CN118586369A - A hardware product report data processing method, system, device and storage medium - Google Patents

Abstract

The application relates to the technical field of data processing, in particular to a hardware product report data processing method, a system, equipment and a storage medium. Firstly, acquiring and standardizing hardware product original data, then receiving a classification rule input by a login user, and classifying the standardized data to obtain classification data; next, a matching template is called from a template library, the structure, style and interaction logic metadata information of the template are extracted, and based on the structure, style and interaction logic metadata information of the template, the classification data are mapped to generate report data by receiving mapping configuration input by a user, and a personalized visual report is dynamically constructed; the template metadata is combined with the data classification result input by the user, so that the dynamic construction of the report generation process is realized, the flexibility and individuation degree of the report display form are improved, and the restriction caused by template solidification is reduced.

Description

A hardware product report data processing method, system, device and storage medium

Technical Field

The present application relates to the technical field of data processing, and in particular to a method, system, device and storage medium for processing hardware product report data.

Background Art

With the rapid development of the hardware manufacturing industry, enterprises are facing increasingly complex management challenges. Timely and comprehensive grasp of core data such as product sales, inventory, logistics, etc. is crucial for formulating precise marketing strategies, optimizing supply chains, and controlling operating costs. As a key means of digital decision support, report data analysis has become increasingly valuable in the hardware industry. How to efficiently extract valuable analytical insights from massive raw data and generate personalized, visual report outputs has become an important issue that needs to be solved urgently.

Existing automated report processing software mostly uses preset templates to organize and display data. It has limited support for non-standardized hardware product data and diversified reporting requirements, and cannot meet the needs of enterprises to quickly respond to market changes and refined management. This situation needs to be further improved.

Summary of the invention

In order to solve the problem that existing automated report processing software has limited support for non-standardized hardware products and diversified report requirements, the present application provides a hardware product report data processing method, system, device and storage medium, which adopts the following technical solutions:

In a first aspect, the present application provides a method for processing hardware product report data, comprising the following steps:

Obtain and standardize raw data of hardware product reports;

Receiving classification rules input by a logged-in user, and classifying the standardized raw data according to the classification rules to obtain classification data of multiple dimensions;

Determine the target report type, obtain the corresponding report template from the report template library, and extract the report structure, style and interaction logic of the report template;

Receive a mapping configuration input by a user, and based on the report structure, style and interaction logic, generate report data according to the mapping configuration for the classified data to obtain a personalized target report;

An authority control policy is set for the personalized target report, and the personalized target report is published and output.

By adopting the above technical solution, the present application first obtains and standardizes the original data of hardware products, then receives the classification rules input by the logged-in user, classifies the standardized data according to the classification rules, and obtains multi-dimensional classified data; next, according to the target report type, the matching template is called from the template library, and the structure, style and interactive logic metadata information of the template are extracted. Based on the structure, style and interactive logic metadata information of the template, by receiving the mapping configuration input by the user, the classified data is mapped to generate report data, and a personalized visual report is dynamically constructed; different from the traditional method of generating reports based on fixed templates, the present application combines the template metadata with the data classification results input by the user, realizes the dynamic construction of the report generation process, improves the flexibility and personalization of the report presentation form, and reduces the constraints brought by template solidification.

Optionally, before receiving a classification rule input by a logged-in user and classifying the standardized raw data according to the classification rule, the method includes the following steps:

Based on the provided visual configuration interface, obtain the classification rule information configured by the logged-in user;

Based on the historical classification data, the classification rule information is scored for quality to obtain the classification rule quality;

When the quality of the classification rules is lower than a preset threshold, a rule adjustment recommendation scheme is generated.

By adopting the above-mentioned technical solution, this application provides a visual configuration interface, allowing logged-in users to independently configure classification rules according to actual needs, and scores the rule quality based on historical classification data, and generates adjustment suggestions when the quality is below the threshold, thereby realizing dynamic optimization of classification rules and improving the accuracy and personalization of classification results. At the same time, the rule quality is scored for quantitative evaluation, which can discover deficiencies in the rules, and generate optimization suggestions based on the analysis results, providing users with the basis and direction for rule optimization, simplifying the work of manual analysis and adjustment.

Optionally, determining the target report type, obtaining the corresponding report template from the report template library, and extracting the report structure, style, and interaction logic of the report template specifically includes the following steps:

Receive a target report type input by the logged-in user;

Searching for a report template matching the target report type in a report template library;

Extracting the layout information and visual style information of the report template as a report structure and a report style;

Semantic recognition is performed on the interaction description information corresponding to the report template to obtain the interaction logic.

By adopting the above technical solution, this application receives the target report type input by the logged-in user, searches for matching templates in the template library, and extracts the structure, style, and interactive logic information of the template, thereby realizing personalized support for the report building process and improving the pertinence and practicality of the final report. The target report type input by the user is received to meet the personalized needs of different users; through the template type resources in the template library, it is ensured that suitable template resources can be found for different target types. The layout, style, and interactive logic information are extracted from the template to construct a basic metadata model for report generation; in the extraction process, the interaction description is understood through semantic recognition technology to ensure the accurate acquisition of the interaction logic, providing comprehensive data support for the subsequent personalized report building process.

Optionally, before setting a permission control policy for the personalized target report and publishing and outputting the personalized target report, the method further includes the following steps:

Acquire the identity information of the logged-in user, and determine the permission level of the logged-in user based on the identity information;

Analyzing the data sensitivity of the personalized target report;

An operation permission control policy for the personalized template report is generated according to the permission level and the data sensitivity.

By adopting the above technical solution, before publishing and outputting personalized target reports, this application determines the permission level of the logged-in user by obtaining the identity of the logged-in user, then analyzes the sensitivity of the report data, and generates a refined operation permission control strategy based on the permission level and the sensitivity of the report data, thereby achieving effective protection of the report data, ensuring data security, and granting different users reasonable data access rights.

Optionally, before receiving the target report type input by the logged-in user, the method includes the following steps:

Identify and extract product line information from the original data of hardware products, and divide the report templates in the report template library into multiple product line types based on the product line information;

Determine the target product line corresponding to the logged-in user according to the identity information of the logged-in user;

Acquire target report type candidates based on the target product line;

The target report type candidate is recommended to the logged-in user.

By adopting the above technical solution, before receiving the target report type input by the logged-in user, the present application first classifies the template library by identifying the product line information in the original data, and then determines the target product line corresponding to the logged-in user according to the user identity, obtains the candidate report type and recommends it to the user, thereby realizing personalized guidance of the report building process and improving user experience and decision-making efficiency; by extracting the original data product line information to dynamically classify the template library, the report template resources can be highly correlated with the business field, providing a more appropriate template basis for report construction; determining the target product line corresponding to the user can lock the user's work scope and avoid interference caused by presenting irrelevant templates.

Optionally, receiving a mapping configuration input by a user, and based on the report structure, style and interaction logic, generating report data from the classified data according to the mapping configuration to obtain a personalized target report, specifically includes the following steps:

According to the report structure, construct a report data storage model;

Receive a mapping configuration input by a user, traverse the classification data, and map the multi-dimensional values of the classification data to the report data storage model according to the mapping configuration;

Based on the report style, generating a visual style description for the data in the report data storage model;

Based on the interaction logic, interaction events and data query, calculation and rendering rules are extracted to obtain personalized target reports.

By adopting the above technical solution, the present application first constructs a unified data storage model according to the report structure, receives the mapping configuration input by the user, traverses the classified data, and loads the multidimensional classified data according to the mapping configuration, thereby ensuring the standardization of data storage and the retention of dimensional characteristics; then generates a visual description for the data based on the report style, and defines the presentation form of the data in the final report; then extracts various rendering rules from the interactive logic, covering the entire process of report generation, and finally integrates the data model, style description and rendering rules to assemble a personalized target report.

Optionally, after setting a permission control policy for the personalized target report, publishing and outputting the personalized target report, the method further includes the following steps:

Analyze the data distribution and association pattern of the personalized template report to generate report data mining results;

Based on the report data mining results, extract key report indicators and key indicator weights;

Based on the key report indicators and key indicator weights, a visual report analysis view is generated.

By adopting the above-mentioned technical solution, after publishing and outputting personalized target reports, this application analyzes the distribution and correlation patterns of report data, extracts key indicators and their weights, and then generates a visual report analysis view, thereby achieving in-depth mining and insight into report data, and providing analytical support for enterprise digital decision-making.

In a second aspect, the present application provides a hardware product report data processing system, comprising:

Data acquisition module, used to acquire and standardize the raw data of hardware product reports;

A data classification module is used to classify the standardized raw data according to preset classification rules to obtain classified data in multiple dimensions;

The template acquisition module is used to determine the target report type, obtain the corresponding report template from the report template library, and extract the report structure, style and interaction logic of the report template;

A report generation module, configured to receive a mapping configuration input by a user, and based on the report structure, style and interaction logic, generate report data from the classified data according to the mapping configuration to obtain a personalized target report;

The report output module is used to set the authority control policy for the personalized target report, publish and output the personalized target report.

In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned hardware product report data processing method when executing the computer program.

In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned hardware product report data processing method are implemented.

In summary, the present application includes at least one of the following beneficial technical effects:

1. This application first obtains and standardizes the original data of hardware products, then receives the classification rules entered by the logged-in user, classifies the standardized data according to the classification rules, and obtains multi-dimensional classified data; next, according to the target report type, the matching template is called from the template library, the structure, style and interactive logic metadata information of the template is extracted, and based on the structure, style and interactive logic metadata information of the template, the classified data is mapped to generate report data by receiving the mapping configuration entered by the user, and a personalized visual report is dynamically constructed; different from the traditional method of generating reports based on fixed templates, this application combines template metadata with the classification results of data entered by the user, realizes the dynamic construction of the report generation process, improves the flexibility and personalization of the report presentation form, and reduces the constraints brought by template solidification;

2. This application provides a visual configuration interface, allowing logged-in users to configure classification rules according to actual needs, and scores the quality of rules based on historical classification data, and generates adjustment suggestions when the quality is below the threshold, thereby realizing dynamic optimization of classification rules and improving the accuracy and personalization of classification results. At the same time, the quality of rules is scored for quantitative evaluation, which can identify deficiencies in the rules, and generate optimization suggestions based on the analysis results, providing users with the basis and direction for rule optimization, simplifying the work of manual analysis and adjustment;

3. Before publishing and outputting personalized target reports, this application determines the permission level of the logged-in user by obtaining the identity of the logged-in user, then analyzes the sensitivity of the report data, and generates a refined operation permission control strategy based on the permission level and the sensitivity of the report data, thereby achieving effective protection of the report data, ensuring data security, and granting different users reasonable data access rights.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for processing hardware product report data according to an embodiment of the present application;

FIG. 2 is a flow chart of step S130 in a method for processing hardware product report data according to an embodiment of the present application;

FIG3 is a flow chart of step S140 in a method for processing hardware product report data according to an embodiment of the present application;

4 is a flow chart of a rule recommendation scheme for generating a hardware product report data processing method in an embodiment of the present application;

5 is a schematic diagram of a flow chart of generating a permission control strategy in a method for processing hardware product report data in an embodiment of the present application;

6 is a flowchart of a method for processing hardware product report data in an embodiment of the present application that recommends a target report type;

7 is a schematic diagram of a flow chart of generating a report analysis view in a method for processing hardware product report data in an embodiment of the present application;

FIG8 is a module diagram of a hardware product report data processing system according to an embodiment of the present application;

FIG. 9 is a diagram showing the internal structure of an electronic device according to an embodiment of the present application.

DETAILED DESCRIPTION

The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and/or" used in the present application refers to any or all possible combinations comprising one or more listed items.

In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more.

The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

In a first aspect, the present application provides a method for processing hardware product report data, referring to FIG1 , comprising the following steps:

S110. Obtain and standardize the original data of hardware product reports.

In this embodiment, the original data of hardware products can be obtained from the enterprise's data center, ERP system, file server and other data sources, including data on fields such as product number, name, category, specification, price, inventory, etc.; standardization refers to performing data cleaning, format conversion, outlier processing and other operations on the obtained original data to ensure data quality and lay the foundation for subsequent data processing.

Specifically, this application uses standard data access interfaces and APIs to extract the original data of the required fields from the corresponding data sources; then cleans the dirty data, fills in missing values, removes duplicate records, converts the string format into a numeric type, and uses a unified data format specification to store the data.

S120 , receiving the classification rules input by the logged-in user, and classifying the standardized original data according to the classification rules to obtain classification data in multiple dimensions.

Among them, receiving the classification rules input by the logged-in user refers to providing the user with a visual configuration interface, on which the user can customize the classification rules, such as classification by product type, price range, sales volume and other dimensions; classifying the standardized raw data according to the classification rules refers to traversing the original data set, and sorting each record according to different dimensions according to the rules configured by the user, to obtain a data set classified by multiple dimensions.

Specifically, the visual configuration interface can be designed using a drag-and-drop interface. Users can select the dimensions of interest from the provided classification condition list and set specific classification thresholds to form complete classification rules. When classifying standardized data, the data set is traversed, rule matching is performed on each record, and it is assigned to the corresponding classification set. Finally, all records are constructed into multiple classification data sets according to different dimensions.

Furthermore, commonly used classification dimensions are recommended in the visual configuration interface so that logged-in users can select common classification dimensions more quickly.

S130: Determine the target report type, obtain the corresponding report template from the report template library, and extract the report structure, style, and interaction logic of the report template.

2, step S130 specifically includes the following steps:

S131. Receive the target report type input by the logged-in user.

Specifically, provide users with interface controls for inputting target report types, such as drop-down menus and input boxes. For example, design a drop-down box control on the interface, preload common report type options in the drop-down list, and users can select report types that meet their needs, such as sales reports, inventory reports, financial reports, etc. At the same time, provide an input box control to allow users to directly input a custom report type name to support customized needs.

S132. Search the report template library for a report template that matches the target report type.

Specifically, the template library can be a resource library on a local file system, a database, or a remote server; metadata is defined for each report template in the template library, including template name, type, description, and other information; the target type entered by the user is used as a keyword, and a full-text search is performed based on the template metadata or a hierarchical search based on the template classification directory to return a report template resource that best matches the target type.

S133. Extracting layout information and visual style information of the report template as a report structure and a report style.

Specifically, use a parser to parse the template file, extract layout attributes such as row and column layout, partition settings, etc. from the report template to build a layout structure model, and extract visual attributes such as color, font, chart type, etc. to build a style model as the structural model and style model when generating the target report.

S134. Perform semantic recognition on the interaction description information corresponding to the report template to obtain the interaction logic.

Specifically, the interaction description in the report template of this application is defined in natural language, such as "click this area to perform data filtering" and "double-click this cell to view detailed data". NLP technologies such as word segmentation, part-of-speech tagging, and syntactic analysis are used to identify semantic information such as event trigger conditions and related data processing methods from sentences, and build a machine-executable interaction logic model to guide the implementation of the interactive behavior of the final report.

S140 , receiving a mapping configuration input by a user, and based on the report structure, style, and interaction logic, generating report data from the classified data according to the mapping configuration to obtain a personalized target report.

3, step S140 specifically includes the following steps:

S141. Construct a report data storage model according to the report structure.

Specifically, the present application parses the layout definition in the report template, identifies the row and column structure and partition settings in the layout, and constructs a hierarchical data model. The model contains nodes of different levels such as rows, columns, and partitions. These nodes store data of corresponding granularity in a specific format, such as row nodes storing row dimension data and column nodes storing column dimension data.

S142. Receive a mapping configuration input by a user, traverse the classified data, and map the multi-dimensional values of the classified data to a report data storage model according to the mapping configuration.

Specifically, it provides users with a visual interface, allowing users to customize which node level in the report data model to map each dimension in the classified data to, such as mapping product type to row nodes, price range to column nodes, etc.; traverse the classified data, and write the values of different dimensions in each record to the corresponding model nodes according to the user's mapping configuration, to achieve data-to-model conversion mapping. If the mapping configuration has been established before, it can be directly applied in subsequent use.

S143. Based on the report style, generate a visual style description for the data in the report data storage model.

Specifically, by parsing the style information defined in the report template, such as header style, cell style, chart style, etc., these style information are associated and mapped with the nodes in the data model, and corresponding style description objects are generated for each node data, including visual attribute descriptions such as font, color, background, and alignment. These descriptions will be used as input to the report rendering engine to control the final visualization effect.

S144. Based on the interaction logic, extract interaction events and data query, calculation and rendering rules to obtain personalized target reports.

Specifically, traverse the interactive logic model, extract the interactive events defined therein, such as single click, right click, etc., obtain the event triggering conditions, the data operation methods to be executed, such as filtering, calculation, etc., and the corresponding data rendering rules, and inject these interactive capabilities into the final report so that it can respond to user interactions, execute specific data processing logic and correctly render the report view after data update, thereby realizing personalized report generation.

S150: Set a permission control policy for the personalized target report, publish and output the personalized target report.

In one embodiment, referring to FIG. 4 , in step S120 , before receiving the classification rules input by the logged-in user and classifying the standardized raw data according to the classification rules, the method includes the following steps:

S410. Based on the provided visual configuration interface, obtain the classification rule information configured by the logged-in user.

In this embodiment, an interface is provided for the user, allowing the user to customize classification rules by dragging, drop-down boxes, etc., such as selecting classification dimensions, setting dimension thresholds, etc., and the rule information configured by the user on the interface is obtained and saved.

Specifically, the visual configuration interface can list optional classification dimensions. Users can check the dimensions of interest and set specific classification conditions for each dimension, such as price range, sales volume range, etc., to build a complete classification rule set.

S420: Based on the historical classification data, the classification rule information is scored for quality to obtain the classification rule quality.

The quality of the classification rules currently configured by the user is evaluated based on the historical classification data to obtain a rule quality score, which serves as a basis for determining whether the rule needs to be optimized.

Specifically, the current classification rule is applied to the historical data set to generate the classification result, and based on the known historical standard classification, the accuracy, recall rate and other evaluation indicators of the current rule classification are calculated, and these indicator values are combined into a rule quality score.

S430: When the quality of the classification rules is lower than a preset threshold, generate a rule adjustment recommendation plan.

In this embodiment, when the evaluated rule quality score is lower than an acceptable quality threshold, rule optimization suggestions are generated for the user based on historical data and rule features to improve classification quality.

Specifically, by analyzing the reasons for the low quality of rules, such as overly detailed classification of some dimensions and improper division of some thresholds, and based on the distribution characteristics of historical data, we recommend optimization solutions such as adjusting the classification granularity and modifying the threshold range for user reference and improvement of the original rules.

In one embodiment, referring to FIG. 5 , in step S150, setting a permission control policy for a personalized target report, before publishing and outputting the personalized target report, the method further includes the following steps:

S510: Obtain identity information of the logged-in user, and determine the authority level of the logged-in user based on the identity information.

In this embodiment, the user's identity identification information such as position, department, role, etc. is obtained; the user identity is mapped to a predefined permission level model, and the user's permission level is determined based on the identity information to obtain the user's specific permission level, such as read-only, editable, and approveable.

Specifically, the identity information of the logged-in user is obtained from the user directory service or identity authentication system, and based on the permission control policy, it is mapped to the corresponding permission level. High-level users have more data access and operation permissions.

S520. Analyze the data sensitivity of the personalized target report.

Specifically, based on data classification strategies and security strategies, analyze the types and sources of report data. For example, financial data and personal privacy data are highly sensitive. At the same time, combine the application scenarios of the report to conduct a comprehensive assessment of data sensitivity.

S530. Generate an operation permission control policy for the personalized template report according to the permission level and data sensitivity.

In this embodiment, specific operation control rules for reports are formulated based on two dimensions: user authority and data sensitivity, such as permission policies for different operations such as viewing, editing, downloading, and sharing.

Specifically, a permission control matrix is constructed to generate a corresponding set of operation permission control policies based on the intersection of user permission levels and data sensitivity, and these policies are applied to the personalized reports that are finally published, thereby achieving secure and compliant data access control.

In one embodiment, referring to FIG. 6 , in step S110 , before receiving the target report type input by the logged-in user, the method includes the following steps:

S610, identifying and extracting product line information from the original data of hardware products, and dividing the report templates in the report template library into multiple product line types based on the product line information.

In this embodiment, information of different product lines to which the products belong, such as automotive fastener product line, building hardware product line, etc., is extracted from the original data; and the report templates are classified and archived according to the product lines to which the templates are applicable.

Specifically, product line information is automatically extracted from the original data based on feature engineering, or manually labeled by users in data governance to establish a mapping relationship between report templates and different product lines.

S620: Determine the target product line corresponding to the logged-in user according to the identity information of the logged-in user.

In this embodiment, based on the user's identity identification information such as position, department, role, etc., the main product line that the user is responsible for or concerned about is determined as the target product line.

S630. Obtain target report type candidates based on the target product line.

Specifically, according to the user's target product line, the report template classification corresponding to the target product line is retrieved from the report template library previously classified by product line, and a series of report types related to the product line are obtained as candidate items, such as sales reports, inventory reports, etc., as a candidate item set.

S640. Recommend target report type candidates to the logged-in user.

In one embodiment, referring to FIG. 7 , in step S150, after setting a permission control policy for a personalized target report, publishing and outputting the personalized target report, the method further includes the following steps:

S710: Analyze data distribution and association patterns of the personalized template report and generate report data mining results.

In this embodiment, statistical analysis is performed on the data in the report to study the distribution of data of different dimensions, abnormal values, and correlation patterns between multi-dimensional data; report data mining results are generated, and the data statistical information, correlation patterns, etc. obtained from the above analysis are output in a structured form.

Specifically, association rule mining technology is applied to automatically analyze report data and obtain mining results such as data distribution parameters, anomalies, and frequent patterns.

S720. Extract key report indicators and key indicator weights based on the report data mining results.

Specifically, according to the business objectives, combined with the mined anomalies, frequent patterns, etc., the corresponding dimensions or indicators are selected as key indicators; at the same time, the weight scores of the key indicators are calculated based on the support and confidence of the association rules.

S730. Generate a visual report analysis view based on key report indicators and key indicator weights.

Through this, based on key report indicators and key indicator weights, a visual report analysis view is generated, the extracted key indicators and their weights are visualized, and an interactive report analysis view is generated to assist users to better understand and analyze report data.

The implementation principle of the embodiment of the present application is as follows: the present application first obtains and standardizes the original data of the hardware products, then receives the classification rules input by the logged-in user, classifies the standardized data according to the classification rules, and obtains multi-dimensional classified data; next, the matching template is called from the template library according to the target report type, and the structure, style and interactive logic metadata information of the template are extracted. Based on the structure, style and interactive logic metadata information of the template, the classified data is mapped to generate report data by receiving the mapping configuration input by the user, and a personalized visual report is dynamically constructed; different from the traditional method of generating reports based on fixed templates, the present application combines the template metadata with the data classification results input by the user, realizes the dynamic construction of the report generation process, improves the flexibility and personalization of the report presentation form, and reduces the constraints brought by template solidification.

In the second aspect, the present application provides a hardware product report data processing system. The hardware product report data processing system of the present application is described below in combination with the above-mentioned hardware product report data processing method.

Referring to FIG8 , a hardware product report data processing system includes:

Data acquisition module 810, used to acquire and standardize the original data of the hardware product report;

The data classification module 820 is used to classify the standardized raw data according to the preset classification rules to obtain classified data of multiple dimensions;

The template acquisition module 830 is used to determine the target report type, obtain the corresponding report template from the report template library, and extract the report structure, style and interaction logic of the report template;

The report generation module 840 is used to receive the mapping configuration input by the user, and based on the report structure, style and interaction logic, generate report data according to the mapping configuration for the classified data to obtain a personalized target report;

The report output module 850 is used to set the authority control policy for the personalized target report, publish and output the personalized target report.

In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be shown in Figure 9. The electronic device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for processing hardware product report data is implemented.

Those skilled in the art will understand that the structure shown in FIG. 9 is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A method for processing hardware product report data, characterized by comprising: Obtain and standardize raw data of hardware product reports; Receiving classification rules input by a logged-in user, and classifying the standardized raw data according to the classification rules to obtain classification data of multiple dimensions; Determine the target report type, obtain the corresponding report template from the report template library, and extract the report structure, style and interaction logic of the report template; Receive a mapping configuration input by a user, and based on the report structure, style and interaction logic, generate report data according to the mapping configuration for the classified data to obtain a personalized target report; An authority control policy is set for the personalized target report, and the personalized target report is published and output. 2. The method for processing hardware product report data according to claim 1 is characterized in that, before receiving the classification rules input by the logged-in user and classifying the standardized raw data according to the classification rules, the method comprises the following steps: Based on the provided visual configuration interface, obtain the classification rule information configured by the logged-in user; Based on the historical classification data, the classification rule information is scored for quality to obtain the classification rule quality; When the quality of the classification rules is lower than a preset threshold, a rule adjustment recommendation scheme is generated. 3. The method for processing hardware product report data according to claim 1 is characterized in that the target report type is determined, the corresponding report template is obtained from the report template library, and the report structure, style and interaction logic of the report template are extracted, which specifically includes the following steps: Receive a target report type input by the logged-in user; Searching for a report template matching the target report type in a report template library; Extracting the layout information and visual style information of the report template as a report structure and a report style; Semantic recognition is performed on the interaction description information corresponding to the report template to obtain the interaction logic. 4. The method for processing hardware product report data according to claim 3 is characterized in that, before setting a permission control strategy for the personalized target report and publishing and outputting the personalized target report, the method further comprises the following steps: Acquire the identity information of the logged-in user, and determine the permission level of the logged-in user based on the identity information; Analyzing the data sensitivity of the personalized target report; An operation permission control policy for the personalized template report is generated according to the permission level and the data sensitivity. 5. The method for processing hardware product report data according to claim 4, characterized in that before receiving the target report type input by the logged-in user, the method comprises the following steps: Identify and extract product line information from the original data of hardware products, and divide the report templates in the report template library into multiple product line types based on the product line information; Determine the target product line corresponding to the logged-in user according to the identity information of the logged-in user; Acquire target report type candidates based on the target product line; The target report type candidate is recommended to the logged-in user. 6. The method for processing hardware product report data according to claim 1 is characterized in that the mapping configuration input by the user is received, and based on the report structure, style and interaction logic, the classified data is generated into report data according to the mapping configuration to obtain a personalized target report, which specifically includes the following steps: According to the report structure, construct a report data storage model; Receive a mapping configuration input by a user, traverse the classification data, and map the multi-dimensional values of the classification data to the report data storage model according to the mapping configuration; Based on the report style, generating a visual style description for the data in the report data storage model; Based on the interaction logic, interaction events and data query, calculation and rendering rules are extracted to obtain personalized target reports. 7. The method for processing hardware product report data according to claim 1 is characterized in that after setting a permission control strategy for the personalized target report, publishing and outputting the personalized target report, the method further comprises the following steps: Analyze the data distribution and association pattern of the personalized template report to generate report data mining results; Based on the report data mining results, extract key report indicators and key indicator weights; Based on the key report indicators and key indicator weights, a visual report analysis view is generated. 8. A hardware product report data processing system, characterized by comprising: Data acquisition module, used to acquire and standardize the raw data of hardware product reports; A data classification module is used to receive classification rules input by a logged-in user, classify the standardized raw data according to the classification rules, and obtain classified data of multiple dimensions; The template acquisition module is used to determine the target report type, obtain the corresponding report template from the report template library, and extract the report structure, style and interaction logic of the report template; A report generation module, configured to receive a mapping configuration input by a user, and based on the report structure, style and interaction logic, generate report data from the classified data according to the mapping configuration to obtain a personalized target report; The report output module is used to set the authority control policy for the personalized target report, publish and output the personalized target report. 9. An electronic device, characterized in that it comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the hardware product report data processing method according to any one of claims 1 to 7 when executing the computer program. 10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of hardware product report data processing according to any one of claims 1 to 7 are implemented.