Form control adjusting method and apparatus, and electronic device

Abstract

The present disclosure provides a form control adjusting method and apparatus, and an electronic device. The method comprises: acquiring a current form control amongst a plurality of form controls, the current form control being a form control which is being operated amongst the plurality of form controls; predicting from amongst the plurality of form controls a control identifier of a next form control of the current form control, so as to obtain a predicted control identifier; and adjusting the sequential positions of the plurality of form controls on the basis of the predicted control identifier. During the implementation of the solution, the control identifier of the next form control of the current form control is predicted from amongst the plurality of form controls, and the sequential positions of the plurality of form controls are adjusted on the basis of the predicted control identifier, such that users can effectively skip optional questions or questions that are not appropriate to answer, and directly answer a question corresponding to the predicted next form control, thus improving the efficiency of filling in questionnaires or forms of office systems.

Description

Form control adjustment method, device and electronic device

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application number 2024101916288, filed with the China Patent Office on February 21, 2024, entitled “A form control adjustment method, device and electronic device,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The present disclosure relates to the technical field of digital form design and optimization, and in particular to a form control adjustment method, device, and electronic device. Background Art

[0004] In the current questionnaire survey and office automation industries, multiple form controls within questionnaires or office system forms are typically fixed. For example, after receiving a questionnaire link, a user clicks on it and completes the corresponding questions within each form control. These controls may be single-choice, multiple-choice, or fill-in-the-blank. In practice, it has been found that users are inefficient when filling out questionnaires or office system forms.

[0005] Application Contents

[0006] Embodiments of the present disclosure provide a form control adjustment method, device, and electronic device for improving the problem of low efficiency in filling out questionnaires or office system forms.

[0007] The disclosed embodiment provides a form control adjustment method, comprising: obtaining a current form control from a plurality of form controls, where the current form control is the form control currently being operated on among the plurality of form controls; predicting a control identifier of a form control next to the current form control from the plurality of form controls to obtain a predicted control identifier; and adjusting the sequential positions of the plurality of form controls based on the predicted control identifier. In the implementation of the above scheme, by predicting the control identifier of the form control next to the current form control from the plurality of form controls and adjusting the sequential positions of the plurality of form controls based on the predicted control identifier, the user can effectively skip optional questions or questions that are not suitable for answering, and directly fill in the question corresponding to the predicted next form control, thereby improving the efficiency of filling out questionnaires or office system forms.

[0008] Optionally, in an embodiment of the present disclosure, predicting the control identifier of the next form control after the current form control from multiple form controls includes: using a recurrent neural network (RNN) model to predict the control identifier of the next form control after the current form control from multiple form controls; or predicting the control identifier of the next form control based on previously filled-in historical form controls and the current form control from multiple form controls. In the implementation of the above scheme, by using a recurrent neural network (RNN) model or predicting the control identifier of the next form control based on previously filled-in historical form controls and the current form control from multiple form controls, the situation where it is difficult to predict the next form control is improved, thereby allowing users to directly fill in the question corresponding to the predicted next form control, thereby improving the efficiency of filling out questionnaires or office system forms.

[0009] Optionally, in an embodiment of the present disclosure, obtaining a current form control from multiple form controls includes: receiving a current form control sent by a terminal device, where the current form control is selected by the terminal device from multiple form controls; and adjusting the sequential positions of the multiple form controls based on a predicted control identifier, including: sending the predicted control identifier to the terminal device so that the terminal device adjusts the sequential position of the next form control to the back of the current form control based on the predicted control identifier. During the implementation of the above scheme, by dynamically adjusting the sequential position of the next form control to the back of the current form control based on the predicted control identifier, dynamic optimization of the form controls is achieved, thereby reducing the time users spend filling out questionnaires or office system forms and improving the efficiency of filling out questionnaires or office system forms.

[0010] Optionally, in an embodiment of the present disclosure, before obtaining the current form control from the multiple form controls, the method further includes: obtaining a flowchart to be processed, sending the flowchart to be processed to a terminal device, so that the terminal device parses the multiple form controls from the flowchart to be processed; after adjusting the order positions of the multiple form controls according to the predicted control identifier, the method further includes: reconstructing the form flowchart according to the adjusted multiple form controls, and sending the form flowchart to the terminal device, so that the terminal device parses the adjusted multiple form controls from the form flowchart. In the implementation process of the above scheme, by reconstructing the form flowchart according to the adjusted multiple form controls, the terminal device parses the adjusted multiple form controls from the form flowchart, so that the user can directly observe the adjusted multiple form controls through the terminal device, effectively realizing the reconstruction function of the form flowchart.

[0011] Optionally, in an embodiment of the present disclosure, after reconstructing the form flowchart based on the adjusted multiple form controls, the process further includes: validating the form flowchart to obtain a verification text. During the implementation of the above solution, by validating the form flowchart, the designer of the form flowchart can optimize the form flowchart based on the verification text, thereby effectively improving the design efficiency of the form flowchart.

[0012] Optionally, in an embodiment of the present disclosure, verifying the form flowchart includes verifying the form flowchart using a heuristic rule algorithm or a machine learning model. During the implementation of the above solution, by verifying the form flowchart using a heuristic rule algorithm or a machine learning model, the verification result of the form flowchart is made more accurate, allowing designers to optimize the form flowchart based on the verification text, thereby effectively improving the design efficiency of the form flowchart.

[0013] The present disclosure also provides a form control adjustment device, comprising: a current control acquisition module configured to acquire a current form control from a plurality of form controls, where the current form control is the form control currently being operated on; a control identifier prediction module configured to predict, from the plurality of form controls, a control identifier of a form control subsequent to the current form control, and obtain a predicted control identifier; and a sequential position adjustment module configured to adjust the sequential positions of the plurality of form controls based on the predicted control identifier.

[0014] Optionally, in an embodiment of the present disclosure, the control identification prediction module includes: a first identification prediction submodule, configured to use a recurrent neural network (RNN) model to predict the control identification of the next form control of the current form control from multiple form controls; or, a second identification prediction submodule, configured to predict the control identification of the next form control based on the historical form controls that have been filled in among the multiple form controls and the current form control.

[0015] Optionally, in an embodiment of the present disclosure, the current control acquisition module includes: a form control receiving sub-module, configured to receive the current form control sent by the terminal device, where the current form control is selected by the terminal device from multiple form controls; and a sequence position adjustment module, including: a control identifier sending sub-module, configured to send a predicted control identifier to the terminal device, so that the terminal device adjusts the sequence position of the next form control to the back of the current form control according to the predicted control identifier.

[0016] Optionally, in an embodiment of the present disclosure, the form control adjustment device further includes: a first flowchart sending module, configured to obtain the flowchart to be processed, and send the flowchart to be processed to the terminal device, so that the terminal device parses the multiple form controls from the flowchart to be processed; a second flowchart sending module, configured to reconstruct the form flowchart according to the adjusted multiple form controls, and send the form flowchart to the terminal device, so that the terminal device parses the adjusted multiple form controls from the form flowchart.

[0017] Optionally, in an embodiment of the present disclosure, the form control adjustment device further includes: a verification text obtaining module configured to verify the form flow chart and obtain a verification text.

[0018] Optionally, in an embodiment of the present disclosure, the verification text obtaining module includes: a flowchart verification submodule, configured to verify the form flowchart using a heuristic rule algorithm or a machine learning model.

[0019] Optionally, in an embodiment of the present disclosure, the form control adjustment device further includes: a verification text optimization module configured to optimize the verification text using a preprocessing language model to obtain optimization suggestions for the form flow chart.

[0020] An embodiment of the present disclosure further provides an electronic device, including: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the machine-readable instructions execute the above method when executed by the processor.

[0021] An embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is executed.

[0022] The embodiments of the present disclosure further provide a computer program product, including: a computer program or computer instructions, which executes the above method when the computer program or computer instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the embodiments of the present disclosure and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0024] FIG1 is a schematic flow chart showing a method for adjusting form controls according to an embodiment of the present disclosure;

[0025] FIG2 is a schematic diagram showing a flow chart of interaction between an electronic device and a terminal device according to an embodiment of the present disclosure;

[0026] FIG3 is a schematic structural diagram of a form control adjustment device provided by an embodiment of the present disclosure;

[0027] FIG4 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. It should be understood that the drawings in the embodiments of the present disclosure are only for the purpose of illustration and description and are not used to limit the scope of protection of the embodiments of the present disclosure. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the embodiments of the present disclosure illustrate the operations implemented according to some embodiments of the embodiments of the present disclosure. It should be understood that the operations of the flowchart can be implemented out of sequence, and steps that do not have a logical context relationship can be reversed in order or implemented simultaneously. In addition, those skilled in the art, guided by the contents of the embodiments of the present disclosure, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0029] In addition, the described embodiments are only some, not all, of the embodiments of the present disclosure. Components of the embodiments of the present disclosure generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the figures is not intended to limit the scope of the claimed embodiments of the present disclosure, but rather merely represents selected embodiments of the present disclosure.

[0030] It is understandable that the "first" and "second" in the embodiments of the present disclosure are used to distinguish similar objects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not necessarily limit them to be different. In the description of the embodiments of the present disclosure, the term "and / or" is merely a description of the association relationship between associated objects, indicating that there may be three relationships, such as A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the previous and subsequent associated objects are in an "or" relationship. The term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups).

[0031] It should be noted that the form control adjustment method provided in the embodiments of the present disclosure can be executed by an electronic device. An electronic device herein refers to a device terminal or server capable of executing a computer program. Examples of device terminals include smartphones, personal computers, tablet computers, personal digital assistants, and mobile internet devices. A server refers to a device that provides computing services over a network. Examples of servers include x86 servers and non-x86 servers. Non-x86 servers include mainframes, minicomputers, and UNIX servers.

[0032] The following describes an example of an application scenario to which the form control adjustment method is applicable: In the development application scenario of a form page of a web project, the order, layout, and style of multiple form controls in a form page are usually fixed. When a user fills in multiple form controls with a fixed order, he or she may sometimes find that there are many optional questions or questions that are not suitable for answering in the multiple form controls. However, even if the optional questions or questions that are not suitable for answering are not filled in, the user cannot directly switch to the form control corresponding to the question that needs to be answered, thereby affecting the efficiency of the user in filling in the entire survey file or office system form. It is understandable that when a user is filling in the current form control in a questionnaire or office system form, the form control adjustment method can be used to dynamically adjust the order position of multiple form controls based on the control identifier of the next form control predicted by the current form control, so that the user can directly fill in the predicted next form control, effectively improving the low efficiency of filling in the questionnaire or office system form.

[0033] Please refer to Figure 1, which shows a flow chart of a form control adjustment method provided by an embodiment of the present disclosure. The main idea of ​​the form control adjustment method is to adjust the order of multiple form controls based on predicted control identifiers, so that users can effectively skip optional questions or questions that are not suitable for answering, and directly fill in the question corresponding to the next predicted form control. Implementation methods of the above form control adjustment method may include:

[0034] Step S110: obtaining a current form control among the multiple form controls, where the current form control is the form control currently being operated among the multiple form controls.

[0035] It is understandable that, while obtaining the current form control among multiple form controls, context information of the current form control can also be obtained. The context information here includes, for example, historical form controls filled in before the current form control, the control name and control content of the historical form controls, etc.

[0036] Step S120: predicting a control identifier of a form control next to the current form control from a plurality of form controls to obtain a predicted control identifier.

[0037] It is understandable that when predicting the control identifier of the next form control of the current form control from multiple form controls, the control identifier of the next form control can be predicted based on the filled-in historical form controls and the current form control.

[0038] Step S130: adjusting the order of the plurality of form controls according to the predicted control identifier.

[0039] In the implementation process of the above solution, by predicting the control identifier of the next form control of the current form control from multiple form controls and adjusting the order of the multiple form controls according to the predicted control identifier, the user can effectively skip optional questions or questions that are not suitable for answering, and directly fill in the question corresponding to the predicted next form control, thereby improving the efficiency of filling out questionnaires or office system forms.

[0040] As an optional implementation of the above step S110, an implementation of obtaining the current form control from multiple form controls may include:

[0041] Step S111: the electronic device receives a current form control sent by the terminal device, where the current form control is selected by the terminal device from multiple form controls.

[0042] An example implementation of step S111 is as follows: the terminal device selects a current form control from multiple form controls so that the user can fill in the content corresponding to the control title in the current form control. After the user completes filling in the content, the terminal device sends the current form control, its control identifier, control title, and filled-in content, etc., to the electronic device via the Hypertext Transfer Protocol (HTTP) or Hypertext Transfer Protocol Secure (HTTPS). The electronic device receives the current form control, its control identifier, control title, and filled-in content, etc., sent by the terminal device via the HTTP or HTTPS protocol. It is understandable that since the electronic device locally stores multiple form controls, the electronic device can compare the control identifiers of the multiple form controls with the control identifier of the current form control, and the electronic device can thereby determine whether the current form control is selected by the terminal device from the multiple form controls.

[0043] As an optional implementation of the above step S120, when predicting the control identifier of the next form control of the current form control, a recurrent neural network (RNN) model may be used for prediction. This implementation may include:

[0044] Step S121: using a recurrent neural network (RNN) model to predict a control identifier of a form control next to the current form control from a plurality of form controls.

[0045] A recurrent neural network (RNN), also known as a recursive neural network, is a type of recursive neural network that takes sequence data as input, performs recursion in the direction of sequence evolution, and has all nodes (recurrent units) connected in a chain-like manner. The core part of a recurrent neural network is a directed graph; the chain-connected elements in the expanded directed graph are called recurrent units (RNN cells).

[0046] For example, in the implementation of step S121 above, it is assumed that the current form control sent by the terminal device is a "name" input box, and the terminal device may also send serialized feature information of the current form control to the electronic device, such as the identifier (ID) of the current form control, the type of user interaction with the current form control (e.g., click, input, etc.), the timestamp of the interaction, etc. Then, the electronic device may use a recurrent neural network (RNN) model to predict from multiple form controls based on the serialized feature information that the next form control after the current form control is an "email address" form control. The RNN model here may be a long short-term memory (LSTM) network, a bidirectional long short-term memory (Bi-LSTM) network, etc.

[0047] Alternatively, when predicting the control identifier of the next form control of the current form control, the prediction may be performed based on the historical form controls and the current form control. This implementation may include:

[0048] Step S122: predicting a control identifier of the next form control based on the filled-in historical form controls and the current form control in the plurality of form controls.

[0049] Alternatively, combining the above two implementations, the RNN model can be used to make predictions based on historical form controls and current form controls. This implementation may include:

[0050] Step S123: using the RNN model to make predictions based on historical form controls and current form controls, and obtain a predicted control identifier for the next form control.

[0051] For example, in the implementation of step S123, assuming that the RNN model predicts, based on historical form controls and the current form controls, that the user is most likely to fill in the "email address" form control next, the electronic device can move the "email address" input box to a more prominent position in the view. For example, the electronic device can move the "email address" input box to immediately after the "name" input box. It is understandable that the output of the recurrent neural network (RNN) model is a probability distribution or probability list of the control identifiers of the next form control. The control identifier corresponding to the highest probability can be selected from the probability distribution or probability list as the predicted control identifier, thereby making the next form control easier for the user to access and fill in.

[0052] As an optional implementation of step S130, adjusting the order of the plurality of form controls according to the predicted control identifiers includes:

[0053] Step S131: the electronic device sends a predicted control identifier to the terminal device, so that the terminal device adjusts the sequence position of the next form control to the back of the current form control according to the predicted control identifier.

[0054] An implementation example of the above-mentioned step S131 is: assuming that the current form control is a "name" input box, and the RNN model in the electronic device predicts that the user is most likely to fill in the "email address" form control next based on historical form controls and the current form controls, then the electronic device can send the "email address" control identifier to the terminal device via the Transmission Control Protocol (TCP) or the User Datagram Protocol (UDP). The "email address" control identifier here serves as a predicted control identifier, so that the terminal device adjusts the sequential position of the "email address" form control to a position adjacent to the form control of the current "name" input box based on the "email address" control identifier. The adjacent position here can be behind or to the right of the form control of the current "name" input box.

[0055] Alternatively, the electronic device may directly adjust the sequential position of the next form control. This implementation may include:

[0056] Step S132: The electronic device adjusts the sequence position of the next form control to behind the current form control according to the predicted control identifier.

[0057] For example, in the implementation of step S132, the electronic device predicts based on historical form controls and current form controls that the user is most likely to fill in the "email address" form control next. In this case, the electronic device can directly adjust the order position of the "email address" form control to behind the form control of the current "name" input box based on the control identifier of the "email address". In the implementation of the above scheme, by dynamically adjusting the order position of the next form control to behind the current form control based on the predicted control identifier, dynamic optimization of the form controls is achieved, which reduces and increases the time spent by users filling out questionnaires or office system forms, and improves the efficiency of filling out questionnaires or office system forms.

[0058] Please refer to FIG. 2 , which shows a schematic diagram of a flow chart of interaction between an electronic device and a terminal device according to an embodiment of the present disclosure. As an optional implementation of the above-mentioned form control adjustment method, before obtaining the current form control from the multiple form controls, the following steps may also be included:

[0059] Step S210: the electronic device obtains the flowchart to be processed, and sends the flowchart to be processed to the terminal device, so that the terminal device parses and outputs a plurality of form controls from the flowchart to be processed.

[0060] An implementation example of the above step S210 is as follows: the electronic device obtains the flowchart to be processed from a file system, a relational database, or a non-relational database, and sends the flowchart to be processed to the terminal device via the HTTP protocol or the HTTPS protocol. Relational databases that can be used include Mysql, PostgreSQL, Oracle, and SQL Server, and non-relational databases that can be used include Grakn database, Hadoop subsystem HBase, MongoDB, and CouchDB. It is understood that after receiving the flowchart to be processed sent by the electronic device, the terminal device can use natural language processing (NLP) technology to parse multiple form controls and a topological structure of the multiple form controls from the flowchart to be processed, and output multiple form controls based on the topological structure; wherein the NLP technology here includes, for example, named entity recognition (NER), dependency parsing technology, etc., and specifically, it can be to use NER to parse multiple form controls from the flowchart to be processed, and to use dependency parsing technology to parse the topological structure of the multiple form controls from the flowchart to be processed.

[0061] Step S220: the electronic device obtains a current form control among the multiple form controls, where the current form control is the form control currently being operated among the multiple form controls.

[0062] Step S230: The electronic device predicts a control identifier of a form control next to the current form control from the multiple form controls, and obtains a predicted control identifier.

[0063] Step S240: the electronic device adjusts the order of the multiple form controls according to the predicted control identifier.

[0064] Among them, the implementation principles and implementation methods of steps S220 to S240 are similar to those of steps S110 to S130. Therefore, their implementation principles and implementation methods are no longer explained here, and reference can be made to the description of steps S110 to S130.

[0065] Step S250: the electronic device reconstructs the form flow chart according to the adjusted multiple form controls, and sends the form flow chart to the terminal device, so that the terminal device parses and outputs the adjusted multiple form controls from the form flow chart.

[0066] In an implementation example of step S250, the electronic device reconstructs the form flow chart based on the adjusted multiple form controls and sends the form flow chart to the terminal device via the HTTP protocol or the HTTPS protocol. After receiving the form flow chart sent by the electronic device via the HTTP protocol or the HTTPS protocol, the terminal device can parse and output the adjusted multiple form controls from the form flow chart.

[0067] As an optional implementation of the above-mentioned form control adjustment method, after reconstructing the form flow chart according to the adjusted multiple form controls, the form flow chart may be verified. This implementation may include:

[0068] Step S260: The electronic device verifies the form flow chart and obtains a verification text.

[0069] It is understandable that the electronic device can verify the logical rationality of a form flowchart. For example, suppose a user of a terminal device is editing a form flowchart for a software development process. The form flowchart may include form controls such as requirements analysis, design, coding, testing, and deployment. The electronic device may discover that the logical rationality problem of the form process is that the coding step precedes the requirements analysis and store this logical rationality problem in the verification text. In the implementation process of the above solution, by verifying the form flowchart, the designer of the form flowchart can optimize the form flowchart based on the verification text, thereby effectively improving the design efficiency of the form flowchart.

[0070] As an optional implementation of step S260, verifying the form flow chart includes:

[0071] Step S261: Use a heuristic rule algorithm or a machine learning model to verify the form flow chart and obtain verification text.

[0072] The implementation method of the above step S261, for example, can be: using a heuristic rule algorithm, a decision tree or a support vector machine (SVM), a deep neural network (DNN) model, and other machine learning models to verify the form flow chart to obtain the above-mentioned verification text. The verification text here is usually a verification result given in text form. The verification text can list some unreasonable form controls and the probability that these form controls are unreasonable. Among them, the above-mentioned DNN model can include: VGG network model, ResNet network model, Wide ResNet network model and Inception network model, etc.

[0073] Alternatively, assume the form flowchart above is used to describe a production process that includes multiple steps: raw material procurement, manufacturing, product testing, and product sales. After validating the form flowchart using a DNN model, the verification results can be recorded in the verification text. For example, the verification results here might include: manufacturing should come after raw material procurement, and product sales should come after product testing. In implementing the above solution, by validating the form flowchart using a heuristic rule algorithm or a machine learning model, the verification results of the form flowchart are more accurate, allowing designers to optimize the form flowchart based on the verification text, effectively improving the design efficiency of the form flowchart.

[0074] As an optional implementation of the above step S260, after obtaining the verification text, the following may also be included:

[0075] Step S262: Optimize the reasonable verification text using the preprocessing language model to obtain optimization suggestions for the form flow chart.

[0076] An example of an implementation of the above step S262 is: assuming that a user of a terminal device is editing a form flow chart of a software development process, the form flow chart may include form controls such as requirements analysis, design, coding, testing, and deployment. The electronic device finds that the logical rationality problem of the form process is that the coding step is before the requirements analysis. Then the electronic device can use a pre-processed language model to optimize the reasonable verification text, and obtain an optimization suggestion for the form flow chart, which is that the user coding step is after the requirements analysis, or it is recommended to add a prototyping step between the requirements analysis and the design to improve the design efficiency of the form flow chart and the efficiency of software development. Among them, the above-mentioned pre-trained language models may include: ChatGPT model, BLOOM model, LLaMA model or ChatGLM model, etc.

[0077] 3 shows a schematic diagram of the structure of a form control adjustment device provided by an embodiment of the present disclosure. The present disclosure provides a form control adjustment device 300, comprising:

[0078] The current control acquisition module 310 is configured to acquire a current form control from among the multiple form controls. The current form control is the form control currently being operated from among the multiple form controls.

[0079] The control identifier prediction module 320 is configured to predict the control identifier of the next form control after the current form control from a plurality of form controls, and obtain a predicted control identifier.

[0080] The sequence position adjustment module 330 is configured to adjust the sequence positions of the plurality of form controls according to the predicted control identifiers.

[0081] Optionally, in an embodiment of the present disclosure, the control identification prediction module includes:

[0082] The first identifier prediction submodule is configured to use a recurrent neural network (RNN) model to predict a control identifier of a form control next to a current form control from a plurality of form controls.

[0083] Alternatively, the second identifier prediction submodule is configured to predict the control identifier of the next form control based on the filled-in historical form controls and the current form control in the multiple form controls.

[0084] Optionally, in an embodiment of the present disclosure, the current control acquisition module includes:

[0085] The form control receiving submodule is configured to receive a current form control sent by a terminal device, where the current form control is selected by the terminal device from multiple form controls.

[0086] Sequential position adjustment module, including:

[0087] The control identifier sending submodule is configured to send the predicted control identifier to the terminal device, so that the terminal device adjusts the sequence position of the next form control to the back of the current form control according to the predicted control identifier.

[0088] Optionally, in an embodiment of the present disclosure, the form control adjustment device further includes:

[0089] The first flowchart sending module is configured to obtain the flowchart to be processed and send the flowchart to be processed to the terminal device, so that the terminal device parses the flowchart to be processed to obtain multiple form controls.

[0090] The second flow chart sending module is configured to reconstruct the form flow chart according to the adjusted multiple form controls and send the form flow chart to the terminal device so that the terminal device parses the adjusted multiple form controls from the form flow chart.

[0091] Optionally, in an embodiment of the present disclosure, the form control adjustment device further includes:

[0092] The verification text obtaining module is configured to verify the form flow chart and obtain the verification text.

[0093] Optionally, in an embodiment of the present disclosure, the verification text obtaining module includes:

[0094] The flowchart validation submodule is configured to validate the form flowchart using a heuristic rule algorithm or a machine learning model.

[0095] Optionally, in an embodiment of the present disclosure, the form control adjustment device further includes:

[0096] The verification text optimization module is configured to optimize the verification text using the preprocessing language model to obtain optimization suggestions for the form flow chart.

[0097] It should be understood that the device corresponds to the aforementioned form control adjustment method embodiment and is capable of executing each of the steps involved in the aforementioned method embodiment. The specific functions of the device can be found in the description above, and a detailed description is omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or embedded in the device's operating system (OS).

[0098] Please refer to FIG4 , which shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. An electronic device 400 provided by an embodiment of the present disclosure includes: a processor 410 and a memory 420. The memory 420 stores machine-readable instructions executable by the processor 410. When the machine-readable instructions are executed by the processor 410, the above method is performed. The present disclosure also provides a computer-readable storage medium 430 on which a computer program is stored. When the computer program is executed by the processor 410, the above method is performed. Among them, the computer-readable storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0099] An embodiment of the present disclosure provides a computer program product, including: a computer program or computer instructions, and the computer program or computer instructions execute the above method when executed by a processor.

[0100] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For device embodiments, since they are generally similar to method embodiments, their description is relatively simple, and for relevant details, reference can be made to the description of the method embodiments.

[0101] In the several embodiments provided in the embodiments of the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are only schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also be different from the order of occurrence marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which mainly depends on the functions involved.

[0102] In addition, the functional modules of each embodiment in the embodiments of the present disclosure may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part. In addition, in the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "some examples", etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0103] The above description is only an optional implementation of the embodiment of the present disclosure, but the protection scope of the embodiment of the present disclosure is not limited to this. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in the embodiment of the present disclosure, and they should all be covered by the protection scope of the embodiment of the present disclosure. Industrial Applicability

[0104] A form control adjustment method, device, and electronic device provided by the embodiments of the present disclosure can enable users to effectively skip optional questions or questions that are not suitable for answering, and directly fill in the question corresponding to the next predicted form control, thereby improving the efficiency of filling out questionnaires or office system forms.

Claims

1. A form control adjustment method, characterized in that: include: Obtaining a current form control from among multiple form controls, where the current form control is a form control currently being operated on among the multiple form controls; Predicting a control identifier of a form control next to the current form control from the multiple form controls to obtain a predicted control identifier; The sequential positions of the plurality of form controls are adjusted according to the predicted control identifier.

2. The method according to claim 1, characterized in that The step of predicting the control identifier of the next form control of the current form control from the multiple form controls includes: Predicting a control identifier of a next form control of the current form control from the multiple form controls using a recurrent neural network (RNN) model; or, The control identifier of the next form control is predicted based on the filled-in historical form controls and the current form control in the multiple form controls.

3. The method according to claim 1, characterized in that The obtaining of the current form control from the plurality of form controls includes: receiving the current form control sent by a terminal device, where the current form control is selected by the terminal device from the multiple form controls; The adjusting the order of the plurality of form controls according to the predicted control identifier includes: The predicted control identifier is sent to the terminal device, so that the terminal device adjusts the sequence position of the next form control to behind the current form control according to the predicted control identifier.

4. The method according to claim 1, wherein Before obtaining the current form control from the multiple form controls, the method further includes: Acquire a flowchart to be processed, and send the flowchart to be processed to a terminal device, so that the terminal device parses the plurality of form controls from the flowchart to be processed; After adjusting the order of the plurality of form controls according to the predicted control identifier, the method further includes: A form flow chart is reconstructed according to the adjusted multiple form controls, and the form flow chart is sent to the terminal device, so that the terminal device parses the adjusted multiple form controls from the form flow chart.

5. The method according to claim 4, characterized in that After the form flow chart is reconstructed according to the adjusted plurality of form controls, the method further includes: The form flow chart is verified to obtain a verification text.

6. The method according to claim 5, characterized in that The verifying of the form flow chart includes: The form flow chart is verified using a heuristic rule algorithm or a machine learning model.

7. A form control adjustment device, characterized in that: include: a current control acquisition module configured to acquire a current form control from among a plurality of form controls, wherein the current form control is a form control currently being operated from among the plurality of form controls; a control identifier prediction module configured to predict a control identifier of a form control next to the current form control from the plurality of form controls to obtain a predicted control identifier; The sequence position adjustment module is configured to adjust the sequence positions of the plurality of form controls according to the predicted control identifier.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and the machine-readable instructions are executed by the processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.

10. A computer program product, characterized in that include: A computer program or computer instruction, wherein the computer program or the computer instruction is executed by a processor to perform the method according to any one of claims 1 to 6.

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