Urban slow space design method for optimizing way-finding task under guidance of human factor technology

Abstract

The invention discloses an urban slow space design method for optimizing a way-finding task under the guidance of a human factor technology, and the method comprises the following steps: 1, carrying out the simulation modeling of different slow space design schemes, carrying out the detailed decomposition of the way-finding task of the slow space through a path planning algorithm, and determining a human factor space variable and a human factor environment variable; 2, designing a space actual measurement experiment and a scene virtual experiment corresponding to the path-finding task, and carrying out a human dependent variable data acquisition experiment based on a VR virtual simulation technology and a human factor data acquisition technology; 3, utilizing an artificial intelligence deep learning algorithm to analyze and research the coupling effect of the human factor perception data and the spatial design variable data; and 4, on the basis of an artificial intelligence data visualization analysis technology, key variables of human factor design are identified, and the urban slow space planning design method for optimizing the path-finding task under the guidance of the human factor technology is obtained. According to the method, the defect that only the shortest path is recommended without considering the path space quality and the human body comfort in the traditional path-finding technology is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of urban slow-moving space design, and in particular to an urban slow-moving space design method for optimizing path-finding tasks under the guidance of human factors technology. Background Art

[0002] In the context of new urbanization, as society's requirements for spatial quality continue to increase, the importance of spatial design of urban slow-moving spaces has become increasingly prominent.

[0003] The design of urban slow-moving spaces should not only meet modern people’s pursuit of a healthy and environmentally friendly lifestyle, but also provide a more humane and walkable street environment.

[0004] The optimization of slow-moving spaces can greatly improve the quality of life of urban residents and promote social harmony and sustainable development.

[0005] Although the importance of slow-moving space design is increasingly recognized, there is currently a lack of a scientific and quantitative methodology to guide space design in actual operation. Existing relevant design guidelines focus on providing guidance on the use of functions and physical space dimensions (indicators such as aspect ratio, street width, discount rate, etc.), such as: Indian Street Design Manual (2011), Los Angeles Street Design Guidelines (2011), New York Street Design Manual (2009), Street Design with Multiple Demands Balance as a Sculpture - Taking the Abu Dhabi Street Design Manual as an Example (2014), Shanghai Street Design Guidelines (2016), Nanjing Street Design Guidelines (2018), and Shanghai Urban Road Classification System Research (2004). For the consideration of slow-moving space quality and humanistic perspective, traditional space design methods often rely on the designer's intuition and experience, and have not formed a comprehensive, objective, and quantifiable evaluation system. This method has obvious limitations in evaluating and optimizing slow-moving spaces, and it is difficult to accurately meet the sophisticated needs of modern urban residents for space quality.

[0006] In recent years, under the guidance of urban ergonomics, space design can pay more attention to people's behavior patterns, psychological feelings and physiological reactions, so as to create a space that is more in line with ergonomic principles. The introduction of the concept of urban ergonomics provides a new idea for solving this problem. Through quantitative means, a descriptive model is established to accurately support the design decision-making process of urban slow-moving spaces, making the design more scientific, systematic and human-oriented. Compared with traditional methods, urban ergonomics can better capture people's behavior patterns, psychological feelings and physiological reactions, and provide accurate data support for the world of slow-moving spaces.

[0007] In this context, the present invention aims to construct a spatial planning and design method based on modern human factors measurement technologies, such as eye tracking, virtual reality (VR), physiological testing, etc., combined with the theoretical framework of urban ergonomics, to explore the perceptual laws of urban slow-moving spaces and optimize the path-finding tasks in slow-moving spaces.

[0008] Therefore, how to calculate the human spatial variables and human environmental variables of public spaces through computer software to provide suitable travel paths for different travelers in different environments so that the paths can meet the travel needs of different objects in different scenarios, rather than just the shortest (fastest) path, has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0009] In view of the above-mentioned defects of the prior art, the present invention provides an urban slow-moving space design method for optimizing path-finding tasks under the guidance of human factors and technology. The purpose of achieving this is to construct a comprehensive, objective and quantifiable evaluation system through interdisciplinary cooperation to support design decisions and improve the design quality of slow-moving spaces. The ultimate goal is to create a healthier, more comfortable and pleasant urban slow-moving environment, promote the sustainable development of the city, and provide residents with a higher quality living space.

[0010] To achieve the above-mentioned purpose, the present invention discloses a method for designing an urban slow-moving space for optimizing the path-finding task under the guidance of human factors and technology, comprising the following steps:

[0011] Step 1: Based on BIM technology and high-precision simulation technology, simulate and model different design schemes of slow-moving spaces, use path planning algorithms to refine and decompose the path-finding tasks of slow-moving spaces, and determine human-factor spatial variables and human-factor environmental variables;

[0012] Step 2: Design a spatial measurement experiment and a scenario virtual experiment corresponding to the path-finding task, and conduct a data collection experiment on human factors variables based on VR virtual simulation technology and human factors data collection technology;

[0013] Step 3: Use artificial intelligence deep learning algorithms to analyze the coupling effects of human perception data and spatial design variable data;

[0014] Step 4: Based on artificial intelligence data visualization analysis technology, identify the key variables of human factors design, and derive an urban slow-moving space planning and design method that optimizes path-finding tasks under the guidance of human factors technology, which is used to guide the planning and design of slow-moving spaces.

[0015] Preferably, step 1 specifically includes:

[0016] Step 1.1, using BIM technology to simulate different design schemes of the slow-moving space with high precision;

[0017] Step 1.2, obtaining virtual modeling of design solutions for all the slow-moving spaces;

[0018] Step 1.3, decomposing the path-finding task of the slow-moving space;

[0019] Step 1.4: Determine the human-factor spatial variables and the human-factor environmental variables.

[0020] More preferably, step 1.3 is as follows:

[0021] For the environment of the built slow-moving space, the drone real-scene 3D oblique photography technology and NeDF (Neural Radiance Fields) or Gaussian snowball deep learning algorithm based on video materials are used to achieve high-precision 3D real-scene modeling of the slow-moving space;

[0022] For the environment of the unbuilt slow-moving space, BIM technology and virtual simulation technology are used to carry out high-precision three-dimensional real-scene modeling of different design schemes of the slow-moving space.

[0023] More preferably, in step 1.4, according to different categories of the slow-moving space, the path-finding task in a specific scene is decomposed, and the corresponding human-induced spatial variables and human-induced environmental variables are selected, as follows:

[0024] When the category of the slow-moving space is walking space, the element of the slow-moving space is an area dedicated to pedestrians, and the corresponding path-finding task is how pedestrians choose the best walking route;

[0025] The areas exclusively for pedestrian use include pedestrian streets, pedestrian plazas, pedestrian bridges and underground passages;

[0026] How the pedestrians choose the best walking route includes distance factors, time factors, landscape factors, and the role of traffic buildings including the pedestrian bridge and the underground passage in guiding pedestrian direction and path selection;

[0027] When the category of the slow-moving space is a bicycle space, the element of the slow-moving space is an area for cyclists, and the corresponding path-finding task includes bicycle lane path selection, bicycle space layout and cyclist behavior patterns;

[0028] The areas for use by cyclists include bicycle lanes, bicycle parking areas, and shared bicycle stations;

[0029] When the category of the slow-moving space is a mixed-use space, the elements of the slow-moving space are areas for pedestrians and cyclists to use together, and the corresponding wayfinding tasks include walking and cycling route selection. The sign system should be coordinated with the layout of the mixed space and its impact on user wayfinding;

[0030] The areas for shared use by pedestrians and cyclists include mixed walking and cycling paths, urban greenways and waterfront trails;

[0031] When the category of the slow-moving space is the city center, the elements of the slow-moving space include commercial pedestrian streets, squares, pedestrian streets in parks and cycling paths, and the corresponding wayfinding tasks include route selection, signage and route design in the busy city center and the impact of the building and space layout of the city center on wayfinding;

[0032] When the category of the slow-moving space is a residential area, the elements of the slow-moving space include pedestrian paths and bicycle paths within the community, and the corresponding wayfinding tasks include major facilities in the residential area and destination selection;

[0033] When the category of the slow-moving space is campus, the elements of the slow-moving space include walking and cycling paths in the university campus, and the corresponding wayfinding tasks include route selection in classrooms, libraries, dormitories and dining areas;

[0034] When the category of the slow-moving space is a waterfront trail, the elements of the slow-moving space include walking and cycling trails built along rivers, lakes or coastlines, and the corresponding pathfinding task includes the impact of the route design of the waterfront trail on pathfinding;

[0035] When the category of the slow-moving space is commuting space, the elements of the slow-moving space include walking and cycling paths designed for daily commuting, connecting residential areas with workplaces and schools, and the corresponding wayfinding tasks include the efficiency of routes between residential areas and workplaces and schools, and the difficulty of commuting;

[0036] When the category of the slow-moving space is leisure space, the elements of the slow-moving space are spaces for leisure, sports and entertainment, connecting residential areas with workplaces and schools, and the corresponding wayfinding tasks include wayfinding signs and natural landscapes and design elements of leisure space;

[0037] The spaces provided for leisure, exercise and recreation include park trails and ice water trails;

[0038] When the category of the slow-moving space is a tourist space, the elements of the slow-moving space include walking and cycling paths connecting tourist attractions, providing sightseeing and experience services, and the corresponding wayfinding tasks include identification of major attractions, cultural relics and natural landscapes.

[0039] Preferably, step 2 specifically includes:

[0040] Step 2.1, designing a spatial measurement experiment and a scenario virtual experiment corresponding to the path-finding task;

[0041] Step 2.2: Use VR-based virtual simulation technology and human factors data collection technology;

[0042] Step 2.3: Conduct data collection experiments on human variables.

[0043] More preferably, in step 2.2, the subjects wear VR equipment and human factors data collection experimental equipment to conduct behavioral experiments and collect data in a three-dimensionally reconstructed virtual scene or a real street scene;

[0044] In step 2.3, using the data obtained by the VR device and the human factors data collection experimental equipment, a human factors variable data collection experiment is conducted on the path-finding task in the slow-moving space from a human-oriented perspective, and the human factors variable data of the subjects including visual focus data, facial micro-expression data, EEG data, electrodermal data, walking path selection data, and stay time data are collected and recorded.

[0045] Preferably, step 3 is as follows:

[0046] Step 3.1: Use artificial intelligence deep learning algorithm;

[0047] Step 3.2, taking human perception data and spatial design variable data as input;

[0048] Step 3.3: Conduct coupling analysis and establish a variable database of pathfinding tasks and human perception data in different slow-moving spaces.

[0049] More preferably, eye tracking data, facial recognition data and VR experience data are collected from subjects by having them wear VR equipment and human factors data collection experimental equipment, and then a parameter coupling analysis model is constructed based on a decision tree and a self-organizing map neural network to simulate the parameter relationship between multiple variables, and to establish a variable database of pathfinding tasks and human factors perception data in different slow-moving spaces.

[0050] Preferably, step 4 is as follows:

[0051] Step 4.1, artificial intelligence data visualization analysis;

[0052] Step 4.2: Identify the key variables of human factors design;

[0053] Step 4.3: Derive a planning and design method for urban slow-moving space that optimizes path-finding tasks under the guidance of human factors and technology.

[0054] More preferably, based on the variable database of pathfinding tasks and human perception data in different slow-moving spaces, the key variables of human factor design are identified through machine learning and deep learning algorithms including decision trees, random forests, and convolutional neural networks, and the influence of spatial design features on pedestrians is obtained, and then the quantitative human factor parameter design indicators are summarized for different design scenarios;

[0055] Finally, through the quantified human factor parameter design indicators and based on data analysis, an optimized design solution guided by human factor technology is obtained.

[0056] This invention adopts a full-process quantitative research method and proposes an optimization design scheme guided by human factors technology based on data analysis to improve the availability, safety and comfort of urban slow-moving spaces.

[0057] Beneficial effects of the present invention:

[0058] Based on traditional path-finding software, this invention will expand the research scope to the public space where the "road" is located, and consider human variables at the public space level. By collecting, experimenting and analyzing the data of human variables in public spaces, the key variables under different space types are identified, and these variables are incorporated into the influencing factors of travel path selection in different scenarios to find the highest quality and relatively short path.

[0059] The path search results provided by the present invention fully consider the travel needs of different spaces and different groups of people, provide the most comfortable and reasonable path, and avoid the shortcomings of traditional path-finding technology that only recommends the shortest path without considering the spatial quality of the path and human comfort.

[0060] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart showing an embodiment of the present invention is shown.

[0062] Figure 2 A table showing the classification of slow-moving space scenes and path-finding task decomposition according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0063] Example

[0064] like Figure 1 As shown in the figure, the design method of urban slow-moving space for optimizing path-finding tasks under the guidance of human factors technology includes the following steps:

[0065] Step 1: Based on BIM technology and high-precision simulation technology, simulate and model different design schemes of slow-moving spaces, use path planning algorithms to refine and decompose the path-finding tasks of slow-moving spaces, and determine human-factor spatial variables and human-factor environmental variables;

[0066] Step 2: Design a spatial measurement experiment and a scenario virtual experiment corresponding to the path-finding task, and conduct a data collection experiment on human factors variables based on VR virtual simulation technology and human factors data collection technology;

[0067] Step 3: Use artificial intelligence deep learning algorithms to analyze the coupling effects of human perception data and spatial design variable data;

[0068] Step 4: Based on artificial intelligence data visualization analysis technology, identify the key variables of human factors design, and derive an urban slow-moving space planning and design method that optimizes path-finding tasks under the guidance of human factors technology, which is used to guide the planning and design of slow-moving spaces.

[0069] The present invention utilizes modern scientific and technological means, such as eye tracking, virtual reality (VR), physiological testing, etc., combined with the theoretical framework of urban ergonomics, to explore the perception laws of urban slow-moving spaces, and develops a quantifiable spatial design method for slow-moving space path-finding tasks under human factors guidance, so as to enhance the user's travel experience.

[0070] In some embodiments, step 1 specifically includes:

[0071] Step 1.1: Use BIM technology to simulate different slow-moving space design schemes with high precision;

[0072] Step 1.2, obtain virtual modeling of the design schemes of all slow-moving spaces;

[0073] Step 1.3: Decompose the path-finding task in the slow-moving space;

[0074] Step 1.4: Determine human spatial variables and human environmental variables.

[0075] In some embodiments, step 1.3 is as follows:

[0076] In practical applications, the path-finding task of the slow-moving space is decomposed to realize virtual modeling of various urban slow-moving space design schemes and the decomposition of the path-finding task of the slow-moving space.

[0077] For the built slow-moving space environment, the drone real-scene 3D oblique photography technology and the NeDF (Neural Radiance Fields) or Gaussian snowball deep learning algorithm based on video materials are used to achieve high-precision 3D real-scene modeling of the slow-moving space;

[0078] For the environment of unbuilt slow-moving spaces, BIM technology and virtual simulation technology are used to carry out high-precision three-dimensional real-scene modeling of different slow-moving space design schemes.

[0079] like Figure 2 As shown, in some embodiments, in step 1.4, according to different categories of slow-moving spaces, the path-finding task in a specific scene is decomposed, and the corresponding human-induced spatial variables and human-induced environmental variables are selected, as follows:

[0080] When the category of slow-moving space is walking space, the elements of slow-moving space are areas dedicated to pedestrians, and the corresponding wayfinding task is how pedestrians choose the best walking route;

[0081] Among them, areas dedicated to pedestrian use include pedestrian streets, pedestrian plazas, pedestrian bridges and underground passages;

[0082] How pedestrians choose the best walking route includes distance factors, time factors, landscape factors, and the role of transportation buildings including pedestrian bridges and underground passages in guiding pedestrian direction and path selection;

[0083] When the category of slow-moving space is bicycle space, the elements of slow-moving space are areas for cyclists to use, and the corresponding wayfinding tasks include bicycle path selection, bicycle space layout, and cyclists’ behavior patterns;

[0084] Among them, areas for cyclists include bicycle lanes, bicycle parking areas, and shared bicycle stations;

[0085] When the category of slow-moving space is mixed-use space, the elements of slow-moving space are areas for pedestrians and cyclists to use together. The corresponding wayfinding tasks include walking and cycling route selection. The sign system should be coordinated with the layout of the mixed space and its impact on user wayfinding.

[0086] Areas for shared use by pedestrians and cyclists include mixed walking and cycling paths, urban greenways, and waterfront trails;

[0087] When the category of slow-moving space is the city center, the elements of slow-moving space include commercial pedestrian streets, squares, pedestrian streets in parks, and cycling paths. The corresponding wayfinding tasks include route selection in busy city centers, signage and route design, and the impact of the building and space layout of the city center on wayfinding;

[0088] When the category of slow space is residential, the elements of slow space include pedestrian paths and bicycle paths within the community, and the corresponding wayfinding tasks include the main facilities and destination selection in the residential area;

[0089] When the category of slow space is campus, the elements of slow space include walking and cycling paths on the university campus, and the corresponding wayfinding tasks include route selection in classrooms, libraries, dormitories, and dining areas;

[0090] When the category of slow-moving space is waterfront trails, the elements of slow-moving space include walking and cycling trails built along rivers, lakes or coastlines, and the corresponding wayfinding tasks include the impact of the route design of the waterfront trail on wayfinding;

[0091] When the category of slow-moving space is commuting space, the elements of slow-moving space include walking and cycling paths designed for daily commuting, connecting residential areas with workplaces and schools, and the corresponding wayfinding tasks include the efficiency of residential areas, workplaces, and school routes, and the ease of commuting;

[0092] When the category of slow-moving space is leisure space, the elements of slow-moving space are spaces for leisure, sports and entertainment, connecting residential areas with workplaces and schools. The corresponding wayfinding tasks include wayfinding signs and natural landscapes and design elements of leisure spaces;

[0093] Spaces for recreation, exercise and entertainment include park trails and ice water trails;

[0094] When the category of slow space is tourist space, the elements of slow space include walking and cycling paths connecting tourist attractions, providing sightseeing and experience services, and the corresponding wayfinding tasks include identification of major attractions, cultural relics and natural landscapes.

[0095] In some embodiments, step 2 specifically includes:

[0096] Step 2.1, design the space measurement experiment and scenario virtual experiment corresponding to the path-finding task;

[0097] Step 2.2: Use VR-based virtual simulation technology and human factors data collection technology;

[0098] Step 2.3: Conduct data collection experiments on human variables.

[0099] In some embodiments, in step 2.2, a behavioral experiment is performed to collect data in a three-dimensionally reconstructed virtual scene or a real street scene by having the subject wear a VR device and a human factors data collection experimental device;

[0100] In practical applications, the present invention simulates the slow-moving space of an actual city by establishing a virtual environment, allowing researchers to observe and record the behavioral responses of participants in specific scenarios without physical boundaries.

[0101] In step 2.3, using the data obtained from VR equipment and human factors data collection experimental equipment, a human factors variable data collection experiment is conducted from a human-centered perspective on the wayfinding task in slow-moving spaces. The human factors variable data of the subjects, including visual focus data, facial micro-expression data, EEG data, electrodermal data, walking path selection data, and stay time data, are collected and recorded.

[0102] In some embodiments, step 3 is as follows:

[0103] Step 3.1: Use artificial intelligence deep learning algorithm;

[0104] Step 3.2, taking human perception data and spatial design variable data as input;

[0105] Step 3.3: Conduct coupling analysis and establish a variable database of pathfinding tasks and human perception data in different slow-moving spaces.

[0106] In some embodiments, eye tracking data, facial recognition data, and VR experience data are collected from subjects by having them wear VR equipment and human factors data collection experimental equipment. A parameter coupling analysis model is then constructed based on a decision tree and a self-organizing map neural network to simulate the parameter relationship between multiple variables and establish a variable database of pathfinding tasks and human factors perception data in different slow-moving spaces.

[0107] The virtual reality technology of the present invention provides an immersive slow-moving space pathfinding task experience environment, allowing subjects to move and explore freely in a fully controlled virtual environment, and collects data such as eye tracking, facial recognition and VR experience from subjects by having them wear VR equipment and using human factors data collection experimental equipment. A parameter coupling analysis model is constructed based on a decision tree (REP-Tree) and a self-organizing map (SOM) neural network to simulate the parameter relationship between multiple variables and establish a variable database of pathfinding tasks and human factors perception data in different slow-moving spaces.

[0108] In some embodiments, step 4 is as follows:

[0109] Step 4.1, artificial intelligence data visualization analysis;

[0110] Step 4.2: Identify the key variables of human factors design;

[0111] Step 4.3: Derive a planning and design method for urban slow-moving space that optimizes path-finding tasks under the guidance of human factors and technology.

[0112] In some embodiments, based on the variable database of pathfinding tasks and human perception data in different slow-moving spaces, the key variables of human factor design are identified through machine learning and deep learning algorithms including decision trees, random forests, and convolutional neural networks, and the influence of spatial design features on pedestrians is obtained. Then, for different design scenarios, the quantitative human factor parameter design indicators are summarized and summarized;

[0113] Finally, through the quantified human factor parameter design indicators and based on data analysis, an optimized design solution guided by human factor technology is obtained.

[0114] This invention adopts a full-process quantitative research method and proposes an optimization design scheme guided by human factors technology based on data analysis to improve the availability, safety and comfort of urban slow-moving spaces.

[0115] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A design method for urban slow-moving spaces that optimizes path-finding tasks under the guidance of human factors and technology; its characteristics are: The steps include: Step 1: Based on BIM technology and high-precision simulation technology, simulate and model different design schemes of slow-moving spaces, use path planning algorithms to refine and decompose the path-finding tasks of slow-moving spaces, and determine human-factor spatial variables and human-factor environmental variables; Step 2: Design a spatial measurement experiment and a scenario virtual experiment corresponding to the path-finding task, and conduct a data collection experiment on human factors variables based on VR virtual simulation technology and human factors data collection technology; Step 3: Use artificial intelligence deep learning algorithms to analyze the coupling effects of human perception data and spatial design variable data; Step 4: Based on artificial intelligence data visualization analysis technology, identify the key variables of human factors design, and derive an urban slow-moving space planning and design method that optimizes path-finding tasks under the guidance of human factors technology, which is used to guide the planning and design of slow-moving spaces.

2. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 1 is characterized in that: Step 1 specifically includes: Step 1.1, using BIM technology to simulate different design schemes of the slow-moving space with high precision; Step 1.2, obtaining virtual modeling of design solutions for all the slow-moving spaces; Step 1.3, decomposing the path-finding task of the slow-moving space; Step 1.4: Determine the human-factor spatial variables and the human-factor environmental variables.

3. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 2 is characterized in that: Step 1.3 is as follows: For the environment of the built slow-moving space, the drone real-scene 3D oblique photography technology and NeDF (Neural Radiance Fields) or Gaussian snowball deep learning algorithm based on video materials are used to achieve high-precision 3D real-scene modeling of the slow-moving space; For the environment of the unbuilt slow-moving space, BIM technology and virtual simulation technology are used to carry out high-precision three-dimensional real-scene modeling of different design schemes of the slow-moving space.

4. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 3 is characterized in that: In step 1.4, according to different categories of the slow-moving space, the path-finding task in a specific scene is decomposed, and the corresponding human-factor spatial variables and human-factor environmental variables are selected, as follows: When the category of the slow-moving space is walking space, the element of the slow-moving space is an area dedicated to pedestrians, and the corresponding path-finding task is how pedestrians choose the best walking route; The areas exclusively for pedestrian use include pedestrian streets, pedestrian plazas, pedestrian bridges and underground passages; How the pedestrians choose the best walking route includes distance factors, time factors, landscape factors, and the role of traffic buildings including the pedestrian bridge and the underground passage in guiding pedestrian direction and path selection; When the category of the slow-moving space is a bicycle space, the element of the slow-moving space is an area for cyclists, and the corresponding path-finding task includes bicycle lane path selection, bicycle space layout and cyclist behavior patterns; The areas for use by cyclists include bicycle lanes, bicycle parking areas, and shared bicycle stations; When the category of the slow-moving space is a mixed-use space, the elements of the slow-moving space are areas for pedestrians and cyclists to use together, and the corresponding wayfinding tasks include walking and cycling route selection. The sign system should be coordinated with the layout of the mixed space and its impact on user wayfinding; The areas for shared use by pedestrians and cyclists include mixed walking and cycling paths, urban greenways and waterfront trails; When the category of the slow-moving space is the city center, the elements of the slow-moving space include commercial pedestrian streets, squares, pedestrian streets in parks and cycling paths, and the corresponding wayfinding tasks include route selection, signage and route design in the busy city center and the impact of the building and space layout of the city center on wayfinding; When the category of the slow-moving space is a residential area, the elements of the slow-moving space include pedestrian paths and bicycle paths within the community, and the corresponding wayfinding tasks include major facilities in the residential area and destination selection; When the category of the slow-moving space is campus, the elements of the slow-moving space include walking and cycling paths in the university campus, and the corresponding wayfinding tasks include route selection in classrooms, libraries, dormitories and dining areas; When the category of the slow-moving space is a waterfront trail, the elements of the slow-moving space include walking and cycling trails built along rivers, lakes or coastlines, and the corresponding pathfinding task includes the impact of the route design of the waterfront trail on pathfinding; When the category of the slow-moving space is commuting space, the elements of the slow-moving space include walking and cycling paths designed for daily commuting, connecting residential areas with workplaces and schools, and the corresponding wayfinding tasks include the efficiency of routes between residential areas and workplaces and schools, and the difficulty of commuting; When the category of the slow-moving space is leisure space, the elements of the slow-moving space are spaces for leisure, sports and entertainment, connecting residential areas with workplaces and schools, and the corresponding wayfinding tasks include wayfinding signs and natural landscapes and design elements of leisure space; The spaces provided for leisure, exercise and recreation include park trails and ice water trails; When the category of the slow-moving space is a tourist space, the elements of the slow-moving space include walking and cycling paths connecting tourist attractions, providing sightseeing and experience services, and the corresponding wayfinding tasks include identification of major attractions, cultural relics and natural landscapes.

5. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 1 is characterized in that: Step 2 specifically includes: Step 2.1, designing a spatial measurement experiment and a scenario virtual experiment corresponding to the path-finding task; Step 2.2: Use VR-based virtual simulation technology and human factors data collection technology; Step 2.3: Conduct data collection experiments on human variables.

6. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 5 is characterized in that: In step 2.2, the subjects wear VR equipment and human factors data collection experimental equipment to conduct behavioral experiments in three-dimensional reconstructed virtual scenes or real street scenes to collect data; In step 2.3, using the data obtained by the VR device and the human factors data collection experimental equipment, a human factors variable data collection experiment is conducted on the path-finding task in the slow-moving space from a human-oriented perspective, and the human factors variable data of the subjects including visual focus data, facial micro-expression data, EEG data, electrodermal data, walking path selection data, and stay time data are collected and recorded.

7. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to the claim, characterized in that: Step 3 is as follows: Step 3.1: Use artificial intelligence deep learning algorithm; Step 3.2, taking human perception data and spatial design variable data as input; Step 3.3: Conduct coupling analysis and establish a variable database of pathfinding tasks and human perception data in different slow-moving spaces.

8. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 7 is characterized in that: Eye tracking data, facial recognition data and VR experience data are collected from subjects by having them wear VR devices and human factors data collection experimental equipment. A parameter coupling analysis model is then constructed based on decision trees and self-organizing map neural networks to simulate the parameter relationship between multiple variables and establish a variable database of pathfinding tasks and human factors perception data in different slow-moving spaces.

9. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 1 is characterized in that: Step 4 is as follows: Step 4.1, artificial intelligence data visualization analysis; Step 4.2: Identify the key variables of human factors design; Step 4.3: Derive a planning and design method for urban slow-moving space that optimizes path-finding tasks under the guidance of human factors and technology.

10. The method for designing urban slow-moving spaces for optimizing path-finding tasks under the guidance of human factors and technology according to claim 9 is characterized in that: Based on the variable database of pathfinding tasks and human perception data in different slow-moving spaces, the key variables of human factor design are identified through machine learning and deep learning algorithms including decision trees, random forests, and convolutional neural networks, and the influence of spatial design features on pedestrians is obtained. Then, for different design scenarios, the quantitative human factor parameter design indicators are summarized and summarized; Finally, through the quantified human factor parameter design indicators and based on data analysis, an optimized design solution guided by human factor technology is obtained. This invention adopts a full-process quantitative research method and proposes an optimization design scheme guided by human factors technology based on data analysis to improve the availability, safety and comfort of urban slow-moving spaces.

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