CN118246775A - Coal mine index management configuration method and system - Google Patents
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Abstract
The invention relates to the technical field of data index management, and particularly discloses a coal mine index management configuration method and system, wherein the method comprises the following steps: acquiring a coal mine index association detailed map based on a first association relationship and a second association relationship among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources; carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination; determining the association configuration basis of all coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all quantized relation index combinations; acquiring a coal mine index management configuration result based on the association configuration basis and the verification quantitative relation of all the coal mine index data tables; to provide a more intelligent, automated method of coal mine index management and association configuration.
Description
Technical Field
The invention relates to the technical field of data index management, in particular to a coal mine index management configuration method and system.
Background
Currently, coal mine operation is a complex industry involving a number of key aspects of production, safety, environmental, equipment management, etc. In order to effectively manage and monitor coal mine operation, a number of key performance indicators must be tracked and analyzed. These metrics are typically from different data sources, including sensors, databases, monitoring systems, etc., resulting in data dispersion and inconsistency.
Existing data management and analysis implementations typically employ conventional data correlation and data cleansing methods, including:
1. Data analysts manually perform data cleansing, field normalization, and correlation. This involves a lot of manual work, is error prone and is very time consuming.
2. Some may develop their own custom scripts for data correlation and cleaning. This requires high user programming skills and often requires constant adjustments to accommodate new data sources and fields.
In summary, the existing implementation scheme for processing the attribute field association configuration of the coal mine index has the problems of high labor cost, low adaptability, high programming requirement and the like. These problems need to be addressed by a more intelligent, automated approach.
Therefore, the invention provides a coal mine index management configuration method and a system.
Disclosure of Invention
The invention provides a coal mine index management configuration method and a system, which are used for constructing a map of association relations among standardized coal mine indexes capable of representing the association relations from a macroscopic angle through analyzing the association relations before all the standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, and ensuring the accuracy of association configuration of the coal mine indexes from different data sources through carrying out coefficient quantization and distinguishing association configuration basis and verifying the quantization relations on the complete association relations in a quantized relation index combination in a coal mine index association detailed map.
The invention provides a coal mine index management configuration method, which comprises the following steps:
S1: constructing a coal mine index association map based on a first association relationship among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources;
s2: based on the historical configuration example and the artificial intelligence algorithm, analyzing a second association relation among all standardized coal mine indexes;
S3: locally refining the coal mine index association map based on the second association relation among all the standardized coal mine indexes to obtain a coal mine index association detailed map;
s4: carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination;
S5: determining the association configuration basis of all coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all quantized relation index combinations;
S6: and obtaining a coal mine index management configuration result based on the association configuration basis and the verification quantitative relation of all the coal mine index data tables.
Preferably, the coal mine index management configuration method comprises the following steps of S1: constructing a coal mine index association graph based on a first association relationship between all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, comprising:
acquiring a coal mine index data table from a plurality of data sources, and determining attribute information of each coal mine index in all the coal mine index data tables;
Cleaning attribute information of each coal mine index to obtain effective attribute information of each coal mine index;
Based on the effective attribute information of each coal mine index in all coal mine index data tables, carrying out standardization processing on each coal mine index in all coal mine index data tables to obtain all standardized coal mine indexes and standardized attribute information of each standardized coal mine index;
Performing primary association on all the standardized coal mine indexes based on the standardized attribute information of all the standardized coal mine indexes to obtain a first association relation among all the standardized coal mine indexes;
And building a coal mine index association map based on the first association relation among all the standardized coal mine indexes.
Preferably, the coal mine index management configuration method performs primary association on all standardized coal mine indexes based on standardized attribute information of all standardized coal mine indexes to obtain a first association relationship between all standardized coal mine indexes, including:
Constructing an association relationship between at least two standardized coal mine indexes with completely consistent standardized attribute information in all the standardized coal mine indexes, and taking the association relationship as a first sub-association relationship;
Summarizing all the standardized coal mine indexes which are completely consistent with all the remaining sub-attribute information except the field meaning in the standardized attribute information in a stepwise manner into a similar index set to obtain a plurality of similar index sets;
determining all second sub-association relationships based on the quantization relationships between at least two similar index sets with the quantization relationships;
And taking all the first sub-association relations and the second sub-association relations as first association relations among all the standardized coal mine indexes.
Preferably, the coal mine index management configuration method determines all second sub-association relations based on the quantization relations between at least two similar index sets with the quantization relations, and the method comprises the following steps:
inputting all similar index sets into a quantization relation identification model to obtain quantization relations between at least two similar index sets with quantization relations;
and building all incidence relations which belong to at least two similar index sets with quantitative relations and meet at least two standardized coal mine indexes corresponding to the quantitative relations, and taking the incidence relations as all second sub-incidence relations.
Preferably, the coal mine index management configuration method comprises the following steps of S2: based on the historical configuration example and the artificial intelligence algorithm, analyzing a second association relation among all standardized coal mine indexes, wherein the second association relation comprises the following steps:
Building an association relation recognition model based on a historical configuration example and an artificial intelligence algorithm, wherein the historical configuration example comprises a large number of coal mine indexes different from all the current standardized coal mine indexes and second association relations existing among all the coal mine indexes;
and inputting all the standardized coal mine indexes into the association relation identification model to obtain a second association relation among all the standardized coal mine indexes.
Preferably, the coal mine index management configuration method comprises the following steps of S4: carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination, wherein the method comprises the following steps:
Determining a standardized coal mine index combination with a quantization relationship in a coal mine index association detailed map as the quantization relationship index combination;
Judging whether the association relation between adjacent standardized coal mine indexes in the coal mine index association detailed map is the same coal mine index or not, if so, setting the association coefficient between the corresponding adjacent standardized coal mine indexes to be 1;
Otherwise, based on the calculation level in the quantization calculation process in the association relation between the corresponding adjacent standardized coal mine indexes, searching a preset quantization calculation level-association coefficient list to obtain the association coefficient between the corresponding adjacent standardized coal mine indexes;
And carrying out coefficient quantization on the complete association relation in the quantized relation index combination based on the association coefficient between each group of adjacent standardized coal mine indexes to obtain the complete association coefficient in the quantized relation index combination.
Preferably, the coal mine index management configuration method performs coefficient quantization on the complete association relation in the quantized relation index combination based on the association coefficient between each group of adjacent standardized coal mine indexes to obtain the complete association coefficient in the quantized relation index combination, and includes:
determining indirect association times in complete association in the quantized relationship index combination;
and taking the sum of the association coefficients among all groups of adjacent standardized coal mine indexes contained in the complete association relation in the quantized relation index combination and the quotient of the corresponding indirect association times as the complete association coefficient in the quantized relation index combination.
Preferably, the coal mine index management configuration method comprises the following steps of S5: based on the complete association coefficients of the complete association relations in all the quantized relation index combinations, determining the association configuration basis of all the coal mine index data tables and verifying the quantized relation, wherein the method comprises the following steps:
Taking all the complete association relations with the complete relation coefficients larger than the coefficient threshold value as the association configuration basis of all the coal mine index data tables;
and taking the complete association relation with all complete relation coefficients not larger than the coefficient threshold value as the verification quantization relation of all coal mine index data tables.
Preferably, the coal mine index management configuration method comprises the following steps of S6: based on the association configuration basis and verification quantitative relation of all coal mine index data tables, acquiring a coal mine index management configuration result, wherein the method comprises the following steps:
S601: performing association correction on all standardized coal mine indexes based on association configuration basis of all coal mine index data tables to obtain all corrected coal mine indexes;
S602: judging whether all corrected coal mine indexes accord with the verification quantitative relation of all coal mine index data tables, if so, carrying out association configuration on all corrected coal mine indexes based on association configuration basis to obtain a coal mine index management configuration result, otherwise, merging the complete association relation which is not consistent with all corrected coal mine indexes in the verification quantitative relation of all coal mine index data tables with the association configuration basis of all current coal mine index data tables to obtain the latest association configuration basis, and removing all complete association relations which are not consistent with all corrected coal mine indexes in the verification quantitative relation of all current coal mine index data tables to obtain the latest verification quantitative relation;
S603: and (3) circularly executing steps S601 to S602 based on the latest association configuration basis and the latest verification quantitative relation to obtain all latest corrected coal mine indexes until all the latest corrected coal mine indexes accord with the latest verification quantitative relation, and carrying out association configuration on all the latest corrected coal mine indexes based on the latest association configuration basis to obtain a coal mine index management configuration result.
The invention provides a coal mine index management configuration system for executing the coal mine index management configuration method in any one of embodiments 1 to 9, comprising:
The map generation module is used for building a coal mine index association map based on a first association relation among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources;
The relation analysis module is used for analyzing a second association relation among all standardized coal mine indexes based on the historical configuration example and the artificial intelligence algorithm;
the map refining module is used for locally refining the coal mine index association map based on the second association relation among all the standardized coal mine indexes to obtain a coal mine index association detailed map;
the coefficient quantization module is used for carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination;
The relation classification module is used for determining the association configuration basis of all the coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all the quantized relation index combinations;
and the configuration correction module is used for obtaining a coal mine index management configuration result based on the association configuration basis and the verification quantification relation of all the coal mine index data tables.
Compared with the prior art, the invention has the following beneficial effects: through analyzing the incidence relation before all the standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, a map of the incidence relation among the standardized coal mine indexes which can represent the incidence relation from a macroscopic angle is built, and through carrying out coefficient quantization on the complete incidence relation in the quantized relation index combination in the coal mine index incidence detailed map and distinguishing the incidence configuration basis and verifying the quantization relation, the accuracy of the incidence configuration of the coal mine indexes from different data sources is ensured.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objects and other advantages of the application may be realized and obtained by means of the instrumentalities particularly pointed out in the specification.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a coal mine index management configuration method in an embodiment of the invention;
FIG. 2 is a schematic diagram of a recognition result of recognizing a standardized coal mine index with a first association relationship based on a code-set association rule in an embodiment of the present invention;
FIG. 3 is a flowchart showing steps executed in step S6 according to an embodiment of the present invention;
Fig. 4 is a schematic diagram of a coal mine index management configuration system according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
The invention provides a coal mine index management configuration method, referring to fig. 1, comprising the following steps:
S1: constructing a coal mine index association map based on a first association relationship among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources;
s2: based on the historical configuration example and the artificial intelligence algorithm, analyzing a second association relation among all standardized coal mine indexes;
S3: locally refining the coal mine index association map based on the second association relation among all the standardized coal mine indexes to obtain a coal mine index association detailed map;
s4: carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination;
S5: determining the association configuration basis of all coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all quantized relation index combinations;
S6: and obtaining a coal mine index management configuration result based on the association configuration basis and the verification quantitative relation of all the coal mine index data tables.
In this embodiment, the data source is a database or system or file or web service, or the like.
In this embodiment, the coal mine index data table is a table containing coal mine index data.
In this embodiment, the artificial intelligence algorithm is a neural network training algorithm.
In this embodiment, the second association is a quantized relationship between different standardized coal mine indexes, for example: the difference between the selling price and the cost of the unit coal yield is the profit of the unit coal yield, and then the three indexes are as follows: the following quantitative relationship exists among selling price, cost and profit of unit coal yield:
Selling price per unit coal yield-cost = profit;
the quantitative relation is a calculation relation among different standardized coal mine index values.
In the embodiment, based on the second association relation among all standardized coal mine indexes, the coal mine index association map is locally refined to obtain a coal mine index association detailed map, which is:
refining the first association relation contained in the coal mine index association map based on the second association relation;
For example, the first association relationship between the normalized coal mine index a and the normalized coal mine index B included in the coal mine index association map is assumed to be: standardized coal mine index a = standardized coal mine index b+x (where x is a certain value);
And a second association relationship exists: when the standardized coal mine index a=the standardized coal mine index b+the standardized coal mine index c+y (wherein y is a certain value), replacing the standardized coal mine index a=the standardized coal mine index b+x in the coal mine index association map by using the standardized coal mine index a=the standardized coal mine index b+c+y;
Namely, the meaning of local refinement is: and replacing the first association relation between the two corresponding standardized coal mine indexes in the coal mine index association diagram by using a second association relation which is more in standardization and participates in the coal mine indexes, so as to obtain the coal mine index association detailed diagram.
In this embodiment, the coal mine index association detailed map is a picture including all the first association relationships and the second association relationships between all the standardized coal mine indexes.
In this embodiment, the complete association represents a quantized relationship through at least one other standardized coal mine indicator when one of the corresponding two standardized coal mine indicators is calculated based on the other standardized coal mine indicator.
In this embodiment, the complete correlation coefficient is a coefficient representing the degree of correlation between the corresponding two normalized coal mine indicators.
In this embodiment, the association configuration basis of all the coal mine index data tables is an association relationship according to which all the standardized coal mine indexes in all the coal mine index data tables are associated.
In this embodiment, the verification quantitative relationship of all the coal mine index data tables is an association relationship according to which verification (verification of rationality and accuracy of the foregoing association configuration step) is performed on all the corrected coal mine indexes obtained after all the standardized coal mine indexes in all the coal mine index data tables are associated according to the corresponding association configuration basis.
The beneficial effects of the technology are as follows: through analyzing the incidence relation before all the standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, a map of the incidence relation among the standardized coal mine indexes which can represent the incidence relation from a macroscopic angle is built, and through carrying out coefficient quantization on the complete incidence relation in the quantized relation index combination in the coal mine index incidence detailed map and distinguishing the incidence configuration basis and verifying the quantization relation, the accuracy of the incidence configuration of the coal mine indexes from different data sources is ensured.
Example 2:
based on the embodiment 1, the coal mine index management configuration method, S1: constructing a coal mine index association graph based on a first association relationship between all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, comprising:
acquiring a coal mine index data table from a plurality of data sources, and determining attribute information of each coal mine index in all the coal mine index data tables;
Cleaning attribute information of each coal mine index to obtain effective attribute information of each coal mine index;
Based on the effective attribute information of each coal mine index in all coal mine index data tables, carrying out standardization processing on each coal mine index in all coal mine index data tables to obtain all standardized coal mine indexes and standardized attribute information of each standardized coal mine index;
Performing primary association on all the standardized coal mine indexes based on the standardized attribute information of all the standardized coal mine indexes to obtain a first association relation among all the standardized coal mine indexes;
And building a coal mine index association map based on the first association relation among all the standardized coal mine indexes.
In this embodiment, the coal mine index is index data related to all links of coal mine production, circulation, sales, management, etc., for example: the total produced coal amount, total sales coal amount and the like of the A coal mine in 2023 in each month.
In this embodiment, the attribute information of the coal mine index includes: the field meaning of the coal mine index, the data type, the numerical range, the numerical unit, the data format and the like.
In this embodiment, cleaning attribute information of each coal mine index includes:
And removing repeated values and abnormal values in the attribute information of the coal mine indexes, and supplementing the missing values in the attribute information of the coal mine indexes according to a pre-trained attribute information supplementing model.
In this embodiment, the normalization process includes: and processing the coal mine indexes with the same field meaning and the same field meaning into data with the same data format and numerical units, namely obtaining the standardized coal mine index, and taking all new attribute information of the standardized coal mine index as standardized attribute information of the standardized coal mine index.
In the embodiment, based on the first association relation among all standardized coal mine indexes, a coal mine index association map is built, and the association map is as follows:
Connecting standardized coal mine indexes with a first association relation by arrows pointing to calculated quantities from original quantities to obtain an index association map;
Wherein the original amount is a standardized coal mine index which can be directly detected or acquired without calculation, such as unit price of coal, total number of staff of the minimum unit department, and the like;
the calculated amount is a standardized coal mine index which needs to be obtained through calculation, for example: the standardized coal mine index, namely the cost of the unit coal yield, is obtained by calculating the difference between the selling price and the cost of the unit coal yield.
The beneficial effects of the technology are as follows: through carrying out data cleaning and standardization processing on coal mine indexes in a coal mine index data table from a plurality of data sources, all standardized coal mine indexes are obtained, so that the subsequent excavation of relations among the standardized coal mine indexes can be more thorough, and through excavation and incidence relation map construction of incidence relations among all the standardized coal mine indexes, the expression of the incidence relations among all the standardized coal mine indexes from a macroscopic angle is realized.
Example 3:
Based on embodiment 2, the coal mine index management configuration method performs primary association on all standardized coal mine indexes based on standardized attribute information of all standardized coal mine indexes, and obtains a first association relationship between all standardized coal mine indexes, including:
Constructing an association relationship between at least two standardized coal mine indexes with completely consistent standardized attribute information in all the standardized coal mine indexes, and taking the association relationship as a first sub-association relationship;
Summarizing all the standardized coal mine indexes which are completely consistent with all the remaining sub-attribute information except the field meaning in the standardized attribute information in a stepwise manner into a similar index set to obtain a plurality of similar index sets;
determining all second sub-association relationships based on the quantization relationships between at least two similar index sets with the quantization relationships;
And taking all the first sub-association relations and the second sub-association relations as first association relations among all the standardized coal mine indexes.
In this embodiment, the rule setting for identifying the first sub-association relationship refers to fig. 2, and the standardized coal mine index "the amount of precipitation of east yeast" in the different coal mine index data tables can be identified by codes.
In this embodiment, the meaning of the field changes stepwise, for example, the standardized coal mine indexes contained in the similar index sets containing a plurality of single month coal yields are respectively: the number of months of the standardized coal mine index that it contains varies in steps, with a coal yield of 2023 years 1 month, a coal yield of 2023 years 2 months, a coal yield of 2023 years 3 months, etc.
In this example, the similar index set includes, for example, a set of single-month coal yields such as 2023 year 1 month coal yield, 2023 year 2 month coal yield, 2023 year 3 month coal yield, and the like.
In this embodiment, assuming that a quantization relationship S exists between the homogeneous index set M and the homogeneous index set N, a standardized coal mine index M1 exists in the homogeneous index set M and a standardized coal mine index N1 in the homogeneous index set N satisfies the quantization relationship S.
In this embodiment, the quantized relationship between at least two homogeneous index sets represents: a quantitative relationship (calculation relationship) that each normalized coal mine index in each of the at least two homogeneous index sets satisfies with a certain value in each of the other homogeneous index sets in the at least two homogeneous index sets.
The beneficial effects of the technology are as follows: and identifying two association relations between at least two different standardized coal mine indexes by using two judging conditions of standardized attribute information of the standardized unseen indexes.
Example 4:
on the basis of embodiment 3, the coal mine index management configuration method determines all second sub-association relationships based on the quantized relationships between at least two similar index sets having quantized relationships, including:
inputting all similar index sets into a quantization relation identification model to obtain quantization relations between at least two similar index sets with quantization relations;
and building all incidence relations which belong to at least two similar index sets with quantitative relations and meet at least two standardized coal mine indexes corresponding to the quantitative relations, and taking the incidence relations as all second sub-incidence relations.
In this embodiment, the quantized relation recognition model uses a large number of homogeneous index sets, which are marked as homogeneous index set combinations having quantized relation and corresponding quantized relation, and all standardized coal mine indexes meeting the corresponding quantized relation, which belong to each homogeneous index set in each homogeneous index set combination, as training samples (a large number of homogeneous index sets are used as model input amounts, the homogeneous index set combinations having quantized relation and corresponding quantized relation, and all standardized coal mine indexes meeting the corresponding quantized relation, which belong to each homogeneous index set in each homogeneous index set combination, are used as model output amounts), and each homogeneous index set combination (including at least two homogeneous index sets) having quantized relation and quantized relation between the homogeneous index set combinations can be recognized in the trained homogeneous index sets.
In this embodiment, all the association relationships between at least two standardized coal mine indexes which belong to at least two similar index sets having quantization relationships and satisfy the corresponding quantization relationships are built as all the second sub-association relationships, for example:
the same class index set J containing a plurality of single months of "selling prices of unit coal yield" contains the following standardized coal mine indexes:
selling price of unit coal yield of 2023 1 month, … …
The same class index set K containing a plurality of single months of "cost per coal yield" contains the standardized coal mine indexes:
Cost of 1 month unit coal production in 2023, … …
The same class index set L containing "profit per unit coal yield" for a plurality of single months contains standardized coal mine indexes of:
Profit of 1 month of 2023 per unit coal yield, … … per unit coal yield
The quantization relations among the similar index set J, the similar index set K and the similar index set L are as follows: the homogeneous index set J-homogeneous index set k=homogeneous index set L;
The selling price of the unit coal yield of 2023 year 1 month (2 months or three months), the cost of the unit coal yield of 2023 year 1 month (2 months or three months), and the profit of the unit coal yield of 2023 year 1 month (2 months or three months) satisfy the above quantitative relationship, namely:
"cost of unit coal yield of 2023 year 1 month (2 months or three months) -2023 year 1 month (2 months or three months) cost of unit coal yield=2023 year 1 month (2 months or three months) profit of unit coal yield" is all second sub-correlations included in the similar index set J, the similar index set K, and the similar index set L.
The beneficial effects of the technology are as follows: through classification of all standardized indexes and identification of similar quantitative relations, the identification accuracy and speed of the association relations are improved.
Example 5:
Based on embodiment 1, the coal mine index management configuration method, S2: based on the historical configuration example and the artificial intelligence algorithm, analyzing a second association relation among all standardized coal mine indexes, wherein the second association relation comprises the following steps:
Building an association relation recognition model based on a historical configuration example and an artificial intelligence algorithm, wherein the historical configuration example comprises a large number of coal mine indexes different from all the current standardized coal mine indexes and second association relations existing among all the coal mine indexes;
and inputting all the standardized coal mine indexes into the association relation identification model to obtain a second association relation among all the standardized coal mine indexes.
In the embodiment, based on a historical configuration example and an artificial intelligence algorithm, an association relationship identification model is built as follows: and training the historical configuration example by using the historical configuration example as a training sample to obtain an association relation recognition model, wherein in the training process, a large number of coal mine indexes different from all current standardized coal mine indexes in the historical configuration example are used as model input quantities, and a second association relation existing among all the coal mine indexes is used as model output quantity.
In this embodiment, the association relation recognition model is a model capable of recognizing that a second association relation exists between the plurality of input standardized coal mine indexes.
The beneficial effects of the technology are as follows: the association relation recognition model built by the historical configuration example and the artificial intelligence algorithm can further excavate association relations existing among all standardized coal mine indexes, and quantized relations which are not easy to directly recognize among all standardized coal mine indexes can be excavated.
Example 6:
Based on embodiment 1, the coal mine index management configuration method, S4: carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination, wherein the method comprises the following steps:
Determining a standardized coal mine index combination with a quantization relationship in a coal mine index association detailed map as the quantization relationship index combination;
Judging whether the association relation between adjacent standardized coal mine indexes in the coal mine index association detailed map is the same coal mine index or not, if so, setting the association coefficient between the corresponding adjacent standardized coal mine indexes to be 1;
Otherwise, based on the calculation level in the quantization calculation process in the association relation between the corresponding adjacent standardized coal mine indexes, searching a preset quantization calculation level-association coefficient list to obtain the association coefficient between the corresponding adjacent standardized coal mine indexes;
And carrying out coefficient quantization on the complete association relation in the quantized relation index combination based on the association coefficient between each group of adjacent standardized coal mine indexes to obtain the complete association coefficient in the quantized relation index combination.
In this embodiment, the combination of standardized coal mine indexes having a quantization relationship is a combination of at least one standardized coal mine index having a quantization relationship.
In the embodiment, the adjacent standardized coal mine indexes in the coal mine index association detailed map are two standardized coal mine indexes taking other standardized coal mine indexes as intermediate quantities, and the complete association relation between the two standardized coal mine indexes is not included in the coal mine index association detailed map.
In this embodiment, the meaning that the adjacent standardized coal mine indexes corresponding to the association relationship between the adjacent standardized coal mine indexes are the same coal mine indexes is: the corresponding adjacent standardized coal mine indexes are standardized coal mine indexes with identical standardized attribute information.
In this embodiment, the computation hierarchy in the quantization computation process in the association relationship is: the total number of times a single symbol calculation is required in the quantization calculation process in the association relation, for example:
Assuming that the quantization calculation process is a= (b+1) ×2, obtaining a with B requires two operations of addition and multiplication, i.e., the calculation hierarchy of the quantization calculation process is 2.
In this embodiment, the preset quantization and calculation level-association coefficient list is a pre-prepared list including values of association coefficients corresponding to each quantization and calculation level, where the larger the quantization and calculation level is, the smaller the association coefficient is, and vice versa.
The beneficial effects of the technology are as follows: and realizing the quantification of the coefficient of the complete association relation in the standardized coal mine index combination with the quantification relation in the coal mine index association detailed map by quantifying the sub-condition coefficient of the association relation between the adjacent standardized coal mine indexes in the coal mine index association detailed map.
Example 7:
Based on embodiment 6, the coal mine index management configuration method performs coefficient quantization on the complete association relationship in the quantized relationship index combination based on the association coefficient between each group of adjacent standardized coal mine indexes, to obtain the complete association coefficient in the quantized relationship index combination, and includes:
determining indirect association times in complete association in the quantized relationship index combination;
and taking the sum of the association coefficients among all groups of adjacent standardized coal mine indexes contained in the complete association relation in the quantized relation index combination and the quotient of the corresponding indirect association times as the complete association coefficient in the quantized relation index combination.
In this embodiment, the number of indirect association times in the complete association is a value obtained by adding 1 to the number of other standardized coal mine indexes as intermediate quantities included in the complete association, and is also the total number of direct association relationships included in the complete association, where the direct association is an association relationship not including other standardized coal mine indexes as intermediate quantities.
The beneficial effects of the technology are as follows: based on the indirect association times in the complete association in the quantized relationship index combination and the association coefficients between all groups of adjacent standardized coal mine indexes contained in the complete association, accurate coefficient quantization of the complete association in the quantized relationship index combination is realized.
Example 8:
Based on embodiment 1, the coal mine index management configuration method, S5: based on the complete association coefficients of the complete association relations in all the quantized relation index combinations, determining the association configuration basis of all the coal mine index data tables and verifying the quantized relation, wherein the method comprises the following steps:
Taking all the complete association relations with the complete relation coefficients larger than the coefficient threshold value as the association configuration basis of all the coal mine index data tables;
and taking the complete association relation with all complete relation coefficients not larger than the coefficient threshold value as the verification quantization relation of all coal mine index data tables.
In this embodiment, the coefficient threshold is a preset distinguishing threshold for distinguishing the complete relationship coefficient according to which the association configuration basis and the quantized relationship are verified in all the complete association relationships.
The beneficial effects of the technology are as follows: and comparing the complete relation coefficient with a coefficient threshold, taking the association relation with larger relation degree as the basis of association configuration in the complete association relation in all quantized relation index combinations, and taking the association relation with smaller relation degree as the verification quantized relation, thereby providing the basis for the subsequent association configuration process and the verification process of the association configuration result.
Example 9:
Based on embodiment 1, the coal mine index management configuration method, S6: based on the association configuration basis and verification quantization relation of all the coal mine index data tables, a coal mine index management configuration result is obtained, and referring to fig. 3, the method comprises the following steps:
S601: performing association correction on all standardized coal mine indexes based on association configuration basis of all coal mine index data tables to obtain all corrected coal mine indexes;
S602: judging whether all corrected coal mine indexes accord with the verification quantitative relation of all coal mine index data tables, if so, carrying out association configuration on all corrected coal mine indexes based on association configuration basis to obtain a coal mine index management configuration result, otherwise, merging the complete association relation which is not consistent with all corrected coal mine indexes in the verification quantitative relation of all coal mine index data tables with the association configuration basis of all current coal mine index data tables to obtain the latest association configuration basis, and removing all complete association relations which are not consistent with all corrected coal mine indexes in the verification quantitative relation of all current coal mine index data tables to obtain the latest verification quantitative relation;
S603: and (3) circularly executing steps S601 to S602 based on the latest association configuration basis and the latest verification quantitative relation to obtain all latest corrected coal mine indexes until all the latest corrected coal mine indexes accord with the latest verification quantitative relation, and carrying out association configuration on all the latest corrected coal mine indexes based on the latest association configuration basis to obtain a coal mine index management configuration result.
In this embodiment, performing association correction on all standardized coal mine indexes based on association configuration basis of all coal mine index data tables to obtain all corrected coal mine indexes, including:
Combining all the association configuration of the coal mine index data tables according to the adjacent standardized coal mine indexes with the same field meaning, so that the standardized coal mine index Q with the adjacent standardized coal mine indexes with the same field meaning is newly added with a part of quantization relation (because the quantization relation of the adjacent standardized coal mine indexes with the same field meaning is sleeved on the standardized coal mine index Q);
Extracting all direct association relations QL in newly added quantized relations of each standardized coal mine index Q;
Judging whether two standardized coal mine indexes related in the direct association relation QL meet the corresponding direct association relation QL or not, and if so, not needing any operation;
Otherwise, substituting the first standardized coal mine index related in the direct association relation QL into the direct association relation QL to obtain an association calculation value of the second standardized coal mine index related in the direct association relation QL;
Meanwhile, substituting a second standardized coal mine index related in the direct association relation QL into the direct association relation QL to obtain an association calculation value of a first standardized coal mine index related in the direct association relation QL;
taking each standardized coal mine index related in the direct association relation QL and the corresponding association calculation value as the upper limit value and the lower limit value of the range respectively, and obtaining the correction range of each standardized coal mine index related in the direct association relation QL;
And correcting the numerical value of each standardized coal mine index in the correction range of each standardized coal mine index in the direct association relation QL, so that the corrected numerical value of all the standardized coal mine indexes in the direct association relation QL meets the corresponding direct association relation QL, and taking the corresponding corrected numerical value as the corresponding corrected coal mine index.
In this embodiment, the verification quantization relationship for determining whether all corrected coal mine indexes meet all coal mine index data tables is: judging whether the numerical values of all the corrected coal mine indexes meet the calculation relations represented by all the complete association relations in the verification quantized relations, if so, representing that all the corrected coal mine indexes meet the verification quantized relations of all the coal mine index data tables, otherwise, representing that all the corrected coal mine indexes do not meet the verification quantized relations of all the coal mine index data tables.
In this embodiment, performing association configuration on all corrected coal mine indexes based on association configuration basis is as follows: and marking all complete association relations (including newly added quantization relations according to the correction process) contained in the association configuration basis among the corresponding different standardized coal mine indexes.
In this embodiment, all the latest corrected coal mine indexes are associated based on the latest association configuration basis, and a coal mine index management configuration result is obtained, and the specific execution steps are the same as those of the "associated configuration for all corrected coal mine indexes based on the association configuration basis".
The beneficial effects of the technology are as follows: and carrying out association correction on all the standardized coal mine indexes based on the association configuration basis, judging whether the standardized coal mine indexes meet the verification quantitative relation or not, verifying whether the currently determined association configuration basis meets the precision requirement or not, and if not, circularly redetermining to achieve the circulation process until the association configuration basis with higher precision is obtained, so as to ensure the association configuration precision of the standardized coal mine indexes.
Example 10:
The present invention provides a coal mine index management configuration system for executing the coal mine index management configuration method described in any one of embodiments 1 to 9, referring to fig. 4, including:
The map generation module is used for building a coal mine index association map based on a first association relation among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources;
The relation analysis module is used for analyzing a second association relation among all standardized coal mine indexes based on the historical configuration example and the artificial intelligence algorithm;
the map refining module is used for locally refining the coal mine index association map based on the second association relation among all the standardized coal mine indexes to obtain a coal mine index association detailed map;
the coefficient quantization module is used for carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination;
The relation classification module is used for determining the association configuration basis of all the coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all the quantized relation index combinations;
and the configuration correction module is used for obtaining a coal mine index management configuration result based on the association configuration basis and the verification quantification relation of all the coal mine index data tables.
The beneficial effects of the technology are as follows: through analyzing the incidence relation before all the standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, a map of the incidence relation among the standardized coal mine indexes which can represent the incidence relation from a macroscopic angle is built, and through carrying out coefficient quantization on the complete incidence relation in the quantized relation index combination in the coal mine index incidence detailed map and distinguishing the incidence configuration basis and verifying the quantization relation, the accuracy of the incidence configuration of the coal mine indexes from different data sources is ensured.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The coal mine index management configuration method is characterized by comprising the following steps:
S1: constructing a coal mine index association map based on a first association relationship among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources;
s2: based on the historical configuration example and the artificial intelligence algorithm, analyzing a second association relation among all standardized coal mine indexes;
S3: locally refining the coal mine index association map based on the second association relation among all the standardized coal mine indexes to obtain a coal mine index association detailed map;
s4: carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination;
S5: determining the association configuration basis of all coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all quantized relation index combinations;
S6: and obtaining a coal mine index management configuration result based on the association configuration basis and the verification quantitative relation of all the coal mine index data tables.
2. The coal mine index management configuration method according to claim 1, wherein S1: constructing a coal mine index association graph based on a first association relationship between all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources, comprising:
acquiring a coal mine index data table from a plurality of data sources, and determining attribute information of each coal mine index in all the coal mine index data tables;
Cleaning attribute information of each coal mine index to obtain effective attribute information of each coal mine index;
Based on the effective attribute information of each coal mine index in all coal mine index data tables, carrying out standardization processing on each coal mine index in all coal mine index data tables to obtain all standardized coal mine indexes and standardized attribute information of each standardized coal mine index;
Performing primary association on all the standardized coal mine indexes based on the standardized attribute information of all the standardized coal mine indexes to obtain a first association relation among all the standardized coal mine indexes;
And building a coal mine index association map based on the first association relation among all the standardized coal mine indexes.
3. The coal mine index management configuration method according to claim 2, wherein performing primary association on all standardized coal mine indexes based on standardized attribute information of all standardized coal mine indexes to obtain a first association relationship between all standardized coal mine indexes includes:
Constructing an association relationship between at least two standardized coal mine indexes with completely consistent standardized attribute information in all the standardized coal mine indexes, and taking the association relationship as a first sub-association relationship;
Summarizing all the standardized coal mine indexes which are completely consistent with all the remaining sub-attribute information except the field meaning in the standardized attribute information in a stepwise manner into a similar index set to obtain a plurality of similar index sets;
determining all second sub-association relationships based on the quantization relationships between at least two similar index sets with the quantization relationships;
And taking all the first sub-association relations and the second sub-association relations as first association relations among all the standardized coal mine indexes.
4. A coal mine index management arrangement method as claimed in claim 3, wherein determining all second sub-associations based on a quantisation relation between at least two sets of homogeneous indexes for which quantisation relations exist comprises:
inputting all similar index sets into a quantization relation identification model to obtain quantization relations between at least two similar index sets with quantization relations;
and building all incidence relations which belong to at least two similar index sets with quantitative relations and meet at least two standardized coal mine indexes corresponding to the quantitative relations, and taking the incidence relations as all second sub-incidence relations.
5. The coal mine index management configuration method according to claim 1, wherein S2: based on the historical configuration example and the artificial intelligence algorithm, analyzing a second association relation among all standardized coal mine indexes, wherein the second association relation comprises the following steps:
Building an association relation recognition model based on a historical configuration example and an artificial intelligence algorithm, wherein the historical configuration example comprises a large number of coal mine indexes different from all the current standardized coal mine indexes and second association relations existing among all the coal mine indexes;
and inputting all the standardized coal mine indexes into the association relation identification model to obtain a second association relation among all the standardized coal mine indexes.
6. The coal mine index management configuration method according to claim 1, wherein S4: carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination, wherein the method comprises the following steps:
Determining a standardized coal mine index combination with a quantization relationship in a coal mine index association detailed map as the quantization relationship index combination;
Judging whether the association relation between adjacent standardized coal mine indexes in the coal mine index association detailed map is the same coal mine index or not, if so, setting the association coefficient between the corresponding adjacent standardized coal mine indexes to be 1;
Otherwise, based on the calculation level in the quantization calculation process in the association relation between the corresponding adjacent standardized coal mine indexes, searching a preset quantization calculation level-association coefficient list to obtain the association coefficient between the corresponding adjacent standardized coal mine indexes;
And carrying out coefficient quantization on the complete association relation in the quantized relation index combination based on the association coefficient between each group of adjacent standardized coal mine indexes to obtain the complete association coefficient in the quantized relation index combination.
7. The coal mine index management configuration method of claim 6, wherein performing coefficient quantization on the complete association in the quantized relationship index combination based on the association coefficient between each set of adjacent standardized coal mine indexes to obtain the complete association coefficient in the quantized relationship index combination comprises:
determining indirect association times in complete association in the quantized relationship index combination;
and taking the sum of the association coefficients among all groups of adjacent standardized coal mine indexes contained in the complete association relation in the quantized relation index combination and the quotient of the corresponding indirect association times as the complete association coefficient in the quantized relation index combination.
8. The coal mine index management configuration method according to claim 1, wherein S5: based on the complete association coefficients of the complete association relations in all the quantized relation index combinations, determining the association configuration basis of all the coal mine index data tables and verifying the quantized relation, wherein the method comprises the following steps:
Taking all the complete association relations with the complete relation coefficients larger than the coefficient threshold value as the association configuration basis of all the coal mine index data tables;
and taking the complete association relation with all complete relation coefficients not larger than the coefficient threshold value as the verification quantization relation of all coal mine index data tables.
9. The coal mine index management configuration method according to claim 1, wherein S6: based on the association configuration basis and verification quantitative relation of all coal mine index data tables, acquiring a coal mine index management configuration result, wherein the method comprises the following steps:
S601: performing association correction on all standardized coal mine indexes based on association configuration basis of all coal mine index data tables to obtain all corrected coal mine indexes;
S602: judging whether all corrected coal mine indexes accord with the verification quantitative relation of all coal mine index data tables, if so, carrying out association configuration on all corrected coal mine indexes based on association configuration basis to obtain a coal mine index management configuration result, otherwise, merging the complete association relation which is not consistent with all corrected coal mine indexes in the verification quantitative relation of all coal mine index data tables with the association configuration basis of all current coal mine index data tables to obtain the latest association configuration basis, and removing all complete association relations which are not consistent with all corrected coal mine indexes in the verification quantitative relation of all current coal mine index data tables to obtain the latest verification quantitative relation;
S603: and (3) circularly executing steps S601 to S602 based on the latest association configuration basis and the latest verification quantitative relation to obtain all latest corrected coal mine indexes until all the latest corrected coal mine indexes accord with the latest verification quantitative relation, and carrying out association configuration on all the latest corrected coal mine indexes based on the latest association configuration basis to obtain a coal mine index management configuration result.
10. A coal mine index management configuration system for executing the coal mine index management configuration method of any one of claims 1 to 9, comprising:
The map generation module is used for building a coal mine index association map based on a first association relation among all standardized coal mine indexes in a plurality of coal mine index data tables from a plurality of data sources;
The relation analysis module is used for analyzing a second association relation among all standardized coal mine indexes based on the historical configuration example and the artificial intelligence algorithm;
the map refining module is used for locally refining the coal mine index association map based on the second association relation among all the standardized coal mine indexes to obtain a coal mine index association detailed map;
the coefficient quantization module is used for carrying out coefficient quantization on the complete association relation in the quantized relation index combination in the coal mine index association detailed map to obtain the complete association coefficient of the complete association relation in the quantized relation index combination;
The relation classification module is used for determining the association configuration basis of all the coal mine index data tables and verifying the quantized relation based on the complete association coefficients of the complete association relations in all the quantized relation index combinations;
and the configuration correction module is used for obtaining a coal mine index management configuration result based on the association configuration basis and the verification quantification relation of all the coal mine index data tables.
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