Form generation method, device, medium and product

Abstract

The present application discloses a form generation method, device, medium and product, which relate to the field of data processing technology, including an electronic device that can automatically determine the demand data and target files corresponding to user needs, and match the corresponding target data from the target file, thereby constructing a corresponding structured table based on the structural relationship between each target data, and generating a corresponding data form, solving the technical problem of low form generation efficiency, and achieving the technical effect of quickly generating multiple types of structured data forms that match user needs.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a form generation method, device, medium and product. Background Art

[0002] During data processing (such as test data processing), users usually construct data tables by manually searching for data, adding data, and other operations to display data in tabular form.

[0003] However, in business scenarios with large data volumes and complex data types, the above data processing method has problems such as low data search efficiency, low accuracy, and poor flexibility, resulting in low efficiency in table generation. Summary of the Invention

[0004] The present application provides a form generation method, device, medium and product to at least solve the problem of low form generation efficiency in related technologies.

[0005] This application provides a form generation method, including:

[0006] In response to receiving the form generation request, determining first requirement data and at least one target file corresponding to the form generation request;

[0007] Determining, based on each target file, a plurality of first target data corresponding to the first requirement data;

[0008] Determine a structural relationship between each first target data, and construct at least one first structured table based on the structural relationship; wherein the first structured table includes at least one first data column;

[0009] Each first target data is written into a corresponding first structured table to obtain at least one first target form.

[0010] This application also provides a form generation device, including:

[0011] A first determining module, configured to, in response to receiving a form generation request, determine first requirement data and at least one target file corresponding to the form generation request;

[0012] A second determining module is configured to determine, based on each target file, a plurality of first target data corresponding to the first requirement data;

[0013] A first construction module is configured to determine a structural relationship between each first target data and construct at least one first structured table based on the structural relationship; wherein the first structured table includes at least one first data column;

[0014] The first generating module is used to write each first target data into the corresponding first structured table to obtain at least one first target form.

[0015] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any one of the above-mentioned form generation methods when executing the computer program.

[0016] The present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned form generation methods are implemented.

[0017] The present application also provides a computer program product, including a computer program, which implements the steps of any of the above-mentioned form generation methods when executed by a processor.

[0018] Through this application, since the electronic device can automatically determine the demand data and target files corresponding to user needs, and match the corresponding target data from the target file, it can construct a corresponding structured table based on the structural relationship between each target data and generate a corresponding data form. Therefore, it can solve the technical problem of low table generation efficiency in related technologies and achieve the technical effect of quickly generating multiple types of structured data forms that match user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A schematic diagram of an application of a form generation system provided in an embodiment of the present application;

[0021] Figure 2 A flowchart of a form generation method provided in an embodiment of the present application;

[0022] Figure 3 This is one of the application diagrams of a form generation method provided in an embodiment of the present application;

[0023] Figure 4 This is a second application diagram of a form generation method provided in an embodiment of the present application;

[0024] Figure 5 The third application diagram of a form generation method provided in an embodiment of the present application;

[0025] Figure 6 A fourth application diagram of a form generation method provided in an embodiment of the present application;

[0026] Figure 7 This is a fifth application diagram of a form generation method provided in an embodiment of the present application;

[0027] Figure 8 A sixth application diagram of a form generation method provided in an embodiment of the present application;

[0028] Figure 9 The seventh application diagram of a form generation method provided in an embodiment of the present application;

[0029] Figure 10 A schematic diagram of the structure of a form generating device provided in an embodiment of the present application;

[0030] Figure 11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0032] It should be noted that, in the description of this application, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. The terms "first," "second," etc., in this application are used to distinguish similar objects, and are not used to describe a particular order or sequence.

[0033] An agent is an agent that can perceive its environment and take actions to achieve specific goals. An agent can be software, hardware, or a system, possessing autonomy, adaptability, and interaction. An agent perceives changes in its environment (e.g., through sensors or data input), makes judgments and decisions based on learned knowledge and algorithms, and then executes actions to influence the environment or achieve a predetermined goal.

[0034] The Voltage Regulation Test System (VRTS) is a comprehensive test system specifically designed to evaluate the performance and stability of voltage regulation equipment, such as automatic voltage regulators (AVRs). It can simulate voltage fluctuations and load changes found in real power grid environments, measuring key parameters such as the equipment's response time, accuracy, and regulation capability to ensure it can effectively maintain output voltage stability under various conditions.

[0035] Natural Language Processing (NLP) is a discipline that studies how to enable computers to understand, process, and generate human language.

[0036] Fully Integrated Voltage Regulator (FIVR) refers to a technology that integrates the voltage regulator module (VRM) directly into the CPU.

[0037] During the data processing process, users usually construct data tables by manually searching for data, adding data, and other operations to display data in tabular form.

[0038] Taking the test data processing process of the Central Processing Unit (CPU) Voltage Regulation Test System (VRTS) as an example, the system generates test data for various test types including DC calibration, transient scanning, thermal drift, and the design of test data for multiple central processing units (CPUs).

[0039] When users search and add data within a complex data structure to construct a voltage regulation test table, they must manually traverse the hierarchical data storage directory structure, locate the data files containing voltage test data, perform data matching on each file, migrate the data within the files across platforms, perform format standardization operations (such as data alignment, unit conversion, and anomaly annotation), and perform manual data comparison. Alternatively, when users use scripts to generate forms, while the scripts can locate the data files, they still need to manually parse the data and perform manual data migration. This process is time-consuming and error-prone. Furthermore, there are challenges such as difficulty identifying the correspondence between test types and test data (for example, mapping data between different configurations and different CPU models in the voltage regulation test FIVR test item), and the inability to search and match test data when the test data type or storage path changes. This results in low data search efficiency, low accuracy, and poor flexibility, leading to low table generation efficiency.

[0040] In order to solve the above technical problems, the embodiments of the present application provide a form generation method, device, medium and product, which can automatically determine the demand data and target files corresponding to user needs through electronic equipment, and match the corresponding target data from the target file, thereby constructing a corresponding structured table based on the structural relationship between each target data, and obtaining a corresponding data form. Therefore, it can solve the technical problem of low table generation efficiency in related technologies and achieve the technical effect of quickly generating multiple types of structured data forms that match user needs.

[0041] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0042] In conjunction with the specific application environment architecture or specific hardware architecture on which the execution of the form generation method depends, the specific application environment architecture or specific hardware architecture is described herein. Figure 1 This is a schematic diagram of an application of a form generation system provided in an embodiment of the present application.

[0043] See also Figure 1 The form generation system 100 includes an electronic device 101 and at least one knowledge base ( Figure 1 Only three are shown). The electronic device 101 is in communication with each knowledge base, and each knowledge base includes at least one associated file.

[0044] In response to receiving the form generation request, the electronic device 101 determines the requirement data corresponding to the form generation request, and determines at least one target knowledge base ( Figure 1 is a target file in the knowledge base 102 ), thereby obtaining target data from the target file, determining the structural relationship of the target data, and thus constructing a structured table. The target data is written into the structured table to obtain a target form.

[0045] The knowledge base may be used to indicate a self-service storage repository or library stored in a storage space of an external terminal device (such as an external electronic device, a server, or a cloud server) that is communicatively connected to the electronic device.

[0046] In this way, the form generation system 100 can automatically identify target files from a large number of associated files and match the target data corresponding to the required data, achieving automated structured form output, implementing intelligent closed-loop optimization and hierarchical semantic matching operations, improving form generation efficiency and accuracy, and saving time and labor costs. Furthermore, it can be applied to various data processing scenarios. By adjusting and optimizing the system, efficient and intelligent data processing processes can be implemented in multiple data processing tasks, improving the accuracy and efficiency of data processing.

[0047] Figure 2A flow chart of the form generation method provided in the embodiment of the present application is shown as follows: Figure 2 As shown, the embodiment of the present application provides a form generation method, which is described in detail as follows:

[0048] S201: In response to receiving a form generation request, determining first requirement data and at least one target file corresponding to the form generation request.

[0049] Optionally, in response to receiving the form generation request, the form generation request is parsed to obtain first requirement data carried therein, and at least one target file is determined from a plurality of associated files associated with the current electronic device based on the first requirement data.

[0050] The form generation request is an instruction or request for an electronic device to generate a structured form corresponding to the requested data. The form generation request can be an instruction or request sent by a user through a user terminal, or can be an instruction or request automatically generated based on a user operation on the current electronic device (such as pressing an external button, clicking or dragging the screen of the current electronic device, etc.).

[0051] Optionally, the requirement data includes word elements (also referred to as tokens or characters) associated with the structured form required by the user, including but not limited to at least one of a target device and a data type. The electronic device is communicatively connected to a plurality of associated files and, based on the requirement data, can determine at least one key file that matches the requirement data and identify it as the target file.

[0052] S202: Determine a plurality of first target data corresponding to the first requirement data based on each target file.

[0053] Optionally, a search is performed among the data in each target file to determine a plurality of data matching the first required data and determine them as the first target data.

[0054] Optionally, the current electronic device can determine a form generation strategy based on the intelligent agent and the first demand data. The form generation strategy may include selecting one from multiple preset tables based on the first demand data and determining it as a target table, and generating a corresponding target form based on the target data corresponding to the first demand data and the above-mentioned target table. Alternatively, it may include constructing a structured table based on the first demand data, and generating a second strategy of the target form based on the target data corresponding to the first demand data and the above-mentioned structured table. The preset table indicates a pre-constructed structured table including at least one first data column. In the embodiment of the present application, the form generation strategy is mainly described as the second strategy as an example.

[0055] S203: Determine the structural relationship between each first target data, and construct at least one first structured table based on the structural relationship; wherein the first structured table includes at least one first data column.

[0056] Optionally, the structural relationship includes, but is not limited to, a subordinate relationship and a non-subordinate relationship. A non-subordinate relationship indicates that there is no subordinate relationship between target data and can be used as the name of a parallel table. A subordinate relationship indicates that there is a subordinate relationship between target data and can be used to determine the data columns under each table based on the subordinate relationship.

[0057] Optionally, the first target data includes but is not limited to first requirement data (such as target device, data type) and keyword elements. The target device is used to indicate the target object of the constructed structured form. The data type is used to indicate each data item of the constructed structured form. The keyword element is the table filling data determined based on the requirement data. For example, the form generation request is: construct the voltage test data and current test data tables of the central processing unit CPU zero. The target object of the constructed structured form is the central processing unit CPU zero, which can also be called the target device. The data items of the constructed structured form include voltage test data and current test data tables, and voltage test data and current test data can also be called data types. The keyword elements matched based on the requirement data include 10V, 20V, 10mA, and 20mA. Then 10V and 20V are the table filling data corresponding to the data type (voltage test data), and 10mA and 20mA are the table filling data corresponding to the data type (current test data).

[0058] S204: Writing each first target data into a corresponding first structured table to obtain at least one first target form.

[0059] Optionally, each first target data is written into the first data column of the corresponding first structured table to obtain at least one first target form.

[0060] Figures 3 to 8 A schematic diagram of an application of a form generation method provided in an embodiment of the present application.

[0061] See also Figure 3 The electronic device includes an associated file access layer, a data retrieval decision center, a data matching engine, a form generation module and a user interaction layer, and the associated file access layer, the data retrieval decision center, the data matching engine, the form generation module and the user interaction layer are connected in sequence.

[0062] Among them, Figure 4As shown, the associated file access layer is used to implement functions such as target file access, data screening, invalid data deletion, format conversion, and hash verification. Specifically, it reads data from associated files, filters it (such as deleting invalid characters), converts the data format, performs hash value verification, ensures file integrity, and excludes damaged files. Associated files can include text files, plain text table data (Comma-Separated Values ​​(CSV) files, Hypertext Markup Language (HTML) files, etc. Invalid characters may include, but are not limited to, file header comment characters, garbled characters, and invalid identifiers.

[0063] For example, data in a target file (such as a voltage test system test data file, FIVR Transient setup) corresponding to a target device in a target knowledge base can be obtained through a file system interface (for example, a files-process-iter(file_path) interface written in the open source programming language Python).

[0064] like Figure 5 As shown, the data retrieval decision center is used to realize functions such as determining required data, determining target files, identifying target devices and data types, generating data retrieval strategies, and optimizing data retrieval strategies. Specifically, it can extract required data (for example, target devices, data types, etc.) in form generation requests through a three-level processing flow of lexical analysis, syntactic analysis, and semantic understanding based on natural language processing (NLP) technology, determine data retrieval strategies based on the structure of data in the target file, and optimize data retrieval strategies based on error information fed back by users (for example, problems such as misplacement of FIVR Transient setup of voltage test system test data), automatically adjust file parsing rules, optimize heuristic function parameters, or supplement the rule base to achieve continuous iterative optimization of extraction strategies.

[0065] For example, a form-generated request such as "Extract and compare all CPU zero FIVR series test item data" can accurately identify the folder path corresponding to "CPU zero" and the file corresponding to "FIVR series test item." Data retrieval strategies can be specifically configured based on the structure of the data in the target file. For example, if the target file data is unstructured, a heuristic retrieval algorithm can be used to determine a data retrieval priority strategy based on preset priority evaluation functions (e.g., keyword relevance priority evaluation function, path depth weight priority evaluation function), and heuristically retrieve the target data based on this strategy. Alternatively, if the target file data is structured, a linguistic rule-based inference mechanism can be used to extract data logic (e.g., file path matching rule logic, field format validation rule logic, etc.) based on conditional chains (e.g., if-then conditional chains) to match the target data.

[0066] like Figure 6 As shown, the data matching engine is used to implement functions such as determining the target file path, identifying the target file, matching the target data, extracting the required data, determining the target search interval, and data association verification (including target device verification and data type verification). Specifically, it locates the target file (for example, using database-like indexing techniques to construct a file path index to locate the target folder and target file), determines the target search interval corresponding to the first required data from the target file based on keyword matching and contextual logic analysis, and retrieves the target data corresponding to the first required data within the target search interval. For example, for an FIVR transient setup file, the engine can identify and extract the target numerical data based on the contextual grammatical structure of the "Voltage Value" field and perform data association verification. For example, based on a preset configuration file (such as a CPU voltage domain mapping table), a mapping relationship between the target data and the target device is established. The target data is then double-checked: verifying that the device to which the target data belongs is the target device (for example, confirming that the data belongs to CPU 0 or CPU 1); and verifying that the type of the target data corresponds to the data type corresponding to the form generation request (for example, whether the target data belongs to the voltage test FIVR transient setup data), to ensure the accuracy of the data retrieval.

[0067] like Figure 7As shown, the form generation module is used to implement functions such as form generation strategy generation (including selecting preset forms or custom structured forms), form generation, writing target data to corresponding data columns, marking abnormal data, and generating multi-format files (including Excel spreadsheets, PDF portable document formats, and plain text table data (CSV)). Specifically, this module selects a target form from preset forms based on the form generation request, or generates a corresponding structured form based on the required data. It also determines the correspondence between the target data and the structured table (for example, the correspondence between the test data values ​​of the target device's CPU zero voltage test data (FIVR Transient setup) and the corresponding data columns in the structured table), and then populates the target data into the corresponding structured table.

[0068] Optionally, the form generation module can also automatically match data units (such as voltage units in "mV") and mark abnormal data (for example, by adding a red highlight mark) when it is detected. Abnormal data includes, but is not limited to, null data and data outside a preset data range. The preset data range includes the upper and lower data limits of the data type corresponding to the data column.

[0069] Optionally, the form generation module can also generate files in different formats based on user needs. For example, spreadsheets (e.g., pivot tables, analysis charts), PDF files, and plain text table data (CSV files) can be used for different application scenarios such as data analysis, report presentation, and system integration.

[0070] Exemplarily, the preset form may include a form name (corresponding to a target device), at least one data column (corresponding to a data type), a test data cell (corresponding to a keyword element), and other table structures.

[0071] As an example and not a limitation, the user interaction interface can be used to visually adjust the order of data columns, add data analysis columns (for example, data analysis columns such as month-on-month growth rate and threshold judgment), and other configurations to meet user needs.

[0072] In this way, the form generation system can traverse billions of data in seconds in a distributed environment, automatically locate target files and target search intervals, and extract target data, reducing the impact of manual intervention and fixed rules on retrieval efficiency; intelligently identify data types, the correspondence between keyword elements corresponding to data types and target devices, and accurately fill keyword elements into the form. Based on the form, multi-purpose solutions can be achieved for complex matching problems such as synonym recognition and context association, reducing the missed detection rate and false detection rate of data, and can automatically generate structured forms corresponding to user needs, add, delete or modify the format of structured forms based on user needs, automatically map data types and generate multi-format files, reducing labor costs and processing time.

[0073] like Figure 8 As shown, the user interface is a visual operation platform that provides real-time display (including displaying generation progress, error data, receiving and displaying update requests, displaying updated requirement data, and displaying updated forms). Specifically, the user interface supports real-time display, including generation progress (for example, the number of processed target files / total target files during form generation), update requests sent by the user terminal or generated by current electronic device operations (including user-submitted requirement update requests and form update requests). When an error message is detected in an update request, the interface identifies the error data and displays the error data (for example, the voltage test FIVR transient setup file format is abnormal and data cannot be extracted), along with the corresponding updated requirement data and updated forms. Optionally, the user interface can also generate a detailed error log based on the error data for easy user review.

[0074] Based on this, the form generation system supports various configurations, including pre-set and custom structured form generation, enabling intelligent conversion of data into custom structured forms. Furthermore, during the form generation process, it supports real-time user update requests (for example, a request to update the voltage test data type "FIVR Transient setup 2"). This automatically triggers the incremental processing mechanism to directly update the form without retrieving data or restarting the form generation process, thus improving generation efficiency.

[0075] Optionally, the electronic device includes a computing device and a storage device. The computing device is used to determine the target file, retrieve the target data, generate a structured table, and populate the target data to obtain a target form. The storage device is used to store the data in the associated files and the generated target form. Therefore, the electronic device requires high computing power from its central processing unit and storage space (such as a local hard drive or network storage).

[0076] Optionally, before determining a plurality of first target data corresponding to the first requirement data based on each target file, the method further includes:

[0077] Get each data in each associated file;

[0078] In each data, invalid data is identified and deleted to obtain multiple candidate data;

[0079] Based on the target format, each candidate data is format converted to obtain multiple target format data;

[0080] Determining, based on each target file, a plurality of first target data corresponding to the first requirement data, including:

[0081] Based on each first requirement data, determining a target search interval in each target format data of the target file;

[0082] In the target search interval, at least one keyword element matching each first demand data is determined to obtain a plurality of first target data; the plurality of first target data includes each first demand data and each keyword element.

[0083] Optionally, the electronic device is communicatively connected to multiple knowledge bases, each of which includes multiple associated files. Each data item in each associated file is read, invalid data is identified and deleted from the read data, and multiple candidate data items are obtained. Each candidate data item is format-converted based on a target format to obtain multiple target format data items. The target format can be specifically set based on actual circumstances. For example, the target format can be a floating point format.

[0084] Optionally, the first requirement data includes, but is not limited to, a target device, a data type, etc. Based on each piece of first requirement data, the position of each piece of first requirement data in the target file is determined based on keyword matching. Furthermore, based on contextual logic analysis techniques and the position of each piece of first requirement data in the target file, a target search interval corresponding to each piece of first requirement data is determined. Within the target search interval, at least one keyword element matching each piece of first requirement data is retrieved to obtain multiple pieces of first target data. The keyword element includes a data value corresponding to the target device and data type in the first requirement data. The multiple pieces of first target data include each piece of first requirement data and each keyword element.

[0085] Optionally, the target search interval can be specifically set based on the context corresponding to each first requirement data. The target search interval includes an upper limit and an upper limit. The upper limit indicates a first number of tokens preceding the first requirement data in the target document. The lower limit indicates a second number of tokens following the first requirement data in the target document. The first and second limits can be specifically set based on actual circumstances.

[0086] For example, based on contextual logic analysis technology, it is determined that the target search interval of the first demand data includes the 10 words before the first demand data and the 20 words after the first demand data. Taking the position of the first demand data as i, the target search interval is [i-10, i+20].

[0087] Optionally, in the target search interval, at least one keyword element matching each first requirement data is determined to obtain a plurality of first target data, including:

[0088] In a target search interval, determining a first similarity between each first required data and each target format data;

[0089] In the target search interval, target format data having a first similarity greater than or equal to a first threshold is determined as a keyword, and a plurality of first target data are obtained.

[0090] Optionally, within the target search interval, a first similarity is calculated between each first requirement data item and each target format data item. The target format data item corresponding to the first similarity item greater than or equal to a first threshold is identified as the keyword element corresponding to the first requirement data item and serves as the first target data item. The keyword element corresponding to each first requirement data item is then obtained in this manner, resulting in multiple first target data items. The first threshold value can be set based on actual circumstances. For example, the first threshold value can be 85%, or the first threshold value can be 90%.

[0091] Optionally, the structural relationship includes a subordinate relationship and a non-subordinate relationship; the first requirement data includes at least one target device and at least one data type.

[0092] Optionally, determining a structural relationship between each first target data and constructing at least one first structured table based on the structural relationship includes:

[0093] Determine at least one target device that has no subordinate relationship in each first target data, and generate a corresponding first structured table based on each target device;

[0094] Identifying each data type in each first target data;

[0095] For each target device, determining a data type having a subordinate relationship with the target device and determining the data type as the target data type;

[0096] For each target device, at least one first data column in a corresponding first structured table is generated based on the corresponding target data type.

[0097] Optionally, at least one target device with no subordinate relationship is determined in each first target data set. A corresponding first structured table is generated for each target device. The first structured table can be used to store data values ​​under each data type subordinate to the corresponding target device. The data type (e.g., "voltage test data," "current test data") in each first target data set is identified. For each target device, a data type with a subordinate relationship is determined and determined as a target data type corresponding to the target device. For each target device, at least one first data column in the first structured table corresponding to the target device is generated based on the target data type corresponding to the target device, thereby determining the first structured table corresponding to each target device.

[0098] Exemplarily, if the first target database includes CPU 0, CPU 1, CPU zero-voltage test data, CPU zero-current test data, and CPU 1-voltage test data, first structured tables named CPU 0 and CPU 1 can be generated based on CPU 0 and CPU 1, respectively, which do not have a subordinate relationship. Furthermore, it is determined that CPU zero-voltage test data and CPU zero-current test data are target data types with a subordinate relationship with target device CPU 0, and corresponding first data columns are generated: CPU zero-voltage test data and CPU zero-current test data. Furthermore, it is determined that CPU 1-voltage test data is a target data type with a subordinate relationship with target device CPU 1, and a corresponding first data column is generated: CPU 1-voltage test data. This results in a first structured table CPU 0 and a first structured table CPU 1.

[0099] Optionally, each first target data is written into a corresponding first structured table to obtain at least one first target form, including:

[0100] In each first target data, determining a target keyword element associated with each target data type;

[0101] For each target data type, the associated target keyword element is written into the corresponding first data column to obtain at least one first target form.

[0102] Optionally, in each first target data, the data type of each keyword element is determined, and a target data type matching the data type of each keyword element is determined. Keyword elements whose data type matches the target data type are determined as target keyword elements associated with the target data type. For each target data type, the associated target keyword element is written into the first data column corresponding to the target data type, thereby obtaining at least one first target form.

[0103] For example, based on the first requirement data, the keyword elements retrieved from the target file include "CPU 10mA" and "10V." The data type of the keyword element 10mA is determined to be current test data (CPU 0), and the data type of the keyword element 10V is determined to be voltage test data (CPU 1). The target data type matching the data type of the keyword element 10mA is determined to be current test data (CPU 0), and the target data type matching the keyword element 10V is determined to be voltage test data (CPU 1). 10mA is entered into the first data column corresponding to current test data (CPU 0), and 10V is entered into the first data column corresponding to voltage test data (CPU 1).

[0104] Optionally, after obtaining each data in each associated file, the method further includes:

[0105] Obtaining a first hash value for each associated file;

[0106] After converting the format of each candidate data based on the target format to obtain multiple target format data, the following steps are included:

[0107] Calculating a second hash value for each associated file based on each target format data;

[0108] For each associated file, comparing the first hash value and the second hash value;

[0109] Determining, based on each target file, a plurality of first target data corresponding to the first requirement data, including:

[0110] If the first hash value matches the second hash value, determining a target search interval in each target format data of the target file based on each first requirement data;

[0111] In the target search interval, at least one keyword element matching each first demand data is determined to obtain a plurality of first target data; the plurality of first target data includes each first demand data and each keyword element.

[0112] Optionally, after obtaining each data in each associated file, a first hash value for each associated file is obtained. Based on each target format data in each associated file, a second hash value for each associated file is calculated. For each associated file, the corresponding first hash value and second hash value are compared. If the first hash value matches the second hash value, it is determined that the associated file is intact and intact, and the steps of determining a target search interval in each target format data in the target file based on each first requirement data and subsequent steps are performed to obtain a plurality of first target data.

[0113] Optionally, in the target search interval, target format data having a first similarity greater than or equal to a first threshold is determined as a keyword element, and after obtaining a plurality of first target data, the method includes:

[0114] For each target device, calculating a second similarity between the target device and the corresponding target keyword element;

[0115] For each target data type, calculating a third similarity between the target data type and the corresponding target keyword element;

[0116] Determining a structural relationship between each first target data and constructing at least one first structured table based on the structural relationship includes:

[0117] When each second similarity and each third similarity are greater than or equal to a second threshold, a structural relationship between each first target data is determined, and at least one first structured table is constructed based on the structural relationship.

[0118] Optionally, for each target device, a target keyword element corresponding to the target device is determined based on the target data type of the target device, and a second similarity between the target device and the corresponding target keyword element is calculated. For each target data type, a third similarity between the target data type and the corresponding target keyword element is calculated. When the second similarity between each target device and the corresponding target keyword element and the third similarity between each target data type and the corresponding target keyword element are both greater than or equal to a second threshold, it is determined that the retrieved target keyword element is indeed a data value corresponding to the first demand data. At this time, the structural relationship between each first target data is determined, and at least one first structured table is constructed based on the structural relationship, and subsequent steps are performed to construct a target form.

[0119] Exemplarily, the second similarity between the target device CPU zero and 10mA, the third similarity between the current test data (CPU zero) and 10mA, the second similarity between the target device CPU one and 10V, and the third similarity between the voltage test data (CPU one) and 10V are calculated. When the above second similarities and third similarities are all greater than or equal to the second threshold, the structural relationship between the target device CPU zero, the target device CPU one, the current test data (CPU zero), the voltage test data (CPU one), 10mA, and 10V is determined, and the first structured tables of the target device CPU zero and the target device CPU one are respectively constructed based on the structural relationship.

[0120] Optionally, in response to receiving the form generation request, determining first requirement data and at least one target file corresponding to the form generation request includes:

[0121] In response to receiving the form generation request, parsing the form generation request to obtain at least one target device in the form generation request;

[0122] Among the multiple associated files, at least one associated file is selected based on each target device and determined as the target file.

[0123] Optionally, in response to receiving a form generation request, the form generation request is parsed to obtain each word element in the form generation request, and at least one target device is identified in each word element. Keyword matching is performed on each target device in a plurality of associated files to determine a word element that matches each target device, and at least one associated file containing the word element that matches each target device is determined as the target file.

[0124] Optionally, the form generation method further includes:

[0125] In response to receiving the demand update instruction, determining second demand data corresponding to the demand update instruction;

[0126] Preprocessing the plurality of first target data based on the second demand data to obtain second target data; wherein the preprocessing includes at least one of deletion processing and addition processing;

[0127] Determining a structural relationship between each second target data, and constructing at least one second structured table based on the structural relationship; wherein the second structured table includes at least one second data column;

[0128] Each second target data is written into the corresponding second structured table to obtain at least one second target form.

[0129] Optionally, during or after generating the first target form, in response to receiving a requirement update instruction, second requirement data corresponding to the requirement update instruction (including but not limited to a new target device and a new data type) is determined, the first requirement data and the second requirement data are compared, and based on the comparison result, the plurality of first target data are pre-processed to obtain the second target data. The pre-processing includes but is not limited to at least one of deletion and addition. The second target data includes but is not limited to a new target device, a new data type, and a new keyword.

[0130] Exemplarily, the first demand data and the second demand data are compared, and an addition process is performed based on word elements that exist in the second demand data but not in the first demand data, and a deletion process is performed based on word elements that exist in the first demand data but not in the second demand data.

[0131] Optionally, the demand update instruction also includes error data prompt information, and based on the above information, the first target data corresponding to the first demand data is updated (ie, the error data is deleted, and data retrieval is performed again based on the first demand data) to determine new target data.

[0132] Optionally, a structural relationship between the new target device, the new data type, and the new keyword element in each second target data is determined, and at least one second structured table corresponding to each new target device is constructed based on the structural relationship; the second structured table includes at least one second data column. The second data column is mapped one-to-one with the new data type. In the second structured table corresponding to each new target device, each new keyword element is written into the second data column mapped to the target data type corresponding to the new keyword element, thereby obtaining at least one second target table.

[0133] Optionally, after writing each first target data into the corresponding first structured table to obtain at least one first target form, the method further includes:

[0134] In response to receiving a table update instruction, determining at least one third structured table and third target data corresponding to the table update instruction;

[0135] A mapping relationship between each third target data and each third structured table is established, and each third target data is written into the corresponding third structured table based on the mapping relationship to obtain at least one third target form.

[0136] Optionally, after obtaining at least one first target form, in response to receiving a table update instruction, the table update instruction including at least one of adding a data column and deleting a data column, each first structured table is processed based on the table update instruction (e.g., adding a data column, deleting a data column), obtaining each third structured table corresponding to the table update instruction, and each first target data is processed, such as deleting part of the first target data or adding new target data, to obtain third target data. A mapping relationship is established between each third target data and each third structured table, i.e., a one-to-one mapping of the target device in the third target data to the name of the third structured table, a one-to-one mapping of the target data type in the third target data to the third data column in the third structured table, and a one-to-one mapping of the keyword corresponding to each target data type to the cell in the corresponding data column. Based on the above mapping relationship, each third target data is written into the corresponding third structured table to obtain at least one third target form.

[0137] Figure 9 This is the seventh application flowchart of a form generation method provided in an embodiment of the present application.

[0138] See also Figure 9At the beginning of the form generation method, a form generation request is received, such as to extract voltage test data for CPU0. The form generation request is parsed by an agent to extract required data, including: extracting a target device, such as CPU0; and extracting a data type, such as voltage test data. The data structure relationships are analyzed to generate a form generation strategy, such as a first strategy that generates a corresponding target form based on a preset table, or a second strategy that constructs a structured table based on the first required data to generate the target form. Based on the required data, a target file is determined, and based on the required data, a target search interval within the target file is determined, and multiple target data are determined. Target device verification and data type verification are performed based on the multiple target data. If verification passes (for example, if each second similarity in the target device verification and each third similarity in the data type verification is greater than or equal to a second threshold), a preset table is determined or a structured table is generated based on the form generation strategy and the target data, and the target data is written into the table to obtain a target form. Based on the target format request, a target format file is generated. A requirement update instruction and / or a form update instruction are received. If the requirement update instruction or the form update instruction includes an error message, the form generation strategy is optimized, and the process returns to executing the agent parsing and subsequent steps. The error prompt includes an error data prompt message. Based on the requirement update instruction and / or form update instruction, the target form is updated, and the form generation process ends.

[0139] Based on the above-mentioned form generation method, the full process of multi-source data access, intelligent form generation strategy generation, automatic data matching and visual form output can be automated. The hierarchical matching mechanism driven by the intelligent agent reduces the data processing time and improves the efficiency of data form generation. Based on the three-layer matching and verification mechanism of target file positioning, data type matching and data association verification, the data retrieval matching degree is improved and data extraction errors are avoided. In addition, based on the user's update request, data and forms can be automatically updated to reduce the cost of changing the form generation process. And by automatically generating structured target format files (for example, Excel pivot tables and analytical charts), it supports data pivot and chart generation (such as comparing data curves of the same data type on different devices), improving the depth and efficiency of data analysis, and can quickly locate abnormal data, thereby improving data processing efficiency.

[0140] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method.

[0141] Figure 10 This is a schematic diagram of the structure of the form generation device provided in the embodiment of the present application. Figure 10 As shown, an embodiment of the present application further provides a form generating device, comprising:

[0142] A first determining module 1001 is configured to, in response to receiving a form generation request, determine first required data and at least one target file corresponding to the form generation request;

[0143] A second determining module 1002 is configured to determine, based on each target file, a plurality of first target data corresponding to the first requirement data;

[0144] A first construction module 1003 is configured to determine a structural relationship between each first target data and construct at least one first structured table based on the structural relationship; wherein the first structured table includes at least one first data column;

[0145] The first generating module 1004 is configured to write each first target data into a corresponding first structured table to obtain at least one first target form.

[0146] Optionally, the form generating device includes:

[0147] A first acquisition module is used to acquire each data in each associated file;

[0148] The recognition module is used to identify invalid data in each data and delete it to obtain multiple candidate data;

[0149] A conversion module, configured to convert the format of each candidate data based on the target format to obtain multiple target format data;

[0150] The second determination module includes:

[0151] a first determining unit, configured to determine a target search interval in each target format data of a target file based on each first requirement data;

[0152] The second determining unit is configured to determine at least one keyword element matching each first requirement data in a target search interval to obtain a plurality of first target data; the plurality of first target data includes each first requirement data and each keyword element.

[0153] Optionally, the second determining unit is specifically configured to:

[0154] In a target search interval, determining a first similarity between each first required data and each target format data;

[0155] In the target search interval, target format data having a first similarity greater than or equal to a first threshold is determined as a keyword, and a plurality of first target data are obtained.

[0156] Optionally, the structural relationship includes a subordinate relationship and a non-subordinate relationship; the first requirement data includes at least one target device and at least one data type; and the first building block includes:

[0157] a first generating unit, configured to determine at least one target device having no subordinate relationship in each first target data, and generate a corresponding first structured table based on each target device;

[0158] an identification unit, configured to identify each data type in each first target data;

[0159] a third determining unit, configured to determine, for each target device, a data type having a subordinate relationship with the target device, and determine the data type as the target data type;

[0160] The second generating unit is configured to generate, for each target device, at least one first data column in the corresponding first structured table based on the corresponding target data type.

[0161] Optionally, the first generating module is specifically configured to:

[0162] In each first target data, determining a target keyword element associated with each target data type;

[0163] For each target data type, the associated target keyword element is written into the corresponding first data column to obtain at least one first target form.

[0164] Optionally, the form generating device further includes:

[0165] A second acquisition module is used to obtain a first hash value of each associated file;

[0166] A calculation module, configured to calculate a second hash value of each associated file based on each target format data;

[0167] a comparison module, configured to compare the first hash value and the second hash value for each associated file;

[0168] The second determination module is specifically configured to:

[0169] If the first hash value matches the second hash value, determining a target search interval in each target format data of the target file based on each first requirement data;

[0170] In the target search interval, at least one keyword element matching each first demand data is determined to obtain a plurality of first target data; the plurality of first target data includes each first demand data and each keyword element.

[0171] Optionally, the second determining unit is specifically configured to:

[0172] For each target device, calculating a second similarity between the target device and the corresponding target keyword element;

[0173] For each target data type, calculating a third similarity between the target data type and the corresponding target keyword element;

[0174] The first building block is specifically used to:

[0175] When each second similarity and each third similarity are greater than or equal to a second threshold, a structural relationship between each first target data is determined, and at least one first structured table is constructed based on the structural relationship.

[0176] Optionally, the first determining module is specifically configured to:

[0177] In response to receiving the form generation request, parsing the form generation request to obtain at least one target device in the form generation request;

[0178] Among the multiple associated files, at least one associated file is selected based on each target device and determined as the target file.

[0179] Optionally, the form generating device further includes:

[0180] a third determining module, configured to, in response to receiving the demand update instruction, determine second demand data corresponding to the demand update instruction;

[0181] A preprocessing module, configured to preprocess the plurality of first target data based on the second demand data to obtain second target data; wherein the preprocessing includes at least one of a deletion process and an addition process;

[0182] a second construction module, configured to determine a structural relationship between each second target data, and construct at least one second structured table based on the structural relationship; wherein the second structured table includes at least one second data column;

[0183] The second generating module is used to write each second target data into the corresponding second structured table to obtain at least one second target form.

[0184] Optionally, the form generating device further includes:

[0185] a fourth determining module, configured to, in response to receiving the table update instruction, determine at least one third structured table and third target data corresponding to the table update instruction;

[0186] The third generating module is configured to establish a mapping relationship between each third target data and each third structured table, and write each third target data into the corresponding third structured table based on the mapping relationship to obtain at least one third target form.

[0187] For the description of the features in the embodiment corresponding to the form generating device, reference can be made to the relevant description of the embodiment corresponding to the form generating method, which will not be repeated here.

[0188] Figure 11 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 11 As shown, the electronic device provided by this embodiment includes: at least one processor 1101 and a memory 1102. Optionally, the electronic device further includes a communication component 1103. The processor 1101, the memory 1102 and the communication component 1103 are connected via a bus.

[0189] During the specific implementation process, at least one processor 1101 executes the computer-executable instructions stored in the memory 1102, so that the at least one processor 1101 executes the above-mentioned form generation method embodiment.

[0190] The specific implementation process of the processor 1101 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0191] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the application may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0192] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0193] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0194] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above-mentioned form generation method embodiments when running.

[0195] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0196] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of any of the above-mentioned form generation method embodiments are implemented.

[0197] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned form generation method embodiments are implemented.

[0198] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0199] The above is a detailed introduction to the form generation method, device, medium and product provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

Claims

1. A form generation method, characterized in that: include: In response to receiving a form generation request, determining first requirement data and at least one target file corresponding to the form generation request; Determining, based on each of the target files, a plurality of first target data corresponding to the first requirement data; Determine a structural relationship between each of the first target data, and construct at least one first structured table based on the structural relationship; wherein the first structured table includes at least one first data column; Writing each of the first target data into a corresponding first structured table to obtain at least one first target form; The structural relationship includes a subordinate relationship and a non-subordinate relationship; the first demand data includes at least one target device and at least one data type; determining the structural relationship between each of the first target data and constructing at least one first structured table based on the structural relationship includes: determining at least one target device having no subordinate relationship in each of the first target data, and generating a corresponding first structured table based on each of the target devices; Identifying each data type in each of the first target data; For each of the target devices, determining a data type having a subordinate relationship with the target device and determining the data type as the target data type; For each target device, at least one first data column in a corresponding first structured table is generated based on the corresponding target data type.

2. The form generation method according to claim 1, wherein: Before determining a plurality of first target data corresponding to the first requirement data based on each of the target files, the method includes: Get each data in each associated file; In each of the data, invalid data is identified and deleted to obtain a plurality of candidate data; Based on the target format, format conversion is performed on each candidate data to obtain multiple target format data; Determining, based on each of the target files, a plurality of first target data corresponding to the first requirement data, comprising: determining a target search interval in each target format data of the target file based on each of the first requirement data; In the target search interval, at least one keyword element matching each of the first demand data is determined to obtain a plurality of first target data; the plurality of first target data includes each of the first demand data and each of the keyword elements.

3. The form generation method according to claim 2, wherein: Determining at least one keyword element matching each of the first demand data in the target search interval to obtain a plurality of first target data includes: determining, in the target search interval, a first similarity between each of the first required data and each of the target format data; In the target search section, target format data having the first similarity greater than or equal to a first threshold is determined as the keyword element to obtain the plurality of first target data.

4. The form generation method according to claim 2, wherein: The step of writing each of the first target data into the corresponding first structured table to obtain at least one first target form includes: Determining, in each of the first target data, a target keyword element associated with each of the target data types; For each target data type, the associated target keyword element is written into the corresponding first data column to obtain at least one first target form.

5. The form generation method according to claim 2, wherein: After obtaining the data in the associated files, the method further includes: Obtaining a first hash value of each associated file; After converting the format of each candidate data based on the target format to obtain a plurality of target format data, the method includes: Calculating a second hash value for each associated file based on each target format data; For each of the associated files, comparing the first Hash value and the second Hash value; Determining, based on each of the target files, a plurality of first target data corresponding to the first requirement data, comprising: determining a target search interval in each target format data of the target file based on each first requirement data when the first hash value matches the second hash value; In the target search interval, at least one keyword element matching each of the first demand data is determined to obtain a plurality of first target data; the plurality of first target data includes each of the first demand data and each of the keyword elements.

6. The form generation method according to claim 3, wherein: In the target search interval, after determining the target format data whose first similarity is greater than or equal to a first threshold as the keyword element and obtaining the plurality of first target data, the method includes: For each target device, calculating a second similarity between the target device and the corresponding target keyword element; For each target data type, calculating a third similarity between the target data type and the corresponding target keyword element; Determining a structural relationship between each of the first target data, and constructing at least one first structured table based on the structural relationship, including: In a case where each of the second similarities and each of the third similarities is greater than or equal to a second threshold, a structural relationship between each of the first target data is determined, and at least one first structured table is constructed based on the structural relationship.

7. The form generation method according to claim 1, wherein: In response to receiving the form generation request, determining first requirement data and at least one target file corresponding to the form generation request includes: In response to receiving the form generation request, parsing the form generation request to obtain at least one target device in the form generation request; Among the multiple associated files, at least one associated file is selected based on each of the target devices and determined as the target file.

8. The form generation method according to any one of claims 1 to 7, characterized in that: The method further comprises: In response to receiving a demand update instruction, determining second demand data corresponding to the demand update instruction; Preprocessing the plurality of first target data based on the second demand data to obtain second target data; wherein the preprocessing includes at least one of deletion processing and addition processing; Determine a structural relationship between each of the second target data, and construct at least one second structured table based on the structural relationship; wherein the second structured table includes at least one second data column; Each of the second target data is written into a corresponding second structured table to obtain at least one second target form.

9. The form generation method according to any one of claims 1 to 7, characterized in that: After writing each of the first target data into the corresponding first structured table to obtain at least one first target form, the method further includes: In response to receiving a table update instruction, determining at least one third structured table and third target data corresponding to the table update instruction; A mapping relationship between each of the third target data and each of the third structured tables is established, and each of the third target data is written into the corresponding third structured table based on the mapping relationship to obtain at least one third target form.

10. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the form generating method according to any one of claims 1 to 9 when executing the computer program.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the form generating method according to any one of claims 1 to 9 are implemented.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the form generating method according to any one of claims 1 to 9 are implemented.

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