CN113761850B

CN113761850B - Form filling method and device

Info

Publication number: CN113761850B
Application number: CN202011274813.1A
Authority: CN
Inventors: 赵晓艳; 李伟进
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2020-11-13
Filing date: 2020-11-13
Publication date: 2024-10-18
Anticipated expiration: 2040-11-13
Also published as: CN113761850A

Abstract

The invention discloses a form filling method and a form filling device, and relates to the technical field of computers. One embodiment of the method comprises the following steps: determining a form item to be filled of a current form; acquiring the current value of a target form item; wherein the target form item is associated with the form item to be filled; determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled; and filling the current value of the form item to be filled into an input box of the form item to be filled. According to the embodiment, manual form filling operation of a user can be reduced, and form filling time of the user is saved.

Description

Form filling method and device

Technical Field

The present invention relates to the field of computer technologies, and in particular, to a form filling method and apparatus.

Background

As technology advances, users often need to fill in various forms on the network. Such as shopping online or submitting questions online, users are often required to fill out many forms. As another example, in a company, cross-department communication or transaction processing has been gradually performed online, and users also need to fill in various forms such as business approval documents, cross-department worksheets, and the like when processing company business. In the process of filling in the form, the user needs to continuously and manually perform input operation in a longer time, the operation is complicated, and the precious time of the user is wasted.

Disclosure of Invention

In view of the above, the embodiments of the present invention provide a form filling method and apparatus, which can reduce the manual form filling operation of a user and save the form filling time of the user.

In a first aspect, an embodiment of the present invention provides a form filling method, including:

Determining a form item to be filled of a current form;

Acquiring the current value of a target form item; wherein the target form item is associated with the form item to be filled;

determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled;

and filling the current value of the form item to be filled into an input box of the form item to be filled.

Alternatively, the process may be carried out in a single-stage,

The determining the form item to be filled in of the current form comprises the following steps:

And determining whether a filling switch corresponding to a form item of the current form is started, and if so, determining the form item to be filled.

Alternatively, the process may be carried out in a single-stage,

Further comprises:

Acquiring historical values of a plurality of form items; wherein the plurality of forms originate from a plurality of history forms;

integrating the historical values of the plurality of form items into a feature wide table;

performing format conversion on the data in the feature wide table according to a pre-stored feature conversion rule;

Training the prediction model according to the pre-designated form item to be filled and the converted feature width table to obtain a training result; wherein, the training result comprises: and training a good prediction model.

Alternatively, the process may be carried out in a single-stage,

The training result further comprises: the influence values of a plurality of other form items on the form items to be filled; the influence value is used for representing the influence degree of the other form items on the form items to be filled;

further comprises: and determining a target form item associated with the form item to be filled in from the other form items according to the influence value.

Alternatively, the process may be carried out in a single-stage,

The determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled comprises the following steps:

performing format conversion on the current value of the target form item according to a pre-stored characteristic conversion rule;

Determining a link address of the predictive model in online service;

And requesting the prediction service corresponding to the link address by taking the converted current value of the target form item as an input parameter to obtain the current value of the form item to be filled.

Alternatively, the process may be carried out in a single-stage,

The input frame of the form item to be filled is a drop-down frame;

Generating a plurality of predicted values and probabilities thereof of the form to be filled according to the current value of the target form and the predicted model of the form to be filled;

and determining a predicted value corresponding to the maximum probability as the current value of the form to be filled.

Alternatively, the process may be carried out in a single-stage,

Further comprises:

for each of the predicted values: determining the position of the predicted value in a pull-down list of the pull-down frame according to the probability of the predicted value;

and displaying the drop-down frame according to the plurality of predicted values and the positions of the predicted values in the drop-down list.

In a second aspect, an embodiment of the present invention provides a form filling apparatus, including:

the form item determining module is configured to determine form items to be filled of a current form;

the current value acquisition module is configured to acquire the current value of the target form item; wherein the target form item is associated with the form item to be filled;

the prediction module is configured to determine the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled;

and the input frame filling module is configured to fill the current value of the form item to be filled into the input frame of the form item to be filled.

In a third aspect, an embodiment of the present invention provides an electronic device, including:

One or more processors;

Storage means for storing one or more programs,

The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods of any of the embodiments described above.

In a fourth aspect, embodiments of the present invention provide a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the above embodiments.

One embodiment of the above invention has the following advantages or benefits: and determining the current value of the form to be filled according to the current value of the target form and the prediction model of the form to be filled, and filling the current value of the form to be filled into an input box of the form to be filled. The input boxes of the form items to be filled are automatically filled with the predicted current values of the form items to be filled, so that manual form filling operation of a user can be reduced, and form filling time of the user can be saved.

Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

FIG. 1 is a schematic illustration of a flow of a form filling method provided by an embodiment of the present invention;

FIG. 2 is a flow chart of a method for determining a target form item according to one embodiment of the present invention;

FIG. 3 is a schematic illustration of the flow of another form filling method provided by an embodiment of the present invention;

FIG. 4 is a schematic diagram of a framework of an intelligent form-filling system according to one embodiment of the present invention;

FIG. 5 is a schematic illustration of the flow of yet another form filling method provided by an embodiment of the present invention;

FIG. 6 is a schematic diagram of an offline training process provided by an embodiment of the present invention;

FIG. 7 is a schematic diagram of a feature engineering system of the present invention;

FIG. 8 is a schematic diagram of a framework of yet another intelligent form-filling system provided in accordance with one embodiment of the present invention;

FIG. 9 is a schematic diagram of a form filling apparatus according to one embodiment of the present invention;

FIG. 10 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;

Fig. 11 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The embodiment of the invention provides a form filling method, as shown in fig. 1, comprising the following steps:

step 101: and determining the form item to be filled in of the current form.

The current form is the form that the user needs to fill in. The current form may include a number of form to be filled in. The input box of the form item to be filled in may include: text boxes, check boxes, radio boxes, drop down boxes, and the like. The form to be filled in the current form can be predicted and filled in by the method of the embodiment of the invention.

Step 102: acquiring the current value of a target form item; wherein the target form item is associated with the form item to be filled.

The target form item is a form item associated with the to-be-filled. And predicting the value of the form item to be filled according to the value of the target form item. The target form item may be one or a plurality of target form items. The target form may be derived from the same form as the form to be filled in, or may be derived from a different form than the form to be filled in.

For example, the current form is a shopping wish list of the user for storing items that the user likes and wants to purchase. The wish text box is a form item to be filled in a shopping wish list, and a user can fill a plurality of articles liked by the user into the wish text box. The user identification can be used as a keyword, a plurality of form items corresponding to the behavior information of the user are associated from a browse item information table of the user, an order information table of the user and a collection item information table of the user, and the form items corresponding to the behavior information are used as target form items.

Step 103: and determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled.

The predictive model is a model that takes as input the current value of the target form and as output the predicted value of the form to be filled. If a plurality of form items to be filled exist in the current form, a plurality of prediction models can be set for each form item to be filled, and a multi-target joint model can be set for the plurality of form items to be filled. The predictive model may include: regression prediction models, temporal prediction models, and the like.

Step 104: and filling the current value of the form item to be filled into an input box of the form item to be filled.

According to the method, the current value of the form to be filled is determined according to the current value of the target form and the prediction model of the form to be filled, and the current value of the form to be filled is filled into an input box of the form to be filled. The embodiment of the application can automatically fill the input box of the form to be filled, and a user does not need to fill the form to be filled manually. Therefore, the method of the embodiment of the application can solve the problems of complicated user form filling operation and time waste.

In one embodiment of the invention, determining the to-be-filled form item of the current form includes:

And determining whether a filling switch corresponding to the form item of the current form is turned on, and if so, determining the form item as the form item to be filled.

When the filling switch is turned on, the system calls the method of the embodiment of the invention to automatically fill the form to be filled of the current form. When the filling switch is closed, the system will not call the method of the embodiment of the invention to fill the form item to be filled of the current form, but the user fills the form item by himself.

Acquiring an actual value filled by a user aiming at the form item to be filled; and if the deviation between the actual value of the form to be filled and the current value of the form to be filled exceeds a first deviation threshold, setting a filling switch corresponding to the form of the current form to be in a closed state.

The first deviation threshold may be set according to specific needs. If the deviation between the actual value filled in by the user and the current value predicted by the system is large, the prediction result of the current prediction model may not be accurate enough, and the prediction model needs to be further iterated to enhance the accuracy of the prediction result of the system. At this time, the filling switch of the form to be filled can be set to be in a closed state, so as to avoid the adverse effect on the subsequent user form filling process due to inaccurate prediction results.

Acquiring an actual value filled by a user aiming at the form item to be filled; if the deviation between the actual value of the form to be filled and the current value of the form to be filled exceeds a second deviation threshold, form data are stored; wherein, form data includes: the current value of the target form item and the actual value of the form item to be filled; the form data is used for training the prediction model.

The second deviation threshold may be set according to specific needs. If the deviation between the actual value filled in by the user and the current value predicted by the system is large, the prediction result of the current prediction model may not be accurate enough, and the prediction model needs to be further iterated. Form data with larger deviation is recorded and stored and used for subsequent prediction model training, so that the prediction result of the trained prediction model can be more accurate.

In one embodiment of the invention, the method further comprises:

acquiring historical values of a plurality of form items; wherein the plurality of form items originate from a plurality of history forms;

integrating the historical values of a plurality of form items into a feature wide table;

according to a pre-stored feature conversion rule, carrying out format conversion on the data in the feature wide table;

Training the prediction model according to a pre-designated form to be filled and a converted feature wide form to obtain a training result; the training result comprises the following steps: and training a good prediction model.

The embodiment of the invention provides a training method of a prediction model. First, each correlation table is integrated into a feature-wide table. Because the feature wide table comprises different data from a plurality of tables, model training is carried out by utilizing the feature wide table, and the efficiency of iterative computation in the process of predictive model training can be improved.

And secondly, performing feature conversion on each field in the feature wide table so that the converted field can be identified by the prediction model. For example, if the predictive model can only identify input parameters in digital form, the fields in the feature broad table can be converted as follows:

for discrete fields: the original values are coded into 1,2 and 3 values in turn, and the original values are in one-to-one correspondence with the coded values. The encoded value may be a unique identification of the original value. Examples: sex characteristic possible values: men and women can be coded 1 and women can be coded 2.

For the text field: firstly, word segmentation is carried out, word vectors corresponding to each word are obtained respectively, and finally, vector average values of all the segmented words are calculated and used as characteristic values of the text field. Examples: "i want to return", after the words are segmented, i = { "i", "want", "return" }, "i" corresponds to word vector [0.5,0.3,.+ -. O ], "want" corresponds to [0.2,0.3,.], and "return" corresponds to [0.2,0.9,.], and the vector of all the segmented words is calculated and averaged, and the feature value of the text field is [0.3,0.5,.].

And finally, training the prediction model according to the converted feature wide table to obtain a trained prediction model.

In addition, the feature conversion rule can be stored in the feature outline instead of hard-coding the conversion rule in a code, the feature conversion rule can be directly obtained through the feature outline in the later iteration of the model and the processing of the original data in the online service process, and the feature outline can play a role in decoupling the online service engineering and the model service.

In one embodiment of the present invention, determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled includes:

determining a link address of the predictive model in online service;

and taking the current value of the converted target form item as an input parameter, requesting a prediction service corresponding to the link address, and obtaining the current value of the form item to be filled.

And requesting the prediction service based on the prediction model by using the link address of the prediction model in the online service by taking the current value of the converted target form item as an input parameter. The embodiment of the invention provides a method for filling forms by using a prediction model conveniently and effectively.

In addition, the feature conversion rule and the link address of the prediction model in the on-line service can be stored in the model outline without writing in codes, so that the function of decoupling each module can be achieved, and the construction and later maintenance of the whole system are facilitated.

The embodiment of the invention provides a method for determining target form items, which is shown in fig. 2 and comprises the following steps:

Step 201: acquiring historical values of a plurality of form items; wherein the plurality of forms originate from a plurality of history forms.

Step 202: the historical values of several form items are integrated into a feature broad table.

Step 203: and carrying out format conversion on the data in the feature wide table according to a pre-stored feature conversion rule.

Step 204: training the prediction model according to a pre-designated form to be filled and a converted feature wide form to obtain a training result; the training result comprises the following steps: the influence values of the form items to be filled of a plurality of other form items; wherein the impact value is used for representing the impact degree of other form items to the form item to be filled.

Step 205: from the impact value, a target form item associated with the form item to be populated is determined among a number of other form items.

The target form item associated with the form item to be populated may be determined from the impact value in a variety of ways. An influence threshold may be set, and the form items corresponding to the influence values higher than the influence threshold are determined as target form items. The number of target list items can also be set, such as 5, 8, etc. And ordering the other list items according to the influence value, and selecting a plurality of other list items of the front target list item from the ordered list items to serve as target list items.

According to the embodiment of the invention, according to the training result, the influence value of the form item to be filled is obtained, and according to the influence value, other form items with larger influence on the form item to be filled are selected as target form items. In the follow-up prediction process, only the values of the target form items are input into the prediction model, and the values of all form items in the feature wide form are not required to be acquired. Therefore, the method of the embodiment of the invention can more accurately predict the form items to be filled and accelerate the prediction process of the prediction model.

FIG. 3 is a flow chart of another form filling method provided by another embodiment of the present invention. As shown in fig. 3, the method includes:

step 301: determining a form item to be filled of a current form; the input box of the form item to be filled is a drop-down box.

Step 302: acquiring the current value of a target form item; wherein the target form item is associated with the form item to be filled.

Step 303: and generating a plurality of predicted values and probabilities of the form to be filled according to the current value of the target form and the predicted model of the form to be filled.

Step 304: and determining a predicted value corresponding to the maximum probability as the current value of the form to be filled.

Step 305: and filling the current value of the form item to be filled into an input box of the form item to be filled.

According to the method, when the input frame of the form item to be filled is the drop-down frame, the predicted value with the highest probability is used as the current value of the form to be filled, and the predicted value is filled into the drop-down frame to enable the predicted value filled into the drop-down frame to have higher accuracy.

In one embodiment of the invention, the method further comprises:

for each predicted value: determining the position of the predicted value in a pull-down list of the pull-down frame according to the probability of the predicted value;

the drop-down box is shown according to a plurality of predictors and their positions in the drop-down list.

The greater the probability of a predicted value, the greater the probability that the user will select that predicted value as the input value to the drop-down box. Therefore, the predicted value with a larger probability can be arranged at a front position in the drop-down frame to facilitate the selection of the user.

The scheme of the embodiment of the invention aims to provide a learning mechanism which learns filling situations of past forms by using a classification algorithm in machine learning and fills the form to be filled. The explanation is made below taking the form to be filled as an example of the following boxes.

In the method of the embodiment of the invention, the extraction history may influence the target form item filled in by the form item to be filled, and the target form item may include: form types, form completors, form entries, etc. The form types may include: problem consultation, approval of documents, problem documents and the like; the form entry may include: telephone customer service work stations, after-sales work stations, spare parts library work stations, and the like. If the form to be filled has a relationship with the order, then the order related form, the order person related form, etc. may also be taken. And carrying out characterization processing on the related form items, and predicting the optimal default value of a certain drop-down frame. The scheme of the embodiment of the invention can improve the intelligent filling proportion, reduce the cost, improve the form filling efficiency, reduce the manual configuration and also can provide continuous automatic learning capability.

FIG. 4 is a schematic diagram of a framework structure of an intelligent form filling system according to an embodiment of the present invention. As shown in fig. 4, the intelligent form filling system includes: model calculation layer, data service layer and system application layer.

Model calculation layer: concentrating on offline learning, and counting characteristic values according to analysis; collecting offline data, cleaning the data, and establishing a characteristic project; model training is continuously carried out by using a classification model; and outputting a final model result.

Data service layer: focusing on real-time application, and acquiring real-time data of various characteristic values required by the model; processing real-time data and storing management; the calling algorithm API (application program interface, application Programming Interface) receives the model results.

System application layer: a call switch is configured in the system. When the call switch is turned on, filling forms by using the intelligent form filling system; calling the model result in an interface form; when the monitoring finds that the prediction deviation is large, service degradation processing can be performed at any time, so that the application stability and the usability of the system are realized. Service degradation processing is used to stop the method of applying the form filling system, and the traditional list scheme is started.

The system abstracts out three layers with single responsibilities, comprising: offline learning, real-time application and business rule management and control, and a complete system without unmanned form filling is realized. Fig. 5 is a schematic diagram of a flow of yet another form filling method according to an embodiment of the present invention, and as shown in fig. 5, a specific technical solution includes a main flow and a big data model calculation flow. The main flow steps are as follows:

s1: creating a form application: and acquiring the form to be filled by the user and the form to be filled.

S2: and determining whether a filling switch corresponding to the form item of the current form is turned on, and if so, determining the form item as the form item to be filled.

And according to a preset switch, confirming whether each form to be filled needs to call the form filling method of the embodiment of the invention.

S3: and determining the form item to be filled in of the current form.

S4: acquiring the current value of a target form item; wherein the target form item is associated with the form item to be filled.

S5: and determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled.

This step determines the current value of the form item to be filled through model prediction. The step of model prediction is the core of embodiments of the present invention. Model prediction involves offline training, and online service.

FIG. 6 is a schematic diagram of an offline training process according to an embodiment of the present invention. As shown in fig. 6, the offline training stream includes: model calculation includes offline training and online service.

S51: offline data processing: and collecting relevant data tables on a big data mart, performing off-line analysis, integrating required features into a feature wide table, and providing the feature wide table for later links for use, wherein the off-line data processing tasks are updated at regular time.

S52: characteristic engineering: cleaning relevant characteristics such as form types, form filling persons and form inlets, if the relevant characteristics are related to the orders, the relevant characteristics of the orders can be also taken, and the relevant characteristics of the orders such as forms, normalization, missing values and the like are processed; in addition, feature transformation rules are written to the feature outline cache.

Fig. 7 is a schematic structural diagram of a feature engineering system of the present invention. As shown in fig. 7, the discrete features and the text features are converted separately to generate a digital code that can be recognized by the predictive model.

S53: model training: the algorithm is to complete the classification task of the default value of the drop-down frame, and train a multi-classification model according to the drop-down options. If a plurality of drop-down boxes exist, a plurality of multi-classification models or multi-target joint models are respectively generated, and a model file is produced.

S54: model iteration: the model iteration task can be run at regular time, and can also be run based on the command of a user or a system. The model iteration process is continuously and iteratively updated through the model, and the model effect is improved.

The online service deployment involves the following 4 steps:

S55: model deployment: and deploying the model files generated by offline training into online service through engineering packaging, and writing information such as model service url (uniform resource locator ), model version and the like into a model outline cache.

S56: and (3) real-time data acquisition: and calling a multiparty interface or a data storage to obtain all data required by the model.

S57: feature conversion: the original features of the real-time request are converted into the format required by the model through the conversion rules specified by the feature outline.

S58: model prediction: and analyzing the configuration and parameters generated by the model training result, and requesting the model service url specified by the feature outline according to the prediction function and the real-time conversion feature, and requesting the prediction service to obtain a predicted value.

S6: and acquiring input information confirmed by the user. The general model does not provide a completely correct filling and the user needs to confirm whether the underfill is correct or not, and if there is an error, the filling can be manually refilled.

S7: and filling the current value of the form item to be filled into an input box of the form item to be filled. Creating and submitting a complete form to be filled in.

S8: model iteration: for the data with errors, the system records and automatically feeds back to the model training link at regular time, the model iterates and corrects, and the model effect is improved.

FIG. 8 is a schematic diagram of a framework of yet another intelligent form-filling system provided in accordance with an embodiment of the present invention. As shown in fig. 8, the system stores url, model version number, and feature conversion rule dictionary etc. information of the model service applied in the process of performing feature engineering into a feature schema. The online service performs feature conversion on the original features according to feature conversion rules in the feature outline, obtains url of the model service through the feature outline, calls the prediction service, and fills the field to be filled.

The prediction function is a prediction method corresponding to the prediction model. Taking the converted characteristics as the parameters of the prediction function, and calling the prediction class of the prediction function. The embodiment of the invention can adopt xdeepFM models.

The feature outline is a set of model-specific metadata, and the feature outline can be generated by feature engineering and model deployment links and stored in a cache server. The feature conversion module converts original features according to the feature outline instead of hard-coding conversion rules, and directly updates metadata to the feature outline cache after late iteration, addition, deletion and feature change of the model, so that modification and restarting of the feature conversion module are avoided. The outline plays a role in decoupling the online service engineering from the model service.

As shown in fig. 9, an embodiment of the present invention provides a form filling apparatus, including:

The form item determining module 901 is configured to determine a form item to be filled of a current form;

a current value obtaining module 902 configured to obtain a current value of the target form item; wherein the target form item is associated with the form item to be filled;

the prediction module 903 is configured to determine the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled;

the input box filling module 904 is configured to fill the current value of the form to be filled into the input box of the form to be filled.

In one embodiment of the present invention, the form item determination module 901 is specifically configured to:

In one embodiment of the invention, the prediction module 903 is configured to obtain historical values for several form items; wherein the plurality of form items originate from a plurality of history forms;

In one embodiment of the present invention, the training result further includes: the influence values of the form items to be filled of a plurality of other form items; the influence value is used for representing the influence degree of other form items to the form item to be filled;

Further comprises: a prediction module 903 configured to determine a target form item associated with the form item to be filled in among several other form items based on the impact value.

In one embodiment of the invention, the prediction module 903 is specifically configured to:

determining a link address of the predictive model in online service;

In one embodiment of the invention, the input box of the form item to be filled is a drop down box;

the prediction module 903 is specifically configured to: generating a plurality of predicted values and probabilities of the form items to be filled according to the current value of the target form item and the predicted model of the form items to be filled;

In one embodiment of the invention, the prediction module 903 is configured to, for each predicted value: determining the position of the predicted value in a pull-down list of the pull-down frame according to the probability of the predicted value;

The embodiment of the invention provides electronic equipment, which comprises:

One or more processors;

Storage means for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the methods of any of the embodiments described above.

Fig. 10 illustrates an exemplary system architecture 1000 to which the abnormal behavior recognition method or apparatus of the embodiment of the present invention may be applied.

As shown in fig. 10, a system architecture 1000 may include terminal devices 1001, 1002, 1003, a network 1004, and a server 1005. The network 1004 serves as a medium for providing a communication link between the terminal apparatuses 1001, 1002, 1003 and the server 1005. The network 1004 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

A user can interact with a server 1005 via a network 1004 using terminal apparatuses 1001, 1002, 1003 to receive or transmit messages or the like. Various communication client applications such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, office applications, etc. (as examples only) may be installed on the terminal devices 1001, 1002, 1003.

The terminal devices 1001, 1002, 1003 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 1005 may be a server providing various services, such as a background management server (merely an example) providing support for shopping-type websites or company-department internal management systems browsed by the user using the terminal apparatuses 1001, 1002, 1003. The background management server can acquire a form which needs to be filled by a user; determining the current value of the form to be filled according to the current value of the target form and the trained prediction model of the form to be filled; and filling the current value of the form to be filled into an input frame of the form to be filled, and feeding back the filled form to the terminal equipment.

It should be noted that, the method for processing the notification trigger message provided in the embodiment of the present invention is generally executed by the server 1005, and accordingly, the form filling device of the article is generally disposed in the server 1005.

It should be understood that the number of terminal devices, networks and servers in fig. 10 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 11, there is illustrated a schematic diagram of a computer system 1100 suitable for use in implementing the terminal device of an embodiment of the present invention. The terminal device shown in fig. 11 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.

As shown in fig. 11, the computer system 1100 includes a Central Processing Unit (CPU) 1101, which can execute various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1102 or a program loaded from a storage section 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the system 1100 are also stored. The CPU 1101, ROM 1102, and RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.

The following components are connected to the I/O interface 1105: an input section 1109 including a keyboard, a mouse, and the like; an output portion 1107 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 1108 including a hard disk or the like; and a communication section 1109 including a network interface card such as a LAN card, a modem, and the like. The communication section 1109 performs communication processing via a network such as the internet. The drive 1110 is also connected to the I/O interface 1105 as needed. Removable media 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed as needed in drive 1110, so that a computer program read therefrom is installed as needed in storage section 1108.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1109, and/or installed from the removable media 1111. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 1101.

The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes a sending module, an obtaining module, a determining module, and a first processing module. The names of these modules do not in some cases limit the module itself, and for example, the transmitting module may also be described as "a module that transmits a picture acquisition request to a connected server".

As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include:

Determining a form item to be filled of a current form;

According to the technical scheme of the embodiment of the application, the current value of the form to be filled is determined according to the current value of the target form and the prediction model of the form to be filled, and the current value of the form to be filled is filled into the input frame of the form to be filled. According to the embodiment of the application, the input boxes of the form items to be filled are automatically filled with the predicted current values of the form items to be filled, so that the input operation of the form filling of a user can be reduced, and the form filling time of the user can be saved.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A form filling method, comprising:

Determining a form item to be filled of a current form;

filling the current value of the form item to be filled into an input box of the form item to be filled;

the form filling method further comprises the following steps:

And determining target form items associated with the form items to be filled in the form items according to influence values of a plurality of other form items included in training results corresponding to the prediction model, wherein the influence values are used for representing influence degrees of the other form items on the form items to be filled.

2. The method of claim 1, wherein the step of determining the position of the substrate comprises,

3. The method as recited in claim 1, further comprising:

4. The method of claim 1, wherein the step of determining the position of the substrate comprises,

Determining a link address of the predictive model in online service;

5. The method of claim 1, wherein the step of determining the position of the substrate comprises,

The input frame of the form item to be filled is a drop-down frame;

6. The method as recited in claim 5, further comprising:

7. A form filling apparatus, comprising:

the input frame filling module is configured to fill the current value of the form item to be filled into the input frame of the form item to be filled;

the prediction module is further configured to determine target form items associated with the form items to be filled in among the plurality of other form items according to influence values of the plurality of other form items included in training results corresponding to the prediction model on the form items to be filled, wherein the influence values are used for representing influence degrees of the other form items on the form items to be filled.

8. An electronic device, comprising:

One or more processors;

Storage means for storing one or more programs,

When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.

9. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.