CN118211579B

CN118211579B - Special processing method for dynamic rendering table based on header configuration attribute

Info

Publication number: CN118211579B
Application number: CN202410627776.XA
Authority: CN
Inventors: 周波; 夏先东; 余勇辉
Original assignee: Zhejiang Huifu Network Technology Co ltd
Current assignee: Zhejiang Huifu Network Technology Co ltd
Priority date: 2024-05-21
Filing date: 2024-05-21
Publication date: 2024-08-06
Anticipated expiration: 2044-05-21
Also published as: CN118211579A

Abstract

The embodiment of the application provides a special processing method of a dynamic rendering table based on a table header configuration attribute, which comprises the following steps: obtaining header data according to the header configuration data, obtaining table row data according to table data and preset table row data processing rules, carrying out model pre-training on a preset time processing model according to a preset time field type data set and preset time detection data, determining a pre-training time processing model, carrying out model pre-training on the preset text processing model according to a preset text field type data set, determining a pre-training text processing model, stacking a model output result of the pre-training time processing model and a model output result of the pre-training text processing model to obtain a pre-training special processing model, judging whether special processing identification in the header data attribute is true, if true, determining first cell data according to the pre-training special processing model, and effectively improving the speed and flexibility of dynamic table display data through the special data processing means.

Description

Special processing method for dynamic rendering table based on header configuration attribute

Technical Field

The application relates to the field of data processing, in particular to a special processing method for a dynamic rendering table based on a header configuration attribute.

Background

Dynamic forms are forms that can automatically adjust content and structure based on changes in data or user interactions, and are very common in Web development, especially in applications that need to present and manipulate lists of data. Dynamic forms enhance the user experience because they allow users to view, sort, filter, and edit data through interactions.

Dynamic form technology has made significant progress in the last few years, but still has some challenges and drawbacks. The current mainstream dynamic form data processing can realize functions of dynamic loading, sorting, filtering, editing and the like of data, but the data are mainly predefined according to business requirements by a business developer or a data analyst and are realized in a program, wherein the related processing steps are executed by manual code writing or configuration tools.

Along with the diversified development of management modes, more and more companies need to utilize dynamic forms to conduct data display and business management, and the current form configuration needs manual programming, but the manual processing mode needs long time, so that the requirement of quick display of the companies cannot be met more and more, and the dynamic form data processing technology of automatic data display is urgently needed to meet the requirements of quick display and flexible display of the dynamic forms.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a special processing method for a dynamic rendering table based on header configuration attributes, which can effectively improve the speed and flexibility of displaying data of the dynamic table by a special data processing means.

In order to solve at least one of the problems, the application provides the following technical scheme:

In a first aspect, the present application provides a method for special processing of a dynamic rendering table based on a header configuration attribute, including:

The method comprises the steps of obtaining header configuration data and table data, processing a header according to the header configuration data and a preset header configuration data processing rule to obtain header data, and processing a table according to the table data and a preset table data processing rule to obtain table data, wherein header data attributes of the header data comprise at least one of field codes, field names, table widths, special processing identifiers, special class name sets, button methods and field types;

Model pre-training is carried out on a preset time processing model according to a preset time field type data set and preset time detection data, a pre-training time processing model is determined, model pre-training is carried out on the preset text processing model according to a preset text field type data set, a pre-training text processing model is determined, data integration characteristics are obtained through stacking of model output results of the pre-training time processing model and model output results of the pre-training text processing model, the data integration characteristics are input into a preset special processing model, and a corresponding pre-training special processing model is obtained, wherein field types in the text field type data set are at least one of a multi-selection frame, a dictionary, a long text, a list and a slot;

judging whether the special processing identifier in the header data attribute is true, if true, determining first cell data according to the header data, the table row data, a preset table authority rule and the pre-training special processing model, and rendering a first cell display page of a target table according to the first cell data;

If yes, determining second cell data according to the header data, the table row data and the preset table authority rule, and rendering a second cell display page of the target dynamic table according to the second cell data.

Further, the model pre-training the pre-time processing model according to the pre-time field type data set and the pre-time detection data, and determining the pre-training time processing model includes:

Extracting time characteristics in a preset time field type data set, and carrying out model pre-training on a preset time processing model according to the time characteristics to determine a pre-training time characteristic model;

and updating the pre-training time characteristic model according to the pre-training time detection data and the pre-training time interval to determine a pre-training time processing model.

Further, the model pre-training the preset text processing model according to the preset text field type data set, before determining the pre-trained text processing model, includes:

Determining a corresponding preprocessed multi-box data set according to the multi-box data in the text field type data set and a preset multi-box variable conversion rule;

Determining a corresponding pretreatment dictionary structure data set according to dictionary data in the text field type data set and a preset dictionary feature extraction rule;

Determining a corresponding preprocessed long text data set according to the long text data in the text field type data set and a preset long text word segmentation abstract rule;

Determining a corresponding preprocessing list data set according to list data in the text field type data set and a preset list sequence processing rule;

determining a corresponding preprocessing slot data set according to the slot data in the text field type data set and a preset slot feature extraction rule;

And determining a preset text field type data set according to the preprocessing multi-option frame data set, the preprocessing dictionary structure data set, the preprocessing long text data set, the preprocessing list data set, the preprocessing slot data set and a preset data set integration rule.

Further, the model pre-training the preset text processing model according to the preset text field type data set, and determining the pre-trained text processing model includes:

Model pre-training is carried out on a preset text processing model according to a preset text field type data set, and a corresponding pre-training loss function is determined;

Determining a weighted joint loss function according to the pre-training loss function and the weight of the preset field type;

And updating parameters of the preset text processing model according to the optimal weighted joint loss function, and determining a pre-training text processing model.

Further, the determining the data integration feature according to the model output result of the pre-training time processing model and the model output result of the pre-training text processing model includes:

Constructing a stacked training set according to the pre-training time processing training set output by the pre-training time processing model and the pre-training text processing training set output by the pre-training text processing model;

Constructing a stacked test set according to the pre-training time processing test set output by the pre-training time processing model and the pre-training text processing test set output by the pre-training text processing model;

and determining corresponding data integration characteristics according to the stacking training set and the stacking test set.

Further, the determining a pre-training special processing model according to the data integration characteristics and a preset special processing model includes:

performing model training on a preset neural network special processing model according to the data integration characteristics, and determining corresponding model training loss;

and updating the model parameters of the neural network special processing model according to the optimal model training loss to determine a pre-training special processing model.

Further, before determining whether the special processing identifier in the header data attribute is true, the method includes:

Determining a corresponding data transmission parent component and a data transmission sub-component according to a preset data transmission rule and the pre-training special processing model;

and judging whether the special processing identifier is true according to the data transmission sub-component.

Further, the determining the corresponding data transmission parent component and the data transmission sub-component according to the preset data transmission rule and the pre-training special processing model includes:

Respectively determining a corresponding parent component transmission rule and a corresponding child component transmission rule according to a preset data transmission rule;

determining a corresponding data transmission parent component according to the parent component transmission rule and a preset parent component container;

and determining a corresponding data transmission sub-assembly according to the sub-assembly transmission rule, the pre-training special processing model and a preset sub-assembly container.

Further, if true, determining first cell data according to the header data, the table row data, a preset table authority rule, and the pre-trained special processing model, including:

if true, determining a table column data attribute according to the attribute of the table head data, and determining corresponding first cell attribute data according to the table column data attribute and the table row data;

And determining first cell data according to the pre-training special processing model, the first cell attribute data and a preset table authority rule.

Further, if the table is false, determining second cell data according to the header data, the table row data and the preset table authority rule, including:

if the table head data is false, determining a table column data attribute according to the attribute of the table head data, and determining corresponding second cell attribute data according to the table column data attribute and the table row data;

And determining second cell data according to the second cell attribute data and a preset table authority rule.

In a second aspect, the present application provides a table head configuration attribute-based dynamic rendering table special processing apparatus, including:

A table data determining module: the method comprises the steps of obtaining header configuration data and table data, processing a header according to the header configuration data and a preset header configuration data processing rule to obtain header data, and processing a table according to the table data and a preset table data processing rule to obtain table data, wherein header data attributes of the header data comprise at least one of field codes, field names, table widths, special processing identifiers, special class name sets, button methods and field types;

A special processing model determining module: the method comprises the steps of carrying out model pre-training on a preset time processing model according to a preset time field type data set and preset time detection data, determining a pre-training time processing model, carrying out model pre-training on the preset text processing model according to a preset text field type data set, determining a pre-training text processing model, stacking a model output result of the pre-training time processing model and a model output result of the pre-training text processing model to obtain a data integration characteristic, inputting the data integration characteristic into a preset special processing model to obtain a corresponding pre-training special processing model, wherein the field type in the text field type data set is at least one of a multi-selection frame, a dictionary, a long text, a list and a slot;

first page display module: the method comprises the steps of judging whether a special processing identifier in the header data attribute is true, if true, determining first cell data according to the header data, the table row data, a preset table authority rule and the pre-training special processing model, and rendering a first cell display page of a target table according to the first cell data;

and a second page display module: and if the table head data is false, determining second cell data according to the table head data, the table row data and the preset table authority rule, and rendering a second cell display page of the target dynamic table according to the second cell data.

In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the table header configuration attribute-based dynamic rendering table special processing method when the program is executed by the processor.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the table header configuration attribute based dynamic rendering table special handling method.

In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the table header configuration attribute based dynamic rendering table special handling method.

According to the technical scheme, the table head data is obtained according to the table head configuration data, the table row data is obtained according to the table data and the preset table row data processing rule, the model pre-training is carried out on the preset time processing model according to the preset time field type data set and the preset time detection data, the pre-training time processing model is determined, the model pre-training is carried out on the preset text processing model according to the preset text field type data set, the pre-training text processing model is determined, the pre-training special processing model is obtained according to the model output result of the pre-training time processing model and the model output result of the pre-training text processing model in a stacked mode, whether the special processing identifier in the table head data attribute is true is judged, if true, the first cell data is determined according to the pre-training special processing model, and therefore the speed and the flexibility of the dynamic table display data can be effectively improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for dynamically rendering a table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 2 is a second flow chart of a method for dynamically rendering a table based on header configuration attributes according to an embodiment of the present application;

FIG. 3 is a third flow chart of a method for dynamically rendering a table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 4 is a flowchart of a method for dynamically rendering a table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 5 is a flowchart of a method for handling table-header configuration attribute-based dynamic rendering table special processing according to an embodiment of the present application;

FIG. 6 is a flowchart illustrating a method for dynamically rendering a table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 7 is a flow chart of a method for dynamically rendering table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 8 is a flowchart illustrating a method for dynamically rendering a table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 9 is a flowchart illustrating a method for dynamically rendering table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 10 is a flowchart illustrating a method for dynamically rendering a table special processing based on header configuration attributes according to an embodiment of the present application;

FIG. 11 is a block diagram of a table header configuration attribute-based dynamic rendering table special processing apparatus in an embodiment of the present application;

Fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.

In consideration of the problems that in the prior art, along with the diversified development of management modes, more and more companies need to utilize dynamic tables to conduct data display and business management, current table configuration needs to be programmed manually, but a manual processing mode needs to be long, the application provides a special processing method for a dynamic rendering table based on table head configuration attributes.

In order to effectively improve the speed and flexibility of displaying data by a dynamic table, the application provides an embodiment of a table head configuration attribute-based special processing method for a dynamic rendering table, referring to fig. 1, the table head configuration attribute-based special processing method for a dynamic rendering table specifically comprises the following contents:

Step S101: the method comprises the steps of obtaining header configuration data and table data, processing a header according to the header configuration data and a preset header configuration data processing rule to obtain header data, and processing a table according to the table data and a preset table data processing rule to obtain table data, wherein header data attributes of the header data comprise at least one of field codes, field names, table widths, special processing identifiers, special class name sets, button methods and field types;

optionally, in this embodiment, the header configuration data is a lightweight data exchange format object, which may have a plurality of attributes, including but not limited to: field code, field name, table width, field type, special handling identification, special class name set, and button method.

Specifically, the header data attributes function as follows:

1. field code: attribute names in the corresponding table data are used for taking values and displaying the values in the table;

2. Field name: a header name displayed in the table;

3. table width: if the table column has the required corresponding width, setting according to the table width value, and if the value is not set, using the self-adaptive width in page rendering;

4. And (3) special treatment identification: the method is used for judging whether special processing marks exist or not, 1 is the existence, and other representations are not the existence;

5. a special class name set: if the table list has the special style, the corresponding style is matched according to the class name;

6. the button method comprises the following steps: the method for triggering the clicking unit cells, for example, the unit cells with requirements display the number of orders, and the clicking of the number of orders needs to display the details of the orders, so that the method can be realized by configuring the corresponding method for the header;

7. field type: data representing what type the column is, supported types are date and time, multiple boxes, dictionaries, long text thumbnail displays, long text line feed displays, lists, slots and other types, and other types are directly displayed by field code values.

Optionally, in this embodiment, the table data is a lightweight data exchange format object, which is to be displayed, and the displayed content of each column is fetched from the table data according to the field code in the header element.

It can be appreciated that by acquiring header configuration data and table data, the table column and table row attributes of the dynamic table can be obtained, which lays a foundation for subsequent data analysis and processing and rendering of the dynamic table.

Step S102: model pre-training is carried out on a preset time processing model according to a preset time field type data set and preset time detection data, a pre-training time processing model is determined, model pre-training is carried out on the preset text processing model according to a preset text field type data set, a pre-training text processing model is determined, data integration characteristics are obtained through stacking of model output results of the pre-training time processing model and model output results of the pre-training text processing model, the data integration characteristics are input into a preset special processing model, and a corresponding pre-training special processing model is obtained, wherein field types in the text field type data set are at least one of a multi-selection frame, a dictionary, a long text, a list and a slot;

Optionally, in this embodiment, the preset time field type data set is a set of data including a time feature, where the time feature is used to represent a variable of time information and a mode, and the time feature type includes, but is not limited to:

1. Timestamp:

indicating the specific point in time at which the event occurred, is typically recorded in terms of date and time.

2. Time interval:

The difference between the two time points may be minutes, hours, days, weeks, months or years.

3. Date component:

years, months, days of week, quarters, etc. extracted from the date.

4. Periodic features:

such as hours (weekday and weekend), weeks (weekweek and weekend), months (financial period), seasons, and the like.

5. Accumulated time:

the total time from a certain start point to a current time point, for example, the total number of months for which the client becomes a member.

6. Time lapse:

Time elapsed since a particular event (e.g., last purchase, last login).

7. Relative time:

Relative to another point in time, e.g. "7 days after occurrence of event X".

8. Holidays and special dates:

Holidays, anniversaries, seasonal events, etc. may have an effect on the time series.

9. And (3) time window statistics:

the number or sum of events occurring within a particular time window (e.g., last 24 hours, last 7 days).

10. Statistical features of the time series:

Such as average, median, standard deviation, minimum, maximum, etc.

It will be appreciated that these temporal features serve as input variables, helping the model capture the intrinsic rules and patterns of the temporal data, and that proper extraction and use of these features is critical to improving the predictive accuracy and interpretation ability of the model.

Optionally, in this embodiment, the preset time detection data is automatic flow control data, which can update the model periodically or trigger retraining of the model under specific conditions.

It will be appreciated that the preset time detection data relates to a trigger condition, wherein the trigger condition includes, but is not limited to:

1. Performance degradation: retraining is triggered when model performance falls below a certain threshold.

2. Data drift: retraining is triggered when a significant change in data distribution is detected.

3. Time interval: and setting a fixed time interval to update the model.

Optionally, in this embodiment, the preset time processing model is a model suitable for time series prediction, such as at least one of ARIMA, seasonal decomposed time series prediction (STL), long-short-term memory network (LSTM), gate-loop unit (GRU), and transducer model.

It can be understood that the pre-training time processing model obtained by pre-training the pre-training time processing model through the pre-training time field type data set and the pre-training time detection data can effectively predict the time sequence and quickly obtain the needed time data.

Optionally, in this embodiment, the preset text field type data set is a set of data including text features, where the text features are used to extract attributes or information capable of representing text content and structure from the original text, and the types of the text features include, but are not limited to:

1. Word frequency:

The number of times a word appears in a document.

2. Document frequency:

One word appears in how many documents.

3. Inverse document frequency:

for measuring the importance of a word to one of a collection of documents.

4. Word embedding:

words are converted into dense vectors to capture semantic relationships between words, such as Word2Vec and GloVe.

5. Part of speech tagging:

The part of speech (noun, verb, adjective, etc.) of each word in the text.

6. Text length:

Word count of a document or sentence.

7. Text abstract:

A brief summary of the content of the document.

8. Text clustering information:

Grouping information of text in cluster analysis.

9. Language model probability:

the probability of a sentence occurring in the language model.

10. The readability score of the text:

The ranking measures the reading difficulty of the text, such as Flesch-Kincaid.

It will be appreciated that in model learning and natural language processing tasks, one or more combinations of the above features may be used depending on the specific requirements of the task, and that the performance and accuracy of the model may be significantly improved by carefully designed features.

Optionally, in this embodiment, the preset text processing model is a model suitable for performing a text task, such as at least one of a statistical model, a convolutional neural network model, an attention mechanism model, a topic model, an end-to-end model, a multi-modal model, a long text processing model, and a text embedding model.

It can be understood that the pre-trained text processing model obtained after the pre-training of the pre-set text field type data set to the pre-set text processing model can effectively identify and process the text, and the required text data can be obtained quickly.

Optionally, in this embodiment, according to the model output result of the pre-training time processing model and the model output result of the pre-training text processing model, stacking to obtain a data integration feature, and inputting the data integration feature into a preset special processing model to obtain a corresponding pre-training special processing model.

Specifically, model outputs of the pre-training time processing model and the pre-training text processing model are stacked to form model input features of a preset special processing model for special processing model training, wherein the preset special processing model comprises, but is not limited to:

1. linear model:

Such as logistic regression or linear regression, is used when the data set is small or the control model is complex.

2. Decision tree:

Decision making is performed through a tree structure.

3. Support vector machine:

is a powerful classification algorithm, whose kernel function can be adjusted to accommodate different data characteristics.

4. Neural network:

including single layer perceptrons or more complex depth networks, which are capable of capturing complex nonlinear relationships, are suitable for large-scale data sets and complex problems.

5. Bayesian model:

such as naive bayes or gaussian processes, which are useful in probabilistic reasoning and decision making.

6. Gradient lifting tree:

Such as XGBoost and LightGBM, these models perform well in many machine learning contests.

7. The integration method comprises the following steps:

Other integration methods are used as meta learner, such as random forest and AdaBoost, to form nested integrated models.

It can be appreciated that by combining the pre-training time processing model and the pre-training text processing model, a new feature set is obtained that combines the advantages of both models, which helps to improve the accuracy and generalization ability of the subsequently trained special processing model.

Step S103: judging whether the special processing identifier in the header data attribute is true, if true, determining first cell data according to the header data, the table row data, a preset table authority rule and the pre-training special processing model, and rendering a first cell display page of a target table according to the first cell data;

Optionally, in this embodiment, the first cell data is determined according to the header data, the table row data, a preset table authority rule, and the pre-training special processing model.

Specifically, in this embodiment, the preset table authority rule is a table content authority control rule, and the preset authority control rule may be implemented by an authority control rule in the prior art, for example, if a and B have different authorities, then a and B can only modify and edit a table within respective authority ranges, and only see table data adapted to both authorities.

Specifically, in this embodiment, corresponding first cell data may be determined according to header data and table row data, at this time, it is determined that the cell data needs special processing, the table authority is determined before the special processing is performed, if the table authority is determined, adaptive processing is performed on the cell data according to a pre-trained special processing model, the first cell data is determined, and a table display page is rendered.

Step S104: if yes, determining second cell data according to the header data, the table row data and the preset table authority rule, and rendering a second cell display page of the target dynamic table according to the second cell data.

Optionally, in this embodiment, the second cell data is determined according to the header data, the table row data, and the preset table authority rule.

Specifically, in this embodiment, a corresponding second cell data may be determined according to the header data and the table row data, and at this time, the cell data is judged without special processing, the table authority is directly judged, and if the table authority is authorized, the table display page is directly rendered according to the second cell data.

It can be appreciated that the form entitlement rules increase flexibility in form presentation, and through the above operations, the target dynamic form can be quickly and flexibly processed and presented with related data without requiring manual programming.

Step S101 to step S104 show how to integrate a data special processing model in the embodiment, and adaptively process cell data of the dynamic table according to the obtained special processing model, and adaptively display cell data of the dynamic table according to a preset table authority rule, so as to effectively improve the speed and flexibility of displaying data of the dynamic table.

As can be seen from the above description, according to the table head configuration attribute-based dynamic rendering table special processing method provided by the embodiment of the present application, table head data can be obtained according to the table head configuration data, table row data can be obtained according to table data and preset table row data processing rules, model pre-training is performed on a preset time processing model according to a preset time field type data set and preset time detection data, a pre-training time processing model is determined, model pre-training is performed on the preset text processing model according to a preset text field type data set, a pre-training text processing model is determined, a pre-training special processing model is obtained according to a model output result of the pre-training time processing model and a model output result of the pre-training text processing model in a stacked manner, whether a special processing identifier in the table head data attribute is true is judged, and if true, first cell data is determined according to the pre-training special processing model, thereby effectively improving the speed and flexibility of dynamic table display data.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 2, the following may be further specifically included:

Step S201: extracting time characteristics in a preset time field type data set, and carrying out model pre-training on a preset time processing model according to the time characteristics to determine a pre-training time characteristic model;

Step S202: and updating the pre-training time characteristic model according to the pre-training time detection data and the pre-training time interval to determine a pre-training time processing model.

Optionally, in this embodiment, the preset time interval may be 12 a.m. each day, for example, there is a dynamic table in which time data is recorded, the time data is a date, and 12 a.m. each day is output according to the time detection data and the update model of the date of the day, so that the time data in the table can be dynamically and automatically updated. Of course, in other embodiments of the present invention, the preset time interval may be set to two early morning points, three early morning points, or a period of time for updating data, which does not affect the implementation of the embodiments of the present invention.

It will be appreciated that the distribution of data may change over time, and in other embodiments of the present invention, the time detection data may also detect a change in the distribution of data, and update the model when a significant change in the distribution of data is detected, thereby ensuring that the model captures the latest trend of data and avoiding degradation in performance due to data overuse.

Through step S201, the present embodiment obtains a time feature model, which can identify time series data features; through step S202, the embodiment obtains a time processing model capable of performing time detection and time sequence processing based on the recognition of time sequence features, and through the time processing model, automatic data update can be performed on time data related to a target dynamic table without manually writing codes, thereby saving time.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 3, the following may be further specifically included:

Step S301: determining a corresponding preprocessed multi-box data set according to the multi-box data in the text field type data set and a preset multi-box variable conversion rule;

step S302: determining a corresponding pretreatment dictionary structure data set according to dictionary data in the text field type data set and a preset dictionary feature extraction rule;

Step S303: determining a corresponding preprocessed long text data set according to the long text data in the text field type data set and a preset long text word segmentation abstract rule;

Step S304: determining a corresponding preprocessing list data set according to list data in the text field type data set and a preset list sequence processing rule;

Step S305: determining a corresponding preprocessing slot data set according to the slot data in the text field type data set and a preset slot feature extraction rule;

step S306: and determining a preset text field type data set according to the preprocessing multi-option frame data set, the preprocessing dictionary structure data set, the preprocessing long text data set, the preprocessing list data set, the preprocessing slot data set and a preset data set integration rule.

Optionally, in this embodiment, before training the text processing model, the features of the text training set need to be uniformly converted into a format that can be identified by the model, where the feature conversion rule is as follows:

Presetting a multi-selection variable conversion rule: multiple boxes are typically used to collect classification data, which can be converted into binary variables (One for each option) or One-Hot Encoding (One-Hot Encoding) for model training.

Presetting dictionary feature extraction rules: the dictionary data structure may contain key-value pairs, where keys are typically strings of characters, and values may be of various types, which may be converted into a simpler structure, such as a vector or matrix, as desired.

Presetting a long text word segmentation abstract rule: the long text data can be converted into numerical feature vectors through word segmentation, word embedding and other methods, and then used for model training.

Presetting list sequence processing rules: the list (or array) may contain multiple elements of the same type, may be used directly if the elements in the list are numeric, and may be encoded if they are of a type.

Presetting slot feature extraction rules: slots are commonly used for intent recognition and information extraction tasks, and models may be trained to recognize and populate these slots, for example using a pretrained model such as BERT.

Optionally, in step S306 of this embodiment, the preset data set integration rule is aimed at integrating all the above-mentioned canonical data sets subjected to data feature conversion, where the preset data set integration rule may be implemented by a data integration rule in the prior art.

Through step S306, the embodiment integrates the specification data processed by the preset rules into a complete text field type data set through the preset integration rules, and lays a foundation for training a subsequent text processing model.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 4, the following may be further specifically included:

Step S401: model pre-training is carried out on a preset text processing model according to a preset text field type data set, and a corresponding pre-training loss function is determined;

step S402: determining a weighted joint loss function according to the pre-training loss function and the weight of the preset field type;

step S403: and updating parameters of the preset text processing model according to the optimal weighted joint loss function, and determining a pre-training text processing model.

Optionally, in this embodiment, the pre-training loss function is an index for measuring the difference between the predicted value and the actual value of the model, and is used to guide model optimization, and when the model needs to process multiple types of fields, a weighted loss weight can be designed, and different weights are allocated to different fields to optimize the model performance.

Specifically, in this embodiment, a weighted joint loss function is determined according to the pre-training loss function and a preset weight of the field types, where for different field types, a field type weight is allocated to each field type according to the importance of the field and the contribution degree to the final task, and the preset field type weight can be determined by domain knowledge and statistical analysis results. In another embodiment of the present invention, the weights of the preset field types may also be determined according to the preliminary training result of the weight analysis model, without affecting the implementation of the embodiment of the present invention.

Optionally, in this embodiment, the weighted joint loss function adds all the weighted losses, where the weighted losses are obtained by multiplying the value of the pre-training loss function by the field type weight of each field.

Optionally, in this embodiment, the pre-training loss function is a set of loss functions obtained after model pre-training, a function with a minimum loss in the set of functions is determined to be an optimal pre-training loss function, and an optimal weighted joint loss function is determined according to the optimal pre-training loss function and the field weight.

Through step S403, the present embodiment successfully trains a text processing model for processing different text fields through the text field type dataset, and the model can automatically process the text types related in the target dynamic table to obtain the required text fields without manual programming, thereby saving time.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 5, the following may be further specifically included:

step S501: constructing a stacked training set according to the pre-training time processing training set output by the pre-training time processing model and the pre-training text processing training set output by the pre-training text processing model;

step S502: constructing a stacked test set according to the pre-training time processing test set output by the pre-training time processing model and the pre-training text processing test set output by the pre-training text processing model;

step S503: and determining corresponding data integration characteristics according to the stacking training set and the stacking test set.

Optionally, in this embodiment, the stacking training set is constructed according to the pre-training time processing training set output by the pre-training time processing model and the pre-training text processing training set output by the pre-training text processing model, and specifically includes:

And predicting the training set by using the trained pre-training time processing model and the pre-training text processing model to obtain prediction results output by the model, and stacking the prediction results into a new training set.

Optionally, in this embodiment, the stacking test set is constructed according to the pre-training time processing test set output by the pre-training time processing model and the pre-training text processing test set output by the pre-training text processing model, and specifically includes:

And predicting the test set by using the trained pre-training time processing model and the pre-training text processing model to obtain prediction results output by the model, and stacking the prediction results into a new test set.

Optionally, in this embodiment, determining the corresponding data integration feature according to the stacking training set and the stacking test set specifically includes:

The stacked training set and test set are integrated into new features for training and testing of the special process model.

Through step S503, the embodiment obtains a feature for training a special processing model, where the feature integrates a time feature and a text feature, and using the feature to train the special processing model can enable the special processing model to have the capability of processing time data and text data, so as to lay a foundation for processing special data of a subsequent target dynamic table.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 6, the following may be further specifically included:

step S601: performing model training on a preset neural network special processing model according to the data integration characteristics, and determining corresponding model training loss;

step S602: and updating the model parameters of the neural network special processing model according to the optimal model training loss to determine a pre-training special processing model.

Optionally, in this embodiment, the preset special processing model is selected as the neural network model, and the model structure can capture a complex nonlinear relationship, so that the method is applicable to a large-scale data set and a complex problem.

It should be noted that, in another embodiment of the present invention, the preset special processing model may specifically select a bayesian model, a decision tree model, etc. according to different processing problems, which does not affect the implementation of the embodiment of the present invention.

Specifically, the following is a specific application example of the pre-training special processing model to process dynamic table data in this embodiment:

1. If the column data is a date, the data is processed into a date format for display, and meanwhile, the column date can be dynamically changed by combining with the real time, for example, the column date value is automatically +1 after 12 points in midnight. In another embodiment of the invention, the column date may also be compared to real time and an early warning may be automatically generated, for example, automatically marking the red cell as time increases when less than 10 days remain from the preset cutoff day. In another embodiment of the present invention, the listed date may be only used for background time prediction, not be represented, waiting for a subsequent manual addition of a function and use, or may be used in combination with a calendar function for holiday prediction.

2. If the column data is a multi-selection frame, processing the data into a multi-selection frame format for display;

3. If the column data is dictionary values, displaying according to the dictionary item matched with the data;

4. If the column data is a long text, processing the data to display in a long text format, displaying 10 characters by default, replacing more than part of the data with ellipses, binding a selected stop event, and displaying all the texts by bubbles when a mouse hovers over the cell;

5. If the column data is a list, processing the data into a list format for display;

6. Rendering a custom template in the parent component for display if the column data is a slot;

Through step S602, the embodiment successfully obtains a data special processing model, and the model can process time data and text data at the same time, and is applied to adaptively adjusting in a dynamic table to obtain data required by a target dynamic table, so that artificial programming is not required, and data display time is saved.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 7, the following may be further specifically included:

Step S701: determining a corresponding data transmission parent component and a data transmission sub-component according to a preset data transmission rule and the pre-training special processing model;

Step S702: and judging whether the special processing identifier is true according to the data transmission sub-component.

Optionally, in this embodiment, the preset data transmission rule may be implemented by a data transmission rule in the prior art.

Specifically, in this embodiment, a pre-trained special processing model is introduced into a data transmission sub-component to perform data judgment and processing, and the sub-component is used in a data transmission parent component to perform data transmission and communication between the parent component and the sub-component. The sub-component performs the prediction task of the machine learning model inside the sub-component, sends events to the parent component, transmits the prediction results, and after the parent component receives the prediction results, the results can be processed according to the application scenario, for example, the results of text classification can be presented in a table.

It is worth noting that, by the dynamic form processed by the form of the parent-child component, what format data need to be displayed, only the child component is needed to judge and adjust, the parent component and the form data display are not needed to be developed, and the form processing and displaying time is greatly saved.

Through step S702, the embodiment successfully establishes a parent-child component transmission mechanism, thereby enhancing the interactivity and intelligence of the application and greatly saving the application processing time.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 8, the following may be further specifically included:

step S801: respectively determining a corresponding parent component transmission rule and a corresponding child component transmission rule according to a preset data transmission rule;

step S802: determining a corresponding data transmission parent component according to the parent component transmission rule and a preset parent component container;

step S803: and determining a corresponding data transmission sub-assembly according to the sub-assembly transmission rule, the pre-training special processing model and a preset sub-assembly container.

Optionally, in this embodiment, a corresponding parent component transmission rule and a corresponding child component transmission rule are respectively determined according to a preset data transmission rule; determining a corresponding data transmission parent component according to the parent component transmission rule and a preset parent component container; determining a corresponding data transmission sub-assembly according to the sub-assembly transmission rule, the pre-training special processing model and a preset sub-assembly container, wherein the data transmission sub-assembly specifically comprises:

1. creating a vue instance, and then building a file of index vue, which is taken as a data transmission father component;

2. using an import component in the parent component file, taking the component as a data transmission sub-component;

3. Creating a container named class= "tableBox" in the parent component template for showing the contents of the table;

4. Using the sub-component (tableList) in the container (tableBox) just created and binding the table content data (tableData), table list header data (tableTH) and pre-trained special processing model on the sub-component using vue syntax, passing in to the sub-component through the props parent component, making a decision using the sub-component;

5. After the form content data (tableData) and the form list header data (tableTH) are accessed via props in the data transfer subassembly, the data processing can take place.

Through step S803, the embodiment successfully establishes a parent-child component transmission mechanism, thereby enhancing interactivity and intelligence of the application and greatly saving application processing time.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 9, the following may be further specifically included:

Step S901: if true, determining a table column data attribute according to the attribute of the table head data, and determining corresponding first cell attribute data according to the table column data attribute and the table row data;

Step S902: and determining first cell data according to the pre-training special processing model, the first cell attribute data and a preset table authority rule.

Optionally, in this embodiment, whether special processing is required is first determined according to a special processing identifier in the attribute of the header element, if special processing is required, the corresponding cell attribute is determined according to the table row and the table column attribute, whether permission processing exists is determined, if yes, the cell data is processed by using a pre-training special processing model, and the processed cell data is obtained and rendered to display pages.

Through step S902, the embodiment successfully performs automatic data processing on the table data usage model, and does not rely on manual programming any more, thereby greatly increasing the table usage speed.

In an embodiment of the table header configuration attribute-based dynamic rendering table special processing method of the present application, referring to fig. 10, the following may be further specifically included:

Step S1001: if the table head data is false, determining a table column data attribute according to the attribute of the table head data, and determining corresponding second cell attribute data according to the table column data attribute and the table row data;

Step S1002: and determining second cell data according to the second cell attribute data and a preset table authority rule.

Optionally, in this embodiment, if special data processing is not required, the corresponding cell attribute is determined according to the table row and table column attribute, whether permission is displayed is determined, if so, the cell data is directly output, and the display page is rendered.

It can be understood that in this embodiment, for the application of the permission, the flexibility of the table can be effectively increased, and users with different permissions display different data pages, and do not need to write artificial programs again, so that the efficiency of the dynamic table is greatly increased.

Through step S1002, the embodiment successfully uses the pre-trained special processing model to perform judgment and processing on the form data, obtains the processed data to perform page rendering, and performs the processing automatically by machine learning, so that manual programming is avoided, the processing speed of the dynamic form is improved, and the requirements of quick display and flexible display of the dynamic form are met.

In order to effectively improve the speed and flexibility of the dynamic table display data, the present application provides an embodiment of a table configuration attribute-based dynamic rendering table special processing device for implementing all or part of the contents of the table configuration attribute-based dynamic rendering table special processing method, referring to fig. 11, the table configuration attribute-based dynamic rendering table special processing device specifically includes the following contents:

The table data determination module 10: the method comprises the steps of obtaining header configuration data and table data, processing a header according to the header configuration data and a preset header configuration data processing rule to obtain header data, and processing a table according to the table data and a preset table data processing rule to obtain table data, wherein header data attributes of the header data comprise at least one of field codes, field names, table widths, special processing identifiers, special class name sets, button methods and field types;

Special processing model determination module 20: the method comprises the steps of carrying out model pre-training on a preset time processing model according to a preset time field type data set and preset time detection data, determining a pre-training time processing model, carrying out model pre-training on the preset text processing model according to a preset text field type data set, determining a pre-training text processing model, stacking a model output result of the pre-training time processing model and a model output result of the pre-training text processing model to obtain a data integration characteristic, inputting the data integration characteristic into a preset special processing model to obtain a corresponding pre-training special processing model, wherein the field type in the text field type data set is at least one of a multi-selection frame, a dictionary, a long text, a list and a slot;

First page display module 30: the method comprises the steps of judging whether a special processing identifier in the header data attribute is true, if true, determining first cell data according to the header data, the table row data, a preset table authority rule and the pre-training special processing model, and rendering a first cell display page of a target table according to the first cell data;

Second page display module 40: and if the table head data is false, determining second cell data according to the table head data, the table row data and the preset table authority rule, and rendering a second cell display page of the target dynamic table according to the second cell data.

As can be seen from the foregoing description, the table head data can be obtained according to the table head configuration data, the table row data can be obtained according to the table head configuration data and the preset table row data processing rule, the model pre-training is performed on the preset time processing model according to the preset time field type data set and the preset time detection data, the pre-training time processing model is determined, the model pre-training is performed on the preset text processing model according to the preset text field type data set, the pre-training text processing model is determined, the pre-training special processing model is obtained according to the model output result of the pre-training time processing model and the model output result of the pre-training text processing model in a stacked manner, whether the special processing identifier in the table head data attribute is true is judged, if true, the first cell data is determined according to the pre-training special processing model, and therefore the speed and the flexibility of the dynamic table display data can be effectively improved.

In order to effectively improve the speed and flexibility of the dynamic table display data from the hardware level, the application provides an embodiment of an electronic device for implementing all or part of the contents in the table head configuration attribute-based dynamic rendering table special processing method, wherein the electronic device specifically comprises the following contents:

A processor (processor), a memory (memory), a communication interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete communication with each other through the bus; the communication interface is used for realizing information transmission between a dynamic rendering form special processing method based on the header configuration attribute and related equipment such as a core service system, a user terminal and a related database; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and the embodiment is not limited thereto. In this embodiment, the logic controller may refer to an embodiment of the method for dynamically rendering table special processing based on the header configuration attribute and an embodiment of the method for dynamically rendering table special processing based on the header configuration attribute, and the contents thereof are incorporated herein, and are not repeated here.

It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Wherein, intelligent wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical application, part of the special processing method of the dynamic rendering table based on the header configuration attribute can be executed on the electronic device side as described above, or all operations can be completed in the client device. Specifically, the selection may be made according to the processing capability of the client device, and restrictions of the use scenario of the user. The application is not limited in this regard. If all operations are performed in the client device, the client device may further include a processor.

The client device may have a communication module (i.e. a communication unit) and may be connected to a remote server in a communication manner, so as to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementations may include a server of an intermediate platform, such as a server of a third party server platform having a communication link with the task scheduling center server. The server may include a single computer device, a server cluster formed by a plurality of servers, or a server structure of a distributed device.

Fig. 12 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 12, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 12 is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.

In one embodiment, the table header configuration attribute based dynamic rendering table special handling method functionality may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:

As can be seen from the above description, in the electronic device provided by the embodiment of the present application, header data is obtained according to header configuration data, table row data is obtained according to table data and preset table row data processing rules, model pre-training is performed on a preset time processing model according to a preset time field type data set and preset time detection data, a pre-training time processing model is determined, model pre-training is performed on the preset text processing model according to a preset text field type data set, a pre-training text processing model is determined, a pre-training special processing model is obtained according to a stack of a model output result of the pre-training time processing model and a model output result of the pre-training text processing model, whether a special processing identifier in a header data attribute is true is judged, if true, first cell data is determined according to the pre-training special processing model, thereby effectively improving the speed and flexibility of dynamic table display data.

In another embodiment, the table configuration attribute-based dynamic rendering table special processing method may be configured separately from the central processor 9100, for example, the table configuration attribute-based dynamic rendering table special processing method may be configured as a chip connected to the central processor 9100, and the table configuration attribute-based dynamic rendering table special processing method function is implemented by control of the central processor.

As shown in fig. 12, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 need not include all of the components shown in fig. 12; in addition, the electronic device 9600 may further include components not shown in fig. 12, and reference may be made to the related art.

As shown in fig. 12, the central processor 9100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 9100 receives inputs and controls the operation of the various components of the electronic device 9600.

The memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 9100 can execute the program stored in the memory 9140 to realize information storage or processing, and the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.

The memory 9140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, etc. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 storing application programs and function programs or a flow for executing operations of the electronic device 9600 by the central processor 9100.

The memory 9140 may also include a data store 9143, the data store 9143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. A communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, as in the case of conventional mobile communication terminals.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 to implement usual telecommunications functions. The audio processor 9130 can include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100 so that sound can be recorded locally through the microphone 9132 and sound stored locally can be played through the speaker 9131.

The embodiment of the present application further provides a computer readable storage medium capable of implementing all steps in the table configuration attribute-based dynamic rendering table special processing method in which the execution subject in the above embodiment is a server or a client, the computer readable storage medium storing thereon a computer program which, when executed by a processor, implements all steps in the table configuration attribute-based dynamic rendering table special processing method in which the execution subject in the above embodiment is a server or a client, for example, the processor implements the following steps when executing the computer program:

As can be seen from the foregoing description, the computer readable storage medium provided in the embodiments of the present application obtains header data according to header configuration data, obtains table row data according to table data and preset table row data processing rules, performs model pre-training on a preset time processing model according to a preset time field type data set and preset time detection data, determines a pre-training time processing model, performs model pre-training on the preset text processing model according to a preset text field type data set, determines a pre-training text processing model, stacks a model output result of the pre-training time processing model and a model output result of the pre-training text processing model to obtain a pre-training special processing model, determines whether a special processing identifier in a header data attribute is true, if true, determines first cell data according to the pre-training special processing model, thereby effectively improving speed and flexibility of dynamic table display data.

The embodiment of the present application further provides a computer program product capable of implementing all the steps in the table configuration attribute-based dynamic rendering table special processing method in which the execution subject is the server or the client, and the computer program/instructions implement the steps of the table configuration attribute-based dynamic rendering table special processing method when executed by the processor, for example, the computer program/instructions implement the steps of:

As can be seen from the foregoing description, the computer program product provided in the embodiment of the present application obtains header data according to header configuration data, obtains table row data according to table data and preset table row data processing rules, performs model pre-training on a preset time processing model according to a preset time field type data set and preset time detection data, determines a pre-training time processing model, performs model pre-training on the preset text processing model according to a preset text field type data set, determines a pre-training text processing model, stacks a model output result of the pre-training time processing model and a model output result of the pre-training text processing model to obtain a pre-training special processing model, determines whether a special processing identifier in a header data attribute is true, and if true, determines first cell data according to the pre-training special processing model, thereby effectively improving speed and flexibility of dynamic table display data.

It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. A method for dynamically rendering a table special processing based on table header configuration attributes, the method comprising:

extracting time characteristics in a preset time field type data set, carrying out model pre-training on a preset time processing model according to the time characteristics to determine a pre-training time characteristic model, and updating the pre-training time characteristic model according to preset time detection data and preset time intervals to determine a pre-training time processing model; model pre-training is carried out on a preset text processing model according to a preset text field type data set, a corresponding pre-training loss function is determined, a weighted joint loss function is determined according to the pre-training loss function and the weight of the preset field type, parameters of the preset text processing model are updated according to the optimal weighted joint loss function, and a pre-training text processing model is determined;

stacking according to the model output result of the pre-training time processing model and the model output result of the pre-training text processing model to obtain data integration features, inputting the data integration features into a preset special processing model to obtain a corresponding pre-training special processing model, wherein the field types in the text field type dataset are at least one of a multi-selection frame, a dictionary, a long text, a list and a slot;

If yes, determining second cell data according to the header data, the table row data and the preset table authority rule, and rendering a second cell display page of the target table according to the second cell data.

2. The method for table top configuration attribute-based dynamic rendering table special processing according to claim 1, comprising, before the determining a pre-trained text processing model:

3. The method for dynamically rendering table special processing based on table header configuration attribute according to claim 1, wherein stacking the model output result of the pre-training time processing model and the model output result of the pre-training text processing model to obtain the data integration feature comprises:

4. The method for dynamically rendering table special processing based on header configuration attribute according to claim 1, wherein the inputting the data integration feature into a preset special processing model to obtain a corresponding pre-trained special processing model comprises:

inputting the data integration characteristics into a preset neural network special processing model to perform model training, and determining corresponding model training loss;

5. The method for dynamically rendering table special processing based on header configuration attribute according to claim 1, wherein the determining whether the special processing identifier in the header data attribute is true comprises:

6. The method for dynamically rendering table special processing based on table header configuration attribute according to claim 5, wherein determining the corresponding data transmission parent component and data transmission child component according to the preset data transmission rule and the pre-trained special processing model comprises:

7. The method for dynamically rendering table special processing based on table header configuration attribute according to claim 1, wherein if true, determining first cell data according to the table header data, the table row data, a preset table authority rule, and the pre-trained special processing model comprises:

8. The method for dynamically rendering table special processing based on table header configuration attribute according to claim 1, wherein if false, determining second cell data according to the table header data, the table row data and the preset table authority rule comprises: