Develop Compliance Checker Tool for AutoStore Floor Elevation Survey Data - Python or Macros
Budget: $250 – $750 CAD
Project Description
We require a robust Python-based tool to automate the analysis of grid-based concrete floor elevation surveys, ensuring compliance with AutoStore's stringent flatness tolerances. The script will process Excel survey files, perform precise calculations for Properties A1, A2, B1, B2, and C (relative to a datum plane), and generate clear pass/fail reports with visualizations. This tool will help identify and remediate non-compliant areas to prevent operational issues in AutoStore automated storage systems.
Ideal candidates will have experience in geospatial data processing, surveying analysis, or industrial automation specs. I can provide AutoStore's exact tolerance values, formulas, and sample datasets upon project start.
Project Background
A laser survey has been conducted on a concrete warehouse floor designated for an AutoStore system. The data captures elevation measurements (in mm) on a regular grid (e.g., 0.6m x 0.6m or 1m x 1m spacing) across areas up to 50m x 50m. AutoStore demands ultra-precise floor tolerances for reliable robot navigation:
-Properties A1/A2: Local flatness over short spans (e.g., 1.2m–2.4m deviations in wheel paths).
-Properties B1/B2: Global levelness and slope limits (e.g., maximum tilt over 1.2m–2.4m+ spans).
-Property C: Absolute elevation deviations from a best-fit datum (e.g., ±2mm overall).
Non-compliance risks errors or system failures, so the tool must deliver granular, actionable insights.
Input Data
Format: Excel (.xlsx) or CSV files, with one or multiple sheets.
Structure: Columns for X (easting), Y (northing), Z (elevation in mm), and optional grid indices (row/column).
Example: 60x80 grid (4,800 points) spanning 36m x 48m.
Additional: User-specified grid spacing; optional datum (mean Z or benchmark); support for batch imports.
Required Functionality
Data Import & Validation:
Parse files to extract Z-value matrices; prompt for grid spacing if absent.
Validate for gaps, outliers (> ±50mm), or irregularities; apply basic smoothing if needed.
Compute best-fit datum plane via least-squares regression on Z-values if unspecified.
Compliance Calculations:
Implement A1/A2: Local deviations (e.g., max |Z_i - Z_{i+1}| over 1.2m/2.4m spans using straightedge simulation).
Implement B1/B2: Slope/gradient checks (e.g., linear regression on subsections for max tilt).
Implement C: Absolute variances (e.g., max/min Z deviations ≤ tolerance).
Adapt standard metrics (e.g., F-numbers from ASTM E1155) to AutoStore props; interpolate for non-grid points.
Flag per-point/zone failures with remediation estimates (e.g., grinding depths).
Report Generation:
Summary Table: Pass/Fail status per property, with measured values vs. tolerances, % compliant area, and overall verdict.
Visuals: Color-coded heatmaps of deviations (via matplotlib/seaborn), contour plots, and zone highlights.
Details: Log of exceeding points with coordinates and fixes.
Outputs: New Excel sheet + printable PDF; include timestamps and stats (e.g., avg. deviation).
User Interface:
Simple GUI (Tkinter or Streamlit preferred) with file upload, datum input, "Check Compliance," and "Generate Report" buttons.
Support batch processing; modular design for easy tolerance updates.
Technical Preferences
Core Stack: Python 3.x with pandas (data handling), numpy/scipy (calculations), matplotlib/plotly (visuals), openpyxl/xlsxwriter (Excel I/O).
Alternatives: Excel VBA if Python constraints arise (must scale to 10,000+ points).
Outputs: Static Excel/PDF or interactive HTML (via Streamlit/Dash).
Edge Cases: Uneven grids, noisy data (filters), unit conversions (mm/inches).
Deliverables
Full source code (zipped or GitHub repo), fully commented and modular.
Sample input file (dummy data) with matching output report demonstrating all five properties.
Basic GUI executable.
1-2 page README/PDF user guide (setup, usage, troubleshooting).
Verified testing on provided anonymized real data (post-milestone).
Timeline & Budget
Timeline: 5–7 days for MVP (core script + reports); 10 days for full version with GUI.
Budget: Fixed-price proposals welcome; milestones: 50% on draft code/validation, 50% on GUI/testing/report.
Required Skills: Python scripting, data analysis, visualization libraries, surveying/GIS tools, report automation.
If you've worked on floor flatness tools or AutoStore projects, include portfolio examples. I'm responsive to questions and ready to share spec excerpts immediately.
We require a robust Python-based tool to automate the analysis of grid-based concrete floor elevation surveys, ensuring compliance with AutoStore's stringent flatness tolerances. The script will process Excel survey files, perform precise calculations for Properties A1, A2, B1, B2, and C (relative to a datum plane), and generate clear pass/fail reports with visualizations. This tool will help identify and remediate non-compliant areas to prevent operational issues in AutoStore automated storage systems.
Ideal candidates will have experience in geospatial data processing, surveying analysis, or industrial automation specs. I can provide AutoStore's exact tolerance values, formulas, and sample datasets upon project start.
Project Background
A laser survey has been conducted on a concrete warehouse floor designated for an AutoStore system. The data captures elevation measurements (in mm) on a regular grid (e.g., 0.6m x 0.6m or 1m x 1m spacing) across areas up to 50m x 50m. AutoStore demands ultra-precise floor tolerances for reliable robot navigation:
-Properties A1/A2: Local flatness over short spans (e.g., 1.2m–2.4m deviations in wheel paths).
-Properties B1/B2: Global levelness and slope limits (e.g., maximum tilt over 1.2m–2.4m+ spans).
-Property C: Absolute elevation deviations from a best-fit datum (e.g., ±2mm overall).
Non-compliance risks errors or system failures, so the tool must deliver granular, actionable insights.
Input Data
Format: Excel (.xlsx) or CSV files, with one or multiple sheets.
Structure: Columns for X (easting), Y (northing), Z (elevation in mm), and optional grid indices (row/column).
Example: 60x80 grid (4,800 points) spanning 36m x 48m.
Additional: User-specified grid spacing; optional datum (mean Z or benchmark); support for batch imports.
Required Functionality
Data Import & Validation:
Parse files to extract Z-value matrices; prompt for grid spacing if absent.
Validate for gaps, outliers (> ±50mm), or irregularities; apply basic smoothing if needed.
Compute best-fit datum plane via least-squares regression on Z-values if unspecified.
Compliance Calculations:
Implement A1/A2: Local deviations (e.g., max |Z_i - Z_{i+1}| over 1.2m/2.4m spans using straightedge simulation).
Implement B1/B2: Slope/gradient checks (e.g., linear regression on subsections for max tilt).
Implement C: Absolute variances (e.g., max/min Z deviations ≤ tolerance).
Adapt standard metrics (e.g., F-numbers from ASTM E1155) to AutoStore props; interpolate for non-grid points.
Flag per-point/zone failures with remediation estimates (e.g., grinding depths).
Report Generation:
Summary Table: Pass/Fail status per property, with measured values vs. tolerances, % compliant area, and overall verdict.
Visuals: Color-coded heatmaps of deviations (via matplotlib/seaborn), contour plots, and zone highlights.
Details: Log of exceeding points with coordinates and fixes.
Outputs: New Excel sheet + printable PDF; include timestamps and stats (e.g., avg. deviation).
User Interface:
Simple GUI (Tkinter or Streamlit preferred) with file upload, datum input, "Check Compliance," and "Generate Report" buttons.
Support batch processing; modular design for easy tolerance updates.
Technical Preferences
Core Stack: Python 3.x with pandas (data handling), numpy/scipy (calculations), matplotlib/plotly (visuals), openpyxl/xlsxwriter (Excel I/O).
Alternatives: Excel VBA if Python constraints arise (must scale to 10,000+ points).
Outputs: Static Excel/PDF or interactive HTML (via Streamlit/Dash).
Edge Cases: Uneven grids, noisy data (filters), unit conversions (mm/inches).
Deliverables
Full source code (zipped or GitHub repo), fully commented and modular.
Sample input file (dummy data) with matching output report demonstrating all five properties.
Basic GUI executable.
1-2 page README/PDF user guide (setup, usage, troubleshooting).
Verified testing on provided anonymized real data (post-milestone).
Timeline & Budget
Timeline: 5–7 days for MVP (core script + reports); 10 days for full version with GUI.
Budget: Fixed-price proposals welcome; milestones: 50% on draft code/validation, 50% on GUI/testing/report.
Required Skills: Python scripting, data analysis, visualization libraries, surveying/GIS tools, report automation.
If you've worked on floor flatness tools or AutoStore projects, include portfolio examples. I'm responsive to questions and ready to share spec excerpts immediately.
Related categories:
Python
Visual Basic
Excel
Software Architecture
Excel VBA
Data Visualization
Data Analysis
Streamlit