CN111522902B - Data entry method, device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The invention relates to the field of big data, and discloses a data entry method, which comprises the following steps: acquiring an original data set, and preprocessing the original data set to obtain a standard data set; identifying characteristic words in the standard data set, screening out data types of the standard data set according to the characteristic words, and classifying the data types by using a preset classification model to obtain a target data set; performing data type matching on the target data set and a target table of a pre-constructed database; when the data type matching fails, performing parameter adjustment on a classifier function in the classification model, and then performing classification processing on the data type again to obtain a target data set; and when the data type is successfully matched, inputting the data in the target data set into a corresponding target table. The invention also provides a data entry device, electronic equipment and a computer readable storage medium. The invention can realize intelligent data input.
Description
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a method and apparatus for data entry, an electronic device, and a computer readable storage medium.
Background
At present, when data is input into a database, a part of data needs to be manually uploaded by a user, such as business data, product data and financial data, and the data are different in source, usually form of table field types and sizes, different in imported target libraries, difficult to completely unify in template format, complicated in data preparation work in the early stage of non-research and development colleagues, and easy to bring great time and personnel cost.
Disclosure of Invention
The invention provides a data entry method, a data entry device, electronic equipment and a computer readable storage medium, and mainly aims to provide a technical scheme of data entry so as to help a user to reduce time and personnel cost when data is entered into a database.
In order to achieve the above object, the present invention provides a data entry method, including:
acquiring an original data set, and performing preprocessing operation on the original data set to obtain a standard data set;
Recognizing characteristic words in the standard data set by using a pre-constructed semantic recognition model, screening out data types of the standard data set according to the characteristic words, and classifying the data types by using a pre-set classification model to obtain a target data set;
Performing data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm;
If the data type matching fails, performing parameter adjustment on a classifier function in the classification model, and then performing classification processing on the data type again to obtain a target data set;
And if the data type is successfully matched, inputting the data in the target data set into a corresponding target table.
Optionally, the preprocessing operation includes deduplication, anomaly, and missing value detection.
Optionally, the removing the anomaly includes: obtaining abnormal data G in the original data set after duplication removal by utilizing double-side test rejection, minimum-value single-side test rejection or maximum-value single-side test rejection;
The calculation method of the bilateral test elimination data comprises the following steps:
Wherein i is a positive integer, Representing the average value of the original data set after the duplication removal, wherein S represents the standard deviation of the original data set after the duplication removal, and Y i represents the original data set after the duplication removal;
The calculation method for the minimum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the original dataset after the duplication removal, Y min represents the minimum data in the original dataset after the duplication removal, and S represents the standard deviation of the original dataset after the duplication removal;
The calculation method for maximum single-side test rejection comprises the following steps:
Wherein, Represents the average value of the original dataset after deduplication, Y mmax represents the maximum data in the original dataset after deduplication, and S represents the standard deviation of the original dataset after deduplication.
Optionally, the missing value detection includes:
Detecting a data missing value existing in the original data set after the abnormality is removed through a missing function;
Filling the data missing values by using the following filling algorithm:
Wherein L (θ) represents a filled data missing value, x i represents an i-th data missing value, θ represents a probability parameter corresponding to the filled data missing value, n represents the number of the original data sets after the anomaly is removed, and p (x i |θ) represents a filled data missing value probability.
Optionally, the performing data type matching on the target data set and the target table in the pre-constructed database through a preset matching algorithm includes:
Establishing a target data matrix for the data types in the target data set:
H(i,0)＝0，0≤i≤m；
wherein i represents the character string length of the ith data type in the target data set, and m represents the sum of the character string lengths of all the data types in the target data set;
Establishing a target table matrix for the data types of the target table as follows:
H(0,j)＝0，0≤j≤n；
Wherein j represents the string length of the jth data type in the target table, and n represents the sum of the string lengths of all the data types in the target table;
And calculating the matching score of the target data matrix and the target table matrix, comparing the matching score with a preset threshold value, and completing the matching of the data types according to the comparison result.
Optionally, the calculating the matching score of the target data matrix and the target table matrix includes:
Calculating the matching score of the target data matrix and the target table matrix by using the following calculation formula:
w＝(H(i-1,j-1)+S(am,bn))
Where w represents a matching score, a m represents the character strings of all data types in the target data matrix, b n represents the character strings of all data types in the target table matrix, and S (a m,bn) represents the similarity coefficient of the target data matrix and the target table matrix.
In order to solve the above-mentioned problems, the present invention also provides a data entry device, the device comprising:
The preprocessing module is used for acquiring an original data set, and preprocessing the original data set to obtain a standard data set;
The recognition module is used for recognizing the characteristic words in the standard data set by utilizing a pre-constructed semantic recognition model, screening out the data types of the standard data set according to the characteristic words, and classifying the data types by utilizing a pre-set classification model to obtain a target data set;
The matching module is used for matching the data types of the target data set and the target table in the pre-constructed database through a preset matching algorithm;
And the judging module is used for carrying out parameter adjustment on the classifier function in the classification model and then carrying out classification processing on the data types again to obtain a target data set when the data type matching fails, and inputting the data in the target data set into a corresponding target table when the data type matching succeeds.
Optionally, the performing data type matching on the target data set and the target table in the pre-constructed database through a preset matching algorithm includes:
Establishing a target data matrix for the data types in the target data set:
H(i,0)＝0，0≤i≤m；
wherein i represents the character string length of the ith data type in the target data set, and m represents the sum of the character string lengths of all the data types in the target data set;
Establishing a target table matrix for the data types of the target table as follows:
H(0,j)＝0，0≤j≤n；
Wherein j represents the string length of the jth data type in the target table, and n represents the sum of the string lengths of all the data types in the target table;
And calculating the matching score of the target data matrix and the target table matrix, comparing the matching score with a preset threshold value, and completing the matching of the data types according to the comparison result.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
A memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the data entry method of any one of the above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the data entry method of any one of the above.
According to the embodiment of the invention, the acquired original data set is preprocessed, so that the accuracy of the data in the acquired data set is ensured, the input of batch data can be supported, and a large amount of early data preparation time is saved; the data types in the data set after preprocessing are identified by utilizing a semantic identification algorithm to obtain a target data set, the standard data set can be well classified, the target data set is matched with a target table in a pre-built database by combining a preset matching algorithm, and the data can be quickly input into the target table of the database without manually uploading the data step by step again. The data entry method, the data entry device and the computer readable storage medium can help users to reduce the time and personnel cost when the data is entered into the database.
Drawings
FIG. 1 is a flow chart of a data entry method according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a data entry method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an internal structure of an electronic device according to a data entry method according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a method of data entry. Referring to fig. 1, a flow chart of a data entry method according to an embodiment of the invention is shown. The method may be performed by an apparatus, which may be implemented in software and/or hardware.
In this embodiment, a method of data entry includes:
S1, acquiring an original data set, and preprocessing the original data set to obtain a standard data set.
In at least one embodiment of the invention, the raw data set comprises: business data, financial data, and/or product data. For example, in the area of security, the business data may include: the amount of the application, type of the application, etc., the financial data may include: the product data may include, among other things, the fund duty cycle of the application, the profit of the application, the rate of return of the application: life risk category, car risk category, accident risk category, etc.
In a preferred embodiment of the present invention, the preprocessing operation includes duplicate removal, anomaly removal and missing value detection.
Preferably, the embodiment of the present invention performs a deduplication operation on the original data set through a distance formula, where the distance formula includes:
Where d represents the distance value of any two data in the original dataset, and w 1j and w 2j represent any two data in the original dataset. And deleting any one of the data when the distance value is smaller than a preset distance value, and simultaneously reserving two data when the distance value is not smaller than the preset distance value. Preferably, the preset distance value may be 0.1.
The exception removal processing in the embodiment of the invention comprises the following steps: and obtaining the abnormal data G in the original data set after the duplication removal by utilizing double-side test elimination and single-side test elimination. The single-side test rejection comprises minimum single-side test rejection and maximum single-side test rejection. In detail, the calculation method of the bilateral test elimination data comprises the following steps:
Wherein i is a positive integer, Represents the average value of the original dataset after deduplication, S represents the standard deviation of the original dataset after deduplication, and Y i represents the original dataset after deduplication.
The calculation method for the minimum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the original dataset after deduplication, and Y min represents the smallest data in the original dataset after deduplication.
The calculation method for maximum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the original dataset after deduplication, and Y mmax represents the largest data in the original dataset after deduplication.
In an embodiment of the present invention, the missing values include: complete random deletions, and non-random deletions. In detail, the completely random miss refers to a completely random miss in which a certain variable miss value is independent of any other reason; the random missing refers to the fact that the missing of a certain variable is related to other variables but not related to the numerical value of the variable itself; the non-random absence refers to the absence of a variable that is related to the value of the variable itself.
Further, the invention detects whether the original data set after the abnormality removal has a data missing value through missmap function missing functions, if the original data set after the abnormality removal has no data missing value, the invention does not process, and the original data set after the abnormality removal is used as the standard data set.
If the data missing value in the original data set after the abnormality removal is detected, preferably, the embodiment of the invention fills the missing value through a preset filling algorithm to obtain the standard data set. In detail, the preset filling algorithm includes:
Wherein L (θ) represents a filled data missing value, x i represents an i-th data missing value, θ represents a probability parameter corresponding to the filled data missing value, n represents the number of the original data sets after the anomaly is removed, and p (x i |θ) represents a filled data missing value probability.
Based on the embodiment, after the original data set is preprocessed, the accuracy of the data in the obtained standard data set is ensured, and meanwhile, the input of batch data can be supported.
S2, recognizing characteristic words in the standard data set by utilizing a pre-constructed semantic recognition model, screening out data types of the standard data set according to the characteristic words, and classifying the data types by utilizing a pre-set classification model to obtain a target data set.
In the embodiment of the invention, the method for identifying the semantic recognition model by utilizing the pre-constructed semantic recognition model comprises the following steps:
wherein R represents a characteristic word of the standard data set, Representing the data types contained in the standard data set, C j representing the set of all data types of the standard data set, W T representing the representation method of feature word meaning recognition for the standard data set by feature word sets R T (D) and R B (D) respectively.
The preset classification model comprises the following steps:
M＝<D,C,R,T>
wherein D represents data in the standard data set, C represents data type of the standard data set, R represents type semantic analysis function of the standard data set, and T represents classifier function of the standard data set.
Converting the quaternion into a function mapping relation to be expressed as:
(t·r): D→C or C= ((T. R) (D)
Based on the embodiment, the standard data set can be well classified by identifying the data type in the standard data set, so that the extraction of key data of the standard data set can be realized.
And S3, performing data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm.
In at least one embodiment of the present invention, the pre-constructed database may be a library database, an insurance database, a household database, or the like. For example, if the pre-constructed database is a library database, and the library database is a library database, the data types of the target table may include: a scientific and technological information data table, a historical data table, a financial data table and the like.
Preferably, in an embodiment of the present invention, the preset matching algorithm includes:
Establishing a target data matrix for the data types in the target data set as follows:
H(i,0)＝0，0≤i≤m；
where i represents the string length of the i-th data type in the target data set, and m represents the sum of the string lengths of all the data types in the target data set.
Establishing a target table matrix for the data types of the target table as follows:
H(0,j)＝0，0≤j≤n；
where j represents the string length of the jth data type in the target table, and n represents the sum of the string lengths of all the data types in the target table.
And calculating the matching score of the target data matrix and the target table matrix, comparing the matching score with a preset threshold value, and completing the matching of the data types according to the comparison result. The preset threshold may be 0.85. Preferably, the embodiment of the present invention calculates the matching score of the target data matrix and the target table matrix by using the following formula:
w＝(H(i-1,j-1)+S(am,bn))
Where w represents a matching score, a m represents the character strings of all data types in the target data matrix, b n represents the character strings of all data types in the target table matrix, and S (a m,bn) represents the similarity coefficient of the target data matrix and the target table matrix.
S4, judging whether the data types are successfully matched.
Further, if the matching score is smaller than the preset threshold, judging that the data type matching fails, and executing S5 to perform parameter adjustment on the classifier function in the classification model and then re-classifying the data type to obtain a target data set.
And if the matching score is not smaller than the preset threshold, judging that the data type matching is successful, and executing S6 to input the data in the target data set into a corresponding target table.
As shown in fig. 2, is a functional block diagram of the data entry device of the present invention.
The data entry store 100 of the present invention may be installed in an electronic device. Depending on the implemented functionality, the data entry means may comprise a preprocessing module 101, an identification module 102, a matching module 103 and a judgment module 104. The module of the present invention may also be referred to as a unit, meaning a series of computer program segments capable of being executed by the processor of the electronic device and of performing fixed functions, stored in the memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The preprocessing module 101 is configured to obtain an original data set, and perform preprocessing operation on the original data set to obtain a standard data set;
The recognition module 102 is configured to recognize a feature word in the standard data set by using a pre-constructed semantic recognition model, screen a data type of the standard data set according to the feature word, and perform classification processing on the data type by using a pre-set classification model to obtain a target data set;
The matching module 103 is configured to perform data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm;
the judging module 104 is configured to, when the data type matching fails, perform parameter adjustment on a classifier function in the classification model, and then re-perform classification processing on the data type to obtain a target data set, and when the data type matching succeeds, input data in the target data set into a corresponding target table.
In detail, the specific implementation steps of each module of the data input device are as follows:
The preprocessing module 101 acquires an original data set, and performs preprocessing operation on the original data set to obtain a standard data set.
In at least one embodiment of the invention, the raw data set comprises: business data, financial data, and/or product data. For example, in the area of security, the business data may include: the amount of the application, type of the application, etc., the financial data may include: the product data may include, among other things, the fund duty cycle of the application, the profit of the application, the rate of return of the application: life risk category, car risk category, accident risk category, etc.
In a preferred embodiment of the present invention, the preprocessing operation includes duplicate removal, anomaly removal and missing value detection.
Preferably, the embodiment of the present invention performs a deduplication operation on the original data set through a distance formula, where the distance formula includes:
Where d represents the distance value of any two data in the original dataset, and w 1j and w 2j represent any two data in the original dataset. And deleting any one of the data when the distance value is smaller than a preset distance value, and simultaneously reserving two data when the distance value is not smaller than the preset distance value. Preferably, the preset distance value may be 0.1.
The exception removal processing in the embodiment of the invention comprises the following steps: and obtaining the abnormal data G in the original data set after the duplication removal by utilizing double-side test rejection and single-side test rejection. The single-side test rejection comprises minimum single-side test rejection and maximum single-side test rejection.
In detail, the calculation method of the bilateral test elimination data comprises the following steps:
Wherein i is a positive integer, Represents the average value of the original dataset after deduplication, S represents the standard deviation of the original dataset after deduplication, and Y i represents the original dataset after deduplication.
The calculation method for the minimum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the original dataset after deduplication, and Y min represents the smallest data in the original dataset after deduplication.
The calculation method for maximum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the original dataset after deduplication, and Y mmax represents the largest data in the original dataset after deduplication.
In an embodiment of the present invention, the missing values include: complete random deletions, and non-random deletions. In detail, the completely random miss refers to a completely random miss in which a certain variable miss value is independent of any other reason; the random missing refers to the fact that the missing of a certain variable is related to other variables but not related to the numerical value of the variable itself; the non-random absence refers to the absence of a variable that is related to the value of the variable itself.
Further, the invention detects whether the original data set after the abnormality removal has a data missing value through missmap function missing functions, if the original data set after the abnormality removal has no data missing value, the invention does not process, and the original data set after the abnormality removal is used as the standard data set.
If the data missing value in the original data set after the abnormality removal is detected, preferably, the embodiment of the invention fills the missing value through a preset filling algorithm to obtain the standard data set. In detail, the preset filling algorithm includes:
Wherein L (θ) represents a filled data missing value, x i represents an i-th data missing value, θ represents a probability parameter corresponding to the filled data missing value, n represents the number of the original data sets after the anomaly is removed, and p (x i |θ) represents a filled data missing value probability.
Based on the embodiment, after the original data set is preprocessed, the accuracy of the data in the obtained standard data set is ensured, and meanwhile, the input of batch data can be supported.
The recognition module 102 recognizes the feature words in the standard data set by using a pre-constructed semantic recognition model, screens out the data types of the standard data set according to the feature words, and classifies the data types by using a pre-set classification model to obtain a target data set.
In the embodiment of the invention, the method for identifying the semantic recognition model by utilizing the pre-constructed semantic recognition model comprises the following steps:
wherein R represents a characteristic word of the standard data set, Representing the data types contained in the standard data set, C j representing the set of all data types of the standard data set, W T representing the representation method of feature word meaning recognition for the standard data set by feature word sets R T (D) and R B (D) respectively.
The preset classification model comprises the following steps:
M＝<D,C,R,T>
wherein D represents data in the standard data set, C represents data type of the standard data set, R represents type semantic analysis function of the standard data set, and T represents classifier function of the standard data set.
Converting the quaternion into a function mapping relation to be expressed as:
(t·r): D→C or C= ((T. R) (D)
Based on the embodiment, the standard data set can be well classified by identifying the data type in the standard data set, so that the extraction of key data of the standard data set can be realized.
The matching module 103 performs data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm.
In at least one embodiment of the present invention, the pre-constructed database may be a library database, an insurance database, a household database, or the like. For example, if the pre-constructed database is a library database, and the library database is a library database, the data types of the target table may include: a scientific and technological information data table, a historical data table, a financial data table and the like.
Preferably, in an embodiment of the present invention, the preset matching algorithm includes:
Establishing a target data matrix for the data types in the target data set as follows:
H(i,0)＝0，0≤i≤m；
where i represents the string length of the i-th data type in the target data set, and m represents the sum of the string lengths of all the data types in the target data set.
Establishing a target table matrix for the data types of the target table as follows:
H(0,j)＝0，0≤j≤n；
where j represents the string length of the jth data type in the target table, and n represents the sum of the string lengths of all the data types in the target table.
And calculating the matching score of the target data matrix and the target table matrix, comparing the matching score with a preset threshold value, and completing the matching of the data types according to the comparison result. The preset threshold may be 0.85. Preferably, the embodiment of the present invention calculates the matching score of the target data matrix and the target table matrix by using the following formula:
w＝(H(i-1,j-1)+S(am,bn))
Where w represents a matching score, a m represents the character strings of all data types in the target data matrix, b n represents the character strings of all data types in the target table matrix, and S (a m,bn) represents the similarity coefficient of the target data matrix and the target table matrix.
If the data type matching fails, the judging module 104 performs parameter adjustment on the classifier function in the classification model, then performs classification processing on the data type again to obtain a target data set, and if the data type matching succeeds, the judging module 104 inputs the data in the target data set into a corresponding target table.
Fig. 3 is a schematic structural diagram of an electronic device implementing the method of data entry according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a data entry program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of data entry programs, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device 1 and processes data by running or executing programs or modules (e.g., data entry programs, etc.) stored in the memory 11, and calling data stored in the memory 11.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The data entry program 12 stored by the memory 11 in the electronic device 1 is a combination of instructions that, when executed in the processor 10, enable:
acquiring an original data set, and performing preprocessing operation on the original data set to obtain a standard data set;
Recognizing characteristic words in the standard data set by using a pre-constructed semantic recognition model, screening out data types of the standard data set according to the characteristic words, and classifying the data types by using a pre-set classification model to obtain a target data set;
Performing data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm;
If the data type matching fails, performing parameter adjustment on a classifier function in the classification model, and then performing classification processing on the data type again to obtain a target data set;
And if the data type is successfully matched, inputting the data in the target data set into a corresponding target table.
Specifically, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. A method of data entry, the method comprising:
acquiring an original data set, and performing preprocessing operation on the original data set to obtain a standard data set;
Recognizing characteristic words in the standard data set by using a pre-constructed semantic recognition model, screening out data types of the standard data set according to the characteristic words, and classifying the data types by using a pre-set classification model to obtain a target data set;
Performing data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm;
If the data type matching fails, performing parameter adjustment on a classifier function in the classification model, and then performing classification processing on the data type again to obtain a target data set;
If the data type is successfully matched, inputting the data in the target data set into a corresponding target table;
the step of performing data type matching on the target data set and a target table in a pre-constructed database through a preset matching algorithm includes: establishing a target data matrix for the data types in the target data set:
，/>；
wherein i represents the character string length of the ith data type in the target data set, and m represents the sum of the character string lengths of all the data types in the target data set;
Establishing a target table matrix for the data types of the target table as follows:
；
Wherein j represents the string length of the jth data type in the target table, and n represents the sum of the string lengths of all the data types in the target table;
Calculating the matching score of the target data matrix and the target table matrix, comparing the matching score with a preset threshold value, and completing the matching of the data types according to the comparison result;
the pre-constructed semantic recognition model comprises the following components:
wherein R represents a characteristic word of the standard data set, Representing the data types contained in the standard dataset,/>Representing a set of all data types of a standard dataset,/>Representing feature word sets in a standard dataset,/>And respectively carrying out characteristic word meaning identification for the standard data set.
2. The data entry method of claim 1, wherein the preprocessing operation includes deduplication, and missing value detection.
3. The data entry method of claim 2, wherein the de-exception comprises: obtaining abnormal data G in the original data set after duplication removal by utilizing double-side test rejection, minimum-value single-side test rejection or maximum-value single-side test rejection;
The calculation method of the double-side test reject data comprises the following steps:
Wherein i is a positive integer, Representing the average value of the raw dataset after deduplication,/>Representing the standard deviation of the original dataset after deduplication,/>Representing the de-duplicated original data set;
The calculation method for the minimum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the raw dataset after deduplication,/>Representing the minimum data in the original data set after the duplication removal, wherein S represents the standard deviation of the original data set after the duplication removal;
The calculation method for maximum single-side test rejection comprises the following steps:
Wherein, Representing the average value of the raw dataset after deduplication,/>And representing the maximum data in the original data set after the duplication removal, and S represents the standard deviation of the original data set after the duplication removal.
4. A data entry method according to claim 3, wherein said missing value detection comprises:
Detecting a data missing value existing in the original data set after the abnormality is removed through a missing function;
Filling the data missing values by using the following filling algorithm:
Wherein, Representing filled data missing values,/>Representing the i-th data loss value,/>Representing probability parameters corresponding to the filled data missing values, n representing the number of the original data sets after exception removal,/>Representing the probability of a filled data missing value.
5. The data entry method of claim 1, wherein the calculating a match score for the target data matrix and target table matrix comprises:
Calculating the matching score of the target data matrix and the target table matrix by using the following calculation formula:
Where w represents the match score, Character strings representing all data types in the target data matrix,/>Character strings representing all data types in the target table matrix,/>Representing the similarity coefficients of the target data matrix and the target table matrix.
6. A data entry apparatus for implementing a data entry method as claimed in any one of claims 1 to 5, the apparatus comprising:
The preprocessing module is used for acquiring an original data set, and preprocessing the original data set to obtain a standard data set;
The recognition module is used for recognizing the characteristic words in the standard data set by utilizing a pre-constructed semantic recognition model, screening out the data types of the standard data set according to the characteristic words, and classifying the data types by utilizing a pre-set classification model to obtain a target data set;
The matching module is used for matching the data types of the target data set and the target table in the pre-constructed database through a preset matching algorithm;
And the judging module is used for carrying out parameter adjustment on the classifier function in the classification model and then carrying out classification processing on the data types again to obtain a target data set when the data type matching fails, and inputting the data in the target data set into a corresponding target table when the data type matching succeeds.
7. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the data entry method of any one of claims 1 to 5.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the data entry method of any one of claims 1 to 5.
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