A low-code tabular form generation method and component

Abstract

The present invention discloses a low-code tabular form generation method and component, including: after configuring the form data query interface based on the data interface parsing module, reading the form data and parsing the form fields; performing secondary modification on the parsed form fields based on the field management module, setting the attribute information of the fields; performing tabular form rendering processing on the fields with the attribute information; importing the labels of the corresponding fields of excel to cover the settings of the corresponding cells of the table; after the cells and fields are bound, configuring the editing interface, automatically calling the editing interface to save the data after the table is modified; previewing the saved data, and when the preview is qualified, exporting the json format configuration file to obtain the tabular form. The efficiency of generating tabular forms is improved, data redundancy and data island problems are avoided, the flexibility in displaying and operating data is improved, and data retrieval and data interaction functions are realized conveniently.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a low-code tabular form generation method and component. Background Art

[0002] At present, when generating tabular forms, the development process is cumbersome, inefficient, and expensive. The design and use of the strong binding between the form and the data model leads to a large number of data redundancy and data island problems. Different form designs may map the same business data, resulting in data redundancy; at the same time, the lack of associative design in the form design process may make the data unable to be reused in other businesses. Due to the direct correspondence between the form and the data model, the display and operation interface of the data can only be executed through the current form. This limits the flexibility of developers in displaying and operating data, and it is difficult to design complex data retrieval and data interaction functions. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the above-mentioned technologies to a certain extent. To this end, the purpose of the present invention is to propose a low-code tabular form generation method and component to improve the efficiency of generating tabular forms, avoid data redundancy and data island problems, improve the flexibility in displaying and operating data, and facilitate the realization of data retrieval and data interaction functions.

[0004] To achieve the above object, a first embodiment of the present invention proposes a low-code tabular form generation method, comprising:

[0005] Based on the data interface parsing module, after configuring the form data query interface, read the form data and parse the form fields;

[0006] Based on the field management module, the parsed form fields are modified for the second time to set the attribute information of the fields; the attribute information includes display labels, field component types and field component configuration attributes;

[0007] Perform table-style form rendering processing on the fields that set attribute information;

[0008] Import the labels of each field in Excel to overwrite the settings of the corresponding cells in the table;

[0009] After the cells and fields are bound, configure the editing interface, and automatically call the editing interface to save data after the table is modified;

[0010] Preview the saved data. If the preview is qualified, export the configuration file in json format to obtain a table form.

[0011] According to some embodiments of the present invention, a table-format form rendering process is performed on a field for setting attribute information, including:

[0012] Bind the cell to the field in the field management module to set the attribute information. Once bound, the cell automatically reads the corresponding data of the field in the interface, and the component corresponding to the field is displayed when the cell is double-clicked for editing;

[0013] Set the cell content to a fixed string;

[0014] Set the cell as a view field. The view field supports the configuration of formula functions. The formula function contains a data input parameter. The data input parameter contains the data of all the cells and fields in the table. Calculation is performed by combining js code, and the calculation result is used as the final display of the cell.

[0015] Process and merge the cells to obtain the table-format form rendering processing result.

[0016] According to some embodiments of the present invention, the label of each field corresponding to the import excel covers the setting of the corresponding cell of the table, including:

[0017] Use the DSSM deep semantic matching model to match the similarity between each field in the field list and each field in the imported Excel. For those with a similarity greater than the threshold, bind the cell to the field and obtain the label of each field to overwrite the setting of the corresponding cell in the table.

[0018] According to some embodiments of the present invention, the method further includes: introducing a table component, wherein the table component reads a tabular form to render a form-based form.

[0019] According to some embodiments of the present invention, a table component reads a tabular form to render a form-based form, including:

[0020] Tabular forms support text input, number input, date, drop-down selection, and dictionary type;

[0021] Support writing custom input components through jsx and registering them on the table;

[0022] The JSON data describes the input control configuration of each cell in the table through {'x,y':{type:'input component type',field:'corresponding field',title:'title description',controlProps:{configurable items of different input components},};

[0023] The table component reads the JSON object, iterates over all keys, renders controls at corresponding locations according to their configuration, and binds their values ​​to corresponding fields of the global model;

[0024] The table loads data, automatically calls the interface to query remote data, and stores it in the global model;

[0025] When the global model data is updated, the table component responsively updates the value of the bound field to obtain the rendering result of the form-style table.

[0026] According to some embodiments of the present invention, the further comprising:

[0027] In the process of binding the cell to the field, the binding information is obtained; the binding feature vector and the binding plan are extracted according to the binding information, the binding feature vector and the binding plan are input into the pre-trained binding simulation model, and the completion degree of the cell binding to the field is output;

[0028] Re-plan the binding plan according to the completion degree of binding cells to fields, determine the target binding plan, and extract the preset binding progress information;

[0029] Determine actual binding progress information, compare the actual binding progress information with preset binding progress information, and determine a delayed binding plan according to the comparison result;

[0030] Obtaining binding process information of cells bound to fields within a preset time period, establishing a binding management model according to the binding process information, and marking the binding management model according to the binding type classification;

[0031] Binding plans that are behind schedule are marked with different warning colors in the binding management model.

[0032] According to some embodiments of the present invention, the DSSM deep semantic matching model is used to match the similarity between each field in the field list and each field of the imported Excel, including:

[0033] Perform data segmentation on each field in the matching field list and each field in the imported Excel file to obtain n first strings and n second strings, and calculate the similarity S(X) between the ith first string and the ith second string. i , Y i );

[0034]

[0035] Among them, X i is the first string of the i-th character; Y i is the i-th second string; k i is the number of semantic concepts in the first string of the i-th character; m i is the number of semantic concepts in the i-th second string; is the semantic concept w a The number of times it appears in the i-th first string; is the semantic concept w bThe number of times it appears in the i-th second string; w a is the ath semantic concept in the i-th first string; w b is the bth semantic concept in the i-th second string;

[0036] Calculate the similarity S(X,Y) between each field X in the matching field list and each field Y in the imported Excel file.

[0037]

[0038] Among them, x i It is the sum of the number of semantic concepts in the ith first string and the ith second string after data segmentation processing is performed on each field X in the matching field list and each field Y in the imported Excel.

[0039] To achieve the above object, the second embodiment of the present invention proposes a low-code tabular form generation component, including:

[0040] The data interface parsing module is used to read the form data and parse the form fields after configuring the form data query interface;

[0041] The field management module is used to perform secondary modification on the parsed form fields and set the attribute information of the fields; the attribute information includes display labels, field component types and field component configuration attributes;

[0042] A rendering processing module is used to perform table-format form rendering processing on the fields for setting attribute information;

[0043] Import module, used to import the labels of each field in Excel and overwrite the settings of the corresponding cells in the table;

[0044] The configuration module is used to configure the editing interface after the cells and fields are bound, and automatically call the editing interface to save data after the table is modified;

[0045] The export module is used to preview the saved data. When the preview is qualified, the json format configuration file is exported to obtain a table format form.

[0046] The present invention proposes a low-code tabular form generation method and component, which improves the efficiency of generating tabular forms, avoids data redundancy and data island problems, improves the flexibility in displaying and operating data, and facilitates the realization of data retrieval and data interaction functions.

[0047] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 is a flowchart of a method for generating a low-code tabular form according to an embodiment of the present invention;

[0051] Figure 2 This is a block diagram of components for low-code tabular form generation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] like Figure 1 As shown, the first embodiment of the present invention proposes a low-code tabular form generation method, including steps S1-S6:

[0054] S1. Based on the data interface parsing module, after configuring the form data query interface, read the form data and parse the form fields;

[0055] S2. Based on the field management module, the parsed form fields are modified for the second time to set the attribute information of the fields; the attribute information includes display labels, field component types and field component configuration attributes;

[0056] S3, performing table-format form rendering processing on the fields for setting attribute information;

[0057] S4. Import the labels of each field in Excel to cover the settings of the corresponding cells in the table;

[0058] S5. After the cells and fields are bound, configure the editing interface, and automatically call the editing interface to save data after the table is modified;

[0059] S6. Preview the saved data. If the preview is qualified, export the configuration file in json format to obtain a table form.

[0060] The working principle of the above technical solution is: after configuring the form data query interface, the system reads the form data through the interface and parses the form fields. The data query interface is an API (application programming interface) that defines how to obtain form data from a backend database or other data source. When configuring the interface, you need to specify the source of the data, the query parameters (such as form ID, user ID, etc.), and the return format of the data (such as JSON, XML, etc.). Once the interface configuration is completed, the data interface parsing module can read the form data by calling this interface. This process usually involves sending an HTTP request to the backend server and waiting for the server to return a response. The response contains the original data of the form. After reading the form data, the data interface parsing module needs to parse it to extract the field information of the form, including: Data decoding: If the data is encoded in JSON, XML, etc., it first needs to be decoded and converted into a format that the system can handle (such as JavaScript object, XML DOM, etc.). Field extraction: Extract the field information of the form from the decoded data. It involves traversing the data structure, finding the key or node corresponding to the form field, and extracting their value. Field validation: Validate the extracted field information to ensure that they conform to the expected format and type. This can include checking whether the field exists, whether it is empty, whether it conforms to a specific data type (such as number, string, date, etc.), etc. Field mapping: Map the extracted field information to the form field model in the system. This involves creating a new form field object and assigning the extracted field information to the properties of the object. After parsing is completed, the data interface parsing module will return an object or data structure containing the form field information. This object or data structure can be used in subsequent form rendering, data validation, data storage and other steps. Ensure that the configured form data query interface is stable and can reliably return form data. When reading and parsing form data, pay attention to data security. Avoid the leakage of sensitive data and ensure the integrity and consistency of the data. Ensure the stability of the data interface and the standardization of the data format so that the parsing module can accurately read and parse the data.

[0061] Make secondary modifications to the parsed form fields and set the field attribute information, including display labels, field component types (such as input boxes, selection boxes, etc.) and field component configuration attributes (such as whether it is required, default values, etc.). Load the parsed form field information into the field management module by calling the API or interface of the field management module. For each field, you can set its display label (also known as field name or field title). This label will be used to display to users on the form interface to indicate the purpose or content of the field. According to the data type and purpose of the field, you can select the appropriate field component type. For example, for text data, you can select a text box component; for date data, you can select a date selector component, etc. For each field component, you can also configure its specific attributes. For example, for a text box component, you can set its maximum number of characters, whether it is allowed to be empty, whether to enable validation, etc.; for a date selector component, you can set its date format, whether to allow the selection of past or future dates, etc. After completing the secondary modification and attribute setting of the field, you need to save these modifications to the field management module. It involves updating the field information in the database or calling the corresponding API to save the changes. Display label: the field name or title displayed to the user on the form interface. It can accurately reflect the purpose or content of the field. Field component type: determines the display and interaction mode of the field on the form interface. Choosing the right field component type is crucial to improving user experience and form usability. Field component configuration properties: used to further customize the behavior and appearance of field components. These properties include validation rules, style settings, default values, etc. Provides a variety of field component types and configuration properties to meet the needs of different form fields. At the same time, supports batch setting and modification of field properties to improve configuration efficiency.

[0062] Render the form according to the set field attribute information to generate a tabular form. Ensure the compatibility and performance of the rendering engine to support the rendering and interaction of complex forms. At the same time, provide customization of form layout and style to meet the needs of different scenarios.

[0063] Import Excel files and overwrite the labels of the corresponding fields in Excel to the settings of the corresponding cells in the table. Support format verification and error prompts of Excel files to ensure the accuracy of imported data. At the same time, provide a preview function for Excel import so that users can confirm the import results.

[0064] After the cells and fields are bound, configure the editing interface. When the table is modified, the editing interface is automatically called to save the data. Ensure the stability and security of the editing interface to prevent data leakage and tampering. At the same time, provide real-time feedback and error prompts for data saving so that users can handle problems in the saving process in a timely manner.

[0065] Preview the saved data, ensure that the data is correct, and then export the configuration file in json format to obtain the final table format. Provide custom preview interface functions to meet the preview needs of different users. At the same time, support version control and rollback functions of json configuration files to restore the previous configuration when needed.

[0066] The beneficial effects of the above technical solution are: through low-code methods, the rapid generation and configuration of tabular forms are realized, which improves the efficiency and flexibility of form generation, avoids data redundancy and data island problems, improves the flexibility in displaying and operating data, and facilitates the realization of data retrieval and data interaction functions.

[0067] According to some embodiments of the present invention, a table-format form rendering process is performed on a field for setting attribute information, including:

[0068] Bind the cell to the field in the field management module to set the attribute information. Once bound, the cell automatically reads the corresponding data of the field in the interface, and the component corresponding to the field is displayed when the cell is double-clicked for editing;

[0069] Set the cell content to a fixed string;

[0070] Set the cell as a view field. The view field supports the configuration of formula functions. The formula function contains a data input parameter. The data input parameter contains the data of all the cells and fields in the table. Calculation is performed by combining js code, and the calculation result is used as the final display of the cell.

[0071] Process and merge the cells to obtain the table-format form rendering processing result.

[0072] The working principle of the above technical solution is: bind the cells in the table with the fields that set the attribute information in the field management module. Once the binding is completed, the cell will automatically read the data of the corresponding field in the interface. At the same time, when double-clicking the cell to edit, the component corresponding to the field will be displayed so that the user can enter or modify the data. Ensure the accuracy and stability of the binding process to avoid data reading errors or component display abnormalities. In addition, a visual display of the binding relationship can be provided so that users can quickly confirm the binding results.

[0073] Allows users to set the contents of certain cells as fixed strings, which will not change as data is updated. Provides convenient string input methods, such as text boxes or drop-down menus. At the same time, supports string formatting functions, such as bold, italics, underline, etc., to meet the needs of different scenarios.

[0074] Set certain cells as view fields, which are not directly involved in data input or modification. View fields support the configuration of formula functions, which can receive a data input parameter that contains all the cell data and field data of the table. By combining JavaScript code for calculation, the calculation result is used as the final display content of the cell. Provide a rich library of formula functions, such as sum, average, maximum, minimum, etc., to meet common calculation needs. At the same time, support user-defined formula functions to improve the flexibility and accuracy of calculations. In addition, provide verification and error prompt functions for formula functions to avoid errors in the calculation process.

[0075] Merge cells in the table according to actual needs. The merged cells will share the same data and property settings. Provide convenient cell merging operations, such as dragging, selecting boxes, etc. At the same time, support the splitting operation of merged cells, so that users can adjust as needed. In addition, when merging cells, ensure the accuracy and integrity of the data to avoid data loss or duplication.

[0076] After the above steps, the rendering result of the tabular form will be obtained. The result will be displayed on the user interface for users to view and operate. Ensure the accuracy and aesthetics of the rendering result. A variety of table styles and themes can be provided for users to choose from to meet the needs of different scenarios. At the same time, support functions such as table scaling, scrolling and printing to improve user experience.

[0077] The beneficial effect of the above technical solution is to achieve efficient and accurate tabular form rendering processing for fields that set attribute information.

[0078] According to some embodiments of the present invention, the label of each field corresponding to the import excel covers the setting of the corresponding cell of the table, including:

[0079] Use the DSSM deep semantic matching model to match the similarity between each field in the field list and each field in the imported Excel. For those with a similarity greater than the threshold, bind the cell to the field and obtain the label of each field to overwrite the setting of the corresponding cell in the table.

[0080] The working principle of the above technical solution is: prepare an Excel file containing field labels. This file should contain one or more columns of data, each column of data corresponds to a field, and the first row is usually the field name (or label). Provide an interface or screen to enable users to upload or select an Excel file. Once the file is selected, the system should be able to load and parse the file to extract the field names and corresponding data. There is a field list, which contains all the fields that need to be used in the form. These fields have been defined in the system, including their names, types, attributes and other information. DSSM is a deep learning model used to calculate the semantic similarity between two texts. In this scenario, each field name in the field list and each field name in the Excel file are input into the DSSM model to calculate the similarity between them. For each field pair (a field in the field list and a field in the Excel file), if their similarity is greater than a preset threshold (this threshold can be adjusted according to actual conditions), they are considered to match successfully. Once a matching field pair is found, the system can bind the corresponding fields (i.e., columns) in the Excel file to the corresponding fields in the form. When the form is rendered, these fields will be filled with the data provided in the Excel file. The binding process involves setting the Excel field's label (i.e., column name) as the form field's display label (if the user has not specified another label). For those fields in the field list for which no match is found in the Excel file, the user is prompted to manually select or enter a matching field, or the fields are marked as unused.

[0081] The beneficial effects of the above technical solution are as follows: by combining the DSSM model for field matching, the import and field binding process of Excel files can be handled more intelligently and automatically, thereby improving efficiency and accuracy.

[0082] According to some embodiments of the present invention, the method further includes: introducing a table component, wherein the table component reads a tabular form to render a form-based form.

[0083] Working principle and beneficial effects of the above technical solution: The table component is an advanced UI component that allows users to display and edit form data in a table format. This component usually combines the concepts of form fields and table rows, allowing users to intuitively see and modify multiple rows of data. Introducing the table component in the form system includes: selecting a ready-made table component library (such as React Table, Ag-Grid, etc.), which provides rich functions and configuration options; integrating the selected table component into the form system. It involves importing the code or module of the table component into the code of the form system and ensuring that they can interact correctly; configuring the properties of the table component according to the needs and style of the form, including setting the columns, rows, paging, sorting and other functions of the table. Read the JSON or XML definition file of the tabular form and parse out the fields, validation rules, layout and other information therein. Map the form fields to the columns of the table, and the mapping properties include the width, alignment, editability and other properties of the columns. Read the data source of the form (such as a database, API, etc.) and convert the data into a format that the table component can convert. Initialize the table component with the parsed form definition and data. Call the rendering method of the table component to display the table on the user interface. Listen to user interaction events (such as clicks, input, selections, etc.), and update the state and data of the table according to the events. When the user submits the form, verify whether the data in the table meets the validation rules of the form. Introduce the table component into the form system, and read the tabular form to render the form-based table. This will provide users with a powerful and flexible form editing and data processing tool.

[0084] According to some embodiments of the present invention, a table component reads a tabular form to render a form-based form, including:

[0085] Tabular forms support text input, number input, date, drop-down selection, and dictionary type;

[0086] Support writing custom input components through jsx and registering them on the table;

[0087] The JSON data describes the input control configuration of each cell in the table through {'x,y':{type:'input component type',field:'corresponding field',title:'title description',controlProps:{configurable items of different input components},};

[0088] The table component reads the JSON object, iterates over all keys, renders controls at corresponding locations according to their configuration, and binds their values ​​to corresponding fields of the global model;

[0089] The table loads data, automatically calls the interface to query remote data, and stores it in the global model;

[0090] When the global model data is updated, the table component responsively updates the value of the bound field to obtain the rendering result of the form-style table.

[0091] Working principle and beneficial effects of the above technical solution: Basic input types: text input, number input, date picker, dropdown selector, dictionary type (Dictionary / Enum Selector, usually selected from a predefined list of values). Support writing custom input components through JSX and registering these components in the table component. This allows users to create and integrate highly customized input controls according to specific needs. Use JSON objects to describe the input control configuration of each cell in the table. The table component reads the JSON object described above and traverses all the keys (i.e., the cell location identifiers) therein. For each cell, render the corresponding input control at the corresponding position according to its configuration (type, field, title, controlProps). If type points to a custom component, the custom component registered in the table component is used for rendering. Bind the value of each input control to the corresponding field of the global model. Involve two-way data binding or event monitoring to ensure that user input can update the global model in real time. When the table is loaded, the interface is automatically called to query remote data and the query results are stored in the global model. This allows the table to display the latest data at the initial rendering. When the data in the global model is updated, the table component can responsively update the value of its bound field. This ensures that the content displayed in the table is always consistent with the data in the global model. According to the above steps, a form-style table is finally rendered, which combines the layout of the table and the input function of the form. Users can edit data directly in the table and see the results of the changes in real time, which makes it easier to improve the accuracy of the table component reading the tabular form to render the form-style table.

[0092] According to some embodiments of the present invention, the further comprising:

[0093] In the process of binding the cell to the field, the binding information is obtained; the binding feature vector and the binding plan are extracted according to the binding information, the binding feature vector and the binding plan are input into the pre-trained binding simulation model, and the completion degree of the cell binding to the field is output;

[0094] Re-plan the binding plan according to the completion degree of binding cells to fields, determine the target binding plan, and extract the preset binding progress information;

[0095] Determine actual binding progress information, compare the actual binding progress information with preset binding progress information, and determine a delayed binding plan according to the comparison result;

[0096] Obtaining binding process information of cells bound to fields within a preset time period, establishing a binding management model according to the binding process information, and marking the binding management model according to the binding type classification;

[0097] Binding plans that are behind schedule are marked with different warning colors in the binding management model.

[0098] The working principle and beneficial effects of the above technical solution: In the process of binding cells and fields, relevant binding information is first collected. This information includes the content, type, location of the cell, and the field name, data type, validation rules, etc. associated with it. From the collected binding information, feature vectors that can characterize the binding relationship are extracted. These feature vectors include the length, complexity, data type matching degree, etc. of the cell content. Based on the extracted feature vectors, a preliminary binding plan is formulated. This plan includes the order, priority, expected completion time, etc. of the binding.

[0099] The extracted binding feature vector and the developed binding plan are input into the pre-trained binding simulation model. This model can predict the completion of cell binding to field based on historical data and feature vectors. The simulation model outputs an indicator representing the completion of cell binding to field based on the input feature vector and plan. This indicator is a percentage that represents the progress and accuracy of binding.

[0100] According to the completion of the binding simulation model output, the preliminary binding plan is re-planned. It is necessary to adjust the order and priority of the binding or take other measures to improve the completion. After the re-planning, a target binding plan is determined. This plan has a higher degree of completion and accuracy. From the target binding plan, the preset binding progress information is extracted. This information includes the expected completion time of each cell binding, key milestones, etc.

[0101] Through monitoring and recording, the actual binding progress information is obtained. This includes the number of completed bindings, ongoing binding tasks, and binding tasks that have not yet started. Comparing the actual binding progress information with the preset binding progress information makes it easier to identify which plans have been completed on time and which plans are lagging behind.

[0102] During the preset time period, the binding process information of cells bound to fields is continuously collected. This information includes the binding status of each cell, error logs, performance indicators, etc. Based on the collected binding process information, a binding management model is established. This model can reflect the various factors in the binding process and their interrelationships. The binding management model is divided according to the binding type (such as text input, number input, date selection, etc.) and marked. This helps to quickly identify different types of binding tasks in subsequent analysis and monitoring. For binding plans that are lagging behind, different warning colors are marked in the binding management model. This can remind the resource scheduling module to pay attention to these plans and take necessary measures to speed up their completion. Realizing refined management and monitoring of the process of binding cells to fields facilitates improving the accuracy and efficiency of data binding while reducing the occurrence of errors and delays.

[0103] According to some embodiments of the present invention, the DSSM deep semantic matching model is used to match the similarity between each field in the field list and each field of the imported Excel, including:

[0104] Perform data segmentation on each field in the matching field list and each field in the imported Excel file to obtain n first strings and n second strings, and calculate the similarity S(X) between the ith first string and the ith second string. i , Y i );

[0105]

[0106] Among them, X i is the first string of the i-th character; Y i is the i-th second string; k i is the number of semantic concepts in the first string of the i-th character; m i is the number of semantic concepts in the i-th second string; is the semantic concept w a The number of times it appears in the i-th first string; is the semantic concept w b The number of times it appears in the i-th second string; w a is the ath semantic concept in the i-th first string; w b is the bth semantic concept in the i-th second string;

[0107] Calculate the similarity S(X,Y) between each field X in the matching field list and each field Y in the imported Excel file.

[0108]

[0109] Among them, ti It is the sum of the number of semantic concepts in the ith first string and the ith second string after data segmentation processing is performed on each field X in the matching field list and each field Y in the imported Excel.

[0110] The working principle and beneficial effects of the above technical solution are as follows: Each field in the matching field list (denoted as field X) and each field in the imported Excel file (denoted as field Y) are segmented. The field text is split into smaller units, such as words, phrases, or semantic concepts. Natural language processing (NLP) techniques are used in the segmentation process, such as word segmentation, part-of-speech tagging, named entity recognition, etc.

[0111] In the segmented text, semantic concepts are identified and extracted based on the pre-trained word embedding model. Semantic concepts can be words, phrases, or more complex entities that can represent the main meaning of the text.

[0112] Calculate the similarity between the ith first string and the ith second string, and then calculate the similarity S(X,Y) between each field X in the matching field list and each field Y of the imported Excel. When calculating the overall similarity, the importance of different fields is considered to improve the accuracy of the calculation, thereby facilitating the determination of the similarity between each field in the matching field list and each field of the imported Excel, improving the accuracy of comparing the similarity with the threshold, and thus improving the accuracy of binding cells to fields.

[0113] like Figure 2 As shown, the second embodiment of the present invention proposes a low-code tabular form generation component, including:

[0114] The data interface parsing module is used to read the form data and parse the form fields after configuring the form data query interface;

[0115] The field management module is used to perform secondary modification on the parsed form fields and set the attribute information of the fields; the attribute information includes display labels, field component types and field component configuration attributes;

[0116] A rendering processing module is used to perform table-format form rendering processing on the fields for setting attribute information;

[0117] Import module, used to import the labels of each field in Excel and overwrite the settings of the corresponding cells in the table;

[0118] The configuration module is used to configure the editing interface after the cells and fields are bound, and automatically call the editing interface to save data after the table is modified;

[0119] The export module is used to preview the saved data. When the preview is qualified, the json format configuration file is exported to obtain a table format form.

[0120] The beneficial effects of the above technical solution are: through low-code methods, the rapid generation and configuration of tabular forms are realized, which improves the efficiency and flexibility of form generation, avoids data redundancy and data island problems, improves the flexibility in displaying and operating data, and facilitates the realization of data retrieval and data interaction functions.

[0121] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A low-code tabular form generation method, characterized in that: include: Based on the data interface parsing module, after configuring the form data query interface, read the form data and parse the form fields; Based on the field management module, the parsed form fields are modified for the second time to set the attribute information of the fields; the attribute information includes display labels, field component types and field component configuration attributes; Perform table-style form rendering processing on the fields that set attribute information; Import the labels of each field in Excel to overwrite the settings of the corresponding cells in the table; After the cells and fields are bound, configure the editing interface, and automatically call the editing interface to save data after the table is modified; Preview the saved data. If the preview is qualified, export the configuration file in json format to obtain a table form.

2. The low-code tabular form generation method according to claim 1, characterized in that: Perform table-style form rendering on the fields that set attribute information, including: Bind the cell to the field in the field management module to set the attribute information. Once bound, the cell automatically reads the corresponding data of the field in the interface, and the component corresponding to the field is displayed when the cell is double-clicked for editing; Set the cell content to a fixed string; Set the cell as a view field. The view field supports the configuration of formula functions. The formula function contains a data input parameter. The data input parameter contains the data of all the cells and fields in the table. Calculation is performed by combining js code, and the calculation result is used as the final display of the cell. Process and merge the cells to obtain the table-format form rendering processing result.

3. The low-code tabular form generation method according to claim 1, characterized in that: Import the labels of the corresponding fields in Excel to cover the settings of the corresponding cells in the table, including: Use the DSSM deep semantic matching model to match the similarity between each field in the field list and each field in the imported Excel. For fields with a similarity greater than the threshold, bind the cell to the field and obtain the label of each field to overwrite the setting of the corresponding cell in the table.

4. The low-code tabular form generation method according to claim 1, characterized in that: Also includes: Introduce the table component, which reads the tabular form to render the form-based table.

5. The low-code tabular form generation method according to claim 4, characterized in that: The table component reads the tabular form to render the form-based table, including: Tabular forms support text input, number input, date, drop-down selection, and dictionary type; Support writing custom input components through jsx and registering them on the table; The JSON data describes the input control configuration of each cell in the table through {'x,y':{type:'input component type',field:'corresponding field',title:'title description',controlProps:{configurable items of different input components},}; The table component reads the JSON object, iterates over all keys, renders controls at corresponding locations according to their configuration, and binds their values ​​to corresponding fields of the global model; The table loads data, automatically calls the interface to query remote data, and stores it in the global model; When the global model data is updated, the table component responsively updates the value of the bound field to obtain the rendering result of the form-style table.

6. The low-code tabular form generation method according to claim 3, characterized in that: Also includes: In the process of binding a cell to a field, get the binding information; Extract binding feature vectors and binding plans according to binding information, input the binding feature vectors and binding plans into a pre-trained binding simulation model, and output the degree of completion of binding cells to fields; Re-plan the binding plan according to the completion degree of binding cells to fields, determine the target binding plan, and extract the preset binding progress information; Determine actual binding progress information, compare the actual binding progress information with preset binding progress information, and determine a delayed binding plan according to the comparison result; Obtaining binding process information of cells bound to fields within a preset time period, establishing a binding management model according to the binding process information, and marking the binding management model according to the binding type classification; Binding plans that are behind schedule are marked with different warning colors in the binding management model.

7. The low-code tabular form generation method according to claim 3, characterized in that: Use the DSSM deep semantic matching model to match the similarity of each field in the field list with each field in the imported Excel file, including: Perform data segmentation on each field in the matching field list and each field in the imported Excel file to obtain n first strings and n second strings, and calculate the similarity S(X) between the ith first string and the ith second string. i , Y i ); Among them, X i is the first string of the i-th character; Y i is the i-th second string; k i is the number of semantic concepts in the first string of the i-th character; m i is the number of semantic concepts in the i-th second string; is the semantic concept w a The number of times it appears in the i-th first string; is the semantic concept w b The number of times it appears in the i-th second string; w a is the ath semantic concept in the i-th first string; w b is the bth semantic concept in the i-th second string; Calculate the similarity S(X, Y) between each field X in the matching field list and each field Y in the imported Excel file. Among them, t i It is the sum of the number of semantic concepts in the ith first string and the ith second string after data segmentation processing is performed on each field X in the matching field list and each field Y in the imported Excel.

8. A low-code tabular form generation component, characterized in that: include: The data interface parsing module is used to read the form data and parse the form fields after configuring the form data query interface; The field management module is used to perform secondary modification on the parsed form fields and set the attribute information of the fields; the attribute information includes display labels, field component types and field component configuration attributes; A rendering processing module is used to perform table-format form rendering processing on the fields for setting attribute information; Import module, used to import the labels of each field in Excel and overwrite the settings of the corresponding cells in the table; The configuration module is used to configure the editing interface after the cells and fields are bound, and automatically call the editing interface to save data after the table is modified; The export module is used to preview the saved data. When the preview is qualified, the json format configuration file is exported to obtain a table format form.

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