CN113934845A

CN113934845A - Report analysis method, device, equipment and medium

Info

Publication number: CN113934845A
Application number: CN202111199473.5A
Authority: CN
Inventors: 杨志科; 王兴荣; 曹文龙; 蒋秋明
Original assignee: Shanghai Shangshi Longchuang Intelligent Technology Co Ltd
Current assignee: Shanghai Shangshi Longchuang Intelligent Technology Co Ltd
Priority date: 2021-10-14
Filing date: 2021-10-14
Publication date: 2022-01-14

Abstract

The embodiment of the invention discloses a report analysis method, a report analysis device, report analysis equipment and a report analysis medium. The method comprises the steps of obtaining report characteristics of an analysis report created by a user and graph characteristics of a created statistical analysis graph, clustering the obtained report characteristics and graph characteristics to determine various table object classes and the corresponding graph characteristics of the various table object classes, establishing a table analysis model based on the various table object classes and the corresponding graph characteristics of the various table object classes, when a table analysis request is obtained, determining target table diagram data information corresponding to the table analysis request according to the current user information in the table analysis request and a table analysis model, and then automatically generating a target report and a target statistical analysis chart corresponding to the table analysis request, so that the automatic generation of the Internet of things industry data analysis report is realized, a user does not need to consult indexes corresponding to report analysis specially, the efficiency of the Internet of things industry data analysis report is improved, and meanwhile, the quality of the Internet of things industry data analysis report is improved.

Description

Report analysis method, device, equipment and medium

Technical Field

The embodiment of the invention relates to the field of big data of the Internet of things, in particular to a report analysis method, a report analysis device, report analysis equipment and a report analysis medium.

Background

With the advance of industrial informatization, the data of the internet of things is increased explosively, and in the face of a large amount of industrial data of the internet of things, data scientists need to spend a great deal of effort on knowing about specific industries so as to know how to utilize related data to analyze the data and assist production and management. This makes the analysis efficiency of thing networking trade data low to, data scientist usually need to consult the index of analysis data to the system technical staff of this trade, if system technical staff misses certain index, will reduce the quality of analysis. Therefore, the prior art has the technical problems of low analysis efficiency and low analysis quality.

Disclosure of Invention

The embodiment of the invention provides a report analysis method, a report analysis device, equipment and a medium, which are used for improving the efficiency of data analysis reports in the industry of the Internet of things and the quality of the analysis reports.

In a first aspect, an embodiment of the present invention provides a report analysis method, where the method includes:

acquiring report characteristics of an analysis report created by a user and graph characteristics of a created statistical analysis graph;

clustering the report features and the graph features, and determining various table object classes and table graph features corresponding to the table object classes, wherein the table graph features comprise user attribute features used for reflecting the table objects and table graph data features used for reflecting table graph data information corresponding to the table objects;

determining a table analysis model based on the table object classes and the table diagram characteristics corresponding to the table object classes;

if a table analysis request is acquired, determining target table diagram data information corresponding to the table analysis request based on current user information in the table analysis request and the table analysis model, and generating a target report and a target statistical analysis diagram corresponding to the table analysis request based on the target table diagram data information.

In a second aspect, an embodiment of the present invention further provides a report analysis apparatus, where the apparatus includes:

the online report acquisition module is used for acquiring report characteristics of an analysis report created by a user and graph characteristics of a created statistical analysis graph;

the characteristic clustering module is used for clustering the report characteristics and the graph characteristics and determining various table object classes and table graph characteristics corresponding to the table object classes, wherein the table graph characteristics comprise user attribute characteristics used for reflecting the table objects and table graph data characteristics used for reflecting the table graph data information corresponding to the table objects;

the model establishing module is used for determining a table analysis model based on the table drawing characteristics corresponding to each established table object class and each established table object class;

and the chart analysis module is used for determining target chart data information corresponding to the chart analysis request based on the current user information in the chart analysis request and the chart analysis model if the chart analysis request is obtained, and generating a target report and a target statistical analysis chart corresponding to the chart analysis request based on the target chart data information.

In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:

one or more processors;

a storage device for storing one or more programs,

when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the report analysis method provided by any embodiment of the invention.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the report analysis method according to any embodiment of the present invention.

The embodiment of the invention has the following advantages or beneficial effects:

the method comprises the steps of obtaining report characteristics of an analysis report created by a user and graph characteristics of a created statistical analysis graph, clustering the obtained report characteristics and the graph characteristics to determine various table building object classes and table graph characteristics corresponding to the various table building object classes, wherein the table graph characteristics comprise user attribute characteristics used for reflecting the table building objects and table graph data characteristics used for reflecting table graph data information corresponding to the table building objects, establishing a table analysis model based on the table graph characteristics corresponding to the various table building object classes and the various table building object classes, determining target table graph data information corresponding to a table analysis request according to current user information and the table analysis model in the table analysis request when the table analysis request is obtained, and further automatically generating a target report and a target statistical analysis graph corresponding to the table analysis request based on the target table graph data information, thereby realizing the automatic generation of the industry data analysis report, the user does not need to consult indexes corresponding to the report analysis specially, the efficiency of the data analysis report of the Internet of things industry is improved, meanwhile, report analysis errors caused by omission of certain indexes during artificial consultation of the indexes are avoided, and the quality of the data analysis report of the Internet of things industry is improved.

Drawings

In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.

Fig. 1 is a schematic flow chart illustrating a report analysis method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a report analysis apparatus according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Example one

Before describing each embodiment of the present invention in detail, an application scenario of the report analysis method provided by the present invention is first described in an exemplary manner. For example, in a large amount of internet-of-things industry data, if it is necessary to perform report analysis on data of a certain industry, for example, report analysis on monthly energy consumption of a power plant, report analysis on weekly material usage of a chemical plant, report analysis on usage time of each device of the chemical plant every year, report analysis on loss of each device, and the like, a technician in the industry is generally required to be consulted to obtain indexes required by the report analysis. However, such manual consulting of report analysis indicators is inefficient and may be missed. Therefore, the report analysis method provided by the embodiments of the present invention can be used for performing report analysis, so that a technician does not need to consult report analysis indexes manually, a table analysis model capable of automatically determining analysis indexes of data in various industries can be obtained by performing feature analysis and clustering on reports created by a user on line, and further, a statistical analysis chart of the reports and the reports can be automatically generated based on the table analysis model.

Fig. 1 is a schematic flow diagram of a report analysis method according to an embodiment of the present invention, where the present embodiment is applicable to a situation where a table analysis model is built according to an analysis report and a statistical analysis graph created by a user, and a target report and a target statistical analysis graph are automatically generated based on the table analysis model, the method may be executed by a report analysis device, and the device may be implemented by hardware and/or software, and the method specifically includes the following steps:

and S110, acquiring report characteristics of the analysis report created by the user and graph characteristics of the created statistical analysis graph.

The analysis report can be a data report created by a user. Specifically, the user can pull the data required for creating the analysis report from the report database, and then create the analysis report according to the pulled data. The statistical analysis chart can be a statistical chart for performing data analysis on the analysis report, such as a scatter chart, a broken line chart, a pie chart and the like. It should be noted that, the statistical analysis chart obtained in this embodiment is usually generated by the user according to the created analysis report, and therefore, the analysis report and the statistical analysis chart corresponding to the analysis report can be regarded as a combination.

Optionally, the obtaining report features of the analysis report created by the user and the graph features of the created statistical analysis graph includes: acquiring an analysis report created by a user based on an online report system and a statistical analysis chart corresponding to the analysis report; determining report characteristics of the analysis report and graph characteristics of the statistical analysis graph; the report characteristics comprise at least one of table identification, table data, table format and table building user information, and the graph characteristics comprise at least one of graph identification, graph data, graph format and graph building user information.

The online reporting system may be a system in which a user can create and edit reports and analysis graphs online, such as a web site, a sailing or microsoft. The embodiment can acquire the analysis report and the statistical analysis chart created by each user based on the online reporting system, and further extract the report characteristics of each analysis report and the chart characteristics of each statistical analysis chart.

Specifically, the report features include at least one of table identification, table data, table format, and table building user information; the table identifier may be a report name and/or a header name, the table data may be specific data information in the report, such as a data point location ID, a time resolution, a start-stop time, and the like, the table format may include information of a row number, a column number, a row width, a column width, a data format in the table, and the like, and the table building user information may include at least one of a user position, a user ID, and a report use time. The graph characteristics comprise at least one of graph identification, graph data, graph format and graph building user information; the graph identifier may be a graph name, the graph data may include data required for generating the statistical analysis graph and specific data information in the statistical analysis graph, the graph format may include information such as a line format and a graph size in the graph, and the graph creation user information may include at least one of a user position, a user ID, and a usage time of the statistical analysis graph.

In the optional embodiment, by obtaining the analysis report created by the user based on the online reporting system and the statistical analysis chart corresponding to the analysis report, and extracting the report characteristics and the chart characteristics for each analysis report and each statistical analysis chart, respectively, the report analysis method provided by the embodiment is applicable to the existing online reporting system, and the extraction of the automatic report characteristics and the chart characteristics can be realized based on the existing online reporting system, so that the applicability of the report analysis method provided by the embodiment is improved.

And S120, clustering the report features and the graph features, and determining various table creating object classes and table graph features corresponding to the table creating object classes, wherein the table graph features comprise user attribute features used for reflecting the table creating objects and table graph data features used for reflecting table graph data information corresponding to the table creating objects.

Specifically, the user position, the user ID, the report use time or the table identifier in the report features, or the user position, the user ID, the table use time or the table identifier in the table features may reflect the table building purpose of the user, for each report feature and each table feature acquired in the above steps. For example, a user position is a user of an energy consumption analysis specialist, and is typically scheduled to analyze energy consumption quarterly or monthly; the user position is a user of an energy consumption department manager, and the general purpose of establishing the schedule is to analyze annual energy consumption; for another example, a user with a user ID of zhang is generally configured to analyze the device wear times, and a user with a user ID of lie is generally configured to analyze the device running time; or, the report use time or the graph use time is 1 month and 1 day, and the corresponding project establishment purpose is analysis annual energy consumption generally; or, the graph identifier or the table identifier is "monthly energy consumption analysis", and the corresponding table establishment purpose is the analysis monthly energy consumption.

Therefore, the present embodiment can determine the class of each created table by clustering the report features and the graph features. It should be noted that, during the clustering process, the table building purpose may be determined based on a single feature of the features reflecting the table building purpose of the user, or may be determined based on a plurality of features of the features reflecting the table building purpose of the user. Such as determining the purpose of creating a statement based on the user ID and the time of use of the statement, determining the purpose of creating a statement based on the table identification and the user position, etc.

Specifically, each category of the created table and the table map features included in each category of the created table can be obtained by clustering each report feature and each map feature. Exemplarily, the report characteristics and the graph characteristics can be clustered based on a maximum expectation method of a Gaussian mixture model; or clustering the report characteristics and the graph characteristics by a density-based clustering method, a mean shift clustering method or a K-Means clustering method.

The table diagram characteristics corresponding to the table diagram establishing types comprise user attribute characteristics used for reflecting the table diagrams and table diagram data characteristics used for reflecting table diagram data information corresponding to the table diagrams. Illustratively, the user attributes may include user position, user ID, report usage time, table identification, graph usage time, and graph identification; the table data characteristics may include table data, table format, graph data, and graph format.

Optionally, before the clustering process is performed on the report features and the graph features, the method further includes: performing correlation analysis on the report features and the graph features, and determining classification results of the report features and the graph features based on correlation analysis results; and performing principal component analysis on the classification result, and deleting report features or graph features which do not meet preset unconditional conditions based on the principal component analysis result.

In this optional embodiment, correlation analysis may be performed on the report features and the graph features to calculate correlations between the report features and the graph features, and further, preliminary classification may be performed on the report features and the graph features based on the calculated correlations, and the report features and the graph features with high correlations may be classified into one classification, so as to obtain a classification result. Further, the main component analysis is carried out on each classification result, so that report features or graph features which do not meet preset nonlinear correlation conditions are deleted, the features with high correlation or repetition in each classification result are reduced, and the dimensionality of the features in each classification result is reduced. The preset nonlinear correlation condition may be a preset condition for determining whether the features are linearly uncorrelated. By the method, the dimension reduction of the report characteristics and the graph characteristics can be realized, the clustering speed of the report characteristics and the graph characteristics is further improved, and the efficiency of generating the target report and the target statistical analysis graph is further improved.

S130, determining a table analysis model based on the table drawing characteristics corresponding to the table building object classes and the table building object classes.

Specifically, in this embodiment, a table analysis model may be established according to the determined table object classes and the table diagram features corresponding to the table object classes, so that the user may automatically generate a target report and a target statistical analysis diagram through the table analysis model, that is, automatically complete the industry data analysis of the internet of things.

Wherein the table analysis model may be a neural network model; the table object class and the table diagram characteristics corresponding to each table object class can be used as training data to train to obtain a table analysis model.

Exemplarily, a user attribute feature in each table graph feature is used as sample data, a table building class corresponding to the user attribute feature is used as a class label, the sample data is input to the neural network model to obtain a class prediction result output by the neural network model, a loss function is calculated according to the class label and the class prediction result, network parameters of the neural network model are reversely adjusted according to the calculation result of the loss function until an iterative training stopping condition is met, and the neural network model is determined as a table analysis model. At this point, the table analysis model may be used to automatically determine the user's category of the build table. Or, the neural network model can be trained continuously according to the table diagram data characteristics in the table diagram characteristics, so that the neural network model can be used for automatically outputting reports and target statistical analysis diagrams. Such as: taking the table object class as sample data, taking the table image data characteristics in the table image characteristics corresponding to the table object class as table image data labels, inputting the sample data into the neural network model to obtain a table image data prediction result output by the neural network model, calculating a loss function according to the table image data labels and the table image data prediction result, carrying out reverse adjustment on network parameters of the neural network model according to the calculation result of the loss function until an iterative training stopping condition is met, and determining the neural network model as a table analysis model.

S140, if a table analysis request is obtained, determining target table diagram data information corresponding to the table analysis request based on current user information in the table analysis request and the table analysis model, and generating a target report and a target statistical analysis diagram corresponding to the table analysis request based on the target table diagram data information.

The table analysis request may be a request for generating a report and a statistical analysis chart corresponding to the report, which is sent by a user through a terminal. If the user clicks "automatically generate report" on the terminal, a report analysis request may be obtained at this time. The form analysis request includes current user information including at least one of a user job title, a user name, and a time of use.

Illustratively, the user attribute includes at least one of a user position, a user name, and a usage time, and the determining target chart data information corresponding to the table analysis request based on the current user information in the table analysis request and the table analysis model includes: and inputting at least one of the user position, the user name and the use time carried by the table analysis request into the table analysis model to obtain target table diagram data information corresponding to the table analysis request output by the table analysis model.

Specifically, the form analysis model may determine a form creation class corresponding to the form analysis request according to at least one of the user position, the user name, and the usage time, and further determine a corresponding form feature, that is, target form data information, based on the form creation class. Or, the form analysis model may directly determine the target form data information corresponding to the form analysis model according to at least one of the user position, the user name and the use time. The target table diagram data information may be data describing information of a target report and a target statistical analysis diagram, such as table format, diagram format, table data, diagram data, and the like.

In one embodiment, determining the target table data information corresponding to the table analysis request based on the current user information in the table analysis request and the table analysis model includes: and determining a target table building class corresponding to the table analysis request based on the current user information of the table analysis request through the table analysis model, and determining target table map data information corresponding to the table analysis request based on the target table building class determination and the table map characteristics corresponding to the table building class.

That is, the table analysis model may determine the target table creation class corresponding to the table analysis request according to the current user information carried by the table analysis request, and then determine the target table data information corresponding to the table analysis request according to the table map feature corresponding to the determined target table creation class.

In another embodiment, the determining a table analysis model based on each of the categories of the built table objects and the table diagram characteristics corresponding to each of the categories of the built table objects includes: determining the incidence relation between the user attribute characteristics and the table diagram data characteristics based on the user attribute characteristics and the table diagram data characteristics corresponding to the table building object class; correspondingly, the determining target table diagram data information corresponding to the table analysis request based on the current user information in the table analysis request and the table analysis model includes: and determining target table diagram data information corresponding to the current user information based on the current user information of the table analysis request and the incidence relation between the user attribute characteristics and the table diagram data characteristics through the table analysis model.

Namely, the table analysis model can establish the incidence relation between each user attribute characteristic and the table diagram data characteristic, so that the target table diagram data information corresponding to the current user information can be determined directly according to the current user information carried by the table analysis request and the established incidence relation.

Specifically, after the target table diagram data information corresponding to the current user information is determined, the target report and the target statistical analysis diagram corresponding to the table analysis request are generated based on the target table diagram data information, and the method may be that: acquiring source data corresponding to the target table diagram data information in a report database; and generating a target report corresponding to the table analysis request based on the source data, and generating a target statistical analysis chart based on the target chart data information and the target report.

The target table data information may include related information of source data required for generating the target report. Based on the target table diagram data information, source data required for generating the target report can be pulled from the report database, further, the target report is generated according to the pulled source data, and a target statistical analysis diagram is generated according to the target report and the statistical diagram related data in the target table diagram data information.

According to the technical scheme of the embodiment, through acquiring report features of an analysis report created by a user and graph features of a created statistical analysis graph, clustering the acquired report features and graph features to determine various table object categories and table graph features corresponding to the various table object categories, wherein the table graph features comprise user attribute features for reflecting the table objects and table graph data features for reflecting table data information corresponding to the table objects, a table analysis model is established based on the table object categories and the table graph features corresponding to the table object categories, when a table analysis request is acquired, target table graph data information corresponding to the table analysis request is determined according to current user information and the table analysis model in the table analysis request, and then a target report and a target statistical analysis graph corresponding to the table analysis request are automatically generated based on the target table graph data information, the automatic generation of the Internet of things industry data analysis report is realized, a user does not need to consult indexes corresponding to report analysis specially, the efficiency of the Internet of things industry data analysis report is improved, meanwhile, report analysis errors caused by omission of indexes during manual consultation of the indexes are avoided, and the quality of the Internet of things industry data analysis report is improved.

Example two

Fig. 2 is a schematic structural diagram of a report analysis apparatus according to a second embodiment of the present invention, which is applicable to a situation where a table analysis model is built according to an analysis report and a statistical analysis chart created by a user, and a target report and a target statistical analysis chart are automatically generated based on the table analysis model, and the apparatus specifically includes: the online report acquisition module 210, the feature clustering module 220, the model building module 230, and the chart analysis module 240.

An online report obtaining module 210, configured to obtain report features of an analysis report created by a user and graph features of a created statistical analysis chart;

a feature clustering module 220, configured to perform clustering processing on the report features and the graph features, and determine each table creation purpose class and a table graph feature corresponding to each table creation purpose class, where the table graph feature includes a user attribute feature used for reflecting the table creation purpose and a table graph data feature used for reflecting the table graph data information corresponding to the table creation purpose;

a model building module 230, configured to determine a table analysis model based on each of the built table object classes and the table diagram characteristics corresponding to each of the built table object classes;

the graph analysis module 240 is configured to, if a table analysis request is obtained, determine target graph data information corresponding to the table analysis request based on current user information in the table analysis request and the table analysis model, and generate a target report and a target statistical analysis graph corresponding to the table analysis request based on the target graph data information.

Optionally, the chart analysis module 240 includes a first determining unit, where the first determining unit is configured to determine, through the table analysis model, a target table building class corresponding to the table analysis request based on the current user information of the table analysis request, and determine target table map data information corresponding to the table analysis request based on the target table building class determination and the table map features corresponding to the classes of the building tables.

Optionally, the model building module 230 is specifically configured to determine, based on the user attribute feature and the table diagram data feature corresponding to the table building object class, an association relationship between the user attribute feature and the table diagram data feature; correspondingly, the chart analysis module 240 includes a second determination unit, and the second determination unit is configured to determine, through the table analysis model, target chart data information corresponding to the current user information based on the current user information of the table analysis request and the association relationship between the user attribute feature and the chart data feature.

Optionally, the chart analysis module 240 includes a data pulling unit, where the data pulling unit is configured to obtain source data corresponding to the target chart data information from a report database; and generating a target report corresponding to the table analysis request based on the source data, and generating a target statistical analysis chart based on the target chart data information and the target report.

Optionally, the user attribute includes at least one of a user position, a user name, and a use time, and the chart analysis module 240 includes a target chart data obtaining unit, where the target chart data obtaining unit is configured to input at least one of the user position, the user name, and the use time carried by the chart analysis request to the chart analysis model, so as to obtain target chart data information corresponding to the chart analysis request output by the chart analysis model.

Optionally, the feature clustering module 220 includes a feature dimension reduction unit, where the feature dimension reduction unit is configured to perform correlation analysis on the report features and the graph features before the report features and the graph features are clustered, and determine classification results of the report features and the graph features based on correlation analysis results; and performing principal component analysis on the classification result, and deleting report features or graph features which do not meet preset nonlinear correlation conditions based on the principal component analysis result.

Optionally, the online report obtaining module 210 is specifically configured to obtain an analysis report created by a user based on an online report system and a statistical analysis chart corresponding to the analysis report; determining report characteristics of the analysis report and graph characteristics of the statistical analysis graph; the report characteristics comprise at least one of table identification, table data, table format and table building user information, and the graph characteristics comprise at least one of graph identification, graph data, graph format and graph building user information.

In the embodiment, the report characteristics of the analysis report created by the user and the graph characteristics of the created statistical analysis graph are obtained through the online report obtaining module, the obtained report characteristics and the graph characteristics are clustered through the characteristic clustering module to determine the categories of each constructed table and the graph characteristics corresponding to the categories of each constructed table, wherein the graph characteristics comprise user attribute characteristics for reflecting the constructed tables and the graph data characteristics for reflecting the graph data information corresponding to the constructed tables, the model establishing module establishes a table analysis model based on the categories of each constructed table and the graph characteristics corresponding to the categories of each constructed table, and the graph analyzing module determines the target graph data information corresponding to the table analysis request according to the current user information and the table analysis model in the table analysis request when the table analysis request is obtained, and then automatically generating a target report and a target statistical analysis chart corresponding to the chart analysis request based on the target chart data information, so that the automatic generation of the Internet of things industry data analysis report is realized, a user does not need to consult indexes corresponding to report analysis specially, the efficiency of the Internet of things industry data analysis report is improved, meanwhile, report analysis errors caused by omission of indexes during manual consultation of the indexes are avoided, and the quality of the Internet of things industry data analysis report is improved.

The report form analysis device provided by the embodiment of the invention can execute the report form analysis method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

It should be noted that, the units and modules included in the system are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.

EXAMPLE III

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. FIG. 3 illustrates a block diagram of an exemplary electronic device 12 suitable for use in implementing embodiments of the present invention. The electronic device 12 shown in fig. 3 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention. The device 12 is typically an electronic device that undertakes the functions of reporting and statistics generation.

As shown in FIG. 3, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a memory 28, and a bus 18 that couples the various components (including the memory 28 and the processing unit 16).

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

Electronic device 12 typically includes a variety of computer-readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 28 may include computer device readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, the storage device 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk-Read Only Memory (CD-ROM), a Digital Video disk (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product 40, with program product 40 having a set of program modules 42 configured to carry out the functions of embodiments of the invention. Program product 40 may be stored, for example, in memory 28, and such program modules 42 include, but are not limited to, one or more application programs, other program modules, and program data, each of which examples or some combination may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, mouse, camera, etc., and display), one or more devices that enable a user to interact with electronic device 12, and/or any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network such as the internet) via the Network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) devices, tape drives, and data backup storage devices, to name a few.

The processor 16 executes the program stored in the memory 28 to execute various functional applications and data processing, for example, to implement the report analysis method provided by the above embodiment of the present invention, including:

Of course, those skilled in the art can understand that the processor can also implement the technical solution of the report analysis method provided by any embodiment of the present invention.

Example four

The fourth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the report analysis method provided in any embodiment of the present invention, and the method includes:

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A report analysis method, characterized in that the method comprises:

2. The method of claim 1, wherein determining the target table map data information corresponding to the table analysis request based on the current user information in the table analysis request and the table analysis model comprises:

and determining a target table building class corresponding to the table analysis request based on the current user information of the table analysis request through the table analysis model, and determining target table map data information corresponding to the table analysis request based on the target table building class determination and the table map characteristics corresponding to the classes of all the table building requests.

3. The method of claim 1, wherein determining a table analysis model based on each of the categories of built tables and the table map characteristics corresponding to each of the categories of built tables comprises:

determining the incidence relation between the user attribute characteristics and the table diagram data characteristics based on the user attribute characteristics and the table diagram data characteristics corresponding to the table building object class;

correspondingly, the determining target table diagram data information corresponding to the table analysis request based on the current user information in the table analysis request and the table analysis model includes:

and determining target table diagram data information corresponding to the current user information based on the current user information of the table analysis request and the incidence relation between the user attribute characteristics and the table diagram data characteristics through the table analysis model.

4. The method according to claim 2 or 3, wherein the generating of the target report and the target statistical analysis graph corresponding to the table analysis request based on the target table graph data information comprises:

acquiring source data corresponding to the target table diagram data information in a report database;

and generating a target report corresponding to the table analysis request based on the source data, and generating a target statistical analysis chart based on the target chart data information and the target report.

5. The method of claim 1, wherein the user attributes comprise at least one of a user position, a user name, and a usage time, and wherein determining the target chart data information corresponding to the chart analysis request based on the current user information in the chart analysis request and the chart analysis model comprises:

and inputting at least one of the user position, the user name and the use time carried by the table analysis request into the table analysis model to obtain target table diagram data information corresponding to the table analysis request output by the table analysis model.

6. The method according to claim 1, further comprising, before said clustering said report features and said graph features:

performing correlation analysis on the report features and the graph features, and determining classification results of the report features and the graph features based on correlation analysis results;

and performing principal component analysis on the classification result, and deleting report features or graph features which do not meet preset nonlinear correlation conditions based on the principal component analysis result.

7. The method according to claim 1, wherein the obtaining report features of the analytic report created by the user and graph features of the created statistical analysis chart comprises:

acquiring an analysis report created by a user based on an online report system and a statistical analysis chart corresponding to the analysis report;

determining report characteristics of the analysis report and graph characteristics of the statistical analysis graph;

the report characteristics comprise at least one of table identification, table data, table format and table building user information, and the graph characteristics comprise at least one of graph identification, graph data, graph format and graph building user information.

8. A report analysis apparatus, the apparatus comprising:

9. An electronic device, characterized in that the electronic device comprises:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the report analysis method of any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the report analyzing method according to any one of claims 1-7.