CN114037156A - Elastic cloud-based rail transit large passenger flow trajectory analysis method, terminal and storage medium - Google Patents
Elastic cloud-based rail transit large passenger flow trajectory analysis method, terminal and storage medium Download PDFInfo
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Abstract
The embodiment of the invention belongs to the technical field of data analysis, in particular to a method, a terminal and a storage medium for analyzing a large rail transit passenger flow track based on elastic cloud, the rail transit large passenger flow trajectory analysis method based on the elastic cloud obtains real-time multi-source passenger flow data, after the large passenger flow track information is fitted on the basis, the multi-source passenger flow data is communicated, associated and fused through the methods of association, comparison and intersection, so as to verify the fitted large passenger flow track information, improve the integrity and accuracy of the whole data analysis, and after the verification, real-time analysis processing is carried out so as to improve the accuracy and precision of generating the rail transit large passenger flow track data covering the flow density, the flow direction and the flow speed in real time, thereby breaking through the bottleneck that the analysis result error is too large due to the use of single data source analysis and reducing the input cost of human resources.
Description
Technical Field
The embodiment of the invention belongs to the technical field of data analysis, and particularly relates to a method, a terminal and a storage medium for analyzing a track traffic large passenger flow track based on elastic cloud.
Background
With the continuous development of urban rail transit networks in China, the network passenger flow shows a rapid growth trend, and the number and the scale of stations are continuously enlarged. Compared with other transportation modes, subway passenger flow has the characteristics of small bearing space, high density, complex flow line, numerous interlacing points and the like, and especially for large transfer stations, due to the fact that the number of connection lines and entrances and exits is large, the characteristics of passenger flow volume superposition, flow volume intersection and the like are presented, the risk of operation organization is aggravated, and higher requirements are provided for the passenger flow management and control level. The Shanghai rail transit is one of important transportation means for citizens to go out, the average daily passenger capacity is over 100 ten thousand times in 2019, the average daily passenger flow is reduced under the influence of epidemic situations in 2020, and the average daily passenger flow is still over 750 ten thousand times. In the face of the passenger flow of tens of millions of people, once the passenger flow exceeds the bearing limit, the order is out of control, and the risk of group life safety accidents is caused. How to better perceive and cope with the large passenger flow of rail transit is therefore an important challenge for some time in the future.
At present, the management of the passenger flow of the existing station of the urban rail transit mainly comprises the steps of manually staring at and monitoring the station with video assistance, judging the passenger flow situation and taking corresponding management and control measures based on the mode of comparing the operation experience with the objective reality, and the measures not only have defects in accuracy and precision, but also cause huge workload of personnel.
Disclosure of Invention
The embodiment of the invention aims to provide a method, a terminal and a storage medium for analyzing a large rail transit passenger flow trajectory based on elastic cloud, an intelligent data analysis algorithm and an elastic cluster cloud technology are combined and applied to the field of rail transit, and accurate analysis of the large rail transit passenger flow trajectory can be realized on the premise of reducing human resource input cost.
In order to solve the technical problem, an embodiment of the present invention provides an elastic cloud-based rail transit large passenger flow trajectory analysis method, including the following steps:
acquiring multi-source passenger flow data based on a data acquisition unit built by a rail transit station;
building an elastic cluster cloud resource pool for storing, calculating, transmitting and analyzing the multi-source passenger flow data on a cloud computing infrastructure and an elastic cluster cloud platform, and deploying a dependence component, a database and multi-source data acquisition and correlation analysis application services required by big data on PAAS (platform assisted adaptive clustering) services of the elastic cluster cloud resource pool;
the multi-source data acquisition and correlation analysis application service supports real-time acquisition of the multi-source passenger flow data so as to fit real-time large passenger flow trajectory information in any time;
the large passenger flow track information is verified through correlation, comparison and intersection methods, and real-time analysis processing is carried out to generate track traffic large passenger flow track data covering flow density, flow direction and flow speed in real time;
analyzing and processing the multi-source passenger flow data based on the elastic cluster cloud resource pool, and acquiring the required resource use condition;
and dynamically adjusting the elastic resources of the elastic cluster cloud resource pool according to the multi-source passenger flow data and the resource use condition and by executing a flexible scheduling strategy so as to automatically respond to the dynamic analysis requirement of the rail transit large passenger flow trajectory data in real time.
An embodiment of the present invention further provides a terminal, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the elastic cloud-based rail transit mass passenger flow trajectory analysis method.
The embodiment of the invention also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to realize the rail transit large passenger flow trajectory analysis method based on the elastic cloud.
Compared with the prior art, the embodiment of the invention has the advantages that real-time multi-source passenger flow data are obtained, after the large passenger flow track information is fitted on the basis, the multi-source passenger flow data are communicated, associated and fused through the association, comparison and crossing methods, so that the fitted large passenger flow track information is verified, the integrity and the accuracy of the whole data analysis are improved, the real-time analysis processing is carried out after the verification, the accuracy and the precision of the generated real-time large passenger flow track data covering the traffic density, the flow direction and the flow speed are improved, the bottleneck that the analysis result error is too large due to the use of single data source analysis is broken through, and the human resource input cost is reduced. Meanwhile, the work of analyzing and processing the multi-source passenger flow data is placed on an elastic cluster cloud resource pool, the elastic resources are dynamically adjusted according to the multi-source passenger flow data and the resource use condition, the dynamic analysis requirement of automatically responding to the large-traffic passenger flow track data in real time is met, meanwhile, redundant elastic resources can be released for other use when the passenger flow volume is small, other elastic resources can be reused for coping when the passenger flow volume is large, the reasonable allocation of the resources is realized, and the condition of resource vacancy or resource shortage is not easy to occur. The data support in developing daily operation activities such as intelligent perception early warning and the like is provided for the field of rail transit, and scientificity and accuracy are improved for intelligent data operation of each rail transit station.
In addition, the data collector based on the rail transit station is used for acquiring multi-source passenger flow data, and specifically comprises the following steps: the data acquisition units built based on the rail transit stations comprise data acquisition units of various rail transit information systems including a Wi-Fi system, an in-station video system, a gate system and a ticketing system, and acquire various rail transit information basic data including Wi-Fi sniffing data, gate video data, gate data and ticketing data. Of course, all networks including the 5G base station may share the mobile communication base station, thereby obtaining the corresponding rail transit information data.
In addition, the multi-source data acquisition and correlation analysis application service supports real-time acquisition of the multi-source passenger flow data to fit real-time large passenger flow trajectory information in any time, and the method comprises the following steps: positioning a single passenger according to the AP signal in the Wi-Fi sniffing data, and acquiring positioning information corresponding to the passenger in a rail transit range so as to realize real-time analysis of the flow speed and the flow direction of the passenger in the rail transit range; based on the unique identification information of the passengers in the Wi-Fi system, all AP identification information accessed by the passengers in a rail transit range is associated and integrated, and further the dynamic analysis of the behavior track of the single passenger in the rail transit range is realized; and fitting real-time large passenger flow track information in any time for all the passengers through a reduction algorithm according to the dynamic analysis of the behavior track of the single passenger in the rail transit range. Since the Wi-Fi system can provide very accurate track information for each passenger, the track information of each passenger can be combined with the service penetration amount and historical Wi-Fi use data to fit the track information of all passengers in real time, namely the real-time large passenger flow track information in any time.
In addition, the method for verifying the large passenger flow trajectory information through correlation, comparison and intersection and performing real-time analysis processing to generate the large passenger flow trajectory data covering the flow density, the flow direction and the flow speed in real time comprises the following steps: according to the gate video data, passenger positioning and people counting at key gate positions in the rail transit range, gateway positions, transfer passages, stairs and carriages are carried out through an image recognition technology, and positioning information and people counting information of passengers at the key gate positions are obtained; according to the gate data and the ticketing data, the information of the passengers entering and leaving the station is obtained; and based on the positioning information and the number statistical information of the passengers at the key gate positions and the information of the passengers entering and leaving the station in the same route interval, carrying out dynamic verification and approval on the reduction algorithm and the large passenger flow trajectory information in real time within any fitting time by using a correlation, comparison and intersection method so as to generate the large passenger flow trajectory data of the rail intersection covering the flow density, the flow direction and the flow speed in real time. However, the Wi-Fi system has the defect of small access amount, so that the fitting result has some deviation, the obtained multi-source passenger flow data is used for checking, particularly, the real-time large passenger flow track information in any time is dynamically checked and checked by using the bayonet video data, the gate data and the ticket selling data, and the accuracy of generating the real-time large traffic passenger flow track data covering the flow density, the flow direction and the flow speed is further ensured; providing videos at corresponding moments according to the video data of the gate, and acquiring positioning information and people counting information of passengers at the position of the key gate; according to the passenger station entering and exiting information and the ticket selling information of all the rail transit route intervals provided by the gate data and the ticket selling data, the accurate station entering and exiting information of the passenger in a certain route interval can be obtained.
In addition, the analyzing and processing of the multi-source passenger flow data based on the elastic cluster cloud resource pool comprises the following steps: the Wi-Fi system is docked through the elastic cluster cloud resource pool, dynamic analysis of the behavior track of the single passenger in a rail transit range is obtained, and real-time large passenger flow track information in any time is fitted for all the passengers through a reduction algorithm; butting the video system in the station through the elastic cluster cloud resource pool, positioning passengers and counting the number of the passengers at key gate openings, entrances and exits, transfer passages, staircases and key gate openings in carriages in the rail transit range, and acquiring positioning information and number counting information of the passengers at the key gate openings; the gate system and the ticketing system are butted through the elastic cluster cloud resource pool, and the information of the passengers entering and leaving the station is obtained; and based on the positioning information and the number statistical information of the passengers at the key gate positions and the information of the passengers entering and leaving the station in the same route interval, performing dynamic verification and approval on the real-time large passenger flow trajectory information in any time by using a correlation, comparison and intersection method, and performing real-time analysis and processing to generate real-time large rail flow trajectory data covering flow density, flow direction and flow speed. Therefore, the whole processing process of the multi-source passenger flow data is placed on the elastic cluster cloud resource pool, and therefore resources required in the whole processing process can be dynamically adjusted in real time.
In addition, the dynamic adjustment of the elastic resources of the elastic cluster cloud resource pool includes the following steps: establishing a passenger flow volume and resource use relation comparison graph according to the multi-source passenger flow data and the resource use condition; in combination with the passenger flow volume and resource use relation comparison graph, performing dynamic preliminary adjustment on the elastic resource according to the multi-source passenger flow data analysis processing and performing detailed adjustment on the elastic resource according to historical peak passenger flow volume; the content of the dynamic initial adjustment of the elastic resource according to the multi-source passenger flow data analysis processing comprises the horizontal and/or vertical dynamic elastic expansion of the elastic cluster cloud resource pool copy according to the resource request usage amount corresponding to the multi-source passenger flow data analysis processing; the objects for performing detailed adjustment on the elastic resources according to the historical peak passenger flow comprise components and databases on which the multi-source passenger flow data are analyzed and processed, and the multi-source data acquisition and correlation analysis application service. The purpose of the dynamic preliminary adjustment and the detailed adjustment is to be able to meet the amount of elastic resources needed in the normal case of daily operations and in the special case of sudden surges, respectively.
In addition, whether the expansion and contraction rate of the elastic resource can meet the resource requirement in the peak data analysis of the multi-source passenger flow data within a value range is judged; when the expansion and contraction rate of the elastic resources cannot meet the resource requirement of the multi-source passenger flow data during peak data analysis, creating a new instance copy on the elastic cluster cloud resource pool; judging whether the resource configuration value of the new instance copy created on the elastic cluster cloud resource pool is greater than a preset resource threshold value; creating a second instance replica on the elastic cluster cloud resource pool that is identical to the first instance replica when the resources of the first instance replica on the elastic cluster cloud resource pool are greater than the preset resource threshold; when the resources of the second instance copy created on the elastic cluster cloud platform are smaller than the preset resource threshold, releasing the second instance copy and reusing the first instance copy.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
Fig. 1 is a flowchart of a method for analyzing a large rail transit passenger flow trajectory based on elastic cloud according to an embodiment of the present invention;
FIG. 2 is a flowchart of building a cloud resource pool of elastic clusters according to an embodiment of the present invention;
FIG. 3 is a flow diagram of an embodiment of a multi-source data collection and correlation analysis application service support of the present invention for obtaining multi-source passenger flow data in real-time to fit real-time large passenger flow trajectory information at any time;
FIG. 4 is a flowchart illustrating an embodiment of a method for correlating, comparing, and crossing traffic trajectory information according to the present invention;
FIG. 5 is a flowchart illustrating dynamic initial adjustment of elastic resources by an elastic cluster cloud resource pool according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating the elastic resource adjustment performed by the elastic cluster cloud resource pool in detail according to an embodiment of the present invention;
fig. 7 is a working schematic diagram of an embodiment of a rail transit large passenger flow trajectory analysis method based on elastic cloud according to the present invention;
fig. 8 is a schematic diagram of a terminal structure according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not constitute any limitation to the specific implementation manner of the present invention, and the embodiments may be mutually incorporated and referred to without contradiction.
The method comprises the steps of acquiring various types of large passenger flow data through a data acquisition unit built on the basis of a rail transit station; the passenger flow data accuracy is improved through methods of multi-data source association, comparison, intersection, verification and the like; according to the cloud computing infrastructure and the elastic cluster cloud platform, an elastic cluster cloud resource pool is built, and resource use conditions required by analysis of large passenger flow track data corresponding to the elastic cluster cloud platform are obtained; according to the analysis of the large passenger flow trajectory and the use condition of dynamic resources, dynamic adjustment of the elastic resources on the elastic cluster cloud platform is carried out so as to better support the requirement of multi-source data analysis on elastic calculation; and finally fitting the data of the large derailment traffic passenger flow track for intelligent perception early warning equal-rail traffic application scenes. The method is applied to the field of rail transit by combining the elastic cluster cloud technology and the intelligent data analysis algorithm, provides data support for the rail transit in developing daily operation activities such as intelligent perception early warning, ensures the operation safety of rail transit lines by using an informatization means, improves the scientificity and the accuracy for intelligent data operation of various subway stations, and is beneficial to overall planning and construction operation of rail transit infrastructures.
As shown in fig. 1, in an embodiment, the method for analyzing a large rail transit passenger flow trajectory based on elastic cloud includes the following steps:
and step S1, acquiring multi-source passenger flow data based on a data acquisition device built by the rail transit station.
Specifically, a Wi-Fi system, an in-station video system, a gate system, a ticketing system, a 5G multi-source data collector and the like are built in a rail transit station in advance, so that the passenger flow condition in the rail transit station is collected in real time through the data collector, the passenger flow data is obtained from multiple channels, and the multi-source passenger flow data is obtained.
It should be noted that the data acquisition unit includes, but is not limited to, data acquisition devices of hardware layers such as a high-definition monitoring probe, a wireless Wi-Fi signal detector, and the like, and the coverage area of the devices includes most public places of the rail transit station; specifically, the method comprises the steps of human face recognition and passenger flow monitoring based on a video probe, passenger flow analysis based on wireless Wi-Fi intelligent perception and the like.
As shown in fig. 2, in an embodiment, the building of the elastic cluster cloud resource pool specifically includes the following steps:
step S21, taking the basic settings provided by the cloud computing infrastructure and the elastic cluster cloud platform as bottom layer facilities;
specifically, the cloud computing infrastructure and the elastic cluster cloud platform are used as the IAAS layer of the service to provide basic settings including a CPU, a memory, a network and the like, and the basic settings provided by the IAAS layer are used as the bottom layer facilities of the PAAS layer.
Step S22, building an elastic cluster cloud resource pool for storing, calculating, transmitting and analyzing the multi-source passenger flow data on the bottom layer facility;
specifically, an elastic cluster cloud resource pool is built on the basis of an IAAS layer, the elastic cluster cloud resource pool provides PAAS service and serves as an operation environment of application service (including components of big data service, dependency environments of various software layers and databases required by big data), and elastic resources provided by the elastic cluster cloud resource pool have expansion capacity so as to meet different peak passenger flow conditions of rail transit stations.
Step S23, deploying a dependency component, a database and a multi-source data acquisition and correlation analysis application service required by big data in the PAAS service of the elastic cluster cloud resource pool.
Specifically, the PAAS service in the elastic cluster cloud resource pool provides a database, a dependency component and a database required by big data, and the multi-source data acquisition and correlation analysis application service is used as the necessary dependency of the big data acquisition service. And then, the installation correctness, the network interoperability and the data acquisition correctness of each step are tested through joint debugging, so that the normal work of the data acquisition and the correlation analysis of the rail transit is ensured.
As shown in fig. 3, in an embodiment, the multi-source data collection and correlation analysis application service supports real-time acquisition of the multi-source passenger flow data to generate real-time large passenger flow trajectory information in any time, and includes the following steps:
s31, positioning a single passenger according to the AP signal in the Wi-Fi sniffing data, and acquiring positioning information corresponding to the passenger in a rail transit range to realize real-time analysis of the flow speed and the flow direction of the passenger in the rail transit range;
step S32, based on the unique identification information of the passenger in the Wi-Fi system, performing association integration on all AP identification information accessed by the passenger in a rail transit range, and further realizing dynamic analysis on the behavior track of the single passenger in the rail transit range;
and step S33, fitting real-time large passenger flow track information in any time for all the passengers through a reduction algorithm according to the dynamic analysis of the behavior track of the single passenger in the rail transit range.
The multi-source passenger flow data comprises Wi-Fi sniffing data, bayonet video data, gate data, ticket selling data, 5G and other information communication basic data of the existing rail transit system; positioning each passenger according to the AP signal of the Wi-Fi system, acquiring positioning information corresponding to the passenger in a rail transit range, and finally realizing real-time analysis of the flow speed and the flow direction of the passenger in the rail transit range on the basis of the identification information and the positioning information; according to the unique identification information of the passenger in the Wi-Fi system, performing association integration on all AP identification information accessed by the passenger in a rail transit range to realize dynamic analysis on the behavior track of the passenger in the rail transit range; namely, the single passenger flow track can be accurately depicted according to the Wi-Fi system. And fitting real-time large passenger flow track information for all passengers in any time through a reduction algorithm according to the behavior track of the single passenger in the rail transit range realized by the Wi-Fi system.
Step S4, the large passenger flow trajectory information is verified by association, comparison and intersection methods, and real-time analysis and processing are performed to generate large rail transit passenger flow trajectory data covering flow density, flow direction and flow speed in real time, which includes the following steps:
according to the gate video data, passenger positioning and people counting at key gate positions in the rail transit range, gateway positions, transfer passages, stairs and carriages are carried out through an image recognition technology, and positioning information and people counting information of passengers at the key gate positions are obtained;
according to the gate data and the ticketing data, the information of the passengers entering and leaving the station is obtained;
and based on the positioning information and the number statistical information of the passengers at the key gate positions and the information of the passengers entering and leaving the station in the same route interval, carrying out dynamic verification and approval on the reduction algorithm and the large passenger flow trajectory information in real time within any fitting time by using a correlation, comparison and intersection method so as to generate the large passenger flow trajectory data of the rail intersection covering the flow density, the flow direction and the flow speed in real time.
As shown in fig. 4, Wi-Fi sniffing data can be obtained by a Wi-Fi system, so that AP data in a rail transit range, especially in gate ports, stations, station halls, carriages, and the like can be obtained; the video data of the gate can be obtained through the video system in the station, so that the statistical data of the number of passengers at key gate positions such as a gate, an entrance, an exit, a staircase and a carriage can be obtained; the gate opening data and the ticket selling data are obtained through the gate system and the ticket selling system, so that the station entering and exiting data of passengers can be obtained. After multi-source passenger flow data are obtained, primary fitting is carried out by mainly using Wi-Fi sniffing data, the fitting results are verified by using bayonet video data, gate data and ticket selling data, and the fitting results are accurately adjusted, so that the multi-data source is communicated and fused, bottlenecks encountered in the single-data source analysis process can be effectively broken through, and the integrity and accuracy of the whole data analysis are improved.
As shown in fig. 5, in an embodiment, the analyzing and processing the multi-source passenger flow data based on the elastic cluster cloud resource pool includes the following steps:
step S51, the Wi-Fi system is butted through the elastic cluster cloud resource pool, dynamic analysis of the behavior track of the single passenger in a rail transit range is obtained, and real-time large passenger flow track information in any time is fitted for all the passengers through a reduction algorithm;
step S52, butting the video system in the station through the elastic cluster cloud resource pool, positioning passengers and counting the number of people at key gate positions in the rail transit range, the gate, the transfer passage, the staircase and the carriage, and acquiring the positioning information and the number counting information of the passengers at the key gate positions;
step S53, the gate system and the ticketing system are butted through the elastic cluster cloud resource pool, and the station entering and exiting information of the passengers is obtained;
and step S54, based on the positioning information and the people counting information of the passengers at the key gate position and the information of the passengers entering and leaving the station at the same route section, performing dynamic verification and approval on the real-time large passenger flow trajectory information in any time by using a correlation, comparison and intersection method, and performing real-time analysis processing to generate real-time large rail flow trajectory data covering the flow density, the flow direction and the flow speed.
And step S6, dynamically adjusting the elastic resources of the elastic cluster cloud resource pool according to the multi-source passenger flow data and the resource use condition and by executing a flexible scheduling strategy, so as to automatically respond to the dynamic analysis requirement of the rail transit large passenger flow trajectory data in real time.
The dynamic adjustment of the elastic resources of the elastic cluster cloud resource pool specifically comprises the following steps: establishing a passenger flow volume and resource use relation comparison graph according to the multi-source passenger flow data and the resource use condition; in combination with the passenger flow volume and resource use relation comparison graph, performing dynamic preliminary adjustment on the elastic resource according to the multi-source passenger flow data analysis processing and performing detailed adjustment on the elastic resource according to historical peak passenger flow volume;
the content of the dynamic initial adjustment of the elastic resource according to the multi-source passenger flow data analysis processing comprises the horizontal and/or vertical dynamic elastic expansion of the elastic cluster cloud resource pool copy according to the resource request usage amount corresponding to the multi-source passenger flow data analysis processing;
the objects for performing detailed adjustment on the elastic resources according to the historical peak passenger flow comprise components and databases on which the multi-source passenger flow data are analyzed and processed, and the multi-source data acquisition and correlation analysis application service.
As shown in fig. 6, in an embodiment, the method for analyzing the large rail transit passenger flow trajectory based on elastic cloud further includes the following steps:
step S71, judging whether the expansion and contraction rate of the elastic resource can meet the resource requirement when the peak data of the multi-source passenger flow data is analyzed in a value range;
step S72, when the expansion and contraction rate of the elastic resource can not meet the resource requirement of the multi-source passenger flow data during peak data analysis, creating a new instance copy on the elastic cluster cloud resource pool;
step S73, judging whether the resource configuration value of the new instance copy created on the elastic cluster cloud resource pool is larger than a preset resource threshold value;
step S74, when the resource of the first instance copy on the elastic cluster cloud resource pool is larger than the preset resource threshold value, creating a second instance copy which is the same as the first instance copy on the elastic cluster cloud resource pool; when the resources of the second instance copy created on the elastic cluster cloud platform are smaller than the preset resource threshold, releasing the second instance copy and reusing the first instance copy.
Firstly, whether the expansion and contraction rate of the elastic resource meets the maximum possible passenger flow condition, namely the historical peak passenger flow is evaluated. And secondly, after the elastic expansion and contraction of the service are set, when the passenger flow volume is increased and the computing capacity requirement is increased to reach the resource utilization rate of the specified volume of the service, namely the expansion and contraction rate of the elastic resource cannot meet the requirement of the historical peak passenger flow volume, automatically creating a new instance copy so as to share the resource utilization pressure of the single instance copy. Then, when the resource of one of the multiple instance copies is higher than a preset resource threshold, an equivalent instance copy is continuously created; and when the resource of one instance copy is smaller than a preset resource threshold value, releasing the instance copy.
Specifically, after the work is completed and the joint debugging test is passed, the big data service acquisition and analysis work of the rail transit can run normally, and the resource use conditions of the passenger flow data on a big data database, a dependence component, a CPU (central processing unit), a memory, a network, a disk and the like of the data acquisition and analysis application service are monitored by observing the passenger flow conditions of different passenger flows of rail transit stations.
In an embodiment, the resource usage includes usage of any one or a combination of several of a CPU, a memory, a network, and a disk.
As shown in fig. 7, in an embodiment, the specific working principle of the elastic cloud-based rail transit large passenger flow trajectory analysis method is as follows:
after all applications of rail transit are deployed in the elastic cluster cloud resource pool, all data are collected into a data platform by using an algorithm engine and an intelligent data analysis algorithm of the elastic cluster cloud resource pool, the data are classified according to types, and different processing is performed according to different classifications.
The data acquisition method comprises the steps of acquiring data generated in the operation process of all elastic cluster clouds, wherein the data acquisition comprises the data acquisition of operation in the elastic cluster cloud construction environment; specifically, the steps for collecting the information data of the passenger flow application system are as follows:
step one, information acquisition and processing.
Specifically, the steps are mainly divided into two steps, namely, running information acquisition and running data processing.
Acquiring operation data, namely acquiring the operation data of the elastic cluster cloud, performing related processing through a streaming kafka channel, and then performing real-time calculation on the streaming data by adopting a Flink calculation engine frame; for data acquisition, the event monitor in the elastic cluster cloud is realized, after the elastic cluster cloud is started, the monitoring components in the elastic cluster cloud are started synchronously, and monitoring contents are registered on the monitoring bus.
The system defines an executor information collector in a job scheduler running in an execution driver and registers the executor information collector in a listener bus object, the listener bus is a core component responsible for receiving and forwarding messages, a task executor communicates with the task executor through remote process call, the executor information collector monitors event messages such as task start, task completion and the like, and the listener manages and operates recorded data in a specific corresponding event monitoring method.
After monitoring that the data in the elastic cluster cloud are reported, the monitor pushes the data to a Kafka message queue in an original mode, and after receiving the related data, the Kafka channel sets 3 partitions and 3 replicas to finish data backup.
For data operation, after the kafka receives the data, the flink computing engine acquires related data in real time through the stream processing module, the data are computed according to a preset algorithm, the data are pushed to different data stores according to data types after the computation, then presto is adopted for data assembly according to the fact that the computed data and the service information of the elastic cluster cloud are located in different data sources, the data are assembled according to mass data and static data, the assembled data are pushed to a result data store, and at the moment, the data can provide support for upper-layer application.
And step two, data standard.
It should be noted that, in the calculation process of the data standard, there are many problems, and the denoising module is cleaned intelligently through the data for standardized data management.
Specifically, the data standard management mainly realizes unified management of information such as data specification, data format, coding rule, data dictionary value, acquisition frequency and the like, and generates a corresponding data cleaning program according to the definition of the data standard so as to realize manual cleaning of data.
And based on the metadata, performing data standard definition on the access data, the basic indexes and the service indexes, and providing operations of checking, adding, modifying, deleting and the like of data standard information.
It should be noted that the data standard management mainly provides the following functions:
1. newly establishing a data standard: and generating a data standard table based on the data definition in the metadata, setting constraints on values and dimensions in the data, and keeping the definition of the current data standard.
2. Management data standard: and operations such as viewing, searching, editing and the like are provided for the information of the existing data standard.
3. Deleting data standard: the defined data standard can be logically deleted, and the data cleaning task related to the data standard stops running after deletion.
And step three, data cleaning.
Specifically, before analyzing data, the collected data needs to be standardized and clarified, and the data cleaning refers to a last procedure for finding and correcting recognizable errors in a data file, and includes checking data consistency, processing invalid values and missing values, and the like.
It should be noted that the scope of data cleansing includes, but is not limited to, transformation of data indexes, compliance detection, removal of redundant data, clearing of erroneous data, supplementing missing data, and data padding.
Further, the specific work of data cleansing includes the following:
(1) in the data cleaning process, the conversion of the data indexes comprises the steps of carrying out standardized processing on the same type of data objects of all original data according to the basic index definition managed by the metadata, and unifying the attribute description and the dimension data of the data objects.
(2) And error data clearing is carried out, namely, error data in the converted index data stream is identified according to data definition in the metadata, and the error data is deleted.
(3) And supplementing missing data, namely identifying whether the converted index data is missing or not according to data definition in the metadata, and supplementing missing items according to the metadata definition and data standards.
And step four, analyzing data.
After the data is cleaned and denoised, data analysis can be carried out, design-related analysis operation is carried out on the collected data in real time, and the analysis is completed.
It should be noted that, in general, the passenger flow volume of the rail transit station directly affects the computing power of the database, the dependent component, and the data acquisition and analysis application service, and the larger the passenger flow volume is, the more the data to be stored, transmitted, and analyzed is, the higher the computing power required by the service is, the larger the consumed resource amount is, so the passenger flow volume directly affects the use of the resources of the CPU, the memory, the network, and the disk of the relevant service.
It should be noted that the content of the data analysis includes: collecting hardware layer data of a cloud host, a cloud disk, a bare metal server, an IP, a physical host, a server, a storage, a network device, a cloud resource, a monitoring probe, a Wi-Fi site and the like, knowing the operation condition of the whole from hardware to software from different dimensions through statistical analysis, extracting analysis data from the collected flow data, visualizing the original data, supporting the visualization of real-time data flow of the original data, presenting different data views according to different data objects, and simultaneously supporting a northbound interface, wherein a user can self-define and provide the original data to the outside; basic data visualization, which supports visualization of real-time data streams based on basic indexes, presents different data views according to different data objects, and supports a northbound interface, so that a user can self-define basic data to be provided to the outside; the business data visualization supports data visualization based on business indexes, provides classification query display of different dimensionalities of the business data, supports a northbound interface, and can be customized by a user to provide the business data to the outside.
It should be noted that the protection scope of the method for improving the rail transit operation capability based on the elastic cluster cloud platform and the big data is not limited to the execution sequence of the steps illustrated in this embodiment, and all the schemes of adding, subtracting, and replacing the steps in the prior art according to the principle of the present invention are included in the protection scope of the present invention.
In conclusion, the invention is applied to the field of rail transit by combining the elastic cluster cloud technology and the intelligent data analysis algorithm, provides data support for developing daily operation activities such as intelligent perception early warning, ensures the operation safety of rail transit lines by using an informatization means, improves the scientificity and accuracy for intelligent data operation of various subway stations, and is beneficial to overall planning and construction operation of rail transit infrastructures.
In one embodiment, the present invention also relates to a terminal, as shown in fig. 8, comprising at least one processor 81; and a memory 82 communicatively coupled to the at least one processor 81; wherein the memory 82 stores instructions executable by the at least one processor 81 to enable the at least one processor 81 to perform the video clipping method described above.
Where the memory 82 and the processor 81 are coupled in a bus, the bus may comprise any number of interconnected buses and bridges that couple one or more of the various circuits of the processor 81 and the memory 82 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, which further receives the data and passes it to the processor 81.
The processor 81 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 82 may be used to store data used by processor 81 in performing operations.
In an embodiment, the invention further relates to a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (9)
1. The rail transit large passenger flow trajectory analysis method based on the elastic cloud is characterized by comprising the following steps:
acquiring multi-source passenger flow data based on a data acquisition unit built by a rail transit station;
building an elastic cluster cloud resource pool for storing, calculating, transmitting and analyzing the multi-source passenger flow data on a cloud computing infrastructure and an elastic cluster cloud platform, and deploying a dependence component, a database and multi-source data acquisition and correlation analysis application services required by big data on PAAS (platform assisted adaptive clustering) services of the elastic cluster cloud resource pool;
the multi-source data acquisition and correlation analysis application service supports real-time acquisition of the multi-source passenger flow data so as to fit real-time large passenger flow trajectory information in any time;
the large passenger flow track information is verified through correlation, comparison and intersection methods, and real-time analysis processing is carried out to generate track traffic large passenger flow track data covering flow density, flow direction and flow speed in real time;
analyzing and processing the multi-source passenger flow data based on the elastic cluster cloud resource pool, and acquiring the required resource use condition;
and dynamically adjusting the elastic resources of the elastic cluster cloud resource pool according to the multi-source passenger flow data and the resource use condition and by executing a flexible scheduling strategy so as to automatically respond to the dynamic analysis requirement of the rail transit large passenger flow trajectory data in real time.
2. The method for analyzing the large rail transit passenger flow trajectory based on the elastic cloud according to claim 1, wherein the data collector built based on the rail transit station obtains multi-source passenger flow data, and specifically comprises:
the data acquisition units built based on the rail transit stations comprise data acquisition units of various rail transit information systems including a Wi-Fi system, an in-station video system, a gate system and a ticketing system, and acquire various rail transit information basic data including Wi-Fi sniffing data, gate video data, gate data and ticketing data.
3. The elastic cloud based rail transit mass passenger flow trajectory analysis method according to claim 2, wherein the multi-source data collection and correlation analysis application service supports real-time acquisition of the multi-source passenger flow data to fit real-time mass passenger flow trajectory information in any time, comprising the steps of:
positioning a single passenger according to the AP signal in the Wi-Fi sniffing data, and acquiring positioning information corresponding to the passenger in a rail transit range so as to realize real-time analysis of the flow speed and the flow direction of the passenger in the rail transit range;
based on the unique identification information of the passengers in the Wi-Fi system, all AP identification information accessed by the passengers in a rail transit range is associated and integrated, and further the dynamic analysis of the behavior track of the single passenger in the rail transit range is realized;
and fitting real-time large passenger flow track information in any time for all the passengers through a reduction algorithm according to the dynamic analysis of the behavior track of the single passenger in the rail transit range.
4. The elastic cloud based rail transit large passenger flow trajectory analysis method according to claim 2 or 3, wherein the large passenger flow trajectory information is verified by means of association, comparison and intersection, and is analyzed and processed in real time to generate real-time rail transit large passenger flow trajectory data covering density, flow direction and flow speed, and the method comprises the following steps:
according to the gate video data, passenger positioning and people counting at key gate positions in the rail transit range, gateway positions, transfer passages, stairs and carriages are carried out through an image recognition technology, and positioning information and people counting information of passengers at the key gate positions are obtained;
according to the gate data and the ticketing data, the information of the passengers entering and leaving the station is obtained;
and based on the positioning information and the number statistical information of the passengers at the key gate positions and the information of the passengers entering and leaving the station in the same route interval, carrying out dynamic verification and approval on the reduction algorithm and the large passenger flow trajectory information in real time within any fitting time by using a correlation, comparison and intersection method so as to generate the large passenger flow trajectory data of the rail intersection covering the flow density, the flow direction and the flow speed in real time.
5. The elastic cloud-based rail transit mass passenger flow trajectory analysis method according to claim 4, wherein the elastic cluster cloud resource pool-based analysis processing of the multi-source passenger flow data comprises the following steps:
the Wi-Fi system is docked through the elastic cluster cloud resource pool, dynamic analysis of the behavior track of the single passenger in a rail transit range is obtained, and real-time large passenger flow track information in any time is fitted for all the passengers through a reduction algorithm;
butting the video system in the station through the elastic cluster cloud resource pool, positioning passengers and counting the number of the passengers at key gate openings, entrances and exits, transfer passages, staircases and key gate openings in carriages in the rail transit range, and acquiring positioning information and number counting information of the passengers at the key gate openings;
the gate system and the ticketing system are butted through the elastic cluster cloud resource pool, and the information of the passengers entering and leaving the station is obtained;
and based on the positioning information and the number statistical information of the passengers at the key gate positions and the information of the passengers entering and leaving the station in the same route interval, performing dynamic verification and approval on the real-time large passenger flow trajectory information in any time by using a correlation, comparison and intersection method, and performing real-time analysis and processing to generate real-time large rail flow trajectory data covering flow density, flow direction and flow speed.
6. The elastic cloud-based rail transit mass passenger flow trajectory analysis method according to claim 5, wherein the dynamically adjusting elastic resources of the elastic cluster cloud resource pool comprises the following steps: establishing a passenger flow volume and resource use relation comparison graph according to the multi-source passenger flow data and the resource use condition; in combination with the passenger flow volume and resource use relation comparison graph, performing dynamic preliminary adjustment on the elastic resource according to the multi-source passenger flow data analysis processing and performing detailed adjustment on the elastic resource according to historical peak passenger flow volume;
the content of the dynamic initial adjustment of the elastic resource according to the multi-source passenger flow data analysis processing comprises the horizontal and/or vertical dynamic elastic expansion of the elastic cluster cloud resource pool copy according to the resource request usage amount corresponding to the multi-source passenger flow data analysis processing;
the objects for performing detailed adjustment on the elastic resources according to the historical peak passenger flow comprise components and databases on which the multi-source passenger flow data are analyzed and processed, and the multi-source data acquisition and correlation analysis application service.
7. The elastic cloud-based rail transit large passenger flow trajectory analysis method according to claim 1, wherein it is determined whether the elastic resource expansion and contraction rate can meet the resource demand of the multi-source passenger flow data in peak data analysis within a value range;
when the expansion and contraction rate of the elastic resources cannot meet the resource requirement of the multi-source passenger flow data during peak data analysis, creating a new instance copy on the elastic cluster cloud resource pool;
judging whether the resource configuration value of the new instance copy created on the elastic cluster cloud resource pool is greater than a preset resource threshold value;
creating a second instance replica on the elastic cluster cloud resource pool that is identical to the first instance replica when the resources of the first instance replica on the elastic cluster cloud resource pool are greater than the preset resource threshold; when the resources of the second instance copy created on the elastic cluster cloud platform are smaller than the preset resource threshold, releasing the second instance copy and reusing the first instance copy.
8. A terminal, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the elastic cloud based mass transit passenger flow trajectory analysis method of any one of claims 1 to 7.
9. A storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the elastic cloud based rail transit mass passenger flow trajectory analysis method of any one of claims 1 to 7.
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| CN114547228A (en) * | 2022-04-22 | 2022-05-27 | 阿里云计算有限公司 | Track generation method, device, equipment and storage medium |
| CN114860321A (en) * | 2022-04-06 | 2022-08-05 | 网易（杭州）网络有限公司 | External device control method, device, equipment and medium based on raspberry pi |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114860321A (en) * | 2022-04-06 | 2022-08-05 | 网易（杭州）网络有限公司 | External device control method, device, equipment and medium based on raspberry pi |
| CN114547228A (en) * | 2022-04-22 | 2022-05-27 | 阿里云计算有限公司 | Track generation method, device, equipment and storage medium |
| CN114547228B (en) * | 2022-04-22 | 2022-07-19 | 阿里云计算有限公司 | Track generation method, device, equipment and storage medium |
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