NoETL data preprocessing method based on artificial intelligence

Abstract

The invention provides a NoETL data preprocessing method based on artificial intelligence, and belongs to the technical field of data engineering and artificial intelligence, end-to-end automation of dynamic perception, mode inference, cleaning conversion and output of a data source is achieved by eliminating manual intervention of a traditional Extract-Transform-Load process, and the method mainly comprises the steps that S1, artificial intelligence configuration is carried out, the method comprises the steps of S1, business scene recognition, S3, data automatic extraction, S4, data cleaning, S5, data conversion and S6, data result output. According to the method, efficient data cleaning, integration and preparation are achieved through the artificial intelligence, so that various data analysis requirements are met.

Description

Technical Field

[0001] The present invention relates to the field of data engineering and artificial intelligence technology, and in particular to a NoETL data preprocessing method based on an artificial intelligence entity. Background Art

[0002] Traditional ETL relies on manual definition of data schemas, cleansing rules, and transformation logic, resulting in long development cycles and high maintenance costs. It struggles to adapt to the dynamic schema changes of semi-structured and unstructured data (such as JSON and log text). Data engineers must repeatedly write code, making it difficult to cope with frequent data source updates or real-time processing requirements.

[0003] Existing NoETL tools (such as automatic schema inference tools) only support limited rules and lack contextual awareness. Traditional AI pre-processing models (such as automatic data cleaning) are mostly single-task optimizations and lack an end-to-end closed loop.

[0004] Therefore, how to design a more flexible and efficient ETL-free data preprocessing solution has become an urgent problem to be solved. Summary of the Invention

[0005] To address the above technical issues, this paper provides a NoETL data preprocessing method based on an artificial intelligence agent. By building artificial intelligence agents for different scenarios, it automates data collection, cleaning, transformation, and preparation. By eliminating manual intervention in traditional ETL (Extract-Transform-Load) processes, it achieves end-to-end automation of dynamic data source perception, schema inference, cleaning, transformation, and loading.

[0006] The technical solution of the present invention is:

[0007] A NoETL data preprocessing method based on artificial intelligence

[0008] Artificial intelligence agents trained using machine learning algorithms can automatically identify data patterns and make corresponding processing decisions;

[0009] It supports dynamic adjustment of processing logic to cope with changes in data sources, improving the flexibility and efficiency of data processing.

[0010] Preferably, the configuration of the artificial intelligence agent in step S1 needs to be configured according to different business scenarios and stages. This invention mainly includes 5 types of intelligent agents, and defines them as follows:

[0011]

[0012] The specific tasks of the five agents are as follows:

[0013] Connection-aware Agent: Automatically identifies data source types (databases, APIs, files, etc.) and dynamically adapts connection protocols.

[0014] Schema Inference Agent: Based on metadata and data distribution, it uses reinforcement learning to infer data schemas and supports dynamic field addition and deletion.

[0015] Semantic Understanding Agent: parses semantic tags in unstructured data (such as log keywords and user comment sentiment) through NLP.

[0016] Quality Assessment Agent: Detects data anomalies (missing values, noise) in real time, generates quality reports, and triggers cleaning rules.

[0017] Conversion Optimization Agent: Automatically optimizes data partitioning and compression strategies based on the characteristics of the target data warehouse (such as columnar storage).

[0018] Preferably, the main steps of the business scenario identification in step S2 are as follows:

[0019] S2.1: Problem and data target identification; connection perception agent for manual input requirements or regular demand tasks to perform semantic understanding and recognition

[0020] S2.2: Data source determination: After the connection perception agent identifies the needs, it automatically matches the indicators and label rules of the corresponding business and identifies the data to be extracted.

[0021] S2.2.1: Establish a data rule indicator system

[0022] The establishment of the indicator system mainly includes six dimensions: basic indicators, performance indicators, business indicators, service indicators, security configuration indicators, and intelligent indicators (Note: different usage scenarios can be used to develop indicator systems for specific businesses)

[0023] Basic indicators: By quantifying the coverage, quality, timeliness, and consistency of data sources, we ensure the comprehensiveness and reliability of data, providing highly reliable input for IT system operations and maintenance to support intelligent decision-making and risk warnings.

[0024] Business metrics: Track API response time and concurrent requests, evaluate business load and user experience, and guide performance tuning.

[0025] Service indicators: Ensure service continuity, quickly locate faults, and restore services through service availability, heartbeat detection, and transaction success rate.

[0026] Security configuration indicators: Detect configuration drift rates and change anomalies to ensure that configurations meet baseline standards and prevent service interruptions caused by configuration errors.

[0027] Intelligent indicators: Use AI models to predict anomaly accuracy and automated repair rates, enabling proactive risk warnings and unattended repairs, improving operation and maintenance efficiency.

[0028] The indicator system template and examples are as follows:

[0029]

[0030] S2.2.2: Establish a data rule labeling system

[0031] The labeling system mainly includes four dimensions: basic dimension labels, business attribute labels, data feature labels, and automation and governance labels.

[0032] Basic dimension tags: define basic attributes such as data source, collection method, storage location, etc., used to distinguish data source types and technical implementations (such as logs and real-time collection).

[0033] Business attribute tags: mark the business system, service level, and core indicators (such as web server, application layer, and CPU usage) to which the data belongs, supporting business scenario analysis.

[0034] Data feature labels: Describe the abnormal state, severity, and change trend of the data (such as urgency, disk I / O latency, and upward trend), assisting in anomaly detection and root cause location.

[0035] Automation and Governance Tags: These tags reflect data collection frequency, processing tools, and governance strategies (such as minute-level collection, Prometheus processing, and GDPR compliance) to ensure process standardization and controllable permissions.

[0036] The label system template and example are as follows:

[0037]

[0038] Preferably, the main steps of automatic data extraction in step S3 include:

[0039] S3.1 Artificial Intelligence Automatically Extracts Data

[0040] When the user inputs or the automatic scheduled task is started, the connection awareness agent automatically detects the data source type (such as MySQL, Kafka, S3, API, etc.) through the pre-trained protocol recognition model, and calls the corresponding connector to automatically collect data according to the S2 data rule indicator system.

[0041] S3.1.1 Abnormal connection handling and fault tolerance

[0042] The retry interval is selected based on a reinforcement learning strategy. When the data source connection is interrupted or the permissions are changed, the connection-aware agent automatically retries or switches to an alternative data source.

[0043] S3.2 Data Verification and Checking

[0044] S3.2.1 Mode consistency check

[0045] Detect missing fields (e.g., expected fields not present) and type conflicts (e.g., strings mistakenly stored as numbers).

[0046] S3.2.2 Data volume integrity monitoring

[0047] Use historical data volume statistical models (such as time series prediction) to determine whether the current extracted data volume is abnormal.

[0048] S3.2.3 Primary Key / Uniqueness Verification

[0049] Automatically identify primary key or unique constraint fields and check for duplicate or null values.

[0050] Preferably, the data cleaning steps in step S4 are as follows:

[0051] S4.1 Outlier Detection and Repair

[0052] The quality assessment agent identifies abnormal data and triggers cleaning rules through statistical models, domain rules and context awareness.

[0053] S4.2 Missing value filling and processing

[0054] Dynamically select population strategies based on field importance, data distribution, and downstream needs.

[0055] Low missing rate (<5%):

[0056] Numerical: fill in the mean / median or predict missing values based on a regression model.

[0057] Classification: fill in the majority or infer through knowledge graph (such as inferring the missing "gender" field through user historical behavior).

[0058] High missing rate (>30%): Mark the field as "low confidence", trigger an alarm and notify downstream systems.

[0059] For time series data, use interpolation methods (such as linear interpolation) or LSTM to predict missing periods.

[0060] S4.3 Missing key fields (such as primary keys): Discard the record or generate a temporary unique identifier (such as a UUID) to ensure data pipeline continuity.

[0061] S4.4 Identification and removal of duplicate data

[0062] The quality assessment agent detects duplicate records based on uniqueness constraints or semantic similarity.

[0063] Precise deduplication: Perform a hash comparison on the primary key or unique constraint field (such as order ID) to remove duplicates.

[0064] Strategy selection: retain the latest record (such as deduplication by timestamp) or merge records (such as order status update) based on business needs.

[0065] S4.5 Context consistency check

[0066] The semantic understanding agent detects cross-field logical contradictions through knowledge graphs and business rules.

[0067] Multi-source consistency: Check foreign key constraints (e.g., user_id in the order table must exist in the user table). Compare attribute differences for the same entity across multiple data sources (e.g., product prices in the ERP system are inconsistent with those on the website).

[0068] S4.6 Data standardization and formatting

[0069] The conversion optimization agent unifies the data format to adapt to the target storage or downstream application requirements.

[0070] Unit unification: Automatically convert units (such as converting "pounds" to "kilograms").

[0071] Time standardization: unify time zones (such as converting UTC time to local time) and formats (such as YYYY-MM-DDHH:mm:ss).

[0072] Encoding conversion: Mapping categorical fields (such as country codes) to enumeration values or one-hot encoding.

[0073] S4.8 self-optimizing cleaning strategy

[0074] Continuously optimize cleaning logic based on downstream feedback (such as BI report errors or model prediction deviations).

[0075] Reinforcement learning mechanism:

[0076] Define reward functions (e.g., improving downstream model accuracy and reducing data storage costs). Dynamically select the optimal cleaning strategy through Q-Learning (e.g., prioritizing repairs for fields that have the greatest impact on the model).

[0077] Rule base evolution:

[0078] Record historical cleaning decisions and their effects, and generate reusable rule templates (such as "give priority to calling third-party APIs to complete address fields when they are missing").

[0079] Preferably, the data conversion steps in step S5 are as follows:

[0080] The conversion optimization agent automatically maps the original field to the target field according to the user's declared goal (such as "generating a user portrait wide table") or the schema requirements of the target storage, and matches and models the data according to the data rule label system to convert it into data that is easy to output.

[0081] S5.1 Data aggregation and calculation field generation

[0082] Time window aggregation: Aggregate streaming data by fixed window (such as total order amount per hour) or session window (such as user single visit behavior).

[0083] Statistical indicator calculation: automatically generate indicators such as mean, sum, and quantile (such as the average length of stay of users).

[0084] Machine learning feature engineering: Generate lagged features (such as sales in the past 7 days), cross features (such as "unit price × purchase quantity"), or embedding vectors (extract text features through pre-trained models).

[0085] S5.2 Data rule matching and conversion

[0086] According to the data rule labeling system set by S2, the extracted data is labeled according to different dimensions.

[0087] Preferably, the data result output in step S6 is performed as follows:

[0088] The conversion optimization agent automatically selects the storage format, partitioning strategy, and compression algorithm based on the characteristics of the target storage system (such as data warehouse, data lake, BI platform).

[0089] S6.1 Target Storage Dynamic Adaptation

[0090] S6.1.1 Partitioning and Bucketing:

[0091] Dynamic partitioning by time (event_date), region (region), or business key (user_id) improves query performance.

[0092] Bucketing is performed on frequently queried fields (such as product_category) to reduce the amount of data scanned.

[0093] S6.1.2 Compression and encoding:

[0094] Select a compression algorithm based on the field type (such as ZSTD for numeric values and Snappy for text).

[0095] Enable dictionary encoding for enumeration fields (such as order_status).

[0096] S6.2 multi-modal output support

[0097] The connection-aware agent converts data into multiple interface protocols according to downstream requirements to achieve seamless delivery.

[0098] Database write:

[0099] Batch insert into MySQL / PostgreSQL, supporting transaction rollback and conflict handling (such as ONCONFLICT UPDATE), streaming write to ClickHouse / Druid, adapting to high-throughput real-time write scenarios.

[0100] API exposure:

[0101] Automatically generate RESTful API or GraphQL interface, provide aggregated results, and dynamically desensitize sensitive data (such as user privacy fields) (such as replacing names with ***).

[0102] File export: Export to CSV, Excel, or PDF reports (such as daily sales reports) on demand, supporting custom templates.

[0103] S6.3 Metadata and Data Lineage Management

[0104] The pattern inference agent generates a data lineage map, recording the conversion path and version information of the data from source to target.

[0105] Version control: Label output data snapshots (such as v1.2.3) and support historical version backtracking.

[0106] Record schema change logs (such as field deletion or type modification).

[0107] Lineage tracing: Use a graph database (such as Neo4j) to store field-level lineage relationships (e.g., "the age field in the user portrait table comes from the birth_year of the original log").

[0108] Downstream dependency analysis: Detect which BI reports or models use the target table to ensure change compatibility.

[0109] S6.4 Data Verification and Consistency Assurance

[0110] The quality assessment agent performs final state verification on the output results to ensure that they are consistent with the target system constraints.

[0111] Constraint check: primary key uniqueness, foreign key referential integrity, non-null constraints, etc.

[0112] Data comparison: Count the number of records and key field distribution (such as total amount) of source data and target data, and trigger an alarm when the deviation exceeds the threshold.

[0113] For real-time data streams, data integrity is verified through checksum.

[0114] S6.5 downstream system adaptive optimization

[0115] Dynamically adjust output strategies based on downstream application feedback (such as query performance and model effects).

[0116] Automatic pre-aggregation: Create materialized views for high-frequency queries (such as "daily GMV").

[0117] Index optimization: Analyze slow query logs and automatically create missing indexes (for example, add a region index for WHERE region = 'Asia').

[0118] Machine learning ready: Output data is automatically converted to TFRecord or Petastorm format, suitable for TensorFlow / PyTorch training processes.

[0119] Normalize or bin the feature fields to improve the model convergence speed.

[0120] S6.6 Real-time and batch output collaboration

[0121] Dynamically switch between streaming output (low latency) and batch output (high throughput) based on data timeliness requirements.

[0122] Lambda architecture implementation:

[0123] Real-time layer: Outputs incremental data through Kafka or Pulsar, supporting seconds-level delay.

[0124] Batch layer: Output the entire data to HDFS / S3 daily to ensure eventual data consistency.

[0125] Automatic merge strategy:

[0126] Merge small files (such as Parquet) in real-time streams into large files based on time windows to reduce storage overhead.

[0127] S6.7 Security and Access Control

[0128] The connection-aware agent integrates the enterprise permission system to dynamically control the scope of data access.

[0129] Field-level desensitization: Desensitize sensitive fields (such as mobile phone numbers) by role (for example, only display the last four digits).

[0130] Use Homomorphic Encryption to support ciphertext calculations.

[0131] Dynamic permissions: Filter output data based on attributes (such as the department to which the user belongs) (such as only outputting sales records of this department). BRIEF DESCRIPTION OF THE DRAWINGS

[0132] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0133] Figure 2 This is a working diagram of the multi-agent communication protocol;

[0134] Figure 3 It is a workflow diagram for automatic data extraction. DETAILED DESCRIPTION

[0135] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0136] The present invention provides a NoETL data preprocessing method based on an artificial intelligence agent, comprising the following steps:

[0137] S1, artificial intelligence configuration;

[0138] The implementation of this project mainly adopted the following five intelligent agents. Through automatic collaboration between intelligent agents, the problems of difficult, slow and inaccurate data collection for different business scenarios in the operation and maintenance of IT systems were solved.

[0139] The definition of this artificial intelligence entity is as follows:

[0140]

[0141]

[0142] S2, business scenario identification;

[0143] This project automatically sets up scheduled tasks through the internal system, and sends IT system operation status report instructions to the connection awareness agent through scheduled instructions.

[0144] S3, automatic data collection;

[0145] S3.1 Establish a data rule indicator system:

[0146]

[0147]

[0148]

[0149] S3.2 Establish a data rule indicator system

[0150]

[0151]

[0152] S3.3 Automatic data collection

[0153] According to the preset indicator system, the connection awareness agent extracts data from Sql and Kafka

[0154] S4. Data cleaning

[0155] Call the quality assessment agent to detect duplicate records based on unique constraints or semantic similarity and clean the data

[0156] S5. Data conversion

[0157] For the IT system operation and maintenance of this system, the conversion optimization agent is called to first label the collected and cleaned data, and then compression and other conversions are performed on the labeled data.

[0158]

[0159]

[0160]

[0161] S6. Data result output

[0162] After the data conversion is completed, the analysis statistics charts and conclusions are directly generated

[0163] The above description is only a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A NoETL data preprocessing method based on artificial intelligence, characterized in that: include: Automatically adapt the data source protocol and extract sample data through the connection-aware agent; Dynamically generate data patterns and quality assessment reports based on reinforcement learning and metadata; Generate pre-processing workflows and perform real-time data loading based on user-declared goals; Continuously optimize cleaning rules and resource allocation strategies based on downstream application feedback.

2. The method according to claim 1, characterized in that The specific steps include: S1. AI configuration: AI configuration needs to be performed according to different business scenarios and stages. S2. Business scenario identification, identifying problems and data targets, and determining data sources; S3, automatic data extraction, automatic detection of data source type and data collection, and data verification; S4, data cleaning, including outlier detection and repair, missing value filling and processing, key field missing, duplicate data identification and deduplication, context consistency verification, data standardization and formatting, and self-optimization cleaning; S5. Data conversion: The conversion optimization agent automatically maps the original fields to the target fields according to the user's declared target or the target storage schema requirements, and matches and models the data according to the data rule label system to convert it into data that is easy to output; S6. Data result output: The conversion optimization agent automatically selects the storage format, partition strategy, and compression algorithm based on the characteristics of the target storage system.

3. The method according to claim 2, characterized in that The agents involved in step S1 include: Connection-aware Agent: automatically identifies data source types and dynamically adapts connection protocols; Schema Inference Agent: Based on metadata and data distribution, it uses reinforcement learning to infer data schemas and supports dynamic field addition and deletion. Semantic Understanding Agent: parses semantic labels in unstructured data through NLP; Quality Assessment Agent: Detects data anomalies in real time, generates quality reports, and triggers cleaning rules; Conversion Optimization Agent: Automatically optimizes data partitioning and compression strategies based on the characteristics of the target data warehouse.

4. The method according to claim 2, characterized in that The steps of business scenario identification in step S2 are as follows: S2.1: Identify problems and data targets; connect the perception agent to perform semantic understanding and recognition based on manual input requirements or regular demand tasks; S2.2: Data source determination: After the connection perception agent identifies the needs, it automatically matches the indicators and label rules of the corresponding business and identifies the data to be extracted.

5. The method according to claim 4, characterized in that S2.2 also includes S2.2.1: Establish a data rule indicator system The establishment of an indicator system includes six dimensions: basic indicators, performance indicators, business indicators, service indicators, security configuration indicators, and intelligent indicators; Basic indicators: By quantifying the coverage, quality, timeliness, and consistency of data sources, we ensure the comprehensiveness and reliability of data, providing highly reliable input for IT system operations and maintenance, supporting intelligent decision-making and risk warnings. Business metrics: Track API response time and concurrent requests, evaluate business load and user experience, and guide performance tuning; Service indicators: Ensure service continuity, quickly locate faults, and restore services through service availability, heartbeat detection, and transaction success rate; Security configuration indicators: Detect configuration drift rates and change anomalies to ensure that configurations meet baseline standards and prevent service interruptions caused by configuration errors. Intelligent indicators: Use AI models to predict anomaly accuracy and automated repair rates, enabling early warning of risks and unattended repairs, improving operation and maintenance efficiency. S2.2.2: Establish a data rule labeling system The labeling system includes four dimensions: basic dimension labels, business attribute labels, data feature labels, and automation and governance labels; Basic dimension tags: define data sources, collection methods, and storage locations, and are used to distinguish data source types and technical implementations; Business attribute labels: Label the business system, service level, and core indicators to which the data belongs, supporting business scenario analysis; Data feature labels: describe the abnormal state, severity, and change trend of the data, assisting in anomaly detection and root cause location; Automation and Governance Tags: This tag reflects the frequency of data collection, processing tools, and governance strategies to ensure process standardization and controllable permissions.

6. The method according to claim 5, characterized in that The automatic data extraction in step S3 includes: S3.1 Artificial Intelligence Automatically Extracts Data When user input or an automatic scheduled task is started, the connection awareness agent automatically detects the data source type through the pre-trained protocol recognition model, and calls the corresponding connector to automatically collect data according to the S2 data rule indicator system; S3.1.1 Abnormal connection handling and fault tolerance The retry interval is selected based on a reinforcement learning strategy. When the data source connection is interrupted or the permissions are changed, the connection-aware agent automatically retries or switches to an alternative data source. S3.2 Data Verification and Checking S3.2.1 Mode consistency check Detect missing fields and type conflicts; S3.2.2 Data volume integrity monitoring Use the historical data volume statistical model to determine whether the current extracted data volume is abnormal; S3.2.3 Primary Key / Uniqueness Verification Automatically identify primary key or unique constraint fields and check for duplicate or null values.

7. The method according to claim 2, characterized in that The data cleaning steps in step S4 are as follows: S4.1 Outlier Detection and Repair The quality assessment agent uses statistical models, domain rules, and context awareness to identify abnormal data and trigger cleaning rules; S4.2 Missing value filling and processing Dynamically select a filling strategy based on field importance, data distribution, and downstream needs; Low missing rate <5%: Numerical: fill in the mean / median or predict missing values based on regression models; Classification: fill in the majority or infer through knowledge graph; High missing rate > 30%: The field is marked as low confidence, triggering an alarm and notifying downstream systems; For time series data, use interpolation or LSTM to predict missing periods; S4.3 Key fields are missing: discard the record or generate a temporary unique identifier to ensure data pipeline continuity; S4.4 Identification and removal of duplicate data The quality assessment agent detects duplicate records based on uniqueness constraints or semantic similarity; Precise deduplication: Perform hash comparison on primary key or unique constraint fields and delete duplicates; Strategy selection: retain the latest records or merge records based on business needs; S4.5 Context consistency check The semantic understanding agent detects cross-field logical contradictions through knowledge graphs and business rules; Multi-source consistency: check foreign key constraints; compare attribute differences of the same entity in multiple data sources; S4.6 Data standardization and formatting Convert and optimize Agent to a unified data format to adapt to target storage or downstream application requirements; Unit unification: automatic conversion of units; Time standardization: unified time zones; Encoding conversion: Mapping the categorical type field to an enumeration value or one-hot encoding; S4.8 self-optimizing cleaning strategy Continuously optimize cleaning logic based on downstream feedback; Reinforcement learning mechanism: Define the reward function and dynamically select the optimal cleaning strategy through Q-Learning; Rule base evolution: Record historical cleaning decisions and their effects, and generate reusable rule templates.

8. The method according to claim 2, characterized in that The data conversion steps in step S5 are as follows: S5.1 Data aggregation and calculation field generation Time window aggregation: Aggregate streaming data by fixed window or session window; Statistical indicator calculation: automatically generate mean, sum, quantile and other indicators; Machine learning feature engineering: generating lagged features, crossed features, or embedding vectors; S5.2 Data rule matching and conversion According to the data rule labeling system set by S2, the extracted data is labeled according to different dimensions.

9. The method according to claim 2, characterized in that The steps for outputting the data results in step S6 are as follows: S6.1 Target Storage Dynamic Adaptation S6.1.1 Partitioning and Bucketing: Dynamic partitioning by event_date, region, or business key user_id improves query performance; Bucketing is performed on frequently queried fields to reduce the amount of data scanned. S6.1.2 Compression and encoding: Select compression algorithm based on field type; Enable dictionary encoding for enumeration fields; S6.2 multi-modal output support The connection-aware agent converts data into multiple interface protocols according to downstream requirements, achieving seamless delivery. Database write: Batch insert to MySQL / PostgreSQL, supporting transaction rollback and conflict handling, and streaming write to ClickHouse / Druid, suitable for high-throughput real-time write scenarios; API exposure: Automatically generate RESTful API or GraphQL interface, provide aggregated results, and dynamically desensitize sensitive data; File export: Export to CSV, Excel or PDF reports as needed, supporting custom templates; S6.3 Metadata and Data Lineage Management The pattern inference agent generates a data lineage map, recording the conversion path and version information of the data from source to target; Version control: label the output data snapshot and support historical version backtracking; Record schema change logs; Bloodline tracing: storing field-level bloodline relationships through a graph database; Downstream dependency analysis: Detect which BI reports or models use the target table to ensure change compatibility; S6.4 Data Verification and Consistency Assurance The quality assessment agent performs final state verification on the output results to ensure that they are consistent with the target system constraints. Constraint check: primary key uniqueness, foreign key referential integrity, non-null constraints, etc. Data comparison: Count the number of records and key field distribution (such as total amount) of source and target data, and trigger an alarm when the deviation exceeds the threshold; For real-time data streams, data integrity is verified through checksums. S6.5 downstream system adaptive optimization Dynamically adjust output strategies based on downstream application feedback; Automatic pre-aggregation: Create materialized views for high-frequency queries; Index optimization: Analyze slow query logs and automatically create missing indexes; Machine learning ready: Output data is automatically converted to TFRecord or Petastorm format, suitable for TensorFlow / PyTorch training process; Normalize or bin feature fields to improve model convergence speed; S6.6 Real-time and batch output collaboration Dynamically switch between streaming output and batch output based on data timeliness requirements; Lambda architecture implementation: Real-time layer: Outputs incremental data through Kafka or Pulsar, supporting seconds-level delay; Batch layer: Output the entire data to HDFS / S3 daily to ensure eventual data consistency; Automatic merge strategy: Merge small files of real-time streams into large files according to time windows to reduce storage overhead; S6.7 Security and Access Control The connection-aware agent integrates the enterprise permission system to dynamically control the scope of data access; Field-level desensitization: Desensitize sensitive fields by role; Use homomorphic encryption to support ciphertext calculations; Dynamic permissions: filter output data based on attributes.

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