Intelligent business form processing system

Abstract

The invention belongs to the technical field of business form data processing, and discloses a business form intelligent processing system which comprises a form data verification module, a form data extraction module, a processing priority analysis module, a check result collection module, an abnormal data analysis module and a data storage library. According to the method, the form data are checked in real time when the form data are collected, then abnormal data are reminded in real time, and the analysis mode can prevent wrong data from entering a subsequent processing flow, so that the accuracy of the form data is greatly improved, and a reliable basis is provided for analysis, decision making and other operations based on the data. According to the method, the form check result is further analyzed, the specific abnormal business form type and the abnormal creation personnel are further identified, and the analysis mode can accurately position weak links in the process, so that process reconstruction and optimization are performed in a targeted manner, and the efficiency and the quality of the whole business process are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of business form data processing, and relates to an intelligent business form processing system. Background Art

[0002] A business form is a structured document used by an enterprise or organization to collect, record, transmit and process relevant information during various business activities. There are many common types of business forms, such as order forms, contract forms, application forms, production report forms, inventory count forms, etc. They play a key role in different business areas and links and are indispensable tools for enterprise operations management. Traditional form processing methods are inefficient, have high error rates, and weak data mining and analysis capabilities. Therefore, research on intelligent processing of business forms is of great significance.

[0003] In the prior art, there are also related schemes of business form data processing technology, for example, the Chinese invention patent application for business form generation method, device and storage medium with publication number CN117829111A, which includes: receiving a business form generation request sent by a target account, and determining an initial form, wherein the initial form is used to record the form information filled in by the target account. Based on the form information, the business process and participating accounts are determined, and based on the business process and the participating accounts, a business form to be reviewed is generated. The business form to be reviewed is sent to the participating accounts for review and processing, and the review results are obtained. Based on the review results, a target business form is generated. The method of the present application increases the development efficiency of business forms and improves the working efficiency of the business system.

[0004] In addition, a Chinese invention patent application for a business form processing method, device, equipment and storage medium with publication number CN116306489A includes: displaying a target business form, the target business form includes multiple target field objects. Obtaining a field update strategy. According to a preset update frequency, extracting the target field data of each target field object, and determining whether the target field data has changed. When the target field data changes, determining a field object to be processed from multiple target field objects according to the field update strategy and the changed target field data. Update the field object to be processed according to the field update strategy and the changed target field data to update the target business form. Displaying the updated target business form.

[0005] Although the above two schemes have proposed some solutions for business form data processing technology, there are still certain limitations: on the one hand, the existing schemes lack real-time correction of the creation process of a specified business form. This analysis method affects the accuracy of subsequent analysis, decision-making and other operations based on the form data, leading to wrong conclusions or unreasonable decisions. This causes delays in the entire business process and reduces work efficiency. On the other hand, there is a lack of further analysis of abnormal business form data, such as further identification of abnormal business form types and abnormal creators. This analysis method makes it difficult to optimize business processes in a targeted manner, causing hidden problems in the process to exist for a long time, affecting overall operational efficiency. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a business form intelligent processing system is now proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: a business form intelligent processing system, including: a form data verification module, used to obtain each collection form corresponding to each collection data, and determine whether there are abnormalities in each collection form corresponding to each collection data, the collection data specifically includes text data, date data and digital data, and data verification prompts are provided for collection data with abnormalities.

[0008] The form data extraction module is used to extract each uploaded specified form and obtain each specified data corresponding to each specified form.

[0009] The processing priority analysis module is used to obtain the form priority influencing data of each specified form, wherein the form priority influencing data specifically includes the receiving time, the required feedback time and the form data volume, analyze the priority of each specified form, and then sort the processing priority of each specified form.

[0010] The verification result collection module is used to collect the abnormal specified data of each specified form, and then analyze the abnormal situation of the specified data of each specified form, and then determine whether there is any abnormality in each specified form.

[0011] The abnormal data analysis module is used to record each designated form that is judged to have an abnormality as an abnormal designated form, extract the business form type and creator of each designated form, and further identify the specific abnormal business form type and abnormal creator, and issue an early warning.

[0012] Data repository for saving historical forms.

[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention verifies form data in real time when collecting form data, and then gives real-time reminders for abnormal data. This analysis method can prevent erroneous data from entering the subsequent processing flow, thereby greatly improving the accuracy of form data and providing a reliable basis for analysis, decision-making and other operations based on these data. It avoids duplication of work caused by data errors, making the form data collection process smoother and more efficient.

[0014] The present invention further analyzes the form verification results and further identifies the specific abnormal business form types and abnormal creators. This analysis method can accurately locate the weak links in the process, thereby carrying out targeted process reengineering and optimization, and improving the efficiency and quality of the overall business process. It helps to solve the abnormal data problem from the root and avoid the repeated occurrence of the same type of abnormal data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0017] Figure 2 A flow chart of identifying and judging abnormal business form types provided by the present invention.

[0018] Figure 3 A flowchart of identifying and judging abnormal creation personnel provided by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1As shown, the present invention provides a business form intelligent processing system, including a form data verification module, a form data extraction module, a processing priority analysis module, a verification result collection module, an abnormal data analysis module and a data storage library, wherein the form data verification module is connected to the form data extraction module, the form data extraction module is connected to the processing priority analysis module, the processing priority analysis module is connected to the verification result collection module, the verification result collection module is connected to the abnormal data analysis module, and the data storage library is connected to the form data verification module.

[0021] The form data verification module is used to obtain each collected data corresponding to each collection form, determine whether there is an abnormality in each collected data corresponding to each collection form, and the collected data specifically includes text data, date data and digital data, and perform data verification prompts for collected data with abnormalities.

[0022] Exemplarily, the text data may be a department name, a person name, a project name, etc., the date data may be a monitoring time, a completion time, etc., and the digital data may be a monitoring indicator quantity.

[0023] In a preferred embodiment of the present invention, the form data verification module includes determining whether there are any anomalies in the text data corresponding to each collection form, and the specific method is as follows: extracting the text data of each collection form and obtaining the business form type of the corresponding collection form.

[0024] Exemplarily, the business form type may be an order form, a contract form, an application form, etc.

[0025] The historical forms saved in the data repository are extracted to obtain the business form type corresponding to each historical form, and the business form type corresponding to each historical form is matched with the business form type corresponding to each collection form to obtain each historical form corresponding to each collection form.

[0026] Extract the text data in each historical form corresponding to each collection form, and then summarize to obtain a reference text database for each text data corresponding to each collection form, and match the text data in each collection form with the text data in the corresponding reference text database. If the match is successful, it is determined that there is no abnormality in the text data corresponding to the collection form. Otherwise, it is determined that there is an abnormality in the text data corresponding to the collection form.

[0027] In a preferred embodiment of the present invention, the form data verification module includes determining whether there are abnormalities in the date data corresponding to each collection form, and the specific method is as follows: extracting the date data corresponding to each collection form, and at the same time obtaining the date type of each date data corresponding to each collection form, the date type including the time that has occurred and the time that has not occurred.

[0028] The date data corresponding to the occurred time in each collection form is compared with the creation time corresponding to each collection form. At the same time, the date data corresponding to the occurred time in each collection form is compared with the preset reference occurred time threshold. If the date data corresponding to the occurred time in a collection form is earlier than the preset reference occurred time threshold or later than the creation time of the corresponding collection form, it is judged that there is an abnormality in the corresponding date data in the collection form. Otherwise, it is judged that there is no abnormality in the corresponding date data in the collection form.

[0029] For example, taking the project log as an example, the completion time of the corresponding completed project construction is 2021.03.26, the form creation time is 2021.3.25, and the pre-set reference occurred time threshold is 2021.01.01. At this time, the completion time of the completed project construction is later than the form creation time. Therefore, it is determined that there is an abnormality in the completion time of the completed project construction in the project log.

[0030] The date data corresponding to each non-occurrence time in each collection form is compared with the creation time corresponding to each collection form, and the date data corresponding to the non-occurrence time in each collection form is compared with the preset reference non-occurrence time threshold. If a date data corresponding to the non-occurrence time in a collection form is earlier than the creation time of the corresponding collection form or later than the preset reference non-occurrence time threshold, it is judged that there is an abnormality in the corresponding date data in the collection form; otherwise, it is judged that there is no abnormality in the corresponding date data in the collection form.

[0031] For example, taking the leave form as an example, the corresponding leave end time is 2021.05.01, the form creation time is 2021.05.12, and the pre-set reference non-occurrence time threshold is 2021.12.31. At this time, the leave end time is earlier than the form creation time. Therefore, it is determined that the leave end time of the leave form is abnormal. It should be noted that the leave form corresponding to this embodiment is carried out according to the normal process. In theory, the leave end time should be later than the form creation time.

[0032] In a preferred embodiment of the present invention, the form data verification module includes determining whether there are any abnormalities in the digital data corresponding to each collection form, and the specific method is as follows: extracting the digital data of each collection form and obtaining the business form type of the corresponding collection form.

[0033] The historical digital data corresponding to each digital data in each historical form corresponding to each collection form is extracted, and then the maximum and minimum values ​​of each digital data in each historical form corresponding to each collection form are obtained by comparison, and then the historical filling range of each digital data in each collection form is constructed.

[0034] It should be noted that the historical filling range of each digital data in each collection form refers to a value less than or equal to the maximum value of each digital data in each historical form corresponding to each collection form and greater than or equal to the minimum value of each digital data in each historical form corresponding to each collection form.

[0035] Compare each digital data corresponding to each collection form with the corresponding historical filling range. If a digital data corresponding to a collection form is within the corresponding historical filling range, it is judged that there is no abnormality in the digital data corresponding to the collection form. Otherwise, it is judged that there is an abnormality in the digital data corresponding to the collection form.

[0036] It should be noted that the present invention verifies the form data in real time when collecting form data, and then gives real-time reminders for abnormal data. This analysis method can prevent erroneous data from entering the subsequent processing flow, thereby greatly improving the accuracy of the form data and providing a reliable basis for operations such as analysis and decision-making based on these data. It avoids repeated work caused by data errors, making the form data collection process smoother and more efficient.

[0037] The form data extraction module is used to extract each uploaded specified form and obtain each specified data corresponding to each specified form.

[0038] It needs to be explained that the collection form and designated form mentioned in the present invention refer to the names of the forms at different stages. When the creator creates the form, the form is recorded as a collection form. After the form creation is completed, it is recorded as a designated form, and then subsequent data analysis, processing, review and other steps are carried out.

[0039] It needs to be explained that the collected data and designated data mentioned in the present invention refer to the names of data at different stages. When the creator creates the form, the data is recorded as collected data. After the form is created, it is recorded as designated data, and subsequent data analysis, processing, review and other steps are carried out.

[0040] The processing priority analysis module is used to obtain form priority influencing data of each designated form, wherein the form priority influencing data specifically includes receiving time, required feedback time and form data volume, analyze the priority of each designated form, and then sort the processing priority of each designated form.

[0041] In a preferred embodiment of the present invention, the analysis of the priority of each designated form requires constructing a priority index for each designated form, and the specific method is as follows: extracting the receiving time, feedback request time and form data volume of each designated form, and recording them as , , ,in Indicates the number of the specified form. , Indicates the number of the specified form.

[0042] Using the formula Analyze and obtain the priority index of each specified form ,in Indicates the current time. Indicates the allowable difference between the preset required feedback time and the current time. Indicates the permissible difference between the preset receiving time and the current time. Indicates the preset reference data volume.

[0043] It should be noted that the reasons for selecting the receiving time, the time required for feedback and the amount of form data as the factors affecting the priority index of each specified form are: 1. The receiving time is a key factor in measuring the urgency of the form. Forms received earlier may represent more urgent business needs. 2. Many forms come from external partners or customers, who often have clear expectations for feedback time. For example, the quotation provided by the supplier may require the enterprise to respond within a certain period of time, otherwise it may affect the cooperative relationship. Using the time required for feedback as a priority index factor can ensure that the enterprise can meet these external expectations, maintain a good cooperative relationship, and avoid business disputes or reputation losses caused by overdue feedback. 3. The size of the form data directly affects the difficulty and time required for the audit. Forms with larger data volumes usually require more time and effort to process. Using the amount of form data as a priority factor can enable auditors to have an intuitive expectation of the difficulty of processing, reasonably allocate time and energy, and give priority to forms with smaller data volumes and relatively easy to complete, thereby improving the overall audit efficiency.

[0044] In a preferred embodiment of the present invention, the specific method of ranking the processing priorities of each designated form is as follows: extract the priority index of each designated form, and then arrange them in order from large to small to obtain the priority ranking of each designated form, and use the priority ranking obtained after arrangement as the subsequent review order.

[0045] It should be explained that the reasons for prioritizing each designated form are: 1. Optimize resource allocation, focus manpower and time on key forms, and improve the efficiency of auditors; 2. Improve business response speed, give priority to emergency matters and key process forms, and ensure business continuity and timeliness; 3. Enhance decision-making timeliness, help to obtain important data and prevent and control risks in a timely manner; 4. Improve overall business efficiency, avoid process blockages and optimize cross-departmental work collaboration, promote smooth and efficient business operations, enhance the ability to respond to risks and seize opportunities, and improve customer satisfaction and corporate competitiveness.

[0046] The verification result collection module is used to collect abnormal specified data of each specified form, and then analyze the abnormal situation of the specified data of each specified form, and then determine whether there is an abnormality in each specified form.

[0047] In a preferred embodiment of the present invention, the analysis of the abnormality of the designated data of each designated form requires the construction of an abnormality index of the designated data of each designated form, and the specific method is as follows: extracting each abnormal designated data of each designated form.

[0048] It should be added that the specific method of extracting each abnormal specified data of each specified form is as follows: extract each text data, date data and digital data corresponding to each collection form judged to have an abnormality, and then record them as each abnormal specified data of each specified form respectively.

[0049] If a certain designated data of a certain designated form is abnormal designated data, the abnormal index of the designated form corresponding to the designated data is set to 1; otherwise, the abnormal index of the designated form corresponding to the designated data is set to 0.

[0050] The abnormal index of each designated data of each designated form is multiplied by the corresponding preset influence weight factor and then accumulated to obtain the abnormal index of each designated data of each designated form.

[0051] It should be noted that for the same designated form, different designated data may have different influence weights, which depends on the meaning represented by the different designated data. For example, for a certain contract form, the registered purchase quantity, purchase price, and purchase date may be relatively important, so the impact weight factor assigned is larger. Correspondingly, the registered contact person name and order creation date may be relatively less important, so the impact weight factor assigned is smaller.

[0052] In a preferred embodiment of the present invention, the specific method of determining whether each designated form has an abnormality is as follows: extracting the designated data abnormality index of each designated form, and then comparing it with a preset designated data abnormality index threshold; if the designated data abnormality index of a designated form is greater than the preset designated data abnormality index threshold, it is determined that the designated form has an abnormality; otherwise, it is determined that the designated form does not have an abnormality.

[0053] Exemplarily, the data anomaly index threshold is specified as .

[0054] The abnormal data analysis module is used to record each designated form that is judged to have an abnormality as an abnormal designated form, extract the business form type and creator of each designated form, and further identify the specific abnormal business form type and abnormal creator, and issue an early warning.

[0055] In a preferred embodiment of the present invention, please refer to Figure 2 As shown, the method for identifying the abnormal business form type is as follows: extract the business form type of each specified form, classify each specified form according to the business form type to obtain each business form type corresponding to each specified form, and then count the number of specified forms corresponding to each business form type and the number of abnormal specified forms.

[0056] The ratio of the number of abnormal designated forms corresponding to each business form type and the number of designated forms is calculated to obtain the ratio of abnormal forms corresponding to each business form type. The ratio of abnormal forms corresponding to each business form type is compared with the preset abnormal form ratio threshold. If the ratio of abnormal forms corresponding to a business form type is greater than the abnormal form ratio threshold, the business form type is judged to be an abnormal business form type.

[0057] For example, the threshold value of abnormal form ratio is .

[0058] In a preferred embodiment of the present invention, please refer to Figure 3 As shown, the method for identifying abnormal creators is as follows: extract the creators of each designated form, and then classify them according to the creators to obtain the designated forms corresponding to each creator, and count the number of designated forms corresponding to each creator and the number of abnormal designated forms.

[0059] The abnormal form ratio of each creator is calculated by comparing the number of abnormal designated forms corresponding to each creator with the number of designated forms, and then compared with the preset abnormal form ratio threshold. If the abnormal form ratio of a creator is greater than the abnormal form ratio threshold, the creator is judged as an abnormal creator.

[0060] It should be noted that the present invention further analyzes the form verification results and further identifies the specific abnormal business form types and abnormal creators. This analysis method can accurately locate the weak links in the process, thereby carrying out targeted process reengineering and optimization, and improving the efficiency and quality of the overall business process. It helps to solve the abnormal data problem from the root and avoid the repeated appearance of the same type of abnormal data.

[0061] The data repository is used to store historical forms.

[0062] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A business form intelligent processing system, characterized in that: include: The form data verification module is used to obtain each collected data corresponding to each collected form, determine whether each collected data corresponding to each collected form has an abnormality, and the collected data specifically includes text data, date data and digital data, and perform data verification prompts for the collected data with abnormalities; The form data extraction module is used to extract each uploaded specified form and obtain each specified data corresponding to each specified form; A processing priority analysis module is used to obtain form priority influencing data of each designated form, wherein the form priority influencing data specifically includes receiving time, required feedback time and form data volume, analyze the priority of each designated form, and then sort the processing priority of each designated form; The verification result collection module is used to collect the abnormal specified data of each specified form, and then analyze the abnormal situation of the specified data of each specified form, and then determine whether there is an abnormality in each specified form; The abnormal data analysis module is used to record each designated form that is judged to have an abnormality as an abnormal designated form, extract the business form type and creator of each designated form, and further identify the specific abnormal business form type and abnormal creator, and issue an early warning; Data repository for saving historical forms.

2. A business form intelligent processing system as claimed in claim 1, characterized in that: The form data verification module includes determining whether each text data corresponding to each collection form is abnormal, and the specific method is as follows: Extract the text data of each collection form and obtain the business form type of the corresponding collection form; Extract the historical forms saved in the data repository, and then obtain the business form type corresponding to each historical form, match the business form type corresponding to each historical form with the business form type corresponding to each collection form, and then obtain each historical form corresponding to each collection form; Extract the text data in each historical form corresponding to each collection form, and then summarize to obtain a reference text database for each text data corresponding to each collection form, and match the text data in each collection form with the text data in the corresponding reference text database. If the match is successful, it is determined that there is no abnormality in the text data corresponding to the collection form. Otherwise, it is determined that there is an abnormality in the text data corresponding to the collection form.

3. A business form intelligent processing system as claimed in claim 1, characterized in that: The form data verification module includes determining whether the date data corresponding to each collection form is abnormal, and the specific method is as follows: Extracting the date data corresponding to each collection form, and obtaining the date type of each date data corresponding to each collection form, wherein the date type includes the time that has occurred and the time that has not occurred; Compare each date data corresponding to the occurred time in each collection form with the creation time corresponding to each collection form, and at the same time compare each date data corresponding to the occurred time in each collection form with a preset reference occurred time threshold. If the date data corresponding to the occurred time in a certain collection form is earlier than the preset reference occurred time threshold or later than the creation time of the corresponding collection form, it is judged that the corresponding date data in the collection form is abnormal. Otherwise, it is judged that the corresponding date data in the collection form is not abnormal. The date data corresponding to each non-occurrence time in each collection form is compared with the creation time corresponding to each collection form. At the same time, the date data corresponding to the non-occurrence time in each collection form is compared with the preset reference non-occurrence time threshold. If a date data corresponding to the non-occurrence time in a collection form is earlier than the creation time of the corresponding collection form or later than the preset reference non-occurrence time threshold, it is judged that there is an abnormality in the corresponding date data in the collection form. Otherwise, it is judged that there is no abnormality in the corresponding date data in the collection form.

4. A business form intelligent processing system as claimed in claim 1, characterized in that: The form data verification module includes determining whether each digital data corresponding to each collection form is abnormal, and the specific method is as follows: Extract each digital data of each collection form, and obtain the business form type of the corresponding collection form; Extract the historical digital data corresponding to each digital data in each historical form corresponding to each collection form, and then compare to obtain the maximum and minimum values ​​of each digital data in each historical form corresponding to each collection form, and then construct the historical filling range of each digital data in each collection form; Compare each digital data corresponding to each collection form with the corresponding historical filling range. If a digital data corresponding to a collection form is within the corresponding historical filling range, it is judged that there is no abnormality in the digital data corresponding to the collection form. Otherwise, it is judged that there is an abnormality in the digital data corresponding to the collection form.

5. A business form intelligent processing system as claimed in claim 1, characterized in that: The analysis of the priority of each specified form requires constructing a priority index of each specified form, and the specific method is as follows: Extract the receiving time, feedback time and form data volume of each specified form and record them as , , ,in Indicates the number of the specified form. , Indicates the number of specified forms; Using the formula Analyze and obtain the priority index of each specified form ,in Indicates the current time. Indicates the allowable difference between the preset required feedback time and the current time. Indicates the permissible difference between the preset receiving time and the current time. Indicates the preset reference data volume.

6. A business form intelligent processing system as claimed in claim 5, characterized in that: The specific method of sorting the processing priorities of each specified form is as follows: The priority index of each designated form is extracted, and then arranged in order from large to small to obtain the priority ranking of each designated form, and the priority ranking obtained after arrangement is used as the subsequent review order.

7. A business form intelligent processing system as claimed in claim 1, characterized in that: The analysis of the abnormality of the specified data of each specified form requires the construction of the abnormality index of the specified data of each specified form, and the specific method is as follows: Extract each abnormal specified data of each specified form; If a certain specified data of a certain specified form is abnormal specified data, the abnormal index of the specified form corresponding to the specified data is set to 1; otherwise, the abnormal index of the specified form corresponding to the specified data is set to 0; The abnormal index of each designated data of each designated form is multiplied by the corresponding preset influence weight factor and then accumulated to obtain the abnormal index of each designated data of each designated form.

8. A business form intelligent processing system as claimed in claim 7, characterized in that: The specific method of determining whether each specified form has an abnormality is as follows: The specified data anomaly index of each specified form is extracted and then compared with the preset specified data anomaly index threshold. If the specified data anomaly index of a specified form is greater than the preset specified data anomaly index threshold, it is determined that the specified form has an anomaly. Otherwise, it is determined that the specified form does not have an anomaly.

9. A business form intelligent processing system as claimed in claim 1, characterized in that: The identification method of the abnormal business form type is as follows: Extract the business form type of each designated form, classify each designated form according to the business form type to obtain each business form type corresponding to each designated form, and then count the number of designated forms corresponding to each business form type and the number of abnormal designated forms; The ratio of the number of abnormal designated forms corresponding to each business form type and the number of designated forms is calculated to obtain the ratio of abnormal forms corresponding to each business form type. The ratio of abnormal forms corresponding to each business form type is compared with the preset abnormal form ratio threshold. If the ratio of abnormal forms corresponding to a business form type is greater than the abnormal form ratio threshold, the business form type is judged to be an abnormal business form type.

10. The business form intelligent processing system according to claim 1, characterized in that: The person who created the exception is identified as follows: Extract the creators of each specified form, and then classify them according to the creators to obtain the specified forms corresponding to each creator, and count the number of specified forms corresponding to each creator and the number of abnormal specified forms; The abnormal form ratio of each creator is calculated by comparing the number of abnormal designated forms corresponding to each creator with the number of designated forms, and then compared with the preset abnormal form ratio threshold. If the abnormal form ratio of a creator is greater than the abnormal form ratio threshold, the creator is judged as an abnormal creator.

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