Report generation method and device, electronic equipment and storage medium

Abstract

The invention provides a report generation method and device, electronic equipment and a storage medium. The method comprises the steps that a service data set and a data report template containing at least one field are acquired; performing text analysis on the description text of each field in the data report template to determine a business data type associated with each field; and based on the business data type associated with each field, extracting business data corresponding to the business data type from the business data set, and based on the extracted business data, carrying out data filling on each field so as to generate a data report corresponding to the business data set. The data report generated based on the service data set can be automatically obtained, so that the report does not need to be manually made in a time-consuming and labor-consuming manner, and the report generation efficiency is improved.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the technical field of report generation, and in particular, to a report generation method, device, electronic device, and storage medium. Background Art

[0002] Business Intelligence (BI) tools play a vital role in modern enterprises. They can collect, integrate, analyze and visualize large amounts of data from different business domains, and provide enterprises with in-depth and valuable data insights by providing intuitive dashboards, data visualization charts and interactive analysis tools, thereby improving the quality of decision-making, optimizing business processes, enhancing customer experience, predicting market trends, and ultimately driving enterprise growth and competitiveness.

[0003] However, although BI tools provide rich data analysis and intuitive data display functions, this data presentation method still has many inconveniences, such as making it inconvenient for users to view, print, share, and archive data offline. Summary of the invention

[0004] The present disclosure provides a report generation method, the method comprising:

[0005] Obtain a business data set and a data report template containing at least one field;

[0006] Performing text analysis on the description text of each field in the data report template to determine the type of business data associated with each field;

[0007] Based on the business data type associated with each field, business data corresponding to the business data type is extracted from the business data set, and data is filled in for each field based on the extracted business data to generate a data report corresponding to the business data set.

[0008] Optionally, the business data type associated with the target field includes a direct business data type corresponding to the target field;

[0009] The extracting business data corresponding to the business data type from the business data set based on the business data type associated with each field, and filling data for each field based on the extracted business data, includes:

[0010] Business data corresponding to the direct business data type is extracted from the business data set, and the extracted business data is filled into the target field.

[0011] Optionally, the business data type associated with the target field includes an indirect business data type corresponding to the target field, and a direct business data type used to generate the indirect business data type;

[0012] The extracting business data corresponding to the business data type from the business data set based on the business data type associated with each field, and filling data for each field based on the extracted business data, includes:

[0013] Extracting business data corresponding to the direct business data type from the business data set, and generating business data corresponding to the indirect business data type based on the extracted business data;

[0014] Fill the generated business data into the target field.

[0015] Optionally, before performing text analysis on the description text of each field in the data report template to determine the business data type associated with each field, the method further includes:

[0016] The acquired business data set is subjected to data cleaning and / or data integration; the data cleaning includes performing missing value processing, outlier detection, consistency verification and / or removing redundant and invalid data on the data in the business data set; the data integration includes converting the data in the business data set into data in a preset format.

[0017] Optionally, obtaining a data report template including at least one field includes:

[0018] Searching for a data report template matching the search information input by the user in a data report template library, outputting the found data report template to the user, and obtaining a data report template selected by the user from the found data report templates; or,

[0019] Based on the data report template recommendation rules, a data report template matching the data report keyword input by the user is determined from the data report template library; wherein the data report template recommendation rules are constructed based on the association relationship between the business domain and the data report template, and the user's historical preference for the data report template.

[0020] Optionally, obtaining a data report template including at least one field includes:

[0021] Based on the data report template generation rules and the data report requirement information input by the user, a data report template matching the data report requirement information is generated.

[0022] Optionally, the method further includes:

[0023] Based on the authority information corresponding to the generated data report, the data report is pushed to a data report viewing party having the authority to view the data report.

[0024] The present disclosure also provides a report generating device, the device comprising:

[0025] An acquisition unit, used for acquiring a business data set and a data report template including at least one field;

[0026] An analysis unit, configured to perform text analysis on the description text of each field in the data report template to determine the type of business data associated with each field;

[0027] A filling unit is used to extract business data corresponding to the business data type from the business data set based on the business data type associated with each field, and to fill data for each field based on the extracted business data to generate a data report corresponding to the business data set.

[0028] The present disclosure also provides an electronic device, comprising a communication interface, a processor, a memory and a bus, wherein the communication interface, the processor and the memory are interconnected via the bus;

[0029] The memory stores machine-readable instructions, and the processor executes the above method by calling the machine-readable instructions.

[0030] The present disclosure also provides a machine-readable storage medium, wherein the machine-readable storage medium stores machine-readable instructions, and when the machine-readable instructions are called and executed by a processor, the above method is implemented.

[0031] Through the embodiments of the present disclosure, a business data set and a data report template containing at least one field are obtained; the business data type associated with each field is determined by performing text analysis on the description text of each field in the data report template; then, based on the business data type associated with each field, business data corresponding to the business data type is extracted from the business data set, and data is filled in for each field based on the extracted business data to generate a data report corresponding to the business data set. Accordingly, the present disclosure can automatically extract business data corresponding to the business data type associated with the field in the business data set according to each field in the data report template, and then fill in the field based on the business data to automatically obtain a data report based on the output of the business data set, thereby eliminating the need for time-consuming and labor-intensive manual report production, thereby improving the efficiency of report generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0033] Figure 1 is a flow chart of a report generation method shown in an exemplary embodiment;

[0034] Figure 2 is an architectural diagram of a report generation system shown in an exemplary embodiment;

[0035] Figure 3 is a flow chart of another report generating method shown in an exemplary embodiment;

[0036] Figure 4 is a hardware structure diagram of an electronic device shown in an exemplary embodiment;

[0037] Figure 5 It is a block diagram of a report generating device shown in an exemplary embodiment. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0039] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0040] Business Intelligence (BI) tools play a vital role in modern enterprises. They can collect, integrate, analyze and visualize large amounts of data from different business domains, and provide enterprises with in-depth and valuable data insights by providing intuitive dashboards, data visualization charts and interactive analysis tools, thereby improving the quality of decision-making, optimizing business processes, enhancing customer experience, predicting market trends, and ultimately driving enterprise growth and competitiveness.

[0041] However, although BI tools provide rich data analysis and intuitive data display functions, this data presentation method still has many inconveniences, such as making it inconvenient for users to view, print, share, and archive data offline.

[0042] In order to solve the above problems, in actual situations, users need to make additional reports according to their own business needs, which not only takes up extra time and energy of users, but may also cause problems such as data statistical errors, causing many inconveniences to users.

[0043] In view of this, the present specification aims to propose a technical solution for automatically filling in data on a data report template according to a business data set, thereby automatically generating a data report.

[0044] During implementation, first, a business data set and a data report template containing at least one field are obtained; further, a text analysis is performed on the description text of each field in the data report template to determine the business data type associated with each field; finally, based on the business data type associated with each field, business data corresponding to the business data type is extracted from the business data set, and based on the extracted business data, data is filled in for each field to generate a data report corresponding to the business data set.

[0045] For example, obtain a dataset in the field of daily necessities sales and a data report template containing the "monthly sales" field and the "quarterly average sales" field. Analyze the text of the two fields, "monthly sales" and "quarterly average sales", and determine that the data type associated with the "monthly sales" field is monthly sales data, and the data type associated with the "quarterly average sales" field is quarterly sales data. Then, extract the sales from January to June of this year as 20,000, 25,000, 18,000, 31,000, 27,000, and 29,000 from the dataset in the field of daily necessities sales, and fill the "monthly sales" field based on these sales data; extract the sales from January to March of the first quarter of this year as 20,000, 25,000, and 18,000 from the dataset in the field of daily necessities sales, and the sales from April to June of the second quarter of this year as 31,000, 27,000, and 29,000, and fill the "quarterly average sales" field based on these sales data. In the above way, a data report corresponding to the dataset in the field of daily necessities sales is finally generated.

[0046] It can be seen that in the technical solution of the present disclosure, a business data set and a data report template containing at least one field are obtained; the business data type associated with each field is determined by performing text analysis on the description text of each field in the data report template; then, based on the business data type associated with each field, business data corresponding to the business data type is extracted from the business data set, and data is filled in for each field based on the extracted business data to generate a data report corresponding to the business data set. Accordingly, the present disclosure can automatically extract business data corresponding to the business data type associated with the field in the business data set according to each field in the data report template, and then fill in the field based on the business data to automatically obtain a data report based on the output of the business data set, thereby eliminating the need for time-consuming and labor-intensive manual report preparation, thereby improving the efficiency of report generation.

[0047] The present disclosure is described below through specific embodiments in combination with specific application scenarios.

[0048] See also Figure 1 , Figure 1 The following is a flow chart of a report generation method according to an exemplary embodiment. The method may perform the following steps:

[0049] Step 102: Acquire a business data set and a data report template including at least one field.

[0050] For example, obtain a dataset in the field of daily necessities sales, and a data report template that includes the "monthly sales" field and the "quarterly average sales" field.

[0051] Among them, business data sets refer to data sets from various business fields, including but not limited to sales fields, production fields, quality control fields, delivery fields, etc. Each field can be further divided. For example, the sales field includes but is not limited to the cosmetics sales field, the daily necessities sales field, the furniture sales field, etc. The type of business data set collected is determined according to the user's business needs, and the present disclosure does not limit this. For the collection method of business data sets, it can be obtained from a data warehouse. The data warehouse uniformly collects, cleans and integrates data from different business domains, and provides a comprehensive and accurate data basis for report generation in the scheme of the present disclosure. The collection method of business data sets can also be through an application program interface (which defines how software applications exchange data through functions and processes), an ETL (Extract-Transform-Load, data extraction-transformation-loading) tool or a data pipeline to connect with the business domain management system, so as to extract key business data in real time or regularly. For the collection method of business data sets, the present disclosure does not limit this.

[0052] Step 104: Perform text analysis on the description text of each field in the data report template to determine the business data type associated with each field.

[0053] For example, by analyzing the text of the "Monthly Sales" field and the "Quarterly Average Sales" field, it is determined that the data type associated with the "Monthly Sales" field is monthly sales data, and the data type associated with the "Quarterly Average Sales" field is sales data for each quarter.

[0054] Among them, by analyzing the field text, the associated data type can be inferred by identifying the keywords in the field description. For example, "sales" may be related to sales data in the financial field, and "inventory" may be related to inventory data in the logistics field. In addition, the field text can also be analyzed by natural language processing, pattern recognition, machine learning, etc. The present disclosure does not limit the specific method of field text analysis.

[0055] Step 106: Based on the business data type associated with each field, extract the business data corresponding to the business data type from the business data set, and fill in data for each field based on the extracted business data to generate a data report corresponding to the business data set.

[0056] For example, from the dataset of daily necessities sales, we extract the sales from January to June of this year as 20,000, 25,000, 18,000, 31,000, 27,000, and 29,000 respectively, and fill the "Monthly Sales" field based on these sales data; from the dataset of daily necessities sales, we extract the sales from January to March of the first quarter of this year as 20,000, 25,000, and 18,000 respectively, and from April to June of the second quarter of this year as 31,000, 27,000, and 29,000 respectively, and fill the "Quarterly Average Sales" field based on these sales data. In the above way, a data report corresponding to the dataset of daily necessities sales is generated.

[0057] Among them, since the "Monthly Sales" field corresponds to the monthly sales data, the sales data from January to June this year does not need to be calculated again and can be directly filled into the "Monthly Sales" field. Since the "Quarterly Average Sales" field corresponds to the average sales data for each quarter, the sales of 20,000, 25,000, and 18,000 from January to March in the first quarter of this year need to be averaged as a whole, and the sales of 31,000, 27,000, and 29,000 from April to June in the second quarter of this year also need to be averaged as a whole and filled into the "Quarterly Average Sales" field. Field filling involves entering the extracted and calculated data into the corresponding fields, which can be done by manual input, dragging and dropping data, using data import functions, or script automation. The present disclosure does not limit the specific method of field filling.

[0058] In this embodiment, through the above data extraction, calculation and field filling process, a data report corresponding to the dataset in the field of daily necessities sales is finally generated. This data report includes the specific values ​​of monthly sales and the statistical data of quarterly average sales.

[0059] In one embodiment shown, the business data type associated with the target field includes a direct business data type corresponding to the target field; based on the business data type associated with each field, the business data corresponding to the business data type is extracted from the business data set, and data is filled in for each field based on the extracted business data, including: extracting the business data corresponding to the direct business data type from the business data set, and filling the extracted business data into the target field.

[0060] For example, the business data types associated with the "Monthly Sales" field include the direct business data types corresponding to the "Monthly Sales" field, that is, the monthly sales data. The sales data from January to June of this year is extracted from the business data set and filled into the "Monthly Sales" field.

[0061] The direct business data type corresponding to the target field refers to the specific data items associated with the field name and content that can be directly obtained from the business data set without further data conversion. These data types usually reflect the key indicators in business operations and form the basis of the field data. For example: for the "Monthly Sales" field, the direct business data type refers to the total sales revenue for each month.

[0062] In one embodiment shown, the business data type associated with the target field includes an indirect business data type corresponding to the target field, and a direct business data type used to generate the indirect business data type; based on the business data type associated with each field, the business data corresponding to the business data type is extracted from the business data set, and data is filled in for each field based on the extracted business data, including: extracting the business data corresponding to the direct business data type from the business data set, and generating the business data corresponding to the indirect business data type based on the extracted business data; and filling the generated business data into the target field.

[0063] For example, the business data types associated with the "Quarterly Average Sales" field include indirect business data types corresponding to the "Quarterly Average Sales" field, namely, the average sales data for each quarter, and the sales data for each month of the quarter used to generate the average sales data for each quarter. The sales of 20,000, 25,000, and 18,000 from January to March in the first quarter of this year, and the sales of 31,000, 27,000, and 29,000 from April to June in the second quarter of this year are extracted from the business data set, and the average sales data for each quarter is generated based on these sales data, thereby filling the "Quarterly Average Sales" field.

[0064] Among them, the indirect business data type corresponding to the target field refers to the specific data items associated with the field name and content that cannot be directly obtained from the business data set and need to be converted. For example: for the "Quarterly Average Sales" field, the indirect business data type is the average sales data for each quarter. The sales of each month in a quarter need to be added up and divided by the number of months in the quarter to obtain the quarterly average sales revenue. The sales of each month in a quarter is the direct business data type that generates the quarterly average sales data (indirect business data type).

[0065] In one embodiment shown, before performing text analysis on the description text of each field in the data report template to determine the business data type associated with each field, the method also includes: performing data cleaning and / or data integration on the acquired business data set; the data cleaning includes performing missing value processing, outlier detection, consistency verification and / or removal of redundant and invalid data on the data in the business data set; the data integration includes converting the data in the business data set into data in a preset format.

[0066] For example, before analyzing the text of the "monthly sales" field and the "quarterly average sales" field to determine the data type associated with the two fields, the acquired dataset in the field of daily necessities sales is processed in at least one of the following ways: 1. Missing value processing, such as inferring missing values ​​based on the previous and subsequent associated data; 2. Outlier detection, such as removing garbled values ​​that do not conform to the specified numerical type when such data is detected; 3. Consistency check, such as checking whether the data in the dataset meets the predetermined consistency rules or standards; 4. Removing redundant data, such as the dataset may contain exactly the same data records, which are unnecessary for analysis and need to be identified and deleted; 5. Removing invalid data: Invalid data may include format errors, values ​​outside the reasonable range, obvious incorrect input, etc. These data need to be identified and deleted from the dataset to prevent them from affecting the accuracy of the analysis results.

[0067] Among them, if there is missing data in the data set, it is necessary to decide how to deal with these missing values, such as by filling in missing values, deleting records with missing values, or estimating missing values ​​using statistical methods. Data consistency is a key aspect of data quality, ensuring that the data is logically reasonable and accurate throughout the data life cycle. Data consistency verification can prevent analysis bias or business decision errors caused by erroneous, missing or contradictory data. Through the above data cleaning and integration steps, the data set will be more suitable for data analysis or other data processing tasks, which will help improve the reliability and effectiveness of the analysis results. This specification does not limit the specific methods of data cleaning and data integration.

[0068] In one embodiment shown, obtaining a data report template containing at least one field includes: searching a data report template library for a data report template that matches the search information input by a user, and outputting the found data report template to the user, and obtaining a data report template selected by the user from the found data report templates; or, based on a data report template recommendation rule, determining a data report template from the data report template library that matches the data report keyword input by the user; wherein the data report template recommendation rule is constructed based on the association relationship between the business domain and the data report template, and the user's historical preference for the data report template.

[0069] For example, if the search information input by the user is "sales", the data report template library will be searched for a data report template that matches the search information "sales", and the three data report templates found (respectively: monthly sales template, quarterly average sales template, and a template that combines monthly sales with quarterly average sales) will be output to the user, and the data report template selected by the user from these three data report templates will be obtained: a template that combines monthly sales with quarterly average sales.

[0070] Data report templates may also be obtained in the following manner: for example, if the data report keyword input by the user is "monthly sales", then based on the data report templates associated with the sales field (mainly three types, namely: monthly sales template, quarterly average sales template, and template combining monthly sales and quarterly average sales) and the user's historical preference for data report templates (monthly sales template is often used), a data report template matching "monthly sales" is determined from the data report template library, namely, the monthly sales template.

[0071] Among them, the data report template library is a database or system that stores various predefined data report templates, from which users can select or search for templates. The data report template recommendation rule is an algorithm or rule used to recommend data report templates that match the keywords entered by the user. The association between the business domain and the template means that each data report template is associated with a specific business field (such as sales, production, etc.), and the recommendation rule takes this association into account. In order to assist users in making quick choices, the data report template library can also support category browsing functions and template keyword search functions. This manual does not limit the auxiliary functions of the data report template library. The above process ensures that users can quickly and easily obtain the data report templates they need. At the same time, the data report template library improves user experience and work efficiency through intelligent recommendations.

[0072] In one embodiment shown, the user may also customize the required data report template, and the data report template library obtains the data report template input by the user and stores the data report template.

[0073] For example, a user interface is provided to the user, allowing the user to enter or edit various components of a data report template, such as a title, field, chart type, etc. The user can also select the type of data report template, such as a table report, a chart report, or a mixed report, and specify the data fields they want to display in the report, such as sales, customer name, date range, etc. In addition, the user can add charts to visualize the data, such as a bar chart, a line chart, or a pie chart. For data that needs to be calculated, the user can enter or edit a formula to automatically calculate the result. After the user completes the design of the data report template, the data report template can be saved to the data report template library for future use or modification. This specification does not limit the user customization process of the data report template.

[0074] Among them, the user interface allows users to enter their required information in an intuitive way, enabling users to select data fields, define data ranges, and specify chart types. Users specify the type of data, format, and presentation method they want to be included in the report through specific instructions provided by the user interface. The system selects the corresponding data fields from the predefined data model based on the user's needs, which may involve database queries, data model mapping, or data extraction logic. The system dynamically builds the layout and structure of the data report based on the data report template generation rules and user needs, which may include the sorting of data fields, grouping logic, paging mechanism, etc. The system may use scripting languages ​​or calculation engines to implement complex data calculations, such as Excel formulas, SQL aggregate functions, or custom scripts. Through these technologies, the system can flexibly meet the user's personalized data reporting needs while providing sufficient control and customization capabilities.

[0075] In an illustrated embodiment, the obtaining of a data report template including at least one field includes: based on a data report template generation rule and data report requirement information input by a user, generating a data report template matching the data report requirement information.

[0076] For example, the system administrator or developer predefines the data report template generation rules, which determine how to generate the data report template according to user needs. The rules may involve the selection of data fields, the design of the report structure, the application of visual styles, etc. In terms of specific implementation, a friendly interface can be provided to users, allowing them to easily enter their required information, such as selecting data fields, specifying data ranges, and selecting chart types. According to the required information entered by the user, the system matches and selects the corresponding fields from the available data fields to be included in the generated report template, and designs the structure of the report according to the data report template generation rules and user needs, including the arrangement order, grouping method and paging of data fields, etc. For data that needs to be calculated, the system will apply the corresponding formulas and calculation logic according to user needs. After the data report template is generated, the user can preview the generated data report template and adjust it when necessary until it meets their own needs. Once the user is satisfied, the user can also name and add a description to the generated data report template for easy identification and retrieval. The system will store the generated data report template for future use or further modification.

[0077] Through the above method, it is convenient for users to design and use personalized data report templates according to their own needs to meet the diverse needs of users. The present disclosure does not limit the specific method of generating data report templates according to user needs.

[0078] In one illustrated embodiment, the method further includes: based on permission information corresponding to the generated data report, pushing the data report to a data report reviewer who has permission to review the data report.

[0079] For example, only some decision makers or team members (such as all employees in the sales department) have access to the generated data report (a data report combining monthly sales and quarterly average sales), so the data report combining monthly sales and quarterly average sales is only pushed to all employees in the sales department.

[0080] Among them, pushing the generated data report according to the permission information corresponding to the generated data report can achieve fine-grained access control, enhance security, and ensure that only authorized users can access specific data reports, thereby protecting sensitive data from unauthorized access or leakage; and the automated push mechanism can also reduce the time of manual distribution of data reports and improve work efficiency.

[0081] In order to help those skilled in the art better understand this method, Figure 2 and Figure 3 This method is described.

[0082] See also Figure 2 , Figure 2 FIG. 1 is an architecture diagram of a report generation system shown in an exemplary embodiment. Figure 2 As shown, the report generation system mainly includes a data source layer, a data processing layer, a storage layer, a report generation layer and a user interaction layer. The data source layer is responsible for collecting and integrating data from different business domains, including but not limited to leads, orders, production, delivery, OTA (Over-the-Air Technology, over-the-air download technology), etc. Among them, the lead data may include the contact information of potential customers (such as name, phone number, email), the type of products or services they are interested in, their needs, and the interaction history with the sales team. OTA refers to any type of remote data transmission or service update over a wireless network, such as updates to mobile applications, configuration changes to smart home devices, etc. The present disclosure does not limit the type of business domain and the type of data in the business domain. The data processing layer uses BI tools to clean, integrate and analyze the data collected by the data source layer, generate business data sets that can be used by the report generation layer, and store the business data sets in the storage layer. The storage layer can be various database types, such as Mysql, Hologres, Clickhouse, etc. The present disclosure does not limit the type of database adopted by the storage layer. The report generation layer automatically generates data reports for each business domain based on the preset report template and the data set provided by the data processing layer. The user interaction layer provides a user interface, allowing users to select data report templates or customize data report templates, and set filtering conditions for the data source used in the data report (such as only counting this year's data, only counting the sales data of a certain department), etc.

[0083] See also Figure 3 , Figure 3 FIG. 1 is a flow chart of another report generation method shown in an exemplary embodiment. Figure 3 As shown, at the beginning of the method, first, a data report template selected by the user is obtained, which may be a user-defined data report template or a recommended data report template selected by the user in a data report template library, etc.; then, a business data set is obtained, and data in the business data set is cleaned and integrated; then, according to the data report template selected by the user, text analysis is performed on each field in the data report template, the business data that needs to be extracted from the business data set is determined, data analysis and processing are performed on the data, and data is filled in for each field in the data report template to generate a data report; finally, the generated data report is pushed to a data report reviewer who has the data report review authority, such as a review device or platform, so that the user can review the automatically generated data report.

[0084] Corresponding to the above-mentioned embodiment of the report generating method, this specification also provides an embodiment of a report generating device.

[0085] See also Figure 4 , Figure 4 It is a hardware structure diagram of an electronic device shown in an exemplary embodiment. At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, a memory 408 and a non-volatile memory 410, and of course may also include other required hardware. One or more embodiments of this specification can be implemented based on software, such as the processor 402 reading the corresponding computer program from the non-volatile memory 410 into the memory 408 and then running it. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0086] See also Figure 5 , Figure 5 FIG. 5 is a block diagram of a report generating device according to an exemplary embodiment. The report generating device 500 can be applied to Figure 4 In the electronic device shown in the figure, the technical solution of this specification is implemented. The device includes:

[0087] An acquisition unit 502 is used to acquire a business data set and a data report template including at least one field;

[0088] A determination unit 504, configured to perform text analysis on the description text of each field in the data report template to determine the type of business data associated with each field;

[0089] The generating unit 506 is used to extract business data corresponding to the business data type from the business data set based on the business data type associated with each field, and fill in data for each field based on the extracted business data to generate a data report corresponding to the business data set.

[0090] In this embodiment, the business data type associated with the target field includes a direct business data type corresponding to the target field; the generating unit includes:

[0091] The first filling subunit is used to extract business data corresponding to the direct business data type from the business data set, and fill the extracted business data into the target field.

[0092] In this embodiment, the business data type associated with the target field includes an indirect business data type corresponding to the target field and a direct business data type used to generate the indirect business data type; the generating unit includes:

[0093] an extraction subunit, configured to extract business data corresponding to the direct business data type from the business data set, and generate business data corresponding to the indirect business data type based on the extracted business data;

[0094] The second filling subunit is used to fill the generated business data into the target field.

[0095] In this embodiment, the device further includes:

[0096] A cleaning and integration unit is used to perform data cleaning and / or data integration on the acquired business data set; the data cleaning includes missing value processing, outlier detection, consistency verification and / or removal of redundant and invalid data on the data in the business data set; the data integration includes converting the data in the business data set into data in a preset format.

[0097] In this embodiment, the acquisition unit includes:

[0098] an acquisition subunit, configured to search a data report template library for a data report template that matches the search information input by the user, output the found data report template to the user, and acquire a data report template selected by the user from the found data report templates; or

[0099] A determination subunit is used to determine, from a data report template library, a data report template that matches a data report keyword input by a user based on a data report template recommendation rule; wherein the data report template recommendation rule is constructed based on an association relationship between a business domain and a data report template, and a user's historical preference for a data report template.

[0100] In this embodiment, the acquisition unit includes:

[0101] The generating subunit is used to generate a data report template matching the data report requirement information based on the data report template generating rules and the data report requirement information input by the user.

[0102] In this embodiment, the device further includes:

[0103] The pushing unit is used to push the data report to a data report viewing party having viewing authority for the data report based on authority information corresponding to the generated data report.

[0104] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0105] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this specification. Ordinary technicians in this field can understand and implement it without paying creative work.

[0106] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, a game console, a tablet computer, a wearable device or a combination of any of these devices.

[0107] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0109] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0110] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0111] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0112] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "one", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0114] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0115] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. A report generation method, characterized in that: The method comprises: Obtain a business data set and a data report template containing at least one field; Performing text analysis on the description text of each field in the data report template to determine the type of business data associated with each field; Based on the business data type associated with each field, business data corresponding to the business data type is extracted from the business data set, and data is filled in for each field based on the extracted business data to generate a data report corresponding to the business data set.

2. The method according to claim 1, characterized in that The business data type associated with the target field includes a direct business data type corresponding to the target field; The extracting business data corresponding to the business data type from the business data set based on the business data type associated with each field, and filling data for each field based on the extracted business data, includes: Business data corresponding to the direct business data type is extracted from the business data set, and the extracted business data is filled into the target field.

3. The method according to claim 1, characterized in that The business data type associated with the target field includes an indirect business data type corresponding to the target field and a direct business data type used to generate the indirect business data type; The extracting business data corresponding to the business data type from the business data set based on the business data type associated with each field, and filling data for each field based on the extracted business data, includes: Extracting business data corresponding to the direct business data type from the business data set, and generating business data corresponding to the indirect business data type based on the extracted business data; Fill the generated business data into the target field.

4. The method according to claim 1, characterized in that: Before performing text analysis on the description text of each field in the data report template to determine the business data type associated with each field, the method further includes: The acquired business data set is subjected to data cleaning and / or data integration; the data cleaning includes performing missing value processing, outlier detection, consistency verification and / or removing redundant and invalid data on the data in the business data set; the data integration includes converting the data in the business data set into data in a preset format.

5. The method according to claim 1, characterized in that The step of obtaining a data report template including at least one field comprises: Searching for a data report template matching the search information input by the user in a data report template library, outputting the found data report template to the user, and obtaining a data report template selected by the user from the found data report templates; or, Based on the data report template recommendation rules, a data report template matching the data report keyword input by the user is determined from the data report template library; wherein the data report template recommendation rules are constructed based on the association relationship between the business domain and the data report template, and the user's historical preference for the data report template.

6. The method according to claim 1, characterized in that The step of obtaining a data report template including at least one field comprises: Based on the data report template generation rules and the data report requirement information input by the user, a data report template matching the data report requirement information is generated.

7. The method according to claim 1, characterized in that The method further comprises: Based on the authority information corresponding to the generated data report, the data report is pushed to a data report viewing party having the authority to view the data report.

8. A report generating device, characterized in that: The device comprises: An acquisition unit, used for acquiring a business data set and a data report template including at least one field; A determination unit, configured to perform text analysis on the description text of each field in the data report template to determine the type of business data associated with each field; A generating unit is used to extract business data corresponding to the business data type from the business data set based on the business data type associated with each field, and to fill in data for each field based on the extracted business data to generate a data report corresponding to the business data set.

9. An electronic device, characterized in that: It includes a communication interface, a processor, a memory and a bus, wherein the communication interface, the processor and the memory are interconnected via the bus; The memory stores machine-readable instructions, and the processor executes the method according to any one of claims 1 to 7 by calling the machine-readable instructions.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores machine-readable instructions, and when the machine-readable instructions are called and executed by the processor, the method according to any one of claims 1 to 7 is implemented.

Images