CN-121545382-B - Urban traffic collaborative intelligent parking management system based on Internet of things

CN121545382BCN 121545382 BCN121545382 BCN 121545382BCN-121545382-B

Abstract

The invention relates to the technical field of intelligent transportation and discloses an urban traffic collaborative intelligent parking management system based on the Internet of things. The system comprises a data acquisition and preprocessing module, a characteristic extraction and anomaly detection module, a congestion prediction and modeling module, a resource allocation and strategy generation module, a performance and feedback monitoring module and a performance and feedback monitoring module, wherein the data acquisition and preprocessing module captures vehicle parking behavior and parking space state data in real time, generates a standardized parking event stream through time sequence alignment and noise filtration, extracts parking space occupation time length fluctuation and vehicle parking frequency characteristics, judges abnormal deviation degree of parking behavior, constructs a node congestion association model, predicts potential traffic resistance of a surrounding road network, and establishes a parking space resource dynamic allocation strategy based on the potential traffic resistance, calculates data migration urgency score, generates an isolation configuration scheme comprising path planning and authority adjustment, and continuously monitors parking space use mode changes to position a behavior anomaly source through comparison of access sequence differences before and after configuration.

Inventors

ZHANG FENG
LIU BENLI
WANG YANYAN

Assignees

山东巨泽信息工程有限公司

Dates

Publication Date: 20260505
Application Date: 20260116

Claims (10)

1. Urban traffic collaborative intelligent parking management system based on Internet of things, which is characterized by comprising the following modules: The data acquisition and preprocessing module is used for capturing vehicle parking behavior data and parking space state change signals in real time through a sensing equipment group deployed in an urban parking area, and carrying out time sequence alignment and noise filtering processing on the captured multi-source data to generate a standardized parking event stream; The feature extraction and anomaly detection module is used for carrying out mode analysis on the standardized parking event stream, extracting the fluctuation feature of the parking space occupation time length and the vehicle parking frequency feature, and judging the anomaly deviation degree of the parking behavior of the current area by comparing the historical normal behavior feature library; The congestion prediction and modeling module is used for positioning physical nodes where the high-frequency abnormal parking behavior occurs according to the abnormal deviation degree, constructing a node congestion association model by combining real-time traffic flow data, and predicting potential passing resistance of a road network around the nodes; The resource allocation and strategy generation module is used for making a parking space resource dynamic reallocation strategy based on the potential passing resistance, calculating data migration urgency scores of different parking space nodes and generating an isolation configuration scheme containing path planning and permission adjustment instructions; And the execution and feedback monitoring module is used for continuously monitoring the change of the parking space use mode after the isolation configuration scheme is executed, and locating the abnormal behavior source by comparing the access sequence difference before and after the configuration is effective.
2. The internet of things-based urban traffic collaborative intelligent parking management system according to claim 1, wherein the time series alignment and noise filtering processing of the captured multi-source data comprises the steps of receiving an original data stream with inconsistent time stamps from a geomagnetic sensor, a camera and a payment terminal, carrying out segmented buffering on the data stream by adopting a sliding window mechanism, carrying out time stamp normalization processing on the data in the segments by taking a GPS clock signal as a reference, removing transient noise data generated by equipment faults by adopting an outlier detection algorithm based on variance, and extracting feature vectors of the processed data segments, wherein the feature vectors comprise vehicle parking starting time, duration and adjacent event intervals.
3. The urban traffic collaborative intelligent parking management system based on the Internet of things, which is characterized in that the extracting of the parking space occupation duration fluctuation characteristic and the vehicle parking frequency characteristic comprises the steps of counting an occupation total duration sequence of a single parking space in a preset time period from a normalized parking event stream, calculating variance and autocorrelation coefficients of the occupation total duration sequence to represent duration fluctuation, counting a vehicle parking times sequence in the same period, calculating variation coefficients to represent frequency stability, combining the duration fluctuation and the frequency stability into a multidimensional characteristic vector, and inputting the multidimensional characteristic vector into a pre-trained automatic encoder for characteristic dimension reduction to obtain low-dimensional dense characteristics.
4. The internet of things-based urban traffic collaborative intelligent parking management system according to claim 3, wherein the determining of the abnormal deviation degree of the parking behavior of the current area by comparing the historical normal behavior feature library comprises calling a historical normal parking behavior feature template stored in a distributed database, wherein the template comprises reference feature ranges of different parking space types in different time periods, calculating the mahalanobis distance between a low-dimensional dense feature extracted in real time currently and the reference feature of the corresponding template, mapping a distance value to be zero to a deviation degree score of one hundred, and marking a parking space node as the abnormal deviation degree and recording an abnormal time window when the deviation degree score exceeds a dynamic threshold.
5. The urban traffic collaborative intelligent parking management system based on the Internet of things, which is characterized by comprising the steps of obtaining real-time traffic flow data provided by a municipal traffic management platform, establishing a road network grid with an abnormal parking node as a center and a radiation radius of five hundred meters, analyzing the average speed change rate and the traffic flow density increment of each road section in the grid in an abnormal time window, modeling the nonlinear relation between the abnormal degree of the parking node and the speed change rate of the surrounding road sections by adopting a graph rolling network, and outputting the influence weight of each node on the road network traffic resistance.
6. The urban traffic collaborative intelligent parking management system based on the Internet of things according to claim 5, wherein the calculating of the data migration urgency scores of different parking space nodes comprises determining a parking space node set to be adjusted according to the influence weight, analyzing parking order data quantity and user sensitive information grade stored in nodes in the set, evaluating the strength of data dependency relationship among nodes, quantifying the dependency strength through the weighted sum of cross-node order association proportion and synchronous access frequency, and calculating migration urgency scores of each node by using a multi-layer perceptron model in combination with the influence weight, the data quantity and the dependency strength, wherein the higher the score is, the higher the priority is.
7. The urban traffic collaborative intelligent parking management system based on the internet of things according to claim 6, wherein the generating of the isolation configuration scheme comprising path planning and authority adjustment instructions comprises the steps of sorting nodes according to migration urgency scores, selecting the highest-scoring node as a source node to be migrated, selecting a target storage node cluster in a low-load area, calculating a data transmission optimal path from the source node to the target cluster based on Yu Dijie Style algorithm, wherein the path needs to avoid a current network congestion link, and generating the isolation configuration scheme comprising a source node identifier, a target cluster address, migration data quantity, a transmission path sequence and an execution time window.
8. The internet of things-based urban traffic collaborative intelligent parking management system according to claim 7, wherein the continuous monitoring of parking space usage pattern changes after the execution of the isolation configuration scheme comprises collecting usage logs of affected parking spaces in a monitoring period after data migration is completed, extracting access time distribution, single access duration and concurrent access number, dynamically time-aligned access pattern features after migration with a baseline pattern of the same period before migration, and calculating accumulated difference values of the access pattern features and the baseline pattern at key pattern points.
9. The urban traffic collaborative intelligent parking management system based on the Internet of things, which is characterized in that the positioning of the behavior anomaly sources through the access sequence difference before and after the comparison configuration is effective comprises the steps of backtracking an access event sequence with obvious difference when the accumulated difference value exceeds an adaptive threshold value, analyzing the number of equipment of the operation source and the user identification, associating access records of other adjacent nodes in the same time window, identifying whether a collaborative anomaly mode exists, performing pattern matching on the anomaly access sequence and a system vulnerability feature library, and determining the type of the behavior source causing the anomaly.
10. The internet of things-based urban traffic collaborative intelligent parking management system according to claim 9, further comprising executing an adaptive response mechanism that automatically triggers a predefined response script based on the determined behavior source type, the script including but not limited to temporarily locking the abnormal source associated parking reservation rights, adjusting the parking pricing coefficients for the abnormal area, sending a detailed diagnostic report to the operation and maintenance terminal and prompting a manual audit key decision point.

Description

Urban traffic collaborative intelligent parking management system based on Internet of things Technical Field The invention relates to the technical field of intelligent transportation, in particular to an urban traffic collaborative intelligent parking management system based on the Internet of things. Background The current urban parking management system mainly adopts a fixed-point detection and independent guiding mode. In the prior art, the monitoring of parking behavior is mostly dependent on a single sensor, and the vehicle parking data and the parking space state change cannot be effectively associated. The data processing method is simple, the time synchronization precision of the multi-source heterogeneous data is insufficient, and the noise interference influences the data quality. Feature extraction dimensions are limited, and time sequence features and frequency features of parking behaviors are not fully mined. The abnormal detection mechanism is solidified, and is mostly judged by adopting a fixed threshold value, so that the abnormal detection mechanism cannot adapt to the dynamic changes of parking behaviors in different areas. The congestion prediction model is linearized and the inherent correlation of parking anomalies with traffic flow is not analyzed in depth. The resource allocation strategy is static, and the parking space resource adjustment lacks real-time traffic state support. The existing method needs to solve key technical problems of multi-source data fusion, behavior pattern recognition, congestion association prediction, dynamic resource allocation and the like. Traditional parking management systems have significant shortcomings in terms of collaborative management and intelligent decision-making. The coverage of the sensor network is incomplete, and the data acquisition of the key area is missing. The time sequence alignment algorithm is simple, and the data synchronization errors of different sampling frequencies are large. The noise filtering parameters are fixed, and the non-stationary signal processing effect is not ideal. The feature extraction method is single, and periodic and sudden features of parking behaviors cannot be captured. The historical behavior feature library is updated with hysteresis, and the latest change of the parking requirement cannot be reflected. The abnormal deviation degree calculation is linearized, and the complex abnormal pattern recognition accuracy is low. The node positioning accuracy is insufficient, and the mapping between the physical position and the logic area is inaccurate. The congestion association model is idealized without considering the effects of road topology and signal control. The prediction deviation of the passing resistance is large, and the reliability of potential congestion evaluation is insufficient. The resource allocation algorithm is low in efficiency, and the large-scale node scheduling response is delayed. Migration urgency scoring criteria are subjective, and important node priority settings are unreasonable. The isolation configuration scheme generates rigidification, and the path planning and the authority adjustment are poor in cooperativity. The execution effect monitoring mechanism is absent, and the feedback optimization loop of the configuration adjustment is not closed. Disclosure of Invention The invention aims to provide an urban traffic collaborative intelligent parking management system based on the Internet of things, which aims to solve the problems in the background technology. In order to achieve the above purpose, the invention provides an urban traffic collaborative intelligent parking management system based on the internet of things, which comprises: The data acquisition and preprocessing module is used for capturing vehicle parking behavior data and parking space state change signals in real time through a sensing equipment group deployed in an urban parking area, and carrying out time sequence alignment and noise filtering processing on the captured multi-source data to generate a standardized parking event stream; The feature extraction and anomaly detection module is used for carrying out mode analysis on the standardized parking event stream, extracting the fluctuation feature of the parking space occupation time length and the vehicle parking frequency feature, and judging the anomaly deviation degree of the parking behavior of the current area by comparing the historical normal behavior feature library; The congestion prediction and modeling module is used for positioning physical nodes where the high-frequency abnormal parking behavior occurs according to the abnormal deviation degree, constructing a node congestion association model by combining real-time traffic flow data, and predicting potential passing resistance of a road network around the nodes; The resource allocation and strategy generation module is used for making a parking space resource dynamic reallocation strategy