CN114003631A - Report conclusion intelligent generation method and device and computer readable storage medium - Google Patents
Report conclusion intelligent generation method and device and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses an intelligent generation method and device of report conclusions and a computer readable storage medium, wherein the intelligent generation method of the report conclusions comprises the following steps: receiving a report conclusion analysis request, and acquiring configuration information contained in the report conclusion analysis request; based on a preset knowledge base, calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result; obtaining a report conclusion contained in an intelligent analysis result according to the configuration information; and adding the report conclusion to an intelligent analysis report according to a report conclusion designated position contained in the configuration information. By implementing the invention, the intelligent analysis and automatic generation of the report conclusion are realized, and the time for the user to conclude the report analysis conclusion is greatly saved.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a report conclusion intelligent generation method and device and a computer readable storage medium.
Background
A company value service team needs to output results of product support services to clients regularly, various indexes of service delivery can be timely transparent to the clients, perception of the clients on company service values is improved, and in the delivery process, checking analysis needs to be conducted on the basis of service work order data, alarm data, product service index data and the like.
However, during the analysis the deliverer may face the following pain points: the analysis conclusion does not know how to write, the service points are numerous, the service knowledge points of the delivery personnel cannot be well met, new personnel enter the job and need to understand all the service points, and the early training cost is high; aiming at different customers, the data logic and the analysis logic of a plurality of dimensions are the same, but the data contents are different, the analysis conclusions are different, and if the analysis conclusions are written one by one, a large amount of manpower is consumed; the analysis conclusion written by the individual is comparatively simple, each individual has the idea of each individual, but the idea of the individual may not be representative, and the conclusion written by other individuals needs to be integrated for reference to summarize a more appropriate and more meaningful analysis conclusion.
Disclosure of Invention
The invention mainly aims to provide a report conclusion intelligent generation method, a report conclusion intelligent generation device and a computer readable storage medium, and aims to solve the technical problem of rapidly summarizing an optimal analysis conclusion in a report analysis process.
In order to achieve the above object, the present invention provides an intelligent generation method of report conclusion, including the following steps:
receiving a report conclusion analysis request, and acquiring configuration information contained in the report conclusion analysis request;
based on a preset knowledge base, calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result;
obtaining a report conclusion contained in an intelligent analysis result according to the configuration information;
and adding the report conclusion to an intelligent analysis report according to a report conclusion designated position contained in the configuration information.
Optionally, the configuration information includes data set information, and the step of obtaining the configuration information included in the report conclusion analysis request includes:
and selecting a corresponding preset intelligent analysis component according to the data set information in the configuration information.
Optionally, the step of calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result includes:
and calling a preset intelligent analysis component to analyze the data set information layer by layer to obtain a service layer analysis result, and regarding the service layer analysis result as an intelligent analysis result.
Optionally, the step of calling a preset intelligent analysis component to perform layer-by-layer analysis on the data set information includes:
and calling a preset intelligent analysis component to perform logic abstract basic analysis, data set dimension analysis and business layer analysis on the data set information.
Optionally, the preset intelligent analysis component includes a basic analysis component, and the step of calling the preset intelligent analysis component to perform logic abstract basic analysis on the data set information includes:
and calling the basic analysis component to perform logic abstract basic analysis on the data set information to obtain a logic abstract basic analysis result.
Optionally, the preset intelligent analysis component further includes a dimension analysis component, and the step of obtaining the logical abstract basic analysis result includes:
and calling the dimension analysis component to perform data set dimension analysis on the logical abstract basic analysis result so as to obtain a data set dimension analysis result.
Optionally, the preset intelligent analysis component further includes a business layer analysis component, and the step of obtaining a data set dimension analysis result includes:
and calling the service layer analysis component to perform service layer analysis on the data set dimension analysis result so as to obtain the service layer analysis result.
Optionally, the preset intelligent analysis component further includes an overview analysis component, and the step of obtaining the business layer analysis result includes:
and calling the overview analysis component to classify and integrate the business layer analysis results so as to generate an overview page of the intelligent analysis report.
In addition, to achieve the above object, the present invention also provides an intelligent report conclusion generating apparatus, including: the intelligent report conclusion generating program comprises a memory, a processor and an intelligent report conclusion generating program which is stored on the memory and can run on the processor, wherein the intelligent report conclusion generating program realizes the steps of the intelligent report conclusion generating method when being executed by the processor.
In addition, to achieve the above object, the present invention also provides a computer readable storage medium having a report conclusion intelligence generation program stored thereon, which when executed by a processor implements the steps of the report conclusion intelligence generation method as described above.
The invention provides an intelligent report conclusion generation method, an intelligent report conclusion generation device and a computer readable storage medium. The user side does not need to write codes, does not need to write analysis conclusions one by one for each report, only needs to configure the analysis conclusions needed by the user side, and then automatically generates the analysis conclusions. The background can arrange an algorithm engineer to continuously increase service content according to the requirements of the user, and an interface is provided for the user to use, so that the work is an accumulated process, and rich intelligent analysis capability of report conclusion can be provided for the user.
Drawings
Fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first embodiment of a report conclusion intelligent generation method according to the present invention;
FIG. 3 is a schematic flow chart of intelligent analysis from the data layer to the application layer according to the first embodiment of the present invention;
fig. 4 is a schematic flow chart of the product _ failure analysis and diagnosis as an example to implement intelligent analysis according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: an intelligent generation method of report conclusions, comprising the following steps:
receiving a report conclusion analysis request, and acquiring configuration information contained in the report conclusion analysis request;
based on a preset knowledge base, calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result;
obtaining a report conclusion contained in an intelligent analysis result according to the configuration information;
and adding the report conclusion to an intelligent analysis report according to a report conclusion designated position contained in the configuration information.
Since the delivery person may face the following pain points during the analysis of the report conclusions: the analysis conclusion does not know how to write, the service points are numerous, the service knowledge points of the delivery personnel cannot be well met, new personnel enter the job and need to understand all the service points, and the early training cost is high; aiming at different customers, the data logic and the analysis logic of a plurality of dimensions are the same, but the data contents are different, the analysis conclusions are different, and if the analysis conclusions are written one by one, a large amount of manpower is consumed; the analysis conclusion written by the individual is comparatively simple, each individual has the idea of each individual, but the idea of the individual may not be representative, and the conclusion written by other individuals needs to be integrated for reference to summarize a more appropriate and more meaningful analysis conclusion.
The invention provides an intelligent report conclusion generation method, which meets various analysis requirements of actual production and effectively increases the efficiency of the actual production. The user side does not need to write codes, does not need to write analysis conclusions one by one for each report, only needs to configure the analysis conclusions needed by the user side, and then automatically generates the analysis conclusions. The background can arrange an algorithm engineer to continuously increase service content according to the requirements of the user, and an interface is provided for the user to use, so that the work is an accumulated process, and rich intelligent analysis capability of report conclusion can be provided for the user.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, and can also be a mobile terminal device such as a smart phone, a tablet computer, a portable computer and the like.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the terminal may further include a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WiFi module, and the like. Such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the mobile terminal is stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer and tapping) and the like for recognizing the attitude of the mobile terminal; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a report conclusion intelligence generation program.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to invoke the report conclusion intelligence generation program stored in the memory 1005 and perform the following operations:
receiving a report conclusion analysis request, and acquiring configuration information contained in the report conclusion analysis request;
based on a preset knowledge base, calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result;
obtaining a report conclusion contained in an intelligent analysis result according to the configuration information;
and adding the report conclusion to an intelligent analysis report according to a report conclusion designated position contained in the configuration information.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the configuration information includes data set information, and the step of obtaining the configuration information included in the report conclusion analysis request includes:
and selecting a corresponding preset intelligent analysis component according to the data set information in the configuration information.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the step of calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result comprises the following steps:
and calling a preset intelligent analysis component to analyze the data set information layer by layer to obtain a service layer analysis result, and regarding the service layer analysis result as an intelligent analysis result.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the step of calling a preset intelligent analysis component to analyze the data set information layer by layer comprises the following steps:
and calling a preset intelligent analysis component to perform logic abstract basic analysis, data set dimension analysis and business layer analysis on the data set information.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the preset intelligent analysis component comprises a basic analysis component, and the step of calling the preset intelligent analysis component to perform logic abstract basic analysis on the data set information comprises the following steps:
and calling the basic analysis component to perform logic abstract basic analysis on the data set information to obtain a logic abstract basic analysis result.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the preset intelligent analysis component further comprises a dimension analysis component, and the step of obtaining the logical abstract basic analysis result comprises the following steps:
and calling the dimension analysis component to perform data set dimension analysis on the logical abstract basic analysis result so as to obtain a data set dimension analysis result.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the preset intelligent analysis component further comprises a business layer analysis component, and the step of obtaining the dimension analysis result of the data set comprises the following steps:
and calling the service layer analysis component to perform service layer analysis on the data set dimension analysis result so as to obtain the service layer analysis result.
Further, the processor 1001 may call the report conclusion intelligence generation program stored in the memory 1005, and further perform the following operations:
the preset intelligent analysis component further comprises an overview analysis component, and the step of obtaining the business layer analysis result comprises the following steps:
and calling the overview analysis component to classify and integrate the business layer analysis results so as to generate an overview page of the intelligent analysis report.
Referring to fig. 2, a first embodiment of the present invention provides an intelligent report conclusion generating method, where the intelligent report conclusion generating method includes:
step S10, receiving a report conclusion analysis request, and acquiring configuration information contained in the report conclusion analysis request;
in this embodiment, the execution subject is an intelligent report conclusion generation system, and the system is constructed based on the python language. The report conclusion analysis request is sent by a user using the report conclusion intelligent generation system, and the system can know the content to be analyzed, the conclusion to be obtained and the position of the report where the intelligent analysis conclusion should be filled in by the user after receiving the request.
In this embodiment, the configuration information includes data set information, that is, a data set selected by a user and needing to be analyzed, and step S10 includes:
and selecting a corresponding preset intelligent analysis component according to the data set information in the configuration information.
It should be noted that, in this embodiment, the standardized data set needs to be set in advance, because each basic analysis content needs to be transmitted into the standard analysis data set.
In step S20, the logical abstraction basic analysis component to be used needs to set a standard data transmission style, and the contents to be agreed are: the number of transmitted data columns, the type of transmitted data, the number of transmitted data lines, etc. if the transmitted data does not satisfy the condition, the related information is transmitted to the foreground, and the reason why the analysis component can not be used is prompted to the user.
When data is selected, a user needs to know the data pattern required to be transmitted by the logic abstract basic analysis component, and the corresponding place of the page can be reminded to the user.
Step S20, based on a preset knowledge base, calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result;
it should be noted that this embodiment provides a method for automatically generating an intelligent analysis of report conclusion, which fully uses the data analysis capability of the basic packages such as pandas and numpy of python, refines the basic logical abstract basic analysis, performs automatic analysis based on a data set according to the transmitted data, provides the data to a single-dimensional application at a business level, and provides an overview analysis of all data sets of the whole report. When various report analyses are carried out, a large amount of text analysis data are accumulated, and when product support services are provided, a large number of manually filled text data assets such as various alarm event lists, client consultation lists, client service fault lists and the like are also accumulated. The construction of the preset knowledge base comprises the steps of data source acquisition, alarm reason analysis, realization of an alarm solution classification algorithm, result induction, result entry and the like.
In this embodiment, step S20 includes:
and calling a preset intelligent analysis component to analyze the data set information layer by layer to obtain a service layer analysis result, and regarding the service layer analysis result as an intelligent analysis result.
Specifically, the step of calling a preset intelligent analysis component to perform layer-by-layer analysis on the data set information includes:
and calling a preset intelligent analysis component to perform logic abstract basic analysis, data set dimension analysis and business layer analysis on the data set information.
In this embodiment, the preset intelligent analysis component includes a basic analysis component, a dimension analysis component, a business layer analysis component, and an overview analysis component.
The step of obtaining the service layer analysis result comprises the following steps:
calling the basic analysis component to perform logic abstract basic analysis on the data set information to obtain a logic abstract basic analysis result;
calling the dimension analysis component to perform data set dimension analysis on the logical abstract basic analysis result to obtain a data set dimension analysis result;
and calling the service layer analysis component to perform service layer analysis on the data set dimension analysis result so as to obtain the service layer analysis result.
It should be noted that the basic analysis component is a logical abstract basic analysis module, and is used for providing a basic analysis service. The data analysis dimensionalities of the data analysis bottom layer mainly comprise data quantification, data change analysis, data label selection according with characteristics, metering comparison and the like.
The basic analysis component sample of the current stage encapsulation is as follows:
tag name for TOP value acquisition: tag names for ranking TOP3(TOP5) are returned in ascending (descending) order of numerical rank;
and (3) percentage analysis: selecting the data volume ratio of the specific label according to the data value;
obtaining the label name and proportion of the TOP value: tag names and odds for ranking TOP3(TOP5) are returned in ascending (descending) order of numerical rank;
total amount analysis: screening according to conditions, and returning the sum of numerical values;
calculating an average value: screening according to the conditions, and returning the average value which accords with the condition numerical value;
frequency analysis: screening according to the conditions, and returning the frequency of the tags meeting the conditions;
screening according to numerical conditions: returning tag names that are greater (less) than the filter value based on the data value;
screening according to specific conditions: according to the data values, returning label names of which the data values are larger than the data mean, smaller than the data mean, larger than the mode, smaller than the mode and the like;
obtaining the numerical ratio of the label columns of the screening conditions: screening the data value proportion of the label rows meeting the conditions according to the labels;
and (3) trend analysis: returning a qualitative rule of data change, such as ascending, descending, ascending first and descending second, descending first and ascending second and the like according to the label and the numerical value;
matching a knowledge base: and selecting the type of the knowledge base to be matched, inputting, and returning main matched problem points and suggested solutions.
At the present stage, 39 logic abstract basic analysis capabilities are packaged, logic abstract basic analysis methods which need to be added in actual production are provided and used according to the framework method of the module, and the needed logic abstract basic analysis control is selected according to the selection data in the using process.
It should be noted that the dimension analysis component is a data set analysis abstraction module, and is used for providing a data set dimension analysis service.
The data set dimension analysis conclusion needs not only a logical abstract result but also a specific expression language of the data set, the data set analysis abstraction mainly realizes the analysis of a single-dimension data set, the realization method is an intelligent report service platform of the department, a text control and abstract analysis control method, the platform transmits the content of a filled text space, the abstract space is selected to analyze the data, and then pulling and splicing are carried out to form the analysis method corresponding to the data set dimension.
At present, a 189-dimensional data set sampling analysis method is realized, and actual production use is provided.
It should be noted that the business layer analysis component is a business layer analysis abstraction module.
The method is used for a service layer, one service layer analysis is related to a plurality of data, the service layer analysis abstraction is based on the data analysis abstraction, the implementation method is a data set required by the selected service layer, then a method for analyzing and abstracting each dimension data set is appointed, and the final result is the splicing of each data analysis result.
The user needs to select the data set required by the service dimension at the front end, and a final analysis result, namely an intelligent analysis result containing the report conclusion, can be obtained after the analysis from the data layer to the application layer.
Based on the above process, intelligent analysis from the data layer to the application layer can be realized, and based on business layer application as a basic unit, each business layer application comprises a plurality of data sets, each data set corresponds to an intelligent analysis method, and the intelligent analysis method of each data set can comprise a plurality of logical abstract basic analysis modules.
Referring to fig. 3, fig. 3 is a schematic diagram of a process of intelligent analysis from a data layer to an application layer in this embodiment, assuming that a data set selected by a user includes application support data, product support data, and service analysis data, the logical abstraction basic analysis module performs operations such as TOP acquisition, proportion analysis, mean analysis, total analysis, frequency analysis, trend analysis, summation measurement analysis, knowledge base matching, threshold screening, cause and effect matching on the data set, provides the obtained results to the data set analysis abstraction module to perform monthly trend analysis of fault amount and fault rate, proportion analysis of alarm categories, ranking list analysis of personal examination and approval, data inspection result analysis of subsystems, score analysis of system health degree, subscription period analysis, and data inspection without rule analysis, and provides the obtained results to the service layer analysis module to perform product _ fault analysis and diagnosis, And finally obtaining applicable report conclusions, namely report page title analysis, report page overview analysis, report page detail analysis, report page summary analysis, report page first page analysis and the like through the operation of the sales system account use condition, examination and approval efficiency analysis and the like.
Further, in the present embodiment, step S20 is followed by:
and calling the overview analysis component to classify and integrate the business layer analysis results so as to generate an overview page of the intelligent analysis report.
It should be noted that, when the overview analysis module is used to output an analysis report, the top page is usually an overview page, the overview page is a summary and summary of all contents of the entire analysis report, and it is considered that analysis data of the entire report is needed to be considered, and if the overview page is intelligently analyzed in a single dimension, all data of the report needs to be transmitted at one time, which is very large in transmission amount, high in performance pressure, and not very fast in calculation speed. Finally overview page intelligent analysis we employ a divide-and-conquer approach,
and splitting the summary page analysis content, reserving an interface to return an analysis conclusion required by the summary page when carrying out single-service-layer dimension intelligent analysis, and carrying out intelligent analysis on the summary page content by the final summary page intelligent analysis in a mode of providing integration service. The realization method avoids the multiple transmission of the whole report data content, completes the analysis of the content, does not additionally increase the data transmission and the large calculation amount, and has very high efficiency.
In this embodiment, the preset knowledge base may also be directly called to implement intelligent analysis.
Specifically, when the knowledge base is needed for analyzing the related dimension data, the knowledge base content of the related dimension is acquired from the knowledge base table which is put in storage, matching of actual production data and the knowledge base content is carried out, the final analysis result is obtained, and data value output is achieved.
If monthly work order type analysis is carried out, the aim to be achieved is to inform customers of main problem points of the product failure in the month and a suggested solution, and the actual production implementation steps are as follows: (1) data acquisition: work order detail data and product fault support knowledge base content; (2) matching similar problems of 'work order details', and positioning to 'standard problems'; (3) the sum of the work unit quantity is recorded as the total work unit quantity according to the standard problem; (4) sorting the total workload, taking the first three: "standard problem" + "proposed solution"; (5) and outputting a conclusion.
By the method, intelligent analysis of 3 lines can be realized: intelligent analysis based on datasets, intelligent analysis based on a knowledge base, and overview analysis based on the global.
In the implementation process, the intelligent analysis scene, the configuration data set, the configuration basic analysis component and the text writing expressive language are configured by using the intelligent report conclusion generating system provided in the embodiment, and the intelligent analysis result can be displayed in second level when the report is generated.
Referring to fig. 4, fig. 4 is a schematic flow chart of the product _ fault analysis and diagnosis implemented intelligent analysis in this embodiment, where from left to right, the data sets respectively represent a first group of data sets "fault amount and fault rate monthly trend", a second group of data sets "type distribution of fault work orders", and a third group of data sets "fault SLA monthly trend"; the corresponding product fault support _ knowledge base data are respectively 'months 2021-02, work order amount 1754, fault amount 78 and fault rate 4.45%' the fault type is an environmental fault, the corresponding fault detail is a client environmental problem, and the work order amount is 33; the fault type is program fault, the corresponding fault subclass is program abnormity, and the work order quantity is 32; the fault type is interface abnormality, the corresponding fault detail type is third-party interface abnormality, and the work order quantity is 9; the fault type is environmental fault, the corresponding fault subclass is server environmental stomachache, the work order amount is 4 ' and ' months 2021-02, SLA98.72, target value 99, standard value 95 '; the failure rate of the month with title being lower can be obtained from the first group of data sets and the corresponding knowledge base data, and detail _1, namely the failure rate trend analysis of the month, can be obtained, wherein the system stability is better, and the failure rate of the month is 4.45 percent; from the second set of data sets and their corresponding knowledge base data, detail _2, the product failure description "product failure: the abnormity is subject to investigation and radical treatment by submitting a question list to research and development; and the detail _3 namely the failure resolution SLA analysis can be obtained from the third group of data sets and the corresponding knowledge base data, wherein the failure resolution SLA analysis is that the failure resolution SLA in the month reaches 98.72 percent and reaches the standard. The final title _ last is 'lower failure rate in this month' based on the report conclusion automatically generated by the title; analyzing and obtaining a final summary _ last based on detail _1, detail _2 and detail _3, wherein the final summary _ last is 'to pay attention to high-frequency faults, promote fault radical treatment and maintain a high SLA solution achievement rate'; and combining detail _1, detail _2 and detail _3 to obtain the final detail _ last, namely' failure rate trend analysis in this month: the stability of the system is better, and the failure rate in this month is 4.45%. Product failure description: product failure: the abnormity is already checked and cured by submitting a question list to research and development.
And (3) analyzing the failure resolution SLA: the resolution of SLA in this month is 98.72 percent and reaches the standard. "
Step S30, obtaining report conclusion contained in the intelligent analysis result according to the configuration information;
it can be understood that the intelligent analysis result includes many different report conclusions generated by different data sets, but only a part of the report conclusions is needed by the user, and the acquisition requirement of the user can also be known from the configuration information, so that the report conclusion needed by the user can be acquired according to the requirement of the user.
And step S40, adding the report conclusion into the intelligent analysis report according to the report conclusion designated position contained in the configuration information.
It should be understood that, as described above, the configuration information includes information of the user-specified intelligent analysis conclusion filling position, and after the report conclusion is successfully obtained, it is only necessary to fill the user-specified position in the intelligent analysis report.
In this embodiment, in all the above processes, the user side does not need to participate in code testing and writing, and only needs to configure the content to be analyzed and the desired conclusion in the foreground, and specify the position of the intelligent analysis conclusion in the report, so that the intelligent analysis can be realized when the report is generated, the analysis conclusion is automatically filled in, and the user does not need to write again. Depending on the report platform of my department, the intelligent analysis content can be displayed in various file formats, ppt, word, pdf and the like.
For the regular report and the report with the same analysis logic, the user only needs to set the report template once, and the required report is generated regularly according to the set report plan at the later stage (the report can be in various forms such as ppt, word and pdf).
The intelligent generation method for the report conclusion provided by the embodiment can greatly save labor cost for enterprises, for example, a certain company delivery team needs to output a monthly report of product and application support service, a quarterly report, a semiannuity report, a yearbook report, a product support event investigation report, a safety risk informing letter and the like to more than 2000 customers every month, if analysis contents are manually written, a large amount of labor cost is consumed, by using the method, the generation of the report at the second level is realized, an engineer can complete the report only by checking the report and carrying out partial personalized modification, and the report is sent to the customers, and the working time of one report is shortened from the small-time level to the minute level.
The intelligent report conclusion generating method provided in the embodiment can meet various analysis requirements of actual production, and is high in actual production efficiency. The user side does not need to write codes, does not need to write analysis conclusions one by one for each report, only needs to configure the analysis conclusions needed by the user side, and then automatically generates the analysis conclusions. The background can continuously increase service contents according to the requirements of the user, and an algorithm engineer provides an interface for the user to use, so that the work is an accumulation process and rich intelligent analysis capability is provided.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, where a report conclusion intelligent generation program is stored on the computer-readable storage medium, and when executed by a processor, the report conclusion intelligent generation program implements the steps of the report conclusion intelligent generation method in any of the above embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An intelligent generation method of report conclusion, which is characterized by comprising the following steps:
receiving a report conclusion analysis request, and acquiring configuration information contained in the report conclusion analysis request;
based on a preset knowledge base, calling a preset intelligent analysis component to analyze the configuration information to obtain an intelligent analysis result;
obtaining a report conclusion contained in an intelligent analysis result according to the configuration information;
and adding the report conclusion to an intelligent analysis report according to a report conclusion designated position contained in the configuration information.
2. The intelligent generation method of report conclusions as claimed in claim 1, wherein the configuration information includes data set information, and the step of obtaining the configuration information included in the report conclusion analysis request is followed by:
and selecting a corresponding preset intelligent analysis component according to the data set information in the configuration information.
3. The intelligent generation method of report conclusions according to claim 2, characterized in that said step of invoking a preset intelligent analysis component to analyze said configuration information to get intelligent analysis results comprises:
and calling a preset intelligent analysis component to analyze the data set information layer by layer to obtain a service layer analysis result, and regarding the service layer analysis result as an intelligent analysis result.
4. The intelligent generation method of report conclusions as claimed in claim 3, characterized in that said step of invoking a preset intelligent analysis component to analyze said data set information layer by layer comprises:
and calling a preset intelligent analysis component to perform logic abstract basic analysis, data set dimension analysis and business layer analysis on the data set information.
5. The intelligent generation method of report conclusions as claimed in claim 4, wherein said preset intelligent analysis component comprises a basic analysis component, and said step of calling said preset intelligent analysis component to perform a logical abstract basic analysis on said data set information comprises:
and calling the basic analysis component to perform logic abstract basic analysis on the data set information to obtain a logic abstract basic analysis result.
6. The intelligent generation method of report conclusions as claimed in claim 5, wherein said preset intelligent analysis component further comprises a dimension analysis component, said step of obtaining logical abstract base analysis results comprising, after:
and calling the dimension analysis component to perform data set dimension analysis on the logical abstract basic analysis result so as to obtain a data set dimension analysis result.
7. The intelligent method of generating report conclusions according to claim 6, wherein said preset intelligent analysis component further comprises a business layer analysis component, and said step of obtaining data set dimension analysis results comprises, after:
and calling the service layer analysis component to perform service layer analysis on the data set dimension analysis result so as to obtain the service layer analysis result.
8. The intelligent generation method of report conclusions as claimed in claim 7, wherein said preset intelligent analysis component further comprises a summary analysis component, said step of obtaining said business layer analysis results comprising, after:
and calling the overview analysis component to classify and integrate the business layer analysis results so as to generate an overview page of the intelligent analysis report.
9. An intelligent report conclusion generating device, characterized in that the intelligent report conclusion generating device comprises: a memory, a processor and a report conclusion intelligence generation program stored on the memory and executable on the processor, the report conclusion intelligence generation program when executed by the processor implementing the steps of the report conclusion intelligence generation method of any of claims 1-8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a report conclusion intelligence generation program, which when executed by a processor implements the steps of the report conclusion intelligence generation method according to any one of claims 1 to 8.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708472A (en) * | 2012-06-20 | 2012-10-03 | 江西省电力公司超高压分公司 | Intelligent processing method for warning information of combined power grid |
| CN103530230A (en) * | 2013-10-11 | 2014-01-22 | 珠海金山网络游戏科技有限公司 | Automatic generation method and automatic generation system of report |
| US20190303770A1 (en) * | 2018-03-28 | 2019-10-03 | International Business Machines Corporation | Architectural composite service solution builder |
| CN111198942A (en) * | 2018-10-31 | 2020-05-26 | 合肥神策数据网络科技有限公司 | Data analysis report generation method and device, mobile terminal and storage medium |
| US10706056B1 (en) * | 2015-12-02 | 2020-07-07 | Palantir Technologies Inc. | Audit log report generator |
-
2021
- 2021-12-30 CN CN202111639224.3A patent/CN114003631A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708472A (en) * | 2012-06-20 | 2012-10-03 | 江西省电力公司超高压分公司 | Intelligent processing method for warning information of combined power grid |
| CN103530230A (en) * | 2013-10-11 | 2014-01-22 | 珠海金山网络游戏科技有限公司 | Automatic generation method and automatic generation system of report |
| US10706056B1 (en) * | 2015-12-02 | 2020-07-07 | Palantir Technologies Inc. | Audit log report generator |
| US20190303770A1 (en) * | 2018-03-28 | 2019-10-03 | International Business Machines Corporation | Architectural composite service solution builder |
| CN111198942A (en) * | 2018-10-31 | 2020-05-26 | 合肥神策数据网络科技有限公司 | Data analysis report generation method and device, mobile terminal and storage medium |
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