Urban sign monitoring and intelligent early warning system for multi-stage cooperative treatment

Abstract

The invention relates to the technical field of smart city governance, and discloses a city sign monitoring and intelligent early warning system for multi-stage collaborative governance. The system comprises a data acquisition module, a hierarchy evaluation module, a region division module, a correlation analysis module and an early warning output module. Multi-source data are collected and processed in a unified mode; health indexes of all levels are calculated according to administrative grading, deep analysis is automatically triggered when abnormity occurs, and accurate risk positioning is achieved; dividing a city space into management units, detecting an abnormal trend and combining the abnormal trend into risk blocks; a graph model technology is adopted to identify the collaboration of the abnormal trend, and cross-domain association risks are distinguished; and evaluating the threat level according to the number and amplitude of the collaborative and independent anomalies, and controlling early warning release. According to the system, multi-stage accurate positioning and collaborative abnormity identification of risks are realized, and the accuracy and collaborative response efficiency of urban risk early warning are improved.

Description

Technical Field

[0001] This invention relates to the field of smart city governance technology, specifically to a city vital signs monitoring and intelligent early warning system for multi-level collaborative governance. Background Technology

[0002] Currently, urban governance commonly employs various monitoring systems to collect and monitor data on indicators such as traffic, environment, and public safety. These systems typically rely on setting uniform static thresholds at the platform level, triggering alarms when a data indicator exceeds the threshold. However, a city is a complex mega-system, and its operational status, manifested across different administrative levels and geographical locations, is closely related to governance responsibilities. Existing flat monitoring models struggle to effectively link macro-level systemic risk warnings with micro-level, specific management units and responsible entities. This results in significant manpower required for secondary analysis and task allocation even after warnings are issued, leading to long response and decision-making chains that fail to meet the agility requirements of multi-level collaborative governance.

[0003] In anomaly identification, existing technologies mostly focus on sudden changes in individual sensors or independent data indicators, judging anomalies by comparing historical data or fixed rules. This method is sensitive to isolated events but struggles to identify potential systemic risks arising from multiple spatially adjacent, trend-related minor anomalies. Urban risks often arise not from the failure of a single node, but from anomalies exhibiting correlation and synergy across multiple management units in space and time. Conventional technologies lack the ability to automatically detect such cross-domain, collaborative anomaly patterns.

[0004] A technological solution is needed to address two key issues: how to deeply integrate urban performance assessment with the administrative hierarchical governance structure to achieve precise hierarchical positioning of risk issues and automated in-depth analysis triggering; and how to overcome the limitations of independent indicator alarms and intelligently identify potentially collaborative risk events with diffusion potential from the perspective of spatial correlation and multi-dimensional trend synergy, thereby improving the foresight and accuracy of early warning. Summary of the Invention

[0005] The purpose of this invention is to provide an urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an urban vital sign monitoring and intelligent early warning system for multi-level collaborative governance, the system comprising: The data acquisition module is used to collect raw data streams of urban vital signs from various sensors and databases, and to perform preliminary cleaning and format standardization on the raw data streams to generate a standardized data set. The hierarchical assessment module is used to receive the standardized data set and perform multi-level health calculations on the standardized data set according to the city's administrative hierarchy, producing a health index for each level. When the health index deviates from the normal range, the deep analysis process is activated. The region division module is used to divide the urban geographic space into several management units during the deep analysis process, track the data trends within each management unit, detect abnormal trend patterns, and combine adjacent management units based on spatial dependencies to form suspicious risk blocks. The correlation analysis module is used to perform multidimensional correlation analysis on abnormal trends within the suspected risk blocks, assess the degree of consistency between trends, and use graph model technology to determine whether there is synergy among abnormal trends, and record the number of synergistic abnormal events. The early warning output module is used to deduce the overall threat level of urban vital signs based on the number of coordinated abnormal events and the magnitude of independent abnormal events, and to control the issuance of early warning signals according to the overall threat level. The magnitude of the independent abnormal event refers to the severity of a single abnormal event that is not determined to be a coordinated abnormal event and its deviation from the normal level.

[0007] Preferably, the operations performed by the data acquisition module when processing the raw data stream include: Perform integrity verification on the raw data stream, fill in missing values ​​and correct deviation values; Convert data in different formats into a standard data model; Data smoothing eliminates random fluctuations and generates a standardized dataset.

[0008] Preferably, when the hierarchical assessment module performs multi-level health calculations, the steps involved include: The administrative hierarchy of a city is defined as the overall city level, the administrative district level, and the street level. Assign baseline health indicators to each level; The deviation of the standardized data set at each level from the benchmark health indicators is calculated to obtain the health index; When the health index exceeds the upper or lower limit of the allowable fluctuation, a deep analysis process is triggered.

[0009] Preferably, the method used when dividing the management units in the region division module includes: The city map is divided into regularly shaped unit areas based on geocoding; Assign a spatial identifier to each unit region; Extract time-series data from each unit area and calculate trend characteristic values; Identify unit regions with abnormal trend characteristic values ​​and mark them as suspicious units.

[0010] Preferably, when combining management units in the region division module, the processing includes: Measure the intensity of spatial interaction between suspected units; If the spatial interaction intensity is higher than the connection threshold, the suspicious units are merged into the same suspicious risk block, and the comprehensive anomaly degree is calculated for each suspicious risk block.

[0011] Preferably, when the association analysis module performs multidimensional association analysis, it includes the following steps: Extract multi-dimensional feature vectors for each anomalous trend from suspicious risk blocks; Calculate the similarity matrix between feature vectors; When the values ​​in the similarity matrix exceed the association threshold, the abnormal trend is considered to be consistent.

[0012] Preferably, when the association analysis module uses graph model technology, the process includes: Construct a graph-structured database of historical anomalies; Train the graph neural network model to learn cooperative patterns; Input the graphical features of the current abnormal trend into the model and output the collaboration probability. If the collaboration probability is greater than the decision threshold, it is confirmed as a collaboration abnormal event.

[0013] Preferably, the methods used by the early warning output module to derive the overall threat level include: Cluster risk scores are calculated based on the number of collaborative anomalies, and discrete risk scores are calculated based on the magnitude of independent anomalies. The cluster risk score and the discrete risk score are combined into an overall threat level.

[0014] Preferably, the strategy used by the early warning output module to control the early warning signal includes: Preset threat level classification criteria; When the overall threat level falls into the high-risk category, an early warning signal is automatically sent; when the overall threat level is in the medium-risk category, a tracking and monitoring mechanism is activated; and when the overall threat level is in the low-risk category, a silent state is maintained.

[0015] Preferably, the specific steps implemented when calculating the comprehensive anomaly degree for each suspected risk block include: For each suspicious unit within a suspicious risk block, calculate the sum of its spatial interaction strength with all other suspicious units within the block, and use this sum as the connectivity weight of the suspicious unit. Obtain the trend feature value for each suspicious unit, which is extracted from time series data and represents the degree of anomaly; Multiply the trend feature value of each suspicious unit by its corresponding connectivity weight to obtain the weighted outlier value; The sum of all weighted outliers is divided by the sum of all connectivity weights to obtain the overall outlier score.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By constructing a multi-level health calculation model corresponding to the city's administrative hierarchy, the system achieves hierarchical measurement and assessment of the city's operational status. When the health index of a certain level deviates from its normal range, the system automatically activates a deep analysis process without manual intervention. This mechanism enables governance responsibility to be precisely delegated according to administrative levels, and macro-level index anomalies can directly drive targeted investigations of micro-level management units. It changes the disconnect between early warning and response levels in traditional monitoring systems, directly linking the generation of early warning information to specific governance responsibility units, shortening the decision-making path from problem perception to precise response, and improving the efficiency and targeting of cross-level collaborative governance.

[0017] By dynamically dividing urban geospatial space into management units and combining them into suspected risk blocks based on spatial dependencies, and then using graph modeling technology to collaboratively identify anomalous trends within these blocks, the system can identify potential risks that traditional methods cannot detect. This technology focuses on the correlation patterns and consistency of anomalous trends among multiple management units, capturing collaborative anomalous events that influence each other spatially and are interconnected temporally. This approach reduces false positives and false negatives caused by dependence on sudden changes in a single indicator, enabling earlier detection of systemic risk precursors formed by the interweaving and coupling of multiple mild anomalies. This allows for early insight and warning of complex urban risks, enhancing the depth and breadth of risk identification. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the working principle of the urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in this invention.

[0019] Figure 2 A flowchart for calculating the health status of the hierarchical assessment module;

[0020] Figure 3 A flowchart for dividing the region division module into management units;

[0021] Figure 4 Heatmap of spatial interaction intensity of suspected units;

[0022] Figure 5 This is a scatter plot showing the number of collaborative anomalies and the collaborative probability distribution. Detailed Implementation

[0023] 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.

[0024] Please see Figure 1 This invention provides an urban health monitoring and intelligent early warning system for multi-level collaborative governance. The system comprises a data acquisition module, a hierarchical evaluation module, a regional division module, a correlation analysis module, and an early warning output module. The data acquisition module collects raw data streams of urban health characteristics from various sensors and databases, performs preliminary cleaning and format standardization on the raw data streams, and generates a standardized data set. The hierarchical evaluation module receives the standardized data set, performs multi-level health calculations based on the city's administrative hierarchy, and generates a health index for each level. When the health index deviates from the normal range, a deep analysis process is activated. During the deep analysis, the regional division module divides the urban geographic space into several management units, tracks data trends within each management unit, detects abnormal trend patterns, and combines adjacent management units based on spatial dependencies to form suspected risk blocks. The correlation analysis module performs multi-dimensional correlation analysis on abnormal trends within suspected risk blocks, assesses the degree of consistency between trends, uses graph modeling technology to determine whether abnormal trends exhibit synergy, and records the number of synergistic abnormal events. The early warning output module derives the overall threat level of urban characteristics based on the number of coordinated abnormal events and the magnitude of independent abnormal events, and controls the issuance of early warning signals according to the overall threat level.

[0025] Example 1: In specific implementation, integrity verification is performed on the original data stream. Integrity verification includes detecting missing values ​​and outliers in the data stream. Missing values ​​are filled using interpolation methods, and outliers are identified and adjusted using statistical thresholds to correct deviations. In some embodiments, a time series forecasting model is selected as the interpolation method, and deviation correction is based on setting dynamic boundaries based on historical data distribution. In specific implementation, when the time series forecasting model is used as the interpolation method, a forecasting model is first built based on historical data. This model estimates the location of missing values ​​by analyzing the trends and periodic patterns of the time series data. The model training process uses complete historical data segments to learn the inherent patterns of the data, and then applies the learned patterns to predict the missing periods, thereby generating a continuous data sequence. Deviation correction sets dynamic boundaries based on historical data distribution, and determines the normal value range by statistically analyzing the mean and standard deviation of historical data. When the predicted value exceeds this range, adjustments are made to ensure that the interpolation result conforms to the historical distribution characteristics. Data in different formats is converted into a standard data model, which is defined as a unified time series data structure, including timestamps, numerical attributes, and metadata fields. In practice, the format conversion process uses a parser component to identify the source data format and execute mapping rules, converting heterogeneous data into a standard data model. Optionally, the standard data model is stored in tabular or graphical format. Data smoothing eliminates random fluctuations by applying filtering algorithms to reduce noise and generate a standardized dataset. It can be understood that data smoothing improves data quality for subsequent analysis. In some embodiments, data smoothing uses a moving average algorithm, which is calculated as follows: ; in: This represents the smoothed value at position j. Indicates the width of the smooth window. Indicates the index position in the data sequence. Represents the i-th original data point. Optional, smoothing window width. The data sampling rate is configured as a fixed value or an adaptive parameter. In practice, after data smoothing, a standardized data set is output, which is stored in a central database system.

[0026] Example 2: See Figure 2In specific implementation, the urban administrative hierarchy is defined as the overall city level, the administrative district level, and the street level. The overall city level covers the entire geographical area of ​​the city, the administrative district level includes municipal districts or county-level administrative units, and the street level consists of the management areas at the street or township level. In some embodiments, the administrative hierarchy structure can be extended to the community or grid level. Benchmark health indicators are assigned to each level. These benchmark health indicators are a set of reference values ​​based on historical data statistics or urban planning goals. Indicator types include environmental quality index, traffic congestion index, and public safety index. In specific implementation, the allocation process of benchmark health indicators is differentiated according to the management responsibilities and data availability of each level. Optionally, the benchmark health indicators use static thresholds or dynamic adjustment models. The deviation between the standardized data set of each level and the benchmark health indicators is calculated to obtain the health index. The health index is calculated using a weighted average method to synthesize the deviation of each indicator. It can be understood that the health index is used to quantitatively characterize the operational status of each level of the city. In some embodiments, the formula for calculating the health index is: ; in: This represents the health index at the k-th level. This indicates the total number of indicators monitored at this level. This represents the actual observed value of the i-th indicator at the k-th level in the normalized dataset. This represents the baseline health index value of the i-th indicator at the k-th level. When the health index exceeds the upper or lower limit of the allowable fluctuation range, a deep analysis process is triggered. The upper and lower limits of the allowable fluctuation range are preset tolerance boundaries based on the baseline health index. In practice, the deep analysis process is activated by generating an interrupt signal and transmitting it to the region partitioning module. It can be understood that the deep analysis process aims to investigate the root cause of abnormal health index values.

[0027] Example 3: See Figure 3In specific implementations, city maps are divided into regularly shaped unit areas based on geocoding. Geocoding employs latitude and longitude grids or administrative boundary coding systems. Regularly shaped unit areas are square or hexagonal grids of equal area. In some embodiments, the size of the unit area is configured according to data density and governance granularity requirements. A spatial identifier is assigned to each unit area; this spatial identifier is a unique code used for indexing and locating the unit area in the database. In specific implementations, the allocation of spatial identifiers follows hierarchical coding rules, reflecting the spatial location relationships of the unit areas. Optionally, the spatial identifier may include the code of the superior administrative region. Time-series data is extracted from each unit area, and trend feature values ​​are calculated. The time-series data is a time series filtered from the standardized dataset according to the spatial identifier. Trend feature values ​​are statistics used to quantify the direction and rate of data change. It can be understood that trend feature values ​​are used to identify abnormal data dynamics. In some embodiments, trend feature values ​​are obtained through the slope of linear regression or the parameters of nonlinear fitting. Unit areas with abnormal trend feature values ​​are identified and marked as suspicious units. Anomaly identification is achieved by comparing the trend feature values ​​with a preset threshold range. In practice, the marking operation involves setting a Boolean flag in the metadata of the unit region. When the region segmentation module combines management units, it measures the spatial interaction strength between suspected units. This spatial interaction strength is a correlation metric calculated based on inter-unit distance, traffic flow, or population movement data. It can be understood that spatial interaction strength reflects the degree of mutual influence between geographic units. In some embodiments, the spatial interaction strength is calculated as follows: ; in: This indicates the spatial interaction strength between suspicious unit m and suspicious unit n. This represents the normalized flow value from suspicious unit m to suspicious unit n. This represents the distance between the center points of the normalized suspicious cell m and the suspicious cell n. It is a very small positive number to prevent the denominator from being zero. Normalization scales the original flow and distance values ​​to the [0,1] interval, thus reducing the spatial interaction intensity. The values ​​are dimensionless. If the spatial interaction strength is higher than the connectivity threshold, suspicious units are merged into the same suspicious risk block. The connectivity threshold is a value set based on historical spatial interaction patterns. In practice, the merging operation uses the connected component analysis algorithm in graph theory to group suspicious units with spatial interaction strengths higher than the connectivity threshold into the same set. A comprehensive anomaly degree is calculated for each suspicious risk block. When calculating the comprehensive anomaly degree, the sum of the spatial interaction strengths of each suspicious unit within the block with all other suspicious units in the block is calculated as the connectivity weight. The connectivity weight represents the relative influence of a single suspicious unit within the block. In practice, the calculation of the connectivity weight involves summing the spatial interaction strengths of the target suspicious unit with all other suspicious units in the block. The trend feature value of each suspicious unit is obtained, extracted from time-series data and representing the degree of anomaly. The trend feature value of each suspicious unit is multiplied by its corresponding connectivity weight to obtain a weighted anomaly value. The weighted anomaly value considers both the anomaly degree of the unit itself and its connectivity importance within the block. In practice, the multiplication operation is performed element-wise within the numerical computation framework. The summation of all weighted outliers and the division by the sum of all connectivity weights yield the overall outlier, which is a weighted average of the degree of anomaly within the suspected risk block.

[0028] See Figure 4 This heatmap, depicting the spatial relationships between urban management units, primarily serves the technical process of combining suspected units into regional division modules. The horizontal and vertical axes represent the target and source units, respectively, with color gradients corresponding to the intensity of spatial interaction. This heatmap provides spatial correlation data for subsequent calculations of the comprehensive anomaly degree of blocks, serving as a crucial visualization tool for analyzing clustered risk blocks from isolated suspected units. It embodies the quantitative analysis logic of spatial dependencies in multi-level collaborative governance of the city.

[0029] Example 4: In specific implementation, a multi-dimensional feature vector is extracted from each anomalous trend in the suspected risk block. The multi-dimensional feature vector is a numerical representation extracted from the time-series data of the anomalous trend, containing statistical features such as mean, variance, and slope. In some embodiments, the dimensions of the multi-dimensional feature vector are predefined according to the analysis requirements, for example, containing ten feature components. The extraction process is automatically completed using feature engineering algorithms, with each anomalous trend corresponding to one feature vector instance. A similarity matrix is ​​calculated between the feature vectors. The similarity matrix is ​​a symmetric matrix, where each element represents the degree of similarity between the feature vectors of two anomalous trends. In specific implementation, the cosine similarity method is used for similarity calculation, with the formula: ; in: This represents the similarity value between anomalous trends u and v. This represents the total number of dimensions of the feature vector. The value of the feature vector representing the abnormal trend u at the k-th feature. The value of the feature vector representing the abnormal trend v at the k-th feature is used. The similarity matrix is ​​constructed by traversing all pairs of abnormal trends and calculating the similarity value for each pair. It can be understood that the similarity matrix is ​​used to quantify the correlation strength between abnormal trends. When the value in the similarity matrix exceeds the correlation threshold, the abnormal trends are determined to be consistent. The correlation threshold is a threshold set based on historical data analysis, used to distinguish between strong and weak correlations. In specific implementations, the determination operation involves scanning the similarity matrix, finding all elements greater than the correlation threshold, and recording the corresponding abnormal trend pairs as consistent pairs. Optionally, the correlation threshold can be dynamically adjusted to adapt to different data distributions. When the correlation analysis module uses graph model technology, a graph structure database of historical abnormal trends is constructed. The graph structure database stores past abnormal trends and their correlation relationships, where nodes represent abnormal trends and edges represent spatial or temporal correlations between trends. In some embodiments, the graph structure database is stored and queried using a graph database management system. A graph neural network model is trained to learn collaborative patterns. The graph neural network model is a deep learning architecture capable of processing graph structure data and capturing complex dependencies between nodes. The graph neural network model used in this embodiment is a graph convolutional neural network model suitable for spatiotemporal anomaly analysis of urban vital signs. The model's layers are adapted to the operational characteristics of urban vital sign monitoring, and it consists of four core layers: a node feature extraction layer, a spatial correlation convolutional layer, a temporal collaborative fusion layer, and a probability output layer. These layers are sequentially cascaded. The output of the node feature extraction layer serves as the input to the spatial correlation convolutional layer, and the output of the spatial correlation convolutional layer is fed into the temporal collaborative fusion layer. The feature output of the temporal collaborative fusion layer is then mapped through a fully connected layer and fed into the probability output layer. Specifically, the node feature extraction layer adapts to the need for multi-dimensional feature vector extraction of urban vital sign anomaly trends; the spatial correlation convolutional layer adapts to the need for spatial dependency analysis of urban management units; the temporal collaborative fusion layer adapts to the need for temporal change trend analysis of urban vital sign data; and the probability output layer adapts to the need for quantitative judgment of collaborative anomaly events, achieving a deep integration of the model structure with the multi-level collaborative governance scenario of urban vital sign monitoring. In specific implementation, historical graph data is used as input during training, and the model parameters are optimized through a backpropagation algorithm, enabling the model to predict collaborative anomalies. It is understandable that a well-trained model possesses the ability to identify the coherence of new anomalous trends. The graph features of the current anomalous trend are input into the model, and the coherence probability is output. Graph features are input features extracted from the current anomalous trend and its relationships. The coherence probability is a value between 0 and 1, representing the likelihood that the anomalous trend belongs to a coherent event. In specific implementations, the extraction of graph features involves constructing a subgraph of the anomalous trends of the current suspicious risk blocks and calculating graph metrics such as the degree centrality of nodes. After outputting the coherence probability, if the coherence probability is greater than a decision threshold, it is confirmed as a coherent anomalous event. The decision threshold is a classification threshold set based on a balance between precision and recall. Optionally, the decision threshold can be determined through cross-validation.The number of collaborative anomalies is recorded; this statistical count is used for subsequent threat level assessment. See Table 1, which shows a multi-dimensional feature vector extraction result to illustrate the composition of the feature vector.

[0030] Table 1: Multidimensional Feature Vector Table of Abnormal Trends

[0031] In practice, the specific feature types of the feature vectors can be selected according to the actual application scenario, such as adding frequency features or morphological features. It is understood that the quality of the feature vectors directly affects the accuracy of the association analysis. In some embodiments, the feature extraction process includes a standardization step to eliminate the influence of dimensions.

[0032] See Figure 5 This scatter plot serves as the component for the collaborative anomaly detection process in the correlation analysis module. It primarily illustrates the relationship between the number of collaborative anomaly events and their probabilities in suspicious risk blocks. The horizontal axis represents the number of collaborative anomaly events, the vertical axis represents the collaborative probability, and the color gradient corresponds to the numerical range of the number of collaborative anomaly events. The correlation analysis module first extracts multi-dimensional feature vectors of the anomaly trend and calculates the similarity matrix. Then, it outputs the collaborative probability through a trained graph neural network model. If the probability exceeds a critical value, it is recorded as a collaborative anomaly event. This plot visually demonstrates the distribution characteristics of both. This type of distribution relationship can help identify special risk blocks with high event counts and low probabilities. It serves as a visual representation of the quantitative statistics of collaborative anomaly events and provides crucial data correlation for the subsequent early warning output module to calculate cluster risk scores.

[0033] Example 5: In specific implementations, a cluster risk score is calculated based on the number of coordinated anomalies. The cluster risk score reflects the degree of spatial or logical aggregation of anomalies, and is calculated using a linear weighted or nonlinear mapping function. In some embodiments, the cluster risk score is directly proportional to the number of coordinated anomalies. A discrete risk score is calculated based on the magnitude of independent anomalies. The magnitude of an independent anomaly refers to the severity of its deviation from the normal level for a single anomaly not identified as a coordinated anomaly. The discrete risk score quantifies the potential threat posed by these isolated anomalies. In specific implementations, the magnitude of an independent anomaly is obtained from its trend characteristic value or the deviation of the original data from the baseline. It can be understood that the discrete risk score is used to capture local high-risk points. In some embodiments, the discrete risk score is calculated by taking the maximum or average of the magnitudes of all independent anomalies. The cluster risk score and the discrete risk score are merged into an overall threat level. The fusion operation uses a weighted summation or rule-based reasoning method. In specific implementations, the formula for calculating the overall threat level is: ; in: Indicates the overall threat level. The weighting coefficients represent the cluster risk scores. Indicates the cluster risk score. The weighting coefficients represent the discrete risk scores. Represents the discrete risk score. Represents the natural logarithm function. Weighting coefficients. and Pre-set parameters are determined through expert knowledge or historical data analysis. It's understood that the logarithmic function is used to smooth out the potential extreme values ​​of discrete risk scores. When controlling the warning signal, the warning output module presets a threat level classification standard. This standard divides the overall threat level into continuous intervals, each corresponding to a risk category. In practice, risk categories are typically defined as low-risk, medium-risk, and high-risk. When the overall threat level falls into the high-risk category, a warning signal is automatically sent. This signal is sent to the designated monitoring terminal or responsible person via a message queue or interface call. In some embodiments, the warning signal includes a threat level description and associated suspicious risk block information. When the overall threat level is in the medium-risk category, a tracking and monitoring mechanism is activated. This mechanism includes increasing the data sampling frequency or extending the analysis time window. In practice, activating the tracking and monitoring mechanism generates a configuration command that is sent to the data acquisition module and the hierarchical evaluation module. Optionally, the tracking and monitoring mechanism may involve preparing a warning signal but not sending it immediately. When the overall threat level is classified as low-risk, the system remains silent. This means that the system does not generate any warnings or alerts, but the internal monitoring and analysis processes continue to operate as usual.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A city vital signs monitoring and intelligent early warning system for multi-level collaborative governance, characterized in that, The system includes: The data acquisition module is used to collect raw data streams of urban vital signs from various sensors and databases, and to perform preliminary cleaning and format standardization on the raw data streams to generate a standardized data set. The hierarchical assessment module is used to receive the standardized data set and perform multi-level health calculations on the standardized data set according to the city's administrative hierarchy, producing a health index for each level. When the health index deviates from the normal range, the deep analysis process is activated. The region division module is used to divide the urban geographic space into several management units during the deep analysis process, track the data trends within each management unit, detect abnormal trend patterns, and combine adjacent management units based on spatial dependencies to form suspicious risk blocks. The correlation analysis module is used to perform multidimensional correlation analysis on the abnormal trends within the suspected risk blocks, assess the degree of consistency between trends, and use graph model technology to determine whether there is synergy among the abnormal trends, and record the number of synergistic abnormal events. The early warning output module is used to deduce the overall threat level of urban vital signs based on the number of coordinated abnormal events and the magnitude of independent abnormal events, and to control the issuance of early warning signals based on the overall threat level. The magnitude of the independent abnormal event refers to the severity of a single abnormal event that is not determined to be a coordinated abnormal event and its deviation from the normal level.

2. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 1, characterized in that, When the data acquisition module processes the raw data stream, the operations it performs include: Perform integrity verification on the raw data stream, fill in missing values ​​and correct deviation values; Convert data in different formats into a standard data model; Data smoothing eliminates random fluctuations and generates a standardized dataset.

3. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 1, characterized in that, When the hierarchical assessment module performs multi-level health score calculations, the steps involved include: The administrative hierarchy of a city is defined as the overall city level, the administrative district level, and the street level. Assign baseline health indicators to each level; The deviation of the standardized data set at each level from the benchmark health indicators is calculated to obtain the health index; When the health index exceeds the upper or lower limit of the allowable fluctuation, a deep analysis process is triggered.

4. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 1, characterized in that, The methods used when dividing the management units in the region division module include: The city map is divided into regularly shaped unit areas based on geocoding; Assign a spatial identifier to each unit region; Extract time-series data from each unit area and calculate trend characteristic values; Identify unit regions with abnormal trend characteristic values ​​and mark them as suspicious units.

5. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 4, characterized in that, When combining management units in the region division module, the processing includes: Measure the intensity of spatial interaction between suspected units; If the spatial interaction intensity is higher than the connection threshold, the suspicious units are merged into the same suspicious risk block, and the comprehensive anomaly degree is calculated for each suspicious risk block.

6. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 1, characterized in that, When performing multidimensional correlation analysis, the correlation analysis module includes the following steps: Extract multi-dimensional feature vectors for each anomalous trend from suspicious risk blocks; Calculate the similarity matrix between feature vectors; When the values ​​in the similarity matrix exceed the association threshold, the abnormal trend is considered to be consistent.

7. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 6, characterized in that, When the association analysis module uses graph model technology, the process includes: Construct a graph-structured database of historical anomalies; Train the graph neural network model to learn cooperative patterns; Input the graphical features of the current abnormal trend into the model and output the collaboration probability. If the collaboration probability is greater than the decision threshold, it is confirmed as a collaboration abnormal event.

8. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 1, characterized in that, The methods used by the early warning output module to derive the overall threat level include: Cluster risk scores are calculated based on the number of collaborative anomalies, and discrete risk scores are calculated based on the magnitude of independent anomalies. The cluster risk score and the discrete risk score are combined into an overall threat level.

9. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 8, characterized in that, When the early warning output module controls the early warning signal, the strategies it uses include: Preset threat level classification criteria; When the overall threat level falls into the high-risk category, an early warning signal is automatically sent; when the overall threat level is in the medium-risk category, a tracking and monitoring mechanism is activated; and when the overall threat level is in the low-risk category, a silent state is maintained.

10. The urban vital signs monitoring and intelligent early warning system for multi-level collaborative governance as described in claim 5, characterized in that, The specific steps implemented when calculating the overall anomaly degree for each suspected risk block include: For each suspicious unit within a suspicious risk block, calculate the sum of its spatial interaction strength with all other suspicious units within the block, and use this sum as the connectivity weight of the suspicious unit. Obtain the trend feature value for each suspicious unit, which is extracted from time series data and represents the degree of anomaly; Multiply the trend feature value of each suspicious unit by its corresponding connectivity weight to obtain the weighted outlier value; The sum of all weighted outliers is divided by the sum of all connectivity weights to obtain the overall outlier score.

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