CN118228020A - Efficient data identification method in big data environment - Google Patents
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Abstract
The invention discloses a high-efficiency data identification method in a big data environment, which relates to the technical field of high-efficiency data identification and solves the problem that the identification efficiency is lower because the related data is not identified in a unified identification mode.
Description
Technical Field
The invention relates to the technical field of efficient data identification, in particular to a method for efficient data identification in a big data environment.
Background
The concept of big data plays an increasingly important role in modern society, and the amount of data involved is so large that it is difficult for conventional data processing applications to process efficiently in a short time. Big data is typically characterized as 5V, namely Volume, high speed, variety, value, and Veracity (authenticity).
The application with the publication number of CN108415355A discloses a high-efficiency identification system of big data, which comprises a data acquisition module, a data transmission module and a cloud computing identification platform; the data acquisition module acquires real-time data from the operation equipment or directly reads the real-time data from the target data system; the data transmission module sends the real-time data to the cloud computing identification platform; and the cloud computing identification platform receives the real-time data, performs data screening, conversion, calculation and comparison to obtain data containing comparison results, and sends the data containing the comparison results to operation equipment so that the operation equipment executes corresponding control operation according to the data containing the comparison results. The high-efficiency identification system for the big data can identify the running equipment by utilizing the big data cloud platform.
In the process of identifying the high-efficiency data in the big data environment, the related data related to the big data is generally identified based on a specific identification mark to determine the high-efficiency data, but the original identification mode is a one-to-one identification mode, the identification process is longer, the identification rate is slower, the related data is not identified in a unified identification mode to ensure the identification rate, and when the characteristic items are compared later, the comparison mode is also a one-to-one comparison mode, the comparison efficiency is too slow, and the corresponding comparison effect cannot be ensured.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a high-efficiency data identification method in a big data environment, which solves the problem that the identification efficiency is lower because the related data is not identified in a unified identification mode.
In order to achieve the above purpose, the invention is realized by the following technical scheme: a high-efficiency data identification method in a big data environment comprises the following steps:
S1), identifying high-efficiency data existing in big data based on preset related data identification, and determining optimal identification logic based on a specific identification process, wherein the specific mode is as follows:
S11, marking preset relevant data identifiers as Ti, wherein i=1, 2, … … and n, i represents different relevant data identifiers, n represents the total number of relevant data identifiers, preferentially adopting a single group of relevant data identifiers Ti to identify relevant data in big data one by one, determining the identification rate Vi of the single group of relevant data identifiers Ti, and marking relevant data with all relevant data identifiers as high-efficiency data;
S12, selecting a relevant data identifier corresponding to the minimum value of Vi from recognition rates Vi of different relevant data identifiers Ti, calibrating the relevant data identifier as a main identifier, randomly selecting a group of Ti from the rest relevant data identifiers to be combined with the main identifier to serve as a combined recognition item, simultaneously recognizing the same group of relevant data by the combined recognition item, determining the recognition rate VS of the combined recognition item based on the corresponding recognition process, determining Vi generated by two groups of data features in the combined recognition item, summing the two groups of Vi, determining a combined reference HB, calibrating the combined recognition item as an error item if HB is more than 2X VS, calibrating the combined recognition item as a reference item if HB is less than or equal to 2X VS, and calibrating the reference item as a logic item based on VS generated by different reference items if the reference item has a plurality of groups;
S13, randomly selecting a group of Ti from the rest related data identifications to be combined with a logic item to serve as a combined identification item, simultaneously identifying the same group of related data by the combined identification item, determining the identification rate XS of the combined identification item based on the corresponding identification process, determining Vi generated by three groups of data features in the combined identification item, summing the three groups of Vi, determining a combined parameter BB, if BB is greater than 3 xVS, calibrating the combined identification item as an error item, if HB is less than or equal to 2 xVS, calibrating the combined identification item as a reference item, and if the reference item has a plurality of groups, selecting a minimum reference item based on VS generated by different reference items, and calibrating the minimum reference item as the logic item;
S14, sequentially confirming whether the related data identifiers are added to the logic item subsequently or not, extracting the logic item in the combination identification item if the finally confirmed logic item belongs to the error item, processing the residual related data identifiers in the same mode of steps S12-S13, and determining other logic items;
s15, carrying out one-to-one identification on subsequent related data based on the determined logic item and related data identifiers which do not belong to the logic item, and determining high-efficiency data;
S2), constructing a group of standard polygons based on related standard processing values preset by the corresponding high-efficiency data processor, wherein the center point dots of the standard polygons are points where the corresponding standard processing values are located, and the specific mode is as follows:
s21, determining the number of polygons corresponding to the same number based on the number of the relevant standard processing values, wherein the relevant standard processing values are preset values;
S22, based on the connection line between the round dot and the corresponding corner point, the standard line is used as an evaluation standard of the corresponding relevant standard processing value, and the numerical value of the unit length inside each standard line is inconsistent because each group of relevant standard processing values are inconsistent, and the numerical value point of the standard line is positioned on the extension line of the standard line aiming at the relevant numerical value exceeding the relevant standard processing value;
S3) sequentially confirming the correlation values with the same standard from the high-efficiency data, locking the correlation points in the standard line inside the standard polygon according to the sequentially confirmed correlation values, interconnecting the correlation points to generate a correlation polygon belonging to the high-efficiency data, and determining the superposition value based on the superposition area of the correlation polygon and the standard polygon; the specific method is as follows:
S31, confirming a correlation value corresponding to the correlation standard from the high-efficiency data according to the correlation standard corresponding to the correlation standard processing value, determining the point of the correlation value in a standard line corresponding to the correlation standard, and calibrating the point as a correlation point;
S32, connecting the relevant points of the adjacent standard lines to generate relevant polygons constructed by a plurality of groups of relevant points, determining the overlapping areas of the relevant polygons and the standard polygons, then determining the area parameters of the overlapping areas, and calibrating the area parameters as the overlapping values of the high-efficiency data.
Preferably, the method further comprises the following steps:
s4) according to different coincidence values generated by different high-efficiency data, according to a mode that the numerical value is from large to small, if the coincidence values of the two groups of high-efficiency data are the same, randomly sequencing, sequencing the different high-efficiency data to generate a sequencing table, and allowing an external person to check or directly sequentially process the high-efficiency data in the sequencing table through a corresponding high-efficiency data processor.
The invention provides a high-efficiency data identification method in a big data environment. Compared with the prior art, the method has the following beneficial effects:
According to the invention, the related identification items are uniformly identified, the original identification mode is changed, whether the uniform identification process is better than the original single identification process is determined based on the related identification rate generated in the uniform identification process, and the uniform identification mode is adopted, and the identification rate can be faster than the single identification mode, so that the related identification rate of high-efficiency data can be ensured, and a better identification effect is achieved;
Based on the determined relevant characteristic values, corresponding polygons are constructed, and based on the superposition conditions of the constructed relevant polygons, superposition areas and superposition areas are determined, so that a plurality of groups of high-efficiency data are ordered.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the combination of standard polygons or related polygons according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the application provides a high-efficiency data identification method in a big data environment, which comprises the following steps:
S1) identifying high-efficiency data existing in big data based on preset related data identifiers, determining optimal identification logic based on a specific identification process, accelerating identification efficiency, shortening identification time of the high-efficiency data, and regarding different high-efficiency data, having different related data identifiers, and respectively being drawn up by operators according to experience, so as to ensure effective identification of the high-efficiency data, for example: if the corresponding data contains relevant request instruction data or appointed logic data, the identified data is marked as high-efficiency data as long as the corresponding data contains relevant characteristic data;
Wherein the specific sub-steps of determining the optimal recognition logic comprise:
S11, marking preset related data identifiers as Ti, wherein i=1, 2, … … and n, i represents different related data identifiers, n represents the total number of related data identifiers, n is generally 5, a single group of related data identifiers Ti is preferentially adopted to identify related data in big data one by one, the identification rate Vi of the single group of related data identifiers Ti is determined, the related data with all related data identifiers are marked as high-efficiency data, the first stage can be understood as a first stage, confirmation is carried out from the identified first group of high-efficiency data, after the first group of high-efficiency data is identified, the identification rate of the related data identifiers can be determined because all preset related data identifiers exist in the high-efficiency data, for example, when an A feature is identified, an identification process exists, the identification rate belonging to the A feature exists, each feature has a designated feature parameter, other data also has similar feature parameters, a plurality of identification processes are needed to be executed for carrying out accurate identification, for example, the A feature exists in a designated data scene environment, the A feature A1 and the A feature A2 can be identified accurately, and the A feature can be identified accurately;
S12, selecting a relevant data identifier corresponding to the minimum value of Vi from recognition rates Vi of different relevant data identifiers Ti, calibrating the relevant data identifier as a main identifier, randomly selecting a group of Ti from the rest relevant data identifiers to be combined with the main identifier to serve as a combined recognition item, simultaneously recognizing the same group of relevant data by the combined recognition item (namely, synchronously recognizing two groups of features instead of a single recognition mode), determining the recognition rate VS of the combined recognition item based on the corresponding recognition process, determining Vi generated by the two groups of data features in the combined recognition item, summing the two groups of Vi, determining a combined reference HB, calibrating the combined recognition item as an error item if HB is larger than 2X VS, calibrating the combined recognition item as a reference item if HB is smaller than or equal to 2X VS, and calibrating the combined recognition item as a logic item based on VS generated by different reference items if the reference item exists in a plurality of groups;
S13, randomly selecting a group of Ti from the rest related data identifications to be combined with a logic item to serve as a combined identification item, simultaneously identifying the same group of related data by the combined identification item, determining the identification rate XS of the combined identification item based on the corresponding identification process, determining Vi generated by three groups of data features in the combined identification item, summing the three groups of Vi, determining a combined parameter BB, if BB is greater than 3 xVS, calibrating the combined identification item as an error item, if HB is less than or equal to 2 xVS, calibrating the combined identification item as a reference item, if the reference item has a plurality of groups, selecting a minimum reference item based on VS generated by different reference items, and calibrating the minimum reference item as the logic item, wherein the example is as follows: if five groups of identifiers exist, namely A, B, C, D and E respectively, a group of main identifiers A is selected from the five groups of identifiers, then A-B, A-C, A-D, A-E is taken as a combined identification item, different related data are uniformly identified, the identification rate VS of A-B is determined, if the VS meets the evaluation condition, A-B is taken as a group of logic items, and the identification rate is faster than that of a single identification mode of A and B when A-B is uniformly identified, so that the logic items can be taken as a group of identification items for uniform identification, the identification rate is reduced, then A-B-C, A-B-D or A-B-E is taken as a combined identification item for uniform analysis, and the related logic items can be confirmed one by sequential processing, so that the corresponding identification rate is ensured;
S14, sequentially confirming whether the related data identifiers are added to the logic item subsequently or not, extracting the logic item in the combination identification item if the finally confirmed logic item belongs to the error item, processing the residual related data identifiers in the same mode of steps S12-S13, and determining other logic items;
S15, carrying out one-to-one identification on subsequent related data based on the determined logic item and related data identifiers which do not belong to the logic item, and determining high-efficiency data, for example: after each group of logic items are subjected to single recognition, relevant data which do not belong to the logic items are recognized, for example, two groups of logic items A and B exist, a group of relevant data identifiers C which do not belong to the logic items exist at the same time, and for one group of relevant data, recognition is performed by the aid of the A and the B preferentially, and finally recognition is performed by the aid of the C, so that compared with an original one-to-one recognition mode, the efficiency is faster, and the specific time of recognition is shorter;
The original identification mode can identify the high-efficiency data from the related data, but the identification mode is one by one, and the next identification can be identified after the identification of the previous identification is finished, so that the identification rate is slower, and if the uniform identification mode can be adopted and the identification rate can be faster than the single identification mode, the related identification rate of the high-efficiency data can be ensured, and the better identification effect can be achieved.
Example two
The first embodiment mainly aims at determining the recognition rate, and the recognition rate is improved by determining the related logic items, and the first embodiment mainly aims at the recognized high-efficiency data and determines the processing sequence of the high-efficiency data based on the related characteristic values of the high-efficiency data;
s2), constructing a group of standard polygons based on related standard processing values preset by the corresponding high-efficiency data processor, wherein the center point dots of the standard polygons are points where the corresponding standard processing values are located, and the specific construction mode is as follows:
s21, combining with FIG. 2, based on the number of the relevant standard processing values, wherein the relevant standard processing values are preset values, the specific values are determined by an operator based on the efficient data processor, the corresponding polygons with the same number are determined, and if six groups of relevant standard processing values exist, a group of hexagons are constructed;
S22, based on the connection line between the round dot and the corresponding corner point, the standard line is used as an evaluation standard of the corresponding relevant standard processing value, and the numerical value of the unit length inside each standard line is inconsistent because each group of relevant standard processing values are inconsistent, and for the relevant numerical value exceeding the relevant standard processing value, the numerical value point is positioned on the extension line of the standard line, for example: the two groups of standard lines have the same length, but the standard values corresponding to the endpoints of the two groups of standard lines are 40 and 50 respectively, 10 sections exist respectively, the value of the standard section with the standard value of 40 is expressed as 4, and the value of the standard section with the standard value of 50 is expressed as 5;
Specifically, the preset relevant standard processing value is extracted from the corresponding high-efficiency data, and certain characteristics of the high-efficiency data have the best condition, and the relevant characteristic value of the high-efficiency data can be determined by data analysis, for example: the data discrete degree of the high-efficiency data is a characteristic value, the data discrete degree adopts a variance processing mode to evaluate the discrete degree of the corresponding high-efficiency data, when the discrete degree is in a certain numerical value, the numerical value is optimal, the processing evaluation is easiest, other related characteristic values exist, such as the number of logic processing processes existing in the high-efficiency data, the central value of the data or the response time requirement of the data are all data characteristics, and the characteristic values generated by different high-efficiency data are different;
S3), sequentially confirming correlation values with the same standard from the high-efficiency data, locking correlation points in standard lines in the standard polygons according to the sequentially confirmed correlation values, interconnecting the correlation points to generate correlation polygons belonging to the high-efficiency data, and determining superposition values based on superposition areas of the correlation polygons and the standard polygons, wherein the specific mode for determining the superposition values is as follows:
S31, confirming a correlation value corresponding to the correlation standard from the high-efficiency data according to the correlation standard corresponding to the correlation standard processing value, determining the point of the correlation value in a standard line corresponding to the correlation standard, and calibrating the point as a correlation point;
S32, connecting the relevant points of adjacent standard lines to generate relevant polygons constructed by a plurality of groups of relevant points, determining the overlapping areas of the relevant polygons and the standard polygons, then determining the area parameters of the overlapping areas, and calibrating the area parameters as the overlapping values of the high-efficiency data;
S4), according to different coincidence values generated by different high-efficiency data, according to a mode that the numerical value is from large to small, if the coincidence values of the two groups of high-efficiency data are the same, randomly sequencing the different high-efficiency data to generate a sequencing table, and allowing an external person to check or directly sequentially process the high-efficiency data in the sequencing table through a corresponding high-efficiency data processor;
Specifically, with reference to fig. 2, according to different relevant points corresponding to different relevant standards, corresponding relevant polygons can be confirmed, according to the combined area of the relevant polygons and the standard polygons, the overlapping area is determined, and the overlapping area is used as an overlapping value for evaluation;
The original comparison method is as follows: based on the determined relevant standard value and the relevant value generated by the high-efficiency data, only a comparison result of a single value can be confirmed by adopting a numerical value one-comparison mode, and the overall comparison result cannot be determined.
Example III
This embodiment includes all of the implementations of the two sets of embodiments described above.
Some of the data in the above formulas are numerical calculated by removing their dimensionality, and the contents not described in detail in the present specification are all well known in the prior art.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. The high-efficiency data identification method in the big data environment is characterized by comprising the following steps:
s1, identifying high-efficiency data existing in big data based on a preset related data identifier, and determining optimal identification logic based on a specific identification process;
S2), constructing a group of standard polygons based on related standard processing values preset by the corresponding high-efficiency data processor, wherein the center point dots of the standard polygons are points where the corresponding standard processing values are located;
S3), sequentially confirming correlation values with the same standard from the high-efficiency data, locking correlation points in standard lines in the standard polygons according to the sequentially confirmed correlation values, interconnecting the correlation points to generate correlation polygons belonging to the high-efficiency data, and determining the superposition values based on superposition areas of the correlation polygons and the standard polygons.
2. The method for efficient data identification in big data environment according to claim 1, wherein in step S1, the specific manner of determining the optimal identification logic is:
S11, marking preset relevant data identifiers as Ti, wherein i=1, 2, … … and n, i represents different relevant data identifiers, n represents the total number of relevant data identifiers, preferentially adopting a single group of relevant data identifiers Ti to identify relevant data in big data one by one, determining the identification rate Vi of the single group of relevant data identifiers Ti, and marking relevant data with all relevant data identifiers as high-efficiency data;
S12, selecting a relevant data identifier corresponding to the minimum value of Vi from recognition rates Vi of different relevant data identifiers Ti, calibrating the relevant data identifier as a main identifier, randomly selecting a group of Ti from the rest relevant data identifiers to be combined with the main identifier to serve as a combined recognition item, simultaneously recognizing the same group of relevant data by the combined recognition item, determining the recognition rate VS of the combined recognition item based on the corresponding recognition process, determining Vi generated by two groups of data features in the combined recognition item, summing the two groups of Vi, determining a combined reference HB, calibrating the combined recognition item as an error item if HB is more than 2X VS, calibrating the combined recognition item as a reference item if HB is less than or equal to 2X VS, and calibrating the reference item as a logic item based on VS generated by different reference items if the reference item has a plurality of groups;
S13, randomly selecting a group of Ti from the rest related data identifications to be combined with a logic item to serve as a combined identification item, simultaneously identifying the same group of related data by the combined identification item, determining the identification rate XS of the combined identification item based on the corresponding identification process, determining Vi generated by three groups of data features in the combined identification item, summing the three groups of Vi, determining a combined parameter BB, if BB is greater than 3 xVS, calibrating the combined identification item as an error item, if HB is less than or equal to 2 xVS, calibrating the combined identification item as a reference item, and if the reference item has a plurality of groups, selecting a minimum reference item based on VS generated by different reference items, and calibrating the minimum reference item as the logic item;
s14, sequentially confirming whether the logic item is added with related data identifiers or not;
And S15, carrying out one-to-one identification on subsequent related data based on the determined logic item and related data identifiers which do not belong to the logic item, and determining high-efficiency data.
3. The method for efficient data identification in a big data environment according to claim 2, wherein said step S14 further comprises:
if the finally confirmed logical item belongs to the error item, extracting the logical item in the combined identification item, processing the residual related data identification in the same way as the steps S12-S13, and determining other logical items.
4. The method for efficiently identifying data in a big data environment according to claim 1, wherein in the step S2, the specific manner of constructing the corresponding standard polygon is as follows:
s21, determining the number of polygons corresponding to the same number based on the number of the relevant standard processing values, wherein the relevant standard processing values are preset values;
s22, based on the connection line between the round dot and the corresponding corner point, the standard line is used as an evaluation standard of the corresponding relevant standard processing value, and the numerical value of the unit length inside each standard line is inconsistent because each group of relevant standard processing values are inconsistent, and the numerical value point of the standard line is positioned on the extension line of the standard line aiming at the relevant numerical value exceeding the relevant standard processing value.
5. The method for efficient data identification in big data environment according to claim 4, wherein in the step S3, the specific manner of determining the coincidence value is:
S31, confirming a correlation value corresponding to the correlation standard from the high-efficiency data according to the correlation standard corresponding to the correlation standard processing value, determining the point of the correlation value in a standard line corresponding to the correlation standard, and calibrating the point as a correlation point;
S32, connecting the relevant points of the adjacent standard lines to generate relevant polygons constructed by a plurality of groups of relevant points, determining the overlapping areas of the relevant polygons and the standard polygons, then determining the area parameters of the overlapping areas, and calibrating the area parameters as the overlapping values of the high-efficiency data.
6. The method for efficient data identification in a big data environment of claim 5, further comprising the steps of:
s4) according to different coincidence values generated by different high-efficiency data, according to a mode that the numerical value is from large to small, if the coincidence values of the two groups of high-efficiency data are the same, randomly sequencing, sequencing the different high-efficiency data to generate a sequencing table, and allowing an external person to check or directly sequentially process the high-efficiency data in the sequencing table through a corresponding high-efficiency data processor.
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| CN102884539A (en) * | 2010-03-12 | 2013-01-16 | 日升研发控股有限责任公司 | System and method for product identification |
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