Built Environment Quality Assessment Using Dynamic Interval Division
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Solution Overview
Problem
Current quality assessment methods for built environments lack scientific accuracy and comprehensive analysis, leading to unreliable and imprecise measurements due to static interval division and neglect of complex interactions between sub-elements, resulting in unreasonable assessment results.
Innovation Solution
A method involving identifying key influencing factors, establishing an index system, analyzing relationships, forming a theoretical model, calculating path coefficients, and dynamically dividing data intervals to perform comprehensive quality measurement, incorporating regression equations and weight calculations for accurate and objective assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static parameter interval division is used for assessment elements, then the assessment process is simple, but the assessment accuracy deteriorates due to data concentration in unreasonable intervals
Solution Approach 1:
The patent applies dynamic interval division by using data-driven clustering algorithms to automatically determine assessment intervals based on actual data distribution characteristics. This replaces static, predetermined intervals with dynamic intervals that adapt to the specific dataset, preventing data concentration in unreasonable intervals while maintaining assessment efficiency through automated processing.
Solution Approach 2:
The patent changes the parameter of interval division from static to dynamic by introducing data distribution analysis. The assessment intervals are no longer fixed but are determined by analyzing the actual data characteristics, thereby improving measurement precision without significantly increasing complexity.
2Ease of operation
If expert assessment or questionnaire survey is used for non-quantifiable index elements, then the assessment can be performed, but the objectivity and scientific validity deteriorate
Solution Approach 1:
The patent replaces the mechanical system of expert assessment and questionnaire surveys with a data-driven computational system. By using statistical analysis and data mining techniques on large sample databases, the system objectively determines assessment results without relying on subjective human judgment, thereby improving reliability while maintaining ease of operation through automated processing.
3Device complexity
If simple comparative analysis of sub-elements is performed, then the analysis process is straightforward, but the comprehensive quality assessment deteriorates due to ignoring complex interactions between elements
Solution Approach 1:
The patent uses composite analysis by integrating multiple sub-element assessments with their interaction relationships. Instead of simply comparing individual elements, the system combines them into a comprehensive quality assessment that accounts for interactions between elements, achieving accurate comprehensive evaluation while managing complexity through systematic integration.
4Ease of manufacture
If assessment intervals are determined based on past research experience, then the assessment can be conducted, but the rationality and systematicness deteriorate without considering data distribution
Solution Approach 1:
The patent enables the assessment system to determine its own intervals automatically through data-driven clustering algorithms. The system serves itself by analyzing its own data distribution characteristics and generating appropriate assessment intervals without relying on external expert input or past experience, thereby improving systematicness and rationality while maintaining ease of implementation through automation.
Data Source
AI summary
Disclosed are a method, an apparatus and a storage medium for measuring the quality of a built environment. The method includes the following steps: identifying key influencing factors determining environmental quality, and establishing an index system of environmental quality influencing factors, wherein the key influencing factors include non-observation elements and observation elements; analyzing the relationship between the environmental quality and the key influencing factors to form a theoretical model; acquiring observation elements to form a large sample database; calculating path coefficients of each key influencing factor of environmental quality according to the distribution of sample data in the large sample database, and converting the path coefficients into weights; dividing distribution intervals of all observation elements dynamically according to the distribution of sample data, and defining quality assignments of all observation elements; and performing an environmental quality measurement of samples in combination with the weights and the quality assignments.