CN115080594A - Method and system for carrying out multi-dimensional analysis on data and electronic equipment - Google Patents
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Abstract
The application provides a method, a system and electronic equipment for carrying out multi-dimensional analysis on data, which relate to the technical field of data calculation and comprise the steps of collecting data to be analyzed, preprocessing the data to generate a dimension table; creating a plurality of menu configuration items based on at least one of the base dimension values and the category of the target dimension, the plurality of menu configuration items including a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels; identifying a range label, the limit label and the secondary limit label selected by a user by acquiring a personalized analysis request of the user; matching feature identifiers based on the secondary qualified labels, and generating SQL statements through SQL statement generation templates associated with the feature identifiers; the target dimension value is called based on the SQL sentence, and the auxiliary analysis result is obtained to be used for auxiliary analysis of the operation condition of the hospital.
Description
Technical Field
The invention relates to the technical field of data calculation, in particular to a method, a system and electronic equipment for carrying out multi-dimensional analysis on data.
Background
At present, financial departments, management departments, medical departments and the like such as companies and public institutions establish respective information systems successively, but partial systems can be constructed dispersedly, and the organization mode of data has the characteristics of dispersity and independence.
Taking a hospital as an example, the information standardization and standard of the hospital in China are started late at present, and due to the lack of unified planning and the dispersive data, the dimension of the hospital is single when data analysis is carried out, the analysis height and depth are not enough, the early warning is not enough, and special analysis and diagnosis are not carried out on the operation condition generally or rarely. Meanwhile, the hospitals with different levels have the characteristics of incomplete analysis and variable personality for data analysis by combining different clinical characteristics and different management ideas of different hospitals.
At present, aiming at various personalized auxiliary analysis results, separate development may be needed. Because the statistical calibers required by the same auxiliary analysis result when used in different scenes are not completely the same, a large amount of manpower, material resources and financial resources are required to be invested, and the later maintenance difficulty and the communication cost are increased along with the continuous increase and change of the demand.
At present, tools for data analysis in the market, such as SSAS, custom report tools, etc., are also only used as a tool for developers, and cannot fundamentally solve the problems of tedious definition, low value-taking efficiency, etc., of taking auxiliary analysis results, and may also require developers to manually write SQL or MDX statements. The requirement on developers is high, time and labor are wasted, the system cannot be reused, and unified management and maintenance are not convenient.
Therefore, a method, a system and an electronic device for multidimensional data analysis are provided.
Disclosure of Invention
The specification provides a method, a system and electronic equipment for multidimensional analysis of data, a dimension table is generated based on data to be analyzed, a plurality of menu configuration items are created based on the dimension table for a user to select, an SQL statement generation template is determined based on the selection of the user and an SQL statement is generated, a target dimension value is called based on the SQL statement, and an auxiliary analysis result is obtained for auxiliary analysis of the operation condition of a hospital.
The method for performing multidimensional analysis on data provided by the application adopts the following technical scheme that:
acquiring data to be analyzed and preprocessing the data to generate a dimension table, wherein the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension;
creating a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, the plurality of menu configuration items including a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
acquiring a personalized analysis request of a user, wherein the personalized analysis request comprises the range label, the limit label and the secondary limit label;
generating an SQL statement through an SQL statement generation template associated with the feature identifier based on the secondary qualified label matching the feature identifier;
and calling a target dimension value based on the SQL sentence to obtain an auxiliary analysis result.
Optionally, the acquiring data to be analyzed and preprocessing the data to generate the dimension table includes:
collecting the data to be analyzed, wherein the data to be analyzed comprises a preset dimension and a preset dimension value, and grouping the data to be analyzed based on the preset dimension and the preset dimension value to generate a plurality of dimension tables; the preset dimension comprises a base dimension.
Optionally, the creating a plurality of menu configuration items based on the target dimension value and the dimension value category includes:
generating a plurality of range labels based on the table name of the dimension table corresponding to the basic dimension value, the basic dimension value and the category of the target dimension, and summarizing the range labels to construct a range menu configuration item;
determining a plurality of defined labels, summarizing the defined labels to construct defined menu configuration items;
determining the secondary defined label corresponding to the defined label based on the defined label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item associated with the defined label.
Optionally, the method further includes:
presetting a plurality of feature labels and a plurality of secondary limit labels associated with the feature labels based on a template association model;
one of said secondary qualified labels being associated with one of said signature identifications;
and associating one feature identifier with one SQL statement generation template.
Optionally, the determining a plurality of defined tags, and summarizing the defined tags to construct a defined menu configuration item, includes:
calling the feature label to generate the definition label, and summarizing a plurality of definition labels to construct a definition menu configuration item.
Optionally, the determining a plurality of defined tags, and summarizing the defined tags to construct a defined menu configuration item, includes:
creating the defined label based on the basic dimension value, and summarizing a plurality of defined labels to construct a defined menu configuration item;
the step of determining the secondary defined label corresponding to the defined label based on the defined label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item associated with the defined label comprises the following steps:
finding the feature tag matching the qualified tag;
determining the secondary defined label corresponding to the defined label based on the feature label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item.
Optionally, the generating an SQL statement by using the SQL statement generation template associated with the feature identifier includes:
determining the SQL statement generation template based on the feature identifier;
and inputting the table name, the basic dimension value corresponding to the range label and the basic dimension value corresponding to the limited label into the SQL statement generation template to generate the SQL statement.
The data analysis system based on multiple dimensions provided by the application adopts the following technical scheme that:
the system comprises a preprocessing module, a dimension table generating module and a processing module, wherein the preprocessing module is used for acquiring data to be analyzed and preprocessing the data to generate the dimension table, and the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension;
a menu generating module, configured to create a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, where the plurality of menu configuration items include a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
the acquisition module is used for acquiring a personalized analysis request of a user, wherein the personalized analysis request comprises the range label, the limit label and the secondary limit label;
the statement generation module is used for generating an SQL statement through an SQL statement generation template associated with the feature identifier on the basis of matching the feature identifier with the secondary qualified label;
and the calling module is used for calling the target dimension value based on the SQL statement to obtain an auxiliary analysis result.
Optionally, the preprocessing module includes:
the preprocessing submodule is used for acquiring the data to be analyzed, the data to be analyzed comprises preset dimensions and preset dimension values, and the data to be analyzed are grouped based on the preset dimensions and the preset dimension values to generate a plurality of dimension tables; the preset dimension comprises a base dimension.
Optionally, the menu generating module includes:
the range menu configuration item constructing submodule is used for generating a plurality of range labels based on the table name of the dimension table corresponding to the basic dimension value, the basic dimension value and the category of the target dimension, and summarizing the range labels to construct a range menu configuration item;
the limited menu configuration item constructing submodule is used for determining a plurality of limited labels and summarizing the limited labels to construct a limited menu configuration item;
and the secondary limited menu configuration item building sub-module is used for determining the secondary limited label corresponding to the secondary limited label based on the limited label and summarizing a plurality of secondary limited labels to build the secondary limited menu configuration item associated with the limited label.
Optionally, the method further includes:
the template association module is used for presetting a plurality of feature labels and a plurality of secondary limit labels associated with the feature labels based on a template association model;
one of said secondary qualified labels being associated with one of said signature identifications;
and associating one feature identifier with one SQL statement generation template.
Optionally, the limited menu configuration item building submodule includes:
and the calling unit is used for calling the feature label to generate the limit label and summarizing a plurality of limit labels to construct a limit menu configuration item.
Optionally, the limited menu configuration item building submodule includes:
the summarizing unit is used for creating the limiting label based on the basic dimension value, summarizing a plurality of limiting labels to construct a limiting menu configuration item;
the secondary qualified menu configuration item building submodule comprises:
a searching unit, configured to search for the feature tag matching the definition tag;
determining the secondary defined label corresponding to the defined label based on the feature label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item.
Optionally, the statement generating module includes:
the template determining sub-module is used for determining the SQL statement generation template based on the feature identification;
and the statement generation submodule is used for inputting the table name, the basic dimension value corresponding to the range label and the basic dimension value corresponding to the limited label into the SQL statement generation template to generate the SQL statement.
The present specification also provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory storing computer executable instructions that, when executed, cause the processor to perform any of the methods described above.
The present specification also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement any of the methods described above.
In the method, a dimension table is generated by collecting data to be analyzed and preprocessing the data, wherein the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension, so that data can be called in the later period conveniently; creating a plurality of menu configuration items based on at least one of the base dimension values and the category of the target dimension, the plurality of menu configuration items including a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels for selection by a user; acquiring a personalized analysis request of a user, wherein the personalized analysis request comprises the range label, the limit label and the secondary limit label; determining a data dimension based on a user selection; generating an SQL statement through an SQL statement generation template associated with the feature identifier based on the secondary qualified label matching the feature identifier; and calling a target dimension value based on the SQL sentence to obtain an auxiliary analysis result for auxiliary analysis of the operation condition of the hospital. The method and the device realize repeated use of one-time configuration, effectively reduce the calculation time of the auxiliary analysis result and reduce the calculation workload.
Drawings
FIG. 1 is a schematic diagram illustrating a method for performing multidimensional analysis on data according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating a method for performing multidimensional analysis on data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a system for performing multidimensional analysis on data according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present specification.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments described below are by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic diagram of a method for performing multidimensional analysis on data according to an embodiment of the present disclosure, where the method includes:
s1, collecting data to be analyzed and preprocessing the data to generate a dimension table, wherein the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension;
s2 creating a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, the plurality of menu configuration items including a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
s3, obtaining a personalized analysis request of a user, wherein the personalized analysis request comprises the range label, the limit label and the secondary limit label;
s4, based on the secondary qualified label matching the feature identifier, generating an SQL statement through an SQL statement generation template associated with the feature identifier;
s5, calling the target dimension value based on the SQL sentence to obtain an auxiliary analysis result.
Take a hospital as an example: when data analysis is performed on operation conditions of different hospitals or departments, requirements on auxiliary analysis results may be different, and dimensions of analysis of different hospitals or departments may also be different, data to be analyzed of specific hospitals or departments are preprocessed to generate a dimension table, a plurality of menu configuration items related to the data to be analyzed of the hospitals or departments are generated based on contents of the dimension table, and the menu configuration items are added into corresponding menus for selection. And generating a corresponding SQL statement based on the personalized selection of the user, and further obtaining an auxiliary analysis result so as to meet different requirements.
S1, collecting data to be analyzed and preprocessing the data to generate a dimension table, wherein the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension;
in the present application, the data to be analyzed is stored in the original table in the database. The original table includes a plurality of initial dimensions and initial dimension values. The number of the original tables may be multiple because the initial dimensions involved may differ due to different item specifications in the data to be analyzed. The initial dimensions include: time, department name, project detail (expenditure), amount of money involved, number of patients involved, project detail (income), number of beds involved, and the like.
The data to be analyzed of the database has huge information quantity, and if data are directly extracted from the data to be analyzed each time, traversal search is needed, so that time is consumed, an operating memory is occupied, and the SQL performance is influenced. Therefore, before data analysis, the data to be analyzed in the original table is collected, and the data is grouped based on the initial dimension and the initial dimension value, or a plurality of dimension tables are generated based on the arrangement and combination of a plurality of dimensions, so that when an auxiliary analysis result is obtained each time, the data can be called as soon as possible to reduce waiting time and reduce calculation cost. When the original table is updated, the contents of the dimension tables are updated in real time, and the integrity of data analysis is improved.
The dimension table includes a base dimension, a base dimension value corresponding to the base dimension, a target dimension, and a target dimension value corresponding to the target dimension. In the dimension table, the dimensions other than the target dimension are basic dimensions. The basic dimension is a row where the target dimension value is located, and the target dimension is a column where the target dimension value is located.
The target dimension value is obtained by extracting data from an original table, converting and processing the extracted data, and finally aggregating the data into a scalar value. Wherein, the data extraction is completed through the evaluation environment to which the data extraction belongs, and the aggregation is completed through an aggregation function. The target dimension may be a specific numerical value such as the number of people, amount of money, number of times, etc. The basic dimension of each hospital or department is subject to the initial dimension and the initial dimension value in the original table of each hospital or department.
In one embodiment of the present application, table 1 shows the data of original table 1, and table 2 shows one of the dimension tables 1 obtained based on the data of original table 1; table 3 shows the original table 2 data, and table 4 shows one of the dimension tables 2 obtained based on the original table 2 data.
(Table 1)
(Table 2)
(Table 3)
(Table 4)
In an embodiment of the present application, the basic dimension may be a time dimension, and the corresponding time dimension values may be equal to 6 months in 2021, the first quarter in 2022, and the like; the base dimension may be a department name and the corresponding dimension value may be internal medicine, surgery, gynecology, pediatrics, etc.; the base dimension may be item detail and the corresponding dimension value may be drug cost expenditure, medical material cost expenditure, treatment income, care income, and the like.
In one embodiment of the present application, table 5 shows the data of original table 3 (item detail (income) of each department in 2021), and table 6 is one of the dimension tables obtained based on table 5; table 7 shows the data of the original table 4 (item details (expenses) of each department in 2021), and table 8 shows one of the dimension tables obtained based on the data of the original table 2.
(Table 5)
(Table 6)
(Table 7)
(watch 8)
S2 creating a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, the plurality of menu configuration items including a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
in an embodiment of the application, before data analysis is performed on a certain hospital or department, a plurality of menu configuration items are created, and the contents of the menu configuration items are added into corresponding menus for selection by a user, so that the menu configuration items are generally created only once for the same hospital or department, the requirement of multiple data analysis of the hospital or department can be met, and the reuse rate of the dimension table is improved.
If the number of the range labels or the limited labels corresponding to the menu configuration items is more, the menu corresponding to the menu configuration items is preferably a pull-down menu; if the range label or the dimension of the limited label corresponding to the menu configuration item is the time dimension, preferably selecting a time selection plug-in for the user to quickly select; if the number of the range labels corresponding to the menu configuration items is less, the range labels can be presented at one time for the user to directly select.
S21, generating a plurality of range labels based on the table name of the dimension table corresponding to the basic dimension value, the basic dimension value and the category of the target dimension, and summarizing the range labels to construct range menu configuration items;
in one embodiment of the present application, the scope tags include a base scope tag and a composite scope tag, wherein the base scope tag refers to a set of concepts that are not separable and that are related to business entity atomic quantization attributes. The composite range label is a set established on the basic range label and formed by a certain operation rule. The category of the target dimension comprises the number of times of the metric value and the metric value. The number of the range menu configuration items can be multiple.
Take the data in table 2as an example (the following is only an example of a partial simple range label):
the result of the metric value (degree) with dimension value a1 in dimension a, and the simple range label is denoted as s1_ a1_ degree;
the result of the metric value (degree) with dimension value a2 in dimension a, and the simple range label is denoted as s1_ a2_ degree;
the result of the metric value (degree) with dimension value b1 in dimension b, the simple range label is denoted as s1_ b1_ degree;
the result of the metric value (degree) with dimension value b2 in dimension b, the simple range label is denoted as s1_ b2_ degree;
the result of the metric value 1 with dimension value a1 in dimension a, the simple range label is denoted as s1_ a1_ metric value 1;
the result of metric value 2 with dimension value a1 in dimension a, the simple range label is denoted as s1_ a1_ metric value 2;
the result of the metric value (times) with the dimension value of a1 in the dimension a and the dimension value of b1 in the dimension b is recorded as s1_ a1b1_ times;
the result of the metric value (degree) with dimension a of a1 and dimension b of b2, the simple range label is denoted as s1_ a1b2_ degree;
……
take the data in table 2as an example (the following is an example of only a partial composite range label):
the dimension a is the mean value of the metric value 1 (metric value 1/times) of a1, namely s1_ a1_ metric value 1/s1_ a1_ times, and the compound range label is marked as s1_ a1_ metric value 1 mean value;
……
taking the data in tables 2 and 4 as an example (the following is an example of only a partial composite range label), the composite range label may be:
the ratio of the metric value 3 to the metric value 1 (metric value 3/metric value 1) with the dimension a1 is s2_ a1_ metric value 3/s1_ a1_ metric value 1, and the compound range label is s12_ a1_ metric value 31;
……
wherein s represents a table name, and s1 represents dimension table 1;
the range labels are based on the dimension table, the number of the range labels is exponentially increased along with the increase of the dimension number of the multiple dimensions, and the range of all available dimensions is covered in an all-round mode so as to improve the analysis height and the analysis depth.
S22 determining a plurality of limit labels, summarizing the limit labels to construct limit menu configuration items;
in one embodiment of the application, a plurality of feature tags and a plurality of secondary definition tags associated with the feature tags are preset based on a template association model;
one of said secondary qualified labels being associated with one of said signature identifications;
and associating one feature identifier with one SQL statement generation template.
The feature tag is used for restricting the style of the generated auxiliary analysis result. In one embodiment of the present application, the feature label may be a time dimension value (time dimension value range), for example, if 2019, month 1 — 2019, month 6 is selected, the auxiliary analysis result will generate a statistical graph for a target dimension value based on month; if the first quarter of 2020 is chosen-the fourth quarter of 2020, the auxiliary analysis results will generate a statistical map for the target dimension values based on the quarters. Of course, if only the year 1 month in 2019 is selected, the auxiliary analysis result generates a target dimension value corresponding to the year 1 month in 2019. The feature tag includes a plurality of patterns of time or time periods, which are not illustrated here. The preset feature tag may be a time plug-in, based on which a time point or a time period may be directly selected on the time presentation interface.
When the feature label represents time, the secondary qualified label corresponding to the feature label comprises a current value, an in-phase value, a last-phase value and a ring ratio value.
When the secondary limited label is a current value, the corresponding feature identifier is a current value feature identifier, and a SQL statement generation template associated with the current value is generated;
similarly, when the secondary qualified label is a "contemporaneous value", the corresponding feature identifier is a "contemporaneous value feature identifier", and a SQL statement generation template associated with the contemporaneous value is generated;
when the secondary limiting label is the same ratio value, the corresponding feature identifier is the same ratio value feature identifier, and the SQL statement generation template with the same ratio value is associated;
when the secondary limit label is the 'last-stage value', the corresponding feature identifier is the 'last-stage value feature identifier', and an SQL statement generation template of the last-stage value is associated;
when the secondary limiting label is a ring ratio value, the corresponding feature identifier is a ring ratio value feature identifier, and the SQL statement associated with the ring ratio value generates a template.
In an embodiment of the present application, as shown in tables 6 and 8, the time dimension values are already included in the header, and if the determination of the auxiliary analysis result involves a long time period or some specific time period, it may be necessary to retrieve the target dimension values of a plurality of dimension tables.
In an embodiment of the application, all or part of the feature tags are called to generate the definition tags, and a plurality of definition tags are summarized to construct definition menu configuration items. When the dimension table is established, the specific analyzable dimension is fixed, the preset feature label is directly called as a limit label, and the condition that the target dimension value corresponding to part of the limit labels may be empty may exist. If the user selects the limit label in the later period, an abnormal reminding is triggered to prompt the user.
The limit label may be the same as the range label, and there is a base dimension value b corresponding to the limit label 1 +b 2 +…+b n (the dimension value corresponding to dimension b in the range label includes b 1 ,b 2 ,…b n ) In essence, the definition tag does not play a limiting role at this time.
In another embodiment of the application, the limit label specific to the hospital or department may be created based on the basic dimension value, a plurality of limit labels are collected to construct a limit menu configuration item, and the limit menu configuration item is added to a limit menu for a user to select.
S23 determining the secondary limit label corresponding to the limit label based on the limit label, summarizing a plurality of the secondary limit labels to construct the secondary limit menu configuration item associated with the limit label.
The secondary qualified labels are associated with the qualified labels, that is, the secondary qualified label corresponding to the selected qualified label a may be different from the secondary qualified label corresponding to the selected qualified label B. In the present application, if both the definition tag a and the definition tag B are in the same dimension, that is, if both represent time or a time period, the secondary definition tag corresponding to the definition tag a is the same as the secondary definition tag corresponding to the selected definition tag B.
In an embodiment of the application, if all or part of the feature tags are called to generate the definition tags, and a plurality of definition tags are summarized to construct a definition menu configuration item, a secondary definition menu configuration item is directly constructed based on the secondary definition tags corresponding to the feature tags.
In another embodiment of the present application, for example, the definition tags specific to the hospital or department are created based on the basic dimension value, a plurality of definition tags are collected to construct a definition menu configuration item, when a secondary definition menu configuration item is constructed, the feature tags matching the definition tags are first searched, and if the definition tags cannot establish a one-to-one relationship with the feature tags, the feature tags matching the definition tags may also be manually associated based on a human. Determining the secondary defined label corresponding to the defined label based on the feature label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item. And adding the configuration items of the secondary limited menu to the secondary limited menu for the user to select.
S3, obtaining a personalized analysis request of a user, wherein the personalized analysis request comprises the scope label, the limit label and the secondary limit label;
the personalized analysis request is specific content selected by the user in a plurality of menus, and after menu configuration items corresponding to the menus are constructed, the user can select a range label, a limited label and a secondary limited label with different dimensions. The range label comprises a table name of a table where the required target dimension value is located, and partial condition limit and/or dimension limit of the target dimension value; the limiting label limits the target dimension value partially and conditionally; the secondary limited label is used for rapidly determining the characteristic mark and further determining the SQL statement generation template so as to accelerate the generation efficiency of the auxiliary analysis result.
S4, based on the secondary qualified label matching the feature identifier, generating an SQL statement through an SQL statement generation template associated with the feature identifier;
associating one of said feature identifiers with one of said SQL statement generating templates, thereby determining said SQL statement generating template based on said feature identifier;
and inputting the table name, the basic dimension value corresponding to the range label and the basic dimension value corresponding to the limited label into the SQL statement generation template to generate the SQL statement. The SQL statement generation template is preset so as to reduce the maintenance difficulty and communication cost in the later period and facilitate unified management and maintenance.
In an embodiment of the present application, the preset SQL statement generation template for the simple current value of the single table is:
selecting a target dimension value from a table where the current time and the basic dimension value corresponding to the range label;
in conjunction with table 2 in the present application, the incoming range label (s1_ a1_ times), limit label (current time), and secondary limit label (current value) are derived, as requested based on the user's personalized analysis.
Determining the SQL statement generation template of the single-table simple current value corresponding to the single-table simple current value feature identification according to the incoming secondary qualified label:
from the incoming range label it can be made explicit: the table name is dimension table 1, the dimension value corresponding to the range label is a1, the target dimension value is the measure value (times)
Substituting the corresponding SQL sentence generation template to obtain a corresponding SQL sentence:
select metric (times) from dimension table 1where current time and dimension a is a1 in an embodiment of the present application, a preset single table simple equal ratio SQL statement generation template is:
with current value as: (
Select target dimension value from table where current time and base dimension value corresponding to range tag
),
The same term as (
Select target dimension value from table where contemporaneous time and base dimension value corresponding to range tag
)
Select iso-ratio (current value, target dimension value-iso-value, target dimension value)/iso-value, target dimension value from current value, iso-value
In conjunction with table 2 in the present application, the incoming range label (s1_ a1_ times), limit label (current time), and secondary limit label (same ratio) are obtained, as requested based on the user's personalized analysis.
Determining a single-table simple same-ratio value SQL statement generation template corresponding to the single-table simple same-ratio value feature identification according to the transmitted secondary limited label:
from the incoming range label it can be made explicit: the table name is dimension table 1, the dimension value corresponding to the range label is a1, the target dimension value is the measure value (times)
Substituting the corresponding SQL sentence generation template to obtain a corresponding SQL sentence:
and substituting the template to obtain:
with current value as: (
Select metric (degree) from dimension table 1where current time and dimension a is a1
),
The same term as (
Select metric (times) from dimension table 1where contemporaneous time and dimension a is a1
)
Select same ratio value (current value, metric value (number) -same-period value, metric value (number))/same-period value, metric value (number) from current value, and same-period value
In the calculation, it is determined with priority that the dividend cannot be 0.
The generation manner of other simple SQL statements of the single table is the same as that of the embodiment, and is not described again.
In an embodiment of the present application, the preset SQL statement generation template for the single-table multidimensional current value is:
the base dimension values corresponding to the Select and the limit label, the target dimension value from the current time of the from table, the base dimension value group by corresponding to the range label and the base dimension value corresponding to the limit label
In conjunction with table 2 in the present application, the incoming range label (s1_ a1_ times), qualifier label (all base dimension values for dimension b), and secondary qualifier label (current value) are obtained, as requested based on the user's personalized analysis.
Determining an SQL statement generation template of the single-table multi-dimensional current value corresponding to the single-table multi-dimensional current value characteristic identification according to the incoming secondary qualified label:
from the incoming range label it can be made explicit: the table name is dimension table 1, the dimension value corresponding to the range label is a1, the dimension corresponding to the limit label is b, and the target dimension value is the measurement value (times);
substituting the corresponding SQL sentence generation template to obtain a corresponding SQL sentence:
select dimension b, measure (degree) from dimension table 1where current time and dimension a is a1group by dimension b
The other single-table multi-dimensional SQL statement generation methods are the same as those of this embodiment, and are not described again. The single-table multi-dimension refers to that a dimension table and a definition tag are involved in the SQL statement generation process (the dimension involved in the definition tag is a further definition of the target dimension value).
In an embodiment of the present application, the preset SQL statement generation template for the single-table composite current value is:
with simple Range tag 1as (
Select target dimension value from table where current time and base dimension value corresponding to simple range tag 1
),
Simple range tag 2as (
Select target dimension value from table where current time and base dimension value corresponding to simple range tag 2
)
Select simple Range tag 1, target dimension value/simple Range tag 2, target dimension value from simple Range tag 1, simple Range tag 2
In conjunction with table 2 in the present application, the incoming range label (s1_ a1_ metric 1 mean), limit label (current time), and secondary limit label (current value) are derived, as requested based on the user's personalized analysis.
Determining the SQL statement generation template of the current value corresponding to the current value template identification according to the incoming secondary qualified label:
from the incoming range label it can be made explicit: the table name is dimension table 1, the dimension value corresponding to the range label is a1, and the target dimension value is the number of times of the metric value 1 and the metric value 1;
substituting the corresponding SQL sentence generation template to obtain a corresponding SQL sentence:
with simple Range tag 1as (
Select metric 1from dimension table 1where current time and dimension a is a1
),
Simple range tag 2as (
Select metric 1 (degree) from dimension table 1where current time and dimension a is a1
)
Select simple Range tag 1. metric 1/simple Range tag 2. metric (times) from simple Range tag 1, simple Range tag 2
Other single table compounding and single table compounding multidimensional SQL statement generating modes are the same as the above embodiments, and are not described herein again. The single table composition refers to that one dimension table and a plurality of simple range labels are involved in the SQL statement generation process. The single-table composite multidimensional refers to that a dimension table, a plurality of simple range labels and a limit label are involved in the generation process of the SQL statement (the dimension involved in the limit label is a further limit to a target dimension value).
In an embodiment of the present application, the preset SQL statement generation template for the multi-table complex current value is:
with dimension tables nas (
The base dimension value corresponding to the Select and the limit label, the base dimension value group by corresponding to the target dimension value from the current time of the table where and the range label, and the base dimension value corresponding to the limit label
),
Dimension table mas (
The base dimension value corresponding to the Select and the limit label, the base dimension value group by corresponding to the target dimension value from the current time of the table where and the range label, and the base dimension value corresponding to the limit label
)
Base dimension value corresponding to Select and limited label, dimension table n
from dimension Table n
full join dimension table m on dimension table n, basic dimension value corresponding to the qualified label, i.e. dimension table m
Basic dimension value corresponding to group by and limited label
In conjunction with tables 2 and 4 in the present application, the incoming range label (s12_ a1_ metric value 31), qualified label (all base dimension values for dimension a), and secondary qualified label (current value) are obtained, as requested based on the user's personalized analysis.
Determining the SQL statement generation template of the current value corresponding to the current value template identification according to the incoming secondary qualified label:
from the incoming range label it can be made explicit: the table names are dimension table 1 and dimension table 2, the dimension value corresponding to the range label is a1, the dimension corresponding to the limit label is a, and the target dimension value is the measurement value 1, the times of the measurement value 1, the measurement value 3 and the times of the measurement value 3;
substituting the corresponding SQL sentence generation template to obtain a corresponding SQL sentence:
with dimension Table 2as (
Select dimension a, measure 3from dimension table 2where current time and dimension a is a1group by dimension a
),
Dimension tables 1as (
Select dimension a, measure 1from dimension table 1where current time and dimension a is a1group by dimension a
)
Select dimension a, dimension table 2, metric 3/dimension table 1, metric 1
from dimension Table 2
full join dimension table 1on dimension table 2 dimension a dimension table 1 dimension a
group by dimension a
It should be noted that the analyzable dimension d in this embodiment is a dimension common to the dimension tables 1 and 2.
The generation manner of other multi-table compounded SQL statements is the same as that of the embodiment, and is not described again. Multi-table compounding refers to the involvement of multiple dimensional tables and multiple simple scope tags in the generation of the SQL statement.
The SQL sentence generated according to the SQL sentence generation template avoids the problems of error and complexity of handwritten SQL, and the method for generating the SQL sentence determines the corresponding basic dimension value through the range tag and the limit tag by utilizing the reusability of the SQL sentence generation template, so that the SQL sentence and the corresponding personalized auxiliary analysis result can be obtained, the workload of personnel is reduced, and the efficiency of generating the SQL sentence is improved. The SQL statement can be selected whether to be output according to the requirements of users.
S5, calling a target dimension value based on the SQL statement to obtain an auxiliary analysis result.
In the application, a feature identifier corresponding to a secondary limit label and an SQL statement generation template corresponding to the feature identifier are preset; the data to be analyzed are collected and preprocessed, three menu configuration items can be obtained, the three menu configuration items are respectively used as the content of a range menu and a limited menu secondary limited menu for a user to select, the user generates a personalized analysis request through the respective selection in the three menus by combining the figure 2, and after the personalized analysis request is obtained, a range label, a limited label and a secondary limited label specifically selected by the user can be obtained. Wherein the range label comprises a table name and a base dimension value (such as drug cost income) corresponding to the range label, and the limit label comprises a base dimension value (such as 1 month in 2019-3 months in 2019) corresponding to the limit label. Determining a characteristic mark based on a secondary limit label (such as a current value), further determining an SQL statement generating template, substituting the table name, a basic dimension value corresponding to a range label and a basic dimension value corresponding to the limit label into the SQL statement generating template to generate an SQL statement, executing the SQL statement to obtain an auxiliary analysis result (namely obtaining a current value of the drug expense income of the month 1 in 2019, a current value of the drug expense income of the month 2 in 2019, a current value of the drug expense income of the month 3 in 2019 and generating a statistical chart based on the three results) so as to assist the analysis of the operation condition of the hospital. If auxiliary analysis results of other dimensions are needed, the user can directly select menu contents again according to requirements to generate a new personalized analysis request, and then a new auxiliary analysis result is obtained. The method is oriented to different hospitals/departments, and auxiliary analysis results are generated in a personalized mode; aiming at the same hospital/department, the system can be configured for multiple use at one time, thereby effectively reducing the calculation time of the auxiliary analysis result and reducing the calculation workload.
Fig. 3 is a schematic structural diagram of a multidimensional-based data analysis system provided in an embodiment of the present specification, where the system includes:
the calling module 301 is configured to collect data to be analyzed and perform preprocessing to generate a dimension table, where the dimension table includes a base dimension, a base dimension value corresponding to the base dimension, a target dimension, and a target dimension value corresponding to the target dimension;
a menu generating module 302, configured to create a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, where the plurality of menu configuration items include a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
an obtaining module 303, configured to obtain a personalized analysis request of a user, where the personalized analysis request includes the range tag, the limit tag, and the secondary limit tag;
a statement generating module 304, configured to generate an SQL statement by using an SQL statement generating template associated with the feature identifier based on the secondary qualified label matching the feature identifier;
and the invoking module 305 is configured to invoke the target dimension value based on the SQL statement to obtain an auxiliary analysis result.
Optionally, the invoking module 301 includes:
the preprocessing submodule is used for acquiring the data to be analyzed, the data to be analyzed comprises preset dimensions and preset dimension values, and the data to be analyzed are grouped based on the preset dimensions and the preset dimension values to generate a plurality of dimension tables; the preset dimension comprises a base dimension.
Optionally, the menu generating module 302 includes:
the range menu configuration item constructing submodule is used for generating a plurality of range labels based on the table name of the dimension table corresponding to the basic dimension value, the basic dimension value and the category of the target dimension, and summarizing the range labels to construct a range menu configuration item;
the limited menu configuration item constructing submodule is used for determining a plurality of limited labels and summarizing the limited labels to construct a limited menu configuration item;
and the secondary limited menu configuration item building sub-module is used for determining the secondary limited label corresponding to the secondary limited label based on the limited label and summarizing a plurality of secondary limited labels to build the secondary limited menu configuration item associated with the limited label.
Optionally, the method further includes:
the template association module is used for presetting a plurality of feature labels and a plurality of secondary limit labels associated with the feature labels based on a template association model;
one of said secondary qualified labels being associated with one of said signature identifications;
and associating one feature identifier with one SQL statement generation template.
Optionally, the restricted menu configuration item building sub-module includes:
and the calling unit is used for calling the feature label to generate the limit label and summarizing a plurality of limit labels to construct a limit menu configuration item.
Optionally, the limited menu configuration item building submodule includes:
the summarizing unit is used for creating the limiting label based on the basic dimension value, summarizing a plurality of limiting labels to construct a limiting menu configuration item;
the secondary qualified menu configuration item building submodule comprises:
a searching unit, configured to search for the feature tag matching the definition tag;
determining the secondary defined label corresponding to the defined label based on the feature label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item.
Optionally, the statement generating module 304 includes:
the template determining submodule is used for determining the SQL statement generation template based on the feature identifier;
and the statement generation submodule is used for inputting the table name, the basic dimension value corresponding to the range label and the basic dimension value corresponding to the limited label into the SQL statement generation template to generate the SQL statement.
The functions of the system in the embodiment of the present invention have been described in the above method embodiments, so that details that are not described in the embodiment of the present invention can be referred to the relevant descriptions in the foregoing embodiments, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for multidimensional data analysis, comprising:
acquiring data to be analyzed and preprocessing the data to generate a dimension table, wherein the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension;
creating a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, the plurality of menu configuration items including a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
acquiring a personalized analysis request of a user, wherein the personalized analysis request comprises the range label, the limit label and the secondary limit label;
matching feature identifiers based on the secondary qualified labels, and generating SQL statements through SQL statement generation templates associated with the feature identifiers;
and calling a target dimension value based on the SQL sentence to obtain an auxiliary analysis result.
2. The method of claim 1, wherein collecting and preprocessing data to be analyzed to generate a dimension table comprises:
collecting the data to be analyzed, wherein the data to be analyzed comprises a preset dimension and a preset dimension value, and grouping the data to be analyzed based on the preset dimension and the preset dimension value to generate a plurality of dimension tables; the preset dimension comprises a base dimension.
3. The method of claim 1, wherein creating a plurality of menu configuration items based on the target dimension value and the dimension value category comprises:
generating a plurality of range labels based on the table name of the dimension table corresponding to the basic dimension value, the basic dimension value and the category of the target dimension, and summarizing the range labels to construct a range menu configuration item;
determining a plurality of defined labels, summarizing the defined labels to construct defined menu configuration items;
determining the secondary defined label corresponding to the defined label based on the defined label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item associated with the defined label.
4. The method of claim 3, further comprising:
presetting a plurality of feature labels and a plurality of secondary limit labels associated with the feature labels based on a template association model;
one of said secondary qualified labels being associated with one of said signature identifications;
and associating one feature identifier with one SQL statement generation template.
5. The method of claim 4, wherein the determining a plurality of defined tags, summarizing the defined tags to construct a defined menu configuration item, comprises:
calling the feature label to generate the definition label, and summarizing a plurality of definition labels to construct a definition menu configuration item.
6. The method of claim 4, wherein the determining a plurality of defined tags, summarizing the defined tags to construct a defined menu configuration item, comprises:
creating the defined label based on the basic dimension value, and summarizing a plurality of defined labels to construct a defined menu configuration item;
the step of determining the secondary defined label corresponding to the defined label based on the defined label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item associated with the defined label comprises the following steps:
finding the feature tag matching the qualified tag;
determining the secondary defined label corresponding to the defined label based on the feature label, and summarizing a plurality of secondary defined labels to construct a secondary defined menu configuration item.
7. The method of claim 3, wherein generating the SQL statement through the SQL statement generation template associated with the feature identifier comprises:
determining the SQL statement generation template based on the feature identifier;
and inputting the table name, the basic dimension value corresponding to the range label and the basic dimension value corresponding to the limited label into the SQL statement generation template to generate the SQL statement.
8. A multidimensional-based data analysis system, comprising:
the system comprises a preprocessing module, a dimension table generating module and a processing module, wherein the preprocessing module is used for acquiring data to be analyzed and preprocessing the data to generate the dimension table, and the dimension table comprises a basic dimension, a basic dimension value corresponding to the basic dimension, a target dimension and a target dimension value corresponding to the target dimension;
a menu generating module, configured to create a plurality of menu configuration items based on at least one of the base dimension value and the category of the target dimension, where the plurality of menu configuration items include a number of range labels, a number of limit labels, and a number of secondary limit labels associated with the limit labels;
the acquisition module is used for acquiring a personalized analysis request of a user, wherein the personalized analysis request comprises the range label, the limit label and the secondary limit label;
the statement generation module is used for generating an SQL statement through an SQL statement generation template associated with the feature identifier on the basis of matching the feature identifier with the secondary qualified label;
and the calling module is used for calling the target dimension value based on the SQL statement to obtain an auxiliary analysis result.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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| CN117497159B (en) * | 2024-01-02 | 2024-04-16 | 智业软件股份有限公司 | Method for managing hospital beds by using cards based on automatic SQL loading |
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