Optimization Method of Disaster-Prevention Living Circle Based on Evacuation-Search Virtual Emergency Route Finding Features

Abstract

This invention discloses a disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features, comprising: S1: extracting the spatiotemporal boundary of the disaster prevention living circle; S2: establishing emergency action units of the disaster prevention living circle; S3: establishing a virtual emergency route finding spatial model of the emergency action units; S4: establishing evacuation-search virtual emergency route finding interactive functions; S5: conducting evacuation-search virtual emergency route finding experiments; S6: analyzing the effectiveness index and route finding feature correlation index of the virtual emergency route finding experiments to obtain evacuation-search multi-role emergency route finding features; S7: proposing a disaster prevention living circle optimization strategy.

Description

Technical Field

[0001] This invention relates to the field of urban disaster prevention and evacuation, and more specifically, to a method for optimizing disaster-prevention living circles based on evacuation-search virtual emergency route finding features. Background Technology

[0002] With the continuous increase in land use intensity and population density, urban space faces new security challenges under the impact of new usage patterns. The traditional grassroots disaster prevention spatial architecture, which relies on ideal walking distances, is no longer sufficient to cope with complex urban risks. The concept of living circles based on dynamic spatiotemporal behavioral needs, applied to urban safety systems, has spurred a renewal of the urban safety landscape centered on the disaster prevention living circle model. Therefore, systematically revealing the laws governing the influence and intrinsic connections between spatial elements of disaster prevention living circles on emergency behavior patterns has become a crucial foundation for ensuring urban grassroots safety.

[0003] The current situation reveals two main problems: First, while urban-scale population evacuation research has received some attention, existing findings largely rely on group behavior algorithms, making it difficult to accurately capture the impact mechanisms of spatial elements on wayfinding cognition and behavioral characteristics. This leads to discrepancies between planning strategies and disaster prevention effectiveness. Second, in addition to evacuation behavior, the efficient and accurate search behavior of emergency rescue "first responders" is crucial for screening disaster sites, assisting in disaster control, and reducing casualties. However, current research focusing on urban search and wayfinding behavior is relatively lacking. To strengthen the first line of defense in urban disaster prevention, it is urgent to improve the wayfinding cognition efficiency of multiple roles in "evacuation-search" within complex spatial environments by optimizing the planning of disaster-prevention living circles. Summary of the Invention

[0004] This invention provides a disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features to solve the technical problems existing in the prior art.

[0005] To achieve the above objectives, this invention provides a disaster prevention living circle optimization method based on evacuation-search virtual emergency wayfinding features, which includes:

[0006] S1: Extract the spatiotemporal boundaries of the disaster prevention living circle;

[0007] S2: Establish emergency response units within disaster preparedness living circles;

[0008] S3: Establish a virtual emergency route finding space model for emergency action units;

[0009] S4: Establish virtual emergency wayfinding interaction function for evacuation and search;

[0010] S5: Conduct a virtual emergency route finding experiment for evacuation and search;

[0011] S6: Analyze the effectiveness indicators and pathfinding feature correlation indicators of the virtual emergency pathfinding experiment to obtain the emergency pathfinding features of multiple roles in evacuation and search;

[0012] S7: Propose strategies for optimizing disaster-prevention living circles.

[0013] In one embodiment of the present invention, optionally, step S1 includes: taking the emergency shelter as the center, extracting the actual reachable range of people within 10 minutes on foot as the spatiotemporal boundary of the disaster prevention living circle.

[0014] In one embodiment of the present invention, step S2 may optionally include: selecting two roads with a vertical or near-vertical relationship centered on the emergency shelter as reference axes, establishing a virtual coordinate system, wherein the two roads are approximately north-south and east-west reference axes respectively, projecting the disaster prevention living circle area into four approximate geographical quadrants: northwest approximate quadrant, northeast approximate quadrant, southwest approximate quadrant, and southeast approximate quadrant, and correcting the quadrant area boundaries according to road, overpass, and river elements to form an emergency action unit.

[0015] In one embodiment of the present invention, step S3 may optionally include:

[0016] S31: Collect basic spatial data

[0017] Select an emergency action unit and collect high-resolution satellite imagery and related vector data of the area where the emergency action unit is located. The vector data includes road network structure, road width, block outline, building layout, building outline, building height, water system distribution and boundaries, and green space distribution and boundaries. The road network structure includes urban roads and roads within blocks.

[0018] S32: Constructing a 3D Scene Model

[0019] Parametric batch modeling is used to generate a basic 3D scene containing the aforementioned road network, building, water system, and green space spatial information.

[0020] Based on satellite information and measured data, the basic 3D scene was manually corrected, and the following spatial information was added: carriageway width, sidewalk width, roadside tree type, road sign type, road sign location and height;

[0021] S33: Building a Virtual Platform Scenario

[0022] Import the completed 3D scene model into the virtual simulation engine, and adjust the lighting parameters and material texture parameters according to the real scene to make the lighting effect of the virtual scene closer to the real and natural state.

[0023] In one embodiment of the present invention, step S4 may optionally include: developing the interactive functions of the virtual platform using script programming, realizing the evacuation and pathfinding interactive functions and the search and pathfinding interactive functions, setting the evacuation and search and pathfinding tasks, the start and end positions of the pathfinding, the criteria for determining the completion of the experiment, the type of information collected, and the pathfinding environment.

[0024] In one embodiment of the present invention, the optional evacuation pathfinding interaction function includes the following elements:

[0025] (1) Evacuation route finding task: The experimenter walks from the designated starting position to the designated refuge space through the urban roads within the emergency action unit, which is considered the end of the task;

[0026] (2) Evacuation route finding start and end locations: a certain refuge space within the experimental area is designated as the evacuation route finding end point. Other locations in the experimental model, except for refuge spaces, are allowed to be set as evacuation start points.

[0027] (3) Criteria for determining the completion of evacuation and route finding: The completion status of the experiment is determined by the time taken to find the route;

[0028] (4) Information type for evacuation and pathfinding: During the experiment, the evacuation and pathfinding time, the time spent in place, and the time spent moving were recorded, and the evacuation and pathfinding trajectory form and length were generated;

[0029] (5) Evacuation and wayfinding immersive scene: In the wayfinding scene, non-experimental personnel roles that walk / run are selectively set, warning sounds issued by emergency vehicles are set, and background broadcast sounds that express the meaning of "evacuate immediately" are set.

[0030] In one embodiment of the present invention, the optional search and pathfinding interactive function includes the following elements:

[0031] (1) Search and pathfinding task: consists of two parts: main task and sub-tasks.

[0032] The main task is for the experimenters to start from a designated search point and walk along a route covering all levels of roads within the emergency response unit, including city roads and neighborhood roads.

[0033] The sub-tasks are: walking routes cover the internal roads of the neighborhood. The number of sub-tasks is the same as the number of neighborhoods in the emergency action unit, and they are recorded as sub-task 1, sub-task 2... sub-task n. During the search and pathfinding process, the experimenters can view a thumbnail map of the search area on the experimental interface.

[0034] (2) Search and pathfinding start and end points: Set the refuge space as the search and pathfinding start point, and do not set an end point for the experiment;

[0035] (3) Criteria for determining the completion of the search and pathfinding task: The completion of the main task search and pathfinding task is divided into two types: system-based determination and subjective determination. System-based determination judges the completion status of the task by the coverage rate of the route. When the coverage rate is 100%, the task is considered complete. The main task route road coverage rate = (length of covered roads / total road length) × 100%, where the length of covered roads = length of covered roads in the city + length of covered roads in the neighborhood, and the total road length = length of roads in the city + length of roads in the neighborhood. Subjective determination is based on the experimenter's own judgment of the pathfinding completion status. When the experimenter subjectively believes that the route road coverage rate has reached or is close to 100%, the user selects "complete pathfinding" on the experiment interface.

[0036] Subtask pathfinding completion is divided into two types: system-based assessment and subjective assessment. System assessment judges the task completion status based on the coverage rate of the neighborhood path. When the coverage rate is 100%, the task is considered complete. The coverage rate of subtask n path is calculated as (the length of the covered road in neighborhood n / the total road length in neighborhood n) × 100%. Subjective assessment is based on the experimenter's own judgment of the pathfinding completion status. When the experimenter subjectively believes that the coverage rate of the neighborhood n path has reached or is close to 100%, it means that the experimenter can leave neighborhood n through the neighborhood entrance / exit.

[0037] (4) Search and pathfinding information types: During the experiment, the search and pathfinding time, the time spent in place, and the time spent moving were recorded. The search and pathfinding trajectory form, the length of the search and pathfinding trajectory, and the road coverage of the route were generated. The location information of the search and pathfinding start point and end point was also recorded.

[0038] (5) Search and wayfinding immersive scene: In the wayfinding scene, non-experimental personnel roles that walk / run can be selectively set, and warning sounds issued by emergency vehicles can be set.

[0039] In one embodiment of the present invention, step S5 may optionally include:

[0040] S51: Pre-experimental preparation: Explain the experimental procedure and the wayfinding task to the participants in detail, conduct pre-experimental positive and negative emotion scale and simulator dizziness scale assessment, selectively provide the participants with a wayfinding area map, and adjust the participants’ understanding of the wayfinding area by reading the map time.

[0041] S52: Wayfinding Experiment Procedure: Participants enter the evacuation / search wayfinding scenario, read the task instructions, enter the experimental phase, and complete the wayfinding task;

[0042] S53: Post-experiment data collection: Complete questionnaires and conduct post-experiment interviews. Questionnaires include: Positive and negative affect scale, simulator dizziness scale, presence scale, and questionnaire on the impact of urban spatial elements on evacuation / search and wayfinding efficiency. Post-experiment interviews include oral reports and open-ended interviews.

[0043] In one embodiment of the present invention, step S6 may optionally include:

[0044] Through a virtual emergency pathfinding experiment, we obtained experimental effectiveness indicators and pathfinding feature correlation indicators. We analyzed the effectiveness of the virtual experiment based on the experimental effectiveness indicators, and analyzed the multi-role emergency pathfinding characteristics of evacuation and search based on the pathfinding feature correlation indicators.

[0045] For the validity indicators of the experiment, the positive and negative affect scales before and after the experiment were used to determine whether the subjects exhibited psychological stress response characteristics in the virtual emergency scenario. The simulator dizziness scale before and after the experiment was used to determine whether the subjects were affected by dizziness. The presence scale after the experiment was used to determine whether the subjects achieved a high level of immersion in the virtual scenario.

[0046] The correlation indicators for evacuation wayfinding characteristics include three aspects: evacuation wayfinding trajectory, evacuation wayfinding performance, and evacuation wayfinding cognition.

[0047] (1) Evacuation pathfinding trajectory analysis: The evacuation pathfinding trajectories of all subjects are superimposed to obtain the cumulative evacuation trajectory. The evacuation pathfinding trajectory analysis includes steps S61 to S63:

[0048] S61: Extract high-frequency overlapping trajectories from the cumulative evacuation trajectory as regular evacuation trajectories, and obtain information on the path form, roads, intersections, and spatial node composition of the regular evacuation trajectories.

[0049] S62: Extract special evacuation trajectories that deviate from the conventional evacuation routes, obtain the path format of the special evacuation trajectories, and the location information of roads and intersections where abnormal behaviors such as detours, turns, and stops occur in the special evacuation trajectories.

[0050] S63: Combining open-ended interview data, obtain the reasons for the selection of roads, intersections, and spatial nodes in regular evacuation trajectories, as well as the reasons for detours, reversals, and pauses in special evacuation trajectories;

[0051] (2) Evacuation route finding performance analysis: This includes evacuation route finding time and the ratio of additional evacuation routes, where the ratio of additional evacuation routes = (actual evacuation route length - shortest evacuation route length) / shortest evacuation route length. The evacuation route finding performance analysis includes steps S64 to S66:

[0052] S64: Regarding evacuation route finding time, statistics are provided on the mean evacuation completion time, standard deviation of evacuation completion time, and the percentage of evacuation tasks completed within ten minutes.

[0053] S65: Calculate the mean and standard deviation of the additional evacuation route ratio.

[0054] S66: Using one-way ANOVA, we obtained the differential impact of different emergency action unit planning forms on evacuation route finding performance indicators;

[0055] (3) Pathfinding cognitive analysis includes steps S67 to S68:

[0056] S67: A subjective questionnaire survey was conducted to investigate the impact of spatial elements in emergency response units on evacuation and wayfinding. Spatial elements included road network structure, spatial nodes, signage systems, and landmarks. Respondents used a 7-point scale: -3 for significant interference; -2 for considerable interference; -1 for slight interference; 0 for no help; 1 for some help; 2 for considerable help; and 3 for significant help. Descriptive statistics were used to determine the degree of impact of spatial elements on evacuation and wayfinding.

[0057] S68: Search pathfinding feature correlation indicators: including three aspects: search pathfinding trajectory, search pathfinding performance, and search pathfinding cognition.

[0058] The search path analysis includes steps S681 to S686:

[0059] S681: Overlay the search pathfinding trajectories of all subjects to obtain the cumulative search trajectory. The cumulative search trajectory is divided into the main task cumulative search trajectory and the subtask n cumulative search trajectory.

[0060] S682: Extract the high-frequency overlapping trajectories from the cumulative search trajectories of the main task and subtask n to obtain the regular search trajectories of the main task and subtask n, respectively, and obtain the path form, road, intersection, and spatial node composition information.

[0061] S683: Extract special search trajectories that deviate from the normal search trajectory from the cumulative search trajectory of the main task and subtask n, obtain the special search trajectory of the main task and the special search trajectory of subtask n, as well as the location information of roads and intersections that produce abnormal behaviors such as detours, turns, and stops in the special search trajectories.

[0062] S684: Extract paths not covered by the regular search trajectories of the main task and subtask n, and obtain the search blind spots of the main task and subtask n.

[0063] S685: Extract the location information of the regular / special pathfinding start and end points from the search trajectories of the main task and subtask n.

[0064] S686: Based on open-ended interview data, obtain the reasons for the selection of roads, intersections, spatial nodes, starting paths, and ending locations in the regular search trajectories of the main task and sub-task n; the reasons for detours, turns, and stops, as well as abnormal starting road segments and ending locations in the special search trajectories of the main task and sub-task n; the reasons for the occurrence of search blind spots in the main task and sub-task n; and the reasons for the selection of the pathfinding starting location.

[0065] Search and pathfinding performance analysis includes steps S681′~S686′:

[0066] S681′: The main task performance indicators include the main task search pathfinding time, the main task additional search path ratio, and the main task path road coverage. The main task additional search path ratio = (the main task actual search path length - the main task shortest search path length) / the main task shortest search path length.

[0067] S682′: Performance metrics for subtask n include subtask n search pathfinding time, subtask n additional search path ratio, and subtask n path coverage. Subtask n additional search path ratio = (subtask n actual search path length - subtask n shortest search path length) / subtask n shortest search path length.

[0068] S683′: Statistically analyze the mean and standard deviation of the search completion times for the main task and subtask n in terms of search pathfinding time.

[0069] S684′: Statistics on the ratio of additional search paths for main tasks and subtasks n, including the mean and standard deviation of the ratio of additional search paths for main tasks and subtasks n.

[0070] S685′: Statistically calculate the mean and standard deviation of road coverage for the main task and sub-task n in terms of route road coverage.

[0071] S686′: Using one-way ANOVA, the impact of different emergency action unit planning forms on search and pathfinding performance indicators was obtained;

[0072] The cognitive analysis of search and wayfinding was conducted using a subjective questionnaire survey method. A questionnaire was established to investigate the impact of spatial elements in emergency action units on search and wayfinding. Spatial elements included road network structure, spatial nodes, signage systems, and landmarks. Participants used a 7-point scale: -3 for extremely large interference; -2 for relatively large interference; -1 for slightly large interference; 0 for no help; 1 for some help; 2 for relatively large help; and 3 for extremely large help. Descriptive statistics were used to determine the degree of impact of spatial elements on evacuation and wayfinding.

[0073] In one embodiment of the present invention, step S7 optionally includes: analyzing the causes of abnormal behaviors such as inefficient wayfinding, getting lost, misreading, detours, and stopping based on evacuation-search pathfinding trajectory characteristics, wayfinding performance characteristics, and wayfinding cognitive characteristics; extracting related spatial elements; and proposing optimization strategies.

[0074] In terms of evacuation wayfinding: evacuation routes are established based on wayfinding trajectories and conventional evacuation routes; low-performing emergency action units are identified based on wayfinding performance, and the density of evacuation routes is adjusted; and error-prone areas in evacuation wayfinding are diagnosed based on wayfinding cognition, and the spatial node guidance capability is strengthened.

[0075] In terms of search and wayfinding: Based on wayfinding trajectories, uncovered and easily detourable areas are extracted to create differentiated road interfaces; based on wayfinding performance, low-performance emergency action units are extracted to strengthen landmarks and achieve sequential spatial guidance; based on wayfinding cognition, planning elements that trigger spatial positioning obstacles are analyzed to weaken the visual interference caused by unfavorable elements.

[0076] The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features provided by this invention has the following beneficial technical effects:

[0077] (1) It makes up for the lack of research on emergency search behavior at the urban scale and improves the understanding of the laws of evacuation-search behavior at the urban scale.

[0078] (2) Improved the accuracy of determining the safe evacuation situation and efficient search capabilities of the disaster prevention living circle.

[0079] (3) Propose spatial optimization strategies for disaster prevention living circles from the perspectives of planning and design and urban renewal, form experiences that can be promoted, and provide reference and guidance for the construction of urban safety systems. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 This is a flowchart of the disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features of the present invention;

[0082] Figure 2 An ideal model for emergency response units;

[0083] Figure 3 This is an emergency response unit according to an embodiment of the present invention.

[0084] Attached diagram labels: 1-Refuge space, 2-Disaster preparedness living circle, 3-Emergency action unit, 4-Virtual coordinate system, 5-Neighborhood, 6-Road. Detailed Implementation

[0085] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] Figure 1 This is a flowchart of the disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features of the present invention. Figure 2 An ideal model for emergency response units. Figure 3 This is an emergency response unit according to an embodiment of the present invention. For example... Figures 1-3 As shown, this invention provides a disaster prevention living circle optimization method based on evacuation-search virtual emergency wayfinding features, which includes:

[0087] S1: Extract the spatiotemporal boundaries of the disaster prevention living circle;

[0088] S2: Establish emergency response units within disaster preparedness living circles;

[0089] S3: Establish a virtual emergency route finding space model for emergency action units;

[0090] S4: Establish virtual emergency wayfinding interaction function for evacuation and search;

[0091] S5: Conduct a virtual emergency route finding experiment for evacuation and search;

[0092] S6: Analyze the effectiveness indicators and pathfinding feature correlation indicators of the virtual emergency pathfinding experiment to obtain the emergency pathfinding features of multiple roles in evacuation and search;

[0093] S7: Propose strategies for optimizing disaster-prevention living circles.

[0094] In one embodiment of the present invention, optionally, step S1 includes: taking the emergency shelter as the center, extracting the actual reachable range of people within 10 minutes on foot as the spatiotemporal boundary of the disaster prevention living circle.

[0095] In one embodiment of the present invention, step S2 may optionally include: selecting two roads with a vertical or near-vertical relationship centered on the emergency shelter as reference axes, establishing a virtual coordinate system, wherein the two roads are approximately north-south (S'-N') and east-west (E'-W') reference axes respectively, projecting the disaster prevention living circle area into four approximate geographical quadrants: northwest approximate quadrant, northeast approximate quadrant, southwest approximate quadrant, and southeast approximate quadrant, correcting the quadrant area boundaries according to road, overpass, and river elements to form an emergency action unit.

[0096] In one embodiment of the present invention, step S3 may optionally include:

[0097] S31: Collect basic spatial data

[0098] Select an emergency action unit and collect high-resolution satellite imagery and related vector data of the area where the emergency action unit is located. The vector data includes road network structure, road width, block outline, building layout, building outline, building height, water system distribution and boundaries, and green space distribution and boundaries. The road network structure includes urban roads and roads within blocks.

[0099] S32: Constructing a 3D Scene Model

[0100] Parametric batch modeling is used to generate a basic 3D scene containing the aforementioned road network, building, water system, and green space spatial information.

[0101] Based on satellite information and measured data, the basic 3D scene was manually corrected, and the following spatial information was added: carriageway width, sidewalk width, roadside tree type, road sign type, road sign location and height;

[0102] S33: Building a Virtual Platform Scenario

[0103] Import the completed 3D scene model into the virtual simulation engine, and adjust the lighting parameters and material texture parameters according to the real scene to make the lighting effect of the virtual scene closer to the real and natural state.

[0104] In one embodiment of the present invention, step S4 may optionally include: developing the interactive functions of the virtual platform using script programming, realizing the evacuation and pathfinding interactive functions and the search and pathfinding interactive functions, setting the evacuation and search and pathfinding tasks, the start and end positions of the pathfinding, the criteria for determining the completion of the experiment, the type of information collected, and the pathfinding environment.

[0105] In one embodiment of the present invention, the optional evacuation pathfinding interaction function includes the following elements:

[0106] (1) Evacuation route finding task: The experimenter walks from the designated starting position to the designated refuge space through the urban roads within the emergency action unit, which is considered the end of the task;

[0107] (2) Evacuation route finding start and end locations: a certain refuge space within the experimental area is designated as the evacuation route finding end point. Other locations in the experimental model, except for refuge spaces, are allowed to be set as evacuation start points.

[0108] (3) Evacuation route finding completion criteria: The completion status of the experiment is determined by the route finding time. For example, route finding completed within 10 minutes is considered a successful evacuation, 10 to 15 minutes is considered an abnormal evacuation, and more than 15 minutes is considered a failed evacuation. When the route finding time reaches 10 minutes or 15 minutes, the system will issue a reminder on the route finding interface;

[0109] (4) Information type for evacuation and pathfinding: During the experiment, the evacuation and pathfinding time, the time spent in place, and the time spent moving were recorded, and the evacuation and pathfinding trajectory form and length were generated;

[0110] (5) Evacuation and wayfinding immersive scene: In the wayfinding scene, non-experimental personnel roles that walk / run are selectively set, warning sounds are set by emergency vehicles (ambulances, police cars, fire trucks), and background broadcast sounds that express the meaning of "evacuate immediately" are set.

[0111] In one embodiment of the present invention, the optional search and pathfinding interactive function includes the following elements:

[0112] (1) Search and pathfinding task: consists of two parts: main task and sub-tasks.

[0113] The main task is for the experimenters to start from a designated search point and walk along a route covering all levels of roads within the emergency response unit, including city roads and neighborhood roads.

[0114] The sub-tasks are: walking routes cover the internal roads of the neighborhood. The number of sub-tasks is the same as the number of neighborhoods in the emergency action unit, and they are recorded as sub-task 1, sub-task 2... sub-task n. During the search and pathfinding process, the experimenters can view a thumbnail map of the search area on the experimental interface.

[0115] (2) Search and pathfinding start and end points: Set the refuge space as the search and pathfinding start point, and do not set an end point for the experiment;

[0116] (3) Criteria for determining the completion of the search and pathfinding task: The completion of the main task search and pathfinding task is divided into two types: system-based determination and subjective determination. System-based determination judges the completion status of the task by the coverage rate of the route. When the coverage rate is 100%, the task is considered complete. The main task route road coverage rate = (length of covered roads / total road length) × 100%, where the length of covered roads = length of covered roads in the city + length of covered roads in the neighborhood, and the total road length = length of roads in the city + length of roads in the neighborhood. Subjective determination is based on the experimenter's own judgment of the pathfinding completion status. When the experimenter subjectively believes that the route road coverage rate has reached or is close to 100%, the user selects "complete pathfinding" on the experiment interface.

[0117] Subtask pathfinding completion is divided into two types: system-based assessment and subjective assessment. System assessment judges the task completion status based on the coverage rate of the neighborhood path. When the coverage rate is 100%, the task is considered complete. The coverage rate of subtask n path is calculated as (the length of the covered road in neighborhood n / the total road length in neighborhood n) × 100%. Subjective assessment is based on the experimenter's own judgment of the pathfinding completion status. When the experimenter subjectively believes that the coverage rate of the neighborhood n path has reached or is close to 100%, it means that the experimenter can leave neighborhood n through the neighborhood entrance / exit.

[0118] (4) Search and pathfinding information types: During the experiment, the search and pathfinding time, the time spent in place, and the time spent moving were recorded. The search and pathfinding trajectory form, the length of the search and pathfinding trajectory, and the road coverage of the route were generated. The location information of the search and pathfinding start point and end point was also recorded.

[0119] (5) Search and wayfinding immersive scene: In the wayfinding scene, non-experimental personnel roles that walk / run can be selectively set, and warning sounds issued by emergency vehicles can be set.

[0120] In one embodiment of the present invention, step S5 may optionally include:

[0121] S51: Pre-experimental preparation: Explain the experimental procedure and the wayfinding task to the participants in detail, conduct pre-experimental positive and negative emotion scale and simulator dizziness scale assessment, selectively provide the participants with a wayfinding area map, and adjust the participants’ understanding of the wayfinding area by reading the map time.

[0122] S52: Wayfinding Experiment Procedure: Participants enter the evacuation / search wayfinding scenario, read the task instructions, enter the experimental phase, and complete the wayfinding task;

[0123] S53: Post-experiment data collection: Complete questionnaires and conduct post-experiment interviews. Questionnaires include: Positive and negative affect scale, simulator dizziness scale, presence scale, and questionnaire on the impact of urban spatial elements on evacuation / search and wayfinding efficiency. Post-experiment interviews include oral reports and open-ended interviews.

[0124] In one embodiment of the present invention, step S6 may optionally include:

[0125] Through a virtual emergency pathfinding experiment, we obtained experimental effectiveness indicators and pathfinding feature correlation indicators. We analyzed the effectiveness of the virtual experiment based on the experimental effectiveness indicators, and analyzed the multi-role emergency pathfinding characteristics of evacuation and search based on the pathfinding feature correlation indicators.

[0126] For the validity indicators of the experiment, the positive and negative affect scales before and after the experiment were used to determine whether the subjects exhibited psychological stress response characteristics in the virtual emergency scenario. The simulator dizziness scale before and after the experiment was used to determine whether the subjects were affected by dizziness. The presence scale after the experiment was used to determine whether the subjects achieved a high level of immersion in the virtual scenario.

[0127] The correlation indicators for evacuation wayfinding characteristics include three aspects: evacuation wayfinding trajectory, evacuation wayfinding performance, and evacuation wayfinding cognition.

[0128] (1) Evacuation pathfinding trajectory analysis: The evacuation pathfinding trajectories of all subjects are superimposed to obtain the cumulative evacuation trajectory. The evacuation pathfinding trajectory analysis includes steps S61 to S63:

[0129] S61: Extract high-frequency overlapping trajectories from the cumulative evacuation trajectory as regular evacuation trajectories, and obtain information on the path form, roads, intersections, and spatial node composition of the regular evacuation trajectories.

[0130] S62: Extract special evacuation trajectories that deviate from the conventional evacuation routes, obtain the path format of the special evacuation trajectories, and the location information of roads and intersections where abnormal behaviors such as detours, turns, and stops occur in the special evacuation trajectories.

[0131] S63: Combining open-ended interview data, obtain the reasons for the selection of roads, intersections, and spatial nodes in regular evacuation trajectories, as well as the reasons for detours, reversals, and pauses in special evacuation trajectories;

[0132] (2) Evacuation route finding performance analysis: This includes evacuation route finding time and the ratio of additional evacuation routes, where the ratio of additional evacuation routes = (actual evacuation route length - shortest evacuation route length) / shortest evacuation route length. The evacuation route finding performance analysis includes steps S64 to S66:

[0133] S64: Regarding evacuation route finding time, statistics are provided on the mean evacuation completion time, standard deviation of evacuation completion time, and the percentage of evacuation tasks completed within ten minutes.

[0134] S65: Calculate the mean and standard deviation of the additional evacuation route ratio.

[0135] S66: Using one-way ANOVA, we obtained the differential impact of different emergency action unit planning forms on evacuation route finding performance indicators;

[0136] (3) Pathfinding cognitive analysis includes steps S67 to S68:

[0137] S67: A subjective questionnaire survey was conducted to investigate the impact of spatial elements in emergency response units on evacuation and wayfinding. Spatial elements included road network structure, spatial nodes, signage systems, and landmarks. Respondents used a 7-point scale: -3 for significant interference; -2 for considerable interference; -1 for slight interference; 0 for no help; 1 for some help; 2 for considerable help; and 3 for significant help. Descriptive statistics were used to determine the degree of impact of spatial elements on evacuation and wayfinding.

[0138] S68: Search pathfinding feature correlation indicators: including three aspects: search pathfinding trajectory, search pathfinding performance, and search pathfinding cognition.

[0139] The search path analysis includes steps S681 to S686:

[0140] S681: Overlay the search pathfinding trajectories of all subjects to obtain the cumulative search trajectory. The cumulative search trajectory is divided into the main task cumulative search trajectory and the subtask n cumulative search trajectory.

[0141] S682: Extract the high-frequency overlapping trajectories from the cumulative search trajectories of the main task and subtask n to obtain the regular search trajectories of the main task and subtask n, respectively, and obtain the path form, road, intersection, and spatial node composition information.

[0142] S683: Extract special search trajectories that deviate from the normal search trajectory from the cumulative search trajectory of the main task and subtask n, obtain the special search trajectory of the main task and the special search trajectory of subtask n, as well as the location information of roads and intersections that produce abnormal behaviors such as detours, turns, and stops in the special search trajectories.

[0143] S684: Extract paths not covered by the regular search trajectories of the main task and subtask n, and obtain the search blind spots of the main task and subtask n.

[0144] S685: Extract the location information of the regular / special pathfinding start and end points from the search trajectories of the main task and subtask n.

[0145] S686: Based on open-ended interview data, obtain the reasons for the selection of roads, intersections, spatial nodes, starting paths, and ending locations in the regular search trajectories of the main task and sub-task n; the reasons for detours, turns, and stops, as well as abnormal starting road segments and ending locations in the special search trajectories of the main task and sub-task n; the reasons for the occurrence of search blind spots in the main task and sub-task n; and the reasons for the selection of the pathfinding starting location.

[0146] Search and pathfinding performance analysis includes steps S681′~S686′:

[0147] S681′: The main task performance indicators include the main task search pathfinding time, the main task additional search path ratio, and the main task path road coverage. The main task additional search path ratio = (the main task actual search path length - the main task shortest search path length) / the main task shortest search path length.

[0148] S682′: Performance metrics for subtask n include subtask n search pathfinding time, subtask n additional search path ratio, and subtask n path coverage. Subtask n additional search path ratio = (subtask n actual search path length - subtask n shortest search path length) / subtask n shortest search path length.

[0149] S683′: Statistically analyze the mean and standard deviation of the search completion times for the main task and subtask n in terms of search pathfinding time.

[0150] S684′: Statistics on the ratio of additional search paths for main tasks and subtasks n, including the mean and standard deviation of the ratio of additional search paths for main tasks and subtasks n.

[0151] S685′: Statistically calculate the mean and standard deviation of road coverage for the main task and sub-task n in terms of route road coverage.

[0152] S686′: Using one-way ANOVA, the impact of different emergency action unit planning forms on search and pathfinding performance indicators was obtained;

[0153] The cognitive analysis of search and wayfinding was conducted using a subjective questionnaire survey method. A questionnaire was established to investigate the impact of spatial elements in emergency action units on search and wayfinding. Spatial elements included road network structure, spatial nodes, signage systems, and landmarks. Participants used a 7-point scale: -3 for extremely large interference; -2 for relatively large interference; -1 for slightly large interference; 0 for no help; 1 for some help; 2 for relatively large help; and 3 for extremely large help. Descriptive statistics were used to determine the degree of impact of spatial elements on evacuation and wayfinding.

[0154] In one embodiment of the present invention, step S7 optionally includes: analyzing the causes of abnormal behaviors such as inefficient wayfinding, getting lost, misreading, detours, and stopping based on evacuation-search pathfinding trajectory characteristics, wayfinding performance characteristics, and wayfinding cognitive characteristics; extracting related spatial elements; and proposing optimization strategies.

[0155] In terms of evacuation wayfinding: evacuation routes are established based on wayfinding trajectories and conventional evacuation routes; low-performing emergency action units are identified based on wayfinding performance, and the density of evacuation routes is adjusted; and error-prone areas in evacuation wayfinding are diagnosed based on wayfinding cognition, and the spatial node guidance capability is strengthened.

[0156] In terms of search and wayfinding: Based on wayfinding trajectories, uncovered and easily detourable areas are extracted to create differentiated road interfaces; based on wayfinding performance, low-performance emergency action units are extracted to strengthen landmarks and achieve sequential spatial guidance; based on wayfinding cognition, planning elements that trigger spatial positioning obstacles are analyzed to weaken the visual interference caused by unfavorable elements.

[0157] The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features provided by this invention has the following beneficial technical effects:

[0158] (1) It makes up for the lack of research on emergency search behavior at the urban scale and improves the understanding of the laws of evacuation-search behavior at the urban scale.

[0159] (2) Improved the accuracy of determining the safe evacuation situation and efficient search capabilities of the disaster prevention living circle.

[0160] (3) Propose spatial optimization strategies for disaster prevention living circles from the perspectives of planning and design and urban renewal, form experiences that can be promoted, and provide reference and guidance for the construction of urban safety systems.

[0161] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0162] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing disaster-prevention living circles based on evacuation-search virtual emergency route finding features, characterized in that, include: S1: Extract the spatiotemporal boundaries of the disaster prevention living circle; S2: Establish emergency response units within disaster preparedness living circles; S3: Establish a virtual emergency route finding space model for emergency action units; S4: Establish virtual emergency wayfinding interaction function for evacuation and search; S5: Conduct a virtual emergency route finding experiment for evacuation and search; S6: Analyze the effectiveness indicators and pathfinding feature correlation indicators of the virtual emergency pathfinding experiment to obtain the emergency pathfinding features of multiple roles in evacuation and search; S7: Propose strategies for optimizing disaster-prevention living circles.

2. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S1 includes: taking the emergency shelter as the center, extracting the actual reachable range of people within a 10-minute walk as the spatial and temporal boundary of the disaster prevention living circle.

3. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S2 includes: selecting two roads with a vertical or near-vertical relationship centered on the emergency shelter as reference axes, establishing a virtual coordinate system, with the two roads being the approximate south-north and east-west reference axes respectively, projecting the disaster prevention living circle area into four approximate geographical quadrants: the northwest approximate quadrant, the northeast approximate quadrant, the southwest approximate quadrant, and the southeast approximate quadrant, correcting the quadrant area boundaries based on road, overpass, and river elements to form an emergency action unit.

4. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S3 includes: S31: Collect basic spatial data Select an emergency action unit and collect high-resolution satellite imagery and related vector data of the area where the emergency action unit is located. The vector data includes road network structure, road width, block outline, building layout, building outline, building height, water system distribution and boundaries, and green space distribution and boundaries. The road network structure includes urban roads and roads within blocks. S32: Constructing a 3D Scene Model Parametric batch modeling is used to generate a basic 3D scene containing the aforementioned road network, building, water system, and green space spatial information. Based on satellite information and measured data, the basic 3D scene was manually corrected, and the following spatial information was added: carriageway width, sidewalk width, roadside tree type, road sign type, road sign location and height; S33: Building a Virtual Platform Scenario Import the completed 3D scene model into the virtual simulation engine, and adjust the lighting parameters and material texture parameters according to the real scene to make the lighting effect of the virtual scene closer to the real and natural state.

5. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S4 includes: using script programming to develop the interactive functions of the virtual platform, realizing the evacuation and pathfinding interactive functions, setting the evacuation and pathfinding tasks, the start and end positions of the pathfinding, the criteria for determining the completion of the experiment, the types of information collected, and the pathfinding environment.

6. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 5, characterized in that, The evacuation wayfinding interactive function includes the following elements: (1) Evacuation route finding task: The experimenter walks from the designated starting position to the designated refuge space through the urban roads within the emergency action unit, which is considered the end of the task; (2) Evacuation route finding start and end locations: a certain refuge space within the experimental area is designated as the evacuation route finding end point. Other locations in the experimental model, except for refuge spaces, are allowed to be set as evacuation start points. (3) Criteria for determining the completion of evacuation and route finding: The completion status of the experiment is determined by the time taken to find the route; (4) Information type for evacuation and pathfinding: During the experiment, the evacuation and pathfinding time, the time spent in place, and the time spent moving were recorded, and the evacuation and pathfinding trajectory form and length were generated; (5) Evacuation and wayfinding immersive scene: In the wayfinding scene, non-experimental personnel roles that walk / run are selectively set, warning sounds issued by emergency vehicles are set, and background broadcast sounds that express the meaning of "evacuate immediately" are set.

7. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 5, characterized in that, The search and wayfinding interactive function includes the following elements: (1) Search and pathfinding task: consists of two parts: main task and sub-tasks. The main task is for the experimenters to start from a designated search point and walk along a route covering all levels of roads within the emergency response unit, including city roads and neighborhood roads. The sub-tasks are: walking routes cover the internal roads of the neighborhood. The number of sub-tasks is the same as the number of neighborhoods in the emergency action unit, and they are recorded as sub-task 1, sub-task 2... sub-task n. During the search and pathfinding process, the experimenters can view a thumbnail map of the search area on the experimental interface. (2) Search and pathfinding start and end points: Set the refuge space as the search and pathfinding start point, and do not set an end point for the experiment; (3) Criteria for determining the completion of the search and pathfinding task: The completion of the main task search and pathfinding task is divided into two types: system-based determination and subjective determination. System-based determination judges the completion status of the task by the coverage rate of the route. When the coverage rate is 100%, the task is considered complete. The main task route road coverage rate = (length of covered roads / total road length) × 100%, where the length of covered roads = length of covered roads in the city + length of covered roads in the neighborhood, and the total road length = length of roads in the city + length of roads in the neighborhood. Subjective determination is based on the experimenter's own judgment of the pathfinding completion status. When the experimenter subjectively believes that the route road coverage rate has reached or is close to 100%, the user selects "complete pathfinding" on the experiment interface. Subtask pathfinding completion is divided into two types: system-based assessment and subjective assessment. System assessment judges the task completion status based on the coverage rate of the neighborhood path. When the coverage rate is 100%, the task is considered complete. The coverage rate of subtask n path is calculated as (the length of the covered road in neighborhood n / the total road length in neighborhood n) × 100%. Subjective assessment is based on the experimenter's own judgment of the pathfinding completion status. When the experimenter subjectively believes that the coverage rate of the neighborhood n path has reached or is close to 100%, it means that the experimenter can leave neighborhood n through the neighborhood entrance / exit. (4) Search and pathfinding information types: During the experiment, the search and pathfinding time, the time spent in place, and the time spent moving were recorded. The search and pathfinding trajectory form, the length of the search and pathfinding trajectory, and the road coverage of the route were generated. The location information of the search and pathfinding start point and end point was also recorded. (5) Search and wayfinding immersive scene: In the wayfinding scene, non-experimental personnel roles that walk / run can be selectively set, and warning sounds issued by emergency vehicles can be set.

8. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S5 includes: S51: Pre-experimental preparation: Explain the experimental procedure and the wayfinding task to the participants in detail, conduct pre-experimental positive and negative emotion scale and simulator dizziness scale assessment, selectively provide the participants with a wayfinding area map, and adjust the participants’ understanding of the wayfinding area by reading the map time. S52: Wayfinding Experiment Procedure: Participants enter the evacuation / search wayfinding scenario, read the task instructions, enter the experimental phase, and complete the wayfinding task; S53: Post-experiment data collection: Complete questionnaires and conduct post-experiment interviews. Questionnaires include: Positive and negative affect scale, simulator dizziness scale, presence scale, and questionnaire on the impact of urban spatial elements on evacuation / search and wayfinding efficiency. Post-experiment interviews include oral reports and open-ended interviews.

9. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S6 includes: Through a virtual emergency pathfinding experiment, we obtained experimental effectiveness indicators and pathfinding feature correlation indicators. We analyzed the effectiveness of the virtual experiment based on the experimental effectiveness indicators, and analyzed the multi-role emergency pathfinding characteristics of evacuation and search based on the pathfinding feature correlation indicators. For the validity indicators of the experiment, the positive and negative affect scales before and after the experiment were used to determine whether the subjects exhibited psychological stress response characteristics in the virtual emergency scenario. The simulator dizziness scale before and after the experiment was used to determine whether the subjects were affected by dizziness. The presence scale after the experiment was used to determine whether the subjects achieved a high level of immersion in the virtual scenario. The correlation indicators for evacuation wayfinding characteristics include three aspects: evacuation wayfinding trajectory, evacuation wayfinding performance, and evacuation wayfinding cognition. (1) Evacuation pathfinding trajectory analysis: The evacuation pathfinding trajectories of all subjects are superimposed to obtain the cumulative evacuation trajectory. The evacuation pathfinding trajectory analysis includes steps S61 to S63: S61: Extract high-frequency overlapping trajectories from the cumulative evacuation trajectory as regular evacuation trajectories, and obtain information on the path form, roads, intersections, and spatial node composition of the regular evacuation trajectories. S62: Extract special evacuation trajectories that deviate from the conventional evacuation routes, obtain the path format of the special evacuation trajectories, and the location information of roads and intersections where abnormal behaviors such as detours, turns, and stops occur in the special evacuation trajectories. S63: Combining open-ended interview data, obtain the reasons for the selection of roads, intersections, and spatial nodes in regular evacuation trajectories, as well as the reasons for detours, reversals, and pauses in special evacuation trajectories; (2) Evacuation route finding performance analysis: This includes evacuation route finding time and the ratio of additional evacuation routes, where the ratio of additional evacuation routes = (actual evacuation route length - shortest evacuation route length) / shortest evacuation route length. The evacuation route finding performance analysis includes steps S64 to S66: S64: Regarding evacuation route finding time, statistics are provided on the mean evacuation completion time, standard deviation of evacuation completion time, and the percentage of evacuation tasks completed within ten minutes. S65: Calculate the mean and standard deviation of the additional evacuation route ratio. S66: Using one-way ANOVA, we obtained the differential impact of different emergency action unit planning forms on evacuation route finding performance indicators; (3) Pathfinding cognitive analysis includes steps S67 to S68: S67: A subjective questionnaire survey was conducted to investigate the impact of spatial elements in emergency response units on evacuation and wayfinding. Spatial elements included road network structure, spatial nodes, signage systems, and landmarks. Respondents used a 7-point scale: -3 for extremely high interference; -2 for significant interference; -1 for slight interference; 0 for no help; 1 for some help; 2 for significant help; and 3 for extremely high help. Descriptive statistics were used to determine the degree of impact of spatial elements on evacuation and wayfinding. S68: Search pathfinding feature correlation indicators: including three aspects: search pathfinding trajectory, search pathfinding performance, and search pathfinding cognition. The search path analysis includes steps S681 to S686: S681: Overlay the search pathfinding trajectories of all subjects to obtain the cumulative search trajectory. The cumulative search trajectory is divided into the main task cumulative search trajectory and the subtask n cumulative search trajectory. S682: Extract the high-frequency overlapping trajectories from the cumulative search trajectories of the main task and subtask n to obtain the regular search trajectories of the main task and subtask n, respectively, and obtain the path form, road, intersection, and spatial node composition information. S683: Extract special search trajectories that deviate from the normal search trajectory from the cumulative search trajectory of the main task and subtask n, obtain the special search trajectory of the main task and the special search trajectory of subtask n, as well as the location information of roads and intersections that produce abnormal behaviors such as detours, turns, and stops in the special search trajectories. S684: Extract paths not covered by the regular search trajectories of the main task and subtask n, and obtain the search blind spots of the main task and subtask n. S685: Extract the location information of the regular / special pathfinding start and end points from the search trajectories of the main task and subtask n. S686: Based on open-ended interview data, obtain the reasons for the selection of roads, intersections, spatial nodes, starting paths, and ending locations in the regular search trajectories of the main task and sub-task n; the reasons for detours, turns, and stops, as well as abnormal starting road segments and ending locations in the special search trajectories of the main task and sub-task n; the reasons for the occurrence of search blind spots in the main task and sub-task n; and the reasons for the selection of the pathfinding starting location. Search and pathfinding performance analysis includes steps S681′~S686′: S681′: The main task performance indicators include the main task search pathfinding time, the main task additional search path ratio, and the main task path road coverage. The main task additional search path ratio = (the main task actual search path length - the main task shortest search path length) / the main task shortest search path length. S682′: Performance metrics for subtask n include subtask n search pathfinding time, subtask n additional search path ratio, and subtask n path coverage. Subtask n additional search path ratio = (subtask n actual search path length - subtask n shortest search path length) / subtask n shortest search path length. S683′: Statistically analyze the mean and standard deviation of the search completion times for the main task and subtask n in terms of search pathfinding time. S684′: Statistics on the ratio of additional search paths for main tasks and subtasks n, including the mean and standard deviation of the ratio of additional search paths for main tasks and subtasks n. S685′: Statistically calculate the mean and standard deviation of road coverage for the main task and sub-task n in terms of route road coverage. S686′: Using one-way ANOVA, the impact of different emergency action unit planning forms on search and pathfinding performance indicators was obtained; The cognitive analysis of search and wayfinding was conducted using a subjective questionnaire survey method. A questionnaire was established to investigate the impact of spatial elements in emergency action units on search and wayfinding. Spatial elements included road network structure, spatial nodes, signage systems, and landmarks. Participants used a 7-point scale: -3 for extremely large interference; -2 for relatively large interference; -1 for slightly large interference; 0 for no help; 1 for some help; 2 for relatively large help; and 3 for extremely large help. Descriptive statistics were used to determine the degree of impact of spatial elements on evacuation and wayfinding.

10. The disaster prevention living circle optimization method based on evacuation-search virtual emergency route finding features according to claim 1, characterized in that, Step S7 includes: analyzing the causes of abnormal behaviors such as inefficient wayfinding, getting lost, misreading, detours, and stopping based on evacuation-search wayfinding trajectory characteristics, wayfinding performance characteristics, and wayfinding cognitive characteristics; extracting related spatial elements; and proposing optimization strategies. In terms of evacuation wayfinding: evacuation routes are established based on wayfinding trajectories and conventional evacuation routes; low-performing emergency action units are identified based on wayfinding performance, and the density of evacuation routes is adjusted; and error-prone areas in evacuation wayfinding are diagnosed based on wayfinding cognition, and the spatial node guidance capability is strengthened. In terms of search and wayfinding: Based on wayfinding trajectories, uncovered and easily detourable areas are extracted to create differentiated road interfaces; based on wayfinding performance, low-performance emergency action units are extracted to strengthen landmarks and achieve sequential spatial guidance; based on wayfinding cognition, planning elements that trigger spatial positioning obstacles are analyzed to weaken the visual interference caused by unfavorable elements.

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