CN112632136A - Data statistical analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
One or more embodiments of the present specification provide a data statistical analysis method, an apparatus, an electronic device, and a storage medium. The invention is suitable for statistical analysis of service data with complex sources. Firstly, collecting service data from different systems and storing the service data in a database, then searching the database by a user-defined Structured Query Language (SQL) so as to obtain a data set meeting preset conditions, and then screening the obtained data set by the user-defined screening conditions according to actual service requirements. And after the screening is finished, correlating the obtained data with the chart template and outputting the chart containing the screened data, wherein the statistical dimension of the chart template also needs to be customized by a user according to the actual condition. Therefore, the data statistical analysis is realized by a reliable and simple method, and the visualized result is output.
Description
Technical Field
One or more embodiments of the present description relate to the field of statistical analysis of data, and more particularly, to the analysis of retrieval of business data based on business requirements.
Background
Data statistics function each information system is the most core part, data statistics in traditional statistics work generally designs corresponding functions by obtaining user requirements, and defining data source, statistical dimension, statistical mode, display mode and filing mode of statistical data, but along with continuous change of information system service, data quantity is continuously increased, and user requirements are continuously changed, so that original statistics work is not flexible, statistical results required by users are difficult to achieve, and data statistics becomes a bottleneck of development of information systems.
Therefore, it is important to improve the statistical efficiency, make the statistical function adaptive to the information system with continuously changing services, and make a standard statistical analysis method with perfect functions, diversified statistical modes, and capable of meeting the statistical requirements of various information systems.
Disclosure of Invention
In view of the above, one or more embodiments of the present disclosure are directed to a method, an apparatus, an electronic device, and a storage medium for statistical data analysis, so as to solve the problems in the existing systems.
In view of the above, one or more embodiments of the present disclosure provide a method for statistical analysis of data, comprising the steps of:
collecting service data and storing the service data in a database; the service data comprises user data and user behavior data;
determining the data type of data to be retrieved by a user;
retrieving in the database based on the data type, acquiring data conforming to the data type and generating a data set;
screening the data in the data set according to the user-defined screening condition to obtain the data required by the user;
and importing the data required by the user into the chart template obtained by the user through self-defining at least one statistical dimension, and generating and outputting a data chart.
Based on the same inventive concept, one or more embodiments of the present specification further provide a data statistical analysis apparatus, including:
the data collection module is used for collecting data from different service systems and storing the data in a database;
the data retrieval module is used for self-defining and retrieving the required Structured Query Language (SQL) by a user according to the data type required to be retrieved and executing the SQL to obtain an information set in the database;
the data screening module is used for customizing screening conditions by the user and screening the information set to obtain required data information;
and the chart generation module is used for customizing the statistical dimension of the icon template by the user and associating the data information with the chart template to generate a chart.
Based on the same inventive concept, one or more embodiments of the present specification further provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the statistical data analysis method as described in any one of the above items when executing the computer program.
Based on the same inventive concept, one or more embodiments of the present specification further provide a non-transitory computer-storable medium that stores computer instructions for performing any of the statistical data analysis methods described above.
As can be seen from the above description, one or more embodiments of the present disclosure provide a data statistical analysis method, apparatus, electronic device, and storage medium, which have the following advantages compared to the prior art:
most of systems provide relatively fixed statistical functions for users, and because of diversity of business requirements and uncertainty of leadership requirements, a new statistical report needs to be generated each time, software needs to be upgraded, which brings great inconvenience to users.
The query statistics function is generated from a data source, a report template, statistics dimensions, a display mode, a filing mode and query conditions in a user-customized mode.
Cross-database and cross-system combined query and statistics are allowed, and multi-database and multi-system combined statistics reports are generated through user customization.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic diagram illustrating the steps of a statistical data analysis method described herein;
FIG. 2 is a schematic flow diagram illustrating data screening in accordance with one or more embodiments of the present disclosure;
FIG. 3 is a schematic diagram of a data statistical analysis device according to one or more embodiments of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.
As described in the background section, the existing statistical analysis methods of data have deficiencies, and the applicant has discovered the following problems in implementing the present disclosure: in the conventional statistical system, the statistical function provided for the user is relatively fixed, and when the endless new service requirements and uncertain leader requirements are met, corresponding software needs to be continuously upgraded to meet the requirements, which brings great inconvenience to the user.
In view of this, one or more embodiments of the present disclosure provide a method for statistical data analysis, which includes the following steps: firstly, collecting service data from different systems and storing the service data in a database; during query, searching data in a database by a user-defined SQL statement to obtain data meeting requirements, and then putting the data meeting the requirements into a data set; the user self-defines the screening condition and screens the data in the obtained information set to obtain the data meeting the screening condition; the user self-defines the chart template, mainly self-defines the statistical dimension of the template, then imports the data which is queried by SQL sentences and screened by conditions into the chart template, processes the data according to the statistical dimension and outputs the chart.
It can be seen that the data statistical analysis method described in one or more embodiments of the present specification can implement conditions such as data source, report template, statistical dimension, presentation mode, archiving mode, query condition, etc. customized by a user, allow the user to customize and generate a multi-database and multi-system joint statistical report, support the user to perform SQL query on full data, and visualize the query result.
The technical solution of the method is described below by means of specific examples.
Referring to fig. 1, a statistical data analysis method according to one or more embodiments of the present disclosure includes the following steps:
and step S101, collecting service data and storing the service data in a database.
In this step, the data such as the key problems and key clues reported by each unit and the entered "situation management ledger" are collected, and the collected data are uploaded to the database.
Step S102, determining the data type of the data to be retrieved by the user.
And S103, retrieving in the database based on the data type, acquiring data conforming to the data type and generating a data set.
In the step, the database is retrieved by defining retrieval SQL sentences, query types, query parameters and operational characters, so that an information set meeting the requirements of users is formed.
And step S104, screening the data in the data set according to the user-defined screening condition to obtain the data required by the user.
In this step, for the important point problem, the important clue, the entered "situation management standing book" and the like, the data set user can define the screening condition and screen according to the actual business requirements, for example, the inspection and inspection work situation specifically developed by a certain unit in a certain year.
The unit name and time can be used as parameters to be input in the browser and clicked for query, the browser sends a request containing the parameters to the server, the server identifies the parameters and fills the parameters into the SQL sentence defined by the user in the step S103 to form a complete SQL sentence, and the server calls the complete SQL sentence to search the database and responds the result to the browser to display the result to the user.
Step S105, importing the data required by the user into the chart template obtained by the user through self-defining at least one statistical dimension, and generating and outputting a data chart.
In this step, all data sets are shared by defining a chart template: data collected by a user of a central patrol working office is displayed in a self-defined chart form, attributes of the chart are defined through a template, the user only needs to define statistical dimensions in a self-defined mode when using the chart (for example, a line chart, the user only needs to define 'legend', 'abscissa' and 'ordinate' by himself, and other attributes such as text background colors of the chart and the like can be uniformly defined through the template), the user associates screened data information with the chart template, displays the screened information in the chart form, and stores historical query retrieval records.
In one or more embodiments of the present specification, when the service data is uploaded to the database, a plurality of check manners including CRC check are required to ensure accuracy of the data. An advanced data compression algorithm is adopted when business data are stored, prototype compression is carried out on a large amount of real-time data, a real-time database storage system is designed by combining with an efficient data retrieval strategy, data compression is traditionally used for reducing disk space, an efficient compression algorithm based on time and space dimensions is adopted, and dead zones and linear compression are combined.
The storage database adopts Oracle, and in order to fully utilize the functional characteristics of the Oracle database, the optimization is mainly carried out on three aspects of database standardized design, database partitioning technology and database fragmentation technology:
the database standardization design: in the case of completely following the third paradigm, the association between tables is a simplest configuration, so that there is no redundancy in the association between tables, and low coupling connection between tables is achieved, thereby enhancing the independence between tables.
Partitioning a database: in order to improve the access speed of data, a data partitioning technology is adopted to partition key nodes, so that the performance of the system can be optimized, and the data table can be partitioned through the Oracle self-technology. After the data table is partitioned, the data table appears to be a complete table to outsiders, and the data in the data table is stored on a plurality of table spaces. Each time data is queried, the full query is not performed, and the corresponding table space is scanned independently. The partitioning technique subdivides the data table, data index, or data index organization table into data segments. Each data segment has its own identity, and its own storage characteristics can be selected individually. Such operation results in a significant increase in system performance.
Database fragmentation: the fragmentation technology is a data division of an application layer, and a large database file is divided into a plurality of small data block files, so that the performance problem can be relieved. When there is a large amount of data, affinity tables may be properly partitioned onto a database. Under the condition of huge data quantity of the single table, the single table can be divided into a plurality of databases according to certain rules, the system is firstly vertically divided by combining with the actual development of vertical division and horizontal division, and then the actual single table is horizontally divided.
In one or more embodiments of the present description, referring to fig. 2, the flow of data screening is as follows.
Step S201, the user selects a data set to be filtered, and defines a filtering condition.
Step S202, the user sends a request to the server through the browser, and transmits the selected information set and the customized screening condition as parameters to the server.
And step S203, the server generates complete retrieval SQL through processing after acquiring the parameters.
And step S204, calling the database through SQL to obtain the screened data, responding the data to a client browser and displaying the data to a user.
The application scenario is as follows, for example, a user wants to perform statistical query on an expert talent base: firstly, establishing an expert talent base data set in a first step, defining a statistical range of an expert talent base, for example, if a user wants to count expert personnel with sex of male and belonging to a unit of national grid headquarters, the user only needs to define the conditions on an interface, and then click to inquire; the browser sends a request to the server, the request comprises parameters defined by a user, the server extracts the parameters of sex, affiliated units and the like contained in the request and fills the extracted parameters into basic SQL sentences of the retrieval data set to form a complete retrieval SQL sentence; and finally, calling the retrieval SQL sentence by the server side to retrieve the database, and displaying the obtained personnel data which conform to the sex of male and belong to the unit of national grid headquarter on a browser for users to look up or further process.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, corresponding to the method of any one of the above embodiments, one or more embodiments of the present specification further provide a data statistical analysis device.
Referring to fig. 3, the data statistical analysis apparatus includes:
the data collection module 301 collects data from different service systems and stores the data in a database;
the data retrieval module 302 defines and retrieves the required structured query language SQL by a user and executes the SQL to obtain an information set in the database;
the data screening module 303 defines screening conditions by the user and screens the information set to obtain required data information;
the chart generation module 304 customizes the statistical dimensions of the icon template, and associates the data information with the chart template to generate a chart.
In the data collection module 301, collected data is related data of each unit, such as that the unit mentions key problems and key clues in the report, and that the situation management ledger is already entered.
In the data retrieval module 302, the database built in the module 201 is retrieved through an SQL statement to obtain a data set meeting the requirement, wherein the parameter of the SQL statement is obtained by user-defined.
In the data filtering module 303, a user formulates a filtering condition according to an actual service requirement, for example, a patrol inspection working condition specifically performed by a certain unit in a certain year, and filters the data set obtained by the module 202 according to the filtering condition, so as to obtain data meeting the condition.
In the chart generating module 304, a chart template is selected according to the service requirement of the user and the statistical dimension is customized, for example, when the chart template is selected as a line chart, the user needs to customize a legend, a horizontal coordinate and a vertical coordinate. After the data is completed, the data obtained by screening in the module 303 is imported into the template, and the data can be displayed in a chart form.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
The apparatus of the foregoing embodiment is used to implement the corresponding statistical data analysis method in the foregoing embodiment, and has the beneficial effects of the corresponding embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, one or more embodiments of the present specification further provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the statistical data analysis method according to any of the above-mentioned embodiments is implemented.
Fig. 4 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the above embodiment is used to implement the corresponding data statistical analysis method in the foregoing embodiment, and has the beneficial effects of the corresponding embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, one or more embodiments of the present specification further provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the statistical data analysis method according to any of the above-described embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the foregoing embodiment are used to enable the computer to execute the data statistical analysis method according to any one of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiment, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the understanding of one or more embodiments of the present description, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A method of statistical data analysis, comprising:
collecting service data and storing the service data in a database; the service data comprises user data and user behavior data;
determining the data type of data to be retrieved by a user;
retrieving in the database based on the data type, acquiring data conforming to the data type and generating a data set;
screening the data in the data set according to the user-defined screening condition to obtain the data required by the user;
and importing the data required by the user into the chart template obtained by the user through self-defining at least one statistical dimension, and generating and outputting a data chart.
2. The method of claim 1, the method further comprising:
and before the service data is stored in the database, checking the service data by adopting a checking mode including Cyclic Redundancy Check (CRC).
3. The method according to claim 1, wherein the service data is stored in a database, in particular:
adopting a data compression algorithm to perform prototype compression on the service data, storing the service data in the database, and optimizing the database, wherein the optimization content comprises the following steps:
designing a database according to a third mode, so that the association among the data tables in the database has no redundancy, and low coupling connection is achieved;
partitioning the key nodes by adopting a data partitioning technology, and subdividing the data table, the data index and the data index organization table into data segments with different identifications;
and segmenting the large database file into a plurality of small data block files.
4. The method of claim 3, wherein the database employs a relational database management system Oracle.
5. The method of claim 1, wherein retrieving in the database based on the data type comprises:
generating a Structured Query Language (SQL) retrieval statement based on the data type and the user-defined content; wherein the user-defined content comprises: querying parameters and operational characters;
and executing the SQL retrieval statement, and retrieving in the database to acquire data conforming to the data type and generate a data set.
6. The method of claim 1, wherein filtering the data in the dataset according to the user-defined filtering condition comprises:
generating a Structured Query Language (SQL) retrieval statement based on the user-defined screening condition and the data set;
and executing the SQL retrieval statement, and screening in the data set to obtain the data required by the user.
7. The method of claim 1, wherein the at least one statistical dimension comprises: legend, abscissa, ordinate of the chart template.
8. A data statistics analysis device is used for carrying out service data statistics, and can be divided into the following modules according to the functions, and comprises the following modules:
the data collection module is used for collecting service data and uploading and storing the service data in a database;
the data retrieval module is used for self-defining and retrieving the required Structured Query Language (SQL) by a user according to the data type required to be retrieved and executing the SQL to obtain a data set meeting the requirement;
the data screening module is used for customizing screening conditions by the user, supplementing the SQL sentences into complete retrieval SQL sentences according to the screening conditions, and calling the database by the retrieval SQL sentences for screening to obtain required data;
and the chart generation module is used for self-defining the statistical dimension of the icon template by the user, importing the data obtained by the data screening module into the chart template, and generating and outputting a chart.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of statistical data analysis according to any one of claims 1 to 7 when executing the program.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the statistical data analysis method of any one of claims 1 to 7.
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| CN115495518A (en) * | 2022-09-22 | 2022-12-20 | 奇点浩翰数据技术(北京)有限公司 | Method and device for generating chart |
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| CN114610793A (en) * | 2022-03-09 | 2022-06-10 | 东莞市创为新科技有限公司 | Interaction method, system and storage medium based on big data statistical analysis |
| CN114610793B (en) * | 2022-03-09 | 2022-10-04 | 东莞市创为新科技有限公司 | Interaction method, system and storage medium based on big data statistical analysis |
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