Methods for generative design of cold-region urban pedestrian space layout based on dynamic thermal comfort prediction

Patent metadata

Field	Value
Publication number	US-2026073085-A1
Application number	US-202519325460-A
Country	US
Kind code	A1
Filing date	Sep 10, 2025
Priority date	Sep 10, 2024
Publication date	Mar 12, 2026
Grant date	—

Abstract

A method for generative design of a cold-region urban pedestrian space layout based on dynamic thermal comfort prediction is provided. The method includes: constructing a wayfinding agent model in a cold-region pedestrian space based on big data and IoT data to obtain pedestrian trajectories during a plurality of travel periods in the cold-region pedestrian space; constructing a mapping between thermal environment data of the cold-region pedestrian space and pedestrian thermal sensation based on physiological indicator data of pedestrians to obtain pedestrian thermal sensation under different thermal environment changes; optimizing the cold-region pedestrian space layout design based on prediction results of pedestrian thermal sensation under typical pedestrian trajectories during the plurality of travel periods; and determining a cold-region pedestrian space layout decision model guided by preferences of the designers based on machine learning models and decision feedback of designers on visual solutions.

First claim

1 . A method for generative design of a cold-region urban pedestrian space layout based on dynamic thermal comfort prediction, comprising:
S1, constructing a wayfinding agent model in a cold-region pedestrian space, including:
S1.1: collecting trajectory data of pedestrian groups during a plurality of travel periods in a typical cold-region pedestrian space based on Internet of Things (IoT) perception;
S1.2: constructing a wayfinding grey-box model during each of the plurality of travel periods in the cold-region pedestrian space;
S1.3: extracting accurate pedestrian trajectories during the plurality of travel periods based on drone image data of the typical cold-region pedestrian space; and
S1.4: calibrating the wayfinding agent model based on IoT perception data of the typical cold-region pedestrian space;
S2, constructing a mapping between thermal environment data of the cold-region pedestrian space and dynamic thermal comfort data of pedestrians, including:
S2.1: obtaining pedestrian thermal sensation in the typical cold-region pedestrian space based on ecological momentary assessment (EMA);
S2.2: constructing a pedestrian thermal sensation model under winter and summer thermal environment conditions of the cold-region pedestrian space; and
S2.3: calibrating the pedestrian thermal sensation model under the winter and summer thermal environment conditions of the cold-region pedestrian space based on reinforcement learning;
S3, generating a cold-region pedestrian space layout design driven by dynamic thermal comfort data, including:
S3.1: obtaining a cold-region pedestrian space layout scheme driven by generative rules;
in step S3.1, obtaining and voxelizing layout model data of a cold-region block, applying a three-dimensional convolutional neural network to extract features of layout and building masses to obtain extracted features; dividing, based on scale features of the cold-region block, a predefined cold-region pedestrian space into three-dimensional units, performing constraint settings on a three-dimensional matrix based on design conditions of the cold-region pedestrian space and the extracted features so that a generated scheme meets a geometric and topological requirement, and under a constraint condition, allocating the three-dimensional units based on a multi-agent system to generate a cold-region pedestrian space layout, and obtaining a cold-region pedestrian space layout optimization prototype; and

S3.2: optimizing the cold-region pedestrian space layout design driven by the dynamic thermal comfort data, including:
S3.2.1: constructing a cold-region pedestrian space layout optimization model;
S3.2.2: obtaining the dynamic thermal comfort data of the pedestrians during travel periods under the cold-region pedestrian space layout; and
S3.2.3: optimizing the cold-region pedestrian space layout oriented by the dynamic thermal comfort data; and

S3.3: providing decision support for the cold-region pedestrian space layout design based on human-computer interaction;
in the step S3.3, after obtaining an optimal scheme of the cold-region pedestrian space layout oriented by the dynamic thermal comfort data, applying, based on dynamic thermal comfort data of the cold-region pedestrian space layout scheme and morphological evaluation data of the cold-region pedestrian space layout scheme by a designer, a random forest model to construct a cold-region pedestrian space layout decision model, and obtaining a preliminary decision scheme; applying a VR device and an environmental control device to provide users with a walking experience simulation of the cold-region pedestrian space layout scheme, obtaining evaluations of the cold-region pedestrian space layout scheme from the users, and using the evaluations as feedback for the cold-region pedestrian space layout decision model to further adjust the cold-region pedestrian space layout decision model, to provide an efficient and comprehensive decision support for the designer.

2 . The method according to claim 1 , wherein the step S2.1 includes:
S2.1.1: obtaining pedestrian positions in the cold-region pedestrian space; S2.1.2: collecting real-time thermal sensation information of the pedestrians under changes in the pedestrian positions in the cold-region pedestrian space; and S2.1.3: collecting real-time thermal environment data of the typical cold-region pedestrian space.

3 . The method according to claim 2 , wherein the step S2.1.2 includes:
collecting real-time skin temperature, heart rate, and electrodermal activity (EDA) data under the changes in the pedestrian positions based on wearable devices, and obtaining subjective evaluation data under the changes in the pedestrian positions; and constructing a mapping relationship between physiological indicator data and the pedestrian thermal sensation in the cold-region pedestrian space to obtain the real-time thermal sensation information of the pedestrians under the changes in the pedestrian positions in the cold-region pedestrian space.

4 . The method according to claim 1 , wherein the step S3.2.2 includes:
S3.2.2.1: constructing a typical thermal environment prediction model during the plurality of travel periods in winter and summer under the cold-region pedestrian space layout; S3.2.2.2: obtaining thermal environments along typical routes of the pedestrians during the plurality of travel periods in winter and summer in the cold-region pedestrian space; and S3.2.2.3: obtaining the pedestrian thermal sensation during the plurality of travel periods along the typical routes under the cold-region pedestrian space layout.

5 . The method according to claim 4 , wherein the step S3.2.2.1 includes:
obtaining thermal environment images during the plurality of travel periods in winter and summer under the cold-region pedestrian space layout, clustering the thermal environment images during the plurality of travel periods, respectively, to obtain clustered images, and then constructing a mapping relationship between the cold-region pedestrian space layout and the clustered images for the plurality of travel periods in winter and summer.

6 . An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the method according to claim 1 .

7 . A non-transitory computer-readable storage medium for storing computer instructions, wherein the computer instructions, when executed by a processor, cause the processor to implement the method according to claim 1 .

Assignees

• Harbin Inst Technology

Inventors

• Sun Cheng
• Han Yunsong
• Xu Shuangyu
• Cui Hongji
• Zhang Qihui
• Bai Zhaohan

Classifications

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06F2111/04

  Constraint-based CAD · CPC title

• G06F30/12

  characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD · CPC title

• G06F2119/08

  Thermal analysis or thermal optimisation · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title