_NORM).

[0136] The above subscripts are used to characterize the position of a numerical value in the numerical group. Exemplarily, assume the numerical group is (-2, 1, -1, 2, 3, -3, 4, -4). Then, the subscript of the first numerical value -2 in this numerical group is 1, the subscript of the second numerical value 1 is 2, and so on, which will not be elaborated here.

[0137] Based on the example of the numerical group (-2, 1, -1, 2, 3, -3, 4, -4), the maximum value of the numerical values included in this numerical group is 4; the minimum value is -4; the mean is (-2 + 1 + -1 + 2 + 3 + -3 + 4 + -4) / 8 = 0; the median is (-1 + 1) / 2 = 0; the standard deviation std satisfies the formula where x1 corresponds to -2, x8 corresponds to -4, and the rest are similar. is the average of -2, 1, -1, 2, 3, -3, 4, -4. The calculated standard deviation is 2.93; Range is the difference between the maximum value and the minimum value, that is, 4 - (-4) = 8; Index_min is the subscript of the minimum value, which is 8; Index_max is 7; Stat_downtrend is -2 - 6 - 8 = - sixteen; Stat_uptrend is 3 + 3 + 1 + 7 = 14; Slope satisfies the slope formula: It is calculated that the Slope is -0.21428571; Positive_sum is 10; Positive_max is 2 + 3 = 5; Positive_maxstart is 4. The interval with the largest sum in the continuously rising interval is [2, 3], and the subscript of 2 is 4; Positive_maxend is 5. The interval with the largest sum in the continuously rising interval is [2, 3], and the subscript of 3 is 5; Negative_sum is the sum of negative values, that is, -2 - 1 - 3 - 4 = -10; Negative_max is the maximum value in the sum of the descending interval. -4 is the maximum value in the sum of the descending interval; Negative_maxstart is the starting Index of the interval with the largest sum in the continuously descending interval. -4 is the maximum value in the sum of the descending interval, and the subscript of -4 is 8, that is, 8; Negative_maxend is the ending Index of the interval with the largest sum in the continuously descending interval. Similarly, it is 8; L1_NORM is 10 - (-10) = 20; Assume that the statistical indicators include the above 20 values, then the first value of the statistical indicators of this value group can be obtained as the eigenvector [-4, 4, 0, 0, 2.93, 8, 8, 7, -16, 14, -0.214, 10, 5, 4, 5, -10, -4, 8, 8, 20].

[0138] S103 - 2: The electronic device determines the second value of the statistical indicators of the Mth value group in the positive samples.

[0139] Based on the example of S103 - 1, assume that the value group in S103 - 1 is the first value group of the device parameters of the negative sample. The electronic device obtains the second value of the statistical indicators of the first value group of the device parameters of each positive sample.

[0140] S103 - 3: The electronic device determines the difference between the first value and the second value.

[0141] In a possible implementation, the electronic device determines the difference between the first value and the second value according to the characteristic parameters of the first value and the characteristic parameters of the second value.

[0142] The characteristic parameters may include the value at the target position and / or the overall mean.

[0143] In an example, the electronic device can use the Kruskal - Wallis test to determine the difference value between the first value and the second value.

[0144] Exemplarily, assume that the target position is the median. The electronic device obtains the median of multiple first values and the median of multiple second values. The electronic device determines the difference between the two medians as the difference between the first value and the second value.

[0145] In another example, the electronic device can use a t-test to determine the difference between the first value and the second value.

[0146] Exemplarily, the electronic device obtains the overall mean of multiple first values and obtains the overall mean of multiple second values. The electronic device determines the difference between the two overall means as the difference value between the first value and the second value.

[0147] In another possible implementation, the electronic device determines the first difference between the value at the target position of the first value and the value at the target position of the second value. The electronic device determines the second difference between the overall mean of the first value and the overall mean of the second value. The electronic device determines the difference value between the first value and the second value according to the first difference, the second difference, and a preset weight.

[0148] Exemplarily, as Figure 6 shown, the electronic device determines the first difference according to the Kruskal-Wallis test and determines the second difference according to the t-test. The electronic device determines the sum of 50% of the first difference and 50% of the second difference as the difference value p value between the first value and the second value.

[0149] S103-4: The electronic device determines relevant quantization values according to the difference.

[0150] It can be understood that the larger the difference value, the greater the correlation between the numerical group and the test result. The greater the correlation, the larger the relevant quantization value of the numerical group of the device parameter.

[0151] It can be understood that in one possible implementation, the electronic device can determine the first value of all statistical indicators of the numerical group of the device parameter in the negative sample; determine the second value of the corresponding all statistical indicators of the numerical group of the device parameter in the positive sample, and obtain the relevant quantization value of the numerical group according to the difference value between the first value and the second value.

[0152] In another possible implementation, the electronic device can also determine the first value of each statistical indicator of the numerical group of the device parameter in the negative sample, determine the second value of the corresponding statistical indicator of the numerical group of the device parameter in the positive sample, obtain the difference value between the first value of the positive sample and the second value of the negative sample of each statistical indicator, obtain multiple relevant quantization values of the numerical group according to the multiple difference values, and the electronic device sorts the multiple relevant quantization values and outputs them to the user, which can facilitate the user to determine which statistical indicator can better reflect the influence degree of the numerical group on the sample defect.

[0153] Optionally, S104: The electronic device sorts the determined relevant quantization values and outputs the sorting of the numerical group of the device parameter corresponding to the relevant quantization values.

[0154] Exemplarily, the electronic device sorts the numerical groups of the device parameters corresponding to the relevant quantization values in descending order according to the magnitudes of the relevant quantization values. In this way, the numerical group with the greatest impact on the defective samples will be ranked at the top, facilitating the user to investigate the reasons for the defective samples.

[0155] In the embodiments of the present disclosure, the electronic device obtains the sample data of each sample among a plurality of samples produced within a preset time period; divides the sample data into positive samples and negative samples according to the inspection results of the samples; determines the sample cut-off point of each sample; the sample cut-off point reflects the mutation point in the numerical values of the device parameters, and the sample cut-off point of each sample divides the target numerical value corresponding to each sample into multiple numerical groups; in this way, the trends of the numerical values in each numerical group tend to be the same. According to the differences between the numerical groups of the device parameters in the negative samples and the corresponding numerical groups of the positive samples, relevant quantization values are determined. The greater the difference, the greater the relevant quantization value, indicating that the numerical group has a greater impact on the defective samples, thereby facilitating the user to identify the reasons for the defective samples.

[0156] As Figure 7 shown in the flowchart of another data processing method provided by the embodiments of the present disclosure, this method can be applied to Figure 2 the electronic device shown in Figure 7 The method shown in

[0157] S200: The electronic device receives the sample screening conditions input by the user on the condition selection interface.

[0158] The sample screening conditions may include: sample model, factory identification, site, process, start time, and end time, etc.

[0159] Exemplarily, the condition selection interface is as Figure 8 shown, Figure 8 where the start time and end time are used to receive the input time period, Figure 8 the input box corresponding to the factory in Figure 8 is used to receive the factory identification, the process input box is used to receive the process, the site input box is used to receive the site, and the product model input box is used to receive the sample model. After the user inputs relevant information in the input boxes in

[0160] and clicks the confirmation button, the electronic device receives the input sample screening conditions.

[0161] Optionally, the sample screening conditions may further include inspection result variables.

[0162] In one possible implementation, the electronic device reads the preset inspection result variables.

[0163] Exemplarily, the result variable input interface is as follows Figure 9 shown, and the user clicks on Figure 9 the result variable input box shown in Figure 9 the interface in Figure 9 where the raw material can be a panel master, the inspection site can be used for the user to select the inspection site, and there are at least six types of variables for inspection results under this inspection site: the number of non-conformities of type 1 can be used for the user to select the number of non-conformities of samples of type 1 as a variable for the inspection result, the non-conformity rate of type 1 can be used for the user to select the non-conformity rate of samples of type 1 as a variable for the inspection result, the non-conformity rate of the raw material of type 1 can be used for the user to select the non-conformity rate of this raw material of type 1 as a variable for the inspection result, the number of non-conformities of type 2 can be used for the user to select the number of non-conformities of samples of type 2 as a variable for the inspection result, the non-conformity rate of type 2 can be used for the user to select the non-conformity rate of samples of type 2 as a variable for the inspection result, and the non-conformity rate of the raw material of type 2 can be used for the user to select the non-conformity rate of this raw material of type 2 as a variable for the inspection result.

[0164] Optionally, the sample screening conditions may further include equipment parameters, and the electronic device obtains the equipment parameters in response to the user's input in the cause variable input interface.

[0165] Exemplarily, as Figure 10 shown is the cause variable input interface. Figure 10 The raw material in Figure 10 can be a panel master. The inspection site in Figure 10 is an inspection site that can be used for the user to select, and the product can be used for the user to select the product model. Figure 10 The process identifier in Figure 10 can be used for the user to select the corresponding process, and one process corresponds to at least one process step.

[0166] S201: The electronic device obtains the sample data of each sample in a plurality of samples corresponding to the sample screening conditions; the sample data includes the values of the equipment parameters of the equipment through which the sample passes at each acquisition time, and the inspection result of the sample.

[0167] S202: The electronic device divides the sample data into positive samples and negative samples according to the inspection result of the sample.

[0168] Optionally, after the electronic device divides the sample data into positive samples and negative samples, it is shown as Figure 11 the sample distribution shown. Figure 11The abscissa represents the production time, and the ordinate represents the inspection result.

[0169] S203: The electronic device determines the sample cutting point of each sample according to the value of the device parameter, and obtains N value groups of the target value corresponding to each sample; the sample cutting point of each sample is used to characterize the mutation point of the value of the device parameter of each sample, the target value is the value whose time difference between two adjacent acquisition times in the value of the device parameter is less than the first threshold, and N is a positive integer greater than or equal to 1.

[0170] Specifically, refer to the description of S102 in the above embodiment, and details are not described herein again.

[0171] S204: The electronic device determines the relevant quantization value according to the difference between the Mth value group in the positive sample and the Mth value group in the negative sample, and the relevant quantization value is used to characterize the influence degree of the device parameter on the defective sample, where M is a positive integer less than or equal to N.

[0172] Specifically, refer to the description in S103 above, and details are not described herein again.

[0173] S205: The electronic device displays the relevant quantization value on the analysis result display interface.

[0174] Optionally, the electronic device sorts the magnitudes of the relevant quantization values, and the electronic device displays the sorting of the value groups of the device parameters corresponding to the relevant quantization values on the analysis result display interface.

[0175] In one example, the electronic device displays each value group corresponding to the obtained relevant quantization value on the analysis result display interface as Figure 12 shown. The electronic device takes the value group as a unit and sorts the multiple relevant quantization values of the value group from high to low. Figure 12 The one ranked first is device parameter 1. This device parameter 1 has only one process and one value group. The correlation quantization values of 20 statistical indicators of this device parameter 1 are sorted from high to low, and the correlation quantization value of feature 1 is the highest, which is 0.9682.

[0176] Optionally, the electronic device obtains output parameters, where the output parameters include: information parameters of the value group, at least one of the range percentage, the first ratio or the second ratio; the first ratio is the ratio of the number of samples including the device parameter to the total number of multiple samples, and the second ratio is the ratio of the number of defective samples corresponding to the device parameter to the total number of negative samples; the electronic device displays the output parameters on the analysis result display interface. Among them, the information parameter includes the position of the value group in the device parameter and / or the percentage of the value group in the target value.

[0177] Exemplarily, the electronic device displays the relevant quantization value of each obtained value group on the analysis result display interface as Figure 13 shown.Figure 13 It includes the relevant quantization values of two numerical groups of device parameter 1. Figure 13 Among them, the maximum relevant quantization value in the numerical group of the first row is 0.9682. The name of the device parameter corresponding to this numerical group is Parameter 1. The numerical group: 0(1 / 2) indicates that there is only one device formula during the sample generation process; this device formula is divided into 2 numerical groups, and this numerical group is the first numerical group; the numerical group percentage: 94.85% indicates the percentage of this numerical group in the entire device formula; the percentage of the range: 100.0% indicates the percentage of the range (maximum value - minimum value) of this device formula in the range of the entire process; the defective ratio: (89 / 89) indicates the number of defective samples reporting this device parameter / the number of all defective samples; the parameter sample ratio: (1095 / 1143) indicates the number of samples reporting this device parameter / the number of all samples.

[0178] The above mainly introduces the solution provided by the embodiments of the present disclosure from the perspective of the method. To implement the above functions, it includes the corresponding hardware structure and / or software module for each function. Those skilled in the art should easily realize that, combined with the units and algorithm steps of each example described in the embodiments disclosed in this article, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but this implementation should not be considered to exceed the scope of this application.

[0179] The embodiments of the present disclosure can divide the electronic device in the above embodiments into functional modules according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the embodiments of the present disclosure is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0180] Such as Figure 14As shown in the figure, it is a structural diagram of a data processing device 80 provided by an embodiment of the present disclosure. The data processing device 80 includes: an acquisition module 801, a division module 802, and a determination module 803. The acquisition module 801 is used to acquire the sample data of each sample in a plurality of samples generated within a preset time period; the sample data includes the values of the device parameters of the devices passed by the samples at each acquisition time, and the test results of the samples; the division module 802 is used to divide the sample data into positive samples and negative samples according to the test results of the samples; the determination module 803 is used to determine the sample cut-off point of each sample according to the values of the device parameters, and obtain N value groups of the target values corresponding to each sample; the sample cut-off point of each sample is used to represent the mutation point of the values of the device parameters of each sample, the target value is the value whose time difference between two adjacent acquisition times in the values of the device parameters is less than the first threshold, and N is a positive integer greater than or equal to 1; determine the relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples, and the relevant quantization value is used to represent the influence degree of the device parameters on the defective samples, and M is a positive integer less than or equal to N. For example, in combination with Figure 3 The acquisition module 801 can be used to execute S100, the division module 802 can be used to execute S101, and the determination module 803 can be used to execute S102 and S103.

[0181] In some embodiments, the determination module 803 is specifically used to: determine the first value of the statistical index of the Mth value group in the negative samples and the second value of the statistical index of the Mth value group in the positive samples; the statistical index is used to represent the central tendency or change trend of the values in the value group; determine the difference between the first value and the second value; determine the relevant quantization value according to the difference.

[0182] In other embodiments, the determination module 803 is specifically used to: determine the difference between the first value and the second value according to the characteristic parameters in the multiple first values in the negative samples and the characteristic parameters in the multiple second values in the positive samples.

[0183] In other embodiments, the characteristic parameters include the value at the target position and / or the overall mean.

[0184] In other embodiments, the determination module 803 is specifically used to: determine the first difference between the values at the target positions of the multiple first values in the negative samples and the values at the target positions of the multiple second values in the positive samples; determine the second difference between the overall means of the multiple first values in the negative samples and the overall means of the multiple second values in the positive samples; determine the difference between the first value and the second value according to the first difference, the second difference, and the preset weight.

[0185] In other embodiments, the determination module 803 is specifically used to: determine the sample data of the reference sample based on the numerical value of the device parameter; the reference sample is a sample in the positive sample; determine the signal-to-noise ratio of the reference sample, and determine the absolute value of the signal-to-noise ratio as the absolute value of the signal-to-noise ratio; use the value of the filtered device parameter whose absolute value is greater than the absolute value of the signal-to-noise ratio as the reference sample cutting point; determine the sample cutting point of each sample based on the reference ratio and the reference sample cutting point, and obtain N numerical value groups of the target numerical value corresponding to each sample; the reference ratio is the ratio of the number of numerical values of the device parameter of the reference sample to the number of numerical values of the device parameter of each sample.

[0186] In other embodiments, the determination module 803 is specifically used to: determine the preliminary sample cutting point of each sample based on the reference ratio and the reference sample cutting point; the acquisition module is also used to: obtain the correlation between the numerical group of device parameters whose distance from the sample cutting point is within the preset window size range and the numerical group of device parameters whose distance from the reference sample cutting point is within the preset window size range based on the determined sample cutting point and the preset window size; the data processing device also includes a correction module 804, which is used to correct the sample cutting point of each sample based on the correlation.

[0187] In other embodiments, the determination module 803 is also used to: perform Fourier transform on the numerical value of the device parameter of each sample in the positive sample; use the minimum number of numerical values of the device parameter in the transformed positive sample as the truncation number; obtain the first truncation number of numerical values of the device parameter of each sample in the positive sample to obtain multiple truncation value groups; the number of numerical values included in each truncation value group is the truncation number; according to the arrangement order of the numerical values in each truncation value group, obtain the median of the numerical values at each position in the multiple truncation value groups to obtain a median sequence; determine the sample data of the reference sample from the positive sample; the reference sample is the sample in the positive sample with the smallest difference value from the median sequence.

[0188] In other embodiments, the acquisition module 801 is specifically configured to: acquire sample data of each sample generated within a preset time period; acquire the number of values included in the target value of the positive sample; determine a value range based on the number of values included in the target value of the positive sample; filter out the positive samples whose number of values included in the target value of the positive sample in the sample data of each sample generated within the preset time period is outside the value range, to obtain sample data of each sample in the plurality of samples generated within the preset time period;

[0189] And / or, obtain sample data of each sample produced within a preset time period; determine the cutting length based on the median of the number of values included in the target value of each sample, cut the sample data of each sample obtained according to the cutting length, and obtain sample data of each sample in the multiple samples generated within the preset time period.

[0190] In some other embodiments, the data processing device 80 further includes: a sorting module 805 for sorting the magnitudes of relevant quantization values; and an output module 806 for outputting the sorted numerical group of device parameters corresponding to the relevant quantization values.

[0191] In some other embodiments, the output module 806 is further configured to: output the information parameters of the numerical group of device parameters, where the information parameters include the position of the numerical group in the device parameters and / or the percentage of the numerical group in the target value.

[0192] In one example, referring to Figure 2 , the receiving function of the above-mentioned obtaining module 801 can be implemented by Figure 2 the interface unit 304 in Figure 2 . The processing function of the above-mentioned obtaining module 801, the dividing module 802, the determining module 803, the correcting module 804, the sorting module 805, and the output module 806 can all be implemented by the processor 301 in

[0193] calling a computer program stored in the memory 302.

[0193] For the specific descriptions of the above optional manners, refer to the foregoing method embodiments, which will not be elaborated here. In addition, the explanations and beneficial effects descriptions of any of the above-provided application examples of the data processing device 80 can refer to the corresponding method embodiments above, and will not be elaborated.

[0194] It should be noted that the actions corresponding to the above-mentioned respective modules are only specific examples, and the actual actions executed by each unit refer to the actions or steps mentioned in the descriptions of the embodiments based on Figure 3 described above.

[0195] As Figure 15The following is a structural diagram of a data processing device 90 provided by an embodiment of the present disclosure. The data processing device 90 includes: a receiving module 901, an obtaining module 902, a dividing module 903, a determining module 904, and a display module 905. The receiving module 901 is configured to receive sample screening conditions input by a user in a condition selection interface. The obtaining module 902 is configured to obtain sample data of each sample among a plurality of samples corresponding to the sample screening conditions. The sample data includes the values of device parameters of devices in the sample path at each acquisition time, and the test results of the samples. The dividing module 903 is configured to divide the sample data into positive samples and negative samples according to the test results of the samples. The determining module 904 is configured to determine a sample cut-off point for each sample according to the values of the device parameters, and obtain N value groups of target values corresponding to each sample. The sample cut-off point of each sample is used to characterize the mutation point of the values of the device parameters of each sample. The target value is a value whose time difference between two adjacent acquisition times in the values of the device parameters is less than a first threshold, and N is a positive integer greater than or equal to 1. Determine a relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples. The relevant quantization value is used to characterize the influence degree of the device parameters on the defective samples, and M is a positive integer less than or equal to N. The display module 905 is configured to display the relevant quantization value on an analysis result display interface. For example, in combination with Figure 7 , the receiving module 901 can be used to execute S200, the obtaining module 902 can be used to execute S201, the dividing module 903 can be used to execute S202, the determining module 904 can be used to execute S203 and S204, and the display module 905 can be used to execute S205.

[0196] In some other embodiments, the data processing device further includes: a sorting module 906, configured to sort the magnitudes of the relevant quantization values. The display module 905 is specifically configured to: display the sorting of the value groups of the device parameters corresponding to the relevant quantization values on an analysis result display interface.

[0197] In some other embodiments, the display module 905 is further configured to: display information parameters of the value groups of the device parameters on an analysis result display interface. The information parameters include the position of the value group in the device parameters and / or the percentage of the value group in the target value.

[0198] In an example, referring to Figure 3 , the receiving functions of the above receiving module 901 and obtaining module 902 can be implemented by Figure 3 the interface unit 304 in. The processing function of the above obtaining module 902, the dividing module 903, the determining module 904, the display module 905, and the sorting module 906 can all be implemented by Figure 3 the processor 301 in calling a computer program stored in the memory 302.

[0199] For the specific description of the above optional manner, please refer to the foregoing method embodiments, which will not be elaborated herein. In addition, the explanations of any of the above-provided application examples of the data processing device 90 and the descriptions of the beneficial effects can be referred to the corresponding method embodiments above, which will not be elaborated.

[0200] It should be noted that the actions corresponding to the above-mentioned respective modules are only specific examples, and the actions actually executed by each unit refer to the actions or steps mentioned in the description of the foregoing embodiments based on Figure 7 the embodiments described above.

[0201] Some embodiments of the present disclosure further provide an electronic device, including: a processor and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the executable instructions to implement the data processing method described in any of the foregoing embodiments.

[0202] Some embodiments of the present disclosure provide a computer-readable storage medium (for example, a non-transitory computer-readable storage medium), in which computer program instructions are stored. When the computer program instructions run on a processor, the processor is caused to execute one or more steps in the data processing method described in any of the foregoing embodiments.

[0203] Exemplarily, the above computer-readable storage medium may include, but is not limited to: magnetic storage devices (such as hard disks, floppy disks or magnetic tapes, etc.), optical discs (such as CDs (Compact Disks), DVDs (Digital Versatile Disks), etc.), smart cards and flash memory devices (such as EPROMs (Erasable Programmable Read-Only Memories), cards, sticks or key drives, etc.). The various computer-readable storage media described in the present disclosure may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0204] Some embodiments of the present disclosure further provide a computer program product. The computer program product includes computer program instructions. When the computer program instructions are executed on a computer, the computer program instructions cause the computer to execute one or more steps in the data processing method described in the foregoing embodiments.

[0205] Some embodiments of the present disclosure further provide a computer program. When the computer program is executed on a computer, the computer program causes the computer to execute one or more steps in the data processing method described in the foregoing embodiments.

[0206] The beneficial effects of the above computer-readable storage medium, computer program product, and computer program are the same as those of the data processing method described in some of the above embodiments, and will not be elaborated here.

[0207] The above is only a specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure who thinks of changes or substitutions should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A data processing method, comprising: Obtaining sample data of each sample among a plurality of samples generated within a preset time period; The sample data includes the values of the device parameters of the devices passed by the sample at each acquisition time, and the test results of the sample; Dividing the sample data into positive samples and negative samples according to the test results of the samples; Determining the sample cut-off point of each sample according to the values of the device parameters, and obtaining N value groups of target values corresponding to each sample; the sample cut-off point of each sample is used to characterize the mutation point of the values of the device parameters of each sample, and the target value is the value with a time difference less than a first threshold between two adjacent acquisition times among the values of the device parameters, and N is a positive integer greater than or equal to 1; Determining a relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples, and the relevant quantization value is used to characterize the influence degree of the device parameters on the defective samples, and M is a positive integer less than or equal to N.

2. The data processing method according to claim 1, wherein the determining the relevant quantization value according to the difference between the Mth value group in the positive samples and the Mth value group in the negative samples includes: Determining a first value of the statistical index of the Mth value group in the negative samples and a second value of the statistical index of the Mth value group in the positive samples; The statistical index is used to characterize the central tendency or the change trend of the values in the value group; Determining the difference between the first value and the second value; Determining the relevant quantization value according to the difference.

3. The data processing method according to claim 2, wherein the determining the difference between the first value and the second value includes: Determining the difference between the first value and the second value according to the characteristic parameters among the multiple first values in the negative samples and the characteristic parameters among the multiple second values in the positive samples.

4. The data processing method according to claim 3, wherein the characteristic parameters include the value at the target position and / or the overall mean.

5. The data processing method according to claim 3, wherein the determining the difference between the first value and the second value according to the characteristic parameters among the multiple first values in the negative samples and the characteristic parameters among the multiple second values in the positive samples includes: Determining a first difference between the values at the target position of the multiple first values in the negative samples and the values at the target position of the multiple second values in the positive samples; Determining a second difference between the overall means of the multiple first values in the negative samples and the overall means of the multiple second values in the positive samples; Determining the difference between the first value and the second value according to the first difference, the second difference and a preset weight.

6. The data processing method according to any one of claims 1-5, wherein the determining the sample cut-off point of each sample according to the values of the device parameters, and obtaining N value groups of target values corresponding to each sample includes: Determining the sample data of a reference sample according to the values of the device parameters; The reference sample is a sample in the positive samples; Determine the signal-to-noise ratio of the reference sample, and determine the absolute value of the signal-to-noise ratio as the absolute signal-to-noise ratio value; Use the values in the filtered numerical values of the device parameters whose absolute values are greater than the absolute signal-to-noise ratio value as the reference sample cut-off points; Determine the sample cut-off points of each sample according to the reference ratio and the reference sample cut-off points, and obtain N numerical value groups of the target values corresponding to each sample; The reference ratio is the ratio of the number of numerical values of the device parameters of the reference sample to the number of numerical values of the device parameters of each sample.

7. The data processing method according to claim 6, wherein the determining the sample cut-off points of each sample according to the reference ratio and the reference sample cut-off points includes: Determine the preliminary sample cut-off points of each sample according to the reference ratio and the reference sample cut-off points; According to the determined sample cut-off points and the preset window size, obtain the correlation between the numerical value group of the device parameters within the preset window size range from the sample cut-off points and the numerical value group of the device parameters within the preset window size range from the reference sample cut-off points; Correct the sample cut-off points of each sample according to the correlation.

8. The data processing method according to claim 6, wherein the determining the sample data of the reference sample according to the numerical values of the device parameters includes: Perform Fourier transform on the numerical values of the device parameters of each sample in the positive samples; Use the minimum number of the numerical values of the device parameters in the transformed positive samples as the truncation number; Obtain the first truncation number of numerical values in the numerical values of the device parameters of each sample in the positive samples to obtain a plurality of truncated numerical value groups; the number of numerical values included in each truncated numerical value group is the truncation number; According to the arrangement order of the numerical values in each truncated numerical value group, obtain the median of the numerical values at each position in the plurality of truncated numerical value groups to obtain a median sequence; Determine the sample data of the reference sample from the positive samples; The reference sample is the sample in the positive samples with the smallest difference value from the median sequence.

9. The data processing method according to any one of claims 1-5, wherein the obtaining the sample data of each sample in a plurality of samples generated within a preset time period includes: Obtain the sample data of each sample generated within the preset time period; Obtain the number of numerical values included in the target value of the positive samples; Determine a numerical value range according to the number of numerical values included in the target value of the positive samples; filter the positive samples in the sample data of each sample generated within the preset time period whose number of numerical values included in the target value of the positive samples is outside the numerical value range, to obtain the sample data of each sample in the plurality of samples generated within the preset time period; And / or, obtain the sample data of each sample produced within the preset time period; Determine a trimming length according to the median of the number of numerical values included in the target value of each sample, and trim the obtained sample data of each sample according to the trimming length to obtain the sample data of each sample in the plurality of samples generated within the preset time period.

10. The data processing method according to any one of claims 1-5, the method further comprising: Sorting the magnitudes of the relevant quantization values, and outputting the sorting of the numerical groups of the device parameters corresponding to the relevant quantization values.

11. The data processing method according to any one of claims 1-5, the method further comprising: Outputting the information parameters of the numerical groups of the device parameters, where the information parameters include the position of the numerical group in the device parameters and / or the percentage of the numerical group in the target value.

12. A data processing method, comprising: Receiving a sample screening condition input by a user in a condition selection interface; Obtaining the sample data of each sample among a plurality of samples corresponding to the sample screening condition; The sample data includes the numerical values of the device parameters of the devices in the sample path at each acquisition time, and the test result of the sample; Dividing the sample data into positive samples and negative samples according to the test result of the sample; Determining the sample cut-off point of each sample according to the numerical value of the device parameter, to obtain N numerical groups of the target value corresponding to each sample; the sample cut-off point of each sample is used to characterize the mutation point of the numerical value of the device parameter of each sample, the target value is the numerical value with a time difference less than a first threshold between two adjacent acquisition times in the numerical values of the device parameter, and N is a positive integer greater than or equal to 1; Determining a relevant quantization value according to the difference between the Mth numerical group in the positive samples and the Mth numerical group in the negative samples, where the relevant quantization value is used to characterize the influence degree of the device parameter on the defective samples, and M is a positive integer less than or equal to N; Displaying the relevant quantization value on an analysis result display interface.

13. The data processing method according to claim 12, the method further comprising: Sorting the magnitudes of the relevant quantization values; The displaying the relevant quantization value on the analysis result display interface includes: Displaying the sorting of the numerical groups of the device parameters corresponding to the relevant quantization values on the analysis result display interface.

14. The data processing method according to claim 12 or 13, the method further comprising: Displaying the information parameters of the numerical groups of the device parameters on the analysis result display interface, where the information parameters include the position of the numerical group in the device parameters and / or the percentage of the numerical group in the target value.

15. A data processing device, comprising: An acquisition module, configured to acquire the sample data of each sample among a plurality of samples generated within a preset time period; The sample data includes the numerical values of the device parameters of the devices in the sample path at each acquisition time, and the test result of the sample; A division module, configured to divide the sample data into positive samples and negative samples according to the test result of the sample; A determination module, configured to determine a sample cut-off point for each sample according to the value of the device parameter, so as to obtain N value groups of target values corresponding to each sample; the sample cut-off point of each sample is used to characterize the mutation point of the value of the device parameter of each sample, the target value is the value whose time difference between two adjacent acquisition times in the value of the device parameter is less than a first threshold, and N is a positive integer greater than or equal to 1; determine a relevant quantization value according to the difference between the Mth value group in the positive sample and the Mth value group in the negative sample, and the relevant quantization value is used to characterize the influence degree of the device parameter on the defective sample, and M is a positive integer less than or equal to N.

16. The data processing device according to claim 15, wherein the determination module is specifically configured to: determine a first value of a statistical index of the Mth value group in the negative sample and a second value of the statistical index of the Mth value group in the positive sample; the statistical index is used to characterize the central tendency or the change trend of the values in the value group; determine the difference between the first value and the second value; determine the relevant quantization value according to the difference.

17. A data processing device, comprising: a receiving module, configured to receive a sample screening condition input by a user in a condition selection interface; an obtaining module, configured to obtain sample data of each sample corresponding to the sample screening condition; the sample data includes the value of the device parameter of the device in the sample path at each acquisition time, and the test result of the sample; a partitioning module, configured to partition the sample data into a positive sample and a negative sample according to the test result of the sample; a determination module, configured to determine a sample cut-off point for each sample according to the value of the device parameter, so as to obtain N value groups of target values corresponding to each sample; the sample cut-off point of each sample is used to characterize the mutation point of the value of the device parameter of each sample, the target value is the value whose time difference between two adjacent acquisition times in the value of the device parameter is less than a first threshold, and N is a positive integer greater than or equal to 1; determine a relevant quantization value according to the difference between the Mth value group in the positive sample and the Mth value group in the negative sample, and the relevant quantization value is used to characterize the influence degree of the device parameter on the defective sample, and M is a positive integer less than or equal to N; a display module, configured to display the relevant quantization value on an analysis result display interface.

18. An electronic device, characterized in that, including: a processor and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the executable instructions to implement the data processing method according to any one of claims 1-11, or to implement the data processing method according to any one of claims 12-14.

19. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the data processing method according to any one of claims 1-11, or execute the data processing method according to any one of claims 12-14.

20. A computer program product, characterized in that, The computer program product includes computer instructions which, when running on a computer device, cause the computer device to execute the data processing method according to any one of claims 1-11, or to execute the data processing method according to any one of claims 12-14.

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