Urban toughness real-time judgment method and system based on distributed sound wave sensing

Abstract

The invention discloses a city toughness real-time determination method and system based on distributed sound wave sensing, and the method comprises the steps: carrying out the optical cable distribution of a target region, carrying out the connection with a data channel, and carrying out the channel number mapping; acquiring a strain rate signal of each channel of the optical cable; caching the acquired DAS data; detecting, screening and judging the signal quality of each channel; if the SNR value and the DR value of one channel are lower than 80% of the whole channel, the coupling is abnormal; performing multi-domain feature extraction on the data; rendering the evolution trend of the activity intensity, the disturbance duration and the regional synchronization index along with time and space; constructing a multi-index fusion matrix; different indexes are mapped to color depth and saturation, an abnormal region track is identified, and high-intensity cooperative event prompting and recording are triggered; and obtaining a regional risk evolution situation. The system comprises a sensing optical fiber unit, a DAS demodulation unit, a data storage unit, a data processing and analysis unit, a toughness collaborative analysis unit and a toughness evolution visualization unit. The method is low in cost, wide in space coverage, high in timeliness and friendly in privacy.

Description

Technical Field

[0001] This invention relates to fiber optic sensing technology, intelligent monitoring systems, and urban resilience assessment methods in the field of urban infrastructure monitoring and resilience management, and particularly to a real-time urban resilience assessment method and system based on distributed acoustic wave sensing. Background Technology

[0002] In recent years, extreme weather events, such as torrential rains, typhoons, earthquakes, and high-temperature droughts, have become increasingly frequent and intense, posing serious challenges to urban infrastructure, resident safety, and social order. At the same time, these public emergencies have exposed significant shortcomings in urban systems regarding emergency response, risk coordination, and resource allocation. To address complex disaster risks and enhance urban self-organization and recovery capabilities, building "resilient cities" has become an important development direction for global urban governance. Resilience is not only reflected at the infrastructure level but also encompasses diverse and dynamic processes such as population activities, social systems, and spatial organization. How to achieve real-time perception and quantitative evaluation of the multi-level and multi-dimensional resilience status of cities has become a core scientific issue in smart city construction.

[0003] Currently, urban operation status monitoring mainly relies on video surveillance, satellite remote sensing, IoT devices, and mobile signal data. These methods can be effective in specific scenarios, but they also have obvious shortcomings: (1) Video surveillance depends on line of sight, and the deployment density is limited, which raises privacy and security concerns; (2) Although remote sensing has a wide coverage capability, it suffers from poor timeliness and insufficient resolution; (3) Although mobile signaling and Wi-Fi tracing can partially identify the distribution of people, their spatial accuracy and dynamic response capabilities are limited; (4) Various sensor devices have high deployment costs and complex maintenance, making it difficult to form a wide-coverage, low-cost, and long-term stable city-level monitoring network.

[0004] On the other hand, distributed acoustic sensing (DAS) technology, as an emerging fiber optic sensing method, has shown great potential in recent years in fields such as earthquake monitoring, traffic detection, and safety early warning. DAS utilizes existing fiber optic cable resources to achieve meter-level resolution and second-level time accuracy in vibration signal sensing in a passive state, unaffected by lighting, weather, or obstructions, and possesses advantages such as wide coverage, flexible deployment, and privacy protection. However, existing research mostly focuses on the application of DAS in extracting single physical signals or identifying specific events, and a systematic approach for multi-dimensional data fusion, collaborative evaluation, and dynamic decision support for urban resilience goals has not yet been developed. Summary of the Invention

[0005] Purpose of the Invention: Addressing the shortcomings of existing technologies, this invention proposes a real-time urban resilience assessment method and system based on distributed acoustic wave sensing (DAS). It constructs a closed-loop technical path encompassing vibration sensing, data preprocessing, resilience modeling, and dynamic visualization, possessing the following key advantages and innovations: Systematic Introduction of Adaptive Cycle Theory: This invention incorporates DAS monitoring data into the four-stage modeling of urban system development—steady-state—release—reorganization, achieving a quantitative expression of the entire process from single-point event identification to system resilience evolution. High-Quality Signal Screening Mechanism Enhances Robustness: Introducing indicators such as signal-to-noise ratio (SNR) and differential ratio (DR) to construct a channel-level signal quality evaluation system ensures stable and reliable output of monitoring data under complex surface coupling conditions. Multi-Dimensional Resilience Indicator Collaborative Assessment and Cloud-Based Visualization Linkage: A comprehensive evaluation model covering indicators such as infrastructure vibration, population behavior intensity, and regional synergy is constructed, enabling real-time rendering and trend prediction of resilience status on a cloud platform, significantly improving the scientific rigor and timeliness of urban governance. This invention breaks through the limitations of traditional urban monitoring methods in terms of timeliness, privacy protection, and system integration, and provides an economical, efficient, and scalable intelligent sensing and evaluation solution for urban resilience construction.

[0006] Technical solution: The present invention provides a real-time urban resilience assessment method based on distributed acoustic wave sensing, comprising the following steps:

[0007] 1) Based on the building layout, traffic roads and functional zoning of the target area, optical cables are laid out, and data channel connection and channel number mapping with the DAS system are realized through physical access interfaces.

[0008] 2) Connect the access optical cable to the DAS demodulation unit to obtain the strain rate signal along each channel of the optical cable, and realize vibration sensing in a wide frequency range.

[0009] 3) The collected DAS data is cached and managed by the data storage unit using a hot storage layer, a warm storage layer, and a cold storage layer. The hot storage layer caches data for several hours to support second-level queries; the warm storage layer manages rolling data and organizes it using a B+ tree index structure; and the cold storage layer compresses and archives historical data, creating a timestamp index by day. This supports the following time query complexity calculations:

[0010]

[0011] Among them, T total T represents the total data duration, Δt represents the query granularity, and T represents the total data duration. q To reduce retrieval complexity, this step supports rapid callback and analysis task scheduling for massive amounts of multi-channel data, improving system operating efficiency.

[0012] 4) In the data processing stage, the signal quality of each channel is first detected and screened based on the signal-to-noise ratio (SNR) and the differential ratio (DR). If the SNR and DR values ​​of a certain channel are lower than the 80th percentile of all channels, it is determined to be an abnormal coupling or poor signal quality. After quality control, multi-domain feature extraction is performed on the effective data, including frequency domain analysis (using the Welch method to calculate the power spectral density (PSD)) and time-varying rate analysis. The rate of change R of PSD relative to midnight (00:00) is calculated for different time periods each day. PSD Used to characterize the intensity of diurnal fluctuations:

[0013]

[0014] Among them, PSD t This represents the power spectral density at time t, and PSD0 is the PSD value at 00:00.

[0015] Furthermore, the Pearson correlation coefficient is used to quantify inter-regional activity synergy:

[0016]

[0017] Where i represents the number of optical cable channels in the first region and j represents the number of optical cable channels in the second region, the coordination relationship represents the synchronicity of vibration frequency and intensity between regions. Regions are divided according to building functional areas, traffic nodes, or densely populated areas, and the coordination relationship reveals the synchronous response characteristics when multiple regions are disturbed.

[0018] 5) In the resilience synergy analysis unit, the monitoring results are converted into quantitative indicators, including activity intensity a(t), disturbance duration DT, and regional synchronization index RSI:

[0019]

[0020] The activity intensity is obtained through PSD integration, the disturbance duration is identified using a threshold method to determine the length distribution of consecutive disturbance segments, and the regional synchronization index is calculated based on the average correlation coefficient between multiple channels. Furthermore, combining the adaptive cycle theory of urban system lifecycles, a comprehensive analysis is conducted on the temporal evolution trends and spatial distribution changes of the above quantitative indicators. Specifically, by analyzing the temporal evolution trends and spatial distribution changes of activity intensity, disturbance duration, and regional synchronization index, the stage characteristics of urban resilience are identified. Each stage exhibits a specific indicator coupling pattern; for example, increased activity and synergy may reflect the system's expansion stage, while high-frequency disturbances and decreased synchronicity indicate a structurally unstable state.

[0021] 6) Deploy the resilience evolution visualization unit on the cloud platform console to render the evolution trends of activity intensity a(t), disturbance duration DT, and regional synchronization index RSI over time and space in real time; construct a multi-indicator fusion matrix M. pq (t): M pq (t)=[a pq (t),RSI pq (t),DT pq (t)](7)

[0022] Where p and q represent city grid coordinates, t is time, and M is... pq (t) is a multi-indicator fusion matrix;

[0023] Different indicators are mapped to color depth and saturation; abnormal area trajectories are identified and mutation points are marked based on clustering algorithms; high-intensity collaborative events are automatically triggered and recorded by combining thresholds; and the risk evolution trend of key areas is obtained through the resilience evolution visualization unit, that is, the spatiotemporal change patterns of activity intensity a(t), disturbance duration DT and regional synchronization index RSI are dynamically presented.

[0024] This step provides heatmap rendering, activity trajectory overlay, and abnormal event marking based on different indicators. Through the resilience evolution visualization unit, city managers can obtain the risk evolution trends of key areas, such as core urban functional areas (around transportation hubs, energy and communication facilities), high-population-density areas, areas with a concentration of major projects or infrastructure, and areas prone to disasters or historical events. In other words, it dynamically presents the spatiotemporal changes in resilience indicators, enabling more precise scheduling and strategic response.

[0025] In step 6), the spatiotemporal variation patterns of activity intensity a(t), disturbance duration DT, and regional synchronization index RSI include a sudden increase in activity intensity, an extension of disturbance duration, and a sharp drop in synchronization index.

[0026] In step 1), the optical cable is laid out by means of pipe threading or shallow burial, and is connected to the data channel of the DAS system and mapped with the channel number through the physical interface.

[0027] In step 3), the hot storage layer caches data for several hours to support second-level queries, the warm storage layer manages rolling data within a week and organizes it with a B+ tree index structure, and the cold storage layer compresses and archives historical data and creates a timestamp index on a daily basis.

[0028] In step 4), the signal differential ratio DR i for:

[0029] in, For single-channel strain rate, The average strain rate across the entire channel.

[0030] In step 4), the multi-domain feature extraction includes frequency domain analysis and time rate of change analysis.

[0031] In step 5), the activity intensity a(t) is obtained by PSD integration, the disturbance duration DT is identified by thresholding continuous disturbance segments and the length distribution is statistically analyzed, and the regional synchronization index RSI is calculated based on the average correlation coefficient between multiple channels.

[0032] In step 6), a multi-indicator fusion matrix M is constructed. pq (t) performs heatmap rendering, activity trajectory overlay, and abnormal event marking; heatmap rendering maps different indicators to color depth and saturation; activity trajectory overlay identifies abnormal area trajectories and marks mutation points based on clustering algorithms; abnormal event marking automatically triggers high-intensity collaborative event prompts and records based on thresholds.

[0033] In step 6), the risk evolution trend of urban core functional areas, high-density population areas, major project or infrastructure clusters, and disaster-prone or historically event-prone areas is obtained through the resilience evolution visualization unit.

[0034] This invention relates to a real-time urban resilience assessment system based on distributed acoustic wave sensing, comprising a sensing fiber optic unit, a DAS demodulation unit, a data storage unit, a data processing and analysis unit, a resilience co-analysis unit, and a resilience evolution visualization unit. The sensing fiber optic unit includes existing low-light cables and newly laid cables for monitoring purposes. The DAS demodulation unit demodulates laser scattering signals and acquires high-resolution vibration data. The data storage unit supports high-capacity, long-term DAS data acquisition and storage. The data processing and analysis unit filters signal quality and extracts key features such as power spectral density and rate of change. The resilience co-analysis unit enables the co-judgment of multi-dimensional resilience indicators. The resilience evolution visualization unit visualizes and analyzes the spatiotemporal distribution and evolution trend of resilience.

[0035] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0036] (1) This invention constructs a real-time urban resilience assessment system based on DAS: This invention introduces distributed acoustic wave sensing technology into the field of urban resilience assessment, and innovatively constructs a closed-loop workflow of "data acquisition - quality evaluation - resilience modeling - dynamic visualization". Unlike traditional DAS applications that are only used for vibration monitoring, this system achieves dynamic identification and trend analysis of resilience levels under different stages of urban development (development, steady state, release, and reorganization) by coupling the "adaptive cycle" theory. It expands the application boundary of DAS in complex socio-physical coupling systems and has the advantages of low deployment cost, wide spatial coverage, strong timeliness, and privacy protection. It is suitable for urban-level resilience status perception, abnormal event response, and decision support scenarios.

[0037] (2) To address the issues of uneven optical cable coupling conditions and large signal-to-noise ratio differences in complex urban scenarios, this invention introduces for the first time a channel quality evaluation mechanism based on signal-to-noise ratio (SNR) and differential ratio (DR). By combining time-frequency characteristics to screen high-quality channels, it significantly improves the reliability and computational efficiency of monitoring results. This ensures the stable operation of the system under large-scale deployment and the output of reliable monitoring results.

[0038] (3) This invention constructs a multi-dimensional resilience index system that includes infrastructure vibration, population activity intensity, and regional collaborative relationships, and realizes its dynamic evolution rendering on a cloud platform. The system not only senses abnormal events in real time (such as crowd gathering, traffic congestion, and sudden vibrations), but also combines building functional areas and activity cycles to assist in the implementation of differentiated strategies, providing intelligent support for resilience planning, risk governance, and urban scheduling. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the composition of the urban resilience real-time assessment system based on distributed acoustic wave sensing of the present invention.

[0040] Figure 2 This is a schematic diagram of the toughness determination framework of the present invention;

[0041] Figure 3 This is a schematic diagram of the toughness determination results of the present invention. Detailed Implementation

[0042] like Figure 1 As shown, to verify the effectiveness of the system and method described in this invention, a pilot project was conducted in a university area of ​​a city. The proposed real-time urban resilience assessment system based on distributed acoustic sensing was deployed, and the proposed real-time urban resilience assessment method based on distributed acoustic sensing was run. This area integrates various urban functions such as teaching, accommodation, transportation, culture and sports, and catering, exhibiting diverse population behaviors and a complex infrastructure layout, making it suitable as a representative application scenario for urban resilience monitoring.

[0043] The present invention relates to a real-time urban resilience assessment system based on distributed acoustic wave sensing, comprising a sensing fiber optic unit 1, a DAS demodulation unit 2, a data storage unit 3, a data processing and analysis unit 4, a resilience collaborative analysis unit 5, and a resilience evolution visualization unit 6. The sensing fiber optic unit 1 consists of a pre-existing dark optical cable 1-1 and an optical cable 1-2 deployed according to monitoring requirements in the city.

[0044] The present invention provides a real-time urban resilience assessment method based on distributed acoustic wave sensing, comprising the following steps:

[0045] Step 1), Fiber Optic Sensing Deployment and Channel Configuration. A 10-kilometer-long fiber optic cable segment was selected from the road network and underground communication pipelines as the sensing medium. This includes existing underground fiber optic cables and supplementary cables deployed around areas such as sports fields and canteens. The fiber optic cable is divided into channels at 5-meter intervals, for a total of 2000 channels. The fiber optic cable is connected to the DAS demodulation unit via pre-reserved connectors to complete the data channel configuration. Some fiber optic cables are installed under the road, while others are coiled in communication wells to improve coupling stability and scene versatility.

[0046] Step 2) Connect the accessed optical cables to the DAS demodulation unit, set the sampling rate to 100Hz and the gauge length to 10m, and continuously collect environmental vibration signals.

[0047] Step 3) The collected DAS data is efficiently cached and managed by dedicated storage units. The system adopts a "hot-warm-cold" tiered storage architecture. The hot storage layer caches 24-hour data to support second-level queries; the warm storage layer manages rolling data within a week and organizes it using a B+ tree index structure; and the cold storage layer compresses and archives historical data, creating a daily timestamp index. It supports the following time query complexity calculations:

[0048]

[0049] Among them, T total T represents the total data duration, Δt represents the query granularity, and T represents the total data duration. q To reduce retrieval complexity, this design supports rapid callback and analysis task scheduling for massive amounts of multi-channel data, improving system operating efficiency.

[0050] Step 4): In the data processing stage, the system first detects and filters the signal quality of each channel, evaluating it based on the signal-to-noise ratio (SNR) and the signal differential ratio (DR), where the differential ratio is:

[0051]

[0052] in For single-channel strain rate, The average strain rate across the entire channel.

[0053] Only channels 200-230 had SNR and DR values ​​below the 80th percentile for all channels, indicating coupling anomalies or poor signal quality. After quality control, multi-domain feature extraction was performed on the valid data, including frequency domain analysis (using the Welch method to calculate power spectral density PSD) and time-varying rate analysis. The rate of change R of PSD relative to midnight (00:00) was calculated for different time periods each day. PSD Used to characterize the intensity of diurnal fluctuations:

[0054]

[0055] Among them, PSD t This represents the power spectral density at time t, and PSD0 is the PSD value at 00:00.

[0056] Furthermore, the Pearson correlation coefficient is used to quantify inter-regional activity synergy:

[0057]

[0058] Where i represents the number of optical cable channels in the first region and j represents the number of optical cable channels in the second region, the coordination relationship represents the synchronicity of vibration frequency and intensity between regions. Regions are divided according to building functional areas, traffic nodes, or densely populated areas, and the coordination relationship reveals the synchronous response characteristics when multiple regions are disturbed.

[0059] Step 5): In the resilience synergy analysis unit, the system transforms the monitoring results into a quantitative index system, including activity intensity a(t), disturbance duration DT, and regional synchronization index RSI. Activity intensity is obtained through PSD integration, disturbance duration is identified using a threshold method to determine continuous disturbance segments and their length distribution, and the regional synchronization index is calculated based on the average correlation coefficient between multiple channels.

[0060]

[0061] By combining the adaptive cycle theory of urban system life cycle, and through a comprehensive analysis of the temporal evolution trend and spatial distribution changes of the above indicators, the stage characteristics of urban operation status can be identified, such as... Figure 2 As shown, each stage exhibits a specific indicator coupling pattern. For example, increased activity and synergy reflect the system's expansion stage, while high-frequency disturbances and decreased synchronicity may indicate a structurally unstable state. This framework provides a basis for identifying and assessing the changing trends of urban resilience at multiple scales and dimensions, avoiding reliance on single thresholds or expert experience.

[0062] Step 6) Deploy the resilience evolution visualization unit on the cloud platform console to support real-time rendering of the evolution trends of the above resilience indicators over time and space. Construct the indicator matrix M. pq (t):

[0063] M pq (t)=[a pq (t),RSI pq (t),DT pq (t)] (21)

[0064] Where p and q represent city grid coordinates, t is time, and M is... pq (t) is the multi-index fusion matrix.

[0065] This indicator system provides heatmap rendering (mapping different indicators to color depth and saturation), activity trajectory overlay (identifying abnormal region trajectories and marking abrupt change points based on clustering algorithms), and abnormal event marking (automatically triggering high-intensity collaborative event prompts and recordings based on thresholds), such as... Figure 3 As shown, through the visualization platform, city managers can obtain information on the evolving risk trends in key areas, enabling more precise scheduling and strategic responses.

Claims

1. A method for real-time assessment of urban resilience based on distributed acoustic wave sensing, characterized in that: Includes the following steps: 1) Based on the building layout, traffic roads and functional zoning of the target area, optical cables are laid out and connected to the data channel of the DAS system through interfaces and mapped with channel numbers; 2) Connect the accessed optical cable to the DAS demodulation unit to obtain the strain rate signal along each channel of the optical cable; 3) The collected DAS data is cached and managed by the data storage unit using a hot storage layer, a warm storage layer, and a cold storage layer. The hot storage layer caches several hours of data, the warm storage layer is organized using a B+ tree index structure, and the cold storage layer compresses and archives historical data, and creates a timestamp index by day, supporting time query complexity calculations. Among them, T total T represents the total data duration, Δt represents the query granularity, and T represents the total data duration. q For retrieval complexity; 4) In the data processing stage, the signal quality of each channel is first detected and screened based on the signal-to-noise ratio (SNR) and signal differential ratio (DR). If the SNR and DR values ​​of a channel are lower than the 80th percentile of all channels, it is determined to be an abnormal coupling or poor signal quality. Then, multi-domain feature extraction is performed on the effective data. The rate of change R of PSD relative to midnight 00:00 is calculated for different time periods each day. PSD To characterize the intensity of daytime fluctuations: Among them, PSD t This represents the power spectral density at time t, and PSD0 is the PSD value at 00:

00. Quantifying inter-regional activity synergy using Pearson correlation coefficient: Where i represents the number of optical cable channels in the first region and j represents the number of optical cable channels in the second region. The coordination relationship represents the synchronization of vibration frequency and intensity between regions. 5) In the resilience synergy analysis unit, the monitoring results are converted into activity intensity a(t), disturbance duration DT, and regional synchronization index RSI: By analyzing the temporal evolution trends and spatial distribution changes of activity intensity, disturbance duration, and regional synchronization index, the stage characteristics of urban operation status can be identified. 6) Deploy the resilience evolution visualization unit on the cloud platform console to render the evolution trends of activity intensity a(t), disturbance duration DT, and regional synchronization index RSI over time and space in real time; construct a multi-indicator fusion matrix M. pq (t): M pq (t)=[a pq (t),RSI pq (t),DT pq (t)](7) Where p and q represent city grid coordinates, t is time, and M is... pq (t) is a multi-indicator fusion matrix; Different indicators are mapped to color depth and saturation; abnormal area trajectories are identified and mutation points are marked based on clustering algorithms; high-intensity collaborative events are automatically triggered and recorded by combining thresholds; and the risk evolution trend of key areas is obtained through the resilience evolution visualization unit, that is, the spatiotemporal change patterns of activity intensity a(t), disturbance duration DT and regional synchronization index RSI are dynamically presented.

2. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 6), the spatiotemporal variation patterns of the activity intensity a(t), disturbance duration DT, and regional synchronization index RSI include a sudden increase in activity intensity, an extension of disturbance duration, and a sharp drop in synchronization index.

3. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 1), the optical cable is laid out by means of pipe threading or shallow burial, and is connected to the data channel of the DAS system and mapped with the channel number through the physical interface.

4. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 3), the hot storage layer caches several hours of data to support second-level queries, the warm storage layer manages rolling data within a week and organizes it with a B+ tree index structure, and the cold storage layer compresses and archives historical data and creates a timestamp index on a daily basis.

5. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 4), the signal differential ratio DR i for: in, For single-channel strain rate, The average strain rate across the entire channel.

6. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 4), the multi-domain feature extraction includes frequency domain analysis and time rate of change analysis.

7. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 5), the activity intensity a(t) is obtained by PSD integration, the disturbance duration DT is identified by thresholding continuous disturbance segments and the length distribution is statistically analyzed, and the regional synchronization index RSI is calculated based on the average correlation coefficient between multiple channels.

8. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 6), a multi-indicator fusion matrix M is constructed. pq (t), to perform heatmap rendering, activity trajectory overlay and abnormal event marking; Heatmap rendering maps different indicators to color depth and saturation; activity trajectory overlay identifies abnormal area trajectories and marks mutation points based on clustering algorithms; and abnormal event marking automatically triggers high-intensity collaborative event prompts and records by combining thresholds.

9. The method for real-time assessment of urban resilience based on distributed acoustic sensing according to claim 1, characterized in that: In step 6), the risk evolution trend of urban core functional areas, high-density population areas, major project or infrastructure clusters, and disaster-prone or historically event-prone areas is obtained through the resilience evolution visualization unit.

10. A determination system for implementing the real-time urban resilience determination method based on distributed acoustic sensing as described in claim 1, characterized in that: It includes a sensing fiber unit (1), a DAS demodulation unit (2), a data storage unit (3), a data processing and analysis unit (4), a resilience co-analysis unit (5), and a resilience evolution visualization unit (6); The sensing fiber unit (1) includes a deployed dark optical cable (1-1) and an optical cable to be laid (1-2); the data processing and evaluation unit (4) performs signal quality judgment and index calculation; the resilience collaborative analysis unit (5) performs collaborative judgment of multi-dimensional resilience indicators; and the resilience evolution visualization unit (6) performs visualization rendering and analysis of the spatiotemporal distribution and evolution trend of resilience.

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