CN110727710B - Data analysis method and device, computer equipment and storage medium - Google Patents
Data analysis method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a data analysis technology and provides a data analysis method, a data analysis device, computer equipment and a storage medium. The method comprises the following steps: acquiring service data and a pre-configured data standard table; establishing a mapping relation between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data; determining a service product corresponding to the service data and a data analysis template corresponding to the service product; and comparing and analyzing the service data and the reference service data in the data analysis template, and comparing and analyzing the standard data item and the reference standard data item in the data analysis template to obtain a data analysis report. By adopting the method, the data analysis cost can be reduced.
Description
Technical Field
The present application relates to the field of data analysis technologies, and in particular, to a data analysis method, apparatus, computer device, and storage medium.
Background
Data analysis refers to the process of analyzing a large amount of collected data using appropriate statistical analysis methods, extracting useful information and forming conclusions to study and summarize the data in detail. The result of the data analysis has a great influence on the on-line of the business product, and when the data analysis result corresponding to some data is unqualified, the business product cannot be on-line.
Currently, analysis is typically performed directly on the source table data. However, since the data sources are varied, different custom scripts need to be developed for different data sources to analyze corresponding source table data, which increases the development and maintenance costs of the scripts and thus increases the data analysis costs. Therefore, the traditional data analysis mode has the problem of higher data analysis cost.
Disclosure of Invention
In view of the above, it is desirable to provide a data analysis method, an apparatus, a computer device, and a storage medium capable of reducing data analysis cost.
A method of data analysis, the method comprising:
acquiring service data and a pre-configured data standard table;
establishing a mapping relation between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data;
determining a service product corresponding to the service data and a data analysis template corresponding to the service product;
and comparing and analyzing the service data and the reference service data in the data analysis template, and comparing and analyzing the standard data item and the reference standard data item in the data analysis template to obtain a data analysis report.
In one embodiment, the establishing a mapping relationship between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data includes:
performing keyword identification on the service data to obtain service data keywords;
acquiring a standard keyword of each datum in the data standard table;
screening target standard keywords matched with the service data keywords from the standard keywords;
when the target standard keyword is screened out, establishing a mapping relation between the business data and data corresponding to the target standard keyword, and determining a standard data item corresponding to the target standard keyword as a standard data item corresponding to the business data.
In one embodiment, the establishing a mapping relationship between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data further includes:
when the target standard keyword is not screened out, the service data is sent to a terminal for processing;
receiving a data item corresponding to the service data feedback from the terminal;
and determining the received data item as a standard data item corresponding to the service data.
In one embodiment, the comparing and analyzing the service data and the reference service data in the data analysis template, and the comparing and analyzing the standard data item and the reference standard data item in the data analysis template to obtain the data analysis report includes:
acquiring an online rule parameter corresponding to the service product;
extracting reference service data and reference standard data items corresponding to the parameter service data from the data analysis template according to the online rule parameters;
comparing and analyzing the service data and the reference service data, and comparing and analyzing the standard data item and the reference standard data item to obtain a data analysis report
In one embodiment, after the comparing and analyzing the service data and the reference service data in the data analysis template, and the comparing and analyzing the standard data item and the reference standard data item in the data analysis template to obtain the data analysis report, the method further includes:
extracting an analysis result in the data analysis report;
classifying the analysis results to obtain classified analysis results;
marking unqualified online rule parameters according to an analysis result in a preset analysis category;
and determining the service products which do not accord with the on-line condition according to the marked on-line rule parameters.
In one embodiment, the method further comprises:
acquiring product demand information corresponding to the service product;
extracting unqualified online rule parameters in the service product according to the data analysis report;
and adjusting the extracted online rule parameters according to the product demand information.
In one embodiment, the step of creating the data analysis template includes:
acquiring product demand information corresponding to the service product;
extracting reference service data from the product demand information;
determining a reference standard data item corresponding to the reference service data according to the data standard table;
and establishing a data analysis template corresponding to the service product according to the reference service data and the reference standard data item.
A data analysis apparatus, the apparatus comprising:
the acquisition module is used for acquiring the service data and the pre-configured data standard table;
the mapping module is used for establishing a mapping relation between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data;
the determining module is used for determining a service product corresponding to the service data and a data analysis template corresponding to the service product;
an analysis module for comparing and analyzing the service data and the reference service data in the data analysis template, and comparing and analyzing the standard data item and the reference standard data item in the data analysis template to obtain a data analysis report
A computer device comprising a memory storing a computer program and a processor implementing the steps of the data analysis method described in the various embodiments above when the computer program is executed.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data analysis method described in the various embodiments above.
According to the data analysis method, the data analysis device, the computer equipment and the storage medium, the business data to be analyzed are mapped based on the pre-configured data standard table, the mapping relation between the business data and the data in the data standard table is established, the standard data item corresponding to the business data is obtained, complex operations such as data cleaning and conversion are not needed, the standard data item to be analyzed can be obtained based on the data standard table, and the acquisition efficiency and accuracy of the standard data item can be improved. The data analysis template matched with the business data is quickly determined according to the business product corresponding to the business data, the business data and the corresponding standard data items are respectively compared and analyzed based on the reference business data and the reference standard data items in the data analysis template, the efficiency and the accuracy of data analysis can be improved, and a data analysis report is quickly and accurately obtained based on the data analysis, so that further data analysis can be carried out based on the data analysis report, and the cost of data analysis can be reduced.
Drawings
FIG. 1 is a diagram of an exemplary implementation of a data analysis method;
FIG. 2 is a schematic flow chart diagram of a data analysis method in one embodiment;
FIG. 3 is a schematic flow chart diagram of a data analysis method in another embodiment;
FIG. 4 is a block diagram showing the structure of a data analysis apparatus according to an embodiment;
FIG. 5 is a diagram of the internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The data analysis method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 obtains the service data and the pre-configured data standard table, establishes a mapping relation between the service data and data in the data standard table to obtain a standard data item corresponding to the service data, determines a corresponding data analysis template according to a service product corresponding to the service data, and performs data analysis on the service data and the corresponding standard data item according to reference service data and the reference standard data item in the data analysis template to obtain a data analysis report. It is understood that the server 104 may obtain the service data from the terminal 102 and feed back a data analysis report obtained for the service data to the terminal 102. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by multiple servers.
In one embodiment, as shown in fig. 2, a data analysis method is provided, which is described by taking the application of the method to the server in fig. 1 as an example, and includes the following steps:
s202, acquiring service data and a pre-configured data standard table.
The service data is service-related data, and may specifically be sample data related to a service, which is input by a user through a terminal. Business data such as family basic information, personal health profile, or personal income and expense data. The data standard table is a standard table or a reference table which is established according to a preset standard and comprises a plurality of standard data. The data standard table can store data with different formats and can also store standard data items corresponding to the data. The data in the data standard table is standard data, that is, specification data, which is data that conforms to the national standard or specification and can be applied to a plurality of service products or a plurality of service systems.
Specifically, the server detects a data analysis trigger event in real time, and when the data analysis trigger event is detected, obtains service data to be analyzed according to the detected data analysis trigger event, and a pre-configured data standard table. The data analysis triggering event is an event for triggering data analysis operation, such as receiving a data analysis instruction sent by the terminal, or detecting that the current time is consistent with a preset data analysis triggering time.
In one embodiment, after receiving a data analysis instruction sent by the terminal, the server parses the received data analysis instruction to obtain service data to be analyzed, or obtains the service data to be analyzed from other devices through network communication according to the data analysis instruction. Other devices such as a terminal that triggers a data analysis instruction, or a server for storing service data.
In one embodiment, the server obtains a pre-configured table of data criteria from other devices, either locally or via network communication, based on the detected data analysis trigger event. The locally stored data criteria table is either preconfigured or asynchronously generated and synchronized to by other threads or other devices. Such as a server for generating and storing data criteria tables.
In one embodiment, the data in the standard data table is a standard field name, and the standard data item corresponding to the data is a field name identifier corresponding to the standard field name. For example, the standard field name of gender is included in the standard data table, which indicates that the features of "male" and "female" are collectively referred to as gender in the data standard table. The field name identifier is used to uniquely identify a standard field name in the data standard table.
In one embodiment, the service data includes field names and may further include a field value corresponding to each field name. For example, the service data includes a field name and gender, and may further include a male or a female corresponding to the gender.
And S204, establishing a mapping relation between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data.
The standard data item is used for uniquely identifying data in the data standard table, and specifically may be a data identifier corresponding to the data, and the standard data item in the data standard table corresponds to the data. The standard data item may specifically be a character string composed of at least one of characters such as numbers, letters, and symbols, such as a, B, or C.
Specifically, after acquiring the service data to be analyzed and the preconfigured data standard table, the server matches the service data to be analyzed with each data in the data standard table, so as to screen the data corresponding to the service data to be analyzed from the data standard table according to the matching result. The server establishes a corresponding relation or a mapping relation between the service data to be analyzed and the screened data so as to realize the mapping between the service data and the data in the data standard table. And the server acquires the standard data item corresponding to the screened data from the data standard table, and determines the acquired standard data item as the standard data item corresponding to the corresponding service data.
For example, assuming that the service data to be analyzed is a personal health profile, the data included in the data standard table has a personal health profile, and the standard data item corresponding to the personal health profile in the data standard table is a, the mapping relationship between the personal health profile and the service data in the data standard table is established according to the above manner, and the standard data item corresponding to the service data is determined as a. In one embodiment, the server matches the service data to be analyzed with the data in the data standard table, establishes a mapping relationship between the data meeting a preset matching condition and the service data, and takes a standard data item corresponding to the data meeting the matching condition as a standard data item corresponding to the service data. The preset matching condition is, for example, that the matching degree is greater than or equal to the threshold matching degree.
In one embodiment, there are multiple traffic data to be analyzed. And the server respectively establishes a mapping relation between each service data and the data in the data standard table, and determines a standard data item corresponding to each service data based on the mapping relation.
In one embodiment, the business data includes field names and corresponding field values. The data in the data standard table is a standard field name, and the standard data item corresponding to the data is a field name identifier corresponding to the standard field name, that is, the data standard table includes the standard field name and a corresponding field name identifier. The server screens standard field names matched with the field names in the service data from the data standard table, establishes a mapping relation between the field names in the service data and the screened standard field names, namely establishes a mapping relation between the service data and the screened standard field names, and determines field name identifications corresponding to the screened standard field names in the data standard table as field name identifications corresponding to the corresponding field names, so as to obtain the field name identifications corresponding to the service data.
S206, determining a service product corresponding to the service data and a data analysis template corresponding to the service product.
The business product may be an entity product or a virtual product. The physical product refers to a product having a physical entity such as a car, a headset, or a bank card. A virtual product is a product without a physical entity, such as a medical insurance product or voucher, as opposed to a physical product. The data analysis template is a template established according to business data involved in data analysis of the business product. The data analysis template is used for specifying business data to be analyzed and corresponding standard data items.
Specifically, the server determines a corresponding service product according to the service data to be analyzed, and acquires a data analysis template established corresponding to the service product according to the determined service product.
In one embodiment, the server locally stores the correspondence between the service data and the service product. And the server locally searches one or more corresponding service products according to the service data to be analyzed. For example, assuming that the service data to be analyzed is a case of personal medical insurance participation, the corresponding service product may be a hospital medical insurance product, a hospital subsidy medical insurance product, and/or an accident medical insurance product.
In one embodiment, the server locally stores the corresponding relationship between the service data identification and the service product identification. The service data identification is used for identifying the service data, and the service product identification is used for identifying the service product. And the server determines corresponding service product identification according to the service data identification corresponding to the service data to be analyzed and the corresponding relationship stored locally, and determines the service product corresponding to the service data according to the service product identification.
In one embodiment, the data analysis template is pre-configured based on business data corresponding to the business product. The server obtains a pre-configured data analysis template from a local or other device according to the business product. Such as a terminal or server for configuring the data analysis template.
And S208, comparing and analyzing the service data and the reference service data in the data analysis template, and comparing and analyzing the standard data item and the reference standard data item in the data analysis template to obtain a data analysis report.
The data analysis report is generated based on the business data and the analysis result corresponding to the corresponding standard data item, and can be used for intuitively knowing the data analysis result. The data analysis report may be in the form of a chart, a table, or text, and is not limited herein.
Specifically, the server extracts pre-configured reference service data and corresponding reference standard data items from the acquired data analysis template, performs data analysis on the service data to be analyzed based on the extracted reference service data, performs data analysis on the corresponding standard data items based on the extracted reference standard data items, and generates corresponding data analysis reports according to analysis results.
In one embodiment, the data analysis template includes a plurality of pre-configured reference service data and a reference standard data item corresponding to each reference service data. After the server obtains a standard data item corresponding to the service data to be analyzed through mapping, a reference standard data item matched with the obtained standard data item and reference service data corresponding to the reference standard data item are extracted from a data analysis template corresponding to the service data. Therefore, corresponding reference business data are extracted from the data analysis template based on the business data to be analyzed, and data analysis is carried out based on the reference business data, so that the data analysis efficiency can be improved.
In one embodiment, the data analysis template includes reference service data and corresponding reference standard data items. Each reference service data includes a reference field name and may further include a reference field value corresponding to the reference field name. And the reference standard data item corresponding to the reference service data is a field name identifier corresponding to the reference field name in the reference service data. The server extracts the reference field names, the reference field values corresponding to the reference field names and the field name identifications from the data analysis template, compares and analyzes the reference field values corresponding to the reference field names and the corresponding field values in the service data to be analyzed, and compares and analyzes the field name identifications corresponding to the reference field names and the field name identifications corresponding to the service data to obtain a data analysis report.
In one embodiment, the server determines an analysis result corresponding to the service data according to a comparison analysis result corresponding to the field name identifier and/or a comparison analysis result corresponding to the reference field value, and obtains a data analysis report. For example, when a field name identifier that fails to match a field name identifier corresponding to the service data exists in the data analysis template, the server determines that the analysis result of the service data is not qualified. And when the field name identification which is corresponding to the business data and fails to be matched exists in the data analysis template, but the field name identification which is corresponding to the business data comprises the field name identification which is associated with the field name identification which fails to be matched, the server judges that the analysis result of the business data is qualified. And when the field name identification corresponding to the business data is successfully matched with the field name identification in the data analysis template, but the field value corresponding to the field name identification in the business data is unsuccessfully matched with the reference field value corresponding to the field name identification in the data analysis template, the server judges that the analysis result of the business data is unqualified.
In one embodiment, the reference service data in the data analysis template includes a reference field name and does not include a reference field value corresponding to the reference field name. And the server compares and analyzes the field name identification corresponding to the reference field name in the data analysis template with the field name identification corresponding to the service data to obtain a corresponding data analysis report.
According to the data analysis method, the business data to be analyzed is mapped based on the pre-configured data standard table to obtain the standard data item corresponding to the business data, complex operations such as data cleaning and conversion are not needed, the standard data item to be analyzed can be obtained based on the data standard table, and the acquisition efficiency and accuracy of the standard data item can be improved. The data analysis template matched with the business data is quickly determined according to the business product corresponding to the business data, the business data and the corresponding standard data items are analyzed based on the reference business data and the reference standard data item distribution in the data analysis template, the efficiency and the accuracy of data analysis can be improved, and a data analysis report is quickly and accurately obtained based on the data analysis, so that the further data analysis can be carried out based on the data analysis report, and the cost of the data analysis can be reduced.
In one embodiment, step S204 includes: performing keyword identification on the service data to obtain service data keywords; acquiring a standard keyword of each datum in a data standard table; screening target standard keywords matched with the service data keywords from the standard keywords; and when the target standard keyword is screened out, establishing a mapping relation between the service data and the data corresponding to the target standard keyword, and determining a standard data item corresponding to the target standard keyword as a standard data item corresponding to the service data.
The service data keywords are keywords extracted from the service data and can be used for representing the service data. The criteria key is a key used to characterize data in the data criteria table. Business data keywords and labeling keywords such as health profile, employment income, participation condition or sign information, etc.
Specifically, the server performs keyword recognition on the service data to be analyzed according to a preset keyword extraction mode to obtain a service data keyword extracted from the service data. And the server acquires the standard key corresponding to each data from the data standard table. And the server respectively matches the service data keywords with each standard keyword so as to screen the target standard keywords matched with the service data keywords from the acquired standard keywords. When the target standard keywords matched with the business data keywords are screened out from the acquired standard keywords, the server establishes a mapping relation between the business data and data corresponding to the target standard keywords in the data standard table. And the server acquires a standard data item corresponding to the screened target standard keyword from the data standard table, and determines the acquired standard data item as a standard data item corresponding to the corresponding service data. The preset keyword extraction method is, for example, a keyword matching method based on a keyword set, or a natural speech processing technology. The server may extract the service data keywords in the service data according to the existing keyword recognition technology, which is not described herein again.
For example, it is assumed that the data included in the data standard table includes a personal health profile, personal income expenditure data, and personal medical insurance participation situation, the standard keywords and standard data items corresponding to the personal health profile are respectively the health profile and a, the standard keywords and standard data items corresponding to the personal income expenditure data are respectively the employment income and B, and the standard keywords and standard data items corresponding to the personal medical insurance participation situation are respectively the medical insurance participation situation and C. If the business data to be analyzed is a personal health file, and the business data keywords extracted from the business data are health files, determining the target standard keywords matched with the business data keywords as the health files according to the mode, and thus determining the standard data item corresponding to the business data as A.
In one embodiment, the server extracts the standard keyword from each data in the data standard table according to a preset keyword extraction mode. It will be appreciated that the server may extract the service data keywords from the service data and the standard keywords from the data in the standard data table in the same keyword extraction manner. Therefore, the matching degree between the business data keywords and the standard keywords can be improved, and the accuracy of data analysis can be improved.
In one embodiment, the data criteria table stores criteria keywords corresponding to each piece of data. And the server directly acquires the standard key corresponding to each data from the data standard table. In this way, the standard keywords corresponding to each data are extracted and stored in advance, and the acquisition efficiency of the standard keywords can be improved.
In one embodiment, the business data includes field names, and each field name corresponds to a field value and field name description data. And the server identifies keywords of the field names and/or the field name description data to obtain service data keywords. For example, if the service data includes a field name of gender, performing keyword recognition on the field name to obtain a service data keyword of gender; if the business data comprises the field name a and the field name description data corresponding to the field name a, namely 'a is gender', the keyword identification is carried out on the field name description data to obtain the business data keyword of the gender.
In one embodiment, the criteria key specific to the data in the data criteria table may be the data itself. If the data in the data standard table is the standard field name, the standard keyword corresponding to the data may be the standard field name itself. In the embodiment, the mapping relationship between the service data to be analyzed and the data in the data standard table is established based on the keyword comparison mode, so that the standard data item corresponding to the service data is determined, and the accuracy and the efficiency of the determined standard data item can be improved.
In one embodiment, step S204 further comprises: when the target standard keyword is not screened out, the service data is sent to the terminal for processing; receiving a data item fed back by the terminal corresponding to the service data; and determining the received data item as a standard data item corresponding to the service data.
Specifically, when the target standard keyword matched with the service data keyword is not screened out from the data standard table, it indicates that data corresponding to the service data does not exist in the data standard table, and the server sends the service data to the terminal. The terminal displays the received service data, acquires a data item pre-input or pre-selected by the user aiming at the service data when the submission triggering operation of the user aiming at the displayed service data is detected, and feeds back the acquired data item to the server. After receiving the data item fed back by the terminal aiming at the service data, the server determines the received data item as a standard data item corresponding to the service data.
In one embodiment, the server receives a data item and a keyword fed back by the terminal aiming at the service data, determines the data item as a standard data item corresponding to the service data, determines the keyword as a standard keyword corresponding to the service data, and correspondingly updates the service data, the standard data item and the standard keyword into a data standard table. It can be understood that the server may also receive standard data fed back by the terminal for the service data, and correspondingly update the standard data, the standard data item and the standard keyword into the data standard table. By continuously updating and perfecting the data standard table, the data analysis efficiency and accuracy can be improved.
In one embodiment, the data item fed back by the terminal for the service data is a field name identifier. The server receives the field names fed back by the terminal aiming at the service data and the field name identifications corresponding to the field names, establishes a mapping relation between the received field names and the field names in the service data, and determines the received field name identifications as the field name identifications corresponding to the field names in the service data, namely determines the received field name identifications as standard data items corresponding to the service data. For example, the service data includes a field name and a corresponding "man" field. And when the server fails to successfully map the service data to the data in the data standard table, namely when the standard data item corresponding to the service data cannot be determined from the data standard table, the server sends the service data to the terminal for manual mapping. The user can determine that the field name corresponding to the field name a in the data standard table is the gender based on the field value of 'male', thereby determining the field name identification corresponding to the field name in the data standard table as the data item corresponding to the service data, and feeding back the determined data item to the server through the terminal.
In the above embodiment, when the standard data item corresponding to the service data cannot be determined based on the data standard table, the mapping of the service data is implemented in a manual labeling manner, and the standard data item is determined. Therefore, on the basis of a mode of combining automation and manpower, the extraction accuracy can be ensured under the condition of ensuring the extraction efficiency of the standard data item, and the efficiency and the accuracy of data analysis can be ensured.
In one embodiment, step S208 includes: acquiring an online rule parameter corresponding to a service product; extracting reference service data and reference standard data items corresponding to the parameter service data from the data analysis template according to the online rule parameters; and performing data analysis on the service data according to the reference service data, and performing data analysis on the standard data item according to the reference standard data item to obtain a data analysis report.
Wherein, the online rule parameter is a quantization parameter of the online rule. The online rule is a rule or basis for judging whether the user meets the product application condition. Taking a business product as a medical insurance product as an example, the online rule is used for judging whether each item of information of the insured person meets the conditions or requirements of the medical insurance product.
Specifically, the server acquires an online rule parameter pre-configured corresponding to a service product, and extracts corresponding reference service data and a reference standard data item corresponding to the reference service data from a data analysis template corresponding to the service product according to the acquired online rule parameter. The server compares and analyzes the extracted reference service data with the corresponding service data, compares and analyzes the extracted reference standard data item with the corresponding standard data item, and generates a data analysis report according to an analysis result.
In one embodiment, the server determines one or more service data identifications corresponding to the online rule parameters, and extracts reference service data corresponding to the determined service data identifications and reference standard data items corresponding to each reference service data from the data analysis template.
In one embodiment, the data standard table stores the corresponding relationship between the online rule parameter and the service data identifier or the reference service data. And the server matches the online rule parameters with the data standard table so as to extract the reference service data corresponding to the online rule parameters from the data standard table according to the matching result.
In one embodiment, the server obtains an analysis result corresponding to the service data according to a comparison result between the service data to be analyzed and the corresponding reference service data, and/or according to a comparison result between a standard data item corresponding to the service data to be analyzed and the corresponding reference standard data item. It is understood that the server may also determine the analysis result separately for the service data and the standard data item corresponding to the service data.
In one embodiment, each business product corresponds to a plurality of online rule parameters, and each online rule parameter corresponds to one or more reference business data, that is, each online rule parameter corresponds to one or more business data. And the server determines the analysis result of the online rule parameter based on the analysis result of one or more service data corresponding to the online rule parameter. Correspondingly, the analysis result of the business product is determined based on the analysis result of each of the plurality of online rule parameters corresponding to the business product. For example, when the proportion of qualified service data in the service data corresponding to the online rule parameter is greater than or equal to the first proportion threshold, the analysis result of the online rule parameter is determined to be qualified. And when the percentage of qualified online rule parameters in the plurality of online rule parameters corresponding to the service product is greater than or equal to the second percentage threshold, judging that the analysis result of the service product is qualified. Wherein, the first and second percentage thresholds can be customized, such as 100%.
In the above embodiment, according to the online rule parameter corresponding to the service product, the reference service data and the reference standard data item having higher relevance to the applicable scenario of the service product are extracted, and data analysis is performed based on the extracted reference service data and the extracted reference standard data item, so that the accuracy of data analysis can be improved.
In an embodiment, after step S208, the data analysis method further includes: extracting an analysis result in the data analysis report; classifying the analysis results to obtain classified analysis results; marking unqualified online rule parameters according to an analysis result in a preset analysis category; and determining the service products which do not accord with the on-line condition according to the marked on-line rule parameters.
Wherein, the analysis result is used for representing the quality or the qualification of the corresponding service data. The analysis category comprises a mapping category, a checking category, an online category and the like. The preset analysis category is, for example, an on-line category. The on-line condition is a preset condition or basis for determining whether the service product can be on-line, for example, the percentage of qualified on-line rule parameters in the on-line rule parameters corresponding to the service product is greater than or equal to the second percentage threshold.
Specifically, the server extracts an analysis result corresponding to each service data to be analyzed from the data analysis report, and classifies the extracted analysis result according to a preset analysis category to obtain a classified analysis result, that is, classifies each analysis result into a preset analysis category. And the server screens unqualified online rule parameters according to each analysis result classified to a preset classification category, and marks the screened online rule parameters. And the server determines the service products which do not accord with the on-line condition according to the marked on-line rule parameters according to the corresponding relation between the service products and the on-line rule parameters.
In one embodiment, the server determines corresponding online rule parameters according to the service data to be analyzed, extracts analysis results corresponding to the service data to be analyzed from the data analysis report, performs statistical analysis on the extracted analysis results to determine analysis results corresponding to the online rule parameters, and marks the unqualified online rule parameters according to the analysis results corresponding to the online rule parameters.
In one embodiment, the server determines a service product according to the service data to be analyzed, determines an online rule parameter corresponding to the service product, and determines a service product which does not meet the online condition according to the marked online rule parameter and the determined online rule parameter.
In the above embodiment, the unqualified online rule parameters are determined based on the data analysis report, and then the service products which do not conform to the online condition are determined, so that the service products which do not conform to the online condition can be correspondingly processed, development of the service products which do not conform to the online condition is avoided, and cost can be reduced.
In one embodiment, the data analysis method further includes: acquiring product demand information corresponding to a service product; extracting unqualified online rule parameters in the service product according to the data analysis report; and adjusting the extracted online rule parameters according to the product demand information.
The product requirement information is information which is required to be met by qualified online rule parameters in the service products and can be used for limiting the online rule parameters of the corresponding service products. The product demand information includes product policy information, specific demand information corresponding to a business product, and the like. The product policy information may be specifically policy information generated uniformly for a plurality of business products. The product policy information may correspond to a region.
Specifically, the server obtains corresponding product demand information according to a service product, and determines an online rule parameter corresponding to the service product. And the server extracts unqualified online rule parameters from the online rule parameters corresponding to the service products according to the analysis result of each service data in the data analysis report, and dynamically adjusts the extracted online rule parameters according to the acquired product demand information.
In one embodiment, the server dynamically adjusts the data threshold and/or data items in the unqualified online rule parameters according to the product demand information. The data threshold may be a numerical value corresponding to a specified field in the online rule parameter, for example, the medicine code in the online rule is 123456, and the data threshold may be 123456; the data items may be diagnostic codes, dosages, etc. data items in the online rule parameters.
In the above embodiment, the unqualified online rule parameters are dynamically adjusted according to the product demand information corresponding to the service product, so as to obtain the qualified online rule parameters, thereby obtaining the service product meeting the online condition, and thus, the accuracy of data analysis can be improved.
In some embodiments, the step of establishing the data analysis template comprises: acquiring product demand information corresponding to a service product; extracting reference service data from the product demand information; determining a reference standard data item corresponding to the reference service data according to the data standard table; and establishing a data analysis template corresponding to the business product according to the reference business data and the reference standard data item.
Specifically, the server acquires corresponding product demand information according to a service product of the data analysis template to be established, and extracts reference service data from the acquired product demand information. And the server matches the extracted reference service data with data in a pre-configured data standard table to determine data matched with the reference service data and a standard data item corresponding to the data from the data standard table, and determines the determined standard data item as a reference standard data item corresponding to the reference service data. And the server establishes a data analysis template according to the reference service data and the corresponding reference standard data item, and the data analysis template is used as a data analysis template corresponding to the corresponding service product.
In the above embodiment, the data analysis template is pre-established based on the product demand information corresponding to the business product and the data standard table, so that the business data to be analyzed can be quickly and accurately analyzed based on the data analysis template in the data analysis process, and the efficiency and the accuracy of data analysis can be improved.
As shown in fig. 3, in an embodiment, a data analysis method is provided, which specifically includes the following steps:
s302, acquiring service data and a pre-configured data standard table.
And S304, carrying out keyword identification on the service data to obtain service data keywords.
S306, acquiring the standard key words of each datum in the data standard table.
And S308, screening the target standard keywords matched with the service data keywords from the standard keywords.
S310, when the target standard keyword is screened out, establishing a mapping relation between the business data and the data corresponding to the target standard keyword, and determining the standard data item corresponding to the target standard keyword as the standard data item corresponding to the business data.
And S312, when the target standard keyword is not screened out, sending the service data to the terminal for processing.
And S314, receiving the data item fed back by the terminal corresponding to the service data.
And S316, determining the received data item as a standard data item corresponding to the service data.
S318, determining a service product corresponding to the service data and a data analysis template corresponding to the service product.
S320, obtaining the online rule parameters corresponding to the business products.
S322, extracting reference service data from the data analysis template according to the online rule parameters and reference standard data items corresponding to the parameter service data.
And S324, comparing and analyzing the service data and the reference service data, and comparing and analyzing the standard data item and the reference standard data item to obtain a data analysis report.
And S326, extracting the analysis result in the data analysis report.
And S328, classifying the analysis results to obtain classified analysis results.
And S330, marking unqualified online rule parameters according to the analysis result in the preset analysis category.
S332, determining the service products which do not meet the online condition according to the marked online rule parameters.
It should be understood that although the various steps in the flow diagrams of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided a data analysis apparatus 400 comprising: an obtaining module 402, a mapping module 404, a determining module 406, and an analyzing module 408, wherein:
an obtaining module 402, configured to obtain the service data and the preconfigured data standard table.
And the mapping module 404 is configured to establish a mapping relationship between the service data and data in the data standard table, so as to obtain a standard data item corresponding to the service data.
The determining module 406 is configured to determine a service product corresponding to the service data and a data analysis template corresponding to the service product.
The analysis module 408 is configured to perform comparison analysis on the service data and the reference service data in the data analysis template, and perform comparison analysis on the standard data item and the reference standard data item in the data analysis template to obtain a data analysis report.
In an embodiment, the mapping module 404 is further configured to perform keyword recognition on the service data to obtain a service data keyword; acquiring a standard keyword of each datum in a data standard table; screening target standard keywords matched with the service data keywords from the standard keywords; and when the target standard keyword is screened out, establishing a mapping relation between the service data and the data corresponding to the target standard keyword, and determining a standard data item corresponding to the target standard keyword as a standard data item corresponding to the service data.
In an embodiment, the mapping module 404 is further configured to send the service data to the terminal for processing when the target standard keyword is not screened out; receiving a data item fed back by the terminal corresponding to the service data; and determining the received data item as a standard data item corresponding to the service data.
In an embodiment, the analysis module 408 is further configured to obtain an online rule parameter corresponding to a service product; extracting reference service data and reference standard data items corresponding to the parameter service data from the data analysis template according to the online rule parameters; and comparing and analyzing the service data and the reference service data, and comparing and analyzing the standard data item and the reference standard data item to obtain a data analysis report.
In one embodiment, the analysis module 408 is further configured to extract analysis results from the data analysis report; classifying the analysis results to obtain classified analysis results; marking unqualified online rule parameters according to an analysis result in a preset analysis category; and determining the service products which do not accord with the on-line condition according to the marked on-line rule parameters.
In an embodiment, the data analysis apparatus 400 further includes: an adjustment module;
the adjusting module is used for acquiring product demand information corresponding to the service product; extracting unqualified online rule parameters in the service product according to the data analysis report; and adjusting the extracted online rule parameters according to the product demand information.
In an embodiment, the data analysis apparatus 400 further includes: a training module;
the training module is used for acquiring product demand information corresponding to a business product; extracting reference service data from the product demand information; determining a reference standard data item corresponding to the reference service data according to the data standard table; and establishing a data analysis template corresponding to the business product according to the reference business data and the reference standard data item.
For specific limitations of the data analysis device, reference may be made to the above limitations of the data analysis method, which are not described herein again. The modules in the data analysis device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing a standard data table and a data analysis template. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data analysis method.
It will be appreciated by those skilled in the art that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the data analysis method in the above embodiments when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, realizes the steps of the data analysis method in the above-mentioned respective embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of data analysis, the method comprising:
acquiring service data and a pre-configured data standard table; the data standard table is a standard table or a reference table which is established according to a preset standard and comprises a plurality of standard data;
establishing a mapping relation between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data; wherein the standard data item in the data standard table corresponds to the data, and the standard data item is used for uniquely identifying the data in the data standard table;
determining a service product corresponding to the service data and a data analysis template corresponding to the service product;
acquiring an online rule parameter corresponding to the service product; the online rule parameter is a quantitative parameter of an online rule, and the online rule is a rule or basis for judging whether a user meets a product application condition;
extracting reference service data and reference standard data items corresponding to the parameter service data from the data analysis template according to the online rule parameters;
performing data analysis on the service data according to the reference service data, and performing data analysis on the standard data item according to the reference standard data item to obtain a data analysis report;
the establishing of the mapping relationship between the service data and the data in the data standard table to obtain the standard data item corresponding to the service data includes:
performing keyword identification on the service data to obtain service data keywords;
acquiring a standard keyword of each data in the data standard table;
screening target standard keywords matched with the service data keywords from the standard keywords;
when a target standard keyword is screened out, establishing a mapping relation between the business data and data corresponding to the target standard keyword, and determining a standard data item corresponding to the target standard keyword as a standard data item corresponding to the business data;
the data analysis template comprises the reference service data and corresponding reference standard data items, each reference service data comprises a reference field name, and the reference standard data item corresponding to the reference service data is a field name identifier corresponding to the reference field name in the reference service data;
the extracting of reference service data from the data analysis template according to the online rule parameter and a reference standard data item corresponding to the parameter service data includes:
determining one or more service data identifications corresponding to the online rule parameters;
extracting the reference service data corresponding to the determined service data identification and the reference standard data item corresponding to the reference service data from the data analysis template according to the corresponding one or more service data identifications;
when a field name identifier which fails to be matched with a field name identifier corresponding to the service data exists in the data analysis template, judging that the analysis result of the service data is unqualified; when the field name identification which is matched with the field name identification corresponding to the service data fails exists in the data analysis template, and the field name identification corresponding to the service data comprises the field name identification which is associated with the field name identification which is matched with the service data, judging that the analysis result of the service data is qualified; and when the field name identification corresponding to the business data is successfully matched with the field name identification in the data analysis template, and the field value corresponding to the field name identification corresponding to the business data is unsuccessfully matched with the reference field value corresponding to the field name identification in the data analysis template, judging that the analysis result of the business data is unqualified.
2. The method according to claim 1, wherein the establishing a mapping relationship between the service data and data in the data standard table to obtain a standard data item corresponding to the service data further comprises:
when the target standard keyword is not screened out, the service data is sent to a terminal for processing;
receiving a data item corresponding to the service data feedback by the terminal;
and determining the received data item as a standard data item corresponding to the service data.
3. The method according to claim 1, wherein the service data keyword is a keyword extracted from the service data and capable of being used to characterize the service data; the standard key is a key for characterizing data in the data standard table.
4. The method of claim 1, wherein obtaining the service data and the pre-configured data criteria table comprises:
detecting a data analysis trigger event in real time; the data analysis triggering event is an event for triggering data analysis operation, and the data analysis triggering event comprises that the current time is consistent with the preset data analysis triggering time;
when the data analysis trigger event is detected, the service data and the preconfigured data standard table are obtained according to the detected data analysis trigger event.
5. The method of claim 1, wherein after comparing the business data with reference business data in the data analysis template and comparing the standard data item with reference standard data item in the data analysis template to obtain a data analysis report, the method further comprises:
extracting an analysis result in the data analysis report;
classifying the analysis results to obtain classified analysis results;
marking unqualified online rule parameters according to an analysis result in a preset analysis category;
determining a service product which does not accord with the online condition according to the marked online rule parameter;
when the proportion of qualified service data in the service data corresponding to the online rule parameter is greater than or equal to a first proportion threshold value, judging that the analysis result of the online rule parameter is qualified; and when the percentage of qualified online rule parameters in the plurality of online rule parameters corresponding to the service product is greater than or equal to a second percentage threshold, judging that the analysis result of the service product is qualified.
6. The method of claim 1, further comprising:
acquiring product demand information corresponding to the service product;
extracting unqualified online rule parameters in the service product according to the data analysis report;
and adjusting the extracted online rule parameters according to the product demand information.
7. The method of any one of claims 1 to 6, wherein the step of creating the data analysis template comprises:
acquiring product demand information corresponding to the service product;
extracting reference service data from the product demand information;
determining a reference standard data item corresponding to the reference service data according to the data standard table;
and establishing a data analysis template corresponding to the service product according to the reference service data and the reference standard data item.
8. A data analysis apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring the service data and the pre-configured data standard table; the data standard table is a standard table or a reference table which is established according to a preset standard and comprises a plurality of standard data;
the mapping module is used for establishing a mapping relation between the service data and the data in the data standard table to obtain a standard data item corresponding to the service data; wherein the standard data item in the data standard table corresponds to the data, and the standard data item is used for uniquely identifying the data in the data standard table;
the determining module is used for determining a service product corresponding to the service data and a data analysis template corresponding to the service product;
the analysis module is used for acquiring online rule parameters corresponding to the business products; the online rule parameter is a quantitative parameter of an online rule, and the online rule is a rule or basis for judging whether a user meets a product application condition; extracting reference service data and a reference standard data item corresponding to the parameter service data from the data analysis template according to the online rule parameters; performing data analysis on the service data according to the reference service data, and performing data analysis on the standard data item according to the reference standard data item to obtain a data analysis report;
wherein the mapping module is further configured to: performing keyword identification on the service data to obtain service data keywords; acquiring a standard keyword of each data in the data standard table; screening target standard keywords matched with the service data keywords from the standard keywords; when a target standard keyword is screened out, establishing a mapping relation between the business data and data corresponding to the target standard keyword, and determining a standard data item corresponding to the target standard keyword as a standard data item corresponding to the business data;
the data analysis template comprises the reference service data and corresponding reference standard data items, each reference service data comprises a reference field name, and the reference standard data item corresponding to the reference service data is a field name identifier corresponding to the reference field name in the reference service data;
the analysis module is further configured to: determining one or more service data identifications corresponding to the online rule parameters; extracting the reference service data corresponding to the determined service data identification and the reference standard data item corresponding to the reference service data from the data analysis template according to the corresponding one or more service data identifications;
when a field name identifier which fails to be matched with a field name identifier corresponding to the service data exists in the data analysis template, judging that the analysis result of the service data is unqualified; when a field name identifier which is matched with the field name identifier corresponding to the service data in failure exists in the data analysis template, and the field name identifier corresponding to the service data comprises the field name identifier which is associated with the field name identifier which is matched in failure, judging that the analysis result of the service data is qualified; and when the field name identification corresponding to the business data is successfully matched with the field name identification in the data analysis template and the field value corresponding to the field name identification corresponding to the business data is unsuccessfully matched with the reference field value corresponding to the field name identification in the data analysis template, judging that the analysis result of the business data is unqualified.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN114416911A (en) * | 2021-05-21 | 2022-04-29 | 深圳市智尊宝数据开发有限公司 | Analysis report generation method, related device, medium, and program |
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