KR102437098B1 - Method and apparatus for determining error data based on artificial intenigence - Google Patents

Abstract

The present disclosure relates to a method for effectively determining erroneous data among a plurality of data and an apparatus on which the method is performed. In a method performed by a computing device, the method for determining error data according to some embodiments of the present disclosure includes generating a search pool including first data matching a user's search term from among a plurality of data, the search Determining whether the first data included in the pool is erroneous data, and when the determination for the search pool is completed, determining whether the second data having at least some attributes in common with the first data and excluded from the search pool is erroneous data may include the step of

Description

Method and device for determining error data based on artificial intelligence

The present disclosure relates to a method for effectively determining erroneous data among a plurality of data and an apparatus on which the method is performed.

With the generalization of Internet of Things (IoT) devices, a large amount of data is being generated, and the amount of such a large amount of data is continuously increasing. A recent topic is how to utilize such a large amount of data in various fields, and as a prerequisite for data utilization, the reliability of the large amount of data is a problem.

As a conventional method for determining the reliability of data, a method of diagnosing data quality one-time has been attempted, but a method of individually diagnosing continuously generated data has limitations.

Accordingly, as a method for securing the reliability of such a large amount of data, a technique for effectively determining erroneous data is required.

Korean Patent Publication No. 10-2007-0106574 (published on Nov. 1, 2007) Korean Patent Publication No. 10-2016-0133477 (published on November 22, 2016) Korean Patent Publication No. 10-2019-0118064 (published on October 17, 2019) Korean Patent Registration No. 10-2096328 (published on March 27, 2020)

A technical problem to be solved through some embodiments of the present disclosure is to provide a method for effectively determining erroneous data among a large amount of data and an apparatus for performing the method.

Another technical problem to be solved through some embodiments of the present disclosure is to provide a method for quickly determining erroneous data by determining errors in data having common attributes, and an apparatus for performing the method.

Another technical problem to be solved through some embodiments of the present disclosure is to provide a method for easily monitoring frequently occurring types of errors and an apparatus for performing the method.

Another technical problem to be solved through some embodiments of the present disclosure is to provide a method and an apparatus for performing the method for immediately notifying a user of the occurrence of an error when an error occurs.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

In a method performed by a computing device, the method for determining error data according to some embodiments of the present disclosure for solving the technical problem is a search including first data matching a user's search term among a plurality of data Creating a pool, determining whether the first data included in the search pool is erroneous data, and when the determination on the search pool is completed, at least some attributes are common to the first data and are excluded from the search pool It may include determining whether the second data is erroneous data.

In some embodiments, the generating of the search pool includes generating a first search pool including third data matching a first search term and a second search pool including fourth data matching a second search term. Comprising the step of processing generation in parallel, the step of determining whether the first data included in the search pool is erroneous data, processing the determination on the third data and the determination on the fourth data in parallel and determining whether the second data excluded from the search pool is erroneous data, wherein the determining of fifth data excluded from the first search pool and having at least some attributes in common with the third data; and parallelly processing a determination on the sixth data that has at least some attributes in common with the fourth data and is excluded from the second search pool.

In some embodiments, determining whether the first data included in the search pool is erroneous data includes: identifying a first standard data structure corresponding to the first data; determining whether the value of the first data is within a range of standard values to determine whether the first data is erroneous data, wherein determining whether the second data excluded from the search pool is erroneous data, identifying a second standard data structure corresponding to the second data and determining whether a value of the second data is included within a range of a second standard value according to the second standard data structure, so that the second data is erroneous It may include determining whether it is data. Here, the step of determining whether the first data is erroneous data includes, if the first data is determined as erroneous data, analyzing an error type according to a value of the first data, wherein the second data is erroneous Determining whether the data is data may include analyzing an error type according to a value of the second data when the second data is determined to be error data. In this case, if the number of error determinations of data having the first standard data structure or the second standard data structure is equal to or greater than the reference number, monitoring is performed on data having the first standard data structure or the second standard data structure It may further include the step of

In some embodiments, when the first data is determined to be erroneous data or the second data is determined to be erroneous data, the method may further include providing an alarm to the user's terminal.

In some embodiments, the meta data of the first data and the meta data of the second data may have a similarity greater than or equal to a reference value.

An apparatus for determining error data according to some embodiments of the present disclosure includes a processor, a network interface, a memory, and a computer program loaded into the memory and executed by the processor, wherein the computer program includes a plurality of data Among them, an instruction for creating a search pool including first data matching a user's search term, an instruction for determining whether the first data included in the search pool is erroneous data, and an instruction for determining whether the search pool is complete , an instruction for determining whether second data that has at least some attributes in common with the first data and is excluded from the search pool is erroneous data.

1 illustrates an exemplary environment to which an erroneous data determination apparatus according to some embodiments of the present disclosure may be applied.
2 is an exemplary flowchart illustrating a method for determining erroneous data according to some embodiments of the present disclosure.
FIG. 3 is an exemplary diagram for explaining in more detail an operation of parallelly processing the error data determination operation described with reference to FIG. 2 .
4 illustrates an example computing device that may implement devices and/or systems in accordance with various embodiments of the present disclosure.

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Advantages and features of the present disclosure and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the technical spirit of the present disclosure is not limited to the following embodiments, but may be implemented in various different forms, and only the following embodiments complete the technical spirit of the present disclosure, and in the technical field to which the present disclosure belongs It is provided to fully inform those of ordinary skill in the scope of the present disclosure, and the technical spirit of the present disclosure is only defined by the scope of the claims.

In adding reference numerals to components in each drawing, it should be noted that the same components are given the same reference numerals as much as possible even though they are indicated in different drawings. In addition, in describing the present disclosure, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present disclosure, the detailed description thereof will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which this disclosure belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly defined in particular. The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present disclosure. In this specification, the singular also includes the plural, unless specifically stated otherwise in the phrase.

In addition, in describing the components of the present disclosure, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only for distinguishing the elements from other elements, and the essence, order, or order of the elements are not limited by the terms. When it is described that a component is “connected”, “coupled” or “connected” to another component, the component may be directly connected or connected to the other component, but another component is between each component. It should be understood that elements may be “connected,” “coupled,” or “connected.”

As used herein, "comprises" and/or "comprising" refers to the presence of one or more other components, steps, operations and/or elements mentioned. or addition is not excluded.

As used herein, “error data” may mean data having a structure different from a standard data structure. For example, when the standard data structure has uppercase alphabetic values, data having lowercase alphabetical values or other symbolic values may be erroneous data. For another example, when the standard data structure has a social security number value, data having a numeric value that is not appropriate for the range of the social security number value may be erroneous data. In addition to the above-described examples, all data having a structure different from the standard data structure may be understood as erroneous data, and such "error data" may be referred to as "low quality data" complementarily.

Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

1 illustrates an exemplary environment to which an erroneous data determination apparatus 100 according to some embodiments of the present disclosure may be applied. 1 illustrates that one user terminal 200 is applied, this is only for convenience of understanding, and the number of user terminals 200 may be varied. On the other hand, Figure 1 is only showing a preferred embodiment for achieving the object of the present disclosure, some components may be added or deleted as needed.

Hereinafter, each component shown in FIG. 1 will be described in more detail.

First, the error data determination apparatus 100 may be a device for storing and managing a large amount of data. Here, the data may be, for example, data generated by an Internet of Things (IoT) device, or data generated in the process of a person using a terminal (eg, a smart phone, etc.), but in the above examples The scope of the present disclosure is not limited, and any data that can be stored and managed may be included in the scope of the present disclosure. Also, here, the error data determination apparatus 100 may include all known modules for storing and managing a large amount of data. In order to implement the error data determination apparatus 100 having modules for storing and managing a large amount of data, a database-related technology and a data search-related technology may be referred to.

The erroneous data determination apparatus 100 may identify erroneous data from among a plurality of stored and managed data. More specifically, the erroneous data determination device 100 generates a data pool (eg, a search pool) matching a user's search term, determines whether data included in the data pool is in error, and includes By determining whether there are errors in other data related to the data, it is possible to inspect the data more efficiently compared to the method of thoroughly inspecting all stored and managed data. In addition, the error data determination apparatus 100 may provide an alarm to the user terminal according to the identification of the error data, and analyze the identified error data type to monitor the same type of data. In order to exclude redundant description, a more detailed operation performed by the erroneous data determination apparatus 100 will be described in more detail later with reference to the drawings below with reference to FIG. 2 .

The above-described error data determination apparatus 100 may be implemented as one or more computing devices. For example, all functions of the erroneous data determination apparatus 100 may be implemented in a single computing device. As another example, a first function of the erroneous data determination apparatus 100 may be implemented in a first computing device, and a second function may be implemented in a second computing device. Here, the aforementioned computing device may be a notebook, a desktop, a laptop, etc., but is not limited thereto and may include any type of device equipped with a computing function. However, if the error data determination apparatus 100 is an environment in which a large amount of data must be managed, the error data determination apparatus 100 may be preferably implemented as a high-performance server-class computing device. An example of the computing device will be described later with reference to FIG. 4 .

Next, the user terminal 200 calculates a summary index (eg, management status, etc.) and statistical indicators (eg, error rate, etc.) regarding the data managed by the error data determination apparatus 100 for error data determination apparatus 100 It can be received from , and display the indicators. Also, the user terminal 200 may execute an operation tool for executing at least some functions of the error data determination apparatus 100 . Specifically, the user may execute at least some functions (eg, data deletion, data monitoring, new data storage, etc.) of the erroneous data determination apparatus 100 by utilizing the above-described manipulation tool through the user terminal 200 . Here, the user terminal 200 may have a web browser or a dedicated application installed therein to load an operation tool for executing the functions of the error data determination apparatus 100, for example, the user terminal ( 200) may be any one of a desktop, a workstation, a laptop, a tablet, and a smart phone, but is not limited thereto, and any type having a computing function device may include.

In some embodiments, the erroneous data determination apparatus 100 and the user terminal 200 may communicate via a network. The aforementioned network is implemented as all types of wired/wireless networks, such as a local area network (LAN), a wide area network (WAN), a mobile radio communication network, and a Wibro (Wireless Broadband Internet). can be

An exemplary environment to which the erroneous data determination apparatus 100 according to some embodiments of the present disclosure can be applied has been described with reference to FIG. 1 . Hereinafter, methods according to various embodiments of the present disclosure will be described in detail.

Each step of the above-described methods may be performed by a computing device. In other words, each step of the methods may be implemented with one or more instructions executed by a processor of a computing device. All steps included in these methods may be executed by one physical computing device, but the first steps of the method are performed by a first computing device, and the second steps of the method are performed by a second computing device. it might be Hereinafter, it is assumed that each step of the above-described methods is performed by the erroneous data determination apparatus 100 illustrated in FIG. 1 to continue the description. However, for convenience of description, the description of the operating subject of each step included in the above-described methods may be omitted.

Referring to FIG. 2 , in step S100 , a search pool including first data matching the user's search term may be generated. Here, a search pool may be created by comparing the type of data and some of the metadata of the data with the search word. Such a search pool may mean a set of some data matching a search term among all stored data. For example, in the case of a search for 'resident registration number' where the gender is 'male', data with the 7th digit value of '1' among data (ie, string) having the 'resident registration number' attribute will be included in the search pool. can

With respect to step S100, in some embodiments, a search pool may be created to include only a portion of the entire data matching the search term. As an example, even data matching the search term may not be loaded with a large amount of data at once, but the entire data matching the search term may be loaded by dividing the number of times or in parallel. As another example, only main data among data matching the search term may be loaded. Here, the main data may be determined in various ways, for example, the number of requests for data, the number of times of data modification, the storage time of data, an importance score given by a user, a matching rate with a search term, etc. The scope of the present disclosure is not limited to one example. In this case, even data excluded from the main data may not be omitted from the error determination because, in a subsequent operation ( S300 ), data having a common attribute with the main data is subject to error determination. According to the above-described embodiment, it is possible to prevent the load on the database from temporarily increasing, and it is also possible to prevent temporary excessive consumption of computing resources in the subsequent error determination operations ( S200 to S300 ). In addition, computing resources can be further saved by examining only a portion of the data identified through attribute comparison between the data, rather than individually examining the entire stored data.

Next, in step S200, it may be determined whether the first data included in the search pool is erroneous data.

In relation to step S200, first, a first standard data structure corresponding to the first data may be identified. Here, the standard data structure means a standard structure of data, and for example, a 13-digit numeric string for a resident registration number may have a standard structure, and for another example, a Korean name may have a standard structure for a Korean character string. . In some embodiments, standard data structures may be defined as metadata of data. Here, as the metadata is inquired, a standard data structure corresponding to the data may be identified. Next, it may be determined whether the first data is erroneous data by determining whether the value of the first data is included in the range of the first standard value according to the first standard data structure. Here, the standard value range is a range value determined by the standard data structure. For example, the standard value range corresponding to a resident registration number is a 13-digit 0 to 9 value, and a standard value corresponding to a Korean name. The range of may be a value of a range of Hangul characters consisting of Hangul consonants and Hangul vowels. That is, by determining whether the value of the data is within the range of the standard value, it can be determined whether the data is erroneous data.

With respect to step S200, in some embodiments, an artificial intelligence model may be used for determination of erroneous data. Here, the artificial intelligence model may be learned from training data and a pair of data having an error determination value of the training data, for example, may be implemented with reference to a classification algorithm of supervised learning. Such an artificial intelligence model may be trained to extract a pattern inherent in the training data and arrive at an error determination value corresponding to the training data from the pattern. As a result, by using such an artificial intelligence model, an error determination value corresponding to the input data may be output from the input data.

Next, in step S300 , when the determination of the search pool is completed, it may be determined whether the second data having at least some attributes in common with the first data and excluded from the search pool is erroneous data. Here, the second data is data excluded from the search pool, and may mean, for example, data that is not main data. In some embodiments, the meta data of the second data may refer to data having a similarity with the meta data of the first data equal to or greater than a reference value. As a specific example, data having the same standard data structure as the first data may be second data, and when the first data is a resident registration number, data excluded from main data or data that is a resident registration number may be the second data. have. In step S300, a detailed description of the data error determination operation may be understood with reference to the description of step S200. That is, it may be understood that only the object of error determination is different and the method of error determination is the same. According to step S300, by performing error determination on other data having some attributes in common with the first data belonging to the search pool, error determination on the data type of interest of the user may proceed. In particular, compared to the method of individually inspecting stored and managed data, computing resources can be used more efficiently.

With respect to steps S200 and S300, if specific data is determined to be erroneous data, an erroneous type of the data may be analyzed. The error data that can be determined in the above-described steps S200 and S300 may have a kind of pattern or tendency, and the pattern or tendency of the error data may be analyzed. As a specific example, when a numeric value other than 1 or 2 is continuously detected in the 7th digit of the resident registration number, such a pattern or tendency may be analyzed. As another example, when a period is continuously detected at the last digit of a Korean name, such a pattern or tendency may be analyzed. In some embodiments, monitoring may be performed on data having a standard data structure in which the number of error determinations is equal to or greater than the reference number. Here, monitoring can be understood as a measure to increase the degree of management of data, for example, the cycle of error determination is shortened, or when an error is determined, the user terminal is immediately notified, or the target of error determination is expanded. Action may be taken.

Steps S100 to S300 of FIG. 2 described so far may be performed in parallel through two or more threads. Specifically, 1) generating the search pool includes generating a first search pool including third data matching the first search term and creating a second search pool including fourth data matching the second search term It may include the step of processing in parallel. In addition, 2) determining whether the first data included in the search pool is erroneous data may include processing a determination on the third data and a determination on the fourth data in parallel. Further, 3) the step of determining whether the second data excluded from the search pool is erroneous data may include determining whether the third data and at least some attributes are common to the fifth data excluded from the first search pool, and the fourth data and at least and processing in parallel the determination for the sixth data that some attributes are common and that are excluded from the second search pool. Specifically, as shown in FIG. 3 , individual tasks 10a, 10b, and 10c may be performed in parallel through a plurality of threads 20a, 20b, and 20c (30a, 30b, 30c), thereby Advantages of parallel computing may be directly applied to the present disclosure.

Next, in step S400 , when the first data is determined to be erroneous data or the second data is determined to be erroneous data, an alarm may be transmitted to the user terminal. According to this step, the user can immediately check whether erroneous data has occurred by checking the alarm transmitted to the user terminal.

Up to now, various methods according to the present disclosure have been described with reference to FIG. 2 and related exemplary drawings. According to the above-described various methods, the inspection can be efficiently performed without individually inspecting the stored and managed data. Here, the data type of the user's interest by first creating a search pool matching the search term, performing an examination on some data, and further performing a check on data having properties common to data belonging to the search pool Inspection can be carried out without omission. Compared to the method of individually examining stored and managed data, this error determination method can use computing resources more efficiently, and can reduce unnecessary wasted computing resources.

Hereinafter, an exemplary computing device 1500 capable of implementing an apparatus for determining error data according to some embodiments of the present disclosure will be described in more detail with reference to FIG. 4 .

The computing device 1500 includes one or more processors 1510 , a bus 1550 , a communication interface 1570 , a memory 1530 for loading a computer program 1591 executed by the processor 1510 , and a computer A storage 1590 for storing the program 1591 may be included. However, only the components related to the embodiment of the present disclosure are illustrated in FIG. 4 . Accordingly, those skilled in the art to which the present disclosure pertains can see that other general-purpose components other than the components shown in FIG. 4 may be further included.

The processor 1510 controls the overall operation of each component of the computing device 1500 . The processor 1510 includes a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any type of processor well known in the art of the present disclosure. can be In addition, the processor 1510 may perform an operation on at least one application or program for executing the method according to the embodiments of the present disclosure. The computing device 1500 may include one or more processors.

The memory 1530 stores various data, commands, and/or information. The memory 1530 may load one or more programs 1591 from the storage 1590 to execute a method according to embodiments of the present disclosure. The memory 1530 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus 1550 provides communication functions between components of the computing device 1500 . The bus 1550 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 1570 supports wired/wireless Internet communication of the computing device 1500 . Also, the communication interface 1570 may support various communication methods other than Internet communication. To this end, the communication interface 1570 may be configured to include a communication module well known in the technical field of the present disclosure.

According to some embodiments, the communication interface 1570 may be omitted.

The storage 1590 may non-temporarily store the above-described one or more programs 1591 and various data.

The storage 1590 is a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, or well in the art to which the present disclosure pertains. It may be configured to include any known computer-readable recording medium.

The computer program 1591 may include one or more instructions that, when loaded into the memory 1530 , cause the processor 1510 to perform methods/operations according to various embodiments of the present disclosure. That is, the processor 1510 may perform the methods/operations according to various embodiments of the present disclosure by executing one or more of the above-described instructions.

So far, various embodiments of the present disclosure and effects according to the embodiments have been described with reference to FIGS. 1 to 4 . Effects according to the technical spirit of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the specification.

The technical idea of the present disclosure described with reference to FIGS. 1 to 4 may be implemented as computer-readable codes on a computer-readable medium. The computer-readable recording medium described above is, for example, a removable recording medium (CD, DVD, Blu-ray disk, USB storage device, removable hard disk) or a fixed recording medium (ROM, RAM, computer-equipped hard disk) can be A computer program recorded on a computer-readable recording medium may be transmitted to another computing device through a network such as the Internet and installed in the other computing device, thereby being used in the other computing device.

In the above, even though it has been described that all components constituting the embodiment of the present disclosure are combined or operated as one, the technical spirit of the present disclosure is not necessarily limited to this embodiment. That is, within the scope of the present disclosure, all the components may operate by selectively combining one or more.

Although acts are shown in a particular order in the drawings, it should not be understood that the acts must be performed in the specific order or sequential order shown, or that all depicted acts must be performed to obtain a desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of the various components in the embodiments described above should not be construed as necessarily requiring such separation, and the program components and systems described may generally be integrated together into a single software product or packaged into multiple software products. It should be understood that there is

Although embodiments of the present disclosure have been described above with reference to the accompanying drawings, those of ordinary skill in the art to which the present disclosure pertains can practice the present disclosure in other specific forms without changing the technical spirit or essential features. can understand that there is Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present disclosure should be interpreted by the following claims, and all technical ideas within an equivalent range should be interpreted as being included in the scope of the technical ideas defined by the present disclosure.

Claims (8)

A method performed by a computing device, comprising:
generating a search pool including first data matching a user's search term from among a plurality of data - The first data includes the number of requests for data, the number of times to modify data, storage time for data, and an importance score given by the user and data determined based on a matching rate with a search term;
determining whether the first data included in the search pool is erroneous data; and
When the determination of the search pool is completed, determining whether second data that has at least some attributes in common with the first data and is excluded from the search pool is erroneous data; The similarity with the metadata of the data is more than the standard value - including, but
Creating the search pool includes:
Comprising parallel processing of generation of a first search pool including third data matching the first search term and generation of a second search pool including fourth data matching the second search term,
The step of determining whether the first data included in the search pool is erroneous data,
Comprising the step of processing the judgment on the third data and the judgment on the fourth data in parallel,
The step of determining whether the second data excluded from the search pool is erroneous data,
Judgment for fifth data that has at least some attributes in common with the third data and is excluded from the first search pool and for sixth data that has at least some attributes in common with the fourth data and is excluded from the second search pool processing the verdict in parallel;
The step of determining whether the first data included in the search pool is erroneous data,
identifying a first standard data structure corresponding to the first data; and
determining whether a value of the first data is included within a range of a first standard value according to the first standard data structure, and determining whether the first data is erroneous data;
The step of determining whether the second data excluded from the search pool is erroneous data,
identifying a second standard data structure corresponding to the second data; and
determining whether a value of the second data is included in a range of a second standard value according to the second standard data structure, and determining whether the second data is erroneous data;
Determining whether the first data is erroneous data comprises:
If it is determined that the first data is erroneous data, analyzing an error type according to a value of the first data;
Determining whether the second data is erroneous data comprises:
If it is determined that the second data is erroneous data, analyzing an error type according to a value of the second data;
performing monitoring on data having the first standard data structure or the second standard data structure when the number of times of error determination of data having the first standard data structure or the second standard data structure is equal to or greater than a reference number; ; and
When the first data is determined to be erroneous data or the second data is determined to be erroneous data, the method further comprising: providing an alarm to the user's terminal;
How to determine error data. delete delete delete delete delete delete processor;
network interface;
Memory; and
a computer program loaded into the memory and executed by the processor;
The computer program is
An instruction for generating a search pool including first data matching a user's search term from among a plurality of data - The first data includes the number of data requests, the number of data modifications, the data storage time, and the Data determined based on the given importance score and matching rate with the search term - ;
instructions for determining whether the first data included in the search pool is erroneous data; and
When the determination of the search pool is completed, an instruction for determining whether the second data that has at least some attributes in common with the first data and is excluded from the search pool is erroneous data; Metadata and similarity of the data is above the reference value - including, but
The instructions for creating the search pool include:
and instructions for parallelly processing generation of a first search pool including third data matching a first search term and generation of a second search pool including fourth data matching a second search term,
An instruction for determining whether the first data included in the search pool is erroneous data,
Comprising instructions for processing the determination on the third data and the determination on the fourth data in parallel,
An instruction for determining whether the second data excluded from the search pool is erroneous data,
Judgment for fifth data that has at least some attributes in common with the third data and is excluded from the first search pool and for sixth data that has at least some attributes in common with the fourth data and is excluded from the second search pool Including instructions for processing the judgment in parallel,
An instruction for determining whether the first data included in the search pool is erroneous data,
instructions for identifying a first standard data structure corresponding to the first data; and
and determining whether the first data is erroneous data by determining whether a value of the first data is included within a range of a first standard value according to the first standard data structure;
An instruction for determining whether the second data excluded from the search pool is erroneous data,
instructions for identifying a second standard data structure corresponding to the second data; and
and determining whether the second data is erroneous data by determining whether a value of the second data is included within a range of a second standard value according to the second standard data structure;
An instruction for determining whether the first data is erroneous data,
If the first data is determined to be error data, comprising instructions for analyzing an error type according to the value of the first data,
An instruction for determining whether the second data is erroneous data,
If the second data is determined to be error data, including instructions for analyzing an error type according to the value of the second data,
an instruction for performing monitoring on data having the first standard data structure or the second standard data structure when the number of times of error determination of data having the first standard data structure or the second standard data structure is equal to or greater than a reference number ; and
When the first data is determined to be erroneous data or the second data is determined to be erroneous data, further comprising instructions for providing an alarm to the user's terminal,
Error data judgment device.

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