表格大师-AI table organizer
AI-powered table organizer — clean, unify, export
专于多页表格分类处理和组织信息的专家。
这些信息需要按照哪一页的表格分类来组织？
请根据这个多页表格的其中一页来处理这些信息...
我有一个多页表格，需要处理信息，请问按哪一页？
帮我更新这个多页表格，需要按照特定一页的分类...
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About 表格大师 (Table Master)
表格大师 (Table Master) is a specialized assistant designed to process, organize, transform, validate, and present multi-page tabular data with high accuracy and predictable structure. Its design purpose is to bridge the gap between raw, heterogeneous spreadsheet-like inputs (Excel files, CSVs, PDF tables, multi-sheet workbooks, exported database tables) and clean, analysis-ready outputs (standardized tables, normalized databases, pivot-ready datasets, API-ready JSON). Core design principles include: 1) page-aware processing — understanding that multi-page inputs often have distinct layouts and metadata per page and providing mechanisms to map, align, and transform pages consistently; 2) repeatable transformations — generating reusable transformation rules (mappings, column-normalization, type schemas) so workflows can be rerun on new files with the same structure; 3) data safety & traceability — preserving provenance (which page, original cell coordinates), flagging uncertainties, and providing clear change logs; 4) user-configurable rules — letting users choose which page template to follow when multiple page types exist. Typical capabilities include automated detection of table boundaries, header/footer detection, header normalization, column merging/splitting, typeIntroduction to 表格大师 inference and correction, duplicate detection, record deduplication and consolidation, unit and currency normalization, schema generation (JSON Schema/SQL), and export into formats like clean CSV, JSON, SQL, or formatted Excel. Example: you receive a 12-page PDF invoice export where pages 1–3 are customer summaries, pages 4–8 are line-item tables in Template A, and pages 9–12 are line-item tables in Template B; 表格大师 can detect the two line-item templates, ask (or be instructed) to standardize on Template A’s column order, transform Template B pages to Template A mapping, merge all line-items into a single normalized table, produce an export CSV, and generate a change log that lists any ambiguous rows (e.g., merged cells or subtotal lines) for manual review.
Primary functions and real-world applications
Multi-page template detection & page-mapping
Example
Given a 20-sheet workbook where sheets 1–5 are monthly summaries, sheets 6–15 are daily logs with the same columns but different column names, and sheets 16–20 are metadata pages, 表格大师 identifies repeating layouts, clusters pages into templates, and builds a mapping from each template's column names to a canonical schema.
Scenario
A government contractor receives monthly reports from regional teams. Each team uses slightly different column headers ('Emp ID' vs 'EmployeeID' vs 'ID#'). 表格大师 clusters pages by layout, suggests a canonical header set, and automatically remaps all region files to the corporate schema, producing a single combined table ready for BI ingestion.
Data cleaning, normalization and rule-based transformation
Example
A dataset contains dates in mixed formats (YYYY/MM/DD, MM-DD-YYYY, textual months), currency values with different symbols and locales, and inconsistent category labels. 表格大师 applies rules to normalize dates to ISO 8601, converts all currency to a single base unit (with configured exchange rates or currency-unification rules), and uses fuzzy matching + user-defined dictionaries to standardize category labels.
Scenario
An e-commerce analyst receives sales exports from three marketplaces. Each marketplace exports price and date differently. 表格大师 transforms them into uniform datatypes, adds a validated 'sale_timestamp' column, and marks any rows where conversion failed (e.g., ambiguous date like '03/04/05') so the analyst can review only the exception set.
Schema generation, validation and export (CSV/JSON/SQL/Excel)
Example
After cleaning and aligning columns across pages, 表格大师 generates a JSON Schema and SQL CREATE TABLE statement representing the canonical table, runs validation on every row, reports row-wise validation errors, and exports validated data to the requested format (for example, a normalized CSV and a ready-to-load SQL file).
Scenario
A finance team wants to load historical transactions into a data warehouse. 表格大师 produces the SQL DDL with column types, nullability, and constraints, validates the incoming CSV against that schema, produces an error report for failing rows (with original page and cell references), and provides the clean CSV plus the DDL, so DBAs can import the data with confidence.
Who benefits most from 表格大师
Data analysts, BI teams, and data engineering
These users regularly ingest heterogeneous tabular inputs from partners, legacy systems, or ad-hoc reports. They benefit from template detection, canonical schema mapping, automated cleaning, and export-ready outputs (CSV/SQL/JSON). 表格大师 reduces manual pre-processing time, provides reproducible transformation rules for scheduled imports, and generates validation/error reports so analysts only inspect edge cases. Typical use: preparing monthly performance dashboards by consolidating multi-source sales reports into one normalized table and producing the DDL for the data warehouse.
Finance, accounting, operations, researchers, and compliance teams
These groups often work with audited, sensitive, or legally significant tabular data (invoices, contract logs, clinical trial tables). 表格大师 helps ensure traceability (original page and cell references), flags anomalies (duplicate invoices, out-of-range amounts), enforces schema/format constraints (account numbers, tax IDs), and produces audit-ready exports and change logs. Researchers and students also benefit when converting legacy datasets or PDFs into analysis-ready tables with provenance. Typical use: consolidating multi-page supplier invoices into a single validated ledger with a change log for audit trails, or extracting experiment results from published PDFs into a clean dataset for statistical analysis.
How to use 表格大师
Visit aichatonline.org for a free trial without login, also no need for ChatGPT Plus.
Open aichatonline.org and start the free trial immediately — no account signup or ChatGPT Plus subscription required. This gives you instant access to 表格大师's core features for testing.
Prepare your tables and choose a page structure
Gather your multi-page spreadsheets, PDFs, or CSVs. If your dataset spans multiple pages, decide which page layout (column order, headers, primary key) to use as the canonical structure — 表格大师 will apply that layout consistently when organizing data.
Upload files and select processing options
Upload one or more files (CSV, XLSX, or multi-page PDF). Choose actions such as normalize headers, deduplicate rows, split/merge tables, map columns, or classify rows. Use built-in templates for common scenarios (research, finance, inventory) to speed setup.
Review, refine, and apply automated transformations
Preview the parsed tables, correct header mappings or column types, and apply transformations (date parsing, number normalization, categorical tagging). Use the 'apply to all pages' optionHow to use 表格大师 for consistent multi-page processing and run deduplication and validation rules.
Export, integrate, and save templates
Export cleaned data to CSV, Excel, or JSON, or connect to Google Sheets/other apps if available. Save your mapping and transformation as a template for repeatable workflows. Tip: name columns clearly and keep a small sample file to refine templates before bulk processing.
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Common questions about 表格大师
What does 表格大师 do best?
表格大师 automates the organization and normalization of multi-page tabular data: it parses spreadsheets and PDFs, maps headers across pages, deduplicates records, standardizes data types, and exports clean, analysis-ready tables. It’s optimized for repetitive workflows where consistent structure is needed across many pages or files.
Which file formats are supported and how accurate is parsing?
Typical supported formats include CSV, XLSX, and multi-page PDFs. Parsing accuracy depends on source quality—well-formatted spreadsheets and high-resolution PDFs yield near-perfect results. 表格大师 provides a preview step to correct header recognition and column types before bulk processing to ensure accurate outputs.
How does it handle multi-page tables with different headers?
You select one page layout as the canonical structure; 表格大师 maps varying headers to that canonical set using intelligent matching (synonyms, fuzzy matching). You can manually confirm mappings and save them as templates so future multi-page imports are normalized automatically.
Is my data private and how are exports handled?
Uploads are processed to produce local exportable outputs (CSV/XLSX/JSON). 表格大师 is designed to minimize retained data—use the export step to download cleaned files and delete processed files from the session. For sensitive data, follow organizational policies and prefer on-premise or enterprise deployment if available.
What integrations or automation options exist?
表格大师 supports exporting to standard formats (CSV/Excel/JSON) for easy import into analytics tools and can connect to Google Sheets or other endpoints when available. It also offers template saving for automation: once a mapping and transformation sequence is saved, it can be reused to batch-process new files with minimal manual steps.