Intelligent data modeling approach

Abstract

This invention discloses an intelligent data modeling method, comprising: dividing a field of a candidate range in a database into multiple subfields; using the node bytes obtained from the division in each subfield as first node bytes; matching the first node bytes of each subfield with the set data; when a match is successful, matching the successfully matched subfield and its adjacent subfields with the set data; when all matches fail, matching each subfield with the set data in parallel; obtaining modeling data from the fields of the candidate range based on the matching results; and performing modeling using the modeling data in the database. In the technical solution provided by this invention, by dividing the field of the candidate range in the database into multiple subfields and then matching each subfield to find the modeling data, the modeling data can be found quickly.

Description

Technical Field

[0001] The present invention relates to the technical field of data modeling, and in particular to an intelligent data modeling method. Background Art

[0002] When facing big data governance, there are cases where individual systems fail to reach a consensus in advance and disagree on data standards. Before modeling, data is extracted from the data warehouse mechanically from multiple sources and then aggregated and calculated for modeling. The problem is that multi-source data may be difficult to match and associate due to inconsistent field names. Summary of the invention

[0003] The present invention provides an intelligent data modeling method. Through a new data modeling algorithm, even when the field names are not unified, it can still be intelligently identified through the content of the data source to determine whether the two are the same content. For example, data d is named field A and field B in different databases. If it is directly associated through the fields, the association will fail. However, through the intelligent algorithm, it is judged that A and B are actually the same content, and then association modeling can be performed.

[0004] To achieve the above object, the present invention proposes an intelligent data modeling method, comprising the following steps:

[0005] Splitting the field of the to-be-selected range in the database into a plurality of subfields, and taking the node byte obtained by the splitting in each of the subfields as the first node byte;

[0006] Matching the first node byte of each of the subfields with the setting data;

[0007] When the match is successful, the subfield that is successfully matched and its adjacent subfields are matched with the setting data; when all matches are unsuccessful, each subfield is matched with the setting data in parallel;

[0008] According to the matching result, the modeling data in the field of the to-be-selected range is obtained;

[0009] Modeling is performed using the modeling data in the database.

[0010] Optionally, the step of dividing the field of the to-be-selected range in the database into a plurality of subfields, and taking the node byte obtained by the division in each of the subfields as the first node byte, further includes:

[0011] In the database, a preliminary screening is performed by setting the data information to obtain the fields within the selection range.

[0012] Optionally, in the step of “in a database, performing preliminary screening through partial information of the setting data to obtain fields in a to-be-selected range”, the partial information of the setting data includes attribute information of the setting data.

[0013] Optionally, the step of “when the match is successful, matching the successfully matched subfield and its adjacent subfields with the setting data, and when all matches are unsuccessful, matching each subfield with the setting data in parallel” includes:

[0014] When the match is successful, the subfield that has been matched successfully and the subfields adjacent to it are matched with the setting data respectively. When the match is successful, it means that the subfield or the subfield adjacent to it includes modeling data. When the match is unsuccessful, the subfield and the subfields adjacent to it are merged and matched with the setting data.

[0015] When all matches are unsuccessful, a parallel approach is adopted to match each of the subfields with the setting data respectively to find the subfield with the modeling data.

[0016] Optionally, the step of “segmenting the field of the to-be-selected range in the database into a plurality of subfields, and using the node bytes obtained by segmentation in each of the subfields as the first node bytes” includes:

[0017] Get the length of the field in the database to be selected;

[0018] According to the length of the field in the to-be-selected range, obtain the number of segmentations n;

[0019] Split a field in a range to be selected in a database into multiple subfields In the database, split a field in a range to be selected in the database into n subfields.

[0020] The intelligent data modeling method provided by the present invention comprises: dividing a field of a to-be-selected range in a database into a plurality of subfields, and using a node byte obtained by the division in each of the subfields as a first node byte; matching the first node byte of each of the subfields with the set data; when the match is successful, matching the successfully matched subfield and its adjacent subfields with the set data, and when all the matches are unsuccessful, matching each of the subfields with the set data in parallel; according to the matching result, obtaining the modeling data in the field of the to-be-selected range; modeling is performed using the modeling data in the database. In the technical solution provided by the present invention, by dividing the field of the to-be-selected range in the database into a plurality of subfields, and then matching each subfield to find the modeling data, the modeling data can be found quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0022] Figure 1 A flowchart diagram of an embodiment of the intelligent data modeling method provided by the present invention.

[0023] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0026] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0027] The present invention proposes an intelligent data modeling method. Figure 1An embodiment of the intelligent data modeling method provided by the present invention comprises the following steps:

[0028] Step S10, dividing the fields in the to-be-selected range in the database into a plurality of sub-fields, and taking the node bytes obtained by the division in each of the sub-fields as the first node bytes: the to-be-selected range may be all the fields in the database, or may be part of the fields;

[0029] Step S20: Match the first node byte of each of the subfields with setting data: the setting data is data required for modeling, and the setting data may be attribute data of the modeling data.

[0030] Step S30, when the match is successful, the subfield that is successfully matched and its adjacent subfields are matched with the set data; when all matches are unsuccessful, each subfield is matched with the set data in parallel: if the match is successful at the first node byte, it indicates that modeling data exists at the cutting point, that is, the modeling data exists in the subfield that is successfully matched and its adjacent subfields, or in the field where the subfield that is successfully matched and its adjacent subfields are merged, so that the modeling data can be found quickly; if the match is unsuccessful at the first node byte, it indicates that there is no modeling data at the cutting point, that is, the modeling data is in each subfield, so that each subfield is matched with the set data in parallel, and the modeling data can also be found quickly.

[0031] Step S40: acquiring modeling data in the fields of the to-be-selected range according to the matching result;

[0032] Step S50: Modeling is performed using the modeling data in the database.

[0033] The intelligent data modeling method provided by the present invention comprises: dividing the field of the selected range in the database into multiple subfields, taking the node byte obtained by the division in each subfield as the first node byte; matching the first node byte of each subfield with the setting data; when the match is successful, matching the successfully matched subfield and its adjacent subfield with the setting data, and when all the matches are unsuccessful, using a parallel method to match each subfield with the setting data respectively; according to the matching result, obtaining the modeling data in the field of the selected range; modeling through the modeling data in the database, in the technical solution provided by the present invention, by dividing the field of the selected range in the database into multiple subfields, and then matching each subfield to find the modeling data, so that the modeling data can be found quickly. That is, in the present invention, through the new data modeling algorithm, in the case of inconsistent field names, it can still be intelligently identified through the content of the data source to determine whether the two are the same content. For example, data d is named field A and field B in different databases respectively, and the association will fail if it is directly associated through the field. However, through intelligent algorithms, it can be judged that A and B are actually the same content, and then association modeling can be performed.

[0034] In one embodiment of the present invention, before step S10, the method further includes:

[0035] In the database, preliminary screening is performed by setting the data information to obtain the fields in the selection range. In this way, the data in the database is filtered through preliminary screening to reduce the amount of calculation.

[0036] Further, in the step of "in a database, performing preliminary screening through partial information of the setting data to obtain fields in a to-be-selected range", the partial information of the setting data includes attribute information of the setting data.

[0037] In one embodiment of the present invention, step S30 includes:

[0038] Step S31: When the match succeeds, the subfield that has been matched successfully and its adjacent subfields are matched with the setting data respectively. When the match succeeds, it means that the subfield or its adjacent subfield includes modeling data. When the match fails, the subfield and its adjacent subfields are merged and matched with the setting data.

[0039] Step S32: When all matches are unsuccessful, each subfield is matched with the setting data in parallel to find the subfield with the modeling data.

[0040] In one embodiment of the present invention, step S10 includes:

[0041] Get the length of the field in the database to be selected;

[0042] According to the length of the field in the to-be-selected range, obtain the number of segmentations n;

[0043] Split the field to be selected in the database into multiple subfields

[0044] In a database, a field in a range to be selected in the database is divided into n subfields.

[0045] That is, in this embodiment, the number of divisions is selected according to the length of the field in the selected range in the database, so as to ensure that the number and length of the divided sub-fields are reasonable, so as to ensure the accuracy of subsequent calculations.

[0046] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for intelligent data modeling, characterized in that: The following steps are involved: Splitting the field of the to-be-selected range in the database into a plurality of subfields, and taking the node byte obtained by the splitting in each of the subfields as the first node byte; Matching the first node byte of each of the subfields with setting data, the setting data including attribute data of modeling data; When the match is successful, the subfield that is successfully matched and its adjacent subfields are matched with the setting data; when all matches are unsuccessful, each subfield is matched with the setting data in parallel; According to the matching result, the modeling data in the field of the to-be-selected range is obtained; Performing modeling using the modeling data in the database; The step of "when the match is successful, matching the successfully matched subfield and its adjacent subfields with the setting data, and when all matches are unsuccessful, matching each subfield with the setting data in parallel" includes: When the match is successful, the subfield that has been matched successfully and the subfields adjacent to it are matched with the setting data respectively. When the match is successful, it means that the subfield or the subfield adjacent to it includes modeling data. When the match is unsuccessful, the subfield and the subfields adjacent to it are merged and matched with the setting data. When all matches are unsuccessful, a parallel approach is adopted to match each of the subfields with the setting data respectively to find the subfield with the modeling data.

2. The intelligent data modeling method according to claim 1, characterized in that: The step of dividing the field of the to-be-selected range in the database into a plurality of subfields and taking the node bytes obtained by the division in each of the subfields as the first node bytes also includes: In the database, a preliminary screening is performed by setting the data information to obtain the fields within the selection range.

3. The intelligent data modeling method according to claim 2, characterized in that: In the step of "in a database, performing preliminary screening through partial information of the setting data to obtain fields in a to-be-selected range", the partial information of the setting data includes attribute information of the setting data.

4. The intelligent data modeling method according to claim 1, characterized in that: The step of "segmenting a field in a to-be-selected range in a database into a plurality of subfields, and using a node byte obtained by segmentation in each of the subfields as a first node byte" includes: Get the length of the field in the database to be selected; According to the length of the field in the to-be-selected range, obtain the number of segmentations n; In a database, a field in a range to be selected in the database is divided into n subfields.

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