Bill interface type generation method and system based on target data

Abstract

The application discloses a bill interface type generation method and system based on target data, and belongs to the technical field of bill processing. The application solves the problems of insufficient flexibility and uneven data quality of the existing method, can filter out high-quality data sources by extracting the amount of error data in the target data and its copy and comparing with the preset parameter threshold, ensures the accuracy and integrity of the bill interface, reduces business errors and decision-making errors caused by data quality problems, and thus improves the user's use experience; by calculating the multidimensional similarity and combining the weight distribution, the system can give priority to certain key features, thus better meeting the needs in specific business scenarios and ensuring that the bill interface template most consistent with the business logic of the target data is found, thereby improving the accuracy and reliability of bill interface generation.

Description

Technical Field

[0001] This invention relates to the field of document processing technology, specifically to a method and system for generating document interface types based on target data. Background Technology

[0002] In modern enterprise information systems, document management is a core component of business processes. Traditional methods for generating document interfaces mainly rely on manual design and fixed templates. This approach suffers from the following problems when facing diverse and dynamically changing business needs:

[0003] 1. Insufficient flexibility: Fixed templates are difficult to adapt to changes in business scenarios. Every business adjustment requires redesigning the document interface, which cannot quickly respond to changes in business needs, and is time-consuming and labor-intensive, affecting the company's operational efficiency.

[0004] 2. Inconsistent data quality: Data from different sources may contain quality issues, such as erroneous or duplicate data, affecting the accuracy and reliability of the document interface.

[0005] Therefore, since it does not meet the existing requirements, we propose a method and system for generating document interface types based on target data. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for generating document interface types based on target data. By extracting the amount of erroneous data from the target data and its copies and comparing it with preset parameter thresholds, high-quality data sources can be selected, ensuring the accuracy and integrity of the document interface, reducing business errors and decision-making mistakes caused by data quality issues, and improving the user experience. By calculating multi-dimensional similarity and combining it with weight allocation, the system can prioritize certain key features, thereby better meeting the needs of specific business scenarios and ensuring that the document interface template that best matches the business logic of the target data is found. This improves the accuracy and reliability of document interface generation and solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for generating document interface types based on target data, the method comprising the following steps:

[0009] Acquire historical data and design various document interface templates based on different business scenarios and document types within the historical data;

[0010] Based on dynamic metadata technology, historical data is analyzed, and business logic features are defined for each document interface template. These features include field type, number of fields, and business logic mode. The defined document interface templates are then stored in the template matching library.

[0011] The collected target data is mapped, cleaned, and quality assessed, and then stored to form a global metadata directory of the target data. The quality assessment process includes: statistical analysis of the amount of erroneous data in the target data and its copies, and data quality grading assessment combined with field type entropy analysis and dynamic threshold adjustment algorithm.

[0012] The target data's metadata is analyzed using dynamic metadata technology to extract its business logic features.

[0013] Based on the feature matching algorithm, the most matching document interface template is found in the template matching library for the business logic features of the target data;

[0014] Based on the matched document interface template, map the fields of the target data to the form components defined in the document interface template;

[0015] The document interface is dynamically generated based on the field mapping relationship and the document interface template layout.

[0016] Furthermore, the mapping and cleaning of the collected target data also includes:

[0017] Extract the erroneous data and their corresponding quantities from the target data, and use this as the first data volume;

[0018] Obtain data related to the target data from multiple channels and use it as a copy of the target data;

[0019] Extract the erroneous data and their corresponding quantities from the target data copy, and use this as the second data volume;

[0020] A preset parameter threshold is set, and the first data volume and the second data volume are compared with the preset parameter threshold respectively to obtain the comparison result;

[0021] Based on the comparison results, the collection quality of the target data and the target data copy is evaluated to obtain the data quality evaluation results;

[0022] Based on the evaluation results, select the target data for generating the current document interface.

[0023] Furthermore, preset parameter thresholds are defined, including:

[0024] Extract the total number of fields in the document interface;

[0025] Categorize the fields in the document interface and obtain the corresponding field types in the document interface;

[0026] For each field type, extract the percentage of the number of fields corresponding to that field type in the total number of fields;

[0027] The Shannon entropy value corresponding to each field type is obtained by using the proportion of the number of fields corresponding to each field type in the total number of fields.

[0028] The Shannon entropy value is normalized to obtain the normalized Shannon entropy value;

[0029] Compare the first data volume and the second data volume to obtain the data volume difference between the first data volume and the second data volume;

[0030] The first and second data volumes are summed and then averaged to obtain the average data volume corresponding to the first and second data volumes as a whole.

[0031] The data volume difference is compared with the data volume average value to obtain the data volume ratio coefficient.

[0032] The parameter threshold is set by combining the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type.

[0033] Furthermore, the parameter threshold is set using the data volume ratio coefficient combined with the normalized Shannon entropy value corresponding to the field type, including:

[0034] Retrieve the normalized Shannon entropy values ​​corresponding to the data volume ratio coefficient and field type;

[0035] Compare the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type;

[0036] When the data volume ratio coefficient does not exceed the normalized Shannon entropy value corresponding to the field type, the preset benchmark parameter threshold is retrieved from the database as the parameter threshold for subsequent collection quality assessment.

[0037] When the data volume ratio exceeds the normalized Shannon entropy value corresponding to the field type, a preset benchmark parameter threshold is retrieved from the database and adjusted.

[0038] The adjusted parameter thresholds will be used as the parameter thresholds for subsequent data acquisition quality assessment.

[0039] Furthermore, based on the feature matching algorithm, the most matching document interface template for the business logic features of the target data is found in the template matching library, specifically as follows:

[0040] Standardize the business logic features of each document interface template in the template matching library;

[0041] By comparing the similarity of field types, the number of fields, and the business logic patterns between the target data and the template, a multi-dimensional similarity is obtained.

[0042] Based on business requirements, assign different weights to field types, number of fields, and business logic patterns;

[0043] The overall similarity score is obtained by multiplying the similarity scores of each dimension by their respective weights and summing them.

[0044] Select the template with the highest overall similarity as the best matching document interface template;

[0045] If multiple templates with the same similarity exist, the selection will be based on other factors.

[0046] Furthermore, based on dynamic metadata technology, the metadata of the target data is analyzed to extract the business logic characteristics of the target data, specifically:

[0047] Determine the specific objectives of metadata analysis and the sources of the target data, and use metadata management tools to automatically collect the metadata of the target data. The metadata includes technical metadata and business metadata.

[0048] By analyzing the metadata of the target data, the field types in the target data are identified, and the total number of fields in the target data and the number of each field type are counted.

[0049] Analyze the dependencies between fields, extract the validation rules of the fields, and identify the calculation logic of the fields to obtain the business logic characteristics of the target data.

[0050] Furthermore, based on the comparison results, the acquisition quality of the target data and its copy is evaluated, specifically as follows:

[0051] If the comparison result shows that the first data volume exceeds the parameter threshold and the second data volume does not exceed the parameter threshold, it indicates that the collection quality of the target data corresponding to the first data volume is low. Therefore, the target data copy corresponding to the second data volume is used as the target data in the current document generation interface.

[0052] If the comparison result shows that the first data volume does not exceed the parameter threshold, but the second data volume exceeds the parameter threshold, it indicates that the collection quality of the target data copy corresponding to the second data volume is low. Therefore, the target data corresponding to the first data volume is used as the target data for the current document generation interface.

[0053] If the comparison results show that both the first and second data volumes exceed the parameter threshold, the acquisition channels of the target data and the target data copy are verified to determine whether there are any omissions in the transmission process. If the transmission is verified to be normal, the data with the lower proportion exceeding the parameter threshold is used as the target data for the current document generation interface.

[0054] If the comparison results show that neither the first data volume nor the second data volume exceeds the parameter threshold, then the target data corresponding to the first data volume will be used as the target data for the current document generation interface.

[0055] Furthermore, after storing the defined document interface template in the template matching library, it includes:

[0056] Establish fixed review cycles and plans to review document interface templates and business logic features;

[0057] Establish a user feedback mechanism to regularly collect user feedback on document interface templates and business logic features;

[0058] Based on the review results and user feedback, we will develop improvement measures and make timely adjustments and optimizations.

[0059] A document interface type generation system based on target data, the system comprising: a data acquisition unit and a data processing unit, the data processing unit comprising: a quality assessment module, a feature extraction module and a feature matching module;

[0060] The data acquisition unit is configured to retrieve target data from a relational database and obtain copies of the target data from multiple channels;

[0061] The quality assessment module is configured to obtain the amount of data corresponding to erroneous data in the target data and the target data copy, assess the data quality of the target data and the target data copy based on preset parameter thresholds, and select the optimal data source based on the assessment results;

[0062] The feature extraction module is configured to analyze the metadata of the target data based on dynamic metadata technology, identify the dependencies between fields, validation rules and calculation logic, and obtain the business logic features of the target data.

[0063] The feature matching module is configured to use a feature matching algorithm to find the most matching document interface template in the template matching library;

[0064] The template building module is configured to design various document interface templates based on historical data and business scenarios, and store these templates to form a template matching library.

[0065] The interface generation module is configured to associate the fields of the target data with the form components defined in the matching document interface template, and construct the document interface based on the field association results and the layout of the document interface template.

[0066] The user feedback module is configured to periodically collect user feedback on document interface templates and business logic features.

[0067] The review and optimization module is configured to periodically review document interface templates and business logic features, and formulate improvement measures based on user feedback.

[0068] Furthermore, the data acquisition unit includes:

[0069] The data cleaning module is configured to map and clean the target data, its metadata, and copies of the target data.

[0070] The data storage module is configured to store the metadata of the target data, forming a global metadata directory.

[0071] Compared with the prior art, the beneficial effects of the present invention are:

[0072] 1. This invention, by extracting the amount of erroneous data from the target data and its copies and comparing it with preset parameter thresholds, can filter out high-quality data sources, ensuring the accuracy and integrity of the document interface, reducing business errors and decision-making mistakes caused by data quality issues, thereby improving the user experience; when using the document interface, users can obtain accurate information, reduce operational troubles caused by data errors, and improve work efficiency.

[0073] 2. This invention, by calculating multi-dimensional similarity and combining it with weight allocation, enables the system to prioritize certain key features, flexibly adapt to different business needs and data structure changes, ensure that the document interface template that best matches the target data business logic is found, better meet the needs of specific business scenarios, effectively avoid the limitations of single-dimensional evaluation, and thus improve the accuracy and reliability of document interface generation. Attached Figure Description

[0074] Figure 1 This is a diagram illustrating the system composition of the document interface type generation system based on target data according to the present invention.

[0075] Figure 2 This is a flowchart of the document interface type generation method based on target data according to the present invention. Detailed Implementation

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] To address the shortcomings of existing technologies, where traditional document interface generation methods rely heavily on manual design and fixed templates, resulting in insufficient flexibility and inconsistent data quality, please refer to [the relevant documentation / provisions]. Figures 1-2 This embodiment provides the following technical solution:

[0078] The method for generating document interface types based on target data includes the following steps:

[0079] Obtain historical data and design various document interface templates based on different business scenarios and document types within the historical data.

[0080] Based on dynamic metadata technology, historical data is analyzed, and business logic characteristics are defined for each document interface template. These characteristics include: field type, number of fields, and business logic mode. The defined document interface templates are then stored in a template matching library. The following steps are also included:

[0081] Establish fixed review cycles and plans, such as monthly or quarterly, and clearly define the specific time, tasks, and responsible persons for each review;

[0082] Review the document interface template, including: checking whether the template contains all necessary fields and components; verifying whether the field types and display order meet business requirements; evaluating whether the interface layout is reasonable and conforms to user operating habits; and testing the display effect of the template on different devices and browsers.

[0083] Review business logic features, including: verifying whether field types are consistent with business requirements; checking whether the number of fields complies with business rules; reviewing whether the dependencies between fields, calculation logic, and validation rules are correct; and checking whether the data conforms to business logic and validation rules.

[0084] Establish a user feedback mechanism to regularly collect user feedback on document interface templates and business logic features; provide multiple feedback channels, such as online feedback forms and user forums; set a fixed feedback collection cycle, such as monthly or quarterly; classify and analyze the collected feedback to extract key issues and improvement suggestions;

[0085] Based on the review results and user feedback, specific improvement measures were formulated and adjusted and optimized in a timely manner. For example, if users reported that the order entry template lacked a customer_name field, the technical team added the customer_name field to the order entry template, with the field type being string, and then tested it. This ensured the integrity of the document interface template and the user experience, and guaranteed the accuracy and consistency of business logic features.

[0086] The collected target data is mapped, cleaned, and quality-assessed, and then stored to form a global metadata directory for the target data. The quality assessment process includes: statistical analysis of the amount of erroneous data in the target data and its copies, combined with field type entropy analysis and dynamic threshold adjustment algorithms for data quality grading assessment, specifically including:

[0087] Extract the erroneous data and their corresponding quantities from the target data, and use this as the first data volume;

[0088] Obtain data related to the target data from multiple channels and use it as a copy of the target data;

[0089] Extract the erroneous data and their corresponding quantities from the target data copy, and use this as the second data volume;

[0090] A preset parameter threshold is set, and the first data volume and the second data volume are compared with the preset parameter threshold respectively to obtain the comparison result;

[0091] Based on the comparison results, the collection quality of the target data and its copy is evaluated to obtain data quality assessment results; specifically:

[0092] If the comparison result shows that the first data volume exceeds the parameter threshold and the second data volume does not exceed the parameter threshold, it indicates that the collection quality of the target data corresponding to the first data volume is low. Therefore, the target data copy corresponding to the second data volume is used as the target data in the current document generation interface.

[0093] If the comparison result shows that the first data volume does not exceed the parameter threshold, but the second data volume exceeds the parameter threshold, it indicates that the collection quality of the target data copy corresponding to the second data volume is low. Therefore, the target data corresponding to the first data volume is used as the target data for the current document generation interface.

[0094] If the comparison results show that both the first and second data volumes exceed the parameter threshold, the acquisition channels of the target data and the target data copy are verified to determine whether there are any omissions in the transmission process. If the transmission is verified to be normal, the data with the lower proportion exceeding the parameter threshold is used as the target data for the current document generation interface.

[0095] If the comparison result shows that neither the first data volume nor the second data volume exceeds the parameter threshold, then the target data corresponding to the first data volume will be used as the target data for the current document generation interface.

[0096] Based on the evaluation results, select the target data for generating the current document interface.

[0097] The beneficial effects achieved by the above are as follows: by extracting the amount of erroneous data from the target data and its copies and comparing it with parameter thresholds, the data collection quality can be effectively evaluated, high-quality data sources can be filtered out, thereby ensuring the accuracy and completeness of the document interface, reducing business errors and decision-making mistakes caused by data quality issues, and improving the user experience; when using the document interface, users can obtain accurate information and improve work efficiency.

[0098] Specifically, preset parameter thresholds include:

[0099] Extract the total number of fields in the document interface;

[0100] Categorize the fields in the document interface and obtain the corresponding field types in the document interface;

[0101] For each field type, extract the percentage of the number of fields corresponding to that field type in the total number of fields;

[0102] The Shannon entropy value corresponding to each field type is obtained by using the proportion of the number of fields corresponding to each field type in the total number of fields.

[0103] The Shannon entropy value is obtained using the following formula:

[0104]

[0105] Where E represents the Shannon entropy value; n represents the number of field types; p i This represents the proportion of the number of fields corresponding to the i-th field type in the total number of fields.

[0106] The Shannon entropy value is normalized to obtain the normalized Shannon entropy value;

[0107] Compare the first data volume and the second data volume to obtain the data volume difference between the first data volume and the second data volume;

[0108] The first and second data volumes are summed and then averaged to obtain the average data volume corresponding to the first and second data volumes as a whole.

[0109] The data volume difference is compared with the data volume average value to obtain the data volume ratio coefficient.

[0110] The parameter threshold is set by combining the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type.

[0111] The technical effects of the above solution are as follows: Compared with the limitations of existing technologies that rely on manual experience to set fixed thresholds or judge based solely on single-dimensional data indicators, the above solution significantly improves the scientificity and adaptability of parameter threshold settings by integrating a dual-source comparison mechanism of business characteristics and data quality. On the one hand, by using Shannon entropy to quantify the diversity of field type distribution in document interfaces, the complexity of business logic is transformed into a calculable numerical indicator, deeply binding the threshold with the complexity of the business scenario. This avoids misjudgments caused by a "one-size-fits-all" fixed threshold (such as thresholds that are too strict in simple scenarios and too lenient in complex scenarios). Furthermore, normalization processing enables unified measurement across scenarios, allowing the threshold to be dynamically adjusted according to business complexity. After normalizing the Shannon entropy value, the business complexity of different document interfaces can be uniformly mapped to the [0,1] interval, facilitating mathematical integration with data quality indicators (such as data volume ratio coefficients) to achieve dynamic adjustment of thresholds across scenarios. On the other hand, by comparing the erroneous data volume of the target data and its copies, the difference and average data volume are calculated to obtain the data volume ratio coefficient reflecting the stability of data collection, achieving dual-source verification of data quality. Ultimately, by combining business complexity indicators with data quality indicators, a composite threshold setting model is constructed. This model automatically tightens the threshold when the business is complex and the differences between the two sources of data are large, and automatically loosens it when the business is simple and the data is highly consistent. This achieves a shift from "human experience-based presets" to "automatic derivation of data features," effectively solving the problems of rigidity and poor adaptability of traditional thresholds, and improving the accuracy of data quality assessment and the level of intelligence in document generation.

[0112] Specifically, the parameter threshold is set by combining the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type, including:

[0113] Retrieve the normalized Shannon entropy values ​​corresponding to the data volume ratio coefficient and field type;

[0114] Compare the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type;

[0115] When the data volume ratio coefficient does not exceed the normalized Shannon entropy value corresponding to the field type, a preset benchmark parameter threshold is retrieved from the database as the parameter threshold for subsequent collection quality assessment; wherein, the preset benchmark parameter threshold is obtained based on actual application needs combined with experiments or experience.

[0116] When the data volume ratio exceeds the normalized Shannon entropy value corresponding to the field type, a preset benchmark parameter threshold is retrieved from the database and adjusted.

[0117] The adjusted parameter thresholds will be used as the parameter thresholds for subsequent data acquisition quality assessment.

[0118] The adjusted parameter threshold is obtained using the following formula:

[0119]

[0120] Where Y represents the adjusted parameter threshold; Y0 represents the preset baseline parameter threshold; E g Represents the normalized Shannon entropy value; D represents the data volume ratio coefficient; m represents the number of times the target data was collected in the historical data collection; C i This represents the normalized difference in data size between the first and second data sizes corresponding to the target data acquired in the i-th iteration. Specifically, The square root of the product of these two factors reflects the degree to which both business complexity and data quality stability affect threshold adjustment. The more complex the business and the greater the fluctuation in data quality, the larger this value will be, and the greater its impact on the magnitude of threshold adjustment, similar to the combined effect of multiple influencing factors in physics. The difference C between the normalized data volume of each target data point i Summing after taking negative exponents. The negative exponent operation makes C... i The smaller the value (i.e., the smaller the difference between the two source data and the better the data quality), the greater its contribution. The summation reflects the overall data quality over time. This involves dividing the summation result by the number of historical data collections, *m*, and then averaging the results to obtain an index reflecting the overall quality of the historical data. This is similar to statistical averaging of multiple samples in statistical physics to obtain a representative statistic. It adjusts the threshold adjustment based on the historical data quality; the better the overall data quality, the closer the value is to 1, and the more significant the amplification effect on the threshold adjustment. This formula incorporates business complexity (E...). g ), current data quality status (D) and historical data quality status (via C) i (and m). The preset baseline parameter threshold Y0 is adjusted from multiple perspectives, comprehensively considering factors affecting data quality assessment. This avoids the irrationality of setting thresholds based on only a single dimension or one-sided information, ensuring that the final obtained parameter thresholds more scientifically reflect actual data quality requirements. Through a dynamic adjustment mechanism, the threshold is adjusted in real time according to different business scenarios (changes in Shannon entropy) and data quality fluctuations (changes in data volume ratios and historical data differences). This improves the adaptability and effectiveness of the thresholds during the data quality assessment process.

[0121] The technical effects of the above solution are as follows: By extracting the total number of fields and category fields from the document interface and calculating the Shannon entropy value, the complexity of business logic can be accurately quantified. Based on this, parameter thresholds are adjusted to ensure deep adaptation to the business scenario, avoiding data quality assessment bias caused by inaccurate judgment of business complexity, significantly improving the accuracy of data quality assessment results, and thus making the target data selected based on accurate assessment results more reliable, providing a high-quality data foundation for document interface generation. The data volume ratio coefficient is obtained by comparing the target data and its copy, and the threshold is set in conjunction with the Shannon entropy value. Cross-verification of dual-source data effectively identifies fluctuations and errors during the data collection process. Compared to single-source data assessment, it can more accurately locate erroneous data, improve the accuracy of erroneous data identification, and make the parameter thresholds more closely match the actual data quality situation, enhancing the accuracy of data quality assessment. As business operations and data change, the data volume ratio coefficient, Shannon entropy value, etc., will change in real time. The technical solution adjusts the parameter thresholds based on these dynamically changing indicators, responding to data fluctuations in real time. Under conditions of changes in the data acquisition environment and adjustments to business logic, a stable assessment of data quality is maintained at all times. This avoids data quality assessment failure or misjudgment due to fixed thresholds, ensuring the stability of data processing during the document interface generation process. The formula incorporates the number of historical data collections and the difference in data volume each time, using historical data patterns to assist in threshold adjustment. When data experiences short-term fluctuations, comprehensive consideration of historical data can prevent excessive threshold adjustments, maintaining a relatively stable assessment system. When data shows long-term trend changes, the threshold can be adjusted in a timely manner based on historical data accumulation, achieving a balance between stability and adaptability. The technical solution automates the calculation and adjustment of parameter thresholds, eliminating the need for frequent manual intervention and manual threshold modification. From data feature extraction and indicator calculation to threshold determination, the entire process is completed automatically, reducing manual operation time costs, accelerating the data quality assessment process, and improving the overall efficiency of data processing before document interface generation. Reasonable threshold settings can more efficiently filter out target data that meets the requirements, reducing repeated processing and error correction operations caused by data quality issues during subsequent document interface generation. By accurately assessing data quality, high-quality data is directly selected for field mapping and interface generation, optimizing the data processing flow, shortening the document interface generation cycle, and improving the overall system operating efficiency.

[0122] On the other hand, existing technologies often use fixed or simply adjusted parameter thresholds, which are difficult to adapt to complex and ever-changing data environments. This new technology dynamically adjusts the threshold based on a data volume ratio coefficient and the normalized Shannon entropy value. When the data volume ratio coefficient and the Shannon entropy value differ, different strategies are adopted, better adapting to fluctuations in data collection quality and the complexity of business logic. This makes the threshold setting more closely match the actual data situation and improves the accuracy of data quality assessment. It integrates business characteristics (Shannon entropy value reflects the diversity of field types, i.e., business complexity) and data quality characteristics (data volume ratio coefficient reflects the consistency of dual-source data). Compared to existing technologies that only consider a single factor, this solution can comprehensively set thresholds based on multi-dimensional information, fully considering business scenarios and data collection stability, making the threshold more reasonable and avoiding misjudgments or inaccurate assessments caused by a single factor. The formula incorporates the number of historical data collections *m* and the normalized data volume difference *C* of each target data collection. i This approach utilizes historical data to adjust and optimize thresholds. Existing technologies often lack effective use of historical data; this method allows for better learning of data change patterns, further improving the accuracy and adaptability of threshold settings.

[0123] The target data's metadata is analyzed using dynamic metadata technology to extract its business logic characteristics; specifically:

[0124] Define the specific objectives of metadata analysis and the source of the target data. Specific objectives include, for example, extracting the field types, number of fields, and business logic patterns of the target data. The source of the target data includes, for example, a relational database. Use metadata management tools to automatically collect the metadata of the target data. Metadata includes technical metadata and business metadata. Technical metadata includes, for example, data table structure, field definitions, and storage locations. Business metadata includes, for example, the business meaning of fields and business rules.

[0125] By analyzing the metadata of the target data, we can identify the field types in the target data, such as strings, dates, and numbers, and count the total number of fields in the target data as well as the number of each field type.

[0126] Analyze the dependencies between fields, such as whether the value of a certain field depends on other fields; extract the validation rules of the fields, such as format validation and range validation; and identify the calculation logic of the fields, such as whether a certain field is calculated from other fields, to obtain the business logic characteristics of the target data.

[0127] Based on the feature matching algorithm, the most matching document interface template is found in the template matching library for the business logic features of the target data; specifically:

[0128] The business logic characteristics of each document interface template in the template matching library are standardized to facilitate quantitative comparison; for example, the field types are converted into a unified code and the number of fields is normalized.

[0129] By comparing the similarity of field types, number of fields, and business logic patterns between the target data and the template, a multi-dimensional similarity is obtained.

[0130] Based on business requirements, assign different weights to field types, number of fields, and business logic patterns;

[0131] The overall similarity score is obtained by multiplying the similarity scores of each dimension by their respective weights and summing them.

[0132] The template with the highest overall similarity is selected as the most suitable document interface template; if multiple templates with the same similarity exist, the selection is based on other factors, such as the template's usage frequency and update time.

[0133] For example: Suppose the target data is an order system that contains the following fields: string, date, string, table, and number.

[0134] The template matching library contains the following two templates:

[0135] Template A: Field types: 2 string fields, 1 date field, 1 table field, 1 number field; Number of fields: 5 fields; Business logic pattern: order_total depends on order_items.

[0136] Template B: Field types: 3 string fields, 1 date field, 1 number field; Number of fields: 5 fields; Business logic pattern: No field dependency.

[0137] Based on the feature matching algorithm: Field type similarity: Template A is 0.8, Template B is 0.6; Field quantity similarity: Template A is 0.9, Template B is 0.9; Business logic pattern similarity: Template A is 0.9, Template B is 0.1.

[0138] Based on the overall similarity calculation, assuming weights of 0.4, 0.3, and 0.3 respectively:

[0139] The overall similarity of template A is: 0.8×0.4+0.9×0.3+0.9×0.3=0.82;

[0140] The overall similarity of template B is: 0.6×0.4+0.9×0.3+0.1×0.3=0.51;

[0141] Template A was ultimately selected as the most suitable document interface template.

[0142] The beneficial effects achieved by the above are as follows: By calculating multi-dimensional similarity and combining it with weight allocation, the system can flexibly adapt to different business needs and data structure changes; by adjusting the weight allocation, certain key features can be given priority, thereby better meeting the needs of specific business scenarios. This allows the system to more comprehensively and accurately evaluate the matching degree between target data and templates, effectively avoiding the limitations of single-dimensional evaluation, ensuring that the document interface template that best matches the business logic of the target data is found, thereby improving the accuracy and reliability of document interface generation.

[0143] Based on the matched document interface template, the fields of the target data are mapped to the form components defined in the document interface template; the document interface is dynamically generated based on the field mapping relationship and the document interface template layout.

[0144] The document interface type generation system based on target data includes a data acquisition unit and a data processing unit.

[0145] The data acquisition unit is configured to retrieve target data from a relational database and obtain copies of the target data from multiple channels; wherein the target data serves as the optimal data source generated by the document interface, and the copies of the target data serve as backup data; the data acquisition unit includes:

[0146] The data cleaning module is configured to map and clean the target data, its metadata, and copies of the target data, removing duplicate or inconsistent information and ensuring data consistency across different sources.

[0147] The data storage module is configured to store the metadata of the target data, forming a global metadata directory for easy subsequent querying and management.

[0148] The data processing unit includes: a quality assessment module, a template construction module, a feature extraction module, a feature matching module, an interface generation module, a user feedback module, and a review and optimization module.

[0149] The quality assessment module is configured to acquire the amount of erroneous data in the target data and its copy, evaluate the data quality of the target data and its copy based on preset parameter thresholds, and select the optimal data source based on the assessment results. Selecting a high-quality data source to generate the document interface can ensure the accuracy and completeness of the document interface, reduce business errors and decision-making mistakes caused by data quality issues, and thus improve the user experience.

[0150] The template building module is configured to design various document interface templates based on historical data and business scenarios, and store these templates to form a template matching library. This allows for quick matching of the corresponding document interface template in the template matching library based on the business logic characteristics of the target data after it is obtained.

[0151] The feature extraction module is configured to analyze the metadata of the target data based on dynamic metadata technology, extract field types, number of fields and business logic patterns, identify the dependencies, validation rules and calculation logic between fields, and obtain the business logic features of the target data.

[0152] The feature matching module is configured to analyze the business logic features of the target data based on dynamic metadata technology, and use the feature matching algorithm to find the most matching document interface template in the template matching library. By comparing the similarity of field types, the similarity of field numbers, and the similarity of business logic patterns in the target data and the template, different weights are assigned to field types, field numbers, and business logic patterns according to business requirements, the comprehensive similarity is calculated, and the template with the highest comprehensive similarity is selected as the most matching document interface template.

[0153] The interface generation module is configured to associate the fields of the target data with the form components defined in the matching document interface template, and construct the document interface based on the field association results and the layout of the document interface template.

[0154] The user feedback module is configured to periodically collect user feedback on document interface templates and business logic features; the review and optimization module is configured to periodically review document interface templates and business logic features, and formulate improvement measures based on user feedback.

[0155] Working principle: A template matching library is built to obtain the target data and a copy of the target data. The quantity of erroneous data is extracted from the target data and compared with the parameter threshold. The data with the highest quality is selected as the basis for generating the document interface. Based on dynamic metadata technology, the selected target data is feature extracted. Based on its business logic characteristics, the corresponding document interface template is matched in the template matching library, and its fields are mapped to the form components defined in the document interface template to dynamically generate the document interface.

[0156] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0157] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for generating document interface types based on target data, characterized in that, The method includes the following steps: Acquire historical data and design various document interface templates based on different business scenarios and document types within the historical data; Based on dynamic metadata technology, historical data is analyzed, and business logic features are defined for each document interface template. These features include field type, number of fields, and business logic mode. The defined document interface templates are then stored in the template matching library. The collected target data is mapped, cleaned, and quality assessed, and then stored to form a global metadata directory for the target data. The quality assessment process includes: statistical analysis of the amount of erroneous data in the target data and its copies, and data quality grading assessment by combining field type entropy analysis and dynamic threshold adjustment algorithm. The target data's metadata is analyzed using dynamic metadata technology to extract its business logic features. Based on the feature matching algorithm, the most matching document interface template for the business logic features of the target data is found in the template matching library. Specifically: Standardize the business logic features of each document interface template in the template matching library; By comparing the similarity of field types, the number of fields, and the business logic patterns between the target data and the template, a multi-dimensional similarity is obtained. Based on business requirements, assign different weights to field types, number of fields, and business logic patterns; The overall similarity score is obtained by multiplying the similarity scores of each dimension by their respective weights and summing them. Select the template with the highest overall similarity as the best matching document interface template; If multiple templates with the same similarity exist, the selection should be based on other factors. Based on the matched document interface template, map the fields of the target data to the form components defined in the document interface template; The document interface is dynamically generated based on the field mapping relationship and the document interface template layout.

2. The method for generating document interface types based on target data according to claim 1, characterized in that: Mapping and cleaning the collected target data also includes: Extract the erroneous data and their corresponding quantities from the target data, and use this as the first data volume; Obtain data related to the target data from multiple channels and use it as a copy of the target data; Extract the erroneous data and their corresponding quantities from the target data copy, and use this as the second data volume; A preset parameter threshold is set, and the first data volume and the second data volume are compared with the preset parameter threshold respectively to obtain the comparison result; Based on the comparison results, the collection quality of the target data and the target data copy is evaluated to obtain the data quality evaluation results; Based on the evaluation results, select the target data for generating the current document interface.

3. The method for generating document interface types based on target data according to claim 2, characterized in that: Preset parameter thresholds, including: Extract the total number of fields in the document interface; Categorize the fields in the document interface and obtain the corresponding field types in the document interface; For each field type, extract the percentage of the number of fields corresponding to that field type in the total number of fields; The Shannon entropy value corresponding to each field type is obtained by using the proportion of the number of fields corresponding to each field type in the total number of fields. The Shannon entropy value is normalized to obtain the normalized Shannon entropy value; Compare the first data volume and the second data volume to obtain the data volume difference between the first data volume and the second data volume; The first and second data volumes are summed and then averaged to obtain the average data volume corresponding to the first and second data volumes as a whole. The data volume difference is compared with the data volume average value to obtain the data volume ratio coefficient. The parameter threshold is set by combining the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type.

4. The method for generating document interface types based on target data according to claim 3, characterized in that: Setting parameter thresholds using the data volume ratio coefficient combined with the normalized Shannon entropy value corresponding to the field type includes: Retrieve the normalized Shannon entropy values ​​corresponding to the data volume ratio coefficient and field type; Compare the data volume ratio coefficient with the normalized Shannon entropy value corresponding to the field type; When the data volume ratio coefficient does not exceed the normalized Shannon entropy value corresponding to the field type, the preset benchmark parameter threshold is retrieved from the database as the parameter threshold for subsequent collection quality assessment. When the data volume ratio exceeds the normalized Shannon entropy value corresponding to the field type, a preset benchmark parameter threshold is retrieved from the database and adjusted. The adjusted parameter thresholds will be used as the parameter thresholds for subsequent data acquisition quality assessment.

5. The method for generating document interface types based on target data according to claim 1, characterized in that: Based on dynamic metadata technology, the metadata of the target data is analyzed to extract its business logic features, specifically: Determine the specific objectives of metadata analysis and the sources of the target data, and use metadata management tools to automatically collect the metadata of the target data. The metadata includes technical metadata and business metadata. By analyzing the metadata of the target data, the field types in the target data are identified, and the total number of fields in the target data and the number of each field type are counted. Analyze the dependencies between fields, extract the validation rules of the fields, and identify the calculation logic of the fields to obtain the business logic characteristics of the target data.

6. The method for generating document interface types based on target data according to claim 2, characterized in that: Based on the comparison results, the acquisition quality of the target data and its copy is evaluated, specifically as follows: If the comparison result shows that the first data volume exceeds the parameter threshold and the second data volume does not exceed the parameter threshold, it indicates that the collection quality of the target data corresponding to the first data volume is low. Therefore, the target data copy corresponding to the second data volume is used as the target data in the current document generation interface. If the comparison result shows that the first data volume does not exceed the parameter threshold, but the second data volume exceeds the parameter threshold, it indicates that the collection quality of the target data copy corresponding to the second data volume is low. Therefore, the target data corresponding to the first data volume is used as the target data for the current document generation interface. If the comparison results show that both the first and second data volumes exceed the parameter threshold, the acquisition channels of the target data and the target data copy are verified to determine whether there are any omissions in the transmission process. If the transmission is verified to be normal, the data with the lower proportion exceeding the parameter threshold is used as the target data for the current document generation interface. If the comparison results show that neither the first data volume nor the second data volume exceeds the parameter threshold, then the target data corresponding to the first data volume will be used as the target data for the current document generation interface.

7. The method for generating document interface types based on target data according to claim 1, characterized in that: After storing the defined document interface template in the template matching library, it includes: Review the document interface templates and business logic features, regularly collect user feedback on the document interface templates and business logic features, formulate improvement measures based on the review results and user feedback, and make timely adjustments and optimizations.

8. A document interface type generation system based on target data, applied in the document interface type generation method based on target data as described in any one of claims 1-7, characterized in that, The system includes a data acquisition unit and a data processing unit, wherein the data processing unit includes a quality assessment module, a feature extraction module, and a feature matching module. The data acquisition unit is configured to retrieve target data from a relational database and obtain copies of the target data from multiple channels; The quality assessment module is configured to obtain the amount of data corresponding to erroneous data in the target data and the target data copy, assess the data quality of the target data and the target data copy based on preset parameter thresholds, and select the optimal data source based on the assessment results; The feature extraction module is configured to analyze the metadata of the target data based on dynamic metadata technology, identify the dependencies between fields, validation rules and calculation logic, and obtain the business logic features of the target data. The feature matching module is configured to use a feature matching algorithm to find the most matching document interface template in the template matching library; The template building module is configured to design various document interface templates based on historical data and business scenarios, and store these templates to form a template matching library. The interface generation module is configured to associate the fields of the target data with the form components defined in the matching document interface template, and construct the document interface based on the field association results and the layout of the document interface template. The user feedback module is configured to periodically collect user feedback on document interface templates and business logic features. The review and optimization module is configured to periodically review document interface templates and business logic features, and formulate improvement measures based on user feedback.

9. The document interface type generation system based on target data according to claim 8, characterized in that: The data acquisition unit includes: The data cleaning module is configured to map and clean the target data, its metadata, and copies of the target data. The data storage module is configured to store the metadata of the target data, forming a global metadata directory.

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