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Automatically generate estimates from QTO data. Apply pricing rules to BIM quantities for cost estimates.
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SkillHub CLI
skillhub install datadrivenconstruction/auto-estimate-generator打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Manual estimate creation challenges:
Automated estimate generation from BIM/QTO data using configurable pricing rules and assembly mappings.
import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
class ElementType(Enum):
WALL = "wall"
FLOOR = "floor"
CEILING = "ceiling"
DOOR = "door"
WINDOW = "window"
COLUMN = "column"
BEAM = "beam"
FOUNDATION = "foundation"
ROOF = "roof"
STAIR = "stair"
MEP = "mep"
@dataclass
class QTOItem:
element_id: str
element_type: ElementType
name: str
quantity: float
unit: str
properties: Dict[str, Any] = field(default_factory=dict)
@dataclass
class PricingRule:
rule_id: str
name: str
element_type: ElementType
conditions: Dict[str, Any] = field(default_factory=dict)
unit_cost: float = 0
assembly_code: str = ""
cost_breakdown: Dict[str, float] = field(default_factory=dict)
@dataclass
class EstimateItem:
qto_element_id: str
description: str
quantity: float
unit: str
unit_cost: float
total_cost: float
rule_applied: str
wbs_code: str = ""
class AutoEstimateGenerator:
"""Generate estimates from QTO data automatically."""
def __init__(self, project_name: str):
self.project_name = project_name
self.pricing_rules: List[PricingRule] = []
self.qto_items: List[QTOItem] = []
self.estimate_items: List[EstimateItem] = []
self.unmapped_items: List[QTOItem] = []
def add_pricing_rule(self, rule: PricingRule):
"""Add pricing rule."""
self.pricing_rules.appe...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# Auto Estimate Generator
## Business Case
### Problem Statement
Manual estimate creation challenges:
- Time-consuming quantity mapping
- Inconsistent pricing rules
- Errors in calculations
- Difficulty updating estimates
### Solution
Automated estimate generation from BIM/QTO data using configurable pricing rules and assembly mappings.
## Technical Implementation
```python
import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
class ElementType(Enum):
WALL = "wall"
FLOOR = "floor"
CEILING = "ceiling"
DOOR = "door"
WINDOW = "window"
COLUMN = "column"
BEAM = "beam"
FOUNDATION = "foundation"
ROOF = "roof"
STAIR = "stair"
MEP = "mep"
@dataclass
class QTOItem:
element_id: str
element_type: ElementType
name: str
quantity: float
unit: str
properties: Dict[str, Any] = field(default_factory=dict)
@dataclass
class PricingRule:
rule_id: str
name: str
element_type: ElementType
conditions: Dict[str, Any] = field(default_factory=dict)
unit_cost: float = 0
assembly_code: str = ""
cost_breakdown: Dict[str, float] = field(default_factory=dict)
@dataclass
class EstimateItem:
qto_element_id: str
description: str
quantity: float
unit: str
unit_cost: float
total_cost: float
rule_applied: str
wbs_code: str = ""
class AutoEstimateGenerator:
"""Generate estimates from QTO data automatically."""
def __init__(self, project_name: str):
self.project_name = project_name
self.pricing_rules: List[PricingRule] = []
self.qto_items: List[QTOItem] = []
self.estimate_items: List[EstimateItem] = []
self.unmapped_items: List[QTOItem] = []
def add_pricing_rule(self, rule: PricingRule):
"""Add pricing rule."""
self.pricing_rules.appe...generate a bunch of images, then you curate the results to handpick the best ones
generate a bunch of images, then you curate the results to handpick the best ones
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