An academic achievement real-time statistics method and device, electronic equipment and storage medium

Abstract

This invention relates to the field of database analysis and processing technology, and discloses a method, device, electronic device, and storage medium for real-time statistical analysis of academic achievements. It aims to solve the problems of low efficiency in statistical analysis of massive amounts of academic data, inflexible combination of search conditions, and insufficient data accuracy. The method includes receiving user configuration information, dynamically generating an SQL query statement conforming to the target database syntax based on a preset metadata mapping relationship between statistical elements and database table structures, sending the SQL query statement to the target database indicated by the database type identifier, executing the SQL query statement to complete the grouping and aggregation calculations of the data, receiving the result set of the completed grouping and aggregation calculations returned by the target database, and rendering and presenting it as an interactive statistical table on the user interface. This solution significantly reduces network transmission and computational overhead by completely pushing the computational tasks down to the database, achieving efficient and real-time statistical analysis of massive amounts of academic achievements.

Description

Technical Field

[0001] This invention relates to the field of database analysis and processing technology, and in particular to a method, apparatus, electronic device and storage medium for real-time statistical analysis of academic achievements. Background Technology

[0002] As the scale of academic output data continues to expand, research institutions, government departments, universities, and other entities have an increasingly urgent need for efficient and accurate statistical analysis of research outputs. The results have become a core basis for research management, resource allocation, and decision support. However, the current domestic market lacks a professional academic statistical product that can balance efficiency, flexibility, and accuracy. Existing technical solutions have significant shortcomings, specifically as follows:

[0003] (a) Manual statistical mode

[0004] This model requires first obtaining raw data from a database, and then cleaning, classifying, and statistically analyzing it using professional tools. Although manual intervention can ensure relative data accuracy to some extent, it has fundamental flaws: First, the process is cumbersome and time-consuming, with statistical cycles often lasting hours or even days when dealing with massive amounts of data, resulting in extremely low efficiency; second, it is difficult to support multi-dimensional and in-depth flexible cross-analysis, and cannot meet the statistical needs of real-time or frequently changing data; third, manual operation is prone to introducing subjective errors, and data updates are delayed, resulting in poor timeliness of statistical results.

[0005] (ii) Database platform with built-in statistical function mode

[0006] Some database platforms offer basic statistical functions, which avoid manual operation and have relatively fast response times, but they have core technical shortcomings: First, the underlying data has not undergone standardization and normalization, lacking a unified entity identification system, which can easily lead to statistical bias due to ambiguous names (such as different abbreviations of institutions or authors with the same name); Second, the statistical dimensions are fixed and the indicators are single, only supporting preset simple statistical scenarios, and cannot flexibly combine dimensions, filter conditions, and customize indicators according to user needs, making it difficult to meet the needs of complex scientific research evaluation scenarios; Third, the statistical logic is solidified at the application layer, failing to fully utilize the computing power of the database kernel, and still has response latency issues when dealing with ultra-large-scale data.

[0007] In summary, existing technologies cannot provide flexible and efficient real-time statistical services while ensuring the accuracy of results, creating a technical bottleneck where "efficiency and accuracy are mutually exclusive, and flexibility and professionalism are contradictory." This severely restricts the quality and efficiency of scientific research management and decision-making. Therefore, there is an urgent need in this field for a technical solution that can overcome this bottleneck. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention provides a method, device, electronic device, and storage medium for real-time statistical analysis of academic achievements, in order to solve the technical problems of low efficiency in statistical analysis of massive amounts of academic data, inflexible combination of search conditions, insufficient data accuracy, and poor interactive experience, thereby achieving a balance between statistical efficiency, flexibility, accuracy, and interactivity.

[0009] The objective of this invention is achieved through the following technical solution:

[0010] In one aspect, the present invention proposes a method for real-time statistical analysis of academic achievements, the method comprising:

[0011] Receive user configuration information, which includes: database type identifier, statistical dimension, search conditions and statistical indicators;

[0012] A query builder is used to dynamically generate SQL query statements that conform to the target database syntax specifications from user configuration information based on the preset metadata mapping relationship between statistical elements and database table structures; wherein, the SQL query statement includes: SELECT clause, FROM clause, WHERE clause and GROUP BY clause;

[0013] The generation process includes: converting statistical dimensions into grouping columns in the GROUP BY clause and SELECT clause of the SQL query statement; converting search conditions into matching conditions in the WHERE clause of the SQL query statement; and converting statistical indicators into aggregate functions in the SELECT clause of the SQL query statement.

[0014] The SQL query statement is sent to the target database pointed to by the database type identifier, and the target database executes the SQL query statement to complete the grouping and aggregation calculation of the data;

[0015] Receive the result set returned by the target database after the grouping and aggregation calculations have been completed;

[0016] The result set is pushed to the front end, where it is rendered and presented as an interactive statistical table on the user interface.

[0017] Optionally, receiving user configuration information includes: responding to a user's selection event for the database type on the user interface;

[0018] Based on the selected database type, the system dynamically retrieves and presents to the user statistical dimension options, search condition options, and statistical indicator options associated with that database type from the preset configuration library.

[0019] Optionally, the step of dynamically generating user configuration information into an SQL query statement that conforms to the target database syntax includes: responding to the entity name entered by the user on the user interface for any search condition; retrieving a list of candidate entities that match the entered entity name from a pre-established normalized entity library and returning it to the user interface for the user to select.

[0020] Receive the entity selected by the user from the candidate entity list and obtain the unique normalized identifier corresponding to the selected entity;

[0021] The unique normalized identifier is used as the query value and filled into the field corresponding to the search condition in the WHERE clause of the SQL query statement.

[0022] Optionally, the entity type of the entity name includes: author name, institution name, region name, subject classification name, journal name, conference name, and funding project name.

[0023] Optionally, the aggregate functions in the SELECT clause include: the COUNT() function for counting, the SUM() function for summing, the AVG() function for calculating the average, the MAX() function for obtaining the maximum value, and the MIN() function for obtaining the minimum value.

[0024] Optionally, the step of dynamically generating user configuration information into an SQL query statement that conforms to the target database syntax further includes:

[0025] Receive user-specified sorting configuration, which includes the fields on which the sorting is based and the sorting method;

[0026] The sorting configuration is converted into the ORDER BY clause in the SQL query statement, which is used to sort the result set returned after performing aggregation calculations on the target database.

[0027] Optionally, rendering and presenting the statistical table as an interactive interface on the user interface includes:

[0028] In response to a user's trigger operation on the header field of the statistical table, the result set is rearranged in ascending or descending order according to the sorting rule corresponding to the trigger operation;

[0029] In response to the user's download command, the result set is converted into a file in a preset format and pushed to the user's terminal;

[0030] In response to a user's click on any statistical value in the statistical table, a detailed query request for the academic achievement corresponding to that statistical value is generated. Based on the detailed query request, a list of detailed bibliographic information is obtained and displayed on the user interface.

[0031] In another aspect, the present invention provides a real-time statistical device for academic achievements, comprising:

[0032] The user configuration parsing module is used to receive user configuration information, which includes: database type identifier, statistical dimension, search conditions and statistical indicators;

[0033] The query builder module is used to dynamically generate SQL query statements that conform to the target database syntax specifications from user configuration information based on the preset metadata mapping relationship between statistical elements and database table structures. The SQL query statements include: SELECT clause, FROM clause, WHERE clause and GROUP BY clause.

[0034] The generation process includes: converting statistical dimensions into grouping columns in the GROUP BY clause and SELECT clause of the SQL query statement; converting search conditions into matching conditions in the WHERE clause of the SQL query statement; and converting statistical indicators into aggregate functions in the SELECT clause of the SQL query statement.

[0035] The query execution module is used to send the SQL query statement to the target database pointed to by the database type identifier, and the target database executes the SQL query statement to complete the grouping and aggregation calculation of the data;

[0036] The result receiving module is used to receive the result set of grouping and aggregation calculations returned by the target database after the execution is completed;

[0037] The result rendering module is used to push the result set to the front end, render it on the user interface and present it as an interactive statistical table.

[0038] A third aspect of the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the real-time statistical method for academic achievements as described in any one aspect of the first aspect.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the real-time statistical method for academic achievements as described in the first aspect.

[0040] The present invention has the following beneficial effects:

[0041] This invention provides a method, device, electronic device, and storage medium for real-time statistical analysis of academic achievements. It directly delegates complex computational tasks such as grouping and aggregation to the database kernel, avoiding the transmission and processing of massive amounts of raw data between the application layer and the database. This reduces the statistical response time from the traditional hours / days to seconds, enabling real-time statistical analysis of massive amounts of academic data and significantly reducing network load and application layer processing pressure.

[0042] This invention utilizes a dynamic mapping mechanism in the query builder to allow users to freely combine statistical dimensions, search conditions, and statistical indicators according to their needs. It can dynamically adapt the corresponding configuration options based on the selected database type, and adapt to various types of databases such as journals, conferences, and dissertations, thus meeting the complex analysis needs of diverse scenarios such as scientific research management, achievement evaluation, and talent assessment.

[0043] This invention resolves statistical ambiguities caused by author names, differing institutional abbreviations, and varying subject classifications by establishing a pre-built standardized entity database and a unique standardized identifier mechanism. This ensures the accuracy of statistical results and provides reliable data support for research decision-making. Furthermore, the results are rendered and presented as interactive statistical tables on a user interface, supporting features such as custom sorting, format downloading, and detailed viewing. The operation is intuitive and convenient, lowering the user threshold. Simultaneously, it enables rapid association between statistical results and original bibliographic records, enhancing the practicality and usability of the statistical results. Attached Figure Description

[0044] Figure 1 A flowchart of a real-time statistical method for academic achievements provided by this invention;

[0045] Figure 2 A structural block diagram of an academic achievement real-time statistical device provided by the present invention;

[0046] Figure 3 This is a schematic diagram of the electronic device structure used to implement the methods and apparatus embodiments of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1: The first embodiment of this invention proposes a real-time statistical method for academic achievements, particularly a method for real-time, multi-dimensional statistical analysis of massive amounts of centrally stored academic achievement data. For example... Figure 1 As shown, the method specifically includes the following steps:

[0049] Step S101: Receive user configuration information, which includes: database type identifier, statistical dimension, search conditions, and statistical indicators;

[0050] Step S102: Using a query builder, based on the preset metadata mapping relationship between statistical elements and database table structure, the user configuration information is dynamically generated into an SQL query statement that conforms to the target database syntax specification; wherein, the SQL query statement includes: SELECT clause, FROM clause, WHERE clause and GROUP BY clause;

[0051] The generation process includes: converting statistical dimensions into grouping columns in the GROUP BY clause and SELECT clause of the SQL query statement; converting search conditions into matching conditions in the WHERE clause of the SQL query statement; and converting statistical indicators into aggregate functions in the SELECT clause of the SQL query statement.

[0052] Step S103: The SQL query statement is sent to the target database pointed to by the database type identifier, and the target database executes the SQL query statement to complete the grouping and aggregation calculation of the data;

[0053] Step S104: Receive the result set returned by the target database after the grouping and aggregation calculations have been completed;

[0054] Step S105: Push the result set to the front end, render it on the user interface and present it as an interactive statistical table.

[0055] In step S101 above, receiving user configuration information includes: responding to the user's selection event of the database type on the user interaction interface;

[0056] Based on the selected database type, the system dynamically retrieves and presents to the user statistical dimension options, search condition options, and statistical indicator options associated with that database type from the preset configuration library.

[0057] In step S102 above, dynamically generating the user configuration information into an SQL query statement that conforms to the target database syntax includes: responding to the entity name entered by the user on the user interface for any search condition; retrieving a list of candidate entities that match the entered entity name from a pre-established normalized entity library and returning it to the user interface for the user to select.

[0058] Receive the entity selected by the user from the candidate entity list and obtain the unique normalized identifier corresponding to the selected entity;

[0059] The unique normalized identifier is used as the query value and filled into the field corresponding to the search condition in the WHERE clause of the SQL query statement.

[0060] In the above embodiments, the entity types of the entity name include: author name, institution name, region name, subject classification name, journal name, conference name, and funding project name.

[0061] Furthermore, step S102 above, which involves dynamically generating user configuration information into an SQL query statement that conforms to the target database syntax, also includes:

[0062] Receive user-specified sorting configuration, which includes the fields on which the sorting is based and the sorting method;

[0063] The sorting configuration is converted into the ORDER BY clause in the SQL query statement, which is used to sort the result set returned after performing aggregation calculations on the target database.

[0064] In the above embodiments, the aggregate functions in the SELECT clause in step S102 include: the COUNT() function for counting, the SUM() function for summing, the AVG() function for calculating the average, the MAX() function for obtaining the maximum value, and the MIN() function for obtaining the minimum value.

[0065] In the scenario of real-time statistical analysis of academic achievements, step S104 above describes a method for executing SQL queries in the target database and utilizing kernel computing capabilities to perform grouping and aggregation calculations. This method is based on the collaborative work of the database kernel's parsing optimization, storage engine, and execution engine. Specifically, the execution engine calls the storage engine to first accurately filter the raw data and then group the filtered data according to the specified field using GROUP BY. Aggregate functions are then performed on the data within each group, and finally, the aggregation calculation results for each group are cached in kernel memory to avoid redundant calculations.

[0066] If the SQL statement contains an ORDER BY clause (e.g., sorted in descending order by number of papers), the execution engine will perform the sorting after aggregation calculations.

[0067] Sort the aggregated results for all groups according to the specified field and sorting method;

[0068] If the grouping field already has an index, the index's ordering can be reused to reduce sorting overhead (e.g., if the "publication year" index is in ascending order, the sorting of the aggregation results can be adjusted directly based on the index order).

[0069] In step S105 above, rendering and presenting an interactive statistical table on the user interface includes:

[0070] In response to a user's trigger operation on the header field of the statistical table, the result set is rearranged in ascending or descending order according to the sorting rule corresponding to the trigger operation;

[0071] In response to the user's download command, the result set is converted into a file in a preset format and pushed to the user's terminal;

[0072] In response to a user's click on any statistical value in the statistical table, a detailed query request for the academic achievement corresponding to that statistical value is generated. Based on the detailed query request, a list of detailed bibliographic information is obtained and displayed on the user interface.

[0073] Example 2: The method of Example 1 described above will be explained in detail below with reference to the technical solution of Example 2, so as to fully demonstrate the implementation process and advantages of the solution.

[0074] (I) Scenario Setting of Implementation Examples

[0075] This embodiment uses "Statistical Analysis of Journal Papers in Engineering Disciplines of a University from 2019 to 2024" as an example to demonstrate the complete implementation process of the present invention. In this scenario, the user (staff of the university's scientific research management department) needs to statistically analyze indicators such as the number of journal papers published by the university's engineering disciplines between 2019 and 2024, the number of high-impact papers, the number of papers funded by national grants, and the total citation frequency. The system also supports sorting by the number of papers, downloading statistical results, and viewing detailed bibliographic information of the papers.

[0076] (II) Detailed Implementation of Methods and Steps

[0077] 1. Receive user configuration information:

[0078] After logging into the real-time statistics system for academic achievements, users will enter the user interface and perform the following configuration operations:

[0079] Select Database Type: In the database type options, check "Journal Paper Database". The system responds to this selection event and dynamically loads and presents the statistical dimension options (such as publication year, author's department, journal level), search criteria options (such as publication time range, subject classification, institution name), and statistical indicator options (such as number of papers, number of high-impact papers, number of papers funded by the National Natural Science Foundation of China, total citation frequency) associated with the journal paper database from the preset configuration database.

[0080] Configure search criteria: Enter "Institution Name" as "XX University" in the search criteria, select "Subject Classification" as "Engineering", and set the "Publication Time Range" to 2019-2024.

[0081] Select statistical dimensions: Select "Publication Year" and "Author's Department" as statistical dimensions.

[0082] Select statistical indicators: Check "Number of papers", "Number of high-impact papers", "Number of papers funded by the National Natural Science Foundation of China", and "Total citation frequency" as statistical indicators.

[0083] The user configuration parsing module receives the above input events, parses out the database type identifier (journal paper database), dimension selection parameters (publication year, author's department), condition key-value pairs (institution name: XX University, subject classification: engineering, publication time range: 2019-2024), and indicator selection parameters (number of papers, number of high-impact papers, etc.), and completes the reception and parsing of user configuration information.

[0084] 2. Dynamically generate SQL query statements

[0085] The query builder module performs the following operations based on the preset metadata mapping relationship between statistical elements and database table structures:

[0086] (1) Entity normalization matching

[0087] In response to the user's input of "Institution Name: XX University", the query builder module calls a pre-established normalized entity database (institution database) to perform a matching query and returns a candidate list to the user interface, such as "XX University (Normative Code: 10426)" and "XX University XX Branch (Normative Code: 1042601)". After the user selects "XX University (Normative Code: 10426)", the module obtains the unique normalized identifier "10426" corresponding to that entity and uses it as the query value for constructing conditions in the subsequent WHERE clause.

[0088] (2) Generation of core SQL statements

[0089] The query builder module performs dynamic transformations based on metadata mapping relationships:

[0090] Dimension mapping: Convert "publication year" to the "publication year" field in the database table, and "author's department" to the "department code" field, corresponding to the grouping columns in the GROUP BY clause (GROUP BY publication year, department code) and SELECT clause of the SQL query statement.

[0091] Metric mapping: "Number of papers" is mapped to the COUNT (*) function, "Number of high-impact papers" is mapped to the SUM (high-impact identifier) ​​function, "Number of papers funded by the National Natural Science Foundation of China" is mapped to the SUM (National Science Foundation identifier) ​​function, and "Total citation frequency" is mapped to the SUM (citation count) function. All of the above aggregate functions are placed in the SELECT clause.

[0092] Conditional mapping: Convert the institutional code "10426", the subject classification "Engineering" (corresponding to the standard code "08"), and the publication time range "2019-2024" into exact matching conditions for the WHERE clause (WHERE Institution Code='10426' AND Subject Code='08' AND Publication Year BETWEEN '2019' AND '2024').

[0093] (3) Sorting configuration conversion

[0094] The user specifies the sorting configuration in the user interface: the sorting basis field is "number of papers" and the sorting method is "descending". The query builder module converts this configuration into an ORDER BY clause (ORDER BY COUNT (*) DESC) and incorporates it into the SQL query statement.

[0095] 3. Execute the query and complete the calculation.

[0096] The query execution module obtains a connection to the journal article database through the database connection pool and sends the above SQL query statement to the database server. After parsing the statement, the database server uses its kernel-optimized query engine and aggregation computing capabilities to directly perform data filtering, grouping, and aggregation operations at the storage layer. This eliminates the need to transmit the original paper data to the application layer, achieving a "complete pushdown" of the computational task and greatly reducing network load and application server processing pressure.

[0097] 4. Receive the result set

[0098] The result receiving module receives the completed result set through the database driver interface and deserializes it from the database's native format into a Data Transfer Object (DTO) for internal system use. This result set is structured statistical data, not raw bibliographic data, as shown in the table below:

[0099]

[0100] 5. Result rendering and interactive presentation

[0101] The rendering module converts the Data Transfer Object (DTO) into JSON format and pushes it to the front-end user interface via the WebSocket protocol. The front-end rendering engine dynamically loads the table component based on the JSON data, generating an interactive statistical table. Users can perform the following interactive operations:

[0102] Sorting operation: Click the “Total Citations” field in the table header. The system will respond to this trigger operation, sort the result set in descending order by total citations, and update the table display content.

[0103] Download operation: Click the "Export Excel" button, and the system will respond to the download command, convert the current result set into an Excel file and push it to the user's terminal for offline archiving and further analysis;

[0104] To view the details: Click on the value "Number of papers published by the School of Mechanical Engineering in 2024: 58" in the table. The system will generate a details query request. Based on this request, you can query the detailed bibliographic information of the 58 papers (including paper title, author, journal name, publication date, abstract, keywords, etc.) and jump to the details page for display.

[0105] Example 3: Based on the same technical concept as the above method, Example 3 of the present invention provides a real-time statistical device for academic achievements, comprising: a user configuration parsing module 210, a query builder module 220, a query execution module 230, a result receiving module 240, and a result rendering module 250 connected in sequence. These modules can be deployed on the same computing device or distributed across different locations, communicating via a network interface. Figure 3 As shown, the connections between the modules are illustrated.

[0106] Specifically, the user configuration parsing module 210 is used to receive user configuration information, which includes: database type identifier, statistical dimension, search conditions and statistical indicators;

[0107] The query builder module 220 is used to dynamically generate SQL query statements that conform to the target database syntax specifications from user configuration information based on the preset metadata mapping relationship between statistical elements and database table structures using a query builder; wherein, the SQL query statement includes: SELECT clause, FROM clause, WHERE clause and GROUP BY clause;

[0108] The generation process includes: converting statistical dimensions into grouping columns in the GROUP BY clause and SELECT clause of the SQL query statement; converting search conditions into matching conditions in the WHERE clause of the SQL query statement; and converting statistical indicators into aggregate functions in the SELECT clause of the SQL query statement.

[0109] The query execution module 230 is used to send the SQL query statement to the target database pointed to by the database type identifier, and the target database executes the SQL query statement to complete the grouping and aggregation calculation of the data;

[0110] The result receiving module 240 is used to receive the result set of grouping and aggregation calculations returned by the target database after the execution is completed;

[0111] The result rendering module 250 is used to push the result set to the front end, render it on the user interface and present it as an interactive statistical table.

[0112] Example 3: This embodiment of the invention also provides an electronic device and a computer-readable storage medium corresponding to Examples 1 and 2.

[0113] One electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, it causes the processor to perform the steps of any one of S101-S105 of an energy-saving control method for an orthodontic machine.

[0114] like Figure 3 As shown, the electronic device may include: at least one processor 31, at least one network interface 35, user interface 34, memory 36, and at least one communication bus 32.

[0115] The communication bus 32 is used to enable communication between these components.

[0116] The user interface 34 may include a display screen and a camera. Optionally, the user interface 34 may also include a standard wired interface and a wireless interface.

[0117] The network interface 35 may optionally include a standard wired interface or a wireless interface (such as a WIFI interface).

[0118] The processor 31 may include one or more processing cores. The processor 31 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 36, and by calling data stored in the memory 36. Optionally, the processor 31 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 31 and may be implemented as a separate chip.

[0119] The memory 36 may include random access memory (RAM) or read-only memory. Optionally, the memory 36 may include a non-transitory computer-readable storage medium. The memory 36 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 36 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 36 may also be at least one storage device located remotely from the aforementioned processor 31. Figure 3 As shown, the memory 36, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for predicting thin sandstone reservoirs in meandering rivers, a system, electronic equipment, and the storage medium.

[0120] exist Figure 3In the electronic device shown, the user interface 34 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 31 can be used to call an application program stored in the memory 36 that is a real-time statistical method for academic achievements. When executed by one or more processors 31, the electronic device performs one or more steps of the real-time statistical method for academic achievements as described in steps S101-S105 of the above embodiment.

[0121] Those skilled in the art will clearly understand that the technical solutions of this application can be implemented using software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware capable of independently performing or cooperating with other components to perform specific functions. Hardware may include, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0122] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any one of S101-S105, a method for real-time statistical analysis of academic achievements.

[0123] Specifically, the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0124] The code of the computer program can be in the form of source code, object code, executable file, or some intermediate form.

[0125] Computer-readable storage media may include cache, high-speed random access memory (RAM), such as the common double data rate synchronous dynamic random access memory (DDR SDRAM), and may also include non-volatile memory (NVRAM), such as one or more read-only memory (ROM), disk storage devices, flash memory devices, or other non-volatile solid-state storage devices such as optical discs (CD-ROM, DVD-ROM), floppy disks, or data tapes.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for real-time statistical analysis of academic achievements, characterized in that, The method includes: Receive user configuration information, which includes: database type identifier, statistical dimension, search conditions and statistical indicators; A query builder is used to dynamically generate SQL query statements that conform to the target database syntax specifications from user configuration information based on the preset metadata mapping relationship between statistical elements and database table structures; wherein, the SQL query statement includes: SELECT clause, FROM clause, WHERE clause and GROUP BY clause; The generation process includes: converting statistical dimensions into grouping columns in the GROUP BY clause and SELECT clause of the SQL query statement; converting search conditions into matching conditions in the WHERE clause of the SQL query statement; and converting statistical indicators into aggregate functions in the SELECT clause of the SQL query statement. The SQL query statement is sent to the target database pointed to by the database type identifier, and the target database executes the SQL query statement to complete the grouping and aggregation calculation of the data; Receive the result set of grouping and aggregation calculations returned by the target database after the execution is completed; The result set is pushed to the front end, where it is rendered and presented as an interactive statistical table on the user interface.

2. The method according to claim 1, characterized in that, The receipt of user configuration information includes: responding to a user's selection of the database type on the user interface; Based on the selected database type, the system dynamically retrieves and presents to the user statistical dimension options, search condition options, and statistical indicator options associated with that database type from the preset configuration library.

3. The method according to claim 1, characterized in that, The step of dynamically generating user configuration information into an SQL query statement that conforms to the target database syntax includes: responding to the entity name entered by the user on the user interaction interface for any search condition; retrieving a list of candidate entities that match the entered entity name from a pre-established normalized entity library and returning it to the user interaction interface for the user to select; Receive the entity selected by the user from the candidate entity list and obtain the unique normalized identifier corresponding to the selected entity; The unique normalized identifier is used as the query value and filled into the field corresponding to the search condition in the WHERE clause of the SQL query statement.

4. The method according to claim 3, characterized in that, The entity types of the entity names include: author name, institution name, region name, subject classification name, journal name, conference name, and funding project name.

5. The method according to claim 1, characterized in that, The aggregate functions within the SELECT clause include: COUNT() for counting, SUM() for summing, AVG() for calculating the average, MAX() for obtaining the maximum value, and MIN() for obtaining the minimum value.

6. The method according to claim 3, characterized in that, The process of dynamically generating user configuration information into SQL query statements that conform to the target database syntax also includes: Receive user-specified sorting configuration, which includes the fields on which the sorting is based and the sorting method; The sorting configuration is converted into the ORDER BY clause in the SQL query statement, which is used to sort the result set returned after performing aggregation calculations on the target database.

7. The method according to claim 1, characterized in that, The statistical tables rendered and presented as interactive tables on the user interface include: In response to a user's trigger operation on the header field of the statistical table, the result set is rearranged in ascending or descending order according to the sorting rule corresponding to the trigger operation; In response to the user's download command, the result set is converted into a file in a preset format and pushed to the user's terminal; In response to a user's click on any statistical value in the statistical table, a detailed query request for the academic achievement corresponding to that statistical value is generated. Based on the detailed query request, a list of detailed bibliographic information is obtained and displayed on the user interface.

8. A real-time statistical device for academic achievements, characterized in that, include: The user configuration parsing module is used to receive user configuration information, which includes: database type identifier, statistical dimension, search conditions and statistical indicators; The query builder module is used to dynamically generate SQL query statements that conform to the target database syntax specifications from user configuration information based on the preset metadata mapping relationship between statistical elements and database table structures. The SQL query statements include: SELECT clause, FROM clause, WHERE clause and GROUP BY clause. The generation process includes: converting statistical dimensions into grouping columns in the GROUP BY clause and SELECT clause of the SQL query statement; converting search conditions into matching conditions in the WHERE clause of the SQL query statement; and converting statistical indicators into aggregate functions in the SELECT clause of the SQL query statement. The query execution module is used to send the SQL query statement to the target database pointed to by the database type identifier, and the target database executes the SQL query statement to complete the grouping and aggregation calculation of the data; The result receiving module is used to receive the result set of grouping and aggregation calculations returned by the target database after the execution is completed; The result rendering module is used to push the result set to the front end, render it on the user interface and present it as an interactive statistical table.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the real-time statistical method for academic achievements as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the real-time statistical method for academic achievements as described in any one of claims 1 to 7.

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