Report processing method and device, equipment and medium

Abstract

The invention relates to the technical field of computers, and discloses a report processing method and device, equipment and a medium, and the method comprises the steps: obtaining a report field to be processed and corresponding field access information, and carrying out the structural processing of the field access information, and obtaining an access behavior data set; analyzing the access behavior data set through a text processing model to obtain a field access analysis result; performing association processing on the field access analysis result and processing logic data of the report field to obtain a field calculation relationship, and analyzing the field calculation relationship through a relationship analysis model to obtain a blood relationship analysis result; and performing offline condition matching on the report fields based on the blood relationship analysis result and the field access analysis result, and performing offline processing on the report fields meeting offline conditions. The report processing method and device can be applied to financial science and technology or medical care service program systems, field-level refined report processing can be achieved, and therefore the processing efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, device, and medium for processing reports. Background Technology

[0002] In financial institutions and healthcare and elderly care service providers, reporting systems are rapidly expanding, with the number of reports growing exponentially, covering multi-dimensional information such as account management, risk assessment, customer health records, and elderly care plan statistics. However, existing reporting governance methods have significant shortcomings, mainly relying on monitoring report access volume and only taking low-frequency reports offline. They fail to identify redundant fields in high-frequency reports, leading to data redundancy and wasted storage and computing resources.

[0003] The existing technologies lack field-level governance capabilities, only enabling coarse-grained management of overall report dimensions. They fail to identify the actual usage of fields such as financial transaction details and health monitoring indicators. The underlying wide-table processing logic becomes increasingly complex with the accumulation of fields, leading to increased data processing delays, significantly higher maintenance costs, and greater manual workload, impacting financial risk control, wealth management product analysis, and the quality of elderly care services. Furthermore, the technology relies on human experience for field dependency analysis, resulting in low efficiency, high error rates, and difficulty in establishing closed-loop management, thus limiting the intelligence and sustainability of data governance. Therefore, a report processing method is urgently needed that can achieve fine-grained field-level report processing, thereby improving report processing efficiency and accuracy. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for processing reports, in order to solve the technical problem in related technologies that the inability to achieve fine-grained report processing at the field level leads to low report processing efficiency and accuracy.

[0005] Firstly, a method for processing reports is provided, the method comprising: Obtain the report fields to be processed and their corresponding field access information, and perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The access behavior dataset is analyzed using a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The field access analysis results are associated with the processing logic data of the report fields to obtain the field calculation relationship. The field calculation relationship is then parsed using a relationship parsing model to obtain the lineage relationship analysis results. The lineage relationship analysis results are used to describe the source information, calculation path, and upstream and downstream relationship information of the report fields. Based on the bloodline analysis results and the field access analysis results, the report fields are matched for offline conditions, and the report fields that meet the offline conditions are taken offline.

[0006] Secondly, a report processing apparatus is provided, comprising: The acquisition module is used to acquire the report fields to be processed and their corresponding field access information, and to perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The analysis module is used to analyze the access behavior data set through a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The parsing module is used to associate the field access analysis results with the processing logic data of the report fields to obtain the field calculation relationship, and to parse the field calculation relationship through the relationship parsing model to obtain the lineage relationship analysis result; wherein, the lineage relationship analysis result is used to describe the source information, calculation path and upstream and downstream relationship information of the report fields; The processing module is used to perform offline condition matching on the report fields based on the blood relationship analysis results and the field access analysis results, and to offline the report fields that meet the offline conditions.

[0007] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned report processing method.

[0008] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described report processing method.

[0009] The solution implemented by the above-mentioned report processing method, device, computer equipment, and storage medium includes the following steps: obtaining the report fields to be processed and their corresponding field access information, and performing structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; analyzing the access behavior data set through a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; associating the field access analysis results with the processing logic data of the report fields to obtain field calculation relationships, and parsing the field calculation relationships through a relationship parsing model to obtain lineage relationship analysis results; wherein, the lineage relationship analysis results are used to describe the source information, calculation path, and upstream and downstream association information of the report fields; based on the lineage relationship analysis results and the field access analysis results, performing offline condition matching on the report fields, and offline processing on the report fields that meet the offline conditions. In this invention, by structuring the field access information of report fields and analyzing the access behavior data set using a text processing model, the usage of each report field under different time periods and user groups can be accurately characterized. This yields information on field usage activity and field access pattern characteristics, enabling refined governance from the report level to the field level. Simultaneously, the field access analysis results are correlated with the corresponding processing logic data of the report fields. A relational analysis model is used to parse the field calculation relationships, automatically generating lineage analysis results reflecting field source information, calculation paths, and upstream and downstream connections. This avoids the risk of accidental deletion of core business fields during the field decommissioning process. Based on the lineage analysis results and field access analysis results, decommissioning conditions are matched to automate and trace the decommissioning of report fields. This significantly reduces the workload and error probability of manually analyzing field dependencies and modifying processing logic, contributing to the construction of a continuously optimized data governance closed loop. This improves the operational stability, maintainability, and resource utilization efficiency of data platforms in the financial, healthcare, and elderly care sectors, achieving a comprehensive technical effect that combines intelligent, refined, and automated governance. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an application environment for a report processing method according to an embodiment of the present invention; Figure 2This is a flowchart illustrating a report processing method according to an embodiment of the present invention; Figure 3 yes Figure 2 A schematic diagram of a specific implementation method for step S10; Figure 4 This is a schematic diagram of a report processing device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 6 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0012] 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, not all, of the embodiments of the present invention. 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.

[0013] The report processing method provided in this embodiment of the invention can be applied to, for example... Figure 1In this application environment, the client communicates with the server via a network. The server can obtain the report fields to be processed and their corresponding field access information from the client, and perform structured processing on the field access information to obtain an access behavior data set. The field access information includes call count information, call time distribution information, and the association information between the field and the user. The server analyzes the access behavior data set using a text processing model to obtain field access analysis results. These results include field usage activity and field access pattern characteristics. The server associates the field access analysis results with the processing logic data of the report fields to obtain field calculation relationships, and then parses these relationships using a relationship parsing model to obtain lineage relationship analysis results. These results describe the source information, calculation path, and upstream and downstream association information of the report fields. Based on the lineage relationship analysis results and the field access analysis results, the server matches the report fields against offline conditions and removes report fields that meet the offline conditions. In this invention, by structuring the field access information of report fields and analyzing the access behavior data set using a text processing model, the usage of each report field under different time periods and user groups can be accurately characterized. This yields information on field usage activity and field access pattern characteristics, enabling refined governance from the report level to the field level. Simultaneously, the field access analysis results are correlated with the corresponding processing logic data of the report fields. A relational analysis model is used to parse the field calculation relationships, automatically generating lineage analysis results reflecting field source information, calculation paths, and upstream and downstream connections. This avoids the risk of accidental deletion of core business fields during the field decommissioning process. Based on the lineage analysis results and field access analysis results, decommissioning conditions are matched to automate and trace the decommissioning of report fields. This significantly reduces the workload and error probability of manually analyzing field dependencies and modifying processing logic, contributing to the construction of a continuously optimized data governance closed loop. This improves the operational stability, maintainability, and resource utilization efficiency of data platforms in the financial, healthcare, and elderly care sectors, achieving a comprehensive technical effect that combines intelligent, refined, and automated governance.

[0014] The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0015] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a report processing method provided in an embodiment of the present invention is shown, the method comprising the following steps: S10: Obtain the report fields to be processed and their corresponding field access information, and perform structured processing on the field access information to obtain a set of access behavior data.

[0016] The field access information includes call count information, call time distribution information, and association information between the field and the user.

[0017] It should be noted that the report fields to be processed can be various indicators or dimensions in financial business reports, medical operation reports, or elderly care service management reports. The field access information is the field call information recorded by the system in daily operation, including the number of times the field is accessed, the distribution of access time, and the access relationship between the field and specific users or user groups. This application does not limit this.

[0018] For example, by structuring field access information, raw logs or access records can be transformed into a dataset that facilitates subsequent analysis—a dataset of access behavior data. This dataset reflects the usage activity and patterns of each field across different time periods and user groups, providing foundational data support for subsequent access analysis and field lineage analysis based on large models. It also helps in accurately identifying high-frequency and low-frequency fields in risk monitoring of financial statements, indicator tracking of medical statements, and statistical analysis of pension data reports, thus laying a data foundation for field-level governance.

[0019] Among them, such as Figure 3 As shown, in step S10, the structured processing of the field access information to obtain the access behavior data set includes the following steps: S11: Perform field identifier alignment and time information unification processing on the field access information to obtain the original field access data.

[0020] S12: Filter the original data of the field access to obtain cleaned data of the field access.

[0021] S13: Aggregate and calculate the field access cleaning data according to a preset time granularity to generate field access statistics.

[0022] S14: Integrate the field access statistics data with the preset field basic attributes to form an access behavior data set.

[0023] For example, field identifier alignment refers to establishing a field mapping table in a data warehouse or log system to uniformly identify fields that are logically identical but have different names from different reports or systems, in order to ensure consistency in subsequent analysis; unified processing of time information refers to using data processing scripts or ETL tools to standardize the timestamps in access logs, including unifying time zones, format conversion, and abnormal time correction, thereby transforming the raw access records into raw field access data that can be used for analysis, providing a reliable foundation for subsequent cleaning and aggregation.

[0024] Furthermore, the raw data for field access can be filtered. For example, rule engines or data processing scripts can be used to remove duplicate records, eliminate access anomalies, filter invalid access information automatically generated by the system, and handle missing or incorrect field identifiers to obtain cleaned field access data. This ensures the accuracy and completeness of the data, providing reliable input for subsequent statistical analysis. The cleaned access data can be summarized daily, weekly, or monthly through SQL queries or distributed computing frameworks (such as Spark or Flink) to count the number of calls, access frequency, and trend changes of each field, forming quantitative indicators. The cleaned field access data can then be aggregated and calculated according to a preset time granularity to generate field access statistics, thus providing basic data support for subsequent access pattern analysis and field activity assessment of large models. For example, by linking data tables or using large models to assist in matching, access statistics can be integrated with basic field attribute tables (including information such as the report to which the field belongs, field type, and business classification) to generate a structured access behavior data set. This provides comprehensive data support for subsequent field access analysis, lineage analysis, and automated governance based on large models. It can also be applied to scenarios such as financial statement risk control, medical operation indicator monitoring, and elderly care business data management to achieve refined and intelligent field-level governance.

[0025] S20: Analyze the access behavior data set using a text processing model to obtain field access analysis results.

[0026] The field access analysis results include field usage activity and field access pattern characteristics.

[0027] It should be noted that the access behavior data set includes field access statistics and basic field attribute information, which can reflect the usage of each field in different time periods and among different user groups.

[0028] For example, text processing models can leverage natural language processing, vectorized encoding, or deep learning techniques to perform semantic understanding and pattern recognition of field names, descriptions, and access behavior patterns. This allows for the analysis of the actual usage activity of fields, i.e., the frequency, cycle, and trend changes of field access and retrieval in business reports. Simultaneously, the model can extract field access pattern characteristics, such as access patterns, dependencies, and potential reuse across different user groups, reports, or time periods. This provides data support for subsequent large-scale model-based field lineage analysis, offline condition matching, and automated governance. Through this analysis, the value of each field in financial statements, medical operation reports, and elderly care service reports can be quantitatively assessed, laying the foundation for refined and intelligent data governance.

[0029] In some embodiments, the step of analyzing the access behavior data set using a text processing model to obtain field access analysis results includes: extracting features from the access behavior data set using the text processing model to obtain feature vectors of field usage activity and field access pattern; analyzing the feature vectors of field access pattern using the text processing model to obtain field access pattern features; and determining the field usage activity and field access pattern features as the field access analysis results.

[0030] For example, a text processing model can extract features from various field access statistics and basic attribute information in an access behavior dataset. It encodes information such as the number of calls, access frequency, access time distribution, and user associations for each field into vectorized representations, generating field usage activity indicators and field access pattern feature vectors. Subsequently, the text processing model further analyzes these field access pattern feature vectors, using techniques such as pattern recognition, clustering, or sequence modeling to uncover access patterns of fields across different time periods, reports, or user groups, thereby obtaining field access pattern characteristics. Finally, the field usage activity indicators are combined with the field access pattern characteristics to form a complete field access analysis result. This analysis result can quantify the actual usage value and access patterns of each field, providing data support for subsequent large-scale model-based field lineage analysis, offline condition matching, and automated governance. It also supports application scenarios such as financial report risk control, medical operation indicator tracking, and elderly care service data management, enabling refined and intelligent analysis and management of report fields.

[0031] S30: The field access analysis results are associated with the processing logic data of the report fields to obtain the field calculation relationship. This relationship is then parsed using a relationship parsing model to obtain the lineage analysis results. The lineage analysis results describe the source information, calculation path, and upstream and downstream association information of the report fields.

[0032] It should be noted that the processing logic data of the report fields includes information such as the generation rules, SQL scripts, or data processing flow of the fields in the underlying wide table or calculated table, which is not limited in this application.

[0033] For example, by first associating the field access analysis results with the processing logic data, a correspondence can be established between the field usage and its calculation source and rules, thus forming field calculation relationships and clarifying the calculation dependencies of each field in the report. Subsequently, the relationship resolution model uses techniques such as graph structure analysis, dependency resolution, or rule reasoning to parse the field calculation relationships and generate field-level lineage analysis results. These results describe in detail the source information, calculation path, and relationships between upstream and downstream fields for each report field.

[0034] This lineage analysis can clearly track the flow and dependencies of fields throughout the entire data processing chain, providing precise data support for risk control in financial statements, statistics on medical indicators, and the removal, logic optimization, and automated governance of fields in elderly care business reports. This ensures that redundant fields can be identified during the refined governance process, while avoiding impact on the calculation and use of key business fields.

[0035] In some embodiments, associating the field access analysis result with the processing logic data of the report field to obtain the field calculation relationship includes: obtaining the processing logic data of the report field; wherein the processing logic data includes an SQL script or a Python script; performing field mapping parsing on the processing logic data to generate a correspondence between the report field and the processing logic data; matching the field access analysis result with the correspondence, and determining the matching result as the field calculation relationship.

[0036] For example, the processing logic data for report fields can first be obtained. This data may include SQL scripts or Python scripts, which describe the generation rules and processing flow of fields in the underlying wide table or calculated table. Then, field mapping parsing is performed on the processing logic data. That is, by parsing field references, calculation expressions, and function call relationships in SQL statements or Python scripts, a correspondence between report fields and processing logic data is generated, clarifying the calculation source and dependency path of each field. Next, the field access analysis results are matched with the correspondence, associating the field's usage activity and access pattern information with its calculation source and processing logic, thereby forming field calculation relationships.

[0037] This step enables the establishment of a mapping between the access value and processing dependency of fields in financial business reports, medical operation reports, and elderly care service reports. This provides accurate input for subsequent lineage analysis based on relational parsing models, ensuring that when fields are taken offline or logic is optimized, redundant or low-value fields can be identified while avoiding impact on the calculation of key business fields and report results, thus achieving refined and intelligent data governance.

[0038] In some embodiments, the step of parsing the field calculation relationship through the relationship parsing model to obtain the bloodline relationship analysis result includes: extracting features from the field calculation relationship to obtain a field source feature vector, a calculation path feature vector, and an upstream and downstream association feature vector; parsing the field source feature vector, the calculation path feature vector, and the upstream and downstream association feature vector through the relationship parsing model to obtain the source information, the calculation path, and the upstream and downstream association information, and determining them as the bloodline relationship analysis result.

[0039] For example, the calculation relationships of fields can first be feature extracted, encoding the calculation source, calculation path, and dependencies of each field with upstream and downstream fields into vector form, i.e., generating field source feature vectors, calculation path feature vectors, and upstream and downstream association feature vectors. These vectorized representations can transform complex field dependencies and processing logic into numerical features that can be processed by the model. Subsequently, the relationship parsing model analyzes and parses the above feature vectors, automatically identifying the source information, calculation path, and upstream and downstream field association information of each field through graph structure modeling, dependency reasoning, or deep learning methods. Finally, the parsed source information, calculation path, and upstream and downstream association information are integrated to form a complete lineage relationship analysis result. This result can reflect in detail the generation source, processing logic, and dependencies of report fields in the entire data processing chain, providing accurate data support for financial report risk control analysis, medical operation indicator statistics, and field decommissioning, logic optimization, and automated governance of elderly care service reports. This ensures that redundant fields can be cleaned up during the refined governance process, while protecting the calculation integrity of key business fields and the accuracy of reports.

[0040] S40: Based on the bloodline analysis results and the field access analysis results, perform offline condition matching on the report fields, and offline the report fields that meet the offline conditions.

[0041] It should be noted that the lineage analysis results reflect the source information, calculation path, and upstream and downstream dependencies of each report field, while the field access analysis results quantify the field's usage activity and access pattern characteristics.

[0042] For example, fields with low access activity and infrequent usage patterns can be filtered first. Simultaneously, based on lineage analysis results, it can be determined whether these fields depend on downstream reports or key business calculations. If a field has both low access activity and no direct dependency on core business fields in the lineage chain, it meets the criteria for being taken offline. Subsequently, these fields are automatically taken offline, including marking the field as offline in the processing logic code, generating modification suggestions, or disabling field calculations and storage in the data warehouse.

[0043] This step enables the safe and precise removal of redundant fields from financial statement risk control, medical operation indicator statistics, and elderly care business reports, achieving refined field-level governance while avoiding negative impacts on the calculation results and data integrity of core business reports, thereby improving the efficiency of data platform resource utilization and the level of governance automation.

[0044] In some embodiments, the step of matching the report fields with offline conditions based on the lineage analysis results and the field access analysis results, and offlineing the report fields that meet the offline conditions, includes: extracting field dependency features from the lineage analysis results and extracting field activity features from the field access analysis results; identifying the field dependency features and the field activity features according to preset offline rules to obtain the report fields that meet the offline conditions, and performing the offline processing on the report fields that meet the offline conditions.

[0045] For example, firstly, feature extraction can be performed on the lineage analysis results to obtain field dependency features, which describe the upstream and downstream dependencies, calculation paths, and impact range of each field in the report calculation chain. Simultaneously, feature extraction can be performed on the field access analysis results to obtain field activity features, which quantify the actual usage frequency, access patterns, and usage value of the fields. Then, based on preset offline rules, the field dependency features and field activity features are comprehensively identified. For example, fields with low access activity and no direct dependency on core business fields in the lineage chain are identified and determined as report fields meeting the offline criteria. Finally, offline processing is performed on these fields that meet the offline criteria, including inserting offline markers in the processing logic code, generating modification suggestions, or disabling the calculation and storage of the fields in the data warehouse. Through this step, redundant fields can be safely and accurately cleaned up in financial report risk control, medical operation analysis, and elderly care service reports, achieving refined field-level governance while ensuring the calculation integrity of core business fields and the accuracy of reports, improving the resource utilization efficiency and automated governance level of the data platform.

[0046] In some embodiments, the method further includes: performing regression validation on the offline processed report fields to obtain a validation result; the validation result is used to indicate whether the offline processed report fields affect the integrity of the report calculation result; if the validation result indicates that the offline processed report fields do not affect the integrity of the report calculation result, then generating an offline operation log; and updating the lineage analysis result and the field access analysis result based on the offline operation log.

[0047] For example, the report calculation logic can be re-executed for the offline field, and the report results before and after processing can be compared to obtain a verification result. This result indicates whether the offline field affects the completeness of the report calculation results. If the verification result shows that the offline field does not affect the core calculation and report accuracy, the system generates an offline operation log, recording the offline field, offline time, and related operation information for traceability and auditing. Simultaneously, based on this offline operation log, the lineage analysis results and field access analysis results can be updated, removing the offline field and its dependencies from the analysis model to maintain the real-time performance and consistency of the data governance model. Through this step, secure and traceable field-level offline operations can be achieved in financial report risk control, medical operation analysis, and elderly care service reports, while ensuring the integrity of core business report calculations and forming a closed-loop automated governance process, improving the stability, reliability, and management efficiency of the data platform.

[0048] As can be seen, the above solution, by structuring the field access information of report fields and analyzing the access behavior data set using a text processing model, accurately depicts the usage of each report field under different time periods and user groups, thereby obtaining field usage activity and field access pattern characteristics, achieving refined governance from the report level to the field level. Simultaneously, the field access analysis results are correlated with the corresponding processing logic data of the report fields, and the field calculation relationships are analyzed using a relational parsing model, automatically generating lineage analysis results reflecting field source information, calculation paths, and upstream and downstream connections. This avoids the risk of accidental deletion of core business fields during the field decommissioning process. Based on the lineage analysis results and field access analysis results, decommissioning conditions are matched to achieve automated and traceable processing of report field decommissioning, significantly reducing the workload and error probability of manual analysis of field dependencies and modification of processing logic. This helps to build a continuously optimized data governance closed loop, thereby improving the operational stability, maintainability, and resource utilization efficiency of data platforms in the financial, healthcare, and elderly care fields, achieving a comprehensive technical effect of intelligent, refined, and automated governance.

[0049] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] In one embodiment, a report processing apparatus is provided, which corresponds one-to-one with the report processing methods in the above embodiments. For example... Figure 4 As shown, the report processing device includes an acquisition module 101, an analysis module 102, a parsing module 103, and a processing module 104. Detailed descriptions of each functional module are as follows: The acquisition module 101 is used to acquire the report fields to be processed and their corresponding field access information, and to perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The analysis module 102 is used to analyze the access behavior data set through a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The parsing module 103 is used to associate the field access analysis results with the processing logic data of the report fields to obtain the field calculation relationship, and to parse the field calculation relationship through the relationship parsing model to obtain the lineage relationship analysis result; wherein, the lineage relationship analysis result is used to describe the source information, calculation path and upstream and downstream association information of the report fields; The processing module 104 is used to perform offline condition matching on the report fields based on the blood relationship analysis results and the field access analysis results, and to offline the report fields that meet the offline conditions.

[0051] The acquisition module 101 performs field identifier alignment and time information unification processing on the field access information to obtain raw field access data; filters the raw field access data to obtain cleaned field access data; aggregates and calculates the cleaned field access data according to a preset time granularity to generate field access statistics data; and integrates the field access statistics data with preset field basic attributes to form an access behavior data set.

[0052] The analysis module 102 is used to extract features from the access behavior data set through the text processing model to obtain the field usage activity and field access pattern feature vectors; analyze the field access pattern feature vectors through the text processing model to obtain the field access pattern features; and determine the field usage activity and the field access pattern features as the field access analysis results.

[0053] The parsing module 103 is used to obtain the processing logic data of the report fields; wherein, the processing logic data includes SQL scripts or Python scripts; to perform field mapping parsing on the processing logic data to generate a correspondence between the report fields and the processing logic data; to match the field access analysis results with the correspondence, and to determine the matching results as the field calculation relationship.

[0054] The parsing module 103 is used to extract features from the field calculation relationship to obtain the field source feature vector, the calculation path feature vector, and the upstream and downstream association feature vector; the relationship parsing model is used to parse the field source feature vector, the calculation path feature vector, and the upstream and downstream association feature vector to obtain the source information, the calculation path, and the upstream and downstream association information, and to determine them as the blood relationship analysis result.

[0055] The processing module 104 is used to extract field dependency features from the bloodline analysis results and extract field activity features from the field access analysis results; identify the field dependency features and field activity features according to preset offline rules to obtain report fields that meet the offline conditions, and perform offline processing on the report fields that meet the offline conditions.

[0056] The processing module 104 is used to perform regression verification on the report fields after the offline processing to obtain a verification result; the verification result is used to indicate whether the report fields after the offline processing affect the integrity of the report calculation result; if the verification result indicates that the report fields after the offline processing do not affect the integrity of the report calculation result, an offline operation log is generated; the lineage analysis result and the field access analysis result are updated based on the offline operation log.

[0057] This invention provides a report processing device that, by structuring the field access information of report fields and analyzing the access behavior data set using a text processing model, accurately depicts the usage of each report field under different time periods and user groups, thereby obtaining field usage activity and field access pattern characteristics, achieving refined governance from the report level to the field level. Simultaneously, it correlates the field access analysis results with the corresponding processing logic data of the report fields, and uses a relational parsing model to analyze the field calculation relationships, automatically generating lineage analysis results reflecting field source information, calculation paths, and upstream and downstream connections. This avoids the risk of accidental deletion of core business fields during the field decommissioning process. Based on the lineage analysis results and field access analysis results, decommissioning conditions are matched to achieve automated and traceable processing of report field decommissioning, significantly reducing the workload and error probability of manual analysis of field dependencies and modification of processing logic. This helps to build a continuously optimized data governance closed loop, thereby improving the operational stability, maintainability, and resource utilization efficiency of data platforms in the financial, healthcare, and elderly care fields, achieving a comprehensive technical effect of intelligent, refined, and automated governance.

[0058] For specific limitations regarding the report processing device, please refer to the limitations on the report processing methods described above, which will not be repeated here. Each module in the aforementioned report processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0059] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements a report processing method, server-side functions, or steps.

[0060] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements a report processing method, client-side functions, or steps.

[0061] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain the report fields to be processed and their corresponding field access information, and perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The access behavior dataset is analyzed using a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The field access analysis results are associated with the processing logic data of the report fields to obtain the field calculation relationship. The field calculation relationship is then parsed using a relationship parsing model to obtain the lineage relationship analysis results. The lineage relationship analysis results are used to describe the source information, calculation path, and upstream and downstream relationship information of the report fields. Based on the bloodline analysis results and the field access analysis results, the report fields are matched for offline conditions, and the report fields that meet the offline conditions are taken offline.

[0062] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Obtain the report fields to be processed and their corresponding field access information, and perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The access behavior dataset is analyzed using a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The field access analysis results are associated with the processing logic data of the report fields to obtain the field calculation relationship. The field calculation relationship is then parsed using a relationship parsing model to obtain the lineage relationship analysis results. The lineage relationship analysis results are used to describe the source information, calculation path, and upstream and downstream relationship information of the report fields. Based on the bloodline analysis results and the field access analysis results, the report fields are matched for offline conditions, and the report fields that meet the offline conditions are taken offline.

[0063] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0064] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0065] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0066] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for processing reports, characterized in that, The method includes: Obtain the report fields to be processed and their corresponding field access information, and perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The access behavior dataset is analyzed using a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The field access analysis results are associated with the processing logic data of the report fields to obtain the field calculation relationship. The field calculation relationship is then parsed using a relationship parsing model to obtain the lineage relationship analysis results. The lineage relationship analysis results are used to describe the source information, calculation path, and upstream and downstream relationship information of the report fields. Based on the bloodline analysis results and the field access analysis results, the report fields are matched for offline conditions, and the report fields that meet the offline conditions are taken offline.

2. The method according to claim 1, characterized in that, The structured processing of the field access information yields an access behavior data set, including: The field access information is processed by aligning field identifiers and unifying time information to obtain the original field access data; The original data accessed by the field is filtered and processed to obtain cleaned data for the field access. The field access cleansing data is aggregated and calculated according to a preset time granularity to generate field access statistics data; The field access statistics data are integrated with the preset field basic attributes to form an access behavior data set.

3. The method according to claim 1, characterized in that, The analysis of the access behavior data set using a text processing model to obtain field access analysis results includes: The text processing model is used to extract features from the access behavior dataset to obtain feature vectors of field usage activity and field access pattern. The field access pattern features are obtained by analyzing the feature vector of the field access pattern using the text processing model. The field usage activity and the field access pattern characteristics are determined as the field access analysis results.

4. The method according to claim 1, characterized in that, The step of associating the field access analysis results with the processing logic data of the report fields to obtain the field calculation relationship includes: Obtain the processing logic data of the report fields; wherein, the processing logic data includes SQL scripts or Python scripts; The processing logic data is parsed by field mapping to generate the correspondence between the report fields and the processing logic data; The field access analysis results are matched with the corresponding relationships, and the matching results are determined as the field calculation relationships.

5. The method according to claim 1, characterized in that, The process of parsing the relationships between the fields using a relation parsing model to obtain the bloodline analysis results includes: Feature extraction is performed on the calculated relationships of the fields to obtain the field source feature vector, the calculation path feature vector, and the upstream and downstream association feature vector; The relationship parsing model is used to parse the source feature vector of the field, the calculation path feature vector, and the upstream and downstream association feature vector to obtain the source information, the calculation path, and the upstream and downstream association information, which are then determined as the blood relationship analysis results.

6. The method according to claim 1, characterized in that, The process of matching the report fields against offline conditions based on the bloodline analysis results and the field access analysis results, and offlineing the report fields that meet the offline conditions, includes: Field dependency features are extracted from the bloodline relationship analysis results, and field activity features are extracted from the field access analysis results; The field dependency features and field activity features are identified according to the preset offline rules to obtain the report fields that meet the offline conditions, and the offline processing is performed on the report fields that meet the offline conditions.

7. The method according to claim 1, characterized in that, The method further includes: The report fields after the offline processing are subjected to regression validation to obtain the validation results; the validation results are used to indicate whether the offline processing of the report fields affects the integrity of the report calculation results. If the verification result indicates that the report fields after the offline processing do not affect the integrity of the report calculation results, then an offline operation log is generated; The bloodline analysis results and the field access analysis results are updated based on the offline operation log.

8. A report processing device, characterized in that, include: The acquisition module is used to acquire the report fields to be processed and their corresponding field access information, and to perform structured processing on the field access information to obtain an access behavior data set; wherein, the field access information includes call count information, call time distribution information, and association information between the field and the user; The analysis module is used to analyze the access behavior data set through a text processing model to obtain field access analysis results; wherein, the field access analysis results include field usage activity and field access pattern characteristics; The parsing module is used to associate the field access analysis results with the processing logic data of the report fields to obtain the field calculation relationship, and to parse the field calculation relationship through the relationship parsing model to obtain the lineage relationship analysis result; wherein, the lineage relationship analysis result is used to describe the source information, calculation path and upstream and downstream relationship information of the report fields; The processing module is used to perform offline condition matching on the report fields based on the blood relationship analysis results and the field access analysis results, and to offline the report fields that meet the offline conditions.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the report processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the report processing method as described in any one of claims 1 to 7.

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