CN116610681A - Data processing method, device, equipment and computer program for multidimensional table - Google Patents
Data processing method, device, equipment and computer program for multidimensional table Download PDFInfo
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Abstract
The application relates to the technical field of data processing, and discloses a data processing method, device and equipment of a multidimensional form and a computer program; when data is input into an original form, positioning the input data, carrying out multidimensional conversion on historical data of the type of the input data to obtain a multidimensional reference model, generating a theoretical trend range according to the multidimensional reference model, judging whether the input data has possibility of input errors or not by comparing the input change trend of the input data with the theoretical trend range, generating an input prompt table when judging that the input data has risk of input errors, displaying the input prompt table in the original form in a floating window mode, and prompting a user, thereby solving the problem that the inspection of the form data is complicated after the data is filled into the form in the prior art.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a computer program for processing data in a multidimensional table.
Background
In filling data into a form, data input errors may be caused occasionally due to misoperation, and in some forms, the data in which the errors are filled is not easily perceived, so that a user needs to consume more time and effort when checking the data in which the form is filled, and the possibility that the erroneous data cannot be checked exists.
Disclosure of Invention
The application aims to provide a data processing method, device, equipment and computer program for a multi-dimensional form, and aims to solve the problem that in the prior art, after filling data into the form, checking the form data is complicated.
The application is realized in such a way that the application provides a data processing method of a multi-dimensional table, comprising the following steps:
acquiring input data in an original form, and analyzing the input data to acquire a classification identifier of the input data;
retrieving historical data associated with the input data in the original table according to the classification identifier, and performing multidimensional type conversion on the input data and the historical data to generate a multidimensional reference model;
analyzing the historical data based on the multi-dimensional reference model to acquire a historical change trend of the historical data, and generating a theoretical trend range of the input data according to the historical change trend;
analyzing the input data based on the multi-dimensional reference model to obtain an input change trend of the input data, and comparing the input change trend with the theoretical trend range to obtain deviation feature distribution of the input data;
and when the deviation characteristic distribution reaches a preset standard, generating an input prompt table according to the deviation characteristic distribution and the multi-dimensional reference model, and displaying the input prompt table in the original table in a floating window mode.
Preferably, the step of acquiring input data in an original table and analyzing the input data to acquire a classification identifier of the input data includes:
monitoring the data change of the original table, and positioning the input data according to the monitoring result to obtain positioning information of the input data in the original table;
and acquiring the identification information of the input data according to the positioning information, and analyzing the identification information to acquire the classification identification and the ordering identification of the input data.
Preferably, the step of retrieving the historical data associated with the input data in the original table according to the classification identifier and performing multidimensional type conversion on the input data and the historical data to generate a multidimensional reference model comprises:
according to the classification identifier and the sorting identifier of the input data, historical data which are the same as the input data in the original table and have the classification identifier are called, and the historical data and the input data are sorted according to the sorting identifier so as to generate a reference data sequence;
determining a multidimensional type of the reference data sequence, and performing multidimensional type conversion on the reference data sequence according to the multidimensional type to generate the multidimensional reference model; the multi-dimensional type includes at least one of: the comparison type comprises a histogram, a box diagram, a bar diagram, a line diagram and a box diagram, the distribution type comprises a scatter diagram and a histogram, the composition type comprises a histogram, a pie diagram, a bar diagram and a line diagram, and the association type comprises a scatter diagram and a bubble diagram.
Preferably, the step of analyzing the historical data based on the multi-dimensional reference model to obtain a historical variation trend of the historical data, and generating a theoretical trend range of the input data according to the historical variation trend includes:
analyzing the change trend of the part corresponding to the historical data in the multidimensional reference model to obtain the historical change trend of the historical data;
acquiring the maximum value of the historical variation trend, and recording the maximum value as a first trend characteristic;
acquiring the maximum value of the adjacent historical variation trend variation values, and recording the maximum value as a second trend characteristic;
acquiring the maximum value of the adjacent historical variation trend variation proportion, and recording the maximum value as a third trend characteristic;
combining the first trend feature, the second trend feature and the third trend feature to generate a basic theoretical trend feature;
analyzing the digital length and the first digit of the historical data, and recording the maximum digital length and the maximum first digit of the historical data as additional theoretical trend characteristics;
determining the historical data adjacent to the input data according to the reference data sequence and marking the historical data as calibration data;
and combining the calibration data with the basic theoretical trend feature and the additional theoretical trend feature to generate the theoretical trend range of the input data.
Preferably, the step of analyzing the input data based on the multi-dimensional reference model to obtain an input variation trend of the input data, and comparing the input variation trend with the theoretical trend range to obtain a deviation feature distribution of the input data includes:
analyzing according to the calibration data and the input data to obtain the input change trend of the input data;
and comparing the input change trend with the theoretical trend range to obtain deviation characteristic distribution of the input data.
In a second aspect, the present application provides a data processing apparatus of a multidimensional table, comprising:
the analysis unit is used for acquiring input data in the original form and analyzing the input data to acquire a classification identifier of the input data;
the conversion unit is used for retrieving the historical data associated with the input data in the original table according to the classification identifier and carrying out multi-dimensional type conversion on the input data and the historical data so as to generate a multi-dimensional reference model;
the reference unit is used for analyzing the historical data based on the multi-dimensional reference model to acquire a historical change trend of the historical data and generating a theoretical trend range of the input data according to the historical change trend;
the comparison unit is used for analyzing the input data based on the multi-dimensional reference model to acquire an input change trend of the input data, and comparing the input change trend with the theoretical trend range to acquire deviation feature distribution of the input data;
and the prompting unit is used for generating an input prompting table according to the deviation characteristic distribution and the multidimensional reference model when the deviation characteristic distribution reaches a preset standard, and displaying the input prompting table in the original table in a floating window mode.
In this embodiment, for specific implementation of each unit in the above embodiment of the apparatus, please refer to the description in the above embodiment of the method, and no further description is given here.
In a third aspect, the present application provides a data processing apparatus of a multi-dimensional table, comprising a memory and an operator;
the memory is used for storing a computer program, and the computer program is used for realizing the data processing method of the multi-dimensional table according to any one of the first aspect;
the operator is configured to drive the memory to execute the computer program.
In a fourth aspect, the present application provides a data processing storage medium of a multi-dimensional table for storing a computer program for executing a data processing method of a multi-dimensional table according to any one of the first aspects.
The application provides a data processing method of a multi-dimensional table, which has the following beneficial effects:
when data is input into an original form, positioning the input data, carrying out multidimensional conversion on historical data of the type of the input data to obtain a multidimensional reference model, generating a theoretical trend range according to the multidimensional reference model, judging whether the input data has possibility of input errors or not by comparing the input change trend of the input data with the theoretical trend range, generating an input prompt table when judging that the input data has risk of input errors, displaying the input prompt table in the original form in a floating window mode, and prompting a user, thereby solving the problem that the inspection of the form data is complicated after the data is filled into the form in the prior art.
Drawings
FIG. 1 is a schematic diagram of steps of a method for processing data in a multi-dimensional table according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data processing device for multi-dimensional table according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. 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 application.
The same or similar reference numerals in the drawings of the present embodiment correspond to the same or similar components; in the description of the present application, it should be understood that, if there is an azimuth or positional relationship indicated by terms such as "upper", "lower", "left", "right", etc., based on the azimuth or positional relationship shown in the drawings, it is only for convenience of describing the present application and simplifying the description, but it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be constructed and operated in a specific azimuth, and thus terms describing the positional relationship in the drawings are merely illustrative and should not be construed as limitations of the present patent, and specific meanings of the terms described above may be understood by those skilled in the art according to specific circumstances.
The implementation of the present application will be described in detail below with reference to specific embodiments.
Referring to fig. 1 and 2, a preferred embodiment of the present application is provided.
In a first aspect, the present application provides a data processing method of a multidimensional table, including:
s1: acquiring input data in an original form, and analyzing the input data to acquire a classification identifier of the input data;
s2: retrieving historical data associated with the input data in the original table according to the classification identifier, and performing multidimensional type conversion on the input data and the historical data to generate a multidimensional reference model;
s3: analyzing the historical data based on the multi-dimensional reference model to acquire a historical change trend of the historical data, and generating a theoretical trend range of the input data according to the historical change trend;
s4: analyzing the input data based on the multi-dimensional reference model to obtain an input change trend of the input data, and comparing the input change trend with the theoretical trend range to obtain deviation feature distribution of the input data;
s5: and when the deviation characteristic distribution reaches a preset standard, generating an input prompt table according to the deviation characteristic distribution and the multi-dimensional reference model, and displaying the input prompt table in the original table in a floating window mode.
Specifically, when a user inputs data in an original form, the input data is acquired and analyzed to obtain a classification identification of the input data.
It should be noted that, the table has a plurality of rows and a plurality of columns, and the rows and columns are interwoven to form a plurality of input cells, and the input cells are used for data input, it is easy to understand that since one input cell belongs to one row and one column, the input data in one input cell corresponds to one row and one column.
More specifically, each row and column has respective representative meanings, which are displayed in the form of text labels, and by analyzing the text labels, it can be determined which text label represents the classification of the input data, and the text label is the classification of the input data.
More specifically, the history data associated with the input data may be retrieved from the original table based on the classification identifier, and it is understood that the history data herein refers to the input data in the original table having the same classification identifier as the input data, and the history data represents the data condition of the input data in the past time period.
More specifically, since the history data reflects the data status of the input data classified in the past time period, the reasonable range of the input data can be calculated by the history data, and when the input data exceeds the calculated range, the possible error of the input data can be judged, and the judgment is fed back to the user, so as to avoid the status of data input error.
More specifically, in the present application, the range is calculated from the history data by performing multidimensional type conversion based on the input data and the history data, and a multidimensional reference model is generated.
It should be noted that, the multidimensional type conversion is to convert input data and history data according to the type of the multidimensional table, and convert the input data and the history data into tables in other forms.
It will be appreciated that the form of the original table cannot intuitively exhibit the trend of change between the respective data, and therefore, it is necessary to convert to another form suitable table for subsequent calculation and display, and the form suitable table is different for different kinds of data, and some kinds of data may be converted by using multiple forms of tables.
More specifically, the multidimensional reference model may be one or more converted forms, the converted forms are easy to perform data processing and analysis to judge historical variation trend of the historical data, and a theoretical trend range of the input data is generated according to the historical variation trend; the history trend is a trend between histories, and includes a change in adjacent histories and also includes a change in the entire histories.
More specifically, the input data is analyzed based on the multidimensional reference model to obtain an input change trend of the input data, and the input change trend is compared with a theoretical trend range, so that deviation feature distribution of the input data can be obtained.
It should be noted that the theoretical trend range is not a fixed numerical value, but a rough range, and the theoretical trend range may be a range including multiple standards, that is, a smaller range may be included in the maximum range, and a smaller range may be included in the smaller range, where when the range in which the variation trend of the input data is located is different, the input variation trend corresponding to the input data is also different.
It can be understood that the deviation feature distribution is used for describing the deviation between the input variation trend and the history variation trend of the input data, when the deviation feature distribution reaches the predetermined standard, an error may exist in the input data, and at this time, an input prompt table needs to be generated according to the deviation feature distribution and the multidimensional reference fan line, and the input prompt table is displayed in the form of a floating window in the original table.
The application provides a data processing method of a multi-dimensional table, which has the following beneficial effects:
when data is input into an original form, positioning the input data, carrying out multidimensional conversion on historical data of the type of the input data to obtain a multidimensional reference model, generating a theoretical trend range according to the multidimensional reference model, judging whether the input data has possibility of input errors or not by comparing the input change trend of the input data with the theoretical trend range, generating an input prompt table when judging that the input data has risk of input errors, displaying the input prompt table in the original form in a floating window mode, and prompting a user, thereby solving the problem that the inspection of the form data is complicated after the data is filled into the form in the prior art.
Preferably, the step of acquiring input data in an original table and analyzing the input data to acquire a classification identifier of the input data includes:
s11: monitoring the data change of the original table, and positioning the input data according to the monitoring result to obtain positioning information of the input data in the original table;
s12: and acquiring the identification information of the input data according to the positioning information, and analyzing the identification information to acquire the classification identification and the ordering identification of the input data.
Specifically, the original table is monitored for data change, after the user inputs data in the original table, the input data is positioned, corresponding horizontal rows and vertical columns of the input data in the original table are determined, and corresponding row and column information is used as positioning information of the input data.
More specifically, the horizontal rows and the vertical columns corresponding to the input data each have identification information, and when the positioning information of the input data is obtained, the identification information of the input data can be obtained.
For example: the positioning information of the input data in the original table is a third row and a fifth column, the third row of the original table is the selling amount, and the fifth column is the month of five, so that the identification information of the input data is the selling amount and the month of five.
It can be understood that the classification identifier and the sorting identifier of the input data can be determined by analyzing the identification information, wherein the classification identifier is used for classifying the input data, and the sorting identifier is used for sorting the input data.
In the above example, the "sell amount" belongs to the category designation and the "month of five" inattention and ordering designation.
It is understood that the data having the same sort identifier and different sort identifiers is the history data of the input data.
Preferably, the step of retrieving the historical data associated with the input data in the original table according to the classification identifier and performing multidimensional type conversion on the input data and the historical data to generate a multidimensional reference model comprises:
s21: according to the classification identifier and the sorting identifier of the input data, historical data which are the same as the input data in the original table and have the classification identifier are called, and the historical data and the input data are sorted according to the sorting identifier so as to generate a reference data sequence;
s22: determining a multidimensional type of the reference data sequence, and performing multidimensional type conversion on the reference data sequence according to the multidimensional type to generate the multidimensional reference model; the multi-dimensional type includes at least one of: the comparison type comprises a histogram, a box diagram, a bar diagram, a line diagram and a box diagram, the distribution type comprises a scatter diagram and a histogram, the composition type comprises a histogram, a pie diagram, a bar diagram and a line diagram, and the association type comprises a scatter diagram and a bubble diagram.
Specifically, the idea of the application is as follows: and converting the historical data in the original form into other forms of forms, and analyzing the data based on the converted forms to judge the input change trend of the input data.
More specifically, to implement conversion of the historical data, the historical data first needs to be sorted by the sorting identifier to generate the reference data sequence.
More specifically, the reference data sequence is used to perform a multidimensional transformation, which is a transformation of the reference data sequence into a table of other forms for subsequent data analysis steps.
More specifically, there are a plurality of table forms that can be converted, and in actual conversion, the reference data series can be converted into one or more of these table forms.
Preferably, the step of analyzing the historical data based on the multi-dimensional reference model to obtain a historical variation trend of the historical data, and generating a theoretical trend range of the input data according to the historical variation trend includes:
s31: analyzing the change trend of the part corresponding to the historical data in the multidimensional reference model to obtain the historical change trend of the historical data;
s32: acquiring the maximum value of the historical variation trend, and recording the maximum value as a first trend characteristic;
s33: acquiring the maximum value of the adjacent historical variation trend variation values, and recording the maximum value as a second trend characteristic;
s34: acquiring the maximum value of the adjacent historical variation trend variation proportion, and recording the maximum value as a third trend characteristic;
s35: combining the first trend feature, the second trend feature and the third trend feature to generate a basic theoretical trend feature;
s36: analyzing the digital length and the first digit of the historical data, and recording the maximum digital length and the maximum first digit of the historical data as additional theoretical trend characteristics;
s37: determining the historical data adjacent to the input data according to the reference data sequence and marking the historical data as calibration data;
s38: and combining the calibration data with the basic theoretical trend feature and the additional theoretical trend feature to generate the theoretical trend range of the input data.
Specifically, the multidimensional reference model is used for analyzing the change trend of the data, the table forms of different kinds of data suitable for conversion are also different, and after the reference data sequence is converted into the multidimensional reference model, the change trend can be analyzed based on the multidimensional reference model.
More specifically, the above analysis of the trend of change includes various ways, such as: the degree of inclination of the line segment change in the line graph is analyzed, and the ratio of each part of the pie chart is analyzed.
More specifically, the history change trend of the history data refers to the change trend of the adjacent history data, and thus the maximum value of the history change trend, the history change trend change value, and the history change trend change ratio can be derived based on the history change trend.
It should be noted that, the maximum value of the historical variation trend is the maximum variation value between adjacent historical data, the maximum variation value may reflect a reasonable variation range between adjacent data, and the maximum variation value is recorded as the first trend feature.
It should be noted that, the historical change trend change value refers to a change relationship between adjacent historical change trends, for example, the first historical change trend is 30%, and the second historical change trend is 50%, and then the adjacent historical change trend change value is 20%; the history change trend change ratio refers to a change relationship between adjacent history change trends, for example, the first history change trend is 20%, the second history change trend is 30%, and then the adjacent history change trend change ratio is 50%.
More specifically, analyzing the historical variation trend to obtain a maximum value of a variation value of the historical variation trend, recording the maximum value as a second trend characteristic, obtaining a maximum value of a variation proportion of the historical variation trend, and recording the maximum value as a third trend characteristic; and combining the first trend feature, the second trend feature and the third trend feature to obtain the basic theoretical trend feature.
It should be noted that it is reasonable to use the basic theoretical trend feature to describe what range the change relation between the input data and the history data remains.
More specifically, in the reference data sequence, the history data adjacent to the input data is calibration data, and it is easy to understand that the difference between the input data and the calibration data is the input change trend of the input data.
That is, the actual trend between the input data and the calibration data is the input trend of the input data, and the range in which the theoretical trend between the calibration data and the input data is located is the theoretical trend range of the input data.
It can be understood that it is most reasonable when the input variation trend of the input data is within the first trend feature, and it is within a more reasonable range when the input variation trend of the input data is outside the first trend feature but within the superposition of the first trend feature and the second trend feature or the third trend feature; when the input change trend of the input data is beyond the superposition of the first trend feature and the second trend feature or the third trend feature, the input change trend belongs to a less reasonable range.
More specifically, the digital length and the first digit of the historical data can be analyzed, and when the digital lengths of all the historical data are kept at the same length, if the digital lengths of the input data are inconsistent with the lengths of the historical data, the digital lengths belong to an unreasonable range.
More specifically, the first digit is based on the judgment of the digit length, and further judgment is made on the input data, for example: when the number length is 4 and the first number is 3-5, the unreasonable range is reached when the number length of the input data is 3 or 5, and the unreasonable range is reached when the number length of the input data is 4 and the first number is 2 or 6.
According to the digital length range and the first digital range of the historical data, an additional theoretical trend range can be obtained, calibration data are combined with basic theoretical trend characteristics and additional theoretical trend characteristics to generate a theoretical trend range of input data, and according to the specific position relation of the input change trend of the input data in the theoretical trend range, the reasonable degree of the input data can be judged, so that subsequent prompting operation is carried out.
Preferably, the step of analyzing the input data based on the multi-dimensional reference model to obtain an input variation trend of the input data, and comparing the input variation trend with the theoretical trend range to obtain a deviation feature distribution of the input data includes:
s41: analyzing according to the calibration data and the input data to obtain the input change trend of the input data;
s42: and comparing the input change trend with the theoretical trend range to obtain deviation characteristic distribution of the input data.
Specifically, the deviation feature distribution is used to describe the relative relationship between the input variation trend of the input data and the theoretical trend range, and the greater the deviation of the input variation trend from the theoretical trend range, the greater the probability of representing an error of the input data.
In a second aspect, the present application provides a data processing apparatus of a multidimensional table, comprising:
the analysis unit is used for acquiring input data in the original form and analyzing the input data to acquire a classification identifier of the input data;
the conversion unit is used for retrieving the historical data associated with the input data in the original table according to the classification identifier and carrying out multi-dimensional type conversion on the input data and the historical data so as to generate a multi-dimensional reference model;
the reference unit is used for analyzing the historical data based on the multi-dimensional reference model to acquire a historical change trend of the historical data and generating a theoretical trend range of the input data according to the historical change trend;
the comparison unit is used for analyzing the input data based on the multi-dimensional reference model to acquire an input change trend of the input data, and comparing the input change trend with the theoretical trend range to acquire deviation feature distribution of the input data;
and the prompting unit is used for generating an input prompting table according to the deviation characteristic distribution and the multidimensional reference model when the deviation characteristic distribution reaches a preset standard, and displaying the input prompting table in the original table in a floating window mode.
In a third aspect, the present application provides a data processing apparatus for a multi-dimensional table, comprising a memory and a runner.
Specifically, the memory is configured to store a computer program, where the computer program is configured to implement a data processing method of a multi-dimensional table according to any one of the first aspects; the runner is used for driving the memory to execute the computer program.
In a fourth aspect, the present application provides a data processing storage medium of a multi-dimensional table for storing a computer program for executing a data processing method of a multi-dimensional table according to any one of the first aspects.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (8)
1. A method of processing data in a multi-dimensional table, comprising:
acquiring input data in an original form, and analyzing the input data to acquire a classification identifier of the input data;
retrieving historical data associated with the input data in the original table according to the classification identifier, and performing multidimensional type conversion on the input data and the historical data to generate a multidimensional reference model;
analyzing the historical data based on the multi-dimensional reference model to acquire a historical change trend of the historical data, and generating a theoretical trend range of the input data according to the historical change trend;
analyzing the input data based on the multi-dimensional reference model to obtain an input change trend of the input data, and comparing the input change trend with the theoretical trend range to obtain deviation feature distribution of the input data;
and when the deviation characteristic distribution reaches a preset standard, generating an input prompt table according to the deviation characteristic distribution and the multi-dimensional reference model, and displaying the input prompt table in the original table in a floating window mode.
2. The method of claim 1, wherein the steps of obtaining input data in an original table and analyzing the input data to obtain a classification identifier of the input data comprise:
monitoring the data change of the original table, and positioning the input data according to the monitoring result to obtain positioning information of the input data in the original table;
and acquiring the identification information of the input data according to the positioning information, and analyzing the identification information to acquire the classification identification and the ordering identification of the input data.
3. The method of claim 2, wherein retrieving historical data associated with the input data in the original table based on the classification identifier and performing multidimensional type conversion on the input data and the historical data to generate a multidimensional reference model comprises:
according to the classification identifier and the sorting identifier of the input data, historical data which are the same as the input data in the original table and have the classification identifier are called, and the historical data and the input data are sorted according to the sorting identifier so as to generate a reference data sequence;
determining a multidimensional type of the reference data sequence, and performing multidimensional type conversion on the reference data sequence according to the multidimensional type to generate the multidimensional reference model; the multi-dimensional type includes at least one of: the comparison type comprises a histogram, a box diagram, a bar diagram, a line diagram and a box diagram, the distribution type comprises a scatter diagram and a histogram, the composition type comprises a histogram, a pie diagram, a bar diagram and a line diagram, and the association type comprises a scatter diagram and a bubble diagram.
4. The data processing method of a multi-dimensional form according to claim 3, wherein the step of analyzing the history data based on the multi-dimensional reference model to obtain a history trend of the history data, and generating a theoretical trend range of the input data according to the history trend comprises:
analyzing the change trend of the part corresponding to the historical data in the multidimensional reference model to obtain the historical change trend of the historical data;
acquiring the maximum value of the historical variation trend, and recording the maximum value as a first trend characteristic;
acquiring the maximum value of the adjacent historical variation trend variation values, and recording the maximum value as a second trend characteristic;
acquiring the maximum value of the adjacent historical variation trend variation proportion, and recording the maximum value as a third trend characteristic;
combining the first trend feature, the second trend feature and the third trend feature to generate a basic theoretical trend feature;
analyzing the digital length and the first digit of the historical data, and recording the digital length range and the first digit range of the historical data as additional theoretical trend characteristics;
determining the historical data adjacent to the input data according to the reference data sequence and marking the historical data as calibration data;
and combining the calibration data with the basic theoretical trend feature and the additional theoretical trend feature to generate the theoretical trend range of the input data.
5. The method of claim 4, wherein the step of analyzing the input data based on the multi-dimensional reference model to obtain an input trend of the input data, and comparing the input trend with the theoretical trend range to obtain a deviation feature distribution of the input data comprises:
analyzing according to the calibration data and the input data to obtain the input change trend of the input data;
and comparing the input change trend with the theoretical trend range to obtain deviation characteristic distribution of the input data.
6. A data processing apparatus for a multi-dimensional table, comprising:
the analysis unit is used for acquiring input data in the original form and analyzing the input data to acquire a classification identifier of the input data;
the conversion unit is used for retrieving the historical data associated with the input data in the original table according to the classification identifier and carrying out multi-dimensional type conversion on the input data and the historical data so as to generate a multi-dimensional reference model;
the reference unit is used for analyzing the historical data based on the multi-dimensional reference model to acquire a historical change trend of the historical data and generating a theoretical trend range of the input data according to the historical change trend;
the comparison unit is used for analyzing the input data based on the multi-dimensional reference model to acquire an input change trend of the input data, and comparing the input change trend with the theoretical trend range to acquire deviation feature distribution of the input data;
and the prompting unit is used for generating an input prompting table according to the deviation characteristic distribution and the multidimensional reference model when the deviation characteristic distribution reaches a preset standard, and displaying the input prompting table in the original table in a floating window mode.
7. A data processing device for a multi-dimensional form, comprising a memory and an operator;
the memory is used for storing a computer program for implementing a data processing method of a multi-dimensional table according to any one of claims 1-5;
the operator is configured to drive the memory to execute the computer program.
8. A data processing storage medium of a multi-dimensional form, characterized by storing a computer program for executing a data processing method of a multi-dimensional form according to any one of claims 1-5.
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