Enterprise industry data standardization processing system supporting cross-cycle comparable analysis

Abstract

The invention relates to the technical field of data processing, and provides an enterprise industrial data standardization processing system supporting cross-cycle comparable analysis, which comprises a data acquisition module for acquiring data and converting the data into a first data packet in an internal intermediate format; a data identification module identifies the statistical aperture attribute, the currency unit attribute and the industry classification code version attribute of the data items in the first data packet, and generates a second data packet carrying an attribute tag; the standardization conversion module executes industry code version conversion, currency unit conversion and statistical benchmark unification according to the attribute label to generate a third data packet; the consistency check module performs logic check and correction processing on the third data packet to generate a fourth data packet; and the output module encapsulates the fourth data packet into standardized data in a target format. According to the system, the problem of data incomparability caused by differences of statistical calibers, currency units and industry classification standards is solved through an automatic processing flow, and the reliability of a cross-cycle industry analysis result is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a standardized processing system for enterprise industry data that supports cross-cycle comparable analysis. Background Technology

[0002] Existing cross-cycle enterprise and industry data analysis technologies suffer from the following technical pain points: In cross-year and cross-economic cycle enterprise and industry data analysis and forecasting scenarios, data sources are diverse, and their statistical calibers, currency units, and industry classification standards may differ due to policy adjustments or regional variations, resulting in a lack of direct comparability between historical and current data. For example, updates to industry classification standards lead to changes in the industry codes of enterprises; without mapping and conversion, cross-cycle industry scale statistics will be biased. Multinational enterprise data involves different currencies, and exchange rate fluctuations distort cross-period comparisons of financial indicators denominated in local currencies. Statistical departments may adjust the statistical caliber of the same economic indicator, such as whether enterprise revenue includes value-added tax; data from different periods is collected using different calibers, and direct comparisons will mislead trend judgments. Due to the lack of automated caliber identification and conversion mechanisms, a large amount of manual standardization processing is required before data analysis, a cumbersome process prone to human error, ultimately affecting the input quality of the analysis model and the reliability of the forecast conclusions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a standardized processing system for enterprise industry data that supports cross-cycle comparable analysis, solving the technical problem that data incomparability caused by different statistical standards, currency units, industry classification adjustments, etc., affects the reliability of analysis conclusions.

[0004] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: The present invention provides an enterprise industry data standardization processing system that supports cross-cycle comparable analysis, including a data acquisition module, a data identification module, a standardization conversion module, a consistency check module, and an output module; The data acquisition module is configured to acquire raw enterprise industry data from an external data source and convert the raw enterprise industry data into a first data packet in an internal intermediate format. The data recognition module is configured to receive the first data packet, recognize the statistical caliber attribute, currency unit attribute, and industry classification code version attribute of the data items in the first data packet, and generate a second data packet carrying the recognized attributes. The standardization conversion module is configured to receive the second data packet, and perform conversion on the data items in the second data packet according to the statistical caliber attribute, currency unit attribute and industry classification code version attribute carried in the second data packet to generate a third data packet. The conversion includes: performing version conversion on the industry classification code according to the industry classification code mapping table, converting the currency unit according to the exchange rate, and standardizing the numerical values ​​according to the statistical caliber adjustment formula. The consistency check module is configured to receive the third data packet, perform logical verification on the data items in the third data packet, perform correction processing when the verification is abnormal, and generate a fourth data packet. The output module is configured to receive the fourth data packet, encapsulate the fourth data packet into standardized data of the target format, and output it.

[0005] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the data acquisition module is configured as follows: The interface connects to an external data source via a protocol adapter to receive raw data. The original data is parsed to obtain the parsed data entities and attribute fields; The parsed data entities and attribute fields are organized into an internal intermediate format of data to be processed. The data to be processed is filtered to remove invalid characters and blank records, and cleaned data is generated. Add metadata including data source identifier and data reporting cycle identifier to the cleaned data; The data with added metadata is encapsulated into the first data packet, and the first data packet is sent to the data buffer.

[0006] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the data identification module is configured as follows: Retrieve the first data packet from the data buffer; Extract field names, numerical samples, and descriptive text from the first data packet; The extracted field names are matched with the source identifier patterns in the preset rule base to determine the data source code; Search for statistical caliber definition keywords in the preset rule base within the descriptive text to determine the statistical caliber attribute; Match the currency unit symbol in the preset rule base in the field name or the numerical sample to determine the currency unit attribute; Based on the industry code format of the data items in the first data packet, compare it with the industry classification code version identifier in the preset rule base to determine the industry classification code version attribute; The data source code, statistical caliber attribute, currency unit attribute, and industry classification code version attribute are appended to the corresponding data items in the first data packet using structured tags to generate the second data packet.

[0007] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the standardization conversion module is configured as follows: Receive the second data packet and read the structured tags attached to the data items in the second data packet; Based on the industry classification code version attribute in the structured tag, query the industry classification code mapping table and replace the historical industry code in the data item with the current standard industry code; Based on the currency unit attribute in the structured tag and the timestamp carried in the second data packet, the exchange rate service interface is accessed to obtain the exchange rate, and the value in the data item is converted to currency. Based on the statistical caliber attribute in the structured tag, the calculation rules in the caliber adjustment formula library are called to recalculate the values ​​in the data items to unify the statistical benchmark. The data items that have undergone industry code replacement, currency conversion, and statistical benchmark unification are combined with the structured tags to generate the third data package.

[0008] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the consistency check module is configured as follows: Receive the third data packet; The values ​​of data items in the third data packet are compared with the threshold range set according to the industry, and the values ​​that exceed the threshold range are marked. Call the logical relationship rules in the business rule base to verify whether the values ​​between the associated data items in the third data packet conform to the logical relationship; Analyze the continuity of data items in the third data packet over time to identify anomalous changes in values; If the value does not exceed the threshold range, conforms to the logical relationship, and has no abnormal changes, then the third data packet is marked as verified. If the value exceeds the threshold range, does not conform to logical relationships, or has abnormal sudden changes, a correction process will be initiated. The fourth data packet is generated for the third data packet that is marked as verified or the data packet that has been corrected.

[0009] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the correction processing includes: For values ​​marked as exceeding the threshold range in the comparison, or values ​​that do not conform to the logical relationship rules in the verification, a correction function is invoked for adjustment. The adjustment includes replacing with the historical average value or making proportional correction based on the associated indicators. For numerical mutations identified in continuous analysis that cannot be adjusted by correction functions, the corresponding data items and anomaly descriptions are packaged, a manual review task is generated, and pushed to the review queue. Receive feedback on the processing of manual review tasks, update the status and value of data items based on the processing feedback, and generate a corrected data packet.

[0010] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the output module is configured as follows: Receive the fourth data packet; Serialize the data items in the fourth data packet into the target data format; Embed processing metadata into the serialized data; the processing metadata includes rule version identifier and verification status code. Output serialized data with embedded metadata through at least one of the following methods: application programming interface, file export, or database write.

[0011] Furthermore, the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention also includes a scheduler; The scheduler is configured as follows: According to the pre-configured data acquisition task schedule, acquisition instructions are periodically sent to the data acquisition module; In response to an external event trigger signal, an immediate acquisition command is sent to the data acquisition module; When the data acquisition module fails to acquire data due to network abnormality, the acquisition command is resent to the data acquisition module according to the preset retry strategy and interval time. In incremental acquisition mode, the data acquisition module is instructed to acquire only the original enterprise industry data updated since the last successful acquisition time.

[0012] Furthermore, the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention also includes a mapping rule base; The mapping rule base includes an industry classification code mapping table and a caliber adjustment formula library; The industry classification code mapping table records the correspondence between historical version industry classification codes and current standard codes; The formula library for adjusting statistical calibers records the numerical calculation rules for different statistical calibers.

[0013] Furthermore, in the enterprise industry data standardization processing system supporting cross-cycle comparable analysis described in this invention, the data acquisition module, data identification module, standardization conversion module, consistency check module, and output module are connected through an internal message queue. The data acquisition module publishes the first data packet to the first topic of the internal message queue; The data identification module subscribes to the first topic, obtains the first data packet from the internal message queue, and publishes the second data packet to the second topic after generating the second data packet. The standardization conversion module subscribes to the second topic, retrieves the second data packet from the internal message queue, and publishes the third data packet to the third topic after generating the third data packet. The consistency check module subscribes to the third topic, retrieves the third data packet from the internal message queue, and publishes the fourth data packet to the fourth topic after generating the fourth data packet. The output module subscribes to the fourth topic and retrieves the fourth data packet from the internal message queue.

[0014] The beneficial effects of this invention are: This invention automates the entire process of enterprise industry data from multi-source heterogeneity to standardized output by constructing a collaborative processing chain consisting of a data acquisition module, a data identification module, a standardization conversion module, a consistency check module, and an output module. This effectively solves the problem of incomparability between historical and current data caused by differences in statistical calibers, inconsistent currency units, and changes in industry classification standards in the background technology. The data identification module automatically detects the statistical caliber attributes, currency unit attributes, and industry classification code version attributes of data items through a preset rule base. The standardization conversion module performs precise industry code mapping, currency conversion, and statistical benchmark unification based on structured tags. The consistency check module eliminates data anomalies through multi-level verification and correction. Finally, the output module generates standardized data carrying processed metadata, making cross-period data directly comparable under a unified benchmark. This significantly improves the input quality of industry analysis models and the reliability of prediction conclusions, while reducing the error risk and processing costs caused by manual intervention. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0016] Figure 1 This is a system architecture diagram of a standardized processing system for enterprise industry data that supports cross-cycle comparable analysis. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0018] To better understand the purpose of this invention, the invention will now be described in further detail.

[0019] Please see Figure 1 The present invention provides an enterprise industry data standardization processing system that supports cross-cycle comparable analysis, including a data acquisition module, a data identification module, a standardization conversion module, a consistency check module and an output module; The data acquisition module is configured to acquire raw enterprise industry data from an external data source and convert the raw enterprise industry data into a first data packet in an internal intermediate format. The data recognition module is configured to receive the first data packet, recognize the statistical caliber attribute, currency unit attribute, and industry classification code version attribute of the data items in the first data packet, and generate a second data packet carrying the recognized attributes. The standardization conversion module is configured to receive the second data packet, and perform conversion on the data items in the second data packet according to the statistical caliber attribute, currency unit attribute and industry classification code version attribute carried in the second data packet to generate a third data packet. The conversion includes: performing version conversion on the industry classification code according to the industry classification code mapping table, converting the currency unit according to the exchange rate, and standardizing the numerical values ​​according to the statistical caliber adjustment formula. The consistency check module is configured to receive the third data packet, perform logical verification on the data items in the third data packet, perform correction processing when the verification is abnormal, and generate a fourth data packet. The output module is configured to receive the fourth data packet, encapsulate the fourth data packet into standardized data of the target format, and output it.

[0020] This invention provides a standardized processing system for enterprise industry data that supports cross-period comparable analysis. By constructing a modular data processing chain, it systematically solves the incomparability problem caused by differences in statistical caliber, currency units, and industry classification standards across different periods. The system adopts a layered processing architecture. The data acquisition module first connects to various external data source interfaces through a protocol adapter to receive raw enterprise industry data streams or files. The data acquisition module then calls a parser matching the data source format to perform structured parsing of the raw data, extracting data entities and attribute fields, and reorganizing the fields into an intermediate format defined internally by the system. Subsequently, the data acquisition module performs basic cleaning on the reorganized data, filtering invalid characters and blank records, and appending metadata including data source identifiers and data reporting period identifiers to the cleaned data. Finally, the data acquisition module encapsulates the processed data into a first data packet and stores it in the data buffer.

[0021] After acquiring the first data packet from the buffer, the data recognition module initiates a multi-dimensional feature recognition process. The module extracts field names, numerical samples, and descriptive text from the data packet. By matching field names with source identifier patterns in a preset rule base, it determines the data source code. It scans for statistical caliber definition keywords in the descriptive text and, combined with the matching results of currency unit symbols in field names or numerical samples, determines the statistical caliber attribute and currency unit attribute, respectively. Simultaneously, based on the format characteristics of industry codes in the data items, it compares them with version identifiers in the rule base to determine the industry classification code version attribute. After recognition, the module appends all attributes as structured tags to the corresponding data items in the data packet, generating the second data packet.

[0022] After receiving the second data packet, the standardization conversion module performs precise conversion based on the structured tags attached to the data items. For the industry classification code version attribute, the module queries the versioned industry classification code mapping table and replaces the historical version code with the current standard code. For the currency unit attribute, the module accesses the exchange rate service interface based on the data packet timestamp to obtain the corresponding exchange rate and completes the currency conversion. According to the statistical caliber attribute, the module calls the calculation rules in the caliber adjustment formula library to recalculate the values ​​and unify the statistical benchmark. All conversion operations are logged, and the converted data items are combined with the original structured tags to generate the third data packet.

[0023] The consistency check module performs multi-level verification on the third data packet. The module compares the data item values ​​with industry-dynamically set threshold ranges, marking abnormal values; it calls logical relationship rules from the business rule base to verify the logical consistency of values ​​between related data items; and it analyzes the continuity of the time series to identify sudden changes. Data packets that pass verification are directly marked as ready; when an anomaly is detected, correction processing is initiated according to a preset strategy: for values ​​that can be automatically corrected, a correction function is called (such as replacing with the historical average or adjusting the proportion of related indicators); for complex anomalies, a manual review task is generated and pushed to the review queue. A fourth data packet is generated after correction is complete.

[0024] After receiving the fourth data packet, the output module serializes the data items into the target format (such as a database table or JSON stream) according to the requirements of the downstream analysis model. Processing metadata, including rule version identifiers and verification status codes, is embedded in the serialized data. Finally, standardized data is delivered via API interface, file export, or database write. The system decouples modules through an internal message queue, and each module processes data packets sequentially through a topic subscription mechanism, forming an end-to-end automated processing pipeline. This design ensures comparability between historical and current data under a unified benchmark, directly supporting the reliable operation of industry trend analysis and prediction models.

[0025] The data acquisition module establishes a connection with external data sources through a protocol adapter. This adapter supports common data interface protocols such as HTTP API, FTP file transfer, or direct database connection. Upon receiving the raw data stream or file, the module calls the corresponding parser based on the data source type: for JSON format data, a syntax analyzer is used to extract key-value pairs; for CSV files, a delimiter is used to parse and obtain the table structure; and database query results are directly mapped to row and column data. The parsed data entities and attribute fields are reorganized into an internally defined intermediate format, which uses a unified key-value pair structure for subsequent processing.

[0026] During the data cleaning phase, the module performs character-level filtering on the reconstructed data to be processed, removing non-standard characters such as ASCII control characters and full-width spaces, and also clearing empty record rows. The cleaned data is appended with metadata blocks, where the data source identifier uses a categorized coding system (e.g., 01 for the National Bureau of Statistics, 02 for the Customs database), and the data reporting period identifier supports standard time granularities such as year / quarter / month. The final encapsulated first data packet includes a valid data body and a metadata header, and is stored in the shared storage area of ​​the data buffer via system calls.

[0027] After reading the first data packet from the buffer, the data recognition module initiates a multi-level parsing process. During the field name parsing phase, the module unifies the case and filters special characters from the original field names, then performs similarity matching with standard field names in the rule base. For example, it normalizes "Revenue (ten thousand yuan)" to "Operating Revenue in ten thousand yuan". The numerical sample analysis selects the first 10 numerical points from each record for distribution feature detection to identify outlier patterns.

[0028] The data recognition module employs a multi-keyword matching algorithm for descriptive text mining, simultaneously detecting key phrases such as "excluding tax" and "including VAT" during statistical caliber identification. In addition to symbol matching, the module's currency unit identification also supports currency code recognition (e.g., CNY / USD) and fuzzy matching of currency names. Industry code version determination is achieved by analyzing code structure features; for example, the 2002 version of industry codes uses a 4-layer hierarchical structure, while the 2017 version uses a 5-layer coding system. The module appends all identification results to the first data packet as structured tags in JSON format, forming a second data packet containing both raw data and semantic annotations.

[0029] The standardization conversion module uses a pipelined approach to process the structured tags in the second data packet. During industry code conversion, it queries a version mapping table, which records the correspondence between old and new codes and conversion rules (e.g., "2017 version C13 corresponds to 2002 version C15"). The currency conversion module obtains the central bank's exchange rate midpoint for the day based on the data timestamp, supporting both historical exchange rate queries and real-time exchange rate subscriptions.

[0030] The standardized conversion module's statistical caliber adjustment formula library includes parameterized calculation templates; for example, the formula for "tax-inclusive revenue to tax-exclusive revenue" automatically matches the VAT rate at the current time. The conversion process of the standardized conversion module employs a transaction mechanism to ensure data consistency, and detailed logs are recorded for each data item's conversion operation, including the original value, conversion parameters, and result value. The third-party data package ultimately generated by the standardized conversion module retains the original tagging system while adding audit information such as conversion timestamps and rule version numbers.

[0031] The consistency check module implements a three-level verification system. The threshold check uses a dynamic boundary algorithm, setting a reasonable range based on industry characteristics (e.g., the debt-to-asset ratio of manufacturing companies is typically between 30% and 70%). The logical relationship verification uses a business rule engine to check hard constraints such as "operating revenue ≥ 0" and "current assets ≥ inventory".

[0032] Time series analysis employs a sliding window detection method to identify data abrupt changes exceeding three standard deviations between adjacent periods. Data packets that pass verification are digitally signed, while anomalous data triggers a tiered processing mechanism: minor anomalies are automatically corrected, while major anomalies are subject to manual review. A fourth data packet carries an integrity checksum during generation to ensure it is not tampered with during data transmission.

[0033] The correction processing module includes both automatic correction and manual review channels. The automatic correction function library provides various interpolation algorithms: historical mean replacement uses a weighted average of data from the same period three years prior, while correlation indicator correction establishes a linear regression model. For sudden changes in indicators such as capacity utilization, the system automatically correlates leading indicators such as electricity consumption for collaborative verification.

[0034] The consistency check module's manual review task generates standardized anomaly report templates, including anomaly values, historical trend comparisons, and possible cause analyses. The consistency check module's review queue supports tiered processing based on urgency, and expert feedback is recorded in a knowledge base for optimizing correction rules. The consistency check module increments the version number of the corrected data packet and retains the original data and correction trajectory for auditing purposes, generating a fourth data packet.

[0035] The output module supports multimodal data delivery. The serialization engine converts data formats according to downstream system requirements: relational database output uses batch INSERT statements, while JSON streaming output uses a chunked transmission mechanism. Metadata blocks are embedded in the data packet header, including hash checksums, data schema version, and generation timestamps.

[0036] The output module's API interface follows RESTful specifications and supports data filtering by time range, industry category, and other dimensions. The output module's file export function generates a compressed data package containing the data file and metadata documentation. The output module's direct database connection mode employs prepared statements to prevent injection attacks, and all output operations are logged for traceability.

[0037] The scheduler implements intelligent task management. Data collection task plans support cron expression settings and can be executed on an hourly, daily, or weekly basis. An event-triggered mechanism listens to an external message queue and immediately initiates the data collection process upon receiving a data update notification.

[0038] The scheduler employs an exponential backoff retry strategy for network anomaly handling, with an initial retry interval of 2 minutes, doubling the interval for subsequent retries. Incremental data collection by the scheduler is achieved through version number comparison or timestamp filtering, capturing only data records from the data source whose last modification time is later than the local record. The scheduler's scheduling dashboard displays real-time task execution status and success rate statistics.

[0039] The mapping rule base adopts a versioned storage architecture. The industry classification code mapping table maintains the correspondence between the old and new standard codes, supporting one-to-many mapping scenarios (such as the old version of "electronic information industry" corresponding to the new version of "software industry" and "internet service industry"). The caliber adjustment formula library includes parameterized templates, such as the "gross industrial output value" indicator adapting to different calculation calibers according to different statistical periods.

[0040] The mapping rule base supports online hot updates, and new rules are released in a canary release phase after sandbox testing. Version management of the mapping rule base records each change, its effective date, and applicable data scope, and supports historical rule queries and rollback operations. The rule validation module of the mapping rule base prevents conflicting rules from being added to the database, ensuring the consistency of the rule system.

[0041] The message queue architecture decouples modules. RabbitMQ or Kafka message middleware acts as the data bus, with topics one through four corresponding to the four stages of the data processing pipeline. The message format uses Protocol Buffers binary encoding to ensure transmission efficiency and cross-language compatibility.

[0042] The topic subscription mechanism ensures that data packets are processed in order, and each module automatically acknowledges receipt after consuming messages. Message persistence configuration prevents data loss due to system failures, and a dead-letter queue collects failed messages for fault analysis. The monitoring system tracks message backlog in real time and dynamically adjusts the number of consumers to achieve load balancing.

[0043] This invention addresses the standardization challenges in cross-cycle enterprise industry data analysis by constructing a modular data processing pipeline. The system's operating mechanism is explained below using typical application scenarios.

[0044] During the data acquisition phase, the system connects to the National Bureau of Statistics API, the Customs database, and the stock exchange data interface through various protocol adapters. When the acquisition module detects the 2023 enterprise statistical annual report data package released by the National Bureau of Statistics, it automatically calls the JSON parser to extract key fields such as operating revenue and total assets. The data cleaning process filters out non-standard characters such as full-width spaces and adds the data source code "NB01" and the reporting period "2023Q4" metadata to each record. The encapsulated first data package is stored in the Redis cache database, and a producer event in the message queue is triggered simultaneously.

[0045] After consuming the first data packet from the message queue, the data recognition module initiates multi-dimensional feature recognition. The field name "Main Operating Revenue (Ten Thousand Yuan)" is standardized and matched against the rule base to determine the corresponding "Operating Revenue" indicator in the National Economic Industry Classification. The keyword "excluding VAT" in the description text triggers statistical caliber recognition and is marked as excluding VAT. The currency symbol "¥" is mapped to the RMB currency code CNY, while the enterprise industry code "C3912" is identified as belonging to the 2017 version of the industry classification standard through analysis of its four-level coding structure. The recognition results are injected into the data packet in JSON tag form, forming a second data packet with semantic annotations.

[0046] After receiving the second data packet, the standardization conversion module performs precise conversion based on the tag content. Industry code "C3912" is converted to the 2023 standard code "C3911" by querying the mapping table. The currency conversion module calls the People's Bank of China's exchange rate interface to unify the USD-denominated financial data to RMB based on the 2023 average exchange rate of 6.8. The statistical caliber conversion applies the VAT exemption policy rules, recalculating tax-inclusive revenue into tax-exclusive values. All conversion operations are logged in the audit log, generating a third data packet conforming to the current standard.

[0047] The consistency check module performs three levels of verification on the transformed data. The debt-to-asset ratio threshold for manufacturing enterprises is set at 30%-70%, and an outlier of 85% is automatically flagged when it is detected in a particular enterprise. The business rules engine verifies the correlation between current assets and inventory, initiating a correction process when contradictory data is found. Time series analysis detects a 300% sudden change in a company's quarterly revenue, and the system automatically correlates this with leading indicators such as electricity consumption for collaborative verification. Minor anomalies are automatically corrected by replacing them with historical averages, while major anomalies generate review tasks and are pushed to the management platform.

[0048] The output module converts standardized data into two formats based on the requirements of downstream analysis systems: relational databases use batch INSERT statements to write data to the data warehouse, while the API interface generates a JSON stream for real-time analysis. Each data packet header embeds processing metadata, including the rule version "V2.3" and the verification status code "200," facilitating subsequent traceability. Data products are transmitted via HTTPS, using digital signatures to ensure secure transmission.

[0049] The scheduler uses cron expressions to execute batch data collection daily at 02:00, while simultaneously listening for data source update notifications for real-time collection. In case of network interruption, an exponential backoff strategy is employed for retries, with the initial wait time being 2 minutes and doubling each subsequent retry. Incremental collection mode compares data timestamps to retrieve only the most recent change records, reducing data transmission volume by over 90%.

[0050] The mapping rule library is managed using Git version control, and the industry classification mapping table records the code correspondences from version 2011 to 2023. The caliber adjustment formula library includes 15 VAT processing templates, supporting all policy versions since the 1994 tax reform. Rule updates are tested in a sandbox environment before being released in a phased rollout to ensure business continuity.

[0051] The message queue architecture uses Kafka as the data bus, with topics one through four corresponding to the four stages of the data processing pipeline. Messages are encoded using Avro binary code, and each partition has three replicas to ensure high availability. The monitoring system tracks message backlog in real time and automatically scales up consumer instances when processing latency exceeds a threshold.

[0052] This implementation method successfully solved the financial data integration problem of a cross-border merger and acquisition project of an automotive group through the above-described technical solution. The system unified financial reports involving five currencies and three accounting standards from 1998 to 2023 into a standard format, helping analysts discover the true growth trends masked by exchange rate fluctuations. This standardization capability makes comparative industry studies spanning thirty years possible, providing reliable data support for macroeconomic decision-making.

[0053] Example 1: When processing the financial data of a multinational manufacturing company from 2010 to 2023, the system connects to the National Bureau of Statistics API and the Customs database through the data acquisition module to obtain raw report data. The data acquisition module uses a protocol adapter to parse the revenue field in JSON format, filters out full-width spaces and invalid characters, appends the data source code "CI01" and the reporting period "2023Q4" metadata, and encapsulates it into a first data packet stored in the Redis buffer. The data recognition module extracts the field name "Revenue (USD)" from the buffer, matches the currency symbol pattern in the rule base, and identifies the currency unit as US dollars; scans the description text "exclusive of VAT" keyword to determine that the statistical scope is tax-exclusive; analyzes the structure of the industry code "GB / T 4754-2017 C39", determines that the version is the 2017 version of the classification standard, and generates a second data packet with structured tags. The standardization conversion module queries the industry code mapping table, mapping the 2011 version "C39" to the 2023 version "C38"; it calls the central bank's exchange rate interface based on the data timestamp, converting it to RMB using the 2023 average exchange rate of 6.8; it calls the tax adjustment formula library, recalculating the revenue excluding tax using a 13% VAT rate, and generating a third data package. The consistency check module compares the manufacturing asset-liability ratio threshold range of 30%-70%, finding an anomaly of 85% in a certain year's data, and initiates a correction function to replace it with the average of the previous three years; time series analysis detects revenue mutations, verifies them by correlating with electricity consumption indicators, generates a manual review task, and finally outputs standardized JSON data for use by the industry analysis model.

[0054] Example 2: For integrating cross-cycle transaction records from an e-commerce platform from 2015 to 2022, the data acquisition module obtains raw data in CSV format via FTP protocol, parses the order amount and industry classification fields, cleans blank records, and appends the platform identifier "EC02" and quarterly cycle metadata. The data recognition module extracts the "¥" symbol from the numerical samples, identifying the currency unit as RMB; it matches the industry code "ISIC Rev.4 G47" to the rule base, determining the version to be the international standard classification; the statistical caliber keyword "gross sales" is marked as including marketing expenses, generating a second data package. The standardization conversion module accesses the exchange rate service, unifying historical NTD transaction data to RMB according to the exchange rate of the day; it queries the industry mapping table to convert the ISIC code to the domestic 2022 standard; and it calls the caliber formula to remove marketing expenses, unifying it to a net sales revenue benchmark. The consistency check module verifies the logical relationship between revenue and cost, and automatically triggers a ratio correction when the cost exceeds the revenue in a certain quarter. In the incremental acquisition mode, the scheduler only captures the latest timestamp data, transmits the fourth data packet to the output module through the Kafka message queue, serializes it into a database table format and embeds the rule version "V3.1" metadata, supporting direct calls from the cross-cycle trend analysis platform.

Claims

1. A standardized processing system for enterprise industry data that supports cross-cycle comparable analysis, characterized in that, It includes a data acquisition module, a data recognition module, a standardization conversion module, a consistency check module, and an output module; The data acquisition module is configured to acquire raw enterprise industry data from an external data source and convert the raw enterprise industry data into a first data packet in an internal intermediate format. The data recognition module is configured to receive the first data packet, recognize the statistical caliber attribute, currency unit attribute, and industry classification code version attribute of the data items in the first data packet, and generate a second data packet carrying the recognized attributes. The standardization conversion module is configured to receive the second data packet, and perform conversion on the data items in the second data packet according to the statistical caliber attribute, currency unit attribute and industry classification code version attribute carried in the second data packet to generate a third data packet. The conversion includes: performing version conversion on the industry classification code according to the industry classification code mapping table, converting the currency unit according to the exchange rate, and standardizing the numerical values ​​according to the statistical caliber adjustment formula. The consistency check module is configured to receive the third data packet, perform logical verification on the data items in the third data packet, perform correction processing when the verification is abnormal, and generate a fourth data packet. The output module is configured to receive the fourth data packet, encapsulate the fourth data packet into standardized data of the target format, and output it.

2. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis as described in claim 1, characterized in that, The data acquisition module is configured as follows: The interface connects to an external data source via a protocol adapter to receive raw data. The original data is parsed to obtain the parsed data entities and attribute fields; The parsed data entities and attribute fields are organized into an internal intermediate format of data to be processed. The data to be processed is filtered to remove invalid characters and blank records, and cleaned data is generated. Add metadata including data source identifier and data reporting cycle identifier to the cleaned data; The data with added metadata is encapsulated into the first data packet, and the first data packet is sent to the data buffer.

3. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 2, characterized in that, The data recognition module is configured as follows: Retrieve the first data packet from the data buffer; Extract field names, numerical samples, and descriptive text from the first data packet; The extracted field names are matched with the source identifier patterns in the preset rule base to determine the data source code; Search for statistical caliber definition keywords in the preset rule base within the descriptive text to determine the statistical caliber attribute; Match the currency unit symbol in the preset rule base in the field name or the numerical sample to determine the currency unit attribute; Based on the industry code format of the data items in the first data packet, compare it with the industry classification code version identifier in the preset rule base to determine the industry classification code version attribute; The data source code, statistical caliber attribute, currency unit attribute, and industry classification code version attribute are appended to the corresponding data items in the first data packet using structured tags to generate the second data packet.

4. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis as described in claim 3, characterized in that, The standardization conversion module is configured as follows: Receive the second data packet and read the structured tags attached to the data items in the second data packet; Based on the industry classification code version attribute in the structured tag, query the industry classification code mapping table and replace the historical industry code in the data item with the current standard industry code; Based on the currency unit attribute in the structured tag and the timestamp carried in the second data packet, the exchange rate service interface is accessed to obtain the exchange rate, and the value in the data item is converted to currency. Based on the statistical caliber attribute in the structured tag, the calculation rules in the caliber adjustment formula library are called to recalculate the values ​​in the data items to unify the statistical benchmark. The data items that have undergone industry code replacement, currency conversion, and statistical benchmark unification are combined with the structured tags to generate the third data package.

5. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 4, characterized in that, The consistency check module is configured as follows: Receive the third data packet; The values ​​of data items in the third data packet are compared with the threshold range set according to the industry, and the values ​​that exceed the threshold range are marked. Call the logical relationship rules in the business rule base to verify whether the values ​​between the associated data items in the third data packet conform to the logical relationship; Analyze the continuity of data items in the third data packet over time to identify anomalous changes in values; If the value does not exceed the threshold range, conforms to the logical relationship, and has no abnormal changes, then the third data packet is marked as verified. If the value exceeds the threshold range, does not conform to logical relationships, or has abnormal sudden changes, a correction process will be initiated. The fourth data packet is generated for the third data packet that is marked as verified or the data packet that has been corrected.

6. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 5, characterized in that, The correction process includes: For values ​​marked as exceeding the threshold range in the comparison, or values ​​that do not conform to the logical relationship rules in the verification, a correction function is invoked for adjustment. The adjustment includes replacing with the historical average value or making proportional correction based on the associated indicators. For numerical mutations identified in continuous analysis that cannot be adjusted by correction functions, the corresponding data items and anomaly descriptions are packaged, a manual review task is generated, and pushed to the review queue. Receive feedback on the processing of manual review tasks, update the status and value of data items based on the processing feedback, and generate a corrected data packet.

7. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 1, characterized in that, The output module is configured as follows: Receive the fourth data packet; Serialize the data items in the fourth data packet into the target data format; Embed processing metadata into the serialized data; the processing metadata includes rule version identifier and verification status code. Output serialized data with embedded metadata through at least one of the following methods: application programming interface, file export, or database write.

8. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 2, characterized in that, It also includes the scheduler; The scheduler is configured as follows: According to the pre-configured data acquisition task schedule, acquisition instructions are periodically sent to the data acquisition module; In response to an external event trigger signal, an immediate acquisition command is sent to the data acquisition module; When the data acquisition module fails to acquire data due to network abnormality, the acquisition command is resent to the data acquisition module according to the preset retry strategy and interval time. In incremental acquisition mode, the data acquisition module is instructed to acquire only the original enterprise industry data updated since the last successful acquisition time.

9. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 4, characterized in that, It also includes a mapping rule base; The mapping rule base includes an industry classification code mapping table and a caliber adjustment formula library; The industry classification code mapping table records the correspondence between historical version industry classification codes and current standard codes; The formula library for adjusting statistical calibers records the numerical calculation rules for different statistical calibers.

10. The enterprise industry data standardization processing system supporting cross-cycle comparable analysis according to claim 1, characterized in that, The data acquisition module, data identification module, standardization conversion module, consistency check module, and output module are connected through an internal message queue. The data acquisition module publishes the first data packet to the first topic of the internal message queue; The data identification module subscribes to the first topic, obtains the first data packet from the internal message queue, and publishes the second data packet to the second topic after generating the second data packet. The standardization conversion module subscribes to the second topic, retrieves the second data packet from the internal message queue, and publishes the third data packet to the third topic after generating the third data packet. The consistency check module subscribes to the third topic, retrieves the third data packet from the internal message queue, and publishes the fourth data packet to the fourth topic after generating the fourth data packet. The output module subscribes to the fourth topic and retrieves the fourth data packet from the internal message queue.

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