A big data-based business data difference analysis method, system and medium

Abstract

The application discloses a business data difference analysis method and system based on big data, and a medium, wherein the method comprises the following steps: acquiring business data information; extracting features and corresponding feature values in the business data information; obtaining the grade number of the corresponding feature values in the business data information according to the preset feature value range in which the feature values fall; constructing a single grid according to the features in the business data information, and sending and storing the grade number of the corresponding feature values in the business data information to the single grid; recombining the single grids corresponding to different business data information to obtain recombination grids of the different business data information, and displaying the feature values corresponding to the different business data information in the corresponding recombination grids. The application extracts the features and feature values of business data, displays the feature grades of the business data through the single grid, and displays the feature grade difference between different business data in combination with the recombination grid, so that the difference between different business data can be intuitively displayed.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method, system, and medium for business data difference analysis based on big data. Background Technology

[0002] As the business grows, the amount of business data accumulates. Currently, due to the increasing transparency of information, some business data needs to be presented to a wider audience, including industry professionals and non-industry professionals. In the past, simply describing the data with numbers and using simple graphics made it easy for industry professionals to understand, but it was difficult for non-industry professionals to comprehend the data.

[0003] Therefore, existing technologies have shortcomings and urgently need improvement. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a method, system and medium for business data difference analysis based on big data, which can directly display the differences between business data.

[0005] The first aspect of this invention provides a method for analyzing business data discrepancies based on big data, comprising:

[0006] Obtain business data information;

[0007] Extract features and corresponding feature values ​​from business data;

[0008] Based on the preset range of feature values ​​that the feature values ​​fall into, the level number of the corresponding feature value in the business data information is obtained;

[0009] Construct individual grids based on the features in the business data information, and send the level numbers of the corresponding feature values ​​in the business data information to the individual grids and store them;

[0010] The individual grids corresponding to different business data information are recombined to obtain recombined grids of different business data information, and the feature values ​​corresponding to different business data information are displayed in the corresponding recombined grids.

[0011] This plan also includes:

[0012] Based on the business data information, obtain the time nodes corresponding to the business data;

[0013] Based on the time nodes corresponding to the business data, the business data is sorted and numbered to obtain the business data number;

[0014] The number of the business data is sent to a preset grid system for storage.

[0015] This plan also includes:

[0016] Based on a pre-defined comment system, obtain the impact score of features in business data information on the business data.

[0017] Based on the characteristics in the business data information, sort them in descending order of influence score to obtain the feature numbers in the business data information;

[0018] The feature numbers in the business data information are sent to a preset grid system for storage.

[0019] In this solution, the step of constructing a single grid based on features in the business data information specifically includes:

[0020] Construct horizontal grids of individual rasters based on the features in the business data information according to their numerical order;

[0021] The vertical grid of a single grid is constructed by using the level number of the corresponding feature value in the business data information.

[0022] In this solution, the step of recombining individual rasters corresponding to different business data information to obtain recombined rasters of different business data information specifically includes:

[0023] The features in the business data information are used to construct horizontal and vertical grids for reorganizing the grid according to the numbering order;

[0024] The difference between the number of levels of the same feature in different business data information is calculated to obtain the corresponding feature level difference, and the feature level difference is displayed in the corresponding grid.

[0025] When the corresponding feature does not exist in the business data information, the raster corresponding to the recombined raster will display 0 and the corresponding feature level difference.

[0026] This plan also includes:

[0027] Based on the reconstructed raster, obtain the maximum feature level difference in the reconstructed raster;

[0028] Determine whether the maximum feature level difference in the reconstructed grid is greater than a preset first level threshold; if so, trigger the first difference information.

[0029] The first difference information is sent to a preset management terminal for display.

[0030] A second aspect of the present invention provides a business data difference analysis system based on big data, comprising a memory and a processor. The memory stores a business data difference analysis method program based on big data. When the processor executes the business data difference analysis method program based on big data, it performs the following steps:

[0031] Obtain business data information;

[0032] Extract features and corresponding feature values ​​from business data;

[0033] Based on the preset range of feature values ​​that the feature values ​​fall into, the level number of the corresponding feature value in the business data information is obtained;

[0034] Construct individual grids based on the features in the business data information, and send the level numbers of the corresponding feature values ​​in the business data information to the individual grids and store them;

[0035] The individual grids corresponding to different business data information are recombined to obtain recombined grids of different business data information, and the feature values ​​corresponding to different business data information are displayed in the corresponding recombined grids.

[0036] This plan also includes:

[0037] Based on the business data information, obtain the time nodes corresponding to the business data;

[0038] Based on the time nodes corresponding to the business data, the business data is sorted and numbered to obtain the business data number;

[0039] The number of the business data is sent to a preset grid system for storage.

[0040] This plan also includes:

[0041] Based on a pre-defined comment system, obtain the impact score of features in business data information on the business data.

[0042] Based on the characteristics in the business data information, sort them in descending order of influence score to obtain the feature numbers in the business data information;

[0043] The feature numbers in the business data information are sent to a preset grid system for storage.

[0044] In this solution, the step of constructing a single grid based on features in the business data information specifically includes:

[0045] Construct horizontal grids of individual rasters based on the features in the business data information according to their numerical order;

[0046] The vertical grid of a single grid is constructed by using the level number of the corresponding feature value in the business data information.

[0047] In this solution, the step of recombining individual rasters corresponding to different business data information to obtain recombined rasters of different business data information specifically includes:

[0048] The features in the business data information are used to construct horizontal and vertical grids for reorganizing the grid according to the numbering order;

[0049] The difference between the number of levels of the same feature in different business data information is calculated to obtain the corresponding feature level difference, and the feature level difference is displayed in the corresponding grid.

[0050] When the corresponding feature does not exist in the business data information, the raster corresponding to the recombined raster will display 0 and the corresponding feature level difference.

[0051] This plan also includes:

[0052] Based on the reconstructed raster, obtain the maximum feature level difference in the reconstructed raster;

[0053] Determine whether the maximum feature level difference in the reconstructed grid is greater than a preset first level threshold; if so, trigger the first difference information.

[0054] The first difference information is sent to a preset management terminal for display.

[0055] A third aspect of the present invention provides a computer-readable storage medium storing a business data difference analysis method program based on big data, wherein when the business data difference analysis method program based on big data is executed by a processor, it implements the steps of the business data difference analysis method based on big data as described in any of the preceding claims.

[0056] This invention discloses a business data difference analysis method, system, and medium based on big data. It extracts the features and feature values ​​of business data, displays the feature levels of the business data through individual grids, and then displays the feature level differences between different business data by combining recombined grids, so that the differences between different business data can be displayed intuitively. Attached Figure Description

[0057] Figure 1 A flowchart of a business data difference analysis method based on big data according to the present invention is shown;

[0058] Figure 2 A schematic diagram illustrating the construction of a single-unit grid according to the present invention is shown;

[0059] Figure 3 A schematic diagram illustrating the construction of the reconstructed grid according to the present invention is shown;

[0060] Figure 4 The diagram shows a block diagram of a business data difference analysis system based on big data according to the present invention. Detailed Implementation

[0061] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0062] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0063] Figure 1 The flowchart of a business data difference analysis method based on big data according to the present invention is shown.

[0064] S101, Obtain business data information;

[0065] S102, Extract the features and corresponding feature values ​​from the business data information;

[0066] S103, based on the preset range of feature values ​​into which the feature values ​​fall, obtain the level number of the corresponding feature value in the business data information;

[0067] S104, construct a single grid based on the features in the business data information, and send the level number of the corresponding feature value in the business data information to the single grid and store it;

[0068] S105, reassemble the individual grids corresponding to different business data information to obtain reassembled grids of different business data information, and display the feature values ​​of the corresponding different business data information in the corresponding reassembled grids.

[0069] According to embodiments of the present invention, the features in the business data information include the amount, reconciliation time, and business personnel of the corresponding business data. For example, if the corresponding feature is the amount, then the corresponding feature value is the numerical value of the corresponding amount. Different features have different preset feature value ranges. For example, if the corresponding feature is the amount, then it can be divided into levels according to different preset amount value ranges. For example, the preset amount value ranges are (0, 100,000], (100,000, 200,000], (200,000, 300,000] (yuan)... and so on. When the amount value is in the range (0, 100,000], the corresponding amount value is set as the first level. When the amount value is in the range (100,000, 200,000], the corresponding amount value is set as the second level. The higher the level number, the more obvious the corresponding feature value. If the corresponding feature is the business personnel, then the corresponding preset feature value range can be divided according to the role of the corresponding business personnel, such as the role of a salesperson, the role of a business team leader, etc. Constructing a single grid for each business data can directly reflect the feature value of the business data under different features. Recombining different business data to construct a recombined grid can directly reflect the feature differences between different business data under different features. The number of feature value levels is simply referred to as the feature value level.

[0070] According to an embodiment of the present invention, it further includes:

[0071] Based on the business data information, obtain the time nodes corresponding to the business data;

[0072] Based on the time nodes corresponding to the business data, the business data is sorted and numbered to obtain the business data number;

[0073] The number of the business data is sent to a preset grid system for storage.

[0074] It should be noted that the time node corresponding to the business data is the time when the business data is filed in the grid system. The business data is numbered according to the chronological order of the corresponding time nodes and stored in the preset grid system.

[0075] According to an embodiment of the present invention, it further includes:

[0076] Based on a pre-defined comment system, obtain the impact score of features in business data information on the business data.

[0077] Based on the characteristics in the business data information, sort them in descending order of influence score to obtain the feature numbers in the business data information;

[0078] The feature numbers in the business data information are sent to a preset grid system for storage.

[0079] It should be noted that the preset comment system uses multiple experts in the field to score the features of historical business data in the same field, determines the influence scores of different features on the business data in the same field, and sorts and numbers the features in the corresponding business data information in descending order of influence scores.

[0080] Figure 2 A schematic diagram of the construction of a single-unit grid according to the present invention is shown.

[0081] like Figure 2 As shown in the embodiment of the present invention, the step of constructing a single grid based on features in business data information specifically includes:

[0082] Construct horizontal grids of individual rasters based on the features in the business data information according to their numerical order;

[0083] The vertical grid of a single grid is constructed by using the level number of the corresponding feature value in the business data information.

[0084] It should be noted that the horizontal grid of a single-unit grid is set by the feature number, and the vertical grid of the corresponding feature value is set by the level number of the single-unit grid. For example, if the feature number is set to 'a'... n The feature with the greatest impact on the score is assigned the number a1, the feature with the second greatest impact is assigned the number a2, and so on. Adjacent features are distinguished by different colors and fill methods. If a corresponding feature number does not exist in the business data, the corresponding feature value level is not displayed. Figure 2 Feature number a4 in the text.

[0085] Figure 3 A schematic diagram of the reconstruction grid constructed according to the present invention is shown.

[0086] like Figure 3 As shown in the embodiment of the present invention, the step of recombining individual grids corresponding to different business data information to obtain a recombined grid of different business data information specifically includes:

[0087] The features in the business data information are used to construct horizontal and vertical grids for reorganizing the grid according to the numbering order;

[0088] The difference between the number of levels of the same feature in different business data information is calculated to obtain the corresponding feature level difference, and the feature level difference is displayed in the corresponding grid.

[0089] When the corresponding feature does not exist in the business data information, the raster corresponding to the recombined raster will display 0 and the corresponding feature level difference.

[0090] It should be noted that the feature numbers of different business data are set as the horizontal and vertical grids of the reorganized grid, respectively. For example, the feature number of business data A is set as the vertical grid, and the feature number of business data B is set as the horizontal grid. The feature level difference of the same business data is filled in the feature number. For example, if the feature level difference of feature a1 between business data A and business data B is 3, then the number 3 is filled in the grid. If the feature does not exist in any of the business data, then two 0s are filled in the grid, separated by a slash. Figure 3 Feature a2; where the difference in feature level between different business data for the same feature is 0, then fill in the number 0 in the raster corresponding to that feature, for example in Figure 3 Feature a3; if one business data A does not have this feature, but another business data B does have this feature, then the edge corresponding to the business data that does not have this feature is filled with the number 0, separated by a slash. For example, in Figure 3 Feature a4 in the data directly reflects the differences between different business data with the same or different features through the numbers in different grids. Other grids without numbers are treated with diagonal lines to indicate that they have no meaning.

[0091] According to an embodiment of the present invention, it further includes:

[0092] Based on the reconstructed raster, obtain the maximum feature level difference in the reconstructed raster;

[0093] Determine whether the maximum feature level difference in the reconstructed grid is greater than a preset first level threshold; if so, trigger the first difference information.

[0094] The first difference information is sent to a preset management terminal for display.

[0095] It should be noted that the feature level difference in each recombined raster is extracted, and the feature level difference in each recombined raster is compared with the preset first level threshold. For example, if the preset first level threshold is 3, then if the feature level difference in the recombined raster is greater than 3, the first difference information is triggered, and the feature number and feature level difference corresponding to the feature level difference greater than the preset first level threshold are specially marked, such as distinguishing the color of the raster from the color of other rasters.

[0096] According to an embodiment of the present invention, it further includes:

[0097] Based on the reconstructed grid, the sum of all feature level differences in the reconstructed grid is obtained.

[0098] If the sum of the feature level differences is greater than a preset second level threshold, then the second difference information is triggered.

[0099] The second difference information is sent to the preset management terminal for display.

[0100] It should be noted that the feature level differences in each recombined grid are extracted and summed to obtain the sum of feature level differences. The sum of feature level differences is then compared with a preset second level threshold. For example, if the preset second level threshold is 6, then when the sum of feature level differences is greater than 6, the second difference information is triggered, indicating that there is a significant difference between the two business data corresponding to the recombined grid.

[0101] According to an embodiment of the present invention, it further includes:

[0102] Based on the business data information, we obtain the characteristics and characteristic values ​​of the corresponding business data at different time points;

[0103] Based on the characteristic values ​​of the corresponding business data at different time points, the characteristic value levels of the corresponding business data at different time points are obtained;

[0104] The feature value levels at different time points are displayed separately in a single raster.

[0105] It should be noted that the different time nodes refer to different time periods. The characteristics and feature values ​​of the corresponding business data at different time nodes are the characteristics and feature values ​​of the corresponding business data at different time periods. The feature values ​​at different time nodes are classified into levels according to the preset feature value range to determine the feature value level of the corresponding business data at different time nodes, and then displayed separately in a single grid, for example, by using different colors to distinguish them. The feature value level of the same feature at different time nodes is displayed according to different colors.

[0106] According to an embodiment of the present invention, after obtaining the influence score of the features in the business data information on the business data, the method further includes:

[0107] Determine whether the influence score of the features in the business data information on the business data is greater than a preset first score. If so, store the feature corresponding to the influence score; otherwise, delete the feature corresponding to the influence score.

[0108] It should be noted that after scoring the features in the business data information through the preset comment system, the influence score of the features in the business data information on the business data information is obtained. If the influence score is less than or equal to the preset first score, it means that the influence of the feature on the business data can be ignored. Therefore, when constructing the grid, features with influence scores less than or equal to the preset first score are deleted. The constructed grid includes single grids and recombined grids.

[0109] Figure 4 The diagram shows a block diagram of a business data difference analysis system based on big data according to the present invention.

[0110] like Figure 4 As shown, a second aspect of the present invention provides a business data difference analysis system 4 based on big data, including a memory 41 and a processor 42. The memory stores a business data difference analysis method program based on big data. When the processor executes the business data difference analysis method program based on big data, it performs the following steps:

[0111] Obtain business data information;

[0112] Extract features and corresponding feature values ​​from business data;

[0113] Based on the preset range of feature values ​​that the feature values ​​fall into, the level number of the corresponding feature value in the business data information is obtained;

[0114] Construct individual grids based on the features in the business data information, and send the level numbers of the corresponding feature values ​​in the business data information to the individual grids and store them;

[0115] The individual grids corresponding to different business data information are recombined to obtain recombined grids of different business data information, and the feature values ​​corresponding to different business data information are displayed in the corresponding recombined grids.

[0116] According to embodiments of the present invention, the features in the business data information include the amount, reconciliation time, and business personnel of the corresponding business data. For example, if the corresponding feature is the amount, then the corresponding feature value is the numerical value of the corresponding amount. Different features have different preset feature value ranges. For example, if the corresponding feature is the amount, then it can be divided into levels according to different preset amount value ranges. For example, the preset amount value ranges are (0, 100,000], (100,000, 200,000], (200,000, 300,000] (yuan)... and so on. When the amount value is in the range (0, 100,000], the corresponding amount value is set as the first level. When the amount value is in the range (100,000, 200,000], the corresponding amount value is set as the second level. The higher the level number, the more obvious the corresponding feature value. If the corresponding feature is the business personnel, then the corresponding preset feature value range can be divided according to the role of the corresponding business personnel, such as the role of a salesperson, the role of a business team leader, etc. Constructing a single grid for each business data can directly reflect the feature value of the business data under different features. Recombining different business data to construct a recombined grid can directly reflect the feature differences between different business data under different features. The number of feature value levels is simply referred to as the feature value level.

[0117] According to an embodiment of the present invention, it further includes:

[0118] Based on the business data information, obtain the time nodes corresponding to the business data;

[0119] Based on the time nodes corresponding to the business data, the business data is sorted and numbered to obtain the business data number;

[0120] The number of the business data is sent to a preset grid system for storage.

[0121] It should be noted that the time node corresponding to the business data is the time when the business data is filed in the grid system. The business data is numbered according to the chronological order of the corresponding time nodes and stored in the preset grid system.

[0122] According to an embodiment of the present invention, it further includes:

[0123] Based on a pre-defined comment system, obtain the impact score of features in business data information on the business data.

[0124] Based on the characteristics in the business data information, sort them in descending order of influence score to obtain the feature numbers in the business data information;

[0125] The feature numbers in the business data information are sent to a preset grid system for storage.

[0126] It should be noted that the preset comment system uses multiple experts in the field to score the features of historical business data in the same field, determines the influence scores of different features on the business data in the same field, and sorts and numbers the features in the corresponding business data information in descending order of influence scores.

[0127] According to an embodiment of the present invention, the step of constructing a single-unit raster based on features in business data information specifically includes:

[0128] Construct horizontal grids of individual rasters based on the features in the business data information according to their numerical order;

[0129] The vertical grid of a single grid is constructed by using the level number of the corresponding feature value in the business data information.

[0130] It should be noted that the horizontal grid of a single-unit grid is set by the feature number, and the vertical grid of the corresponding feature value is set by the level number of the single-unit grid. For example, if the feature number is set to 'a'... n The feature with the greatest impact on the score is assigned the number a1, the feature with the second greatest impact is assigned the number a2, and so on. Adjacent features are distinguished by different colors and fill methods. If a corresponding feature number does not exist in the business data, the corresponding feature value level is not displayed. Figure 2 Feature number a4 in the text.

[0131] According to an embodiment of the present invention, the step of recombining individual rasters corresponding to different business data information to obtain recombined rasters of different business data information specifically includes:

[0132] The features in the business data information are used to construct horizontal and vertical grids for reorganizing the grid according to the numbering order;

[0133] The difference between the number of levels of the same feature in different business data information is calculated to obtain the corresponding feature level difference, and the feature level difference is displayed in the corresponding grid.

[0134] When the corresponding feature does not exist in the business data information, the raster corresponding to the recombined raster will display 0 and the corresponding feature level difference.

[0135] It should be noted that the feature numbers of different business data are set as the horizontal and vertical grids of the reorganized grid, respectively. For example, the feature number of business data A is set as the vertical grid, and the feature number of business data B is set as the horizontal grid. The feature level difference of the same business data is filled in the feature number. For example, if the feature level difference of feature a1 between business data A and business data B is 3, then the number 3 is filled in the grid. If the feature does not exist in any of the business data, then two 0s are filled in the grid, separated by a slash. Figure 3 Feature a2; where the difference in feature level between different business data for the same feature is 0, then fill in the number 0 in the raster corresponding to that feature, for example in Figure 3 Feature a3; if one business data A does not have this feature, but another business data B does have this feature, then the edge corresponding to the business data that does not have this feature is filled with the number 0, separated by a slash. For example, in Figure 3 Feature a4 in the data directly reflects the differences between different business data with the same or different features through the numbers in different grids. Other grids without numbers are treated with diagonal lines to indicate that they have no meaning.

[0136] According to an embodiment of the present invention, it further includes:

[0137] Based on the reconstructed raster, obtain the maximum feature level difference in the reconstructed raster;

[0138] Determine whether the maximum feature level difference in the reconstructed grid is greater than a preset first level threshold; if so, trigger the first difference information.

[0139] The first difference information is sent to a preset management terminal for display.

[0140] It should be noted that the feature level difference in each recombined raster is extracted, and the feature level difference in each recombined raster is compared with the preset first level threshold. For example, if the preset first level threshold is 3, then if the feature level difference in the recombined raster is greater than 3, the first difference information is triggered, and the feature number and feature level difference corresponding to the feature level difference greater than the preset first level threshold are specially marked, such as distinguishing the color of the raster from the color of other rasters.

[0141] According to an embodiment of the present invention, it further includes:

[0142] Based on the reconstructed grid, the sum of all feature level differences in the reconstructed grid is obtained.

[0143] If the sum of the feature level differences is greater than a preset second level threshold, then the second difference information is triggered.

[0144] The second difference information is sent to the preset management terminal for display.

[0145] It should be noted that the feature level differences in each recombined grid are extracted and summed to obtain the sum of feature level differences. The sum of feature level differences is then compared with a preset second level threshold. For example, if the preset second level threshold is 6, then when the sum of feature level differences is greater than 6, the second difference information is triggered, indicating that there is a significant difference between the two business data corresponding to the recombined grid.

[0146] According to an embodiment of the present invention, it further includes:

[0147] Based on the business data information, we obtain the characteristics and characteristic values ​​of the corresponding business data at different time points;

[0148] Based on the characteristic values ​​of the corresponding business data at different time points, the characteristic value levels of the corresponding business data at different time points are obtained;

[0149] The feature value levels at different time points are displayed separately in a single raster.

[0150] It should be noted that the different time nodes refer to different time periods. The characteristics and feature values ​​of the corresponding business data at different time nodes are the characteristics and feature values ​​of the corresponding business data at different time periods. The feature values ​​at different time nodes are classified into levels according to the preset feature value range to determine the feature value level of the corresponding business data at different time nodes, and then displayed separately in a single grid, for example, by using different colors to distinguish them. The feature value level of the same feature at different time nodes is displayed according to different colors.

[0151] According to an embodiment of the present invention, after obtaining the influence score of the features in the business data information on the business data, the method further includes:

[0152] Determine whether the influence score of the features in the business data information on the business data is greater than a preset first score. If so, store the feature corresponding to the influence score; otherwise, delete the feature corresponding to the influence score.

[0153] It should be noted that after scoring the features in the business data information through the preset comment system, the influence score of the features in the business data information on the business data information is obtained. If the influence score is less than or equal to the preset first score, it means that the influence of the feature on the business data can be ignored. Therefore, when constructing the grid, features with influence scores less than or equal to the preset first score are deleted. The constructed grid includes single grids and recombined grids.

[0154] A third aspect of the present invention provides a computer-readable storage medium storing a business data difference analysis method program based on big data, wherein when the business data difference analysis method program based on big data is executed by a processor, it implements the steps of the business data difference analysis method based on big data as described in any of the preceding claims.

[0155] This invention discloses a method, system, and medium for business data difference analysis based on big data. The method includes: acquiring business data information; extracting features and corresponding feature values ​​from the business data information; obtaining the level number of the corresponding feature value in the business data information based on the feature value falling within a preset feature value range; constructing a single-unit grid based on the features in the business data information, and sending and storing the level number of the corresponding feature value in the business data information to the single-unit grid; recombining the single-unit grids corresponding to different business data information to obtain a recombined grid of different business data information, and displaying the feature values ​​corresponding to different business data information in the corresponding recombined grids. This invention extracts features and feature values ​​from business data, displays the feature levels of the business data through single-unit grids, and then combines the recombined grids to display the feature level differences between different business data, making the differences between different business data intuitively displayed.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0157] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0159] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for analyzing business data discrepancies based on big data, characterized in that, include: Obtain business data information; Extract features and corresponding feature values ​​from business data; Based on the preset range of feature values ​​that the feature values ​​fall into, the level number of the corresponding feature value in the business data information is obtained; Construct individual grids based on the features in the business data information, and send the level numbers of the corresponding feature values ​​in the business data information to the individual grids and store them; The individual grids corresponding to different business data information are recombined to obtain recombined grids of different business data information, and the feature values ​​corresponding to different business data information are displayed in the corresponding recombined grids. The step of recombining individual rasters corresponding to different business data information to obtain recombined rasters of different business data information specifically includes: The features in the business data information are used to construct horizontal and vertical grids for reorganizing the grid according to the numbering order; The difference between the number of levels of the same feature in different business data information is calculated to obtain the corresponding feature level difference, and the feature level difference is displayed in the corresponding grid. When the corresponding feature does not exist in the business data information, the raster corresponding to the recombined raster will display 0 and the corresponding feature level difference.

2. The business data difference analysis method based on big data according to claim 1, characterized in that, Also includes: Based on the business data information, obtain the time nodes corresponding to the business data; Based on the time nodes corresponding to the business data, the business data is sorted and numbered to obtain the business data number; The number of the business data is sent to a preset grid system for storage.

3. The business data difference analysis method based on big data according to claim 1, characterized in that, Also includes: Based on a pre-defined comment system, obtain the impact score of features in business data information on the business data. Based on the characteristics in the business data information, sort them in descending order of influence score to obtain the feature numbers in the business data information; The feature numbers in the business data information are sent to a preset grid system for storage.

4. The business data difference analysis method based on big data according to claim 1, characterized in that, The step of constructing a single raster based on features in the business data information specifically includes: Construct horizontal grids of individual rasters based on the features in the business data information according to their numerical order; The vertical grid of a single grid is constructed by using the level number of the corresponding feature value in the business data information.

5. The business data difference analysis method based on big data according to claim 1, characterized in that, Also includes: Based on the reconstructed raster, obtain the maximum feature level difference in the reconstructed raster; Determine whether the maximum feature level difference in the reconstructed grid is greater than a preset first level threshold; if so, trigger the first difference information. The first difference information is sent to a preset management terminal for display.

6. A business data difference analysis system based on big data, characterized in that, The system includes a memory and a processor. The memory stores a program for analyzing business data differences based on big data. When the processor executes the program for analyzing business data differences based on big data, it performs the following steps: Obtain business data information; Extract features and corresponding feature values ​​from business data; Based on the preset range of feature values ​​that the feature values ​​fall into, the level number of the corresponding feature value in the business data information is obtained; Construct individual grids based on the features in the business data information, and send the level numbers of the corresponding feature values ​​in the business data information to the individual grids and store them; The individual grids corresponding to different business data information are recombined to obtain recombined grids of different business data information, and the feature values ​​corresponding to different business data information are displayed in the corresponding recombined grids. The step of recombining individual rasters corresponding to different business data information to obtain recombined rasters of different business data information specifically includes: The features in the business data information are used to construct horizontal and vertical grids for reorganizing the grid according to the numbering order; The difference between the number of levels of the same feature in different business data information is calculated to obtain the corresponding feature level difference, and the feature level difference is displayed in the corresponding grid. When the corresponding feature does not exist in the business data information, the raster corresponding to the recombined raster will display 0 and the corresponding feature level difference.

7. A business data difference analysis system based on big data according to claim 6, characterized in that, Also includes: Based on the business data information, obtain the time nodes corresponding to the business data; Based on the time nodes corresponding to the business data, the business data is sorted and numbered to obtain the business data number; The number of the business data is sent to a preset grid system for storage.

8. A business data difference analysis system based on big data according to claim 6, characterized in that, Also includes: Based on a pre-defined comment system, obtain the impact score of features in business data information on the business data. Based on the characteristics in the business data information, sort them in descending order of influence score to obtain the feature numbers in the business data information; The feature numbers in the business data information are sent to a preset grid system for storage.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a business data difference analysis method program based on big data. When the business data difference analysis method program based on big data is executed by a processor, it implements the steps of the business data difference analysis method based on big data as described in any one of claims 1 to 5.

Images