CN117275234A - Urban road internet of things data management platform - Google Patents
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Abstract
An urban road Internet of things data management platform collects traffic flow values at a plurality of preset time points in a preset time period through intelligent street lamps deployed on preset roads; arranging the traffic flow values of the plurality of preset time points into traffic flow time sequence input vectors according to the time dimension; extracting the time sequence characteristics of the traffic flow time sequence input vector to obtain traffic flow time sequence characteristics; and determining a congestion degree label of a preset road based on the traffic flow time sequence characteristics. Therefore, the congestion degree of the preset road can be intelligently judged, valuable information is provided for city management and service, and monitoring, prediction and optimization of the traffic congestion degree are facilitated.
Description
Technical Field
The application relates to the technical field of intelligent internet of things, and more particularly, to an urban road internet of things data management platform.
Background
Urban road traffic congestion has been an important issue in urban management and planning. With the acceleration of the urban process, the increase of the number of vehicles and the limitation of road resources lead to the increasing problem of traffic jam, and bring a plurality of inconveniences to urban management and resident trip.
However, urban traffic data is typically a large-scale, high-dimensional dataset that requires efficient processing and analysis to extract useful information. However, the existing data management platform can only screen and utilize data manually, has limitations in data processing and analysis capabilities, and cannot fully utilize the potential of the data. In addition, urban traffic conditions require real-time monitoring and response in order to take timely action to cope with traffic jams and events. However, the existing data management platform has hysteresis in terms of real-time performance and responsiveness, and cannot provide timely traffic condition information and decision support.
Therefore, an optimized urban road internet of things data management platform is desired.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. The embodiment of the application provides an urban road Internet of things data management platform, which is used for acquiring traffic flow values of a plurality of preset time points in a preset time period through intelligent street lamps deployed on preset roads; arranging the traffic flow values of the plurality of preset time points into traffic flow time sequence input vectors according to the time dimension; extracting the time sequence characteristics of the traffic flow time sequence input vector to obtain traffic flow time sequence characteristics; and determining a congestion degree label of a preset road based on the traffic flow time sequence characteristics. Therefore, the congestion degree of the preset road can be intelligently judged, valuable information is provided for city management and service, and monitoring, prediction and optimization of the traffic congestion degree are facilitated.
In a first aspect, there is provided an urban road internet of things data management platform, comprising:
the intelligent street lamp comprises a traffic flow data acquisition module, a traffic flow control module and a traffic flow control module, wherein the traffic flow data acquisition module is used for acquiring traffic flow values at a plurality of preset time points in a preset time period through intelligent street lamps deployed on preset roads;
the vehicle flow time sequence arrangement module is used for arranging the vehicle flow values of the plurality of preset time points into vehicle flow time sequence input vectors according to the time dimension;
the traffic flow time sequence feature analysis module is used for extracting time sequence features of the traffic flow time sequence input vectors to obtain traffic flow time sequence features;
and the preset road congestion degree detection module is used for determining a congestion degree label of the preset road based on the traffic flow time sequence characteristics.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an urban road internet of things data management platform according to an embodiment of the application.
Fig. 2A is a schematic diagram of a smart street lamp according to an embodiment of the present application.
Fig. 2B is an application schematic diagram of the intelligent street lamp according to an embodiment of the present application.
Fig. 3 is a flowchart of an urban road internet of things data management method according to an embodiment of the present application.
Fig. 4 is a schematic diagram of an urban road internet of things data management method architecture according to an embodiment of the application.
Fig. 5 is an application scenario diagram of an urban road internet of things data management platform according to an embodiment of the application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Unless defined otherwise, all technical and scientific terms used in the examples of this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In the description of the embodiments of the present application, unless otherwise indicated and defined, the term "connected" should be construed broadly, and for example, may be an electrical connection, may be a communication between two elements, may be a direct connection, or may be an indirect connection via an intermediary, and it will be understood by those skilled in the art that the specific meaning of the term may be understood according to the specific circumstances.
It should be noted that, the term "first\second\third" in the embodiments of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that the embodiments of the present application described herein may be implemented in sequences other than those illustrated or described herein.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Urban road traffic jam refers to the phenomenon that in an urban road network, due to the fact that the number of vehicles is too large, the road capacity is limited, traffic organization is unreasonable, and the like, the running speed of the vehicles is slow, and the traffic flow is not smooth. Traffic jams not only bring challenges to city management and planning, but also bring a lot of inconveniences to the residents' travel.
The impact of traffic congestion on urban management and planning is mainly manifested in the following aspects:
economic impact: traffic jams can cause the running speed of vehicles to be reduced, the travel time is prolonged, the transportation cost is increased, and timely delivery of goods is affected. In addition, traffic jams can also lead to increased fuel consumption and emissions, negatively impacting the environment.
Social influence: the traffic jam brings inconvenience to the travel of residents, and increases commute time and pressure. Traffic accidents, road conflicts and unsafe factors can be caused by long-time traffic jam, and travel safety and life quality of residents are affected.
Urban planning impact: traffic congestion limits the development space and effectiveness of urban planning. Insufficient road capacity and poor traffic flow limit the possibilities of newly built roads, improved traffic facilities and perfected urban traffic networks. In addition, the traffic jam can also affect urban air quality and noise pollution, and adversely affect urban environment.
In order to solve the problem of urban road traffic jam, means such as traffic management, urban planning, traffic facility construction, intelligent technology and the like are required to be comprehensively utilized. Traffic management measures such as traffic signal lamp optimization, traffic control, traffic restriction and the like are adopted to regulate and control traffic flow and optimize traffic organization, so that traffic jam is reduced. And the urban road network is reasonably planned, the road capacity is increased, the road layout is improved, and the traffic fluidity is improved. Meanwhile, public transportation construction is emphasized, residents are encouraged to use public transportation means, and private automobile use is reduced. And the construction of traffic facilities is enhanced, including road reconstruction and expansion, traffic hub construction, parking facility construction and the like, so that the road capacity and the traffic running efficiency are improved. And the traffic data is monitored and analyzed in real time by utilizing an intelligent traffic system and the technology of the Internet of things, so that traffic condition information and decision support are provided. The intelligent traffic management system can realize intelligent optimization of traffic signal lamps, dynamic regulation and control of traffic flow and the like, so that traffic jams are reduced.
However, urban traffic data is typically a large-scale, high-dimensional dataset that requires efficient processing and analysis to extract useful information. Existing data management platforms are commonly used for collection, storage, processing and analysis of urban traffic data to support urban management and decision making. The data management platform comprises: the traffic sensor network platform is used for acquiring traffic data such as traffic flow, speed, road condition and the like in real time by deploying a sensor network on a road, and the acquired data is transmitted to a back-end system for storage and analysis through wireless communication. The data storage and management platform is used for storing and managing large-scale traffic data. Efficient data storage and retrieval functions may be provided for fast access and processing of large amounts of traffic data. The data processing and analysis platform is used for processing and analyzing traffic data, extracting useful information and insight, and various data processing and analysis algorithms such as time sequence analysis, data mining, machine learning and the like can be applied to reveal traffic conditions, trends and modes. The visualization and decision support platform displays traffic data in a visualization manner, provides interactive data query and analysis functions, can generate maps, charts and reports of traffic conditions, helps city managers and decision makers to understand and evaluate traffic problems, and makes corresponding decisions and measures. The real-time monitoring and early warning platform can monitor traffic conditions in real time, provide real-time traffic early warning and alarming functions, can timely find traffic jams, accidents and abnormal conditions through real-time data updating and analysis, and send alarms and notices to related departments and residents.
The data management platform can help city managers and planners to better understand and cope with traffic jam problems, provide decision support and optimize city traffic management. However, existing platforms still have some limitations in terms of data processing and analysis capabilities, real-time and responsiveness, requiring further improvements and perfection. However, the existing data management platform can only screen and utilize data manually, has limitations in data processing and analysis capabilities, and cannot fully utilize the potential of the data. In addition, urban traffic conditions require real-time monitoring and response in order to take timely action to cope with traffic jams and events. However, the existing data management platform has hysteresis in terms of real-time performance and responsiveness, and cannot provide timely traffic condition information and decision support.
Therefore, in the application, an optimized urban road Internet of things data management platform is provided.
In one embodiment of the present application, fig. 1 is a block diagram of an urban road internet of things data management platform according to an embodiment of the present application. As shown in fig. 1, an urban road internet of things data management platform 100 according to an embodiment of the present application includes: a traffic flow data acquisition module 110 for acquiring traffic flow values at a plurality of predetermined time points within a predetermined period of time by intelligent street lamps deployed on a predetermined road; a traffic flow time sequence arrangement module 120, configured to arrange traffic flow values at the plurality of predetermined time points into traffic flow time sequence input vectors according to a time dimension; the traffic flow time sequence feature analysis module 130 is configured to perform time sequence feature extraction on the traffic flow time sequence input vector to obtain a traffic flow time sequence feature; the predetermined road congestion degree detection module 140 is configured to determine a congestion degree label of a predetermined road based on the traffic flow timing characteristic.
In the traffic data acquisition module 110, the position and coverage of the street lamp need to be considered when the intelligent street lamp is deployed, so as to ensure that the traffic data can be accurately acquired. In addition, it is necessary to ensure that the time points and time periods of acquisition are representative in order to obtain comprehensive traffic flow information. By collecting traffic flow values at a plurality of preset time points in a preset time period, traffic flow conditions of different time periods on a road can be obtained, and basic data are provided for subsequent data analysis and congestion degree evaluation.
In the traffic flow time alignment module 120, when the traffic flow values are aligned in the time dimension, it is necessary to ensure the accuracy of the order and interval of the time points. In addition, the manner of storage and processing of the data is considered for subsequent timing feature extraction and analysis. The vehicle flow values are arranged according to time to form time sequence input vectors, so that the time change trend of the vehicle flow can be reflected better, and the subsequent time sequence characteristic analysis and congestion degree detection are facilitated.
In the traffic flow timing feature analysis module 130, when performing timing feature extraction, a proper feature extraction method and algorithm need to be selected to accurately capture the timing feature of the traffic flow. At the same time, the dimension and representation of the features are considered for subsequent congestion level detection and label determination. By extracting the time sequence characteristics of the time sequence input vector of the traffic flow, important characteristics related to the traffic flow can be extracted from the data, so that the change rule of the traffic flow can be better understood and analyzed, and a basis is provided for the detection and evaluation of the congestion degree.
In the predetermined road congestion level detection module 140, when determining a road congestion level label based on the traffic flow timing characteristics, a standard and a threshold value of the congestion level need to be defined. Meanwhile, the influence of other factors on the congestion degree, such as road capacity, traffic incidents and the like, is considered. By determining the congestion degree label of the preset road based on the traffic flow time sequence characteristics, the traffic condition of the road can be rapidly estimated, the traffic congestion condition can be monitored in real time, and a timely reference basis is provided for traffic management and decision.
The cooperation of the modules can realize the collection of traffic flow data, time sequence characteristic analysis and congestion degree detection of a preset road, thereby providing support for urban traffic congestion management and optimization. However, the accuracy and reliability of the module needs to be verified and adjusted in practical applications to ensure its effectiveness.
In one embodiment of the present application, as shown in fig. 2A and 2B, the intelligent street lamp is an intelligent device that uses the internet of things technology to realize remote control, monitoring and management of the street lamp. The intelligent street lamp can integrate weather sensors, such as a temperature sensor, a humidity sensor, a barometric pressure sensor and the like, and the sensors can monitor weather parameters of the environment in real time, provide weather forecast and weather data and help urban planning and residents to make corresponding decisions. The intelligent street lamp can be provided with the high-definition camera for monitoring the safety conditions of roads and public areas in real time, and the video monitoring can help to monitor traffic flow and detect traffic illegal behaviors and provide safety precaution and crime prevention functions. The intelligent street lamp can be provided with an RFID reader-writer for identifying and tracking objects or vehicles, for example, the automatic identification and charging of the vehicles can be realized by using RFID technology in a parking lot, and convenient parking service is provided. The intelligent street lamp can be provided with emergency call buttons for pedestrians to seek help in emergency, and the buttons can be connected with an emergency service mechanism to provide quick response and rescue. The intelligent street lamp can realize intelligent lighting function through devices such as a light sensor and a motion sensor. According to the environment illumination and people stream condition, the intelligent street lamp can automatically adjust brightness and illumination time, and energy-saving and comfortable illumination effects are provided. The intelligent street lamp can provide wireless network coverage, provides free public Wi-Fi service for urban residents and tourists, and is convenient for the residents and tourists to connect with the Internet and use online service. The intelligent street lamp can be provided with a display screen or an LED lamp strip for issuing real-time information and city notices. Such information may include traffic conditions, weather reports, public activities, emergency notices, etc., providing convenient and secure information services. The intelligent street lamp can integrate the electric automobile fills electric pile, provides the service of charging for electric automobile, and such facility can promote electric automobile's popularization and sustainable traffic's development.
The intelligent street lamp has the advantages that the intelligent level of the city can be improved by the multifunctional application of the intelligent street lamp, and more convenient, safe and sustainable city service is provided. By integrating various technologies and functions, the intelligent street lamp becomes an important component of urban infrastructure, contributing to sustainable development of cities and life quality of residents.
The intelligent street lamp not only can save energy and improve the service life and safety of the street lamp, but also can collect and transmit various data of urban roads, and provides valuable information for urban management and service. In order to effectively utilize data collected by intelligent street lamps, an urban road Internet of things data management platform based on intelligent street lamps is provided. The main functions of the platform are as follows:
and (3) data acquisition: the platform receives various data collected by the intelligent street lamp in real time through a wireless communication technology, such as street lamp states, environment parameters, traffic flow, vehicle types, pedestrian numbers and the like.
And (3) data storage: the platform adopts a distributed database system to classify and store data according to time, space and types, and the integrity and reliability of the data are ensured.
Data analysis: the platform utilizes a big data analysis technology to count, excavate and predict data, generates various reports and charts, and reflects the real-time condition and development trend of urban roads.
Data presentation: the platform provides various data display modes, such as web pages, mobile terminals, large screens and the like, displays data analysis results to different users and departments, and supports data inquiry, downloading and sharing.
Data application: the platform provides intelligent decision support and optimization suggestions for city management and service according to the data analysis result, such as adjusting street lamp brightness, optimizing traffic signals, improving road facilities, improving public safety and the like.
The urban road Internet of things data management platform based on the intelligent street lamp is an innovative urban intelligent solution, can effectively improve the management level and service quality of urban roads, and brings great social and economic benefits for urban construction and development.
Aiming at the technical problems, the technical concept of the application is to utilize the internet of things technology to collect and monitor traffic data in real time by deploying sensors and equipment on urban roads, and provide a large amount of information and decision basis for urban traffic management. The intelligent street lamp is used as a part of the urban road Internet of things system, can provide a lighting function, can collect traffic data such as traffic flow in real time through the built-in sensor, and introduces a data processing and analyzing algorithm at the rear end to conduct time sequence analysis of the traffic flow value, so that the congestion degree of a preset road is judged, valuable information is provided for urban management and service, and monitoring, prediction and optimization of the traffic congestion degree are facilitated.
Specifically, in the technical scheme of the present application, first, vehicle flow values at a plurality of predetermined time points within a predetermined period of time acquired by an intelligent street lamp deployed on a predetermined road are acquired. Traffic flow conditions at different time points in a preset time period can be obtained through traffic flow values acquired by the intelligent street lamp, objective description of road traffic conditions is provided by the data, and a data base is provided for subsequent congestion degree evaluation. Through the traffic flow values of a plurality of preset time points, the change trend of traffic flow on the road can be observed, the peak and valley time periods of the road and the time period change of the traffic flow can be known, and further the congestion condition of the road can be known more comprehensively. The traffic flow timing feature analysis module may use these traffic flow values for timing feature extraction. By analyzing the time sequence characteristics of the traffic flow, such as peak value, volatility, periodicity and the like, the change rule of the traffic flow can be better understood, and a basis is provided for the subsequent congestion degree detection. Based on the traffic flow timing characteristics, the predetermined road congestion level detection module may determine predetermined road congestion level labels that may be used to describe the congestion level of the road, such as clear, light congestion, medium congestion, heavy congestion levels, to assist traffic managers and decision makers in assessing the traffic conditions of the road.
The acquisition of traffic flow values collected by intelligent street lamps can provide comprehensive understanding of predetermined road traffic conditions, and provide important reference information for traffic management and decision making. The determination of the congestion degree label can help traffic managers to know the congestion condition of the road in time, and take corresponding measures to relieve the traffic congestion problem and improve the traffic efficiency.
The congestion degree label is determined according to the traffic flow data and the time sequence characteristic analysis result and is used for describing the traffic congestion degree on the road. These tags can help traffic authorities and drivers to understand real-time traffic conditions of roads and make corresponding decisions.
Congestion level labels are typically represented in discrete fashion, and include: no congestion indicates no traffic congestion on the road and the vehicle can travel at normal speed. The light congestion indicates that a certain degree of traffic congestion exists on the road, and the running speed of the vehicle is slightly reduced, but still relatively smooth flow can be maintained. Moderate congestion indicates that the traffic congestion degree on the road is high, the running speed of the vehicle is obviously reduced, and the traffic flow is affected to a certain extent. The heavy congestion indicates that the degree of traffic congestion on the road is very high, the running speed of the vehicle is remarkably reduced, and the traffic flow is very slow. The serious congestion indicates that the traffic congestion on the road reaches an extremely high degree, the vehicle can hardly run normally, and the traffic flow is seriously blocked.
The specific division of the congestion degree labels can be adjusted according to actual conditions and requirements, and the congestion degree of different levels can be determined based on indexes such as vehicle density, speed, delay time and the like. The use of the labels can help traffic management departments to monitor road congestion in real time, take corresponding traffic control measures, and remind drivers to select proper routes to avoid congestion areas, so that traffic efficiency is improved, and influence of traffic congestion on urban operation is reduced.
Next, considering that the traffic flow value has a dynamic change rule of time sequence in a time dimension, in order to analyze and judge the time sequence change rule and trend of the traffic flow value, so as to more accurately detect and judge the congestion degree of the predetermined road, in the technical scheme of the application, the traffic flow values at a plurality of predetermined time points need to be arranged into traffic flow time sequence input vectors according to the time dimension, so as to integrate the time sequence distribution information of the traffic flow values.
In one embodiment of the present application, the traffic flow timing feature analysis module 130 includes: the vehicle flow time sequence up-sampling unit is used for enabling the vehicle flow time sequence input vector to pass through an up-sampling module based on linear interpolation to obtain an up-sampled vehicle flow time sequence input vector; the vector-image conversion unit is used for inputting the up-sampling traffic flow time sequence input vector into the vector-image conversion module to obtain a traffic flow time sequence image; and the traffic flow time sequence feature extraction unit is used for extracting the image features of the traffic flow time sequence images to obtain the traffic flow time sequence features.
The vehicle flow time sequence up-sampling unit is used for up-sampling given vehicle flow time sequence input vectors, time resolution is increased through a method based on linear interpolation, and vehicle flow time sequence input vectors with finer granularity are obtained, so that the change condition of the vehicle flow can be captured more accurately, and the accuracy and the precision of subsequent processing are improved.
The vector-image conversion unit inputs the up-sampled traffic flow time sequence into a vector-image conversion module to convert the up-sampled traffic flow time sequence into a traffic flow time sequence image. By converting traffic flow time series data into image form, image processing and computer vision methods can be utilized to extract more abundant feature information.
The traffic flow time sequence feature extraction unit performs feature extraction on the traffic flow time sequence image, so as to extract meaningful feature representations from the image, wherein the features can comprise peak values, volatility, periodicity and the like, and are used for describing the time sequence change rule of the traffic flow. Through the extraction of the time sequence characteristics of the traffic flow, the change trend of the traffic flow can be understood and analyzed more deeply, and useful information is provided for the subsequent tasks such as congestion degree detection and traffic prediction.
Therefore, the time resolution of the vehicle flow time sequence up-sampling unit can be improved, the detailed information of the vehicle flow data can be increased, and the accuracy and precision of subsequent processing can be improved. The vector-image conversion unit converts the traffic flow time sequence data into an image form, and the method of image processing and computer vision is utilized to extract richer features, so that the time sequence change of the traffic flow can be more comprehensively described. The traffic flow time sequence feature extraction unit can extract meaningful feature representation from the traffic flow time sequence image, help understand and analyze the change rule of the traffic flow, and provide important reference information for traffic management and decision. The characteristics can be used for tasks such as congestion degree detection, traffic prediction, traffic signal control optimization and the like, so that the traffic efficiency is improved and the congestion problem is reduced.
Then, in order to improve the capturing capability of the traffic flow time sequence fine change feature of the preset road, in the technical scheme of the application, the traffic flow time sequence input vector is further processed through an up-sampling module based on linear interpolation to obtain an up-sampling traffic flow time sequence input vector, so that the time sequence density and smoothness of traffic flow data are increased, and the traffic flow time sequence feature of the preset road is better represented later. It will be appreciated that by up-sampling with linear interpolation, the data points in the original traffic flow timing input vector can be interpolated to generate more data points. In this way, it is helpful to increase the resolution in the time dimension, making the time-series variation of the traffic more finely visible. Meanwhile, the linear interpolation can carry out smooth interpolation among sampling points, so that the influence of noise and abrupt change is reduced, and the continuity and stability of data are improved.
Further, in order to better extract the traffic flow time sequence characteristics of the predetermined road, in the technical scheme of the application, the up-sampling traffic flow time sequence input vector is further input into a vector-image conversion module to obtain a traffic flow time sequence image. By converting the up-sampled traffic timing input vector into the form of an image, the image data may be processed and analyzed using image processing and analysis algorithms to more accurately analyze traffic timing changes and trends of the predetermined link to determine the congestion level of the predetermined link. This is because image data is rich in information and may contain spatial and temporal structural features that vector data cannot capture directly. That is, by converting the up-sampled traffic timing input vector into a traffic timing image, subsequent capture of traffic timing change patterns at different points in time and spatial locations is facilitated. The time sequence features can better reflect the dynamic change of the traffic flow and provide more accurate information for subsequent data processing and road congestion detection.
In one embodiment of the present application, the traffic flow timing feature extraction unit is configured to: and the traffic flow time sequence image is used for obtaining a space visualization traffic flow time sequence characteristic diagram serving as the traffic flow time sequence characteristic through a convolution neural network model using a space attention mechanism.
The traffic flow time sequence feature extraction unit is used for: each layer of the convolutional neural network model using the spatial attention mechanism carries out convolution processing, mean pooling processing and nonlinear activation processing on input data in forward transmission of the layers respectively to output the final layer of the convolutional neural network model using the spatial attention mechanism as the spatial visualization traffic flow time sequence characteristic diagram, wherein the input of the first layer of the convolutional neural network model using the spatial attention mechanism is the traffic flow time sequence image.
Then, after converting the up-sampled traffic timing input vector into the traffic timing image, feature mining is performed on the traffic timing image using a convolutional neural network model having excellent performance in implicit feature extraction of images. In particular, it is considered that in the actual process of monitoring the time sequence change of the traffic flow, the time sequence characteristic information of the traffic flow on the preset road at the space position is more focused, and the rest useless interference characteristics are filtered. In view of the ability of the attention mechanism to select the focus position, a more resolved representation of the feature is produced, and the feature after addition to the attention module will change adaptively as the network deepens. Therefore, in the technical solution of the present application, the traffic flow time sequence image is processed in a convolutional neural network model using a spatial attention mechanism, so as to perform feature enhancement based on a spatial position on the traffic flow time sequence feature related to the predetermined road in the traffic flow time sequence image, thereby obtaining traffic flow time sequence feature information focused on the spatial position of the predetermined road, namely a spatial display traffic flow time sequence feature map. It should be noted that, here, the image features extracted by the spatial attention reflect the weights of the differences of the spatial dimension features, so as to suppress or strengthen the features of different spatial positions, thereby extracting the time sequence feature information of the traffic flow which is spatially focused on the predetermined road.
The spatial attention mechanism is a technology applied in a convolutional neural network, is used for enhancing the attention degree of a model to different spatial positions in input data, and can help the model to automatically learn and focus on the most relevant and meaningful spatial regions in the input data, so that the characteristics with more distinguishing and expression capability are extracted.
In the processing of traffic timing images, a spatial attention mechanism may be used to highlight regions in the images that are related to traffic variations, enhancing the feature expression of these regions to obtain more pronounced and differentiated traffic timing feature patterns.
In particular, the convolution layer is used to extract feature representations in an image by applying a series of convolution kernels to capture features of different scales and levels of abstraction. The attention mechanism is a core component of spatial attention, and based on a characteristic diagram of input data, the attention mechanism is assigned to different spatial positions through learning weights so as to determine attention degrees of the model at different positions. Common attention mechanisms include self-attention mechanism (self-attention) and channel attention mechanism (channel attention), among others. The activation function is used for introducing nonlinear transformation, increasing the expression capacity of the model, and comprises ReLU (Rectified LinearUnit), sigmoid, tanh and the like.
By using a convolutional neural network model of a spatial attention mechanism, areas related to traffic flow change can be automatically learned and focused in a traffic flow time sequence image, and the characteristic expression of the areas is highlighted, so that a spatially-visualized traffic flow time sequence characteristic diagram is obtained. The feature map can better reflect the spatial distribution and the change trend of the traffic flow, and provide more valuable information for the subsequent tasks of congestion degree analysis, traffic prediction and the like.
In one embodiment of the present application, the predetermined congestion degree detection module 140 includes: the characteristic distribution optimizing unit is used for carrying out Hilbert orthogonal space domain representation decoupling on the space-display traffic flow time sequence characteristic vector obtained by expanding the space-display traffic flow time sequence characteristic map so as to obtain an optimized space-display traffic flow time sequence characteristic map; and the road congestion judging unit is used for enabling the optimized space-display vehicle flow time sequence characteristic diagram to pass through a classifier to obtain a classification result, wherein the classification result is used for representing a congestion degree label of a preset road.
Particularly, in the technical scheme of the application, after up-sampling is performed on the traffic flow time sequence input vector based on linear interpolation, the vector-image conversion module is input to obtain the traffic flow time sequence image, and multi-dimensional distribution representation based on time sequence subdivision positions can be performed on the distribution of traffic flow values in time dimension, so that the time sequence multi-dimensional cross-correlation characteristic representation effect of the space-display traffic flow time sequence characteristic diagram on the distribution of the traffic flow values in the time dimension can be improved by using a convolution neural network model of a space attention mechanism, however, considering that the distribution of the traffic flow values in the time sequence direction is uneven, the distribution of the traffic flow values in a time sequence multi-dimensional distribution space is also enlarged when the multi-dimensional distribution representation based on the time sequence subdivision positions is performed, so that the space-display traffic flow time sequence characteristic diagram also has diversified local characteristic representation among all local characteristic distributions, and the accuracy of the space-display traffic flow characteristic diagram as a whole classification result in the classification domain is affected when the space-display traffic flow characteristic diagram is performed through a classifier.
Based on this, when classifying the spatially-developed traffic flow time series feature map, the applicant of the present application preferably performs hilbert orthogonal spatial domain representation decoupling on a spatially-developed traffic flow time series feature vector obtained by expanding the spatially-developed traffic flow time series feature map, for example, denoted as V, and expressed as: performing Hilbert orthogonal space domain representation decoupling on the space-display vehicle flow time sequence feature vector obtained by expanding the space-display vehicle flow time sequence feature map by using the following optimization formula to obtain the optimized space-display vehicle flow time sequence feature map; wherein, the optimization formula is:
V'＝V 1 θV 2
V 2 ＝IθV 1
wherein V is the time sequence feature vector of the space display vehicle flow obtained by expanding the time sequence feature map of the space display vehicle flow,is the global feature mean value of the time sequence feature vector of the space-display traffic flow obtained by expanding the time sequence feature map of the space-display traffic flow, V 2 Is the space visualization traffic flowThe two norms of the space-development traffic flow time sequence feature vector obtained by expanding the volume time sequence feature map, L is the length of the space-development traffic flow time sequence feature vector obtained by expanding the space-development traffic flow time sequence feature map, I is a unit vector, theta is a vector subtraction, cov 1D (. Cndot.) represents covariance matrix, and V' is the optimized space-revealing traffic flow time sequence feature vector obtained by expanding the optimized space-revealing traffic flow time sequence feature map.
Here, the hilbert orthogonal spatial domain representation decoupling is used to improve the domain-adaptive generalization performance of the spatially-visualized traffic timing feature vector V in the classification domain by emphasizing the domain-specific (domain-specific) information within the diversified feature representation of the spatially-visualized traffic timing feature vector V, i.e., by performing the orthogonal spatial domain decoupling of domain-invariant (domain-invariant) representation within the overall domain representation of the spatially-visualized traffic timing feature vector V based on the vector-self-spatial metric and the hilbert spatial metric under the vector-self-product representation, thereby improving the classification effect of the spatially-visualized traffic timing feature map. Therefore, the congestion degree of the road can be judged based on the real-time change of the traffic flow of the road, so that valuable information is provided for city management and service, and monitoring, prediction and optimization of the traffic congestion degree are facilitated.
In one embodiment of the present application, the road congestion judging unit includes: the matrix expansion subunit is used for expanding the optimized space visualization traffic flow time sequence feature diagram into classification feature vectors according to row vectors or column vectors; a full-connection coding subunit, configured to perform full-connection coding on the classification feature vector by using multiple full-connection layers of the classifier to obtain a coded classification feature vector; and the classification subunit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
And then, the optimized space-visualization traffic flow time sequence characteristic diagram passes through a classifier to obtain a classification result, wherein the classification result is used for representing a congestion degree label of a preset road. Specifically, in the technical scheme of the application, the classification label of the classifier is the congestion degree label of the preset road, so that after the classification result is obtained, the congestion degree of the preset road is judged, valuable information is provided for city management and service, and monitoring, prediction and optimization of the traffic congestion degree are facilitated.
The classifier can correlate the optimized feature map with the corresponding congestion degree label, so that the congestion degree of the road is predicted. This enables traffic managers to quickly learn the traffic status of roads, including no congestion, light congestion, medium congestion, heavy congestion, and heavy congestion levels. The traffic condition of the road can be monitored in real time by acquiring the traffic flow data in real time and generating the corresponding feature map. The traffic manager can take measures in time according to the classification result, such as adjusting the traffic signal timing, guiding the traffic flow direction, etc., so as to relieve the congestion and improve the traffic efficiency. The classification result provides accurate prediction of the road congestion degree, and can provide decision support for traffic managers. For example, according to different congestion degrees, traffic planning, road construction and maintenance planning adjustment, traffic signal control strategy improvement and the like can be optimized, so that the running efficiency and travel experience of urban traffic are improved.
In summary, the urban road internet of things data management platform 100 according to the embodiments of the present application is illustrated, which uses the internet of things technology to collect and monitor traffic data in real time by deploying sensors and devices on urban roads, and provides a great amount of information and decision basis for urban traffic management. The intelligent street lamp is used as a part of the urban road Internet of things system, can provide a lighting function, can collect traffic data such as traffic flow in real time through the built-in sensor, and introduces a data processing and analyzing algorithm at the rear end to conduct time sequence analysis of the traffic flow value, so that the congestion degree of a preset road is judged, valuable information is provided for urban management and service, and monitoring, prediction and optimization of the traffic congestion degree are facilitated.
In one embodiment of the present application, fig. 3 is a flowchart of an urban road internet of things data management method according to an embodiment of the present application. Fig. 4 is a schematic diagram of an urban road internet of things data management method architecture according to an embodiment of the application. As shown in fig. 3 and 4, the method for managing data of the internet of things of the urban road includes: 210, acquiring vehicle flow values at a plurality of preset time points in a preset time period by intelligent street lamps deployed on preset roads; 220, arranging the traffic flow values of the plurality of preset time points into traffic flow time sequence input vectors according to a time dimension; 230, extracting the time sequence characteristics of the traffic flow time sequence input vector to obtain traffic flow time sequence characteristics; 240, determining a congestion degree label of a preset road based on the traffic flow time sequence characteristics.
It will be appreciated by those skilled in the art that the specific operations of the respective steps in the above-described urban road internet of things data management method have been described in detail in the above description of the urban road internet of things data management platform with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
Fig. 5 is an application scenario diagram of an urban road internet of things data management platform according to an embodiment of the application. As shown in fig. 5, in the application scenario, first, traffic flow values (e.g., C as illustrated in fig. 5) at a plurality of predetermined time points within a predetermined period are collected by a smart street lamp deployed on a predetermined road; the acquired traffic flow value is then input into a server (e.g., S as illustrated in fig. 5) deployed with an urban road internet of things data management algorithm, wherein the server is capable of processing the traffic flow value based on the urban road internet of things data management algorithm to determine a congestion degree label for a predetermined road.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not intended to be limited to the details disclosed herein as such.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (7)
1. The utility model provides an urban road thing networking data management platform which characterized in that includes:
the intelligent street lamp comprises a traffic flow data acquisition module, a traffic flow control module and a traffic flow control module, wherein the traffic flow data acquisition module is used for acquiring traffic flow values at a plurality of preset time points in a preset time period through intelligent street lamps deployed on preset roads;
the vehicle flow time sequence arrangement module is used for arranging the vehicle flow values of the plurality of preset time points into vehicle flow time sequence input vectors according to the time dimension;
the traffic flow time sequence feature analysis module is used for extracting time sequence features of the traffic flow time sequence input vectors to obtain traffic flow time sequence features;
and the preset road congestion degree detection module is used for determining a congestion degree label of the preset road based on the traffic flow time sequence characteristics.
2. The urban road internet of things data management platform of claim 1, wherein the traffic flow timing feature analysis module comprises:
The vehicle flow time sequence up-sampling unit is used for enabling the vehicle flow time sequence input vector to pass through an up-sampling module based on linear interpolation to obtain an up-sampled vehicle flow time sequence input vector;
the vector-image conversion unit is used for inputting the up-sampling traffic flow time sequence input vector into the vector-image conversion module to obtain a traffic flow time sequence image;
and the traffic flow time sequence feature extraction unit is used for extracting the image features of the traffic flow time sequence images to obtain the traffic flow time sequence features.
3. The urban road internet of things data management platform according to claim 2, wherein the traffic flow timing feature extraction unit is configured to: and the traffic flow time sequence image is used for obtaining a space visualization traffic flow time sequence characteristic diagram serving as the traffic flow time sequence characteristic through a convolution neural network model using a space attention mechanism.
4. The urban road internet of things data management platform according to claim 3, wherein the traffic flow timing feature extraction unit is configured to: each layer of the convolutional neural network model using the spatial attention mechanism carries out convolution processing, mean pooling processing and nonlinear activation processing on input data in forward transmission of the layers respectively to output the final layer of the convolutional neural network model using the spatial attention mechanism as the spatial visualization traffic flow time sequence characteristic diagram, wherein the input of the first layer of the convolutional neural network model using the spatial attention mechanism is the traffic flow time sequence image.
5. The urban road internet of things data management platform of claim 4, wherein the predetermined road congestion level detection module comprises:
the characteristic distribution optimizing unit is used for carrying out Hilbert orthogonal space domain representation decoupling on the space-display traffic flow time sequence characteristic vector obtained by expanding the space-display traffic flow time sequence characteristic map so as to obtain an optimized space-display traffic flow time sequence characteristic map;
and the road congestion judging unit is used for enabling the optimized space-display vehicle flow time sequence characteristic diagram to pass through a classifier to obtain a classification result, wherein the classification result is used for representing a congestion degree label of a preset road.
6. The urban road internet of things data management platform according to claim 5, wherein the feature distribution optimizing unit is configured to: performing Hilbert orthogonal space domain representation decoupling on the space-display vehicle flow time sequence feature vector obtained by expanding the space-display vehicle flow time sequence feature map by using the following optimization formula to obtain the optimized space-display vehicle flow time sequence feature map;
wherein, the optimization formula is:
wherein the method comprises the steps ofV is the time sequence feature vector of the space display vehicle flow obtained by expanding the time sequence feature map of the space display vehicle flow, Is the global feature mean value of the time sequence feature vector of the space-display traffic flow obtained by expanding the time sequence feature map of the space-display traffic flow, V 2 The two norms of the space development traffic flow time sequence feature vector obtained by the expansion of the space development traffic flow time sequence feature map are L, the length of the space development traffic flow time sequence feature vector obtained by the expansion of the space development traffic flow time sequence feature map is L, and I is a unit vector>Representing vector subtraction, cov 1D (. Cndot.) represents covariance matrix, and V' is the optimized space-revealing traffic flow time sequence feature vector obtained by expanding the optimized space-revealing traffic flow time sequence feature map.
7. The urban road internet of things data management platform according to claim 6, wherein the road congestion judging unit comprises:
the matrix expansion subunit is used for expanding the optimized space visualization traffic flow time sequence feature diagram into classification feature vectors according to row vectors or column vectors;
a full-connection coding subunit, configured to perform full-connection coding on the classification feature vector by using multiple full-connection layers of the classifier to obtain a coded classification feature vector; and
and the classification subunit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
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